Appraisal and Valuation: Contemporary Issues and New Frontiers [1st ed.] 9783030495787, 9783030495794

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Green Energy and Technology

Pierluigi Morano · Alessandra Oppio · Paolo Rosato · Leopoldo Sdino · Francesco Tajani   Editors

Appraisal and Valuation Contemporary Issues and New Frontiers

Green Energy and Technology

Climate change, environmental impact and the limited natural resources urge scientific research and novel technical solutions. The monograph series Green Energy and Technology serves as a publishing platform for scientific and technological approaches to “green”—i.e. environmentally friendly and sustainable—technologies. While a focus lies on energy and power supply, it also covers “green” solutions in industrial engineering and engineering design. Green Energy and Technology addresses researchers, advanced students, technical consultants as well as decision makers in industries and politics. Hence, the level of presentation spans from instructional to highly technical. **Indexed in Scopus**.

More information about this series at http://www.springer.com/series/8059

Pierluigi Morano Alessandra Oppio Paolo Rosato Leopoldo Sdino Francesco Tajani •

•

•

•

Editors

Appraisal and Valuation Contemporary Issues and New Frontiers

123

Editors Pierluigi Morano ICAR Polytechnic University of Bari Bari, Italy

Alessandra Oppio DASTU Politecnico di Milano Milan, Italy

Paolo Rosato DIA University of Trieste Trieste, Italy

Leopoldo Sdino ABC Politecnico di Milano Milan, Italy

Francesco Tajani DiAP Sapienza University of Rome Rome, Italy

ISSN 1865-3529 ISSN 1865-3537 (electronic) Green Energy and Technology ISBN 978-3-030-49578-7 ISBN 978-3-030-49579-4 (eBook) https://doi.org/10.1007/978-3-030-49579-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Times are running fast, and with them the issues to which the Appraisal and the Economic Valuation are applied. Several reasons related to the current economic crisis, the high cyclical nature of the real estate market, the scarce availability of financial resources, the complexity of the decision-making contexts, require, both in the public and in the private fields, the widespread use of the Appraisal and the Economic Valuation. Appraisal and Economic Valuation, as a scientific discipline, concerns the methodological and operational principles for properties values and costs assessment, investments’ feasibility analysis, decision support tools definition, public and private real estate assets’ enhancement, forecast of the effects generated by the territorial initiatives on society and natural resources through monetary and qualitative approaches. Due to their cross-sectoral nature, the Appraisal and the Economic Valuation is able to interact with the other disciplines at different scales, to integrate the economic, legal, social, cultural and environmental skills, as well as to play as one of the leading sectors in the training, research and professional practice of Architects and Engineers. The Italian Society of Appraisal and Valuation (SIEV), a scientific organization that gathers professors, scholars and professional technicians working in the appraisal field, supports and spreads the culture of the Appraisal and Valuation through conferences, training and research activities, scientific publications, collaborations with public and private stakeholders (www.siev.org). In the outlined framework, this book features a selection of the best papers presented at two SIEV Conferences, held in Milan (2017) and in Bari (2018), concerning appraisal issues from different geographic and socio-economic contexts. The volume, divided into three main Sections, collects the reflections of the scholars on the transversal and trait d’union role of the Appraisal and the Economic Valuation with the other disciplinary fields, explaining and deepening the main theoretical and practical issues that arise in relation with the following areas: (1) Territory and Urban Planning; (2) Real Estate Assets and Construction Building Process; (3) Real Estate Finance and Property Management. v

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Given the dual nature of the Appraisal and the Economic Valuation, characterized by a robust methodological structure and at the same time a practical attitude, the papers present a theoretical session, with the proposal of a methodological framework, and its application to a case study. The book aims at being an opportunity for further insights and discussions among the real-world decision makers, stakeholders and scholars, in order to stimulate ideas and reflections on which the debate, the future research and the consequent applications can be addressed.

Bari, Italy Milan, Italy Trieste, Italy Milan, Italy Rome, Italy

Editors Pierluigi Morano Alessandra Oppio Paolo Rosato Leopoldo Sdino Francesco Tajani

Contents

Territory and Urban Planning Seismic Vulnerability and Old Towns. A Cost-Based Programming Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Salvatore Giuffrida, Chiara Circo, Margherita Giuffrè, Maria Rosa Trovato, and Vittoria Ventura

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Capturing the Public Value in the Public/Private Zoning Agreements: Evidence from Italian Municipalities . . . . . . . . . . . . . . . . . . . . . . . . . . . Ezio Micelli and Agostino Valier

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An Empirical Study of Land Leverage as a Function of Market Value Using a Spatial Autoregressive Model . . . . . . . . . . . . . . . . . . . . . . . . . . Sergio Copiello

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Cost-Benefit Analysis and Investment Risk Assessment. Threshold Values According to the ALARP Logic . . . . . . . . . . . . . . . . . . . . . . . . . Gabriella Maselli and Antonio Nesticò

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New Public Management and Economic Feasibility Assessment of PPP Projects. A Case Study in Calabria . . . . . . . . . . . . . . . . . . . . . . Francesco Calabrò, Lucia Della Spina, and Basiliana Randò

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An Application to a Spanish Case Study of a Property Valuation Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pierluigi Morano, Francesco Tajani, Marco Locurcio, Felicia Di Liddo, and Debora Anelli The Transformation of Surface Rights into Property Rights. A Financial Resource for Rebalancing Municipal Budgets. The Case of Pescara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sebastiano Carbonara and Davide Stefano

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Contents

Real Estate Assets and Construction Building Process Do Policy Incentives to Buildings Energy Retrofit Encourage Homeowners’ Free-Rider Behavior? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Chiara D’Alpaos A Literature Review on Construction Costs Estimation: Hot Topics and Emerging Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Marta Bottero, Caterina Caprioli, and Alessandra Oppio Public Works in North-East Italy: An Efficiency and Risk Allocation Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Valentina Antoniucci and Giuliano Marella A Parametric Cost-Based Approach to Appraisal Jack-Up Vessels . . . . 147 Vincenzo Del Giudice, Pierluigi Morano, Pierfrancesco De Paola, and Francesco Tajani Cultural Heritage and Seismic Disasters: Assessment Methods and Damage Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 Fabiana Forte, Vincenzo Del Giudice, Pierfrancesco De Paola, and Francesco Paolo Del Giudice The ‘Value of Solidarity’ in the Public Housing Stock Alienation. A Case Study in Palermo (Italy) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 Grazia Napoli, Salvatore Giuffrida, and Maria Rosa Trovato Management of Maintenance Costs in Cultural Heritage . . . . . . . . . . . . 195 Giovanna Acampa and Claudia Mariaserena Parisi The Regional Price Lists for Estimating the Costs of Construction . . . . 213 Paolo Rosasco and Leopoldo Sdino Optimal Design in Energy Retrofit Interventions on Building Stocks: A Decision Support System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 Laura Gabrielli and Aurora Greta Ruggeri How the Italian State Finances Post-seismic Reconstruction: The 2009 Abruzzo Earthquake . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 Sebastiano Carbonara and Davide Stefano Is Investing in Safety Worthwhile? A Methodology for Assessing the Costs and Benefits of Accidents in the Construction Sector . . . . . . . 269 Maria Rosaria Guarini and Rossana Ranieri Real Estate Finance and Property Management To Buy or Rent to Buy? Appraisal Questions . . . . . . . . . . . . . . . . . . . . 291 Francesca Salvo, Pierluigi Morano, Francesco Tajani, and Manuela De Ruggiero

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A Model for Determining a Discount Rate in Market Value Assessment of Buildable Areas Subject to Restrictions . . . . . . . . . . . . . . 303 Fabrizio Battisti and Orazio Campo A Rational Assessment Procedure of Long-Term Sustainable Values for Bank Lending Purposes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315 Francesco Tajani, Pierluigi Morano, Vincenzo Del Giudice, and Pierfrancesco De Paola A Multi-criteria Decision Analysis for the Assessment of the Real Estate Credit Risks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 Marco Locurcio, Francesco Tajani, Pierluigi Morano, and Debora Anelli

Territory and Urban Planning

Seismic Vulnerability and Old Towns. A Cost-Based Programming Model Salvatore Giuffrida, Chiara Circo, Margherita Giuffrè, Maria Rosa Trovato, and Vittoria Ventura

Abstract Seismic vulnerability affects particularly the small inland towns due to its multifaceted nature, concerning the social capital of city in its two main dimensions, the urban capital and the human capital. In some Italian regions, as well as in Emilia Romagna, municipalities are implementing seismic vulnerability reduction policies based on the Emergency Limit Condition, which has become one of the references for ordinary land planning. With reference to the general planning framework of the Faentina Union—a group of five municipalities in the southwestern part of the Province of Ravenna, Italy—this study proposes a cost-based valuation programming approach to the seismic vulnerability reduction for the old town of Brisighella. This approach involves three cognitive areas: knowledge, aimed at the typological, constructive, and technological description of the buildings, specifically concerning their seismic vulnerability; interpretation, as a critical analysis of the urban fabric representation aimed at outlining a range of earthquake damage scenarios; planning, as a generative pattern of multiple vulnerability reduction strategies having comparable costs, and in correspondence of different budgets. This pattern includes a cost modelling tool aimed at defining the trade-off between the extension and the intensity of the vulnerability reduction works, given the budget.

S. Giuffrida (B) · C. Circo · M. R. Trovato (B) Department of Civil Engineering and Architecture, University of Catania, Catania, Italy e-mail: [email protected] M. R. Trovato e-mail: [email protected] C. Circo e-mail: [email protected] M. Giuffrè Institute of Environmental Geology and Geoengineering (IGAG) of the National Research Council (NRC), Rome, Italy e-mail: [email protected] V. Ventura e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Morano et al. (eds.), Appraisal and Valuation, Green Energy and Technology, https://doi.org/10.1007/978-3-030-49579-4_1

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Keywords Urban fabrics · Seismic vulnerability · Critic analysis · Cost modelling · Urban preservation planning · Building works programming

1 Introduction Seismic vulnerability is a condition of the urban fabrics typically affecting the historic cities because of its multifaceted nature, basically concerning the combination of the characteristics of the two main components of the social capital, the urban capital and the human capital [18]. Urban capital is identified as the system of the material, construction, typological and urban aspects of the building fabrics. In the historic city: the load-bearing masonry construction system prevails; in many cases, inadequate transformative interventions—in particular aimed at widening liveliness and accessibility—affect their structural consistency making them more fragile; the building units are typically connected by a structural constraint that make them as portions of larger entities, the building aggregates, which they are an integral part of; a greater urban density, especially in terms of occupied surface area, and their typical orography, often with a high slope, reduces the inner accessibility [5] thus compromising the rescue operations in case of emergency. Human capital is identified in the properties of the social structure, therefore in its “consistency” and “strength”; “consistency” concerns population as for size and composition; “strength” concerns socio-economic status, cultural level, degree of participation, time preference rate of public. In the historic city, and in particular in the inland small towns, the poorness of job opportunities—due to the progressive concentration of the main and the most specialized urban functions within the more large and dense cities—triggered a general and broad depopulation process thus weakening the social structure of these networks of towns. Such criticalities of the urban social capital affect the individual axiological profiles and the identity of the local communities, thus influencing the politicaladministrative system concerning the decision-making about the conservation of the architectural and urban heritage. Typically, seismic vulnerability mitigation involves a specific program of interventions whose promoters, stakeholders, objects, costs and stages, need to be easily identified. The weaker aspects of these processes concern the sphere of the common values as to the importance that public attach to the urban and human dimensions of this heritage. Uncertainty and incomplete information characterize the description of seismic events and their effects: this description has a specific positive connotation. On the other hand, decision-making processes have an intrinsically normative nature, as they are inspired by accountability. The science of evaluations, which attributes value judgments, constitutes the interface between description and decision, as it describes a social reality, different, but not extraneous to physical reality: this social reality is the reality of values. The general objectives of the representation of the reality of

Seismic Vulnerability and Old Towns …

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values are distributive justice in a social and generational sense: this outlines the civil commitment of the science of evaluations. The study we propose, applies the above-mentioned description-valuationdecision making approach to the case of the old town of Brisighella, Italy, that is part of the Union of Municipalities of the Romagna Faentina, within which a coordinated seismic vulnerability reduction planning process has started. Concerning the small urban centres vulnerability, restoration and evaluation identify durability as the vehicle respectively of formal architectural unity and economic significance [15, 20]. The resilience of a social-urban system depends on the amount of social product surplus accrued to protect it from catastrophic events. Literature on risk analysis methods for unreinforced masonry structures highlight remarkable socio-economic implications of seismic vulnerability. Several studies have proposed an innovative holistic approach [4] to the urban seismic risk evaluation based on indicators related to physical exposure, social fragility and lack of resilience. By means of geomatics tools (GIS), the seismic risk is analysed basing both on the expected losses scenarios, and on the probabilities of predefined damage states [1, 10]. Innovative methodologies for assessing the physical vulnerability of the historic centres have been proposed both on an architectural scale and on an urban scale [11]. In some case studies, seismic vulnerability has been measured combining empirical and mechanical methodologies that identify the typological and structural layout of the buildings. In addition, an indices-based method for load-bearing masonry building aggregates has been applied. The obtained vulnerability characteristics and the corresponding assessments provide relevant and consistent information in the form of typological capacity and fragility curves, which can be applied to urban areas presenting similar building classes. This study exposes the methodology and the findings of the vulnerability assessment of the building heritage in the old town of Brisighella, referring both to the building aggregates and to the architectural units; starting from these results, it proposes an integrated model of analysis, evaluation and project initiation [19, 21– 23, 29], aimed at outlining multiple strategies for programming interventions to reduce vulnerability and, therefore, to optimize the seismic vulnerability reduction decision-making process.

2 Materials 2.1 The Emergency Limit Condition and the Historic Centre of Brisighella, Italy The issue of urban vulnerability is studied in the specific context of the EmiliaRomagna Region with a wide range of approaches and tools that were only recently introduced to the regional planning aimed at reducing the urban seismic risk.

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The aim is to ensure that, during a seismic event, an urban centre can persist, regarding both the efficiency of the main strategic activities for the recovery and the identity characteristics that distinguish it. The programmatic document of vulnerability reduction is the Emergency Limit Condition, (ELC) an urban scale analysis aimed at managing the behaviour of the settlement in the post-earthquake phase, carried out by the Technical Office of the Union of Municipalities [6, 7]. By definition, the ELC represents that limit condition for which, after the seismic event, the urban settlement keeps preserving just the basic functions for emergency management. The ELC is, in fact, made up of strategic buildings, emergency areas, and the main links between the elements and the territorial context, as well as their interactions with the interfering elements [2]. The settlement of Brisighella rises from the slopes of the Tuscan-Romagna Apennines in the lower valley of the Lamone river. The first settlement, dating back to the end of the XIII century, consisted of a fortified nucleus, the current Rocca. In the 14th century, the fortification works were extended to the settlement of the “Borgo”, creating an elevated arcaded path integrated into the houses for defensive purposes, (now Via degli Asini). During the 1400 s, the nucleus expanded towards the valley, creating a new fortification wall beyond which, starting from 1500, the city developed. The historical evolutionary process of an urban centre and the orographic peculiarities of its territory have greatly influenced the definition of the urban form, characterised by aggregates of townhouses built against the slope. The residential buildings have incorporated the ancient walls defining the unique configuration of the urban fabric.

2.2 Map of Vulnerabilities and Strengths, and the ELC of Brisighella The elements identified in the context of the field survey are represented by icons in the “Map of vulnerabilities and strengths of the urban fabric”. Depending on the method used to acquire knowledge (observations from the outside), the vulnerability discussed here is related to the possible overturning mechanisms of the walls facing the street. From the analysis emerged a general good state of conservation for the historic centre of Brisighella, which does not present cases of buildings with different masonry structures, because it was not subject to post-war reconstruction. The map of Brisighella (Fig. 1) shows the recurring vulnerabilities and strengths in the whole old town, such as the presence of tall buildings, which are more vulnerable to the overturning mechanism due to the number of uncontained outer walls, and volumes jutting out from the external fronts, which are more frequent in the rear façades of the buildings. Furthermore, the map illustrates some specific vulnerability factors, such as the constructional irregularities of some parts of the urban fabric due

Seismic Vulnerability and Old Towns …

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Fig. 1 Overlapping of the Emergency Limit Condition and vulnerability/strengths factors of the historic Centre of Brisighella

to the integration in the ancient city walls. The contiguity between very different geometrical and structural configurations (in terms of storey height, wall thickness, etc.) can constitute a weakness from a seismic perspective, and this should be taken into consideration in the context of a possible intervention. The strength factors that characterise the urban fabric of Brisighella concern the widespread use of historic anti-seismic devices (e.g., metallic tie-rods and buttresses) and a good construction technique—as concerns its visibility from the outside. The ELC of Brisighella, unlike the other municipalities analysed, is included in the historical urban fabric, since the main connection infrastructures cross the historical centre in a rather extensive way. For this reason, a high number of interfering structural aggregates was noted, which are those that, following an earthquake, could collapse on the escape routes identified in the ELC.

2.3 Intervention Criteria for Vulnerability Reduction The interpretation of information related to vulnerabilities and strengths allows the prefiguration of the seismic damage mechanisms that can affect the analysed urban fabric (Fig. 1). The recurring issues to be faced in seismic risk reduction are clarified and the essential features of a mitigation strategy that is respectful of the constructional and urban peculiarities of the historic centres are specified. The intervention criteria are

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not expressed by technical details but rather by the objectives to which the intervention must aim, which allows a design freedom with only one indispensable restriction: respect for the constructive logic of the masonry technique as a guarantee of effectiveness and compatibility of the intervention with the historical building. With reference to the vulnerabilities observed in the historic centre of Brisighella, the improvement of the seismic response is pursued by means of interventions aimed at control of the thrusts in the roofs and reduction of the thrusts of vaulted elements as well as improvement of the connections between walls and floors with particular regard to the containment of the façade walls. These indications are valid for the entire wall structure of the historic centre of Brisighella and allow a general framework of actions to be implemented in order to define the preventive mitigation of the seismic vulnerability. It follows that the overlapping of the levels of knowledge in terms of vulnerability, strengths and intervention criteria with the previsions of the ELC of Brisighella can help to identify the strategic interventions to be promoted by the public authorities within the historic centre—favouring coordinated management of the financial resources—and to define reward mechanisms to promote the implementation of private interventions.

3 Methods 3.1 The Analysis of the Seismic Vulnerability of the Historic Centre The methodology adopted for the analysis of the seismic vulnerability of the historic centre of Brisighella is based on the direct knowledge of the building aggregates and has been used often in contexts damaged by earthquakes [3] as well as under ordinary conditions. The activities carried out are organized in three strictly connected phases: knowledge, interpretation, and project initiation. The knowledge phase includes the preliminary bibliographical research of the studies already carried out by the Municipal Technical Office (MTO), which outlined the main evolutionary phases of the historic centre; a further on-site survey was carried out to detect the (constructional, typological, evolutionary) factors which may significantly affect, positively or negatively, the seismic behaviour of the urban fabric in its current configuration. The elements that positively influence the seismic response (called resistance factors or strengths), such as the presence of anti-seismic devices, the good quality of the constructional technique, etc., and those that play negative roles (called vulnerability factors) are identified; particular attention was paid to the development of important damage mechanisms, such as the overturning of the façades. All information was collected within a direct survey of the urban fabric through observations from the outside of the building façades and the accessible courtyards.

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Moreover, the survey highlighted the specificities of the aggregates as regards intended use and construction techniques, distinguishing the load-bearing masonry residential buildings from those built in reinforced concrete or with other construction techniques, and buildings with specialized functions (churches and historical buildings). The aim is to identify possible points of constructive discontinuity and the relationships of contiguity between buildings with different geometrical-structural characteristics. This type of analysis conducted in the whole historic centre allows to draw a map of the recurrent vulnerability and strength factors of the urban fabric to be obtained, creating indispensable background knowledge for the definition of intervention criteria aimed at reducing vulnerabilities. In the interpretative phase, the data collected on vulnerability and strength factors are critically selected with the aim of formulating a judgment on the mechanical quality of the urban fabric and therefore prefiguring the expected damage related to the precariousness observed. In the project phase, the intervention criteria for the mitigation of vulnerabilities are established and the economic evaluation of interventions was carried out with the aim of an efficient, effective and fair allocation of the public financial resources.

3.2 Vulnerability Reduction Assessment and Programming Model The convergence of observations, assessments, and decisions toward an appropriate vulnerability reduction process needs the coordination of heterogeneous effects of the public expenditure, that are usually difficult to compare, such as direct and indirect private externalities [32, 33, 36], individual security, property market price increase [27]. From the economic point of view, the coherence between the value of investments and the value of security concerns two components of the calculation of seismic risk: hazard and exposure. The measurement of the hazard is affected by: 1. the randomness of the seismic event, whose natural origin involves geotechnics; 2. the perception of such an eventuality by the public, whose social characterization occupies the individual psychological sphere, and the shared ethics [9, 30]. The extent of the exposure varies according to the different “value qualities” attributable to the vulnerable assets; the latter’s monetary measurement, in fact, can be a significant benchmark in the comparison between the increase in security and the cost to be incurred to achieve it. In this prospect, once the cost of the works has been calculated, it is possible to generate a coherent multiplicity of budget allocation hypotheses by combining complementary performances. The general perspective of such a large and complex program of works reveals the complementarity between the completeness of the interventions and the extension of

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the area involved, between which the trade-off relation needs to be defined in order to maximize the cost-effectiveness function. The evaluation model applies an algorithm to a dataset whose descriptive contents are transformed into assessments generating a structured set of intervention scenarios each of which performs different and complementary degrees of completeness and security on the urban scale. The model coordinates the structural, material, geometric, technological, typological, and maintenance characteristics of the studied units, relevant for: (a) characterising their static attributes (seismic vulnerability); (b) hypothesising the intervention scenarios ranging over the different degrees of vulnerability; (c) calculating the costs of typical bundles of works for each building unit (the façade interfering with the public areas); (d) adjusting the intensity and extent of the interventions; (e) mapping the interventions corresponding to each combination of intensity and extension of the whole program of works; (f) calculating the total cost of each hypothesis thus by defining the trade-off functions [24] between the intensity and extension of the works. The model expands the ELC-based approach taking into account the town as a whole, instead of the strategic facilities and the road network connecting them, and in addition only over the emergency phase.

3.2.1

Calculation of Vulnerability

The calculation of vulnerability measures the risk that the façades interfering with the evacuation and rescue routes may overturn and collapse, obstructing them and/or affecting the safety of the fleeing people and rescuers. Vulnerability associated to each Façade Unit (FU) facing the public spaces is calculated as a numerical index measuring the ground acceleration level capable of triggering elementary over-turning kinematic mechanisms [31]. This indicator (α0 ) (NTC 2008), takes into account: (i) the presence and extent of tapers, (ii) the direction of the floor main beams (parallel or perpendicular to the façade wall), (iii) the presence of tie-rods, (iv) the effectiveness of bonding between the façade and the (orthogonal) shear walls” [8]. As an index of vulnerability, each acceleration coefficient is associated with: works necessary to reduce vulnerability mostly aimed at avoiding the façade overturn; additional or ancillary completion works, such as those for securing elements soaring above the roofs, e.g., the chimneys, as well as the external and internal finishing works. This distinction is important for decisions regarding the level of completeness of the intervention program, as discussed below. In the rows, the database contains all the FU, u i ; in the columns all the characteristics necessary to calculate the acceleration coefficient are reported. A low acceleration coefficient indicates a high vulnerability, and vice versa.

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3.2.2

11

Cost Calculation

Based on the acceleration coefficient of each UF, the algorithm selects the required safety measures, starting from the most common, such as inserting chains and filling of superficial breaks, up to the most complete, substantial and/or invasive ones, such as integration of masonry damaged by passing breaks, addition of reinforced masonry, securing of jutting and towing elements, and external and internal finishing works related to both walls and ceilings; these works are articulated in 36 items each of which is associated to a unit price. The elementary costs are then aggregated to calculate the total cost of each intervention scenario, generated by varying the intensity of the interventions and/or their extension. The intensity depends on the degree of completeness of the interventions with the same extension; the extension is the number of FUs involved with the same degree of completeness. The result is two cost functions, one intensive, C( j), the other extensive C(k). The intensive cost function relates the total cost of each façade unit u i , and the type of intervention, which depends on the bundle of works b jk associated with u i . Each bundle includes works corresponding to the entries of the Emilia Romagna Region Official Bill of Quantities for the public works, b jk ∈ B, where B is the set of all works referable to the specific scenario hypothesis. The b jk package can contain more or less works, according to their different relevance levels, distinguished as strictly necessary, jn , of primary public interest, j p , less invasive, jv , more or less adequate, ja . By combining the five degrees of completeness j with the five safety degrees k, 25 different hypothetical strategies with increasing costs have been defined [17]. The extensive cost function relates the number of FUs included in the scenario according to their vulnerability level. The FUs are grouped basing on five thresholds, k60% , k70% , . . . , k100% , delimitating five corresponding sub-ranges of the acceleration coefficient associated with each FU: k60% defines the sub-range of the façades whose acceleration coefficient is lower than the minimum one (αmin ) which corresponds to the highest vulnerability degree and the minimum number of FUs included; vice versa, k100% corresponds to the largest number of façades to be included in the scenario. The intermediate degrees are defined by progressively  adding up to  αmin = α + α − α a quarter of the range α − α . Then: k 70% max max min  min min    · 0.25; k80% = αmin + αmax − αmin · 0.50; k90% = αmin + αmax − αmin · 0.75.

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4 Application and Results 4.1 Valuation and Programming of the Interventions for Implementing the ELC Scenarios The above-mentioned database includes: identification codes of the FU in the building complex to which it belongs (Block and Architectural Unit); land registry identification; type; façade units per aggregate; number of interfering façades for each room; horizontal extension of the façade; wall thickness; room depth; surface of the room; number of floors above ground; gross surface area of the façade; heights of the different floors; average height of each floor; construction system; wall type; direction of the slab warping respect to the façade; soaring elements; braces, hypothesised to be required if the width of the front façade is greater than 6.50 m and the number of floors is greater than 1; the presence of tie rods; the presence of breaks. In this study, the data were obtained from the documentation provided by the Union’s Technical Office of Brisighella and by means of the quick inspections carried out on site. The tendency to overturn of the façade was calculated according to (a) a pessimistic prudential scenario referred to as the basic configuration, quantified by the coefficient α0b , and (b) an optimistic scenario referred to as the configuration changed and quantified by the coefficient α0v (Table 1 and Fig. 2). Depending on the degree of vulnerability of each of the 749 façade units analysed (only 685 need to be secured), the model identifies the interventions necessary for securing them. It should be noted that the interventions are not activated automatically and unambiguously but based on the type of strategy chosen by the decision maker. The costs for the securing works of each FU depend on the its vulnerability degree and on the characteristics of the AU. Table 1 Portion of the database displaying the calculation of the vulnerability indexes Calculation of the acceleration coefficient AU

FU

S1

H

L

N

p

k

r

n

Base (0) Varied (1)

Alpha0 b

Alpha0 v

1

1

0.4

6.3

8.1

2

2

1

0.0810

72

0

0.068

0.068

1

2

0.4

6.3

6.2

2

2

3

0.0828

72

1

0.068

0.150

1

3

0.4

3.2

6.3

1

1

1

0.1088

72

0

0.141

0.141

1

4

0.4

3.2

7.2

1

1

3

0.0244

72

1

0.130

0.172

1

5

0.5

3.2

6.2

1

1

3

0.0373

72

1

0.164

0.186

1

6

0.4

3.2

4.5

1

1

3

0.0595

72

1

0.134

0.178

1

7

0.4

6.3

4.9

2

2

3

0.1227

72

1

0.071

0.155

2

1

0.4

6.5

4.4

2

2

3

0.1377

72

1

0.070

0.156

2

2

0.4

6.5

3.0

2

2

3

0.1806

72

1

0.073

0.162

…

…

…

…

…

…

…

…

…

…

…

…

…

Seismic Vulnerability and Old Towns …

13

Number of FU 60% 70% 80% 90% 100%

Fig. 2 Map of the vulnerability of the Façade units of the old town of Brisighella. The table classifies the Façade units by vulnerability degree

Figure 3 summarises the pattern for the selection of the works according to each strategy and indicates the costs of the 25 strategies corresponding to the possible combinations of the five different degrees of completeness and security. A further function of the model is mapping the 25 different scenarios providing information on the FU for which the intervention is necessary (given by the position of the bubbles in the map) and a graphic representation of the cost (given by the dimension of the bubbles), as sampled in Fig. 4 displaying four of the 25 strategies. In this figure, the position of the scenario on the different isocost curves of the graph is shown. The sequence represents four strategies with increasing costs due to the simultaneous improvement in completeness and security. Completeness degree 2 3 4 1 1 1 0.3 1 1 1 1 0.5 1 1 1 1 1 0.7 1 1 1 1 1 1 1 1 1 1 1

(a)

5 1 1 1 1 1 1 1 1

Grado di completezza Completeness degree UF Numero Number of fixed in sicurezza Units Façade

Kind of works necessary unnecessary public private min security max security not invasive invasive

1

2

3

4

5

196

0.71

1.11

1.29

1.65

2.44

232

0.83

1.30

1.52

1.95

2.89

374

1.39

2.04

2.33

3.03

4.57

577

2.10

2.96

3.33

4.34

6.60

685

2.46

3.42

3.84

4.99

7.57

(b)

Fig. 3 a Scheme for the progressive definition of completeness; b table of total cost for each strategy given by the combination of completeness and security

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S. Giuffrida et al.

5 Completeness degree

Completeness degree

5

4

3 0.658

2

1

4 1.584

3

2

1 50

99

242

457

684

50

Number of FU involved

99

242

457

684

Number of FU involved

3.842

5

5 Completeness degree

Completeness degree

2.781

4

3

2

1

4

3

2

1 50

99

242

457

684

Number of FU involved

50

99

242

457

684

Number of FU involved

Fig. 4 Mapping of the different layouts of strategies having an increasing total cost

In Fig. 5, instead, the strategies displayed are those having approximately the same cost so that they differ regarding the completeness and security degree.

5 Discussion and Conclusions The proposed model outlined a wide range of options concerning the possible combinations of the overall degree of security, corresponding to the number of buildings secured, from 196 to 685 FU out of 749, and the budget needed to cover the total cost, ranging from 0.71 to e7.57 million. The central scenario envisages that 374 FUs can be secured by means of average completeness level interventions, having a total cost of e 2.33 million.

Seismic Vulnerability and Old Towns …

15

5

5 Completeness degree

Completeness degree

1.620

4 1.584

3

2

1

4

3

2

1 50

99

242

457

684

50

Number of FU involved

242

457

684

5 Completeness degree

5 Completeness degree

99

Number of FU involved

4

3

2

4

3

2

1.159

1

1.159

1 50

99

242

457

Number of FU involved

684

50

99

242

457

684

Number of FU involved

Fig. 5 Mapping of the different layouts of the strategies with (approximately) the same total cost

The table of Fig. 3b and the diagrams of Figs. 4 and 5, taken along the main diagonal, measure the increase in cost corresponding to the increase in both resilience and completeness. If, on the other hand, the table and the diagram are taken along the isocost function, the trade-off relationships between the completeness of the interventions and the degree of resilience given the same budget and for each amount are shown. The combination, integration, and consequentiality of factual, axiological and decisional aspects show that this study considers the ELC as an opportunity to go beyond its original purpose and immediate significance. The ELC provides some early information about the minimum adaptiveness of the urban fabric lest it lose its identity, in the prospect to implement a stricter proactive policy aimed at complementarily reducing the future implementation of the consequent reactive policy [34, 35].

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Since the urban centre can be considered resilient only when the ELC is fully realized, it is necessary to coordinate interests and motivations of all households involved, given the unequal distribution of the positive externalities associated with the ELC [23]. The presence of these externalities allows the local administration to start negotiations on the works to be subsided and, as a consequence, on the dimensions of the incentives [25] according to the general trend of the urban policies focused on the trade-off between efficiency and fairness [28]. Planning the seismic retrofit at the urban scale, in fact, involves a well-structured public–private partnership, able to capture all benefits coming from both the avoided reconstruction costs, and any real estate externalities [12–14, 16, 26, 37], to be taken into account in order to redistribute the value added generated as a result of a seismic retrofit program coordinated by the public.

References 1. Barbat AH, Carreño ML, Pujades LG, Lantada N, Marulanda MC (2010) Seismic vulnerability and risk evaluation methods for urban areas. A review with application to a pilot area. Struct Infrastruct Eng 6:17–38. https://doi.org/10.1080/15732470802663763 2. Carocci CF (2012) Small centres damaged by 2009 L’Aquila earthquake: on site analyses of historical masonry aggregates. Bull Earthq Eng 10:45–71. https://doi.org/10.1007/s10518-0119284-0 3. Carocci CF (2013) Conservazione del tessuto murario e mitigazione della vulnerabilità sismica. Introduzione allo studio degli edifici in aggregato. In Blasi C (ed) Architettura Storica e Terremoti. Protocolli Operativi per La Conoscenza e La Tutela. Wolters Kluwer, Italy, pp 138–153 4. Carreno ML, Cardona O, Barbat AH (2007) Urban seismic risk evaluation: a holistic approach. Nat Hazard 40:137–172 5. Christodoulou SE, Fragiadakis M, Agathokleous A, Xanthos S (2013) From historical and seismic performance to city-wide risk maps. In: Tesfamariam S, Goda K (eds) Handbook of seismic risk analysis and management of civil infrastructure systems. Science Direct, Amsterdam, The Netherlands, pp 247–267. https://doi.org/10.1016/b978-0-12-813652-2.000 08-6 6. Circo C, Giuffrè M (2018) La pianificazione urbanistica come possibile sovrapposizione di strategie per la riduzione del rischio sismico. Considerazioni sul Piano Regolatore della Sismicità dei Comuni dell’Unione della Romagna Faentina. Urban Inf 278:49–51 7. Conti T, Salvini V, Galeati S (2019) Comune Di Brisighella, Piano Di Recupero Del CS di Brisighella; Comune Di Brisighella: Ravenna, Italy, 1994. http://www.romagnafaentina.it/I-ser vizi/Edilizia-e-Urbanistica/Tutela-e-governo-del-territorio/Regolamento-Urbanistico-ed-Edi lizio-RUE/Regolamento-Urbanistico-ed-Edilizio-RUE-Intercomunale-Approvato. Accessed 29 Sept 2019 8. Dolce M, Kappos A, Masi A, Penelis G, Vona M (2005) Vulnerability assessment and earthquake damage scenarios of the building stock of Potenza (Southern Italy) using Italian and Greek methodologies. Eng Struct 28:357–371 9. Dolce M, Manfredi G (2015) Libro Bianco Sulla Ricostruzione Privata Fuori Dai Centri Storici Nei Comuni Colpiti Dal Sisma Dell’Abruzzo Del 6 Aprile 2009, Doppiavoce, Napoli, Italy 10. Formisano A (2017) Local and global-scale seismic analyses of historical masonry compounds in San Pio delle Camere (L’Aquila, Italy. Nat Hazards 86:465–487. https://doi.org/10.1007/s11 069-016-2694-1

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11. Fuentes DD, Laterza M, D’Amato M (2019) Seismic vulnerability and risk assessment of historic constructions: the case of masonry and adobe churches in Italy and Chile, vol 18. Springer, Cham, Switzerland, pp 1127–1137. https://doi.org/10.1007/978-3-319-99441-3_122 12. Gabrielli L, Giuffrida S, Trovato M R (2015) From surface to core: a multi-layer approach for the real estate market analysis of a central area in Catania. In: Gervasi O et al (eds) Proceedings of the international conference on computational science and its applications, ICCSA 2015. LNCS Part III, pp 284–300. https://doi.org/10.1007/978-3-319-21470-2_20 13. Gabrielli L, Giuffrida S, Trovato MR (2016) Functions and perspectives of public real estate in the urban policies: the sustainable development plan of Syracuse. In: Gervasi O, Murgante B, Misra S, Rocha AMAC, Torre CM, Taniar D, Apduhan B O, Stankova E, Wang S (eds) Proceedings of the international conference on computational science and its applications (ICCSA 2016), Beijing, China, 4–7 July 2016, vol IV. Springer International Publishing, Cham, Switzerland, pp 13–28. https://doi.org/10.1007/978-3-319-42089-9_2 14. Gabrielli L, Giuffrida S, Trovato MR (2017) Gaps and overlaps of urban housing sub market: a fuzzy clustering approach. In: Green energy and technology. Springer, Berlin, Germany, pp 203–219. Issue 9783319496757. https://doi.org/10.1007/978-3-319-49676-4_15 15. Giannelli A, Giuffrida S, Trovato MR (2018) Il Parco Madrid Rio. Valori simbolici e valutazione contingente, Valori e valutazioni, E-Flow Dei Tipografia del Genio Civile, Rome, Italy, pp 75–86. ISSN 2036-2404 16. Giuffrida S, Ferluga G, Valenti A (2014) Clustering analysis in a complex real estate market the case of Ortigia (Italy). In: Murgante B et al (eds) ICCSA 2014. LNCS, vol 8581, Springer, Cham, Switzerland, pp 106–121. ISBN 9783319091495 17. Giuffrida S, Gagliano F, Tocci C (2015) Emergency limit condition in the old towns. Axiology of seismic vulnerability and design. Valori Valutazioni 15:55–67 18. Giuffrida S, Ferluga G, Ventura V (2015) Planning seismic damage prevention in the old towns value and evaluation matters. In Gervasi O et al (eds) ICCSA 2015. LNCS Part III. Springer, Berlin, Germany, pp 253–268. https://doi.org/10.1007/978-3-319-21470-2_18 19. Giuffrida S, Napoli G, Trovato MR (2016) Industrial areas and the city. Equalization and compensation in a value-oriented allocation pattern. In Gervasi O et al (eds) ICCSA 2016. LNCS Part IV. Springer International Publishing, Cham, Switzerland, pp 79–94. https://doi. org/10.1007/978-3-319-42089-9_6 20. Giuffrida S, Casamassima G, Trovato MR (2017) Le norme EMAS-ISO nella valutazione della qualità del servizio idrico integrato. Aestimum 70:109–134. https://doi.org/10.13128/ Aestimum-21084 21. Giuffrida S, Trovato MR (2017) A semiotic approach to the landscape accounting and assessment. An application to the urban-coastal areas. In: Salampasis M, Bournaris T (eds) Proceedings of the 8th international conference on information and communication technologies in agriculture, food and environment (HAICTA 2017), Chania, Crete Island, Greece, 21–24 Sept 2017. Springer International Publishing, Cham, Switzerland, pp 696–708. ISSN 16130073 22. Giuffrida S, Napoli G, Trovato MR (2018) The urban being between environment and landscape. On the old town as an emerging subject. In: Bevilacqua C et al (eds) Smart innovation, systems and technologies, 3rd international new metropolitan perspectives. local knowledge and innovation dynamics towards territory attractiveness through the implementation of Horizon/Europe2020/Agenda2030, vol 101. Springer Science and Business Media, Deutschland GmbH, pp 269–276. https://doi.org/10.1007/978-3-319-92102-0_29 23. Giuffrida S, Trovato MR, Giannelli A (2019) Semiotic-sociological textures of landscape values. Assessments in urban-coastal areas. In: Salampasis M, Bournaris T (eds) Proceedings of the international conference on information and communication technologies in agricultural food and environment (HAICTA 2017), Chania, Crete, Greece, 21–24 Sept 2017, vol 953. Springer, Cham, Switzerland, pp 35–50. https://doi.org/10.1007/978-3-030-12998-9_3 24. Manganelli B, Vona M, De Paola P (2018) Evaluating the cost and benefits of earthquake protection of buildings. J Eur Real Estate Res 11:263–278. https://doi.org/10.1108/JERER-092017-0029

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Capturing the Public Value in the Public/Private Zoning Agreements: Evidence from Italian Municipalities Ezio Micelli and Agostino Valier

Abstract In recent decades, the rules of planning have changed radically. From an authoritative approach, planning has become an activity of dialogue between the various operators that finance and accomplish urban projects. Public-private agreements have met with great success and they became an ordinary instrument in city planning. Agreements on zoning allow more advantageous planning rules and, as a result, landlord benefits a capital gain that is shared with public authority. While the nature of economic gain and the reasons why such is shared have been the object of a rather large investment by scholars, less effort has been devoted to understand how these concepts are applied in the administrative action of public authorities. The research investigates methodologies and techniques with which public administrations evaluate public/private partnerships in development projects resulting from zoning agreements. The study identified Veneto Region as a useful area of research. The administrative acts approved by the municipalities with regard to the public/private partnerships allowed by Veneto planning law (LR 11/2004) and by Italian national decree (380/2001, i.e. derogatory building permits) have been examined. The survey considers all the provincial capitals of Veneto region and, in order to have a representative sample of smaller towns, all the municipalities of the Vicenza province. Conclusions point out that most local authorities prescribe automatic or quasi-automatic procedures as assessment methodologies. They use values often established for tax purposes to determine the capital gain resulting from zoning rules modification. The application of automatic procedures does not necessarily lead, though, to quality results. Lastly, in territories substantially homogeneous, only a few kilometers away, levy rates can be very different, posing obvious and relevant problems of effectiveness and fairness. E. Micelli (B) Dipartimento di Culture del progetto, Università Iuav di Venezia, Venice, Italy e-mail: [email protected] A. Valier Dipartimento di Ingegneria Civile Edile e Ambientale, Università degli studi di Padova, Padua, Italy e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Morano et al. (eds.), Appraisal and Valuation, Green Energy and Technology, https://doi.org/10.1007/978-3-030-49579-4_2

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Keywords Planning gain · Rent · Public/private agreements · Public value

1 Introduction Public administrations and private actors have a radically different way of relating to each other than they had in the past. From a purely regulatory relationship to a relationship of dialogue aimed at optimising the objectives of both parties. The regulatory tools used in the new way of planning are public/private agreements, urban equalisation and the transfer of building rights. Partnership processes have made the instrument of expropriation less indispensable. The space requirements and objectives of the collective city can be achieved even without public authority proceedings against private interests. The agreements between the administration and the private sector have thus become an ordinary tool for the transformation of the city. The discontinuity is clear with respect to government formulas with no opportunity for comparison with ownership. Numerous studies have deepened the role that evaluation plays in such instruments, focusing attention on the tools available to local authorities. Less effort has been made to understand how, in the practice of administrative choices, such instruments have found application. The research aims to verify how and with which instruments administrations have undertaken the evaluation of agreements between administration and private individuals. The research is organised in four parts. The first part considers the main evaluation themes related to agreements and explains the objectives of the research. The second part illustrates the data used: the acts produced by several municipalities, of all sizes, in the Veneto Region. The third analyses the data with reference to the economic and estimative aspects. The fourth interprets the analyses carried out for an assessment of the use of evaluation in town planning agreements.

2 The Planning Agreements: From International Experiences to Local Norms The government of the city through agreements is a matter of international interest. Wherever there is a need for rules governing the ways in which private parties benefit from derogations from the general rules of planning. The structure of the rules in fact reflects the distribution of the land rent and any variation can lead to imbalances in the various parts [15, 18, 21, 25, 29, 32]. In Italy, legislative competence for planning is delegated to the regions. Consequently, the tools of public-private agreements are regulated by regional laws. The urban planning legislation of several Italian regions has implemented the possibility

Capturing the Public Value in the Public/Private Zoning …

21

of governing urban transformation projects through agreements. The agreements punctually redefine the legal conditions of transformation of the city. The initiatives of the regional legislator have also been matched by the action of the national legislator. With the amendment of Italian Presidential Decree 380/2001, the local government can make timely changes to urban planning forecasts with permits to build in derogation also in favour of private individuals [2, 7, 12–14, 30, 31]. In the face of new and more advantageous urban planning rules, there is also an economic benefit for the agreed project, the amount of which is shared between the administration and private parties. The levy by means of negotiated agreements is the alternative to the levying of capital gains by tax. In Italy, as in other international experiences, the option of the levy in urban planning has been considered the most effective because it is the one that identifies with greater precision the beneficiaries of the valorization and its amount [1, 16, 17, 23]. In fact, taxation has the disadvantage of affecting indifferently the properties that benefit from the increase in value and properties that, while benefiting from public action, do not intend to monetise the increase in value, at least in the short term. The modalities of value distribution are not specified in regional laws, leaving room for the action of local authorities to determine the rates of levy and the nature of goods and services for the benefit of the community [11]. The national legislator has laid down precise rules the nature of the goods and services to be granted to the administration and on the rate, setting it at a minimum of 50% of the increase in value. For theoretical and methodological reasons, the research has focused on the nature of value. At stake is only one specific component of value—the change of real estate income [8]. In fact, income is public value, and therefore its partial restitution to the community seems not only legitimate, but theoretically necessary to restore conditions of allocative efficiency [6, 9]. With regard to the methods of estimating the surplus value and its withdrawal, the acquisition of the conceptual and operational instruments relating to the agreements and the determination of the benefit in favour of administrations and private individuals appeared less obvious and linear than expected. Research conducted in the areas most characterised by the use of partnership instruments has highlighted the difficulty of determining the levy for the benefit of the public party and has raised doubts about the forms of surplus-value levy [10, 11, 26]. The centrality of the operational phase makes it necessary to carry out more in-depth research on this issue. The question is whether, and how, the municipal administrations have equipped themselves to assess the surplus value deriving from specific variants and how they have proceeded to divide it between property and community. What is at stake is the success of reforms that otherwise risk being outlined only in the part of the principles and that are partial and incomplete in the implementation phase.

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3 Dataset: Regulations of Provincial Capitals and Small Towns in Veneto Region The research considers some cities in the Veneto region. The choice of this region is explained by the long tradition with public-private partnership instruments: the 2004 urban planning law has provided since the middle of the last decade the possibility to modify the instrument with confirmatory value with specific agreements. The municipalities have therefore had a long apprenticeship with the instruments of the agreements. The measures taken in this regard cannot therefore be considered merely experimental. Almost fifteen years after the town planning reform law, the administrations have had sufficient time to prepare all the appropriate instruments to implement the new rules from an operational point of view. The research has examined the administrative measures prepared by local authorities. The research wanted to investigate cities of different rank. The hypothesis is that large municipalities have a different degree of technical capacity from small towns and therefore the choices may be different. The provincial capital cities were therefore examined and the survey then considered all the municipalities of one of the Veneto provinces—the province of Vicenza—in order to have a representative sample of smaller towns. The choice of the municipalities in the province of Vicenza is justified by their location on the A4 highway, the main infrastructure axis of the Region, and by the presence of a wide and differentiated framework of territories—the hills, the high plains, the foothills—which make this province largely representative of the entire region. The survey of the provincial capitals has acquired the documentation of the seven Veneto provincial capitals—Venice, Verona, Rovigo, Padua and Treviso—with the exception of Belluno, which has not yet regulated the agreements. The survey on smaller towns covered all the municipalities in the province of Vicenza. Of the 120 municipalities contacted, 94 replied to the request for documentation. Of these 22 stated that they had not prepared any measures regarding the agreements, while 72 municipalities provided the documentation requested. The municipalities that provided useful documentation for the survey represent less than half of the total number of municipalities. However, local administrations with the appropriate instruments to regulate public-private agreements represent just over 70% of the population and a similar percentage of businesses in the province. This confirms the significance of the documentation collected with respect to the phenomenon under investigation. Adding up the 72 municipalities of the province of Vicenza to the remaining five provincial capitals, the survey sample amounts to 77 municipalities.

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23

4 Analysis. Local Norms: Bases of Valuation, Assessment Methodologies and Rates of Levy On the basis of the data collected, whether we consider the provincial capitals or minor municipalities, the attention of the administrations to this issue seems significant. This is more evident where demographic and economic pressure are most important. The first issue considered in the survey concerns the nature of economic value. The documents of the municipalities reveal how in the absolute majority of cases (71 of the 77 municipalities with documentation) the administration has identified the real estate surplus value resulting from the urban variant as the reference value for the subsequent subdivision between property and local authority. The remaining 6 municipalities choose other values, thus reducing the comparable cases from 77 to 71 municipalities. The differences between the positions of the municipalities are more evident when considering the estimation procedures. The first striking aspect concerns the differentiation between the estimation of the value of assets before and after the change of urban planning instruments. The second aspect that emerges concerns the use of comparative procedures. In the absolute majority of cases (67 out of 71 municipalities) the administrations propose the estimation by comparative procedure of the assets before and after the change of the urban planning instruments. The analytical procedure is identified in 8 cases as the only procedure proposed and in 11 cases as a possible alternative to the comparative procedure. In the absolute majority of cases, administrations still refer to values established for expropriation or tax purposes. The estimate of the value of areas before the variant is often (38 out of 71 municipalities) associated with the average agricultural value VAM,1 traditionally used in the estimate of expropriation allowances, while the value of building areas used for the calculation of the IMU2 represents a reference value in the municipal resolutions examined. A cross-reference between population data and the choice of procedures shows a positive relationship between the size of municipalities and the use of analytical estimation procedures. In fact, if we consider the municipalities surveyed according to their population and assume three different levels of complexity in estimates— exclusively tabular values, tabular values and other procedures, exclusively estimation procedures specifically related to the case of estimation—a significant direct relationship can be identified (Table 1). The determination of rates represents an area of significant differentiation between municipalities. An initial classification distinguishes between municipalities that have opted for a fixed rate—often determined at 50% of the surplus value—and 1 VAM

stands for Valore agricolo medio, which means average agricultural value. In Italy it is used as a value on which to base expropriations of land that cannot be built on. It is updated every year by a provincial commission. 2 IMU is the tax on real estate property in Italy. Its rates vary for each municipality.

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Table 1 Assessment methodologies and municipalities per population Assessment methodologies

Municipalities

Population

Count

Mean

Only tabular values

41

8.438

6.542

8.731

1.038

51.149

Tabular values and other procedures

22

18.978

11.248

29.852

1.317

111.620

4

140.844

147.697

116.171

6.661

261.321

Exclusively estimation procedures

Median

Standard deviation

Minimum

Maximum

those that have opted for a variable measure of the levy, which varies according to the characteristics of the intervention or the applicant. The choice of variable rates is preferred in small centres. The use of differentiated rates of levy concerns 48 municipalities, about twice the 23 administrations that have opted for a fixed rate (Table 2). In non-capital municipalities, the variability of the rate is accompanied by lower rates of levy. Even if the maximum rate is taken as a term, just under a third of the municipalities have set the levy value at 50%, which is the threshold set by the legislator in the case of derogating building permits. The Fig. 1 highlights the relationship between the size of the centres and the value of the rates, confirming the higher taxation by larger centres and the propensity of smaller centres to stay below the 50% threshold. Table 2 Number of municipalities per class of rates

Rate

No of municipalities (fixed rate)

No of municipalities (variable rate)

>50%

2

3

50%

17

11

40–49%

1

11

30–39%

1

13

1, the risk level of the project still moves “in the same direction” as the market but is higher than the average. Mathematically, β is expressed by: β=

cov(ri , rm ) varrm

(2)

In which the numerator is given by the covariance between the return on the generic investment ri and the market return rm , while the denominator coincides with the variance of the market return rm . Graphically, β corresponds to the inclination of the straight line that best interpolates in a Cartesian diagram x-y the excess returns of the investment compared to the excess returns of the market: ri = α + β·rm + ε

(3)

With α = (1 – β) · rf and ε statistical error measuring the reliability of the estimate made [6, 27].

3.2 A Statistical Methodology to Estimate the Tolerability and Acceptability Thresholds of the Investment Risk The objective of the research is to estimate investment risk thresholds for building projects. This can be done by establishing the minimum acceptable return for the investor. This minimum return depends on several factors, among others: the type of project, the risk appetite of the funder and the specific socio-economic conditions of the territory in which the intervention is located. The theoretical reference for establishing risk tolerability and acceptability thresholds is the CAPM, which enables to compare the investment risk not only to the return of the production sector in which the project under consideration falls, but also to the return of the market as a whole.

Cost-Benefit Analysis and Investment Risk Assessment …

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In fact, this model allows assessing the risk-adjusted discount rate r(β), which can be interpreted as the minimum expected rate of return from an investment project with a β risk profile [13, 23]. In particular, the joint use of the ALARP principle, the CAPM and statistical survey methods makes it possible to have the acceptability and tolerability thresholds of risk for building construction projects. The acceptability threshold Ta = r(β a ) is defined as the minimum expected rate of return from an investment project whose risk profile represents, on average, that of companies in the sector with “worst” returns, i.e. those statistically represented in the first quartile (25-th percentile). So: r(βa ) = Ta = rf + βa · (rm −rf )

(4)

In (4) rf is the risk free rate, estimated as the average yield on 10-year government bonds. β a represents the systematic “acceptable” risk, a function of the return of first quartile companies rIQ and the return of an ideal “market portfolio” rm composed by the total number of manufacturing companies in a country. Therefore, β a is given by the inclination of the straight line that best interpolates the average excess return YIQ of the civil enterprises represented in the first quartile compared to the average excess return X of the market. In formula: X = rm −rf

(5)

YIQ = rIQ −rf

(6)

where rIQ is the average Return On Investment (ROI) of lower quartile companies. The tolerability threshold Tt = r (β t ), on the other hand, coincides with the minimum expected rate of return of a project whose risk profile is on average that of companies in the sector statistically represented in the second quartile (50-th percentile): r(βt ) = Tt = rf + βt · (rm −rf )

(7)

with β t “tolerable” systematic risk, a function of the return rIIQ of second quartile (or median) companies and market rate of return rm . β t corresponds to the slope of the straight line that best interpolates the average excess returns YIIQ of civil enterprises in the second quartile compared to the average excess return X of the market: YIIQ = rIIQ −rf with rIIQ equal to the average ROI of companies in the second quartile.

(8)

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3.3 Phases of the Risk Assessment Model The estimation of the acceptability and tolerability thresholds of investment risk is integral part of the proposed risk assessment model. The six phases in Fig. 3 retrace the logical-operational sequence of the risk management process in Fig. 1.

Definition of the objectives The investment failure must be avoided, i.e. the performance indicator must be checked for positive values

Identification of the risks Study of the risky variables of the system, i.e. those that most influence the profitability of the project

Risk Analysis Estimation of the cumulative probability distribution of the Internal Rate of Return (IRR)

Risk Assessment Comparison between the expected value of IRR and thresholds of acceptability (Ta) and tolerability (Tt) of the risk, estimated as described in section 3.2

Risk Treatment If the risk falls in the ALARP or "tolerable" region, then mitigation measures are sought until the costs are out of proportion to the benefits

Assessment of Residual Risk Comparison between the expected value of IRR post mitigation interventions and thresholds of acceptability (Ta) and tolerability (Tt) of the investment risk Fig. 3 Phases of the risk assessment model

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4 Threshold Values for the Investment Risk in the Construction Sector in Campania Region (Italy) The methodology described above is implemented to estimate the risk acceptability and tolerability thresholds for investments in the sector ATECO 41 «construction of buildings» in Campania Region (Italy). For this purpose, the parameters common to the formulas (4) and (7) are first estimated, i.e. the risk-free rate rf and the market rate of return rm . The risk free rate rf is equal to the rate of return on 10-year Treasury bonds (BTP) in the time interval 2009–2018 (source: Ministry of Economy and Finance, Treasury Department). From the elaborations it derives that rf = 3.44%, as in Table 1. The market rate of return rm is assumed equal to the average ROI of the main 2095 Italian manufacturing companies in the decade 2009–2018 (source: MEDIOBANCA Studies Office). The analysis returns a value of rm = 7.76%. The results are summarized in Table 2. For the estimation of the acceptability threshold, the systematic risk β a should be assessed. This risk relates to civil enterprises in Campania in the first quartile, i.e. 25% of the regional enterprises studied with the lowest ROI. Therefore, β a corresponds to the inclination of the straight line that best interpolates the average excess return YIQ = rIQ − rf of the regional construction companies of first quartile compared to the average excess return of the market X = rm − rf in the time interval 2009– 2018. With reference to (6), rIQ is equivalent to the average ROI of companies in Campania with ATECO code 41 of the first quartile. The data are provided by the Analisi Informatizzata delle Aziende Italiane (AIDA) database. It is edited by Bureau Van Dijk and it contains financial and commercial information on over 500,000 corporations operating in Italy. The elaborations, summarized in Table 3 and in Fig. 4, return β a = 1.45. The tolerability threshold is instead a function of the systematic risk β t . This risk is associated with the second quartile—or median—enterprise in Campania. Thus, β t corresponds to the slope of the straight line that best interpolates the average excess Table 1 Average rate of return on BTPs (source Ministry of Economy and Finance, Treasury Department) Year

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

rf (%)

4.32

4.01

5.25

5.65

4.38

3.00

1.70

1.40

2.14

2.54

Average (%)

3.44

Table 2 Average market return (source MEDIOBANCA Studies Office) Year

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

rm (%)

8.10

6.70

8.30

7.30

7.40

7.20

8.40

8.10

7.90

8.02

Average (%)

7.76

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Table 3 Estimation of β a Year

rm (%)

rIQ (%)

rf (%)

X (%)

YIQ (%)

2009

8.10

2.80

4.32

3.78

−1.52

2010

6.70

3.52

4.01

2.69

−0.49

2011

8.30

3.69

5.25

3.05

−1.56

2012

7.30

3.42

5.65

1.65

−2.23

2013

7.40

3.05

4.38

3.02

−1.33

2014

7.20

4.22

3.00

4.20

1.22

2015

8.40

4.41

1.70

6.70

2.71

2016

8.10

6.05

1.40

6.70

4.65

2017

7.90

6.73

2.14

5.76

4.59

2018

8.20

7.19

2.54

5.66

4.65

Average (%)

7.76

4.51

3.44

4.32

1.07

COV(X, YIQ )

0.000460875

VAR X

0.000317697

βa

1.45

Fig. 4 Regression line for β a estimation

return YIIQ = rIIQ − rf of the second quartile company compared to the average excess return X of the market. rIIQ is equivalent to the average ROI of companies with ATECO code 41 of the second quartile. The estimate return a β t = 1.02, as shown in Table 4 and Fig. 5. Ultimately, by implementing (4) and (7): Ta = rf + βa · (rm −rf ) = 3.44% + 1.45 · (7.66%−3.44%) = 9.71% Tt = rf + βt · (rm −rf ) = 3.44% + 1.02 · (7.66%−3.44%) = 7.84%.

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Table 4 Estimation of β t Year

rm (%)

rIIQ (%)

rf (%)

X (%)

YIIQ (%)

2009

8.10

8.32

4.32

3.78

4.00

2010

6.70

7.79

4.01

2.69

3.78

2011

8.30

8.04

5.25

3.05

2.79

2012

7.30

8.23

5.65

1.65

2.58

2013

7.40

8.16

4.38

3.02

3.78

2014

7.20

8.39

3.00

4.20

5.39

2015

8.40

9.03

1.70

6.70

7.33

2016

8.10

9.03

1.40

6.70

7.63

2017

7.90

8.58

2.14

5.76

6.44

2018

8.20

8.96

2.54

5.66

6.42

Average (%)

7.76

8.45

3.44

4.32

5.01

COV(X, YIIQ )

0.000323581

VAR X

0.000317697

βt

1.02

Fig. 5 Regression lines for the estimation of β t

The calculations show that an investment project in the building construction sector in Campania has a risk: – “not tolerable” if the expected value of its return is less than 7.8%; – ALARP if the expected value of its return is between 7.8 and 9.7%; – “broadly acceptable” if the expected value of its return is greater than 9.7%.

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5 Conclusive Remarks By integrating the As Low As Reasonably Practicable logic into traditional economic valuation protocols, the study proposes a model for investment risk assessment based on shared criteria and objective data. From the ALARP logic, widely consolidated in the management procedures of the industrial safety, the concepts of acceptability and tolerability threshold of risk are borrowed. In this field, a risk is as low as reasonably practicable if included between these two thresholds or if the costs to mitigate it appear disproportionate compared to the benefits that can be pursued. Since ALARP is considered to be “a general way of thinking”, it can also be applied in the civil field. As regards the estimation of the two thresholds for risk acceptance, the theoretical reference is the Capital Asset Pricing Model, which enables to compare the investment risk not only to the return of the production sector of the investment, but also to the return of the market as a whole. In particular, the acceptability threshold Ta coincides with the minimum expected return of a project with the risk profile of the “worst” companies in a given territory, i.e. those belonging to the lower quartile. The tolerability threshold Tt , on the other hand, is assessed as the minimum expected return on a project with a second quartile risk profile. The methodology is implemented for investments in the building construction sector in Campania Region (Italy). Thus, starting from the analysis of the official profitability indices of 627 regional construction companies, the values the thresholds of tolerability Tt and acceptability Ta are determined, respectively equal to 7.8% and 9.7%. The comparison between the return on investment expressed in probabilistic terms and the acceptance thresholds of the risk is a useful criterion for improving the informative panel on the economic viability of the project. The implications of such an approach on the selection process of the interventions to be financed, as well as the advantages for decision-makers are evident.

References 1. Ale BJM, Hartford DND, Slater D (2015) ALARP and CBA all in the same game. Saf Sci 76:90–100 2. Aven T (2015) Risk analysis, 2nd edn. Wiley, Chichester, England 3. Aven T (2016) Risk assessment and risk management: review of recent advances on their foundation. Eur J Oper Res 253:1–13 4. Battisti F, Campo O (2018) A procedure for determining the industrial profitability of settlement interventions in the appraisal of exceptional contribution of urbanisation. In: Gervasi O et al (eds) Computational science and its applications—ICCSA 2018. Springer International Publishing, Cham, Switzerland. https://doi.org/10.1007/978-3-319-95168-3_3 5. Benintendi R, De Mare G, Nesticó A (2018) Upgrade the ALARP model as a holistic approach to project risk and decision management: a case study. Hydrocarb Process 97(7):77–82. ISSN 0018-8190 6. Black F (1993) Beta and return. J Portf Manag 20:8–18

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7. Black F, Jensen M, Scholes M (1972) The capital asset pricing model: some empirical tests. In: Studies in the theory of capital markets. Praeger Publishers, New York, pp 79–12 8. Calabrò F, Cassalia G, Tramontana C (2019) Evaluation approach to the integrated valorization of territorial resources: the case study of the Tyrrhenian area of the metropolitan city of Reggio Calabria. In: Calabrò F, Della Spina L, Bevilacqua C (eds) New metropolitan perspectives, ISHT 2018. Smart innovation, systems and technologies, vol 101. Springer, Cham, Switzerland, pp 3–12. https://doi.org/10.1007/978-3-319-92102-0_11 9. D’Alpaos C, Marella G (2014) Urban planning and option values. Appl Math Sci 8(158):7845– 7864. https://doi.org/10.12988/ams.2014.49744 10. De Mare G, Nesticò A, Benintendi R, Maselli G (2018) ALARP approach for risk assessment of civil engineering projects. In: Gervasi O et al (eds) Computational science and its applications— ICCSA 2018. Springer International Publishing, Cham, Switzerland, pp 75–86. https://doi.org/ 10.1007/978-3-319-95174-46 11. Della Spina L (2019) A multi-level integrated approach to designing complex urban scenarios in support of strategic planning and urban regeneration. In: Calabrò F, Della Spina L, Bevilacqua C (eds) New metropolitan perspectives, ISHT 2018. Smart innovation, systems and technologies, vol 101. Springer, Cham, Switzerland, pp 226–237. https://doi.org/10.1007/978-3-319-920993_27 12. French S, Bedford T, Atherton E (2007) Supporting ALARP decision making by cost benefit analysis and multi-attribute utility theory. J Risk Res 8:207–223 13. Gollier C (2011) Pricing the planet’s future: the economics of discounting in an uncertain world. Princeton University Press, New Jersey, US 14. Health and Safety Executive (1992) The tolerability of risk from nuclear power stations. Her Majesty’s Stationery Office, London 15. Health and Safety Executive (2001) Reducing risks, protecting people. Her Majesty’s Stationery Office, London 16. Jones-Lee M, Aven T (2011) ALARP—what does it really mean? Reliab Eng Syst Saf 96:877– 882 17. Lintner J (1965) Security prices, risk, and maximal gains from diversification. J Finance 20:587– 615 18. Maciotta R, Lefsru L (2018) Framework for developing risk to life evaluation criteria associated with landslides in Canada. Geoenviron Disasters 5–10 19. Meyer T, Reniers G (2013) Engineering risk management. De Gruyter Graduate, Berlin 20. Nesticò A (2018) Risk-analysis techniques for the economic evaluation of investment projects. In: Mondini G, Fattinnanzi E, Oppio A, Bottero M, Stanghellini S (eds) Integrated evaluation for the management of contemporary cities. SIEV 2016. Green energy and technology. Springer, Cham, Switzerland, pp 617–629. https://doi.org/10.1007/978-3-319-78271-3_49 21. Nesticò A, He S, De Mare G, Benintendi R, Maselli, G (2018) The ALARP principle in the cost-benefit analysis for the acceptability of investment risk. Sustainability 10(12). https://doi. org/10.3390/su10124668 22. Nesticò A, Maselli G (2020) Sustainability indicators for the economic evaluation of tourism investments on islands. J Clean Prod 248:119217. https://doi.org/10.1016/j.jclepro.2019. 119217 23. Nesticò A, Maselli G (2020) A protocol for the estimate of the social rate of time preference: the case studies of Italy and the USA. J Econ Stud 47(3):527–545. https://doi.org/10.1108/ JES-02-2019-0081 24. Nesticò A, Moffa R (2018) Economic analysis and Operational Research tools for estimating productivity levels in off-site construction [Analisi economiche e strumenti di Ricerca Operativa per la stima dei livelli di produttività nell’edilizia off-site]. Valori e Valutazioni 20:107–126. ISSN: 2036-2404 25. Nesticò A, Morano P, Sica F (2018) A model to support the public administration decisions for the investments selection on historic buildings. J Cult Herit 33:201–207. https://doi.org/10. 1016/j.culher.2018.03.008 (Elsevier)

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26. Redmill F (2010) ALARP explored. Computing Science, University of Newcastle upon Tyne. Newcastle upon Tyne, UK 27. Rosenberg B, Guy J (1976) Beta and investment fundamentals. Financial Anal J 32(4):62–70 28. Sharpe W (1964) Capital asset prices: a theory of market equilibrium under conditions of risk. J Finance 19:425–442 29. Society of Risk Analysis (2015) Glossary society for risk analysis. www.sra.com/resources. Accessed 15 Dec 2019 30. Zio E (2007) An introduction to the basics of reliability and risk analysis. World Scientific Publishing, Singapore

New Public Management and Economic Feasibility Assessment of PPP Projects. A Case Study in Calabria Francesco Calabrò, Lucia Della Spina, and Basiliana Randò

Abstract This contribution aims to illustrate an application case of a model for assessing the economic feasibility/sustainability of projects, in order to test their applicability and, possibly, to identify its limits. The contribution is divided into two parts, one of a methodological nature, the other illustrates a case study. The first part summarizes the SOSTEC model, built to evaluate the feasibility/economic sustainability of PPP projects for the reuse of unused public buildings, within the framework of the Strategic Plans and Integrated Programs. The second part illustrates, as a case study, the application of the SOSTEC Model to the hypothesis of reuse of a historic building located in Mammola, a small town in an Inner Area of Calabria. Keywords Economic feasibility/sustainability of projects · Public-private partnership · Unused public buildings · Strategic plans · Integrated programs

1 Introduction 1.1 The New Demand for Public Investment Assessments The need to develop valuation techniques capable of highlighting the economic implications of public investments has its roots in the first half of the last century. In the expansion phase of public spending, which characterized that historical period, the evaluation questions had essentially two purposes: to verify the effective collective

F. Calabrò (B) · L. D. Spina · B. Randò PAU Department, Mediterranea University, Reggio Calabria, Italy e-mail: [email protected] L. D. Spina e-mail: [email protected] B. Randò e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Morano et al. (eds.), Appraisal and Valuation, Green Energy and Technology, https://doi.org/10.1007/978-3-030-49579-4_5

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utility of the projects and to identify the optimal solutions among several alternatives: it is in this context that are born the various techniques known as Benefit Cost Analysis [1] The progressive tightening of budgetary constraints, also due to the disappearance of the political reasons that had favored the expansion of the public deficit, has partially changed the nature of the assessment questions, in the context of a new organizational model of the Public Administration, known as New Public Management [2]. In fact, the needs mentioned above were accompanied by that of knowing the implications of the projects on public budgets during the management phase. Therefore, in the economic evaluation of public projects, criteria usually used in the evaluation of private investments have been introduced, aimed essentially at verifying the possibility of achieving the balance of the budget, according to a strictly financial logic [3] Or, in alternatively, to know in advance the size of the deficit generated by the project which would weigh on the budget of the managing body. It is in this context that SostEc is born, a model for the evaluation of the economic feasibility/sustainability of PPP projects for the reuse of unused public buildings, developed by the LaborEst Laboratory of the Mediterranea University of Reggio Calabria. This article aims to illustrate an application case of the model, in order to verify its applicability in certain contexts and, possibly, to identify its limits to be overcome through future research activities. Part I—Methodological Aspects

2 The SOSTEC Model in Strategic Planning and Integrated Programming The SostEc Model, in its entirety, has been illustrated in other publications, to which reference is made for exhaustive knowledge [4]: The most salient aspects are summarized below. The model was designed for the development of hypotheses of reuse of public (or of public value) unused buildings in the context of Strategic Planning and Integrated Programming processes; of these processes, it incorporates the path that leads to the identification of the intended use of the buildings: from that point on, we proceed with typical tools of the Economic Evaluation of Projects such as the analysis of cash flows [5]. In fact, SOSTEC contains a peculiar processing of the analysis of cash flows, the main elements of which are: – the alternative use of the Cash Flow Analysis or Discounted Cash Flow Analysis in relation to the envisaged form of PPP is provided: if it includes investments and management or only management. In the first case, obviously, we use the DCFA

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and we discuss more specifically the economic feasibility of the project: the object is the remuneration of the private capital invested, the time horizon is multi-year, equal to the project cycle; in the second case, however, we use the CFA since the assessment is on the sustainability over time of the hypothesised functions, the objective is to verify the balance of the budget in the fully operational year, consequently the time horizon is equal to one year; – the structure of the Cash Flow Analysis is based on the routinely used in the corporate accounting. This structure is then modified, introducing some cost and revenue items not present in the “basic version” and eliminating others deemed irrelevant, given the purposes of the analysis. The model therefore envisages slightly diversified structures for CFA and DCFA, also taking into account the nature of the activities carried out by the private partner, that is, whether of a profit or non-profit nature.

3 The SOSTEC Model The experimental model of “economic feasibility project for the development of unused public buildings”, called SOSTEC, can be used when the public decision maker intends to verify whether the economic conditions exist for the use of privatepublic partnership agreements to implement and/or to manage a project. The model is aimed at verifying the feasibility/economic sustainability of reuse hypotheses of unused public buildings, which is consistent with, and derived from, an overall idea of territorial development. The model, which can also be used in the case of buildings with a certain cultural value, is divided into three sections (Table 1): • Section A—cognitive surveys • Section B—reuse hypothesis • Section C—financial economic plan. The model structure allows to derive the reuse hypotheses from the knowledge of the territory dynamics and to verify the feasibility/sustainability of the formulated hypotheses. Indeed, the model internalizes not only the usual socio-economic surveys (demographic trend, labor market, infrastructure and mobility system, cultural and environmental heritage, etc.), but also the stakeholders’ point of view and this information, which derives from the programs in progress or already finished. Particularly, as far as the programming is concerned, the references are assumed for the strategy and the objectives already identified by the local community, to develop coherent hypotheses, and the other programmed actions, with which eventually operate in synergy [6]. This cognitive framework should not be taken as a constraint, but as an element of awareness: The re-use of the specific building can also follow a different direction compared to the framework of the interventions already planned with other tools, but a similar choice should be motivated and conscious [7, 8].

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Table 1 Structure of the SOSTEC model Section A

Cognitive surveys

A.1 Territorial framework A.2 The territorial context A.3 Census of tangible and intangible cultural heritage A.4 The infrastructure and mobility system A.5 Existing programming A.6 Description of the asset to be enhanced A.7 Recognition of already completed projects A.8 Stakeholders’ point of view A.9 Best Practices Identification A.10 problems and vocations identification

Section B

Reuse hypothesis

B.1 Idea-force of the project B.2 Objectives of the project B.3 Intervention hypothesis B.4 Functional schemes and substance

Section C

Financial economic plan

C.1 Estimation of investment costs C.2 Estimation of revenues C.3 Estimation of management costs C.4 Verification of economic feasibility and/or sustainability of the project

From the design point of view, (Section B—reuse hypothesis) choices to implement the model are synthetic: it is sufficient a functional program, equipped with the physical quantities of spaces intended for the different functions: These hypotheses allow to verify, at first glance, the coherence between the intrinsic characteristics of the building and the hypotheses of reuse formulated. From the economic point of view (Section C—financial economic plan), the model provides for the preliminary evaluation of investment costs (Works for the recovery and re-functionalization of buildings; Furniture, hardware and software equipment for the usability of buildings; communication and marketing; etc.) followed by the economic dynamics analysis of the management phase. These dynamics are influenced, among others, by the type of manager entrusted to manage the asset, that can be a profit or not-for-profit subject: This hypothesis also determines economic implications, as explained later in the article. The main purpose of the model, as mentioned, is the verification of the economic feasibility/sustainability of public-private partnership hypotheses: It serves, in other words, to verify the existence of sufficient conditions of convenience for private subjects, in the project realization and/or management in compliance with the expected public objectives, from which the work itself originates.

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The term “feasibility”, used in this article, applies to verify the profitability of an investment; instead, the term “sustainability” is used (making exclusive reference to its economic dimension), with the attempt to verify the balance during the management phase of a project. The financial economic plan, therefore, will have the purpose of verifying the feasibility/sustainability of the reuse hypotheses and, consequently, identifying the economic conditions that can be placed at the basis of the partnership agreement.

4 SOSTEC Model: Section C—Financial Economic Plan (FEP) As already explained above, the FEP is divided into 4 phases: Phase C.1. Investment costs assessment (Works for the recovery and refunctionalization of buildings; Hardware and software furniture and equipment for the usability of buildings; communication and marketing; etc.). Phase C.2. Revenues assessment (Identification of goods to be produced or services to be provided; estimate of their unitary sale price; identification of the target audience; demand assessment revenues assessment). Phase C.3. management costs assessment (management model and manager description; human resources plan, management costs appraisal, such as consumables, services, workers, etc.). Phase C.4. Project economic feasibility and/or sustainability.

4.1 Phase C.1. Investment Costs Assessment Preliminarily, it is necessary to identify all the investment items, possibly subdivided by macro-category of works (masonry, consolidation, parking, etc.) and by type (furnishings, equipment, software, technical expenses). The estimate will be made through the synthetic procedures for the estimation of the construction/production cost, as concern the building works, and the equipment and furnishings too, that will have to be carried out according to the building re-use hypothesis, with respect the different rooms it is composed by. Then, the capital composition has to be analyzed, establish public and private investment shares; as concern the private share, it the equity the debt share has to be established, to calculate a possible annual loan installment, to be added to the Discounted cash flow analysis. At the end of this phase, it is necessary to hypothesize the residual value of the building at the end of the life cycle, that will be added among the revenues, in the case of the profit management entity, jointly to the public investment shares.

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4.2 Phase C.2. Revenues Assessment After the investment costs assessment, the private subject has to be established, if profit or not-for-profit entity of management, to proceed with the subsequent of costs and revenues assessments. The revenues appraisal requires, first of all, the identification of goods to be produced, or the services to be provided, and the estimate of their unit sale price. Then, it is possible to figure out the demand, through the definition of the reference target of the proposed project. Among the revenues, in the case of no-profit management entity, must consider: membership fees; other private contributions and fundraising; any government grants for management.

4.3 Phase C.3. Management Costs Assessment To assess management costs, the management model has to be established in advance. In order to identify the optimum management model for the proposed project, its sustainability has to be verified, both with a private for-profit entity and a not-forprofit one, also specifying the additional subjects eventually involved in the management phase. The management model is based on the use of an organization chart to list the activities, the foreseen functions and the role assigned to each human resource involved, specifying the taken legal form (consortium, partnership agreement, etc.) Consistently with the management model, the different items of management costs (consumables, services, maintenance etc.) are evaluated, identified through synthetic procedures and surveys, clarifying the reference sources (national labor contracts, best practices, etc.).

4.4 Phase C.4. Project Economic Feasibility and/or Sustainability Basing on the revenues and costs assessments, the economic sustainability of the intervention can be verified in the management phase (non-profit subject), or the investment feasibility in terms of profitability (profit subject) by alternatively drawing up one of the two economic accounts exposed before (Scheme of Cash Flow Analysis and Scheme of Discounted Cash Flow Analysis). Part II—Case Study

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5 Territorial Framework The town of Mammola was inhabited since the times of Magna Grecia and its origins date back to the 4th–5th century BC. The housing nucleus was born on the ruins of Malea (Mαλšα in ancient Greek), a Locrese colony remembered by Thucydides. The nucleus of Mammola developed further at the end of the tenth century AD. In fact, between 950 and 986 a stable village was built, inhabited by populations who had abandoned the Ionian coast to escape Saracen incursions. Over the years the monasteries became a spiritual and cultural center. During the feudal period, Mammola belonged to several families: Ruffo, Caracciolo di Gerace, Correale from Sorrento, Carafa, just to name a few; the De Gregorio family held it until 1806, the year of the suppression of feudalism [9]. Mammola was the seat of the district and home of noble families of the time. Numerous noble palaces of considerable architectural value remain, testified, often enhanced by the presence of lush gardens. The town preserves the medieval layout characterized by houses gathered around numerous squares. The municipal territory falls within the Aspromonte National Park and in the Serre Calabre chain and is crossed by a network of paths, suitable for hiking. The Municipality of Mammola is part of Locride, a very vast territory that includes 42 Municipalities belonging to the province of Reggio Calabria located on the Ionian side of Calabria, in the far south of Italy. The Municipalities that are part of this territory are nestled between the Aspromonte and the Ionian Sea. Locride is characterized by a large white beach that stretches over 90 km of what is commonly called the “Costa dei Gelsomini”. But Locride is not just crystal clear sea and high cliffs, in fact thanks to the presence of the mountain range that separates the Ionian and Tyrrhenian belts, some of the municipalities that make up the area fall within the Aspromonte National Park. These villages, which abound in history and tradition, are the frame of a landscape full of contrasts and, without a doubt, unique in its kind [10]. The Locride territory is therefore characterized by a rich identifying heritage, tangible and intangible.

5.1 Tangible Heritage The tangible heritage that falls within the Locride territory is very conspicuous and widespread in various municipalities; can be divided by type into: archaeological areas, architectural heritage, natural heritage and the main sites are: – Archaeological areas: Locri (Locri Epizephiri); Monasterace (Kaulon); Marina di Gioiosa Ionica (Greek-Roman theater); Gioiosa Ionica (Roman Villa of Naniglio); Casignana (Roman villa).

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– Architectural heritage: Stilo (Cattolica and Norman Castle); Bivongi (Monastery of S. Giovanni Theristis); Pazzano (Monte Stella Sanctuary); Stignano (San Fili Castle); Roccella Ionica (Carafa Castle); Mammola (S. Barbara and Musaba Monastery—Santa Barbara outdoor park-museum); Gerace (historic center, cathedral and castle); Staiti (Roman-Norman abbey of Tridetti). – Naturalistic heritage: Bivongi (Marmarico waterfalls); Canolo (Monte Mutolo also known as “Southern Dolomites”); San Luca (monolith of Pietra Cappa).

5.2 Intangible Heritage Intangible cultural heritage is the set of traditions, oral expressions, performing arts, rituals, festive events, crafts, traditional agricultural practices that are a “living” expression of the identity of the communities and populations that recognize themselves in them. We can divide the intangible heritage of Locride into three categories: historical figures; religious cults and events; food and wine; the intangible heritage of considerable interest is also present in several Municipalities: – Historical characters: Stilo (Tommaso Campanella, philosopher and theologian); Locri (Zaleuco, author of the first code of laws written in the western world); San Luca (Corrado Alvaro, the most important Calabrian writer, and one of the greatest of the Italian twentieth century). – Religious cults and events: San Luca (Madonna della Montagna); Stilo (Palio di Ribusa); Caulonia (Kaulonia Tarantella Festival); Roccella Ionica (Roccella Jazz Festival); – Food & Wine: Bivongi (Wine); White (“Greek” wine); Mammola (Stockfish); Ciminà (Caciocavallo); Platì (Bread).

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Intangible heritage Historical caracters Religious cults and events Food & wine

6 Context Surveys: Tourism in the Villages of Italy Villages and historic centers, a jewel for tourism but at the same time a heritage in danger due to neglect, inadequate policies and lack of planning on the part of the institutions [11]. Data deriving from an investigation by the Centro Studi Turistici of Florence and Confesercenti (Centro Studi Turistici, 2016) indicate a real tourist boom in 2017 for the cities of art and, above all, for the small villages, which recorded a record year with 95 million admissions and a very significant share of foreigners. Foreign tourist presences in the villages rose by 30.3% between 2010 and 2017, against a decrease of 5.4% for Italian tourists. The idea of spending your holiday not visiting a single place, but savoring the most authentic characteristics of a territory, has developed a new form of tourism, the so-called kilometric tourism, that is itinerant tourism, in stages, which can be practiced with different means (car, bicycle, camper, etc.).

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A new phase has begun for tourism in the villages. Recent reports on tourism show that tourism is very popular in minor destinations, for which there is no problem of the opposition between tourist and resident, while in large cities there are problems of compatibility with the various city users [12]. A phenomenon observed in recent years is the significant increase in presences in smaller destinations, due to the tendency of European and national tourists to visit places deemed experientially more authentic and to the greater competitiveness of prices compared to large cities. To take full advantage of this trend, many Municipalities have established alliances, developing a network within a more or less vast territory that meets the needs of itinerant tourists. Thanks to the sum of these factors, kilometric tourism offers destinations new opportunities for: – Check and improve the quality of the flows – Enhancing local aspects and resources by contrasting them with the “mass tourism” model – Develop local identity and hospitality culture – Create social, economic and employment growth – Stimulate craftsmanship, typical products and local entrepreneurship.

7 SWOT Analysis Mammola and all the municipalities belonging to the Locride area present a series of tourist potential that, however, are not sufficiently exploited. All of this is certainly caused primarily by shortcomings at the organizational level, also the inadequacy of complementary services does not facilitate the increase in tourist flows [13]. The SWOT analysis returns a synthetic picture of the main dynamics that distinguish this area [14]: Strengths: – Mammola is located in a central position for the use of the entire Locride territory – Excellent endowment of identity, cultural and natural, material and immaterial resources – Favorable climatic conditions Weakness: – – – – – –

Inadequacy of public transport services Organizational shortcomings in the tourist offer Media image heavily damaged by crime Absence of qualified tourist staff Widespread phenomena of degradation Inadequacy of services for the use of resources

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Opportunity: – Spreading national and international interest in forms of tourism with characteristics similar to the Locride offer – Increased awareness in Locride citizens of the importance of their resources – Dissemination of ICT also for tourism purposes Threats: – Depopulation of the territory due to the massive youth emigration – Reduction of public expenditure for recovery and enhancement of identity resources – Other popular regional destinations with better facilities and services.

8 Hypothesis of Intervention From the elements that emerged through the cognitive phase, it is possible to deduce that the general objective of the intervention is to contribute to the tourist development of the Locride, improving its attractiveness and usability of the resources present. For these reasons, the intervention hypothesis foresees the reuse of one of the noble palaces of Mammola, Palazzo Florimo, to locate a tourist services center there. The specific objectives are: – Enhance the architectural heritage of Mammola through the restoration and reuse of Palazzo Florimo – Enhance the identity, natural and cultural, material and immaterial resources of the entire Locride – Offer job opportunities through the creation of new jobs – Creating wealth through the enhancement of local products.

9 Definition of the Target and Analysis of the Demand to Be Satisfied The center for the tourist enhancement of Mammola and the surrounding area can be defined as a multi target destination [15]. It is a destination for all those who love architecture, food, nature, excursions, travel. According to the 14th report on tourism of the Calabria Region [16] and direct surveys, it can be deduced that in the winter period visitors to the municipality of Mammola have gastronomy and Musaba as their main motivations, and they come mainly from the municipalities of the surrounding area; in the summer, however, there are more visitors from other places (both from Italy and from abroad).

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Again according to the 14th tourism report of the Calabria Region, arrivals in the province of RC stood at around 112,000 and, through direct surveys on the territory of Mammola, arrivals of approximately 30,000 people were recorded, an increase compared to the beginning of the decade. Arrivals are divided into about 2,000 people who have a holiday home, about 5,400 who stay in the 96 beds of the accommodation facilities and 22,600 people who stay one day. The overall presences instead, settle around 16,200, considering an average of 3 nights. The municipality of Mammola is also characterized by the increase in people who stay 1 day, as mentioned above for reasons related to food and wine and Musaba.

10 Best Practices In order to define the functional program and, above all, the management model of the Tourist Services Center, some best practices have been taken into consideration, to be exact 2 in Italy and 2 abroad.

10.1 Segovia (Spain) The tourist area of the city of Segovia is managed by the tourism department and the municipal tourism company (EMT—Municipal tourism company) (Segovia Turismo 2019). This company was created in 2004 by the municipality of Segovia and has the task of encouraging and developing tourist activity in the city. The councilor for tourism acts as vice president of the municipal tourism company, while a tourism technician directs and coordinates the work between the two bodies that are members of the area. The presidency and vice-presidency of the municipal tourism company fall respectively in the office of the mayor of Segovia and in the department for historical heritage and tourism. The EMT technical team is made up of 35 people. In addition to the managers of the various areas we find: guides, tourist informants, IT assistants and technicians of the culture of Segovia. This company takes care of the 5 basic needs of each traveler: eating (restaurants), sleeping (accommodation), easily finding places of interest (signage), historical and cultural references (tourist guides), participating in activities (tourist offer). The goal of the EMT is to develop various tourism products within the territory: religious, gastronomic, family, industrial, sports, naturalistic, literary and accessible tourism. Spanish courses are also offered for interested tourists or students. In the center of the town of Segovia we find a visitor reception center. It is a large space that incorporates the latest innovations in tourist services, interactive information, audiovisual projections and staff that can help tourists in Spanish, English, French, Italian and German. Inside this center you can book tourist packages, guided

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tours, tickets for a wide variety of events, rent audio guides with different routes in the city. There is also a store where we can find typical products, quality craftsmanship and design, magazines, decorative objects, stationery and other gifts related to the historical, touristic and cultural heritage of Segovia. The EMT team also manages a website where we find all the useful information about the city, tourist routes in the city of Segovia and in the surrounding areas, online reservations for guided tours, lists of accommodation facilities, visiting times of the various monuments and so on. To facilitate the stay of tourists, a smartphone app has also been created with all the information and routes proposed and signs have been placed in the main points of interest with QR code to be scanned.

10.2 Ljubljana (Slovenija) In the city of Ljubljana tourism development is managed by the public body “Ljubljana tourism”, in collaboration with the members of the tourist offer and has the task of promoting the development of the tourist offer of Ljubljana and the region of central Slovenia (Visit Ljubljana 2019). Tourists are welcomed into the city of Ljubljana inside the tourist information center. In addition to the classic tourist information about Slovenia, it offers free publications, performs accommodation bookings and trips throughout the state. There is also the sale of information brochures on the territory, tickets for various events, postcards, stamps, T-shirts and tourist souvenirs. You can also rent bikes through a bike sharing service managed by “Ljubljana tourism” and there is an internet point, both services are paid. In the summer months, once a week, Slovenian language courses are held for foreigners. The Ljubljana tourist association has also thought of creating the Ljubljana card for tourists. With the purchase of this card it is possible to have discounts on admission to attractions, bus trips, guided tours, internet access. There is also a website with all the information tourists need.

10.3 Lovere (BG) The tourist area of the city of Lovere (BG) is managed by the “Visit lake Iseo” association (Visit Lake Iseo 2019). It is an association made up of the 16 coastal municipalities of Lake Iseo and the provinces of Bergamo and Brescia. Its mission is to coordinate tourism projects aimed at enhancing and promoting the heritage of the municipalities concerned. The association does not pursue profit-making purposes. The revenue consists of: annual payments of the members, the profit deriving from the performance of the association activities, any other income that contributes to increase the social assets.

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The annual contribution of the various municipalities will be calculated on the basis of: number of inhabitants, number of tourist presences, number of accommodation and catering activities. The contribution from the provinces of Bergamo and Brescia is e 5,000 per year for each. The association may also include natural, legal persons, associations, consortia, entities that carry out tourism-related activities. In the town of Lovere there is “INFOPOINT ALTO LAGO D’ISEO” to welcome tourists. The services offered within this center are various: information and distribution of material relating to the historical, naturalistic, cultural, artistic and sporting aspects of the area of competence and of the entire province of Bergamo; distribution of panoramic maps of the paths and excursions to discover the environmental heritage; information on accommodations and restaurants, information on transport and roads.

10.4 Candelo (BI) In the municipality of Candelo (BI) the tourist activity is entirely managed by an association called “Tourist Association Pro Loco di Candelo”, a non-profit association, formed by volunteers, founded in 1992, which, in close collaboration with the municipal administration concretizes an increasing number of initiatives and projects (Pro Loco Candelo 2019). The association carries out a series of initiatives every day to make Candelo known and grow: welcome services and tourist information; programming, organization and promotion of events; enhancement and communication of tourist-cultural heritages. The Pro Loco brings together all the natural persons (members) who intend to actively work in favor of the tourist development of the municipality of Candelo and its hamlets. The association is a voluntary social life center and carries out its business for the purposes of tourism promotion; the association carries out the enhancement of the naturalistic, cultural, historical, social and gastronomic realities and potentialities of the territory of the municipality of Candelo too, and favors the improvement of the life of its residents. It deals with promoting and organizing, also in collaboration with public and/or private bodies, initiatives such as conferences, excursions, researches, guided tours, public shows, exhibitions, celebrations, sporting events, food and wine and/or other fairs, as well as initiatives of social solidarity, environmental recovery, restoration and management of monuments, etc. that serve to attract and make the stay of tourists and the quality of life of the residents more pleasant. The members of the Pro Loco are divided into: ordinary members, worthy members and honorary members. Ordinary members and supporters must pay the annual membership fee; honorary and honorary members are exempt from paying the annual fee. The economic resources with which the Pro Loco operates and carries out its activities are: members’ contributions and contributions; inheritance, donations and

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legacies; contributions from the European Union and international bodies; contributions from the state, regions, local authorities, public bodies or institutions; revenue deriving from the provision of agreed services; paid guided tour organizations.

11 Hypothesis of Reuse 11.1 State of Fact Palazzo Florimo is located inside the historic center of Mammola. The precise time of construction of the building is not known, but from a direct analysis of the building it can be said that it dates back to a period between the end of the 18th century and the first half of the 19th century. The building consists of three floors above ground and a fourth floor that develops partially on the back of the building and built in the following period. The formal characteristics, more important than this architectural artefact, can be seen in the elevations on Via Dante and Via Magenta, in addition to the internal stone staircase which constitutes a dominant architectural element. This plant was originally probably different from the current one, that is, it occupied a larger area, but in the following era it was divided and sold to other owners. Today the building is in a serious state of neglect, however the vertical structures do not present serious injuries.

11.2 Functional Program It was decided to create a tourist enhancement center inside Palazzo Florimo, not only for the municipality of Mammola, but for the whole area of Locride, which includes a total of 42 municipalities. The hypothesis of dividing the rooms was made on the basis of existing buildings of the same type (comparison with best practices previously analyzed). Ground floor: it was decided to insert shops in which to sell the classic souvenirs and some typical products from Mammola and Locride. – First floor: we will have the infopoint, with specialized staff available to tourists, who will assist in booking 56 stays, guided tours and so on. There will also be a specialized tourist consultancy office and an internet point with the possibility of making DIY reservations. – Second floor: it will be mainly dedicated to the organization of recreational/educational workshops for schools, with an equipped conference room used for the schools themselves or with the possibility of also being rented for conferences of various kinds (convention bureau). – Third floor: it will be the management center of the entire building, with the management offices located.

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The services provided by the center will be manifold, there will be bike sharing and car sharing, the possibility to book guided tours and audio guides, workshops for schools with the aim of making discover the beauty of our land even for the little ones, the possibility of purchasing a card with including inside entrances to some monuments and discounts of various kinds.

12 The Management Model The local tourist offer system (SLOT) is a set of activities and factors which, located in a defined space (site, locality, area), are able to offer an articulated and integrated tourist offer, i.e. they represent a hospitality system specific and distinct tourism that enhances local resources and culture [17]. Three types of destinations can be distinguished according to the different coordination methods and the different level of integration [18]: – Fragmentation model: when an offer characterized by “point-to-point” systems prevails in the destination. It is a model often the result of entrepreneurial “spontaneity”, in which tourism resources and activities are poorly integrated, and therefore the enhancement of possible products takes place mainly through the behavior of demand and the isolated actions of individual companies that act independently – Leadership model: when the prevailing configuration is that of the “package”. It is a model in which the offer and market access are organized and controlled by companies (tour operators, incoming agencies, convention bureaus, etc.) – Cooperation model: when “network” configurations prevail. It is a model characterized by medium-long term collaborative behaviors. In this model, the offer is the result of aggregations of operators capable of guaranteeing differentiation of resources and skills despite the presence of an adequate level of coordination, greater flexibility and ability to reorient the offer.

12.1 The Management Model for the Tourist Enhancement Center of Palazzo Florimo Analyzing the various management models that can be used in the local tourism offer system (SLOT), the one that has the best characteristics and that adapts to the territory considered is certainly the leadership model. In fact the advantages of this system are: – Offer and market access organized and controlled by companies (tour operators, incoming agencies, convention bureaus, etc.) or associations whose purpose is the development of tourism in the area within which they operate. – The perception of a unitary entity is widespread among the subjects of the offer.

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However, we can also highlight some disadvantages related to this system: – Being managed by a leader, this system may not work if the leader does not manage the activity correctly – The links between the subjects involved are not designed and organized in the smallest details, but are often temporary partnerships linked to some specific moments and events.

12.2 The Type of Management Subject The analysis that must be done at this point is to identify within the territory of Mammola which is the best activity that can follow this project. The recognition of the subjects present in the territory highlights that there are only 3 associations linked to tourism and cultural activity in the municipality of Mammola: – Mammola stockfish consortium: Managed by a company born in 1987, which deals with the processing and sale of stockfish, but also with its export at national and international level. The term consortium identifies an association between businesses, of an economic nature, which has the specific purpose of realizing the financial interests of the participants, through collaboration between the member companies, aimed at maximizing the results they intend to achieve. In addition to the company that manages and supplies the raw materials, this consortium includes some restaurants and agritourisms located in the municipality of Mammola. – Tourist Association “Pro Loco”: The birth of this voluntary association is attested by a notarial deed of April 26, 1979. It is a private contract between individual citizens who want to develop, together, forms of tourist attraction for their community. Being an association, everything that is made is the product of many heads. People come together in an association to pursue goals that they do not want or cannot achieve on their own. The associative nature of the Pro Loco places aggregation as a mandatory requirement of their existence. But it is also a voluntary tourism association, therefore the members are volunteers who lend their work for free. It is not a business, therefore its purpose is not profit and any form of income must be reinvested in the association’s activities. He takes care of the organization and management of the stockfish festival, but has always been an active part of all promotional events in the area. – Musaba, Spatari/Maas Foundation: It is an international non-profit moral body for the creation, protection, management, conservation, diffusion, enhancement of the artistic, architectural, archaeological, landscape and botanical heritage. The foundation manages the “MUSABA”, a museum but also an art laboratory. It is a center for the training of young people and the updating of people who have a particular interest in art, architecture and the environment. The structure that houses the foundation has exhibition rooms, beds, refreshment areas, art workshops, library, workshop areas.

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13 The Economic Feasibility Project The SostEc Model is a tool to support decisions in the planning phase of reuse of unused public buildings. It allows first of all to formulate hypotheses of reuse linking the functions to be localized to the real needs and dynamics of the territories; at the same time, it allows to verify already at this stage the economic implications of the hypothesised functions, in terms of cash flows, linking them to a management model hypothesis and to a form of public-private partnership. The feasibility and/or sustainability of the project hypothesis is verified through the drafting of an economic-financial plan, through which the initial investment costs are estimated; the composition of the capital in terms of any distribution between public and private entities was assumed; estimated cash flows, incoming and outgoing, in the management phase in relation to the specific nature of the assumed manager entity [19].

13.1 Investment Costs (A) Estimate of the sums for works to auction base (Construction cost) Building

recoverya

External arrangementsb

Unit cost of construction

Physical quantity

e 1,726.79

580 mq

e 1,001,538.20

e 1,300.00

Entirely

e 1,300.00

(A) Total sums for works to auction base

Total

e 1,002,838.20

Sources a Prezzario tipologico 2013 Regione Calabria b Costs for horizontal and vertical signage from existing bike sharing and car sharing project

Summary I—Investment for buildings recovery and refunctionalization (Production cost) (A) Sums for works to auction base

e 1,002,838.20

(B) Amounts available to the developer

e 146,215.34

I—Total

e 1,149,053.54

Summary of estimated investments I—Investment for building recovery and refunctionalization

e 1,149,053.54

II—Investment for property usability

e 108,040.15

III—Communication and marketing investments

e 36,290.00

Total of investments

e 1,293,383.69

Rounded total of investments

e 1,300,000.00

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13.2 Revenues Estimated annual revenue in fully operational Unit price

Quantity

Revenue

Shop sales

e 5.00

5.000

e 25,000.00

Organized tours with guide

e 10.00

2.000

e 20,000.00

Bike sharing rental

e 5.00

5.000

e 25,000.00

Car sharing rental

e 20.00

3.000

e 60,000.00

Card with discounts and entrance fees to monuments

e 7.00

5.000

e 35,000.00

Audio guide rental

e 5.00

2.000

e 10,000.00

Recreational and educational workshops for schools

e 5.00

2.700

e 13,500.00

Convention bureau

e 50.00

30

e 1,500.00 e 190,000.00

Total revenues from sales

13.3 Estimated Annual Operating Costs

Analytical estimate of the cost of human resources Position

Unit

Unit cost

Total cost

Manager

1

e 0.00

e 0.00

Marketing e web site management

2

e 6,000.00

e 12,000.00

Point of sale employees

2

e 14,520.00

e 29,040.00

Infopoint employees

3

e 15,840.00

e 47,520.00

Volunteers for recreational/educational workshops

2

e 2,400.00

e 4,800.00

Touristic guides

2

e 2,400.00

Estimate of other annual operating costs for services Cost item

e 4,800.00 e 98,160.00

Total annual cost of human resources

Annual cost

Utilities

e 18,500.00

Consumer products

e 2,600.00

Ordinary maintenance

e 8,000.00

Communication and marketing

e 20,000.00

Unforeseens

e 500.00

Total annual operating costs in fully operational

e 49,600.00

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14 Conclusions As can be seen from the previous tables, the hypothesis of reuse of Palazzo Florimo as a center for tourist enhancement, with an initial entirely public investment and management entrusted to a non-profit entity, is sustainable over time. It is clear that the formulation of the initial management model hypothesis is based on the experience of the designer, thanks to which it is possible to identify solutions that have a good chance of being feasible and/or economically sustainable. The SostEc Model, in addition to providing any confirmation of the hypotheses formulated, also allows, through the reiteration of the procedure, to verify the implications of alternative solutions, thus providing the decision maker with the elements to make informed decisions. Research on the issue of economic feasibility/sustainability of projects for the reuse of unused public buildings is directed towards an evolution of the SostEc Model, which also incorporates the public benefit dimension of the hypotheses formulated. It is true that the current budgetary constraints of public administrations require strict control of the financial implications of the choices made, but it is equally true that we must not forget the collective dimension of public choices. This requires us not to limit ourselves to a simple verification of cash flows according to a private logic, which is also indispensable, but we must also look at the implications of the choices made on the community. Years after the start of this process, criticisms of the model are raised from many sides. In particular, the authors who refer to the so-called Public Value Theory, believe that, in public goods, economic value should be accompanied by “the further value constituted by social capital, social cohesion, created social relations, as well as the social meaning and cultural identity, individual well-being and that of communities; moreover, political value (i.e. added value for the public sphere, obtained by stimulating and supporting democratic dialogue and active participation of citizens) and ecological value would also be considered in terms of promoting sustainable development” [20, 21]. The SOSTEC Model responds to the need, typical of the “New Public Management” Model, to guarantee the financial sustainability of PPP projects, without investigating the size of the other effects produced by the projects on communities and territories, typical instead of the Model based on “Public Value Theory” and the debate on the so-called Common Goods. The research will therefore continue in this direction, in identifying, that is, the most appropriate ways to introduce the dimension of public costs and benefits within the SOSTEC Model, starting first of all from the substantial existing literature on Cost Benefit Analysis [22].

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22. Nesticò A, Maselli G (2019) Intergenerational discounting in the economic evaluation of projects. In: New metropolitan perspective. Smart innovation, systems and technologies, vol 101. Springer, Cham, CH, pp 260–268. ISSN: 2190-3018, Reggio Calabria, 22–25 maggio 2018. https://doi.org/10.1007/978-3-319-92102-0_28 23. http://centrostudituristicifirenze.it/blog/borghi-d-italia-213-mln-arrivi-90-mln-presenze2016/ 24. http://www.segoviaturismo.es/en/ven-a-segovia/fiestas-y-eventos/mes-a-mes/otros/3057-emp resa-municipal-de-turismo 25. https://www.visitljubljana.com/en/visitors/ 26. https://visitlakeiseo.info/it/info/visit-lake-iseo/chi-siamo 27. http://www.prolococandelo.it/

An Application to a Spanish Case Study of a Property Valuation Models Pierluigi Morano, Francesco Tajani, Marco Locurcio, Felicia Di Liddo, and Debora Anelli

Abstract In 2017 the European AVM Alliance emphasized the importance of Automated Valuation Methods, to be used to assess market values and/or to monitor the evolution of property prices. This last aspect has acquired a particular cogency for market operators (sellers, buyers, investors, etc.) over time, in order, on the one hand, to make reliable valuations and, on the other hand, to effectively and quickly check the trend of the values. The present research aims at analyzing the functional relationships between the unit selling prices and the explanatory variables that contribute to their formation. The study has been carried out on a sample of two-hundred and ten residential properties, sold in the period 2016–2017 and located in the city of Tarragona (Spain). The main factors considered by sellers and buyers in the preliminary negotiation phase have been collected. The implementation of a data-driven technique has allowed to identify a statistically reliable model which, in addition to highlight the most influencing factors, has shown the interdependences between the variables considered and the unit selling prices. Keywords Data-driven technique · Genetic algorithm · Market value · Automated valuation models · Spanish property market P. Morano · M. Locurcio · F. Di Liddo Department of Civil Engineering Sciences and Architecture, Polytechnic University of Bari, Via E. Orabona 4, 70126 Bari, Italy e-mail: [email protected] M. Locurcio e-mail: [email protected] F. Di Liddo e-mail: [email protected] F. Tajani · D. Anelli (B) Department of Architecture and Design, Sapienza University of Rome, Via Flaminia 359, 00196 Rome, Italy e-mail: [email protected] F. Tajani e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Morano et al. (eds.), Appraisal and Valuation, Green Energy and Technology, https://doi.org/10.1007/978-3-030-49579-4_6

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1 Introduction In the recent years, the spread of large amounts of data from heterogeneous sources— the so-called “spatial” big data—has led to the growing demand for innovative statistical valuation methods capable of managing them, in order to support the evaluation processes and the periodic updates of the public and private assets values [8, 13, 22]. In fact, in the Art. 208 (3) (b) of the Capital Requirements Regulation (EU) no. 575/2013, the possibility for the institutions to “use statistical methods to monitor the value of the immovable property and to identify the immovable property that needs revaluation” has been firmly established. In the sector of the real estate valuations, the different denominations with which the European AVM Alliance defines the property assessment methods—Capital Assisted Mass Appraisal (CAMA), Automated Valuation Models (AVM), Surveyor Assisted AVM, AVM Assisted Appraisal, Mass Valuation, Comparable Based AVM, Analyst Assisted AVM—testify the widespread interest in these techniques, both for the determination of more reliable and objective property values, and for the possibility of monitoring their evolution over time [10, 20]. The numerous applications of the AVMs have highlighted their usefulness, due to the ability of the models to be modified in the short term taking into account the current real time economic indices [2, 11, 14, 21, 26]. Regarding the statistical valuation methods, different analytical approaches can be identified, such as the classical linear and non-linear regressions, the Genetic Algorithms (GA), the Artificial Neural Networks (ANN) and the Fuzzy Logic (FL) [1, 9, 18]. All these techniques allow to determine the property value, starting from the selling prices of the comparables, and they differ from each other in terms of the computational complexity level, the outputs mathematical sophistication and the interpretation of the input data [4, 24]. In particular, in the reference literature the various applications have allowed to suitably analyze the mass appraisal techniques peculiarities, in order to highlight the respective strengths and weaknesses [7, 16]. With reference to the real estate sector, the different mass appraisal methodologies have been implemented in order to investigate and to study each specific issues of the real estate market phenomena, e.g. for (i) the prediction of the property values [3, 6, 17] and the property market segmentation through the ANN technique, (ii) the determination of the relationship between the land use and the future urban planning with the FL implementation [5], (iii) the evaluation of the real estate investment by adopting the rules behind the logic of the GA [27]. In all those cases, the starting database and its quality and detail level significantly influence the final results: therefore, whatever statistical valuation method is adopted, the preparatory steps for obtaining reliable outputs should concern the construction of a sufficiently representative market database—in terms of sample size and influencing factors selected—, the definition of appropriate measurement scales of the variables, the knowledge of the local property price formation mechanisms and the valuer experience in the calibration of the database and in the interpretation of the results obtained.

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The present research aims at testing a “hybrid” statistical valuation method, definable as a Surveyor Assisted Comparable Based AVM. With reference to the city of Tarragona (Spain), a sample of two-hundred and ten residential properties, sold in the period 2016–2017, has been collected. For each residential property of the selected sample, the unit selling price and the main influencing factors have been considered. The methodology applied uses a genetic algorithm to identify the “best” regressive equation, i.e. able (i) to select the variables that, more than the others, contribute to the selling price formation and (ii) to generate reliable functional relationships in empirical terms. The paper is structured as follows. In Sect. 2 the case study has been described, the variables considered in the model and the corresponding descriptive statistics have been specified. In Sect. 3 the methodology implemented has been illustrated and the main criteria used to assess the reliability of the generated models have been explained. In Sect. 4 the application to the case study has been developed and the results obtained in terms of statistic performances and empirical reliability have been outlined. Finally, in Sect. 5 the conclusions of the work have been discussed.

2 Case Study The case study concerns a sample of two hundred and ten residential properties, located in the city of Tarragona (Spain) and sold in the period 2016–2017. In Fig. 1 the localization of each property of the study sample has been represented. Most of the sample properties (85%) are located in the central area (El Serrallo district)

Fig. 1 Localization of the properties of the study sample in the city of Tarragona

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of the city of Tarragona. A small number of properties (11%) has been detected in the industrial area of the city (Camp Clar/Torreforta district) and isolated cases of residential units are in the two peripheral/suburban areas (1% in the Sant Pere i Sant Pau district and 3% in the Llevant district). For each property of the sample, the unit selling price [Pu] (dependent variable) and the technological and locational influencing factors (independent variables) have been collected. In particular, the explanatory variables considered can be classified into technological and locational ones and are described below: Technological variables • • • •

the total surface of the property [S] (m2 ); the floor on which the property is located [P] (n); the number of the bathrooms in the property [B] (n); the presence of the lift in the building in which the residential unit is located [A]. In the model this variable has been considered as a dummy variable: in particular, the presence of the lift has been represented by the value “one”, whereas the absence of the service has been indicated with the value “zero”; • the quality of the maintenance condition of the property. This variable has been considered as a qualitative factor and it has been differentiated by the categories “to be restructured” [Mp], “good” [Mb] and “excellent” [Mo]. In particular, through the logic of the dummy variables, the score “one” has been assigned to the specific category that defines the quality of each property, and the score “zero” for the remaining two categories; • the quality of the maintenance condition of the adjacent buildings [Mf]. The evaluation of this variable has been carried out through an appropriate scale of scores (1 = bad maintenance condition, 3 = good maintenance condition, 5 = excellent maintenance condition). Locational variables • the distance from the nearest supermarket [Dm] (kilometers it takes to walk to it); • the distance from the nearest post office [Du] (kilometers it takes to walk to it); • the distance from the nearest public childhood, primary and secondary school [Ds] (kilometers it takes to walk to it); • the distance from the nearest hospital [Do] (kilometers it takes to walk to it); • the distance from architectural amenities [Ea] (kilometers it takes to walk to it). Architectural amenities have been represented by heritage assets (Roman ruins, Cathedral, etc.) or buildings characterized by appreciable architectural qualities and functions (modern buildings, University, etc.); • the distance from the nearest public green spaces [Dv] (kilometers it takes to walk to it); • road traffic level characterizing the adjacent to the building area [T]. This factor has been assessed through a scale of scores (“1” for the area characterized by a high traffic intensity, “3” in the case of average traffic intensity, “5” in the situation of buildings that are into areas with a low traffic density);

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Fig. 2 Frequency of the technological variables in the case study sample

• the distance from the nearest bus stop [Db] (kilometers it takes to walk to it); • the distance from the nearest motorway [Da] (kilometers it takes to get there by car); • the distance from the train station [Df] (kilometers it takes to walk to it). In Figs. 2 and 3 the frequency of each technological/locational influencing factor in the property sample has been described.

3 Method The method implemented in the present research is a data-driven technique, called Evolutionary Polynomial Regression [12], that is a versatile symbolic regression tool. This method is a generalization of the stepwise regression, in particular it is linear with respect to regression parameters, but it is non-linear in the model structures. The generic polynomial expression is reported in Eq. (1):

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Fig. 3 Frequency of the positional variables in the case study sample

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Y = a0 +

n 

[ai · (X 1 )(i,1) · · · · · (X j )(i, j) · f ((X 1 )(i, j+1) · · · · · (X j )(i,2 j) )]

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(1)

i=1

where n is the number of additive terms of the polynomial expression (bias excluded), ai are numerical parameters to be valued, X i are candidate explanatory variables, (i, l)—with l = (1, …, 2j)—is the exponent of the l-th variable input within the i-th term in Eq. (1), f is a function constructed by the process during the implementation of the methodology. The exponents (i, l) are also selected by the user in the preliminary phase from a range of real numbers. The parameters aj are evaluated by a Least Squares method. In particular, the iterative investigation of model mathematical structures, implemented by exploring the combinations of exponents to be assigned to each candidate input of Eq. (1), is performed through a sample based strategy that employs a genetic algorithm; the quantity and the complexity of the solutions that the methodology will generate depend on the maximum number of terms and of exponents that the user defines in the preliminary phase of the implementation of the data-driven technique. The methodology searches the most performing function, as a combination of the independent variable vectors (i.e. the variables selected as the model inputs) and the appropriate value of the coefficient of each variable. The accuracy of each model generated by the data-driven technique is checked through the Coefficient of Determination (COD). In particular, the closer the COD to the unit value, the higher the statistical performance level of the model [25]. The ability to simultaneously pursue different objective functions represents another potentiality of the proposed methodology: in particular, the methodology considers a Pareto optimal frontier constituted by the following conflicting objectives: (i) the maximization of the accuracy of the model; (ii) the minimization of the number of additive terms of the equation (ai ); (iii) the reduction of the complexity of the model by minimizing the number of explanatory variables (X i ). The technique, therefore, generates a wide range of solutions, each one characterized by a more or less complex algebraic form and by a different level of statistical accuracy in terms of COD. The final choice of the best solution is made by the user, taking into account the knowledge of the study phenomenon and the purpose of the analysis conducted.

4 Application The base model structure reported in Eq. (1) with no function f selected has been considered in the implementation of the method. In particular, in accordance with the reference literature relating the property market [15], the dependent variable to be assessed in the model is represented by the natural logarithm of the unit selling price (Y = ln (Pu)). The mathematical form of the resulting models consists of an algebraic sum of monomial terms, and each one is a combination of the input variables (i.e. the explanatory variables) raised to appropriate numerical exponents. In the present

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research, the possible candidate exponents are equal to (0; 0.5; 1; 2), whereas the maximum number of terms of each returned equation is equal to eight. The result of the implementation of the methodology has been represented by a consistent number of models: each one of them has shown different functional relationships between the independent variables chosen and the unit selling prices. In Table 1 the main models obtained, and the respective COD have been reported. Each model is characterized by a more or less complex mathematical form, consisting of a progressively increasing number of additive terms and with a different level of COD. In particular, each term of the models generated by the methodology consists of one or, in most cases, of several independent variables combined with each other, each one raised to the appropriate exponent identified by the method. The progressive increasing complexity of the functional form of the models, due to a gradually increasing number of additive terms and variables, has been compensated by a gradually improved level of statistical performance. In the case study, the analysis carried out has allowed to identify the most appropriate model, characterized by the functional relationships in Eq. (9) and a COD equal to 69.90%. Analyzing in detail the model of Eq. (9), the variables selected by the methodology as the most influential in the explanation of the housing prices in the city of Tarragona (Spain) are the following: – for the technological factors: the quality of the maintenance condition of the adjacent buildings [Mf], the presence of the lift [A], the “excellent” state of the maintenance condition of the property [Mo], the number of the bathrooms [B], the “good” maintenance conditions [Mb], the floor level [P], the total surface [S]; Table 1 Equations obtained by the implementation of evolutionary polynomial regression Eq. (n) Model

COD [%]

(2)

Y = −0.22094 · Do + 0.31838 · Mo + 0.41921 · B0.5 + 7.0369

34.36

(3)

Y = −0.22247 · Do + 0.59703 · Mo + 0.16374 · B·

37.10

(4)

Y = + 0.33927 · A0.5 · Mo0.5 + 0.12006 · B· Mf 0.5 − 0.055546 · S 0.5 · Do0.5 − 7.4952

40.36

(5)

Y = −0.071924 · Do2 + 0.57619 · Mo 0.5 + 0.15844 · Mb0.5 · T 0.20235· B2 · Da – 0.15529· B2 · Da2 + 7.1204

45.47

(6)

Y = −0.084301 · Do2 + 0.58273· Mo 0.5 + 0.22112 · Mb0.5 + 0.11327· B2 48.61 · Da · Df 0.5 – 0.073943 · P 0.5 · B2 · Dv · Da2 + 7.1485

(7)

Y = −0.2793 · Do + 0.54365 · Mo 0.5 + 0.32624 · B0.5 + 0.111· B2 · Da2 · 51.70 Df 0.5 + 0.053311· P 0.5 · Mb0.5 · T − 0.025522· P 0.5 · B2 · T · Da2 + 7.0197

(8)

Y = + 0.50926 · A0.5 · Mo 0.5 + 0.48348 · B 0.5 + 0.12723 · B2 · Da2 · 54.29 Df 0.5 − 0.046258· P 0.5 · B2 · T 0.5 · Da2 + 0.012181 · P· Mb0.5 · Mf 0.5 · T − 0.031543 · S 0.5 · Do + 6.9059

(9)

Y = + 0.12048 · Dm0.5 · Ea · Mf + 0.61216· A 0.5 · Mo 2 + 0.75646 · B0.5 69.90 + 0.11977· B0.5 · A0.5 · Mb0.5 · Dv0. 5 · T · Df 0.5 + 0.032606· B· Mb0.5 · Du2 · T − 0.070054 · P0.5 · B2 · A0.5 · Du0.5 · Dv · T · Da0.5 − 0.011393 · S 0.5 · Do2 − +0.0066479· S· Dm0.5 · Ea0.5 + 6.587

Mb0.5

+ 7.3236

0.5

+

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– for the locational variables: the distance from the nearest hospital [Do], the distance from the nearest supermarket [Dm], the distance from the nearest architectural amenities [Ea], the distance from the nearest urban green spaces [Dv], the road traffic level [T], the distance from the train station [Df], the distance from the nearest post office [Du], the distance from the nearest motorway [Da]. The variables in the model of Eq. (9) are combined within the same additive terms and, in some cases, also appear several times within the expression. Therefore, as for all the factors it is not possible to immediately verify the empirical evidence of the functional relationships with the unit selling prices, more accurate analyzes are necessary for the empirical interpretation of the model of Eq. (9). In order to quantitatively specify the contribution of each independent variable selected by the model in the formation of the housing prices, an exogenous mathematical approach has been implemented, by varying the i-th influencing factor in the eligible range for the study sample detected, and by keeping constant the values of the other variables—i.e. equal to the mean value for the quantitative factors and equal to “1” for the dummy variables. In Fig. 4 the “ordinary contributions” of the variables considered by the model of Eq. (9) in the formation of the Tarragona housing prices have been represented through a bar graph: in particular, the ordinary contributions are determined by the percentage changes in the unit selling prices generated by the most frequent differential variations in the eligible sample range of the influencing factors. The results obtained give rise to interesting considerations. First of all, the relationships between the locational characteristics concerning the infrastructural level of the area of each property—distance from the nearest supermarket [Dm], distance from the nearest hospital [Do], distance from the nearest post office [Du]—and the unit selling prices are inverse, confirming the empirical evidence of a decrease in

Fig. 4 Ordinary contributions of the influencing factors on the unit selling prices

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the housing prices corresponding to a higher distance from infrastructure, such as the supermarket (−10.57%), the hospital (−8.53%) and the post office (−7.55%). With reference to the accessibility level, the model has selected the characteristics distance from the nearest motorway [Da] and distance from the train station [Df]. In particular, the model has shown an inverse functional relationship between the first characteristic (−11.15%) and the unit selling prices, and a direct relationship with the distance from the train station (25.29%). This correlation highlights that the reference market appreciates more a property that is far from the train station, due, in this case, to the noise and the environmental pollution associated with the closeness to this infrastructure, located between the urban center and the coastline. In the case of the variables road traffic level [T] and distance from the nearest urban green spaces [Dv], both proxy variables of the quality of the urban natural places, the model has confirmed the empirical evidence of a direct functional relationship with the unit selling prices for the variable T (+12.09%) and an inverse correlation for the variable Dv (−14.28%). The inverse functional relationship (−10.16%) between the variable distance from the architectural amenities [Ea] and the unit selling prices denotes the local market appreciation for properties located close to buildings of cultural value, in accordance with the presence of architectural heritage of high historical and artistic value well preserved and integrated in the city. Regarding the technological characteristics, the model has selected the quality of the maintenance condition of the property and the number of bathrooms [B] as the most influential variables in the property price formation: in particular, the ordinary contributions are equal to +39.20% for the “excellent” state of the maintenance condition [Mo], +31.10% for the “good” state of the maintenance condition [Mb], +20.47% for the number of bathrooms. It should be noted that the predominant influence of the factor Mo is subordinated, on the one hand, to the simultaneous presence of the lift in the building [A] in which the residential unit is located, on the other hand, to the specificity of the analyzed sample consisting of a strong discrepancy between properties identified in excellent maintenance condition and those that require important renovation initiatives. The quality of the maintenance condition of the adjacent buildings [Mf] and the presence of the lift [A] are correlated to the unit selling prices through a direct functional relationship, with percentage variations respectively equal to +7.63% and + 10.37%. Finally, the surface of the building [S] and the floor level [P] are characterized by inverse functional correlations with the unit selling prices, characterized by ordinary contributions respectively equal to −19.15% and −3%.

5 Conclusions In the last decades, the growing attention dedicated to the AVMs in the real estate sector, points out the need of reliable and “lean” assessments tools for the market value updates and for the monitoring of the real estate trends.

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With reference to a study sample of residential properties located in the city of Tarragona (Spain), the present research has been performed implementing a “hybrid” statistical valuation method, able to correctly interpret the large number of available market data. In particular, the main influential technological and locational factors in the housing price formation phenomena of the city have been analyzed through the most performing functional expression generated by the genetic algorithm behind the methodology. The chosen model, among all those generated, is characterized by a good statistical reliability and a high empirical coherence of the functional correlations between the influencing factors selected and the unit selling prices. The analysis of the functional relationships between the independent variables and the dependent ones has demonstrated the applicability of the methodology in situations where an adequate tool is required to validate the punctual assessments performed by the independent valuators who directly operate within the territorial context [19]. The work is consistent with a current line of research of high interest, demonstrating the considerable potentialities and the support that can be provided by the proposed data-driven methodology. Further insights may address the application of the procedure in other national and international territorial contexts and the simultaneous implementation of other statistical methods [23], in order to compare the results and to highlight the advantages and the limitations of the models obtained.

References 1. Amato F, Tonini M, Murgante B, Kanevski M (2018) Fuzzy definition of rural urban interface: an application based on land use change scenarios in Portugal. Environ Model Softw 104:171– 187 2. Bolam C (2017) Data, data, everywhere. Prop J 16–17 3. Bonissone PP, Cheetam W, Golibersuch DC, Khedkar P (1998) Automated residential property valuation: an accurate and reliable approach based on soft computing In: Ribeiro R, Zimmermann H, Yager RR, Kacprzyk J (eds) Soft computing in financial engineering. Physica-Verlag, Heidelberg, Germany 4. Calhoun CA (2001) Property valuation methods and data in the United States. Hous Finance Int 16(2):12–23 5. Chang YC, Ko TT (2014) An interactive dynamic multi-objective programming model to support better land use planning. Land Use Policy 36:13–22 6. Collins A, Evans A (1994) Artificial neural networks: an application to residential valuation in the U.K. J Prop Valuat Invest 11:195–204 7. D’Amato M, Kauko T (2017) Advances in automated valuation modeling. Springer, Cham 8. Dachuan C, Baoshan Z (2012) The applications of big data in housing information system. J Inf Commun Technol 5:4 9. Del Giudice V, De Paola P, Torrieri F (2014) An integrated choice model for the evaluation of urban sustainable renewal scenarios. Adv Mater Res 1030:2399–2406 10. European AVM Alliance (2017) Standards for statistical valuation methods for residential properties in Europe, London, UK 11. Gibler KM, Tyvimaa T, Kananen J (2014) The relationship between the determinants of rental housing satisfaction and considering moving in Finland. Prop Manag 32(2):104–124

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12. Giustolisi O, Savic DA (2006) A symbolic data-driven technique based on evolutionary polynomial regression. J Hydroinformatics 8(3):207–222 13. Juan Y (2013) The precise marketing based on big data in the real estate enterprises. J Mark Wkly 9:66–67 14. Kryvobokov M (2007) What location attributes are the most important for market value? Extraction of attributes from regression models. Prop Manag 25(3):257–286 15. Lynch AK, Rasmussen DW (2004) Proximity, neighbourhood and the efficacy of exclusion. Urban Stud 41:285–298 16. McCluskey W, Anand S (1999) The application of intelligent hybrid techniques for the mass appraisal of residential properties. J Prop Invest Finance 17:218–238 17. McGreal S, Adair A, McBurney D, Patterson D (1998) Neural networks: the prediction of residential values. J Prop Valuat Invest 16:57–70 18. Morano P, Locurcio M, Tajani F, Guarini MR (2015) Fuzzy logic and coherence control in multi-criteria evaluation of urban redevelopment projects. Int J Bus Intell Data Min 10(1):73–93 19. Morano P, Rosato P, Tajani F, Manganelli B, Di Liddo F (2019) Contextualized property market models vs. generalized mass appraisals: an innovative approach. Sustainability 11(18):4896 20. Pagourtzi E, Assimakopoulos V, Hatzichristos T, French N (2003) Real estate appraisal: a review of valuation methods. J Prop Invest Finance 21(4):383–401 21. Potepan MJ (1996) Explaining intermetropolitan variation in housing prices, rents and land prices. Real Estate Econ 24(2):219–245 22. Rosato P, Alberini A, Zanatta V, Breil M (2010) Redeveloping derelict and underused historic city areas: evidence from a survey of real estate developers. J Environ Plan Manag 53(2):257– 281 23. Saganeiti L, Favale A, Pilogallo A, Scorza F, Murgante B (2018) Assessing urban fragmentation at regional scale using sprinkling indexes. Sustainability 10(9):3274 24. Sheppard S (1999) Hedonic analysis of housing markets. Handb Reg Urban Econ 3:1595–1635 25. Tajani F, Ntalianis K, Di Liddo F (2017) An assessment model for the periodic reviews of the market values of property assets In: Gervasi O et al (eds) Computational science and its applications—ICCSA 2017. Lecture notes in computer science, vol 10406. Springer Cham, pp 490–500 26. Taltavull de La Paz P (2003) Determinants of housing prices in Spanish cities. J Prop Invest Finance 21(2):109–135 27. Wang WK (2005) A knowledge-based decision support system for measuring the performance of government real estate investment. Expert Syst Appl 29(4):901–912

The Transformation of Surface Rights into Property Rights. A Financial Resource for Rebalancing Municipal Budgets. The Case of Pescara Sebastiano Carbonara and Davide Stefano

Abstract Over the past thirty years the valorisation of public real estate assets has acquired growing importance in political debate in Italy. After the 1990s, though to an even greater degree following the profound economic crisis of 2007, the idea of a more corporate approach to the managerial activities of Public Bodies began gaining ground as a means for increasing their autonomous access to financial resources. In this situation, the valorisation of public real estate represents one of the most interesting options, so long as it is pursued properly. Beginning with these considerations, this paper explores the transformation of surface rights into full property rights in public housing estates, in accordance with Italian Law n. 448/1998. The underlying hypothesis of this law is compared with three possible alternatives. The intention is to define what appears to be the most correct procedure from the vantage point of estimation. Keywords Transformation of surface rights · Cadastral income · Public residential construction · Usufruct

1 Introduction A research project to survey the public real estate assets of the City of Pescara [1, 3] revealed a considerable number of residential units for which the City enjoys only surface rights (approximately 5300 units). Like the majority of Italian municipalities, Pescara implemented the procedure to transform surface rights into full property rights for assets located inside socalled PEEP areas (Piani di Edilizia Economico Popolare or Public Housing Plans), pursuant to Law n. 448/1998. However, this procedure adopts a methodology for S. Carbonara (B) · D. Stefano Department of Architecture, Gabriele d’Annunzio University of Chieti and Pescara, Viale Pindaro 42, 65127 Pescara, Italy e-mail: [email protected] D. Stefano e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Morano et al. (eds.), Appraisal and Valuation, Green Energy and Technology, https://doi.org/10.1007/978-3-030-49579-4_7

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calculating the value of this transaction that differs slightly from that imagined by the legislator. The need to use a different method than that proposed in the law,1 as clarified successively, derives from the impossibility to identify with “certainty the effective value of the concession fees to be detracted” (owing to a lack of documents attesting to fees paid in the past) without committing significant technical, administrative and legal resources to develop estimates, and the scarce revenues expected compared to the notable organisational commitment requested of municipal governments. Inspired by these elements, this paper proposes three hypotheses for defining the aforementioned fees. The intention is to develop an immediate procedure capable of generating certain revenues for public bodies.

2 From the Right to a Home to Public Housing (Italy’s ERP) “In broad terms, public housing can be considered as any intervention that benefits from direct or indirect State funding to build housing units at a lower cost than that requested by the private market; or, using a more restrictive definition, public housing can be defined as only those interventions funded entirely by the State, in other words, subsidised housing” [5]. From the early twentieth century, with the Luzzatti Law of 1903, municipal governments were afforded the faculty of providing housing to those in need. This because in Italy the right to a home has always assumed the connotations of a “legitimate” interest, despite there being no direct reference in the Italian Constitution. In the wake of the Second World War, the scarcity of housing and rising rental fees fanned the flames of discussions centred around the right to a home, making it a growing theme in political debate [13]; charged by the pressures exerted by society, state government promoted three laws that saw it intervene heavily in the arena of social housing (both public and private): the Fanfani (n. 43/1949), Tupini (n. 408/1949) and Aldisio (n. 715/1950). A decade later, Law n. 167/1962 “Measures for Favouring the Acquisition of Buildable Lands for Social and Public Housing”, confronted the problem of building new social housing estates through the forced land acquisitions and governed by the so-called PEEP Public Housing Plans. However, it was only during the 1970s 1 According to article 31 comma 48 of Law n. 448/1998 48. The fees for the areas to which property

rights are granted is determined by the municipal government, based on opinions expressed by its technical department, as 60% of that calculated (using the market value, which the municipal government has the right to reduce by up to 50%), net of surface right concession fees, revalued based on the variation, according to the ISTAT [Italian National Institute of Statistics] consumer pricing index for working class families, from the month the aforementioned fees were originally paid and that of the stipulation of the act of concession. In any case, the cost of the land calculated in this manner may not exceed that established by the municipal government for areas to which property rights are conceded directly at the time of transformation as per comma 47”.

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that Law n. 865/1971 first introduced the term “Edilizia Residenziale Pubblica” (ERP), Public Housing. This term was adopted by the legislator to introduce the concepts of edilizia sovvenzionata, edilizia agevolata and edilizia convenzionata. Edilizia sovvenzionata benefits from State funding for the construction of units permanently destined for rental; edilizia agevolata offers buyers financial assistance when purchasing a home; edilizia convenzionata benefits from State funding that serves to cap rental or purchase costs. The assignment of areas for the realisation of one of the forms of ERP public housing to specific subjects (cooperatives and/or building contractors), takes the form of the concession of surface rights to buildable lands (limited and not to exceed 99 years) and through the transfer of full property rights, though with some restrictions on free sale. Almost thirty years after the formation of the PEEP, with Law n. 448/98 the State offered the municipal governments who owned these lands the possibility to transfer property rights to owners to favour the acquisition of full rights to their properties.

3 The Transformation of Surface Rights into Property Rights in Their PEEP Areas of the City of Pescara The notion of surface rights is rooted in Roman law and was initially linked to the principle of superficies solo cedit,2 according to which anything constructed on a piece of land belonged to the owner of that piece of land. In our time, surface rights are disciplined [in Italy] by art. 952 et seq. of the Italian Civil Code. This article describes the right of the land owner to “to build on a third party’s property and, consequently, to acquire ownership of anything built on the property” until, when the pre-established term expires, the surface right is extinguished and the land owner becomes the owner of any property built on the land. To free up resources in favour of local governments, the aforementioned Law n. 23 December 1998, n. 448 offered municipal governments the possibility to transfer property rights in PEEP areas. There are various aspects of this law worth noting: in primis a non-obligatory condition, as the municipal government may grant these areas, without being obliged to do so; secondly, the right of property owners not to accept this proposal; a condition of unilaterality, as the value of the transfer is calculated by the municipal government; finally, the question of incentive, as the law foresees that the cost of the transfer is reduced by 60% (extendible to 50%), net of concession fees (for the agreement) revalued according to the ISTAT index. For this reason, after numerous interpretations by diverse sections of the Italian Court of Audit ([4]—forthcoming), the fee for the transformation of surface rights into property rights can be calculated as follows:   Vtds = Vv × 0.6∗ − Ocr 2 Nuovo

Compendio di Istituzioni di Diritto Romano, P. Giaquinto, 2017.

94

Vtds Vv Ocr 0.6∗

S. Carbonara and D. Stefano

value of the transformation of surface rights into property rights current market land value; cost of surface right concession revalued at the date of the fee calculation percentage reduction, extendible to 0.5

The City of Pescara, while applying the principles of the Law, proposes3 a different methodology of calculation; the fee for the transformation of surface rights into property rights is calculated by applying a rate of 2.50% to the Rateable Value (revalued cadastral income of a unità immobiliare urbana (u.i.u.)4 multiplied by a coefficient). Specifically5 : Vtds = (Rc × 1.26 × 100) × 2.50% Vtds value of the transformation of surface rights into property rights Rc Cadastral income

4 Hypothesis of Reformulation Pursuant to Law 23 December 1998 n. 448, the City of Pescara proposed the transformation of surface rights into property rights for all areas inside the Piani di Zona (Zoning Plans) present in its territory, for a total of approximately 5300 residential units. This study focused on a representative sample of roughly 2400 units, comprised of properties in cadastral categories A/2 and C/6, on Cadastral Maps n. 12, 18, 19 and 31. Applying the methodology proposed by the City of Pescara generates revenues in the order of 4 million euros (Table 1).

4.1 Hypothesis 1 “Revaluation” An initial hypothesis was formulated by substituting the Rateable Value defined by the City of Pescara with the Asset Value defined in the Unified Text on Local Authorities (TUEL)6 , which foresees applies a coefficient of 160 to buildings classified in cadastral groups A, C/2, C/6 and C/7. Vtds = (Rc × 160) × 2.50% 3 Municipal

Council Deliberation n. 120 dated 29 May 2006. building unit: each building or part thereof that, in its current state, is able, on its own, to produce revenue. 5 Referred to properties in cadastral categories A, B and C, excluding categories A/10 and C/1. 6 Legislative Decree n. 267 dated 18 August 2000, art. 267. 4 Urban

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Table 1 Calculation of the value of the transformation of surface rights into property rights applying the methodology proposed by the City of Pescara Map

Cadastral category

Rateable value

Rate (%)

Transfer value

12

A/2

e 37,012,060

2.50

e 925,301

C/6

e 1,359,432

2.50

e 33,986

18

A/2

e 41,396,047

2.50

e 1,034,901

C/6

e 2,027,413

2.50

e 50,685

19

A/2

e 29,126,310

2.50

e 728,158

C/6

e 2,161,750

2.50

e 54,044

A/2

e 47,420,574

2.50

e 1,185,514

C/6

e 55,351

2.50

e 1,384

31

e 4,013,973

Table 2 Calculation of the value of the transformation of surface rights into property rights applying hypothesis 1 Map

Cadastral category

Rateable value

Rate (%)

Transfer value

12

A/2

e 46,999,441

2.50

e 1,174,986

C/6

e 1,726,262

2.50

e 43,157

18

A/2

e 52,566,409

2.50

e 1,314,160

C/6

e 2,574,493

2.50

e 64,362

A/2

e 36,985,791

2.50

e 924,645

C/6

e 2,745,080

2.50

e 68,627

A/2

e 60,216,602

2.50

e 1,505,415

C/6

e 70,286

2.50

e 1,757

19 31

e 5,097,109

Vtds value of the transformation of surface rights into property rights Rc Cadastral income revalued by 5% This hypothesis, confirming the rate proposed in Municipal Council Deliberation n. 120/2006 (2.50%), generates a potential value in the order of 5 million euros, which translates into an increase in revenue of 26.98% (Table 2).

4.2 Hypothesis 2 “Market” The methodology of calculation proposed in hypothesis 1, while aligned with the general principles of accounting, provides a “fiscal” value that, in reality, owing to the iniquities of the cadastral system, are significantly misaligned with the actual market value of properties.

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Table 3 Calculation of the total surface area Map

Cadastral category

Average room (m2 )

n. rooms/area of garages (n. − m2 )

Total area (m2 )

12

A/2

20.54

2,258

46,368

4,740

4,740

18

A/2

18.96

2,600

49,274

7,683

7,683

19

A/2

19.08

1,884

35,937

4,763

4,763

2,961

59,412

243

243

C/6 C/6 C/6 31

A/2 C/6

20.07

Therefore, the methodology proposed in hypothesis 2 begins with the market value of the properties, followed by the application of the rate proposed in Municipal Council Deliberation n. 120/2006 (2.50%). Cadastral information was used to calculate the areas of the properties, transforming metric cadastral data (dimensions are expressed in vani, rooms) using the average room dimensions: this procedure can be considered legitimate as it is the same utilised by the Revenue Agency to produce the “Statistiche catastali” (Cadastral Statistics) document (Table 3). The average market value was calculated by referring to 199 deeds of sale7 for properties inside the Zoning Plans and immediately adjacent to them: the study revealed a substantial balance between the two markets and an alignment with OMI8 values for civil residential units in a normal state of conservation (and not low-cost dwellings). At this point it is necessary to introduce a specification: the analyses appear to demonstrate the legitimacy of the consideration that the market considers units located inside the Zoning Plans on par with those located outside these areas. Indeed, the characteristics of the properties inside these areas are such that they cannot be considered low-cost dwellings or social housing units, but more precisely civil properties (further demonstrated by their classification in cadastral group A/29 ).” ([4]—forthcoming). It follows that if the choice were made to utilise the prices provided by the OMI, rather than those found in deeds of sale (as in the case study), reference prices should be those for civil dwellings in a normal state of conservation (average values) rather than those for social housing units (Table 4). Proceeding with this hypothesis and confirming the average rate proposed in Municipal Council Deliberation n. 120/2006 (2.50%) provides a potential value of 7 The

deeds are from 2016 to the first semester of 2017. Italian Revenue Agency’s Osservatorio del Mercato Immobiliare, Real Estate Market Observatory. 9 In the city of Pescara, roughly 84% of the u.i.u. related to the transformation of surface rights are classified in category A/2, while the remainder are in category A/3. 8 The

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Table 4 Summary of average market values Map Market value (average) for Market value (average) for OMI value (average) for properties inside PEEP areas properties outside PEEP civil residential units in a (e/m2 ) areas (e/m2 ) normal state of conservation (e/m2 ) 12

1,207.00

1,182.19

1,325.00

18

1,163.00

1,145.49

1,080.00

19

a

1,244.72

1,325.00

973.00

754.91

1,020.00

31 a No

deeds were found for Map 19

Table 5 Calculation of the value of the transformation of surface rights into property rights applying hypothesis 2 Map

Cadastral category

Rateable value

Rate (%)

Transfer value

12

A/2

e 55,391,249

2.50

e 1,384,781

C/6

e 3,231,827

2.50

e 80,796

A/2

e 56,873,717

2.50

e 1,421,843

C/6

e 5,674,433

2.50

e 141,861

19

A/2

e 44,731,727

2.50

e 1,118,293

C/6

e 3,689,658

2.50

e 92,241

31

A/2

e 62,787,093

2.50

e 1,569,677

C/6

e 183,498

2.50

e 4,569

18

e 5,814,061

almost 6 million euros, or an increase in revenue of 44.85% with respect to the method currently used by the City of Pescara (Table 5).

4.3 Hypothesis 3 “Actualisation” This approach is based on an indirect estimation procedure and market value actualisation based on the remaining duration of the concession. The concept of transforming surface rights into full property rights by bare property owners can be compared to the right of usufruct [10]. Usufruct is “a limited real right of a subject (usufructuary) to enjoy the property of another (bare owner) and to take its fruits, though with the obligation to respect its economic objective. This real right to enjoy the property of another has a very vast definition: indeed, the extension of the rights of the usufructuary resemble, though with equalling them, the rights of

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enjoyment of a property belonging to the owner of said property, who retains bare ownership. The right of usufruct is always temporary”.10 Similarly, the owners of urban properties are compared to an usufructuary, while City Government can be assimilated to the bare owner; hence, the value of a bare property is provided by actualising the market value of the asset and can be calculated using the known formula employed in financial mathematics: Vnp = Vm

1 qn

Vnp Value of the bare property Vm Market value of the built property n Remaining duration of the concession 1 Future value with compound interest qn The variables of this question are the capitalisation tax and the number of years left until expiry of the concession. In the case examined here, the number of years is represented by the difference between the duration of the concession (99 years) and the year of stipulation of the contract (which varies from Zoning Plan to Zoning Plan). Market value data can be found both by consulting deeds of sale and the OMI database. The capitalisation rate can be calculated using OMI pricing as the ratio between the value of the annual rental cost (properly reduced11 ) and the market value of the property [8, 9, 11] (Table 6). r=

a V

r Capitalisation rate a Net revenue V Market value This hypothesis results in a potential value of almost 28 million euros, in other words an increase in revenue of 597.88% with respect to the methodology currently applied by the City of Pescara (Table 7). The proposed methodology provides a value for the transformation of surface rights, solely for the properties examined, in the order of 28 million euros, compared to the almost 4 million euros obtainable using the method currently adopted by Pescara’s municipal government. 10 Articles

978 et seq. of the Italian Civil Code. income, or net income is calculated from gross income, net of all operating costs paid by the property owner; according to diverse authors (Realfonzo, Michieli, Forte and De’Rossi) this value is indicated as the percentage of gross income (or rental fee). In the example studied here, considering the percentages proposed by literature in the field of estimation, as well as the evolution of legislation in property taxation (Law 27 December 2013, n. 147 art. 1 comma 639), the authors considered it plausible to propose a value of 30%. 11 Capitalizable

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Table 6 Summary of the data used to formulate hypothesis n. 3 Map

Cadastral category

Market rate (e/m2)

Actualisation rate (%)

Remaining duration in years

12

A/2

e 1,195

2.92

65

C/6

e 682

2.89

65

18

A/2

e 1,154

3.84

65

C/6

e 739

3.94

65

19

A/2

e 1,245

2.93

69

C/6

e 775

2.89

69

A/2

e 1,057

3.42

65

C/6

e 752

3.12

65

31

Table 7 Calculation of the value of the transformation of surface rights into property rights applying hypothesis 3 Map

Cadastral category

Rateable value

Actualisation rate

Remaining duration in years

Transfer value

12

A/2

e 55,391,249

2.92

65

e 8,550,483

C/6

e 3,231,827

2.89

65

e 506,430

18

A/2

e 56,873,717

3.84

65

e 4,902,210

C/6

e 5,674,433

3.94

65

e 461,458

19

A/2

e 44,731,727

2.93

69

e 6,091,241

C/6

e 3,689,658

2.89

69

e 515,850

A/2

e 62,787,093

3.42

65

e 6,960,420

C/6

e 183,498

3.12

65

e 24,806

31

e 28,012,898

5 Conclusions The City of Pescara has implemented the general principles of Law n. 448/98, though developing a procedure that differs from these same principles. The procedure adopted by the city’s government, while interesting for its practical application, does not refer to the market value of the area, but is instead based on cadastral property value; however, even substituting rateable value with market value, the fixed rate of 2.50% does not produce a real and plausible value of incidence of the site of the property. Hypothesis 3, founded on actualised market value, analogous to the calculation of bare property value, appears to be aligned with the principles of the law (in both formal and operative terms) and, in the opinion of the authors, also easier to apply. Indeed, in procedural terms, it permits the constant updating of indices and a more objective definition of fees, as it is aligned with the market rate and remaining duration of the concession.

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Table 8 Comparison between the degree of incidence in the various hypotheses Hypothesis

Rateable value

Transfer value

Municipal Deliberation n. 120/2006

e 160,558,937

e 4,013,973

Incidence (%) 2.50

Hypothesis 1 “Revalued”

e 203,884,364

e 5,097,109

2.50

Scenario 2 “Market”

e 232,563,201

e 5,814,061

2.50

Scenario 3 “Actualised”

e 232,563,201

e 28,012,898

12.00

A second consideration must be made for the incidence of values calculated with respect to property value [12]: the value of a bare property, in other words the cost of transforming surface rights into property rights, can be intended as the incidence of the value of land with respect to the building, as the value of the property is to be understood in its entirety owing to the very complementary relationship that exists between a building and the site upon which it is constructed [7]. Analysing the costs of transformation calculated for the diverse hypotheses, and more specifically for hypothesis n. 3 “Actualised”, it is possible to note how the incidence of market value for an entire property is generally around 12%, in other words inferior to the value of incidence listed in the magazine Il consulente immobiliare de Il Sole 24 Ore that, for the peripheral area of the city of Pescara, is estimated in the order of 20% (Table 8). In light of the considerations expressed and the autonomy of Italy’s municipal governments in determining the fees to be paid for the transformation of surface rights into full property rights (a circumstance fully coherent with the greater autonomy granted to territorial authorities), hypothesis n. 3 “Actualised” appears to respect the objectives established by the legislator, to be linear and transparent in its application and not to suffer from contrasting jurisprudence. It is evident that this proposal, while advantageous for City Government [2], in practical terms would discourage property owners and undermine the original intentions of the law, in other words, combining the need of municipal governments to generate revenue—given the scarcity of public resources that shows little prospect for change [6]—and the need to develop forms of subsidies for property owners. In this perspective, it would be possible, in any case, to apply a percentage reduction (for example, analogous to that foreseen in art. 31, of 60%): using the proposed procedure, this approach would provide revenues in the range of 16 million euros.

References 1. Carbonara S, Stefano D (2016) Repertorio beni immobili di proprietà pubblica nella città di Pescara (Comune, Provincia, Demanio, Regione). In: I Vv.Aa. (Red.) VersoPescara2027. Dossier di ricerca (bd. CD allegato al volume 2). Gangemi, Roma 2. Carbonara S, Stefano D (2020) An operational protocol for the valorisation of public real estate assets in Italy. Sustainability 12(2). https://doi.org/10.3390/su12020732

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3. Carbonara S, Stefano D, Di Ceglie R, Curcetti S (2016) Il censimento del patrimonio immobiliare pubblico della città di Pescara. In: I Vv.Aa. (Red.) VersoPescara2027. Dossier di Ricerca. Gangemi, Roma 4. Carbonara S, Stefano D, Di Prinzio A (2020) Transforming surface rights into property rights: an analysis of current estimation procedures and a comparison with an alternative. Valori e Valutazioni. Teorie ed Esperienze n.24. Dei, Milano 5. Centofanti N, Centofanti P (2008) Gli alloggi di edilizia residenziale pubblica: costruzione - assegnazione - cessione. In: Narducci IF (Red.) Guida normativa (s. 3307–3367). CEL, Bergamo 6. Della Spina L, Calabrò F, Calavita N, Meduri T (2014) Trasferimento di diritti edificatori come incentivi per la rigenerazione degli insediamenti abusivi. LaborEst 9. https://doi.org/10.19254/ LaborEst.09.13 7. Formularo N (1947) La stima dei fabbricati. I. Agricole, Bologna 8. Forte C, De’Rossi B (1974) Principi di economia ed estimo. Etas libri, Milano 9. Michieli I (1987) Trattato di estimo con elementi di economia, di matematica finanziaria e contabilità dei lavori. Edagricole, Bologna 10. Morano P, Tajani F (2013) Estimative analysis of a segment of the bare ownership market of residential property. In: Computational science and its applications—ICCSA 2013. ICCSA 2013. Lecture notes in computer science, vol 7974, pp 433–443. https://doi.org/10.1007/9783-642-39649-6_31 11. Realfonzo A (1994) Teoria e metodo dell’estimo urbano. Carocci, Roma 12. Schmid CU, Hertel C (2005) Real property law and procedure in the European Union. General report. European University Institute (EUI) Florence/European Private Law Forum Deutsches Notarinstitut (DNotI) Würzburg. Henta frå. https://www.eui.eu/Documents/DepartmentsCent res/Law/ResearchTeaching/ResearchThemes/EuropeanPrivateLaw/RealPropertyProject/Gen eralReport.pdf 13. Venuti GC (1972) Amministrare l’urbanistica. Einaudi, Torino

Real Estate Assets and Construction Building Process

Do Policy Incentives to Buildings Energy Retrofit Encourage Homeowners’ Free-Rider Behavior? Chiara D’Alpaos

Abstract To encourage homeowners in undertaking buildings energy-efficient renovations, Governments have introduced a wide range of incentive measures. Nonetheless, the cost-effectiveness of this incentive policy is controversial. The general perception is that incentives are costly and, if they are not optimally designed, they can attract free riders and generate undesirable outcomes to efficient resource allocation and to Society. In this paper, we investigate whether investments in buildings energy retrofit (BER) are be profitable in the phasing out of incentives and if current incentive policy encourage homeowners’ free-rider behavior. In detail, we analyze the investment decision of a homeowner, who has to undertake retrofit interventions of an existing building, regardless incentives are cancelled out. In detail, we develop and implement a Real Option Model to determine the investment value and its optimal timing, by modeling the opportunity to invest as a call option. Our results show that negative NPV investments may turn to be profitable if the option to defer is optimally exercised. The value of flexibility to invest adds to the investment value and make incentive schemes not necessary in encouraging investments in BER. By contrast, current fiscal incentive policy in Italy may attract free riders and generate an over-investment effect, which in turn increase costs for Government and Society. Keywords Buildings energy retrofit · Policy incentives · Free-rider behavior · Real options

1 Introduction Building stock in Europe is responsible for about 30% of global energy consumption and generate about 20% of all energy-related greenhouse gas (GHG) emissions [12, 21, 24, 39, 46]. It is widely recognized in literature that the built environment represents a key sector in climate change mitigation, as it provides, compared to other C. D’Alpaos (B) Department of Civil Environmental and Architectural Engineering and Centro Studi Levi Cases, University of Padova, Padova, Italy e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Morano et al. (eds.), Appraisal and Valuation, Green Energy and Technology, https://doi.org/10.1007/978-3-030-49579-4_8

105

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sectors (e.g., power sector, transport sector), relatively low-cost, but cost-effective opportunities to reduce CO2 emissions by increasing buildings energy efficiency. In this respect, the recent Italian “2017 National Energy Strategy” (SEN 2017) identified the building sector as a key driver in achieving the 2030 EU wide targets on mitigation of climate change effects, set by the 2030 Climate and Energy Framework (EC 2014). The Italian building stock is in fact one of the oldest in Europe and is responsible for 36% of overall primary energy consumption [14]. Therefore, it has the greatest potential among priority policy-target sectors in reducing pollutant emissions and increasing energy savings. As highlighted by Bottero et al. [11], over 55% of the Italian building stock was built before 1960, 90% before 1990 and the replacement rate of old assets with new assets is nearly 1% per year. The renovation of existing buildings is therefore a milestone in the mitigation of climate change effects. Energy performance of existing buildings can be improved by implementing different retrofit options, which ranges from energy-consumption reduction measures to the adoption of low carbon technologies and investments in renewable energy sources (RES) power plants. Their implementation can be very challenging and may generate a set of co-benefits, which can arise from leveraging on environmental sustainability, creating urban identity and providing simultaneously high quality housing standards [28, 36, 40, 42]. To address the issue of financial barriers, which deter investments in buildings energy retrofit (BER), over the last decades most EU countries have implemented incentive policies to encourage energyefficiency upgrades and retrofit of existing buildings [11, 22]. These interventions usually include specific home renovations (e.g. buildings envelope thermal insulation, replacement of windows, etc.) and equipment (e.g. high-efficiency heating and cooling systems, appliances, etc.). To encourage homeowners in undertaking energy-efficient renovations, Governments can introduce a wide range of incentive measures: regulatory instruments, economic and market-based instruments, information campaigns and voluntary actions [18, 17]. Effective February 19 2007, the Italian Government has introduced fiscal incentive programs to accelerate energy retrofit interventions in residential buildings. According to these incentive policies, homeowners can deduct from their income taxes a fixed percentage of the expenses incurred to implement specific energy retrofit measures (ERMs). More recently, Act n. 135/2018 extended to December 31 2019 current incentive of 65% tax deduction on expenses incurred to invest in energy retrofit of existing buildings. Fiscal incentives schemes, introduced starting from 2006, favored capital investments in energy retrofitting. According to the National Energy Agency [25, 26], during the period 2014–2017 total capital investments were about 9.5 billion Euros and about 1 million renovations were undertaken. Out of which 50% involved the replacement of windows with low-emittance ones, whereas 25% involved adoption of thermal insulation packages on external walls roof and ceilings, and 20% the replacement of Heating Ventilation and Air Conditioning (HVAC) systems. Projections on tax deduction value in the next ten years amounts to 2.1 billion Euros and estimated energy savings due to investments undertaken thanks

Do Policy Incentives to Buildings Energy Retrofit Encourage …

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to fiscal incentives during the period 2014–2020 are about 1.38 Mtoe/year [11, 25, 26]. The above-mentioned incentive scheme generated a cost for the Government (in terms of tax deductions) amounting to 108.7 billion Euros whereas tax revenues amounted to 89.8 billion Euros. Consequently, the final balance appears to be negative: about 1 billion Euros per year [25]. Nonetheless, projections reveal a net capital gain for the Government of about 0.3 million Euros due to tax revenues from renovation works, which act as driver for the economy and specifically for construction industry. There is a debate in literature on the cost-effectiveness of these incentives, which is controversial. The general perception is that incentives are costly and not cost-effective as proved by findings of the EU ENTRANZE PROJECT, they may attract free riders and produce an over-investment effect à la Averch-Johnson [1, 4, 5, 6, 11, 22]. This paper contributes to this debate. Results from a recent literature review by D’Alpaos and Bragolusi [21] reveal that many authors in literature argue that BER generates a wide series of co-benefits (e.g., increase in occupants’ well-being and comfort, increase in property market value, carbon emission’s reduction), which may add to investment value and, thus, make BER profitable also in the absence of policy incentives [2, 13, 29, 32, 41, 47]. In line with this strand of literature, it is here investigated whether investments in BER can be profitable in the phasing out of incentives and whether current incentive policy encourage homeowners’ free-rider behavior (i.e., attracts recipients who would have invested anyhow). To answer the above research questions, we provide a theoretical and methodological framework to support investment decisions in BER. Investments in BER are irreversible and their payoffs are strongly affected by energy price uncertainty. Nonetheless, these investments embed the option to defer and wait for more information to come, which can reduce uncertainty and avoid costly errors [19, 20]. The Net Present Value (NPV) rule fails to capture the value of flexibility, which arises from the deferral option. Therefore, negative NPV projects may turn to be positive NPV projects in the future. Starting from the seminal works by Myers [45], Kester [37] and McDonald and Siegel [43, 44], the Real Options Approach explicitly captures the value of flexibility and provides for a consistent treatment of investment risks. In this paper, it is analyzed the investment decision of a homeowner who has to undertake retrofit interventions on an existing building, regardless incentives are cancelled out. In detail, a Real Option Model to determine the investment value and its optimal timing is developed and implemented. The opportunity to invest in energy retrofitting is in fact analogous to a financial call option, whose value may add to the investment value if optimally exercised. The remainder of the paper is organized as follows. Section 2 provides the model; Sect. 3 illustrates model calibration and reports parameters estimates; in Sect. 4 results are discussed and Sect. 5 concludes.

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2 Model In this section, starting from the seminal works by Myers [45], Kester [37] and McDonald and Siegel [44], as in D’Alpaos et al. [23], it is modelled the homeowner decision to invest in BER through the adoption of specific ERMs. The opportunity to invest in ERMs permits to reduce energy consumption and consequently increase households’ energy savings. The following simplifying assumptions are introduced. Assumption 1 The investment decision time horizon is T r . In detail, T r = T b if T p > T b or T r = T b if T b < T r where T b is the building residual life and T p is the project lifetime. Assumption 2 Project A (i.e., a specific ERM or a combination of ERMs), once undertaken generates a decrease in energy consumption, which in turn produces a monetary benefit in terms of energy cost savings, due to a reduced energy demand. The buying price of energy is stochastic and, as shown in literature [3, 7, 8, 9, 27, 31], it evolves over time according to a Geometric Brownian Motion (GBM): dpt = μpt dt + σ pt dz t

p0 = p

(1)

where dzt is the increment of a Wiener process, μ is the drift term (lower than the market risk-adjusted rate of return μ, ˆ i.e., μ < μ) ˆ and σ is the instantaneous volatility. The household’s net benefit  generated by ERM adoption is: Πt = pt E − Ct

(2)

where ΔE are energy savings due to retrofitting, which are considered as constant over time, and Ct are operating and maintenance costs. The project present value V 1 in a risk-neutral world [16, 23, 33] is:  V (Π0 ) = E

Tr −τ 

−rt

∫ e 0

ΠtA

    1 − e−δ(Tr −τ ) Π0 = Π dt ≡ δ

(3)

where E is the expectation operator under a risk-neutral probability measure, r is the risk-free discount rate, T r is time horizon τ is the investment exercise time and δ (i.e., δ ≡ μˆ − μ > 0) is the opportunity cost of investing at time t = 0 in the project instead of a twin security [43].2 Assumption 3 The project investment costs I are irreversible. 1 Under

the hypothesis that markets are complete, the investment present value coincides with the expected value of discounted cash flows generated. i.e. net energy cost savings. 2 According to McDonald and Siegel [43] and D’Alpaos et al. [23] δ ≡ μ ˆ − μ > 0. In addition, as μˆ = r + R P, where RP is the risk premium, μ − R P = r − δ, where r − δ is the certainty equivalent rate of return.

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Based on the above assumptions, the opportunity to invest in project A is analogous to a European call option on a constant dividend-paying asset (i.e., the ERM):    F(t , t) = Et e−r(τ −t) max (V(t ) − I)+ , 0

(4)

Black and Scholes [10] provided the solution to (4): F(t , t) = e−δ(τ −t) Φ(d1 )Vt − e−r(τ −t) Φ(d2 )I

(5)

subject to the terminal condition [23]:  lim F(t , τ ) = lim max (V(t ) − I)+ , 0 = 0

τ →Tr

τ →Tr

(6)

where d1 (Vt ) =

ln(Vt /I )+(r −δ+σ 2 /2)(τ −t) √ , σ τ −t

(7)

√ d2 (Vt ) = d1 (Vt ) − σ τ − t and Φ(x) is the cumulative standard normal distribution function. F is the so-called extended (or strategic) NPV, i.e., NPV plus the value of options embedded in project A.

3 Model Calibration The investment decision of the homeowner of a four-storey building is here considered. The building consists of 15 apartments (5 per storey, same layout) and is located in a suburb of the city of Padova (Fig. 1). The building was built in 1984 and consists on precast concrete elements and brick walls (Fig. 2) and the gross floor area is 1040 m2 .

Fig. 1 Example of i-th floor layout

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Fig. 2 Reference building

Internal and external walls, floors and ceilings have not thermal insulation and heating systems are low energy efficiency. The analysis focuses on the scenario in which policy incentives to BER are not set in place and it is meant to verify whether and when it is optimal to invest in thermal insulation of the building envelope. According to key findings of a recent literature review by D’Alpaos and Bragolusi [21], this ERM is one of the most cost-effective, in that it generates significant energy savings while reducing its Lyfe Cycle Costs (LCC). Building envelope insulation provides the highest energy saving potential, involves negligible maintenance and operating costs and maintains its technical performance over the years [11, 24, 30, 38, 48]. The ERM under investigation to be implemented has to comply with technical requirements set by Italian Government Decree DM 25/06/2015, in which minimum requirements for ERMs are defined. BIM TerMus software was used to compute the primary energy consumption of the building prior to investment (i.e., status quo) and consequent to investment (i.e., after building envelope thermal insulation construction). Table 1 illustrates thermal analysis results for the entire building. Annual energy consumption of the status-quo building is 62.21 MWhe /year, whereas energy consumption of the retrofitted building is 42.76 MWhe /year. Table 1 Thermal analysis results (Source BIM TerMus software) Annual energy consumption (MWhe /y)

Percentage of energy savings with respect to the status quo (%)

Status-quo building

61.22

–

Retrofitted building

42.76

30.15%

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To calibrate the model, market data driven from the Italian energy market and the Italian construction industry were used. The household is a price-taker and is connected to a national grid under a variable rate contract, whose price is indexed to the National Single Price (PUN). • It can be demonstrated that grid-purchased energy price pt evolves over time according to a GBM [7, 8, 9, 15, 27, 31]. Parameters are set according to recent estimates by Andreolli et al. [3], and specifically μ = 1%, σ = 34.87% and pt (t = 0) = 54 e/MWh.3 • Increase in energy savings due to ERM adoption is E = 18.46 MWhe /y and it is constant over time. • Envelope thermal insulation costs were estimated according to market analyses among local suppliers and Veneto Region pricelists for construction works. Investment costs are equal to I = 53600 Euros and are assumed to be not time-dependent. • ERM maintenance and operating costs C t are negligible and set to zero, i.e. C t = 0. • The investment decision time horizon is T r = 30 years. • The risk free rate of return is equal to the interest rate on Italian Treasury Bonds (BTPs) maturing at 30 years. According to the Italian Department of the Treasury (Dipartimento del Tesoro) r = 3% [3, 7]. • The risk-adjusted rate of return is estimated according to the Capital Asset Pricing Model, i.e. μˆ = r + RP = r + βMRP, where MRP = 5% is the market risk premium and β = 0.5 measures systematic risk [7]. Therefore μˆ = 5.5%. • According to the above estimates, the opportunity cost of investing in project A is δ = 4.5%.

4 Results and Discussion Our results show that the project NPV is negative (i.e., −37191 Euros). Nonetheless, by waiting to invest one year it becomes profitable, due to the option value effect, which contribute to increase the investment value. Initially, the longer the deferral, the larger the increase in investment value. It is worth noting that the extended NPV is concave in exercise time and has a maximum, in correspondence to the optimal exercise time of the option. Afterwards, for any additional year of postponement, the investment value reduces. When τ = 0 NPV and F coincide, whereas when τ = 30 years the extended NPV is null as the underlying asset value is null. The option value to invest reflects the opportunity cost of waiting to invest. When δ = 4.5% and σ = 38.4% (i.e., drift and volatility of energy prices in the Italian electricity market), the optimal investment timing is τ = 12 and the investment value is F = 1315.43 Euros. Nonetheless, if the homeowner invest at 3 p is the average PUN yearly price in the period January 2016-Septeber 2019 as provided by 0 Gestore Mercati Energetici (GME), which operates power, gas and environmental markets.

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time τ = 1, the investment value is slightly positive and is equal to F = 2.83 Euros (Fig. 3). These results prove that it may be profitable to invest in building envelope insulation regardless incentive schemes (Fig. 3). To test the model results, comparative statics was performed. Firstly, to take into consideration the recent downturn in electricity prices due to significant reductions in oil prices and to account for possible rises in future prices (which reflect on μ), it was assumed, ceteris paribus, δ = 2%, 3%, 4% respectively. From direct inspection of Fig. 4, it can be easily shown that higher the opportunity cost δ, the lower the investment value F. The optimal investment timing does not change. Secondly, comparative statics was performed by varying σ to account for changes in volatility of energy prices. The project NPV is not affected by changes in price volatility, i.e. NPV = −37191 Euros. According to our findings, the greater the volatility σ, the greater the option value to defer for benefitting from new information to come (Fig. 5). Price volatility affects the optimal investment timing. When σ = 30% the optimal investment timing is τ = 13 years. This results might appear to be counterintuitive as usually the greater the uncertainty, the greater the deferral. It is worth noting that the greater the deferral, the shorter the time-period over which homeowners can gain benefits from investing in terms of cost savings until the buildings residual life (or the ERM residual life) is reached. From analysis of results, it clearly emerges the trade-off between the option value to postpone (which increases as time passes) and the opportunity cost of waiting to invest. The longer the homeowner waits to invest, the lower the benefits arising from costs saving throughout the buildings residual life or the ERM residual life. 1400

Fig. 3 Investment value (extended NPV) for δ = 4.5% and σ = 38.4%

1200

F (Euros)

1000 800 600 400 200 0

0

10

20

τ (year)

30

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Fig. 4 Investment value (extended NPV) for σ = 38.4% and different values of δ

3000

Fig. 5 Investment value (extended NPV) for δ = 4.5% and different values of σ

F (Euros)

2500 2000 1500 1000 500 0

0 σ = 30%

10

τ (year) σ = 40%

20

30 σ = 50%

5 Conclusions In this paper, it was investigated whether homeowners may be willing to invest in BER regardless incentive schemes. In detail, it is proposed a Real Option model to determine the value of investing in ERMs and its optimal timing, by modeling the opportunity to invest as a call option. It is analyzed how uncertainty over energy prices and investment timing flexibility affect investment value. The Real Options theory suggests that investment timing flexibility, and specifically the option to defer,

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has a value and guarantees to avoid costly errors by hedging investment risks and capitalizing on favorable future opportunities. Our results show that negative a NPV investment may turn to be profitable in the future if the option to defer is optimally exercised. At current energy prices, immediate investment in building envelope thermal insulation is not profitable. Nonetheless, by postponing investment by one year, the investment turns out to be profitable. The value of the opportunity to invest is concave in exercise time and the investment value can be maximized if the option is optimally exercised, i.e. investment is undertaken at its optimal investment timing. In this respect, it is identified the optimal investment strategy: to defer investment until the optimal investment timing is reached. Investments in household energy efficiency are cost-effective at current energy prices if uncertainty over future energy prices increases. According to our findings, the value of flexibility to invest adds to investments value and make incentive schemes not necessary to encourage investments in BER. By contrast, current fiscal incentive policy in Italy may attract free riders and generate an over-investment effect, which in turn increases Government and Society costs. In other words, recipients who would have invested anyhow may decide to invest and apply for tax rebates, which de facto reduce investment costs and enlarge monetary benefits for private homeowners, whereas they increase Society costs. Our findings have relevant policy implications, and can support policy makers in the optimal design of incentive policies to boost investments in BER. Despite potential financial gains, homeowners might not invest in the phasing out of policy incentives due to the so-called “bounded-rationality” barrier. Homeowners might not consider energy efficiency as a priority concern. It is likely that many energy users have limited or imprecise information on operating costs and consequently on potential energy savings, rather than on investment costs. In this respect, information campaigns might be cost-effective solutions in favoring investments in BER, while reducing Government costs. To avoid the adoption of opportunistic behavior by homeowners, policy incentives should be targeted to low-income households. De facto, fuel poverty affects severely low-income households, who are not likely to invest due to stringent budget constraints unless they receive state aids.

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A Literature Review on Construction Costs Estimation: Hot Topics and Emerging Trends Marta Bottero, Caterina Caprioli, and Alessandra Oppio

Abstract The aim of the present paper is to investigate the literature on the issues of construction costs in building production and urban development in order to identify the most relevant trends and describe the research context. In particular, a bibliometric analysis was carried out through one of the most acknowledged bibliometric databases, Scopus. Due to the great number of documents related to this topic, the literature review was conducted at three different levels: the first one investigates in a wider manner the cost value, the second one analyses the costs in building production and infrastructure, whereas the third one focuses on the evaluation approaches and new trends emerging from the literature. This study has allowed an advance in the comprehension of the main relevant issues related to this topic and an in-depth understanding of the role of evaluation methods as an instrument to synthesize the full range of aspects involved in the cost and in the project life-cycle. The increasing importance of topics like Building Information Modeling (BIM), Life Cycle Assessment (LCA) or Multicriteria analysis shows clearly a transition of the research to a sustainable view of the production operations and to a life cycle perspective of the projects. Keywords Construction cost · Evaluation approaches · Sustainability · Life cycle · Building · Infrastructure

M. Bottero · C. Caprioli (B) Interuniversity Department of Regional and Urban Studies and Planning (DIST), Politecnico di Torino, Castello del Valentino, Viale Pier Andrea Mattioli, 39, 10125 Turin, TO, Italy e-mail: [email protected] M. Bottero e-mail: [email protected] A. Oppio Dipartimento di Architettura e Studi Urbani (DAStU), Politecnico di Milano, Via Edoardo Bonardi, 3, 20133 Milan, MI, Italy e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Morano et al. (eds.), Appraisal and Valuation, Green Energy and Technology, https://doi.org/10.1007/978-3-030-49579-4_9

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1 Introduction The construction industry is a complex sector of the economy, which involves a broad range of stakeholders and has wide linkages with other activities such as manufacturing, the usage of materials, the energy sector, the finance, the labour and equipment market [14]. Many kinds of research have shown the interdependence between construction and other economic sectors [4, 21]. In particular, the construction industry is able to mobilize material and human resources for the development of building and infrastructure, promoting local employment [15]. By itself, it is responsible for a considerable amount of the Gross Domestic Product (GDP) in both developed and underdeveloped countries [8]. It is for those reasons that decisions within the construction sector and the construction cost in itself are linked, not only to economic but also to social and environmental aspects [1, 16]. As a consequence, it is possible to say that the new goal of the construction sector becomes the implementation of new technologies and solutions in line with the pillars of sustainable development [5, 11, 17]. For achieving that, it is necessary a strong effort of the research in this line. The adoption of new methods and evaluation approaches is needed [19] for a transition to a holistic view of the project over all its components and over this life cycle, i.e. from cradle to grave. The strong impact of constructions on the built environment plays a central role in the promotion of the sustainable development [2, 3, 9, 10] and its connection with other products and services could be also an opportunity to affect changes on those fields [20]. Since many variables must be considered when managing the reliability of costs of projects [6, 7], spanning from project complexity to technology requirements, from vagueness in scope to the project team requirement [12], the estimation of costs is a major issue with many impacts on the performance, not only directly connected with the project but also indirectly [13, 18]. The present research has the aim to give a clearer representation of the research on the estimation of construction costs, considering, in particular, the building and infrastructural sector, and to analyse the role of different methods and approaches in supporting this objective. To achieve this goal, a literature review on these issues was conducted, using the database Scopus. The paper is structured as follow: after this Introduction, a Research Methodology Section describes the method used to conduct the literature review and the different stages of the analysis. Then, the Results Section highlights the results obtained from the different investigations conducted. Finally, in the Conclusions, the paper gives an overall explanation of the most interesting trends of the literature and points out the future implications of the research.

2 Research Methodology As already mentioned, the aim of this paper is to develop a bibliometric analysis of the literature in the context of cost estimation in building and infrastructural sectors. The scope is to highlight the most recent trends and the main relevant issues on this theme in order to give a comprehensive view of the state of art, useful for guiding

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future researches and demonstrating the role of operational researches and evaluation methods in this field. The literature review was conducted using Scopus, one of the most acknowledged and reliable bibliometric databases. The analysis and collection of papers were performed between November 2019 and January 2020. The first step of the literature review was the selection of the keywords to be used for the research. In particular, due to the wide number of documents related to the cost estimation contained in Scopus, the literature review was conducted at three different levels and, so, with different keywords strings. The first analysis investigates in a wider manner the cost estimation using the following keywords: ((“construction cost” OR “production cost”) AND (“evaluation” OR “assessment “ OR “estimation”)). The result is a number of 34,180 documents with a full-text search and 7,289 documents by limiting the search to title, abstract and keywords. The second analysis narrows the research adding some keywords to boundary the disciplinary fields. Two searches were performed, one for the building sector and the other for the infrastructural one. The keywords used are the following: ((“construction cost” OR “production cost”) AND (“evaluation” OR “assessment “ OR “estimation”) AND (“building”)) and ((“construction cost” OR “production cost”) AND (“evaluation” OR “assessment “ OR “estimation”) AND (“infrastructure”)). In this second case, the analysis was carried out only considering the results in the title, abstract, and keywords. The number of documents is respectively 647 for the building sector and 280 for the infrastructural one. In the third analysis—the most interesting one for the purpose of the research—other keywords were added with a specific focus on evaluation approaches, operational researches and new trends emerging from the literature. The new keywords are in most cases derived from the filter keywords suggested by Scopus in the previous searches and are the following: (“Building Information Modeling” OR “BIM”), (“choice experiment” OR “conjoint analysis”), (“cost-benefit analysis”), (“Discounted Cash Flow” OR “DCF”), (“Life Cycle Assessment” OR “LCA”), (“Life Cycle Cost” OR “LCC”), (“Monte Carlo method” OR “Monte Carlo experiment”), (“multicriteria” OR “MCDA” OR “MCA” OR “multi-criteria”), (“Neural network”), (“Regression Analysis” OR “Parametric Model”) and (“revealed preference” OR “stated preference”). Those keywords are added to the string used in the second analyses both for building and infrastructure. In the next paragraphs, these three literature reviews will be named respectively Group A, Group B and Group C. For each group, different analyses were performed which are titled Historical series analysis, Country productivity analysis and Subject area analysis. Figure 1 shows the framework of the literature review performed in this research. The first type of analysis (Historical series) allows the comprehension of the productivity of the literature with respect to these themes over the timespan indexed in Scopus. In this way, it is possible to understand the overall trend of the selected sample. The second analysis (Country Productivity) shows how much the different countries, according to the authors’ affiliation city, have produced in terms of documents published during the lifespan. The third analysis (Subject area) provides an overview of the sectors that deal with the issue of cost evaluation. For the last group (Group C), in addition, a transversal comparison of the keywords selected was given

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Fig. 1 Literature review framework

in order to understand the relevance of specific approaches and methods in the cost estimation of building and infrastructure. In particular, this comparison can support the identification of the gaps in literature production and the most consolidated fields of research.

3 Results 3.1 Group A: Cost Value Estimation The first analysis performed and related to the cost estimation shows a great number of documents (34,180) both with a full-text search and by limiting the search to title, abstract, and keywords (7,289). Historical production starts in 1953, but only in the 70s, the number of documents indexed is more than 10 per year. Nevertheless, the intensive production of the literature on cost estimation starts after the 2000s. From 2000 to 2020, the number of documents is 31,396, equal to 92% of the entire literature

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Fig. 2 Historical production for all text research and limited to title, abstract and keywords

production. In particular, in the last 10 years, the documents published are 25,243, i.e. 74% of the total. The second analysis, which focuses the research on title, abstract and keywords, shows similar results with respect to the Historical production. Also in this case, the first document indexed in Scopus dates back to 1953 and the 1970s shows a slight increase in production with an average of about 14 documents per year. From 2000 to 2020, the literature production increases significantly with a total of 6,226 documents, equal to 85% of the entire production. With the same trend, slightly lower than the first search, from 2010 to 2020, the number of documents published are 4,572, i.e. 63% of the total production. Figure 2 reports the historical production of the literature for the research in all text and limited to title, abstract and keywords. The examination of the Subject areas performed by Scopus and reported in Fig. 3 illustrates how many sectors deal with the topic of cost estimation, spanning from engineering and construction to environmental science, from medicine to biology.

3.2 Group B: Cost Estimation in Building and Infrastructure Construction The second analysis starts with the previous keywords trying to boundary the disciplinary fields. In particular, two searches were performed, one for the building sector and the other for the infrastructural one, in order to verify if different trends emerge. The results show that for the building sector the number of documents is more than double, i.e. 642 documents against 280 for infrastructure, in both cases analyzed

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Fig. 3 Subject areas for all text research and limited to title, abstract and keywords

only searching the string in title, abstract and keywords. The Historical Production is also wider for building, starting from the 70s. For the infrastructure, instead, the first documents date back to 1986, even if only in 1990 the production starts with regularity. In both cases, more than 90% of documents were written after 2000 and more than 70% after 2010. In fact, before 2003 for building and 2007 for infrastructure, the number of documents indexed was less than 10 per year. Figure 4 reports the historical trends of literature for the building and infrastructure sector. As might be expected, with the introduction of other keywords (building and infrastructure), the Subject areas decrease and, in particular, most of the documents are in the field of engineering and construction, environmental sciences and energy (Fig. 5). Also in Group A, these subjects emerge as the main discipline in studying the cost estimation, demonstrating the strong relevance of this topic in these fields. Finally, as regards the Country productivity analysis, it can be noticed that the majority of documents in building and infrastructure sectors were written in the United States. Otherwise, some slight differences can be seen in the countries of the authors’ affiliation. For example, in the building sector, the main production countries are China, South Korea and the UK with more than 30 documents per each. Instead, in the infrastructure sector, the most productivity countries are the UK, Canada, China and Australia with more than 12 documents per each. In both cases,

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Fig. 4 Historical production in building and infrastructural sector Psychology Immunology and Microbiology Nursing Multidisciplinary Biochemistry, Genetics and Molecular Biology Chemistry Arts and Humanities Economics, Econometrics and Finance Agricultural and Biological Sciences Medicine Chemical Engineering Decision Sciences Mathematics Physics and Astronomy Materials Science Social Sciences Earth and Planetary Sciences Computer Science Business, Management and Accounting Energy Environmental Science Engineering 0

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Fig. 5 Subject areas in building and infrastructural research

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Fig. 6 Number of publications per each country

Italy represents one of the main European countries which contributes in this research field respectively with 23 and 8 documents for building and infrastructure sectors, i.e. the second and sixth European countries in the ranking of publications productivity. In general, the two searches show the same countries as to be active on these issues. Figure 6 shows the number of publications produced by each country and those with an incidence lower or equal to the 1% which are considered all together. The only exceptions are Sweden, Denmark and France, which are considered separately, because, even if in building sector the documents have an incident equal to the 1%, they assume a more relevant role in the infrastructure sector.

3.3 Group C: Evaluation Approaches and New Trends in Cost Estimation The aim of the third analysis—the most interesting one for the purpose of the research—is to focus on evaluation approaches, operational researches and new trends emerging from the literature. The search starts from the previous analysis (Group B), maintaining the same division for building and infrastructure. The new keywords are in most cases derived from the filter keywords suggested by Scopus in the previous searches. It is interesting to notice that words like life cycle and sustainable development are in the first twenty keywords suggested by Scopus searching for construction cost estimation both in building and infrastructural sector. Other keywords are defined based on the disciplinary field of the authors, in order to verify the possible gaps in the literature production and highlight the relevance of peculiar approaches and methods in the estimation. Table 1 shows the type of

A Literature Review on Construction Costs Estimation … Table 1 Keywords used in Group C

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Keywords “Building Information Modeling” OR “BIM” “Choice experiment” OR “Conjoint Analysis” “Cost-Benefit Analysis” OR “CBA” “Discounted Cash Flow” OR “DCF” “Life Cycle Assessment” OR “LCA” “Life Cycle Cost” OR “LCC” “Monte Carlo method” OR “Monte Carlo experiment” “Multicriteria” OR “MCDA” OR “MCA” OR “Multi-criteria” “Neural network” “Regression Analysis” OR “Parametric Model” “Revealed preference” OR “Stated preference”

keywords chosen for the analysis of the literature. The results show a similar trend in building and infrastructure, as it is possible to see in Fig. 7. Both in infrastructure and building sector, Choice Experiment/Conjoint Analysis/Discount Cash Flow/Revealed and Stated Preferences are totally absent in the literature production. On the contrary, Cost-benefit analysis is the most common evaluation approach used for estimating the construction or production cost, respectively 39% and 46% of the entire set of documents analyzed. Another similar and relevant result is the strong adoption of Life-cycle approach. The documents which estimate the costs through a Life-cycle approach are 13% in building and 18% in infrastructure, as well as Life-cycle assessment 13% both in building and infrastructure. A greater number of LCC than LCA in infrastructure could be partially explained with a more complex end-of-life disposal operations and its few consolidated estimations, which determine a predilection to the simpler approach of LCC. Similar percentages of documents may be seen in the use of Montecarlo simulation, Multicriteria analysis, Neural network and Regression and Probabilistic models, from 3 to 7% of documents for both searches. A different trend, instead, occurs when focusing on Building Information Modeling (BIM). If in the building sector, the use of BIM is quite consolidated with a percentage of 13% of documents such as LCC and LCA, the number in infrastructure search is much lower with only a percentage of 2. This low percentage of adoption can be partially explained with its heavy use of 2D based design and a large volume of static documentation. Adaptation of the BIM concept to suit the specific requirements of infrastructure projects will be a key aspect in effective BIM deployment [7]. Generally, the Historical production in the context of evaluation approaches and methods to support the estimation of construction and production costs in building starts after 2000, as it is possible to see in Fig. 8. In particular, this happens for the most consolidated approaches, such as Cost-benefit analysis, Neural network, Regression and Probabilistic model and Multicriteria analysis. More recent are, instead, such documents which apply LCC and LCA, all dated after 2005. However, some

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Fig. 7 Percentage of evaluation approaches and methods contained in building search (above) and in infrastructure one (below)

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50 45 40 35 30 25 20 15 10 5 0

BIM

CBA

LCA

LCC

Montecarlo

MCDA

Neural Network

Regression

Fig. 8 Historical production of literature in building sector per each evaluation approach and method considered

researches on these methods had already been done, in particular, in 1994 and 1996 for LCC and in 1998 for LCA, but in the last years, there has been an increase in publications in this field. BIM is definitely the most recent approach in this context, with the first documents published after 2010, but with an intensive increase of publications in the very last years. A similar trend can be seen for the infrastructure search but postponed about 5 years later in 2005 (Fig. 9). As mentioned before, BIM is still underused, while CBA is also, in this sector, the most common approach. LCA and LCC seem quite promising and being consolidated also in this sector. As might be expected, for the Subject area analysis, the results show a major production in the fields of Engineering, Environmental Science and Energy in general for all evaluation approaches considered, as it is possible to see in Figs. 10 and 11. However, it is interesting to notice that in building sector the adoption of a BIM approach is strongly connected also with the computer sciences field, with a number of articles almost similar to those in the engineering sector.

4 Conclusions and Future Implications The present paper has allowed the understanding of the most recent trends and the main relevant issues related to the theme of cost estimation in building and infrastructure production. In particular, the research gives a comprehensive view of the state of art, useful for guiding future researches and demonstrating the role of some evaluation approaches and methods in this field.

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20 18 16 14 12 10 8 6 4 2 0 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 BIM

CBA

LCA

LCC

Montecarlo

MCDA

Neural Network

Regression

Fig. 9 Historical production of literature in infrastructure sector per each evaluation approach and method considered

At the same time, the research shows certainly some limitations. First of all, related to the type of analysis conducted using as source a single database, even if Scopus is one of the most acknowledged and reliable bibliometric databases. Secondly, the way in which the research was implemented is, at this moment, quite general, without any in-depth analysis of single documents. Future investigations will consider in a more advanced way the role and potentials of the single approach and method in the evaluation and performance of cost estimation in the building and infrastructural sectors. The cost estimation literature is not very far from the main trend of the research, where the role of a sustainability perspective and the concept of life-cycle of products is very influent. The two are more relevant when considered together in the production and construction of buildings and infrastructures. This change of paradigm is also reflected by the recent number of documents which adopt LCC or LCA approaches. This demonstrates the transition from a cradle to grave approach to a cradle to cradle approach with attention to the multi-recyclability of goods. In this way, it is possible to reduce the initial costs through the re-use of existing materials, which obtain monetary and environmental values, such as, for example, the reduction of CO2 emission. Another aspect that emerges from this investigation is the increasing influence of BIM models with attention to the role of Project Management in the context of cost estimation. However, as previously underlined, the role of BIM is more consolidated in the building sector than in the infrastructural one. However, this lower interest could be potentially seen as a very promising future field of research. This work has also demonstrated how relevant is and will be the estimation field in this context, with its ability to synthesize the full range of aspects involved in

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Physics and Astronomy Chemistry Nursing Economics, Econometrics and Finance Biochemistru, Gentics and Molecular Biology Agricultural and Biological Sciences Materials Science Chemical Engineering Arts and Humanities Medicine Decision Sciences Mathematics Earth and Planetary Sciences Social sciences Computer Sciences Business, Management and Accounting Energy Environmental Science Engineering 0 Regression

Neural Network

10

MCDA

20

30

Montecarlo

40

50

LCC

60 LCA

70

80

CBA

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BIM

Fig. 10 Subject areas in building sector per each evaluation approach

the cost value and in the project life-cycle, bringing the analysis full circle to the sustainable perspective. Finally, an interesting investigation will be implemented in future research on the issue of maintenance costs, in order to understand the differences in the literature and in the research compared to the construction and investment ones. A preliminary search on Scopus has shown a similar number of documents both in building and infrastructure sector, so the comparison could be particularly interesting in the historical and country production and in the subject areas, as well as in the evaluation approaches and methods, in order to guide and understand the development of new trend in the research.

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Biochemistru, Gentics and Molecular Biology Agricultural and Biological Sciences Materials Science Chemical Engineering Arts and Humanities Medicine Decision Sciences Mathematics Earth and Planetary Sciences Social sciences Computer Sciences Business, Management and Accounting Energy Environmental Science Engineering 0 Regression

Neural Network

MCDA

5

10 Montecarlo

15

20 LCC

25 LCA

30 CBA

35

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BIM

Fig. 11 Subject areas in infrastructural sector per each evaluation approach

References 1. Assumma V, Bottero M, Monaco R, Soares AJ (2019) An integrated evaluation methodology to measure ecological and economic landscape states for territorial transformation scenarios: an application in Piedmont (Italy). Ecol Indic. https://doi.org/10.1016/j.ecolind.2019.04.071 2. Bassi A, Carlotta O, Dell’Ovo M (2019) Minimum environmental criteria in the architectural project. Trade-off between environmental, economic and social sustainability. Valori e Valutazioni 22:35–45 3. Bertolinelli M, Guzzoni L, Masseroni S, Pinti L, Utica G (2018) Innovative participatory evaluation processes: the case of the ministry of defense real-estate assets in Italy. In: Mondini G, Fattinnanzi E, Oppio A, Bottero M, Stanghellini S (eds) Integrated evaluation for the management of contemporary cities. SIEV 2016. Green energy and technology. Springer, Cham, pp 547–557 4. Bon R (1992) The future of international construction. Secular patterns of growth and decline. Habitat Int 16:(3):119–128. https://doi.org/10.1016/0197-3975(92)90068-A 5. Bottero M, Caprioli C, Cotella G, Santangelo M (2019) Sustainable cities: a reflection on potentialities and limits based on existing eco-districts in Europe. Sustainability 11(20):5794. https://doi.org/10.3390/su11205794 6. Bottero M, Datola G, Monaco R (2019) Fuzzy cognitive maps: a dynamic approach for urban regeneration processes evaluation. Valori e Valutazioni

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7. Bradley A, Li H, Lark R, Dunn S (2016) BIM for infrastructure: an overall review and constructor perspective. Autom Constr 71:139–152. https://doi.org/10.1016/j.autcon.2016. 08.019 8. Crosthwaite D (2000) The global construction market: a cross-sectional analysis. Constr Manag Econ 18(5):619–627. https://doi.org/10.1080/014461900407428 9. D’Alpaos C, Bragolusi P (2018) Multicriteria prioritization of policy instruments in buildings energy retrofit. Valori e Valutazioni 21:15–25 10. D’Alpaos C, Bragolusi P (2018) Buildings energy retrofit valuation approaches: State of the art and future perspectives. Valori e Valutazioni 20:79–94 11. Dell’anna F, Vergerio G, Corgnati S, Mondini G (2019) A new price list for retrofit intervention evaluation on some archetypical buildings. Valori e Valutazioni 12. Doloi H (2013) Cost overruns and failure in project management: understanding the roles of key stakeholders in construction projects. J Constr Eng Manag 139(3):267–279. https://doi. org/10.1061/(ASCE)CO.1943-7862.0000621 13. Fattinnanzi E (2011) La valutazione della qualità e dei costi nei progetti residenziali - Il Brevetto SISCo. Valori e valutazioni 7:7–8 14. Hillebrandt PM (2000) Economic theory and the construction industry. Palgrave Macmillan, London, UK 15. Industry BT (2003) Sustainable building and construction: Facts and figures. Ind Environ 26(2–3):5–8 16. Jelokhani-Niaraki M, Malczewski J (2015) The decision task complexity and information acquisition strategies in GIS-MCDA. Int J Geogr Inf Sci. https://doi.org/10.1080/13658816. 2014.947614 17. Le´sniak A, Zima K (2018) Cost calculation of construction projects including sustainability factors using the case based reasoning (CBR) method. Sustainability 10(5):1608. https://doi. org/10.3390/su10051608 18. Morano P, Rosato P, Tajani F, Di Liddo F (2020) An analysis of the energy efficiency impacts on the residential property prices in the city of Bari (Italy). In: Green energy and technology 19. Oppio A, Torrieri F, Dell’Oca E (2018) Land value in urban development agreements: methodological perspectives and operational recommendations. Valori e Valutazioni 21:87–95 20. Stasiak-Betlejewska R, Potkány M (2015) Construction costs analysis and its importance to the economy. Procedia Econ Finance 34:35–42. https://doi.org/10.1016/s2212-5671(15)01598-1 21. Wells J (1986) The construction industry in developing countries: alternative strategies for development. Taylor & Francis

Public Works in North-East Italy: An Efficiency and Risk Allocation Analysis Valentina Antoniucci and Giuliano Marella

Abstract The present contribution provides an overview of public works completed and in progress in the Veneto Region, in north-east Italy. The analysis is conducted on regional data collected by the Italian government in accordance with the European Union’s transparency requirements on public spending. Cost and time overruns are considered as crucial indicators of efficiency in infrastructure development and risk assessment. The paper addresses the relevance of public works, and risk assessments in infrastructure development, also dealing with the theoretical framework and relevant regulatory innovation. We compare the proportion of risks considered by the Authority for the Supervision of Public Works, Services, and Supply Contracts (AVCP) with a sample of 4,331 works retrieved from the OpenData source on public works in Italy. The analysis confirms an improvement in the efficiency of the public administration in the territory analyzed. It also highlights the potential and also the bias of the data source in terms of the reliability of the present results, and for the purposes of future research. Keywords Public works · Risk assessment · Cost overrun · Time overrun · Open data

1 Introduction Promoting the development of relevant infrastructure is one of the main goals of Italy’s national structural reforms to correct macro-economic imbalances. Improving the country’s infrastructure and public services is a prerequisite in order to raise the country’s productivity level and attract international investment. The quality of Italian infrastructure is below the EU average [20], and the present state of repair V. Antoniucci (B) · G. Marella Department of Civil, Architectural and Environmental Engineering, University of Padova, ICEA, Padua, Italy e-mail: [email protected] G. Marella e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Morano et al. (eds.), Appraisal and Valuation, Green Energy and Technology, https://doi.org/10.1007/978-3-030-49579-4_10

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and maintenance is a source of concern. As the EU Commission states: “Italy is not playing its key role in the European Transport Strategy” [20, p. 52] due to a rethinking of its commitment to certain major projects of European significance, such as the Brenner Base tunnel and the Lyon-Turin Base tunnel. Developing public works and infrastructure may also help to reduce the regional and inter-regional disparity across the country. Delays in the development of Italy’s public works are more often down to a matter of efficiency than to a lack of economic resources. Whilst the funding for public works has been constantly decreasing since 2011, a huge amount of money is still allocated for infrastructure and public services by the EU and the national government [24]. According to the EU Commission [20], 2.45 billion euro of European Structural and Investment (ESI) funds have been allocated to expanding the country’s transport networks, 1.4 billion euro to sustainable urban transport infrastructure, and 889 million euro to fast internet networks. Projects in these domains should be finalized by the end of 2020. ESI funds have also been earmarked for risk prevention (2.3 billion euro), the circular economy [36], and water management (1.4 billion euro), mainly in Southern Italy. The schedules for public works, even those already planned and financed, are liable to frequent delays and cost overruns. This phenomenon seems to be endemic in the public works economy worldwide. Although a large body of literature has been devoted to the topic, the extent of these cost and time overruns does not seem to decrease. As Flyvbjerg [21, p. 3] puts it: “No improvement in forecasting accuracy seems to have taken place, despite all claims of improved forecasting models, better data, etc.” The majority of empirical research provides data and describes methods for assessing potential delays in the completion of public works, and the associated increase in their costs, but “the empirical findings are scattered between different strands of literature, ranging from the fields of construction engineering and management to that of applied economics” [13, p. 1]. This makes it hard to manage a taxonomy of possible causes of the phenomena for homogeneous categories of projects. One of the main criticalities in our understanding and assessment of cost and time overruns in public works concerns the availability, accuracy and comparability of the data [42]. The wider the array of data, the greater the difficulty of managing and organizing project timelines and costs in a comparable way. The definition of ‘public works’ includes such diverse projects as railways, public buildings (schools, hospitals, etc.) and dams, but also works for dealing with hydrological instability, and landscape design. The need for transparency regarding public investments is one of the features of the EU’s program for monitoring national funding, and this has led to several national institutions collecting data on public works and making them freely accessible to the public in recent years. This new large body of data is still largely ignored by scholars and researchers, and hardly ever compared with forecasts regarding risk assessments for public works issued by the Italian authorities. The aim of the present contribution is to critically review the available data on public works in Italy, and in the Veneto Region in particular, discussing the reliability of the data sources, and assessing the state of progress of public works, also in the

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light of the related risk allocation by the ANAC [the Italian National Anti-Corruption Authority]. The remainder of this paper is organized as follows: Sect. 2 presents the background literature on the topic, and the regulatory framework; Sect. 3 presents and discusses the available data on public works retrieved from the Italian State’s General Accounting Department, considering a sample of public works planned and implemented between 1999 and 2018 in the Veneto region, in north-east Italy; and Sect. 4 critically discusses the main findings emerging from the data analysis, highlights some limitations of our methodological approach, and suggests directions for future research.

2 Literature Review and Regulatory Background 2.1 Literature Review The literature on public works from the economic and engineering perspective focuses mostly on inefficiency in their development. Since the 1970s (see [29], among others), research has confirmed systematic and substantial time and cost overruns in public procurement in any sector. The pioneering research by Bruzelius et al. [6], and Flyvbjerg et al. [23] provided the first empirical data on cost overruns of the so-called “mega-projects”, mostly regarding public transport works. Flyvbjerg specifically analyzes the causes behind the constant increase in their costs, highlighting the psychological bias affecting decision-makers, among other issues [23]. He describes how the “appraisal optimism” of promoters and forecasters regarding expected project outcomes leads to the estimated costs being lower than the real outlay. Even though they are disproved by the facts, these over-optimistic expectations are endorsed by politicians for reasons of personal interest and power. This attitude can be defined as a “strategic misinterpretation” [31]. Underestimating the costs and, more generally, the risks of a project’s development is not necessarily tantamount to lying, but based on the assumption of an ideal world where “everything goes according to plan” [23]. This so-called “EGAP principle” [45] is one of the core problems in project developments. While this psychological issue is probably the ‘original sin’, the other main reasons underestimating costs recognized in the literature are of a technical or economic nature and, to a smaller scale, related to the regulatory systems governing public works. Technical considerations are used to categorize the main mistakes affecting a project’s development in its design and construction phases [3, 9, 21, 27, 30, 32, 44]. The technological features of cost underestimation are assessed mainly by means of statistical analyses on ex-post project costs, and on the strength of interviews with stakeholders, such as construction companies, project managers, etc. [1, 8, 12, 40]. These analyses attribute several reasons for design mistakes to inefficient construction site management, which can affect all kinds of project.

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The economic issues involve the promotion of a given project by private developers, engineers and construction firms driven by self-interest [10], and by public authorities that may need approval for funding and attracting capital investments to their cities and territories [23, 33]. The last category of reasons for cost overruns concerns the regulatory framework, and the awarding of building contracts in the public economy. Apart from illegal phenomena, such as corruption [4, 14, 46], a complex and unclear system of norms and laws tends to lengthen the time it takes to develop a project, and the costs consequently increase [35]. In many countries, the contract awarding procedures are recognized as a key factor in both time and cost overruns. The requirements for bidding on and awarding public works contracts are complex, time-consuming, and liable to litigations that can interrupt or postpone the a project [16, 17, 19, 34]. Public administrations often lack the skills to manage awards for major infrastructure or civil works [25, 26]. Particular types of contract, such as concessions and private-public partnerships (PPP) frequently suffer from errors and omissions concerning their risk allocation [14, 28, 39, 46], leading to an underestimation of their future construction and running costs, and delays in their development schedule.

2.2 The Italian Regulatory Framework: The ANAC Risk Matrix As Bucciol et al. [7] state, “The final cost of public works is often considerably higher than the price at which the contract is awarded in the tendering process”. The material cost of implementing public works is just one of the risks that a project may face during its development, however. The increase in the costs and the time required is most severe during the concession procedure phase because the effect of forecasting errors may last for decades after the works have been put into service. In an effort to avoid, or at least contain such risks, the ANAC published a few mandatory guidelines for the ex-ante assessment of economic sustainability of PPP in 2015, and made them stricter in 2016, when the latest version of the Code of Public Works, Services, and Supply Contracts (D.Lgs n. 50/2016) came into force. According to the ANAC guidelines, a public administration can promote a project for development by a PPP providing it demonstrates its value for money, as measured with the Public Sector Comparator. This method compares the Net Present Value (NPV) of the work realized with those of the award procedure and concession procedure. According to Italian legislation, this regulatory innovation entails a mandatory risk sharing between the public and private sector, and shifts to the private developer both the construction risk and one or both of the operational and market demand risks. These three main risks can be divided into several other risks, depending on the type and complexity of a project. Table 1 shows a possible risk breakdown risks, as proposed by the ANAC.

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Table 1 Example of a risk matrix Type of risk

Probability of Risk of risk (%) increase in costs and/or delays

Risk Risk for the Risk for the mitigation if it public developer (%) is transferred authority (%) to the developer

Regulatory Administrative Design Construction Financing Market Operational Maintenance Force majeure Source ANAC, Determinazione no. 10 of Sept 10, 2015

This assessment method, long established in other EU countries [5, 41], is an important innovation in Italy for two reasons: (i) private developers must demonstrate ex-ante their ability to guarantee a greater efficiency than the public administrator; and (ii) the legislator recognizes that public authorities promoting projects should shoulder few of the risks. Thus, in the event of time and cost overruns, or temporary or permanent inability of the work and/or related service to operate, the partly responsible is clearly identifiable, both legally and in the eyes of public opinion. In 2009, the Authority for the Supervision on Public Works, Services, and Supply Contracts (AVCP), since absorbed by the ANAC, and the Technical Unit for Project Financing of the Prime Minister’s Office (UTFP), conducted a survey on 31,970 traditionally-funded public works contracts (excluding works under Project Financing schemes) awarded during the years 2000–07, to obtain a picture of the probability of certain risks for local authorities. Table 2 shows the changes in the forecasting costs and development times, distributed in four categories. Table 3 summarizes the probability of such changes in maintenance and operational costs. The performance risk and the market demand risk (which are not represented in the following tables) are divided into just two categories of change from the forecasted values: no risk, or performance under the estimation, that are respectively assessed in 60–40%. To our knowledge, this is the first and only ex-post analysis on such a large sample, and it is still adopted by the ANAC even though it now refers to data more than ten years old [7] conducted a similar analysis, but on a significantly smaller sample). The present work tries to compare the results of this official survey with the analysis of public works data for the Veneto region, also planned and implemented more recently, and to test the current reliability of the AVCP survey applied to a territory where the public administration is particularly efficient.

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Table 2 Results of the AVCP survey on the changing costs and times to completion of public works in Italy (2000–2007) Degree of change

Economic and temporal efficiency Works with changes in costs (%)

None (≤0)

25

Works with changes in time to completion (%) 23

Small (>0 5% 20%) Total works (No.)

12

66

100

100

Source AVCP, UTFP (2009, Sept) Analysis of the assessment method used for the choice of work development’s procedure: the Public Sector Comparator Method and the assessment of value

Table 3 Results of the AVCP survey on the changing maintenance and operational costs for public works in Italy (2000–2007)

Degree of change

Increase in maintenance costs (%)

Increase in operational costs (%)

None (≤0)

20

30

Small (>0 5% 20%)

10

10

100

100

Total works (No.)

Source AVCP, UTFP (2009, Sept) Analysis of the assessment method used for the choice of work development’s procedure: the Public Sector Comparator Method and the assessment of value

3 Analysis of Public Works in the Veneto Region The main source of freely-available data on public works, and the Italian public economy and finance is the OpenData website managed by the State’s General Accounting Department (https://bdap-opendata.mef.gov.it/catalog/?h=search0, last accessed on Jan 7, 2019). Public works data are collected at national and regional level. They are submitted by public contractors’ works managers and each work is identified by a unique code. Data entry is governed by national laws on transparency in the public administration, and the instructions are detailed, but there is no obligation to check the accuracy and completeness of the information required by the State’s General Accounting Department. The database contains the following information: • location and description of the works; • category of the works (e.g. infrastructure, soil protection, school and university buildings, and so on);

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• • • •

type of works (maintenance, restoration, renovation, new build, and so on); state of progress (e.g. ongoing, completed); contractor (State, Regional Authority, Local Authority); estimated and actual dates of starting and completing project design, construction, and start-up; • estimated and actual costs. The OpenData architecture makes it a potentially valuable source of information for systematically monitoring and analyzing the trend of the funding and construction of public works in Italy. It also enables comparisons between levels of efficiency by geographical area—a very relevant issue in a country traditionally affected by differences in productivity and public expenditure across Regions [11, 37]. The data available for the Veneto region are up-to-date as at July 2019, and cover the years from 1999 to 2018. The administration in charge of the public works data can constantly update the information regarding the works, without any control on the part of the State’s General Accounting Department. Given the purpose of the present work, from the original database of 19,117 public works we excluded all items with incomplete information regarding the costs and timing of construction and operation: this left us with a final sample of 4,331 public works. Some of these works were still unfinished (according to the Territorial Cohesion Agency [Agenzia per la Coesione Territoriale] [2], only considering completed works could bias a sample due to an over-representation of certain types of work, e.g. smaller or more efficient projects), so the actual construction time also includes the partial value for ongoing building sites for which no completion date is available. Here we omit the detailed descriptive statistics for the proportions of the various categories of works and types of project. Although the sample is slightly different, we assume that the proportions are the same as in Marella and Antoniucci [34]: extraordinary maintenance accounts for more than 50% of the sample as a whole, and new buildings for about 20%. The three main categories of works, in descending order, are for road infrastructure, schools and other buildings for social uses, and social infrastructure. A glance at the descriptive statistics in Table 4 already reveals some counterintuitive findings. Despite the marked variability, depending largely on the different types and categories of the works, the average time to accomplish the design phase shows a very modest increase over the forecasted time. Even the case with the longer design phase of the sample, has an actual duration less than the expected ones. The mean time is far shorter than the figure reported in official statistics [2], according to which the design phase for the most common categories of works ranges from 2.4 to 2.7 years, whereas in our sample it averages about ten months. The mean duration of the construction phase in our sample is about 1.1 years (see Table 4), again suggesting a greater efficiency in the Veneto than is reflected in the national official statistics, which indicate that construction takes from 9 months to 1.7 years. The relationship between planned and actual time to completion of the works is more consistent with the literature and official data, the actual time being 76% longer on average than the scheduled time. The time actually taken to obtain

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Table 4 Descriptive statistics for projects development phases and funding of the sample Estimated time for design phase (days)

Actual time for design phase (days)

Time to awarding of contracts (days)

Estimated construction time (days)

Actual construction time (days)

Available funding (e)

Mean

306.85

313.97

249.68

230.26

404.90

659,457

Standard deviation

536.64

550.74

293.07

268.16

515.30

2,881,546

Coefficient of variation (%)

174.89

175.41

117.38

116.46

127.27

436.96

Minimum

0

0

1.0

0

0

104.72

Maximum

5,431

5,268

3,673

5,475

5,166

83,820,524

Table 5 Descriptive statistics for estimated and actual costs (subsample) Planned costs (euro)

Actual costs (euro)

Variation between planned and actual costs

Mean

878,468

869,345

−13%

Standard deviation

3,859,596

3,985,786

51%

Coefficient of variation

439%

458%

−388%

Minimum

1,897

1,868

−705%

Maximum

56,521,259

55,926,522

151%

No.

522

522

522

permission to proceed with the works [28] is also significant: this takes more than 7 months on average, which is perfectly in line with Italian official data. To look at the costs, we had to further reduce our sample due to incomplete data on the expected and actual costs of each work (see Table 5). We excluded not only unfinished works (for which there are no actual costs available), but also many other works for which the OpenData figures were clearly incomplete or wrong. Comparing the sample with the ANAC matrix produces some unexpected results. The actual production costs in the majority of cases are lower than expected (we omit the results here for the sake of brevity, and because their detailed representation is irrelevant to the scope of the present work). On the other hand, the increase in the time to completion differs significantly from the AVCP sample (see Table 6); the situation is even reversed. In the AVCP sample, there is a marked increase in the time to completion in 66% of the works, while this is true of only 35% in our sample. On the other hand, most of the works in the Veneto seem to be completed very efficiently, with no time overrun at all (in 59% of cases). The intermediate categories of small and moderate

Public Works in North-East Italy … Table 6 The increase in the time to completion of public works in Italy according to the AVCP survey (2000–2007) and our data for the Veneto region (1999–2018)

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Degree of change Works with changes in time to completion AVCP survey (%) None (≤0)

Veneto sample (%)

23

59

Small (>0 5% 20%)

66

35

Total works (No.) 100

100

time overruns are less represented in our sample than in the AVCP survey, but still proportionally consistent in the two datasets. The Veneto, like most of the regions in northern Italy, is highly efficient in terms of its local administrations, as confirmed by most public economy indicators. Although there are no regional-scale official statistics on the topic, our results seem too good to be true. This picture may be partly explained by the OpenData architecture. The Veneto’s incredibly positive results concerning the costs of public works may simply represent the “physiological gap” between the costs indicated in the tender documents, and those indicated in the contract awarded after the tender procedure has been completed (which is structurally lower). But this cannot explain the numerous cases in which the scheduled costs coincide with the actual costs. Because OpenData is built as an open environment in progress, anyone responsible for data entry at local level can update the information during the course of the works. This often means that scheduled costs and construction times are also revised vis-àvis the original or earlier figures. The managers of OpenData only check for technical issues with the system, and they have no responsibility for checking the accuracy of data provided by single local institutions.

4 Conclusion Public works are an important part of the national and local public economy. They contribute to stimulating the economy in terms of employment and incomes at local level, and exert a countercyclical effect. More importantly, they can attract new local and international investments. Public investments accounted for 2.5% of Italy’s GDP in the decade 2008–2018, while the EU average is 3.1% (Source: IFEL— Territorial Economy Department on Eurostat data). In 2018, investments accounted for more than 37 billion euro: 18.8 billion euro (50.3%) coming from national and EU administrations; 18.2 billion (48.7%) from local authorities; and 355 million (the last 1%) from social security institutions. The quality of public expenditure is even more important than its quantity. European and national institutions are constantly expanding the tools and procedures for monitoring the development of public works and infrastructure, especially those

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involving PPPs. In recent years, a few Italian governmental institutions have developed an open system for collecting and monitoring data on public works during and after their completion to comply with EU requirements on transparency. This is the first collection of data on public works in Italy to be made freely available. Traditionally, there has always been a paucity of data concerning public works, whether completed or underway, making it difficult to conduct ex-post assessments on issues of efficiency and risk allocation [18]. In recent years, the Italian government has adopted new regulations for the development of projects based on PPPs designed to measure and clearly allocate the related risks between private and public actors in the process. This is done using a risk matrix, and the probability of the various risks is also measured on a sample of about 32,000 public works. The newly-available data make it possible to outline the state of the art of infrastructure development in Italy, and its efficiency, also by comparing these data with official statistics on public works, and with the government’s risk assessments. In times of urban regeneration and limited public resources, it is also important to analyze small and medium-sized works, whereas the literature tends to focus mainly on mega-projects [22]. The present analysis concentrates on public works completed and in progress in the Veneto region, in north-east Italy—an area with a good track record for efficiency when compared with the national average for public investments [15, 38, 43]. We analyze a sample of 4,331 public works, also dividing them into two subsamples, to test the proportional risks of cost and time overruns, and compare them with the official statistics. Our analysis confirms that the Veneto’s public authorities as more efficient as contractors, but also raises doubts that the data source considered might be inaccurate and unreliable. Even though it marks a huge innovation for Italy, in terms of both public transparency and new research prospects, the Italian government’s OpenData on public works seems to contain data that suffer from an important bias, which undermines the value of analyzing them. To be specific, because it is an open system, routinely updated by the single public authorities inputting the data, information on predicted development costs can also be continuously revised. This prevents any accurate assessment of the risk of cost overruns, and their actual entity, for a given category and type of works. Data entry is checked not for data quality, but only for accessibility. Any updating of scheduled costs, as well as actual costs, represents a bias for the accuracy of any cost overrun risk assessment or other analyses for risk allocation purposes. The implementation of an open system of data on public works has great potential for research in a field scarcely investigated in Italy (precisely because of a shortage of data). Aside from statistics-based inferences regarding the causes of cost and time overruns (which should be the most important outcome of a research on the topic), providing a precise picture of the state of public investments in infrastructure and territorial development could significantly improve risk assessments on proposed public works in Italy. It should also deliver an up-to-date overview of the country’s infrastructure that would be useful in elucidating the infrastructural and economic divide across Italian regions. As for the question of EU and national funding, a

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representation of the completed infrastructure should facilitate the analysis of how funding is allocated and its effect as a driver of territorial growth.

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A Parametric Cost-Based Approach to Appraisal Jack-Up Vessels Vincenzo Del Giudice, Pierluigi Morano, Pierfrancesco De Paola, and Francesco Tajani

Abstract With reference to “jack up” vessels, this paper has proceeded: to examine the critical aspects of their assessment in the context of the various cases relating to ship appraisal; to catalogue the assessment models proposed in the international literature about the construction cost of similar vessels; to identify the most suitable methodological approach to the study case (parametric cost valuation by functional elements), and to the subsequent relative implementation, aimed at estimating the construction cost of the vessel in terms of “proxy” variable of its value. Keywords “Jack-up” vessel · Vessels construction costs · Parametric cost valuation

1 Introduction The appraisal of vessels value presents many operational complications due to the difficulty to obtain information relating to market data, as well as the use of transparent and rational evaluation procedures, as they are usually based on empirical or unconventional principles [2, 7, 8, 10–13, 15, 17–20]. V. Del Giudice · P. De Paola (B) Department of Industrial Engineering, University of Naples “Federico II”, Piazzale Vincenzo Tecchio 80, 80125 Naples, Italy e-mail: [email protected] V. Del Giudice e-mail: [email protected] P. Morano Department of Civil Engineering Sciences and Architecture, Polytechnic University of Bari, Via E. Orabona 4, 70126 Bari, Italy e-mail: [email protected] F. Tajani Department of Architecture and Design, Sapienza University of Rome, Via Flaminia 359, 00196 Rome, Italy e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Morano et al. (eds.), Appraisal and Valuation, Green Energy and Technology, https://doi.org/10.1007/978-3-030-49579-4_11

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In recent decades, there has been considerable technological innovation in the naval field both in products (vessels) and in the related production processes. From a functional point of view, the vessels which originally adapted to the general transport of various and diversified goods, have undergone processes of progressive specialization, in relation to the function for which the vessel is designed (e.g. offshore platforms, drilling vessels, special vehicles for the study of seabed, etc.) or the type of load to be transported (e.g. liquefied gases, chemical products, food products, etc.) [5]. From the original flexibility of use of the vessels, nowadays there is a substantial “univalence”, with an increasing risk of non-use of the specific vessel and a corresponding higher obsolescence rate. The specialization, together with the standardization process of vessels, has led to the construction of naval vehicles (container holders, barge holders, RO/RO, etc.) which allow to reduce the rotation times in the ports; the “dimensional” increase of vessels has allowed to achieve economies of technological scale, with a consequent reduction in the costs of transporting goods; other types of special vessels—such as the “jack up” subject of this evaluation—are instead more rarely made in typical shipyards and are intended for the production of services related to the use of marine resources, or services other than maritime transport. The extensive typological variety of vessels has significantly influenced the world maritime market, significantly expanding its segmentation. As a consequence, the competitiveness between shipping companies and industrial companies producing the ship-plant has become “global”. It follows that obsolescence, specialization, economies of scale, global competitiveness, prefabrication of technological and plant components, industrialization of production in the shipbuilding sector, are all aspects that must be kept in mind in the assessment process of naval vehicles. Due to the difficulty of obtaining market data or even the limited trading activity of the vessels market, the appraisal methods based on direct comparison are inapplicable. Often, there are market transactions too, but the historical price data may not be representative of the type or size class of the vessel concerned, either because the limited number of cases observed does not allow to have a sufficiently representative sample and because, with the exception of naval units built “in series”, the vessels have almost never the same technical characteristics. It follows that the approaches based on the reconstruction cost depreciated (or reproduction) often are the only solution, thus carrying out the appraisal of the value attributable to the ship based on the relative construction cost, decreased by a rate that takes into account of its state of use and economic or functional obsolescence. In this, it should also be borne that the variability over time of the abatement rates (depreciation) of the vessel value is an aspect strictly dependent on the reference market conditions [6, 14]. For the appraisal of special vessels, i.e. for particular naval units for which it is not possible to implement a procedure of direct comparison or a so-called procedure “analytical-reconstructive” (such as the one based on the “depreciated cost”), the estimative literature suggests to apply—as a last resort—models used in decisionmaking analyzes for the evaluation of industrial and commercial investments.

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2 Literature Review The analysis of the parametric costs relating to vessels of different nature and typology is a topic that is still under-treated in the specialist literature of the sector. In literature there are no studies relating to cost analysis of means similar to “jack up” vessels; by analogy, it is therefore necessary to resort to studies—which are also rare—referring to means that can be assimilated for specific technical and construction characteristics, such as off-shore platforms. Kaluzny et al. [13] presented an application of data mining algorithms to the problem of cost estimation of developing and building ships. This work comes to model the estimation cost of a class of military ships and then compare the value obtained with the actual construction costs of the ship itself. Two estimation approaches were followed: a parametric analysis in which decision trees and linear regression models were combined together, and a hierarchical cluster analysis implemented with nonlinear optimization algorithms from which to obtain a cost estimate for comparison. Assuming different types of military vessels as reference (amphibious assault military ships, auxiliary military supply ships, aircraft carriers, helicopter carriers, platform ships and icebreakers), the results of the study by Bohdan et al. highlighted a deviation from the actual costs of construction between −2 and −16% for the parametric approach and between +6 and −4% for the hierarchical approach. Cocodia [2] compared neuro-fuzzy modeling techniques with multiple linear regression analysis for the purpose of estimating the cost of offshore structures during the design phase. Specifically, this contribution focuses on the implementation of a model in which the changes in construction costs are related to the variability of the technical characteristics of the plant. The characteristics considered in Cocodia’s contribution are the weight of the naval unit, the depth, the number of wells, the operating weight of the topside, the environmental and localization conditions, the storage capacity of the structure. Ennis et al. [7] developed a parametric model of the construction cost of U.S. Navy aid ships. Discriminating the process of building a ship in three phases (conceptual, preliminary, contract), in the first of these phases (conceptual) the total cost of the vessel can be represented by an equation dependent on the weight of the ship (displacement), the speed of navigation and a complexity factor in turn influenced by the type of boat (CF): Price = CF × A × displacement b × speed c , where the values of the coefficient A and of the exponents “b” and “c” are determined by a regression model developed from a database on investment costs. Of great interest, there is the presentation of a case study (roll on/roll off, or ferry for the transport of motor vehicles) from which it is possible to deduce the weights, in percentage terms, of the components the ship’s principal in terms of construction cost (the latter discriminated against in the cost of materials and cost of working hours). Kaiser and Snyder [11] identify generalized cost functions related to marine drilling rigs and offshore platforms. Kaiser and Snyder’s study first presents a

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statistical survey on the construction costs of different structures (jackups, semisubmersible vehicles and drilling vessels) in relation to the operational depth of the vehicle, and then specifies a cost model represented by a multifactorial linear func tion: C(U) = a0 + ai X i , where C(U) represents the cost of the class of plants U, while the number and selection of descriptive variables X i are specified in relation to the type of plant, the preferences of end users and the availability of data. In general, the variables considered in the study refer to the environmental conditions of the project, the technical specifications of the plant, market conditions, the age of the plant, the possibility of upgrades and extension of the economic life of the plant, as well as further correlation factors. For new constructions, the following factors are considered: operating depth, year of construction, drilling depth, project environmental conditions (extreme or moderate environment), variable load on wells, country of construction; for replacement costs, the following factors are instead considered: sea depth, year of construction, actual years up to the plant upgrade, plant upgrade status, project environmental conditions. Through the least squares procedure, the authors have identified the regression coefficients of the multifactorial linear function reported above, also implementing different cost models for each type of plant and identifying the best models on the basis of the R2 determination index. For new constructions of jack-ups vessel, the cost model identified by Kaiser and Snyder is as follows: Newbuild Cost = a0 + a1 HARSH + a2 WD + a3 (WD)2 , where HARSH is the representative variable of the design environmental conditions (dichotomous variable equal to 1 in the presence of extreme conditions, 0 for nonextreme conditions), WD is the sea depth expressed in feet. The model identified by Kaiser and Snyder in terms of replacement cost of jack-up vessels is instead the following: Replacement Cost = a0 + a1 WD + a2 (WD)2 + a3 HARSH + a4 YEAR, where YEAR is the year of construction of the plant. In a subsequent article, Kaiser and Snyder [12] follow a “top-down” approach for estimating the need for manpower and construction materials for offshore plants built in the United States. The construction cost for a drilling rig is divided into five components: drilling rigs, rig kits (leg components, anchor winches, cranes, cantilever components, etc.), labor, materials, shipyard profit. A cost function is therefore developed for each element. The cost estimation procedure refers to a specific case (platform type “LeTourneau Super 116E” built in 2010). The same Kaiser and Snyder, together with Pulsipher, in a more recent publication (2013), analyze the international market for offshore platforms, including the construction costs of jack-up vessels built in the United States between 1996 and 2011. The multifactorial regression model that provided the best results, based on n. 26 platforms analyzed, is as follows: Cost = −96 + 0.42 × WD + 0.003 × DD + 103 × HARSH, where WD is the sea depth expressed in feet, DD is the drilling depth expressed in feet, HARSH is the environmental variable. The labor cost is determined by the relationship between the capital costs and the productivity of the labor factor, multiplied by the average wage. The cost of the material (steel) is determined separately for each component (hull, legs, etc.). The profit margin relating to shipbuilding is finally quantified in percentage and variable between 5 and 10% of the construction cost.

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In a research paper, Ross [17] proposes a parametric approach—based on practical experience gained in the field of shipyards—for the estimation of shipbuilding costs. In the application case developed in his contribution, Ross considers a hull tanker assuming as a reference that half of the construction costs are absorbed by the work of the shipyards ($/hour 30.00), and that approximately 1,100,000 h of work are required for build a tanker with a weight of about 45,700 tons (deadweight). Recalling other studies, Shetelig [19] provides interesting data concerning the percentage incidence of the preliminary project and of the other project phases on the total construction cost of the ships, asserting that about 85/90% of the total construction costs of a ship are fixed already before the start of production of the ship itself [8]. Based on data from different European shipyards (Poland, Germany and Norway), the study of Roy claims that 70/80% of ship production costs are committed already during the conceptual phase of the project [18]. Michalski [15] suggests an econometric approach to determine the unit cost (qh ) of the individual technological groups in relation to their weight (mh ), for example by providing the following functional relationship for the hull tanker: qh = 1.994,663 + . In Michalski’s study, the total construc0.015549 × mh + (−154,0222) × m0.2471932 h tion cost of the ship is assumed to consist of the following macro-categories: hull construction, plants, power plant and propulsion systems, including in each of these categories the costs of material, labor and other construction costs. The general c3,j formula suggested in the study is as follows: qj = c0,j + c1,j × mj + c2,j × mj , where the regression coefficients are determined according to the type of ship. The Istituto Superior Tecnico of Lisbon—Center for Marine Technology and Ocean Engineering [20], in its own in-depth note concerning the bibliographic analysis of parametric cost models for the construction of vessels, suggests some cost functions applicable to specific components functional. The cost of acquiring a vessel is expressed through the following relation: Q = (Ch + Ce + Cm + Cx) × (1 + Kb), where: Ch is the steel cost for the hull, Ce is the cost of equipment/systems, Cm is the machinery cost, Cx is the cost of special equipment (cranes, guides, etc.), Kb is the margin of shipyard profit expressed in percentage terms. A further alternative approach to the one proposed, provides that the cost of acquiring a vessel can be expressed through the following formal relationship: Q = (Ch + Ce + Cm + GE + S + EC), where: GE is the general expenses (about 90% of the labor cost), S is the profit of the shipyard (about 5% of labor costs), EC are the extra costs. The cost of each component, obviously, must be divided into costs for materials (or equipment/systems) and labor costs. Of particular interest are the empiricalexperimental cost functions proposed in the Lisbon IST methodological note, for the various components of the ship.

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3 Methodology The appraisal methods for the vessel are often traced back to actual appraisal cases, are widely diversified and sometimes lead to results of considerable variability even if referring to the same vessel. The common denominator of these methods, however, is the logic behind the evaluation process, which must take into account the simultaneous contribution of utility, singularity and difficulty in acquiring of the naval vehicle being assessed (market conditions). An essential role in these appraisals is assumed by the economic effects that the good to be evaluated produces, by the possibility of finding similar goods on the market and by the usefulness of these goods in relation to the needs of the reference market. A general classification of the appraisal methods that can be used for the evaluation of a vessel is as follows [1]: • Depreciated reconstruction cost, which refers to the cost of rebuilding new vessel and the decay state of the same by reason of age, also taking into account all those elements that are affected by the market; in the case of special naval unit, the use of the reconstruction cost method becomes almost exclusive, since the intrinsic technical content prevails in them, subjecting them only partially to market influences and reactions. • Comparison method, which presupposes the knowledge of market data relating to vessels deemed similar or comparable to the one being estimated on the basis of the relative main technical and productional characteristics (dimensions, gross tonnage, gross capacity, power of the engine equipment, operating speed, year of construction, etc.). • Analytical method, applicable only to newly built vessels for which a detailed technical description is available (construction data and main characteristics of the vessel, bill of materials weights, bill of cost elements, bill of ancillary accounts and overheads); once in possession of all these elements, the functional elements are identified, discriminated by specific technical and construction characteristics, in order to determine their average unit costs. • Analytical method with costs assessment; this is the already illustrated analytical method, corroborated, however, by cost estimates formulated by shipyards on the basis of the preliminary or rough design drawings of the vessel to be estimated. In obtaining quotes, account should be taken of any cost savings obtainable from a series of vessels of the same type. • Simplified analytical methods, the appraisal based on the technical characteristics of the naval unit can take as reference a single technical parameter, such as the weight of the unloaded vessel, or the weights of the three main components of the vehicle: hull, bodywork, engine system; while showing obvious limitations, this procedure is useful when the information available on the vessel to be estimated are scarce.

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In the context of the aforementioned classification, it should be noted the substantial absence on the market of vessels similar to the jack-up to be evaluated. Consequently, its value can only be determined with an analytical method, i.e. previously discriminating the vessel in its constituent functional components, and then carrying out its  appraisal with a parametric cost approach: C jackup = i C i (f i ), where the functional elements are identified after analysis of the technical and design documentation of the vessel. For each functional element it is necessary to define a cost function with empirical, experimental and/or analytical nature, whose inputs relate to the unit costs of materials and services supplies, ordinarily available only on international markets and characterized by high volatility of the relative market prices. The main critical aspects of the parametric cost functions are mostly represented by labor cost and material production factors. The supply chain distribution also come into play in the cost of labor and materials, as these production factors are distributed in several geographical world areas and, therefore, they are characterized by prices expressed in different monetary currencies, subject to speculative phenomena with significant repercussions on currency exchange costs. Particular attention should be paid to the unit cost of steel, making it the higher cost item inherent to materials. The two types of steel generally used in the construction of jack-up vessel are the low carbon one for the structural elements (legs, bridges, parapets, walkways, etc.) and the special high strength steel for the critical components under extreme conditions. Three categories of steel components are considered in literature: hull steel (generally 34–51 ksi), leg steel (generally 100 ksi), steel for various components (generally 72–90 ksi). The steel costs are first estimated separately for each functional element and are then added together, taking into account that the welding works of the steel elements produce an increase in the weight of the assembled components ordinarily equal to 5%.

4 Appraisal of the Jack-up Vessel The analysis of the cost components and related quantification are developed using parameters taken from the most recent literature in the sector [7, 10–12, 19, 20] and the prices obtainable from official price lists published by public or private entities (www.comune.venezia.it; www.regione.lazio.it; www.mosevenezia.eu) or based on average market prices for materials, works and/or services not covered in these price lists. Based on design drawings of the jack up, the different functional elements taken into consideration can be summarized as follows: • • • • •

Metal carpentry (including goats, fisherman’s frame, legs); Antifouling and anti-corrosive protection; Cathodic protection; Cabin bodies (metal carpentry, anticorrosive protection); Machinery, plants and equipment;

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Fig. 1 Jack-up vessel of the “MOSE” hydraulic infrastructure of Venice (www.mosevenezia.eu)

• Engines; • Transportation, hauling, various, docking; • RINA tests and certifications (Fig. 1).

4.1 Metal Carpentry (Including Goats, Fisherman’s Frame, Legs)

Materials

Functional element weight (kg)

Unit cost steel (e/kg)

Total cost steel for the functional element

Metal carpentry 1,522,271.22 (including goats)

e 3.00

e 4,566,813.67

Carpentry of fisherman’s frame

90,660.00

e 3.00

e 271,980.00

Legs carpentry

95,980.00

e 3.00

e 287,940.00 (continued)

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(continued) Direct labor

Functional element weight (kg)

Unit cost direct labor (e/h)

Average productivity (h/ton)

Total cost direct labor

Metal carpentry 1,522,271.22 (including goats)

22.00

22.810

e 763,906.14

Carpentry of fisherman’s frame

90,660.00

22.00

22.810

e 45,495.00

Legs carpentry

95,980.00

22.00

22.810

e 48,164.68

Indirect labor

Functional element weight (kg)

Unit cost indirect labor (e/h)

Average productivity (h/ton)

Total cost indirect labor

Metal carpentry 1,522,271.22 (including goats)

18.00

22.810

e 625,014.12

Carpentry of fisherman’s frame

90,660.00

18.00

22.810

e 37,223.18

Legs carpentry

95,980.00

18.00

22.810

e 39,407.47

General expenses

e 1,403,289.54

90% × (direct labor + indirect labor)

Builder’s profit

e 808,923.38

10% × (materials + direct and indirect labor + general expenses)

Total metal carpentry

e 8,898,157.18

4.2 Antifouling and Anti-corrosive Protection

Materials (antifouling protection)

Functional element surface (sqm)

Unit cost material (e/sqm)

Total cost material for the functional element

Metal carpentry (including goats)

12,119.99

e 7.00

e 84,839.96

Carpentry of 721.82 fisherman’s frame

e 7.00

e 5,052.71

Legs carpentry

764.17

e 7.00

e 5,349.20

Materials (anti-corrosive protection)

Functional element surface (sqm)

Unit cost material (e/sqm)

Total cost material for the functional element

Metal carpentry (including goats)

12,119.99

e 11.00

e 133,319.93 (continued)

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(continued) Carpentry of 721.82 fisherman’s frame

e 11.00

e 7,939.97

Legs carpentry

764.17

e 11.00

e 8,405.89

Direct labor

Functional element weight (kg)

Unit cost direct labor (e/h)

Average productivity (h/ton)

Total cost direct labor

Metal carpentry (including goats)

1,522,271.22

20.00

22.810

e 694,460.13

Carpentry of 90,660.00 fisherman’s frame

20.00

22.810

e 41,359.09

Legs carpentry

95,980.00

20.00

22.810

e 43,786.08

Indirect labor

Functional element weight (kg)

Unit cost indirect Average labor (e/h) productivity (h/ton)

Total cost indirect labor

Carpenteria metallica (incluso Capre)

1,522,271.22

16.00

22.810

e 555,568.11

Carpenteria Telaio 90,660.00 Pescatore

16.00

22.810

e 33,087.27

Carpenteria Gambe

95,980.00

16.00

22.810

e 35,028.86

General expenses

e 1,262,960.59

90% × (direct labor + indirect labor)

Builder’s profit

e 276,149.20

10% × (materials + direct and indirect labor + general expenses)

Total antifouling and anti-corrosive protection

e 3,037,641.19

4.3 Cathodic Protection Functional element

Functional element surface (sqm)

Unit cost cathodic protection (e/sqm)

Total cost cathodic protection for the functional element

Metal carpentry (including goats)

12,119.99

e 65.00

e 787,799.60

Carpentry of fisherman’s frame

721.82

e 65.00

e 46,917.99

Legs carpentry

764.17

e 65.00

e 49,671.18 (continued)

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(continued) General expenses

Included in the unit cost 90% × (direct labor + indirect labor)

Builder’s profit

Included in the unit cost 10% × (materials + direct and indirect labor + general expenses)

Total cathodic protection

e 884,388.77

4.4 Cabin Bodies (Metal Carpentry, Anticorrosive Protection)

Materials (carpentry)

Functional element weight (kg)

Unit cost steel (e/kg)

Total cost steel for the functional element

Forward bridge

40,176.00

e 3.00

e 120,528.00

Aft bridge

9,600.00

e 3.00

e 28,800.00

Cabin CO2

13,692.00

e 3.00

e 41,076.00

Ventilation cabin

17,856.00

e 3.00

e 53,568.00

Fireplace cabin

14,988.00

e 3.00

e 44,964.00

Storehouse

18,000.00

e 3.00

e 54,000.00

Direct labor carpentry

Carpentry weight (kg)

Unit cost direct labor (e/h)

Average productivity Total cost (h/ton) direct labor

Metal carpentry

114,312.00

22.00

22.810

Indirect labor

Carpentry weight (kg)

Unit cost indirect labor (e/h)

Average productivity Total cost (h/ton) indirect labor

Metal carpentry

114,312.00

18.00

22.810

Materials

Carpentry surface (Mq)

Unit cost material (e/Mq)

Total cost anti-corrosive materials

Metal carpentry

910.13

e 7.00

e 6,370.89

Direct labor anticorrosive protection

Carpentry weight (kg)

Unit cost direct labor (e/h)

Average productivity Total cost (h/ton) direct labor

Metal carpentry

114,312.00

20.00

22.810

Indirect labor anti-corrosive protection

Carpentry weight (kg)

Unit cost indirect labor (e/h)

Average productivity Total cost (h/ton) indirect labor

e 57,364.05

e 46,934.22

e 52,149.13

(continued)

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(continued) e 41,719.31

Metal carpentry

114,312.00

16.00

22.810

General expenses

e 178,350.04

90% × (direct labor + indirect labor)

Builder’s profit

e 72,58.,36

10% × (materials + direct and indirect labor + general expenses)

Total cabin bodies

e 798,406.01

4.5 Machinery, Plants and Equipment

Functional element

Functional element Unit cost (e/kg) Total cost for the functional weight (kg) element

Total metal carpentry 1,823,223.22 (included cabin bodies)

e 3.00

e 5,469,669.67

Engines (90% × total cost steel)

e 4,922,702.70

Machinery, plants and equipment (150% × total cost steel—engines cost)

e 3,281,801.80

Direct labor for machinery, plants and equipment

Multiple Total cost direct labor for machinery, plants and equipment

Multiple × total cost direct labor for metal carpentry (included cabin bodies)

3.890

Indirect labor for machinery, plants and equipment

Multiple Total cost indirect labor for machinery, plants and equipment

Multiple × total cost indirect labor for metal carpentry (included cabin bodies)

3.890

e 3,559,077.22

e 2,911,972.27

General expenses

e 5,823,944.55

90% × (direct labor + indirect labor)

Builder’s profit

e 1,557,679.58

10% × (materials + direct and indirect labor + general expenses) (continued)

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(continued) Total machinery, plants and equipment

e 17,134,475.43

4.6 Engines

Functional element

Functional element weight (kg)

Unit cost (e/kg)

Total cost for the functional element

Total metal carpentry (included cabin bodies)

1,823,223.22

e 3.00

e 5,469,669.67

Engines (90% × total cost steel)

e 4,922,702.70

General expenses

e 738,405.40

15% × (materials cost)

Builder’s profit

e 566,110.81

10% × (materials + general expenses)

Total engines

e 6,227,218.91

4.7 Transportation, Hauling, Various, Docking

Functional element

Total cost for the functional element

Transportation, hauling, various, boat docking

e 90,749.82

General expenses

e 13,612.47

15% × (materials cost)

Builder’s profit

e 10,436.23

10% × (materials + general expenses)

Total cost

e 114,798.52

4.8 RINA Tests and Certifications

Functional element

Total technical cost of Average percentage the jack-up vessel incidence

Total cost for the functional element (continued)

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(continued) Prove e Certificazioni RINA

37,095,086.01

Total RINA test and certifications

e 4,451,410.32

12.00%

e 4,451,410.32

5 Conclusions The partial appraisals as formulated above, for the various functional elements, lead to a total technical cost of the naval vessel examined of Euro 37,095,086.01, to which must be added the costs of RINA tests and certifications (Euro 4,451,410.32) and other charges that can ordinarily be linked to the production process of the “jack up” (safety charges, interest expenses relating to capital advances, entrepreneurial profit). Said additional costs may be included in a percentage of the amount of the technical construction cost. For general engineering works [4], as is known, the safety charges can vary from 2 to 5% of the technical cost of the works, and therefore they can assume an average of 3.50% of the technical construction cost; interest expense for the capital advances can be assumed to an average percentage rate of 6.50% (incidence ordinarily found in ordinary pre-financing operations addressed to companies for loans with repayment in installments) of the technical construction cost including safety charges; the profit of the entrepreneur, different from the builder’s profit, can be quantified in a prudential measure of at least 10% on the technical construction cost including safety charges—in relation to the low degree of riskiness of the production process deriving from the uniqueness of the jack-up vessel and by particular market conditions comparable to an offer monopoly. Finally, the following summary framework arises with indication of the percentage weight attributed to each functional element: Macrocategories (separating legs and fisherman frame)

Technical construction cost

Percentage incidence (on the total construction cost) (%)

Metal carpentry

e 8,898,157.18

21.83

e 10,789,037.83

Antifouling and anti-corrosive protection

e 3,037,641.19

7.45

e 3,683,147.54

Cathodic protection

e 884,388.77

2.17

e 1,072,323.59

Deck superstructures

e 798,406.01

1.96

e 968,069.28

Supply and installation of systems and equipments

e 17,134,475.43

42.03

Total cost for the macrocategory

e 20,775,594.29

(continued)

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(continued) Macrocategories (separating legs and fisherman frame)

Technical construction cost

RINA test and certifications

e 4,451,410.32

9.01

e 4,451,410.32

Engines

e 6,227,218.91

15.28

e 7,550,518.50

Transportation, hauling, e 114,798.52 various, docking Total

e 41,546,496.33

Percentage incidence (on the total construction cost) (%)

0.28 100.00

Total cost for the macrocategory

e 139,193.49 e 49,429,294.85

The above results are corroborated by the data and by the cost percentages provided by companies operating nationally and internationally in the specific shipbuilding sector.

References 1. AA.VV (1989) Atti della Tavola Rotonda su Estimo Navale e Nautico, 26.11.1988 In: Aestimum, Centro Studi di Estimo ed Economia Territoriale, vol 20. Firenze University Press 2. Cocodia E (2005) A comparative study of neuro-fuzzy systems and regression analysis in the cost estimation of offshore structures. J Mar Eng Technol 4(1):33–42 3. Consorzio Venezia Nuova (2019). www.mosevenezia.eu. Accessed Dec 2019 4. Del Giudice V (2015) Estimo e Valutazione Economica dei Progetti. Loffredo Iniziative Editoriali, Napoli 5. Del Giudice V, De Paola P (2017) The value of intellectual capital in shipping companies. In: Green energy and technology. Springer, pp 231–239 6. Del Giudice V, Manganelli B, De Paola P (2016) Depreciation methods for firm’s assets. In: ICCSA 2016, Part III. Lecture notes in computer science, vol 9788. Springer, pp 214–227 7. Ennis KJ, Dougherty JJ, Lamb T, Greenwell CR, Zimmermann R (1998) Product-oriented design and construction cost model. J Ship Prod 14(1):41–58 8. Fischer JO, Holbach G (2011) Cost management in shipbuilding: planning, analysing and controlling product cost in the maritime industry. GKP Publishing 9. General Price List Venetian Transportation Consortium—Maintenance of Naval Units and Pontoons (2019). www.comune.venezia.it. Accessed Dec 2019 10. Kaiser MJ, Snyder B, Pulsipher AG (2013) Offshore drilling industry and rig construction market in the Gulf of Mexico. Coastal Marine Institute of Louisiana State University. OCS Study BOEM 2013-0112 11. Kaiser MJ, Snyder BF (2010) Newbuild and replacement cost functions. Oil Gas J 12. Kaiser MJ, Snyder BF (2012) Labor, material needs estimated for US construction of jack ups. Oil Gas J 13. Kaluzny BL, Barbici S, Berg G, Chiomento R, Derpanis D, Jonsson U, Shaw RHAD, Smit MC, Ramaroson F (2011) An application of data mining algorithms for shipbuilding cost estimation. J Cost Anal Parametr 4(1):2–30 14. Manganelli B, Morano P, Tajani F (2014) Companies in liquidation: a model for the assessment of the value of used machinery. WSEAS Trans Bus Econ 11(1):683–691 15. Michalski JP (2004) Parametric method of preliminary prediction of the ship building cost. Pol Marit Res (special issue)

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16. Public Works Price List—Lazio Region—Maritime and Hydraulic Works (2019). www.reg ione.lazio.it. Accessed Dec 2019 17. Ross JM (2004) A practical approach for ship construction cost estimating. COMPIT 18. Roy R (2003) Cost engineering: why, what and how? Decision Engineering Report Series, Cranfield University 19. Shetelig H (2013) Shipbuilding cost estimation—parametric approach. Norwegian University of Science and Technology, Department of Marine Technology 20. Ventura M (2010) Cost estimate, IST—Istituto Superior Tecnico de Lisbona. www.mar.ist. utl.pt. Accessed Dec 2019

Cultural Heritage and Seismic Disasters: Assessment Methods and Damage Types Fabiana Forte, Vincenzo Del Giudice, Pierfrancesco De Paola, and Francesco Paolo Del Giudice

Abstract The identification of adequate evaluation methodologies of earthquake damages to Cultural Heritage is a highly topical subject, considering the frequency and intensity of the seismic phenomenon, in recent times, in Italy. The subject is related to the broader theme of the attribution of a “monetized” economic value to the cultural assets, widely investigated in the appraisal and evaluation Italian disciplines. In this perspective, the article aims to verify the principles and evaluation methods for the monetary assessment of the damages caused by earthquake disasters. Starting from the definition of cultural assets as in the Italian legislative system, the article highlights the characteristics and several values of cultural assets; it then defines, in a systematic way, the damage and its differentiation, subsequently discussing the main damage evaluation approaches. Keywords Earthquake damage · Cultural heritage · Economic evaluation

F. Forte Department of Architecture and Industrial Design, University of Campania “Luigi Vanvitelli”, Via S. Lorenzo, 31, 81031 Aversa, Italy e-mail: [email protected] V. Del Giudice · P. De Paola (B) Department of Industrial Engineering, University of Naples “Federico II”, Piazzale Vincenzo Tecchio 80, 80125 Naples, Italy e-mail: [email protected] V. Del Giudice e-mail: [email protected] F. P. Del Giudice University of Naples “Federico II”, Piazzale Vincenzo Tecchio 80, 80125 Naples, Italy e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Morano et al. (eds.), Appraisal and Valuation, Green Energy and Technology, https://doi.org/10.1007/978-3-030-49579-4_12

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1 Introduction In Europe, Italy is one of the countries most exposed to natural hazards due to its typical geological and geomorphological conformation; it is in fact subject to various types of hazards (seismic and volcanic hazards, landslides and flooding). In accordance with the Civil Protection Department, Italy is also one of the Mediterranean countries with the highest seismic risk due to its particular geographic position. This results in the significant dynamism of its territory that is at the basis of earthquakes and volcanic eruptions [13]. The highest seismicity is concentrated in the centralsouthern part of the peninsula, along the central Apennine region, which recently, as it is sadly known, has experienced several major earthquakes. Generally, an earthquake not only can cause grave risk to human life, but also heavy damage to physical structures and infrastructures as well as historical buildings, monuments and cultural assets, widely consistent in all of the Italian territory, such as the many UNESCO sites. With specific reference to this last, if in Europe 16% of UNESCO sites fall in zones with high seismic degree and the 62% in zones with low seismic degree, for Italy the scenario results overturned, with 28% of the sites in zones with high seismic degree and only 16% in zones with a low seismic degree [23]. The effort to identify adequate evaluation methodologies of the damage suffered by cultural assets in consequence of earthquake disasters, leads back to the broader theme of the attribution of a “monetized” economic value to the cultural assets, widely investigated in the appraisal and evaluation Italian disciplines. From a methodological point of view, in fact, for the evaluation of damage, the difference must be operated between the value of the economic good object of appraisal, assuming the absence of the damaging event, and the minor value subsequent to the same event. Since it is logically impossible to determine depreciation without the preventive determination of the initial value, it follows that, methodologically, for the evaluation of the damage suffered by the cultural asset the determination of the economic value of the same asset necessarily occurs. In other words, the principle at the base of the identification of the measure of the damage is the “with/without” principle: it is necessary to identify and measure all the alterations between the situation after the earthquake and the hypothetic situation existing if the disaster had not occurred. As cultural assets, due to their characteristics, cannot always be monetized, also for the damage evaluation, there is reference to their categories of value estimable through several evaluation approaches. In this perspective, the article, starting from the definition of cultural assets as in the Italian legislative system, highlights the characteristics and several values of cultural assets (Sect. 2); it then defines, in a systematic way, the damage and its differentiation (Sect. 3), subsequently discussing the main damage evaluation approaches (Sect. 4); some conclusions end the article (Sect. 5).

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2 Cultural Assets: Characteristics and Values According to the Italian legislative system, cultural assets belong to the Cultural Heritage which, as in article 2 of the Italian Code of the Cultural and Landscape Heritage (L.D. n. 42/2004), consists of cultural property and landscape assets. Cultural property are immovable and movable things belonging to the State, Regions, other territorial government bodies, as well as any other public body and institution, as well as private non-profit associations, which possess artistic, historical, archaeological or ethno-anthropological interest and any other thing identified by law or in accordance with the law, as testifying to the values of civilization. Cultural property also includes, when the declaration of cultural interest intervenes, immovable and movable things of particularly important artistic, historical, archaeological or ethno-anthropological interest, which belong to subjects other than those previously indicated. Further, cultural property are immovable and movable things, to whomsoever they may belong, which are of particularly important interest because of their reference to political or military history, to the history of literature, art and culture in general, or as testimony to the identity and history of public, collective or religious institutions. Among the things previously indicated are included villas, parks and gardens possessing artistic or historical interest; public squares, streets, roads and other outdoor urban spaces of artistic or historical interest. For the purpose of this article, it is possible to classify immovable cultural assets in the following categories: monuments; historic buildings or dwellings located in historic centers, churches registered as historical heritage, museums, archeological sites. Among this classification, it is possible further to classify public historic heritage buildings, including historic assets declared as such that are the property of the state and private historic heritage buildings, whether owned individually or by foundations. This broad range of immovable cultural assets, are also “economic goods” for their characteristics of utility, considering their capacity to satisfy particular types of learned needs (nowadays, in the knowledge economy, perceived increasingly); accessibility and limited availability or uniqueness [24]. In the same time, cultural goods differ from other goods because they possess not only private but also public goods characteristics (non-exclusive and non-rival character). In other words, not all cultural goods are either purely private or purely public and many of them fall into a category of “quasi- public goods”. Further, cultural assets can also be treated as a “merit good”, which, according to Cwi [3] is a good that «some persons believe ought to be available and whose consumption and allocation are felt by them to be too important to be left to the private market». As well highlighted by Carlo Forte in the 1970s [15], many cultural assets owned by private subjects can be object of exchange and for them it is possible to forecast a market price. Also for the cultural assets owned by the state, despite their juridical regime of «unsaleability», it is possible to hypothesize an exchange and then, it is possible to appraise a market value (hypothetical date), independently from the

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effective exchange that could occur and from the consequent market price (historical data). Ultimately it follows that for the majority of buildings with historical, artistic or environmental characteristics, there is or can be a hypothesis of a demand and, then, a market. Therefore, the evaluative problems which arise about the architectural cultural assets can well concern the determination of the market value, placing themselves within the typically methodological questions of appraisal [5–10]. At the same time, the author, recognizing the utility which, directly or indirectly, can be deployed by the immovable cultural assets for the benefit of the overall community, identified the “social use value”, different from the exchange value, as the appreciation which the community express for that cultural goods on the basis of their social utility and collective availability. He also suggested several methodological approaches for the appraisal of the “social plus value” of the cultural assets (deriving from the difference between the social use value and the exchange value). Starting from these assumptions, Forte [14] dealt with the evaluation of the damage to cultural assets as a consequence of the tremendous earthquake disaster in Friuli Venezia Giulia in 1976. Successively, several further values for the historical and cultural heritage were identified, improving and detailing the components and different categories of the users. The approach to Economic Conservation by the Neapolitan School of Monuments Restoration [22] has found convergence in the main scientific studies at European and international levels. At both these levels, in accordance to CHCfE [2] «the past few decades have witnessed main conceptual and policy developments which have recognized the multiple and valuable benefits that cultural heritage brings at society as a whole». In this perspective, the common approach towards the economic value incorporated into cultural assets has to the “holistic” one, which recognizes that cultural assets possess both cultural and economic values, categorized according to their use and non-use characteristics; this approach has been verified also for the “new architectural assets” characterized by the interaction between creativity, beauty and innovation and expression of the contemporary age [16, 18]. In Fig. 1 several typolo-

Fig. 1 Values of cultural assets

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gies of values are schematized, based on the studies provided by different authors, but not exhaustive, with there being many different interpretations on the overall value of cultural heritage (as the extensive open debate on intrinsic value). As highlighted in current international scientific literature, the non-use value of a cultural asset constitutes an important component of its total value and it is the most difficult to assess, despite several evaluation methods being put forward; some of them refer to the ‘willingness to pay’ concept (under both the conditions of existence or not of a market). In any case, each one of them presents advantages and limits, as shown in Sect. 3.

3 The Damage and Its Differentiations In literature, the term damage presents several definitions, each one enounced with different aims in relation to the specific problematic area. However, the different definitions substantially converge toward a common interpretation of the term, summarized as follow: damage is any “economic prejudice which resolves itself in a patrimony decrease for a determinate subject and occurs as consequence of any human matter of a fortuitous event” [11, 26]. On the basis of this definition, the main elements constituting damage seem to be the following: • an event attributable to the human conduct or to accidental events; • a change for the worse or a detriment in the economic consistency of a certain good; • a subject titular of the good which suffers injury. Such elements assume different characteristics on the basis of the typology of prejudice concretely examined. To this last results in fact subordinated the specification both of the nature of the damaging event and the effects which the same event is able to produce in relation to the characteristics of the goods and the subjects affected. In the specific case of prejudices deriving from an earthquake, a form of natural disaster caused by environmental factors, various types of damage result, both directly and indirectly. Among the several classification proposed, it seems useful to start from a basic distinction between private and public damage.

3.1 Private and Public Damage Damage can be private or public and the characterization does not depend on the juridical nature of the good which suffers the prejudice (private good or public good) but only from the perspective of the analysis from which the prejudicial actions inducted by the event are observed.

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Damage, both to a private and public good, may consist both in a lesion of private or subjective interest (private damage) and in a lesion of public interest (public damage). If the good affected by the damage is private, the damage presents itself under the dual aspect: (a) the worsening of the economic- productive characteristics of the good and consequent impairment of the real right belonging to the owner of the good; (b) the disadvantages resulting for the entire community for the damage of a super individual interest, damage deriving from the compromising of the cited real right. This happen also when the object affected by the event is a public good in a strict sense (pure public good) or a private good, which, playing both private and public functions, appear as a mixed good. In such cases, private damage regards the patrimonial loss and/or not patrimonial (as example, physical and psychic damages) suffered by the single individual member of the community or also, if the object of the damage is a mixed good, by the individual to which juridical has to be attributed the property of the good. Public damage, instead, consists of the prejudice which the event causes to the overall community.

3.2 Physical and Monetary Damage Damage, private or public, can be represented both in physical and monetary terms. In the first case, it is expressed in the form of the modification of the quantitative/qualitative attributes of the good affected by the event (physical damage). In the second case, the modification is translated into a monetary expression, reducing to a common measure the several physical aspects of the damage (monetary damage). Generally, the determination of the physical damage precedes the quantification of the monetary damage. This is obtained by applying to the procedures of the physical damage, and that is to the variations of the quantitative/qualitative consistency of the good, the unit prices of the compromised resources. These prices have to reflect the effective grade of utility of the resources from the point of view of the subject or the community of subjects to whom is referred the quantification of the monetary damage.

3.3 Economic, Material and Financial Damage Monetary damage is economic when it expresses the modification suffered by the capital stock and by the flow of the incomes. Economic damage assumes the dual form of: (a) loss of value regarding a certain capital stock (damnum emergens) and (b) alteration or interruption of a series of future incomes (lucrum cessans). The first form can be traced back to the amount of the expenses required to eliminate or contain

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the harmful effects; the second reflects the diminution of the utility consequent to the worsening of the qualitative/quantitative characteristics of the damaged good [30]. The cost to be sustained for restoring the damaged good represents the material damage, while the financial damage equals the present value of the misses future revenues. Economic damage may also refer to eventual lesions or disablements of the physical integrity of the person, as suggest the juridical principle that the right to health is an integrant part of the individual patrimony.

3.4 Tangible Damage and Intangible Damage Generally, the damage deriving from an earthquake manifest itself as: • variation in input (increasing in costs) and/or output (decrease of revenues) related to the economic activities which have been hit by the earthquake; • losses of well-being suffered by the community located in the territory after the earthquake. The first type of damage is consequent to the productive resources detriment; the second results from the alteration of the environmental and cultural resources. Damage to the productive resources, since it can be expressed in monetary terms, starting from the market prices of the damaged goods, are usually indicated as tangible damage. The damage to the environmental and cultural resources are defined as intangible damage as well as extra-economic damage, since it does not result directly expressible in monetary terms, for their extra-mercantile nature.

3.5 Primary Damage and Secondary Damage Tangible damage can be subdivided into: primary damage (or direct) and secondary damage (or indirect). Primary damage is a direct consequence of the event itself. It mainly consists of: (i) the disruption or degradation of property and structures; (ii) the loss of revenues; (iii) the costs which have to be sustain for eventual emergency measures to activate in recurrence of the earthquakes. Secondary damage is the indirect effects which involve the productive components of the macroeconomic system. These effects involve not only the “internal” sectors of activities in the territorial context affected by the earthquake, but also the “external” sectors which use the outputs which derive from these.

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3.6 Preventive Damage and Consumptive Damage The evaluation of the damage can be elaborated in a preventive way (ex-ante evaluation) or in a consumptive way (ex-post evaluation). In the first case, the evaluation concerns the forecast of the measure of the damage deriving from a certain hypothetic event; in the second case, it is necessary to ascertain the measure of the damage consequent to a certain event concretely occurred. Hence, the damage is preventive when it is evaluated at an initial moment of the time horizon where the event happens. On the other hand, the damage is consumptive when the evaluation is elaborated at a final moment of the time horizon. The determination of the preventive damage has to be developed through the comparison between a real anterior situation and a posterior hypothetic situation. The consumptive damage has to be verified through the comparison between an anterior hypothetic situation and a real posterior situation.

3.7 Compensable Damage and not Compensable Damage Regarding the matter of the compensation of damage, article 2043 of the Italian civil code says “Any malicious fact or culpable which causes an unjust damage to others, obliges the one who has committed the fact to compensate the damage”. Elements of the compensation are: (i) a malicious or culpable fact; (ii) an unjust damage; (iii) the casual link between the fact and the damage. As malicious or culpable fact is understood a human conduct which can consists in an action or in an omission attributable to the will of its author, also when it happens under the “threshold of consciousness” of the same author. When that condition does not exist, it is evident that the obligations of damage compensation do not rise for any subject. It follows the impossibility to frame inside the compensation matter the damage inducted by a natural disaster as the earthquake.

4 The Evaluation of Damage: Direct and Indirect Approaches Generally the damage which cultural assets have incurred as a consequence of earthquake disasters can be divided into two groups: the first includes the material damages, for example the collapse and damage of the physical structure for the shock caused by the earthquake and the loss and deterioration of the art pieces, contained in them [1]. The second group of damage is linked to the relationship between the cultural assets and the community settled in the affected territory. Usually, the methodology for the monetary evaluation of the damage proceeds through the preliminary definition of the physical effects inducted by the event. The

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successive phases of the evaluation methodology consist of researching the prices to apply to the physical damage, or the modification of the physical attributes of the assets which suffer prejudice. The nature of these prices (market prices, shadow prices or accounting prices) varies in function both of the type of the assets damaged (immovable cultural property or environmental resources) and the perspective from which the evaluation is elaborated, from private or the community. In the private perspective, the evaluation of the damage is based on the market prices. The existence of a market makes it possible to assess the direct damage suffered by the immovable cultural assets. The main procedures under the hypothesis of the existence of a market are developed by the orthodox appraisal discipline, as the market prices approach; the costs approach (replacement cost) and the income capitalization approach. When the perspective is public, the market prices cannot be used because they do not reflect the effective grade of utility attributed from the community to the damaged resources. The main reasons are: the level of imperfection implicit in the real markets and the presence of externalities. For the evaluation of public damage, the market prices have to be substituted by the shadow prices, able to reflect the social value of the damaged resources. At a theoretical level, the shadow prices should be measured on the base of the marginal costs which the community sustain for the use of the resources. The shadow prices are obtained “depurating” the markets prices from the aliquots which represent utility effects for the overall community. As mentioned in Sect. 2, cultural assets represent a special category of good, mostly not traded on the market; therefore, in the absence of a market, the damage to the cultural assets has to be evaluated indirectly. In this perspective, several methods have been developed, some of them deriving from Environmental Economics and are based on the willingness to pay concept, rooted in behavioural economics. As it is known, the willingness to pay reflects the individual utility functions and it is anchored to the individual wellbeing variation, as in the Hicksian welfare theory. This variation can be expressed in the form of equivalent or compensating variation of the monetary income. Regarding the evaluation of the damage, the equivalent variation allows to express the income deduction occurring for avoid the wellbeing decrease consequent to an hypothetic event (ex-ante evaluation). The compensating variation, instead, is used to express the income deduction occurring for taking back the individual to the wellbeing level pre-existent to an event concretely occurred (ex-post evaluation), as in the Italian praxis. Here, in fact, contrary to other European countries, in the case of natural disasters, the evaluation of the damage is based on government funding deliberated after the occurrence of the disaster. The main consequences are the uncertainty of the entity of the effective compensation achievement and the scarcity of incentives for prevention investments. From an operative point of view, among the evaluation methods based on the “willingness to pay” and, then, the amount of the damage to cultural asset, it is possible to

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distinguish “revealed preference” and “stated preference” methods. Revealed preference methods, such as hedonic pricing and travel cost, draws data from reviews of actual choices made by individuals in the real world. The hedonic prices method is based on the fact that the prices of goods in a market are functions of their characteristics; through statistical techniques the method tries to isolate the implicit price of each of these characteristics [6–8, 10]. In accordance with Vecvagaras [31], the use of the hedonic pricing method in the damage assessment of cultural assets can be limited to places where detailed data (historical prices) and information on the residential property are available. Furthermore, this method does not capture non-use values of the cultural assets. The travel cost method seeks to put a value on the individuals’ willingness-topay for a cultural asset, by the overall costs incurred to consume it. The method measures the value of the cultural asset beyond to the price effectively paid using concrete information, but, as in the hedonic pricing, it only assesses the use-value and not the non-use one (existence, bequest, etc.). The use of this method in the damage assessment of cultural assets, as Vecvagars highlights, can be limited to countries and places where the data on the total expenditure of visitors are available and has been collected prior to the occurrence of the damage. The stated preference methods, such as contingent valuation, collect data from people responses to a hypothetical market in order to estimate the individual’s willingness to pay for the non-market good. The contingent valuation method, the most frequently used both in the evaluation of the environmental and cultural assets, aims to elicit people’s intended future behavior in the markets by revealing individual preferences, in monetary terms, for a non-market good or service [4]. With this method, the absence of a concrete market value for the cultural assets is replaced by imagining a hypothetical market and its focus is on real choices and implied willingness-to-pay. Even if this method allows to evaluate the non-usevalue of non-market goods, it results complex to apply (being the most controversial one). Regarding the damage evaluation of cultural assets, Vecvagars suggests how the contingent evaluation method can be very useful in assessing the total value of the damaged asset; use of hypothetical scenarios helps to better construct possible alternatives and reconstruct the damaged cultural asset for the respondents. Further, exist several cost based valuation methods, as replacement cost, that estimate value based on costs of replacing, restoring or substituting goods, assuming that the value of cultural assets is equal to such costs. Even if this methods often underestimate the total value of the cultural assets, they are frequently used for post disaster damage evaluation. Finally, for the evaluation of the indirect damages, the economic impact studies occur, assessing the economic significance of a cultural asset/service based on the direct and indirect income that it generates. There is a wide variety of cultural impact studies that use different approaches [12, 17, 19–21, 25, 27–29]. The most wellknown and implemented is the input-output models. In general terms, these models estimate the way in which money spent on cultural heritage may stimulate actions and flows of financial resources in other areas or sectors bringing additional income

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or development to a given place: the multiplier effect. Regarding the specific issues of evaluation of indirect damages to the cultural assets, this model of analysis can be used for the estimation of the consequences which on the cultural heritage can have the losses of output verified in a certain sector hit by the damage.

5 Conclusions Cultural assets, due to their characteristics, cannot always be monetized and for the damage evaluation occurs referring to their different categories of value (use and non-use values). As the article has highlighted, many evaluation methods are available to assess the different values of cultural assets, each one with its strength and weakness. In Italy, many of these methods are implemented in the academic world, while the praxis which characterizes the Italian political system in the case of earthquake disaster, is mainly based on the replacement cost method (ex-post evaluation). For example, regarding the recent earthquake that shook Central Italy in 2016, according to the Civil Protection Department, the total monetary amount of damage has surpassed e 23.53 billion. This figure includes the emergency costs and an estimation of the damages to the infrastructure, private buildings, cultural assets, public buildings, and the production system, both for the agro-industrial sector and livestock. In accordance with the MIBACT (Minister of Cultural Heritage), there are 293 immovable cultural assets collapsed or gravely damaged only in the most restricted area; the total value (use and not-use) of each one of this cultural asset obviously exceeds the total replacement or restoration costs. Beyond the issue of the cultural assets, in Italy, the respect of the budget constrains does not allow to have sufficient economic resources to prevent catastrophic effects consequent to earthquakes or other natural disasters, becoming the ex-ante evaluation methods, as the defensive expenditure one, particularly adapt. It has been discussed for years an assurance system against the natural disasters, as in other European countries, which could incentive a correct prevention approach. The insurance premium should be fixed in function of the risk which depends on from the historical and artistic value of the buildings. This perspective should open new research frontiers for the appraisal and evaluation disciplines.

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The ‘Value of Solidarity’ in the Public Housing Stock Alienation. A Case Study in Palermo (Italy) Grazia Napoli, Salvatore Giuffrida, and Maria Rosa Trovato

Abstract As many low-income households have still been facing the problem of affordable housing, in several countries governmental institutions have implemented various measures of housing policy and are supporting both the “right to housing” and the “right to buy” a dwelling at a subsidized price. Alienation of public housing is a political measure mostly directed towards low-income tenants, by which the dwellings are sold at a price set by law, assumed as a “administered” price. The gap between market and administered prices may be considered as the “value of solidarity” to be taken into account as the monetary reference of the social housing welfare policies aimed at reducing social inequities. The solidarity value approach has been implemented here to identify some measurements of the overall management efficiency (concerning the allocation of the public housing asset) and the social equity between the tenants whose “right to buy” should be fostered. The management efficiency concerns the ratio between the “administered price”, fixed according to the regulations in force, and the potential property market price; the social equity concerns the homogeneity of the prices registered over the actual transfers. The model is applied to a case study in the city of Palermo (southern Italy), where the Municipality implemented an ‘Alienation and Real Estate Development Plan’. Keywords Public housing · Administered price · Real estate market · Alienation of property · Solidarity

G. Napoli (B) Department of Architecture, University of Palermo, Palermo, Italy e-mail: [email protected] S. Giuffrida · M. R. Trovato Department of Civil Engineering and Architecture, University of Catania, Catania, Italy e-mail: [email protected] M. R. Trovato e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Morano et al. (eds.), Appraisal and Valuation, Green Energy and Technology, https://doi.org/10.1007/978-3-030-49579-4_13

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1 Introduction Italian welfare policies assumed the right to housing as a key objective in the 60–70 s of the last century, so that a huge amount of public residential assets were built, a great part of which the Municipalities still own. Nevertheless, many low-income households have still been facing the problem of affordable housing, especially in some metropolitan areas and in poor regions [1]. In several countries, governmental institutions assess housing affordability on the basis of different approaches and indices [2, 3] aimed at measuring the gap between the housing market prices and household incomes [4, 5], and therefore identify and implement several housing policy measures, such as social housing projects, fiscal incentives and public subsidies. An interesting measure of public housing policy is the Right to Buy (RTB), established in the United Kingdom in 1980, that allowed tenants in publicly-owned housing to buy, at a subsidized price, the dwelling they were living in. As a consequence of the heavy discounts applied on the market price (35–70%), the rate of home ownership significantly increased (+15% over 20 years) [6]. In Italy, since the 90s, many laws provided the alienation or enhancement of public real estate assets in order to allow the public authorities to reduce the management and maintenance costs of real estate assets releasing immediate liquidity. The public real estate enhancement programs, on the other hand, although pursuing overall objectives including the energy efficiency performances improvement [7–9], are however aimed at getting the highest profits and subjected to the respect of cost-effectiveness and financial sustainability [10–16]. At first, publicly owned properties were privatized through direct selling or financial instruments such as securitization (Legislative Decree 351/2001), with contradictory results. The securitization procedure usually provides for the sale of properties at market prices, with special tenant protection constraints, such as the right of pre-emption to purchase, and a 40.5% housing price reduction, whereas a 30% reduction is applied to tenants of non-residential properties. Anyway, this measure does not produce an equalizing effect, because the reduction in the market price is independent of the household income, and the tenants don’t necessarily have a low income [17]. Alienation of public housing is a political measure mostly directed towards lowincome tenants; accordingly, the dwellings are sold at a price set by law, assumed as a ‘administered’ price. Administered pricing allows the Municipalities to offer affordable housing and to transfer a substantial part of the social wealth to the households who have been living there for years and may benefit from the acquisition of an asset that is worth much more than the price they pay. More generally, such a policy has an equalization purpose since real estate market prices have increased significantly more than income levels and Index Consume Prices (ICP) [18]. The gap between market and administered prices may be considered as the ‘value of solidarity’ that aims at reducing social inequities [19]. In such issue, the convergence and integration of efficiency and fairness [20–22] in Public Housing (PH) appraisal and management can be considered the basis of “the true value” [23], i.e. the authentic value, which is the “raw material” for “true valuations”, i.e. valuation that are reliable in validating

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projects specifically aimed at societal and environmental justice in the perspective of urban sustainability. The paper proposes a model aimed at appraising the value of solidarity [24] as the differential between the appraised market values and the ‘administered prices’ that are fixed according to the current laws. The solidarity value approach can be used to identify a measure of the overall efficiency of public housing policy on the basis of an indicator defined by the relationship between the two prices [25] and a measure of equity [26] among all the low-income tenants involved, based on the degree of homogeneity of the differential between prices in actual transfers. These results can provide significant information for concerning the reliability of the method used for calculating the administered price and the efficiency of the public housing alienation program. The approach is applied to a case study in the city of Palermo (southern Italy); whereas several models of urban real estate market analysis may be performed to represent the complexity of the peculiar features of the city and submarkets [12, 27–33], we carried out a linear multiple regression model to study the phenomenon over the two districts of Palermo in which Municipality has already transferred 32 dwellings.

2 The Transfer Program of Public Housing Stock of Palermo There are several tools to support households facing financial difficulties, such as leasing of public housing, financial subsidies to housing rent, promotion of social housing. In Palermo, the Public Housing asset consists of 24,035 dwellings most of which are owned by the Municipality and the ‘Istituto Autonomo Case Popolari’ (IACP), which is a public institution that manages the local public housing stock (Fig. 1). The stock of public housing is very small compared to the population of the city (668,405 inhabitants in 2018) and to the index of ‘material and social vulnerability of households’ by Italian National Institute of Statistics ISTAT that is equal to 102 in Palermo, whereas the national average value is 99.3—the higher the index, the worse the social vulnerability [34]. The Municipality of Palermo identified the properties to be sold or to developed, pursuing the objectives of rationalizing the asset management and the recovery of financial resources to be used in the renewal or new construction of public housing. The ‘Alienation and Real Estate Development Plan’ was drafted in 2015 (Municipal Council resolution no. 442 of December 4, 2015), according to the law 133/2008, and became operational in April 2016. It provided for the sale of a high share of public housing owned by the Municipality, whereas the “Public Notice of Sale of Housing of Public Housing (April 16, 2016) owned by the Municipality of Palermo”

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Fig. 1 Public housing stock in Palermo (Italy)

allowed the tenants of 2,569 public dwellings to purchase the ones in which they live (Fig. 2). Though, the tenants must meet many specific requirements to purchase the dwelling, such as being up-to-date with the payment of the rent and the condominium fees, having no member of their family as owner or user of a suitable housing, having the final assignment of the dwelling for at least two years, and other legal requirements (set by Presidential Decree 30/12/1972 no. 1035). The sale price of the dwelling, that is the administered price, is calculated according to the law 560/199 and the main social purpose of allowing low-income households, who otherwise would not afford to pay market prices, to purchase the housing. The administered price is evaluated multiplying the dwelling cadastral income, usually very low, by several coefficients set by law. As first, a baseline

Fig. 2 Public housing sold per district (from January 2016 to May 2018) (left) and the cadastral zones (right) in Palermo (Italy)

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price is calculated multiplying the cadastral income by the coefficient a, which is 100 for housing (1). To take into account of the depreciation of the property, the coefficient b may reduce the baseline price of 1% for each year following the date of construction up to a maximum of 20%. Conversely, the baseline price is increased to take into account of any extraordinary maintenance costs incurred over the last 5 years, as expressed in formula (2). The price P1 thus calculated may be further reduced by 10% in case of single-down payment (3). Pb = Rc · a

(1)

P1 = Pb · (1−b) + Cex

(2)

P2 = P1 · (1−c)

(3)

where: Pb , P1 , and P2 are administered prices; Rc is the cadastral income of a dwelling; a is the coefficient for housing; b is a variable depreciation coefficient; Cex is the extraordinary maintenance cost; c is the reduction coefficient for single-down payment. People who cannot afford a single-down payment is supposed to pay the 30% of price P1 while the residual value may be paid by an up to 15 years mortgage by applying the legal interest rate and by taking out an insurance policy. In any case, the legal and technical expenses to get the Energy Performance Certificate and the verification of the legal conformity of the dwelling are charged to the purchaser. The administered prices for all sold public housing are calculated based on the categories and classes to which they belong, as shown in the example of Table 1 for the 2nd cadastral area (Table 1). The baseline price Pb is the maximum administered price, whereas the price P2 indicates the minimum price as it includes all the possible reductions by law. Pb ranges from 452 to 148 euros/sq.m corresponding the former to the highest cadastral class of a dwelling in the category A2 and the latter to the worst class of the category A4.

3 Methods As the value of solidarity is equal to the gap between the appraised market value of the public properties to be transferred and their administered price, this value provides also a measure of the efficiency and fairness of the PH policy the Municipality is committed in, and may be a starting point from which to reconsider the rules for the administered pricing. Moreover, in order to grant a homogeneous discount of the real estate market values, for the purpose of the equalization between the current tenants (potential owners), possible variations in the administered prices could be suggested

A4

A3

25.31 29.44 34.60 40.80

6

7

67.14

7

5

56.81

6

4

48.55

5

77.47

7 41.32

64.56

6

4

54.23

2.38

2.02

1.72

1.48

3.91

3.31

2.83

2.41

4.52

3.76

3.16

A2

2°

5

Cadastral income EUR/room

Category

Zone

Class

Cadastral data EUR/sq.m

Table 1 The administered prices of public housing in the 2° cadastral zone of Palermo Administered prices (by law)

238

202

172

148

391

331

283

241

452

376

316

Baseline Pb EUR/sq.m

190

161

137

118

313

265

226

193

361

301

253

Price 1 EUR/sq.m

171

145

124

106

282

239

204

173

325

271

228

Price 2 EUR/sq.m

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for those properties of which, due to any higher location characteristics, the market values are greater, and in respect of which the discount is greater than the average. The appraisal framework consists of three stages: • A real estate market survey aimed at providing a wide and articulated database intended to appraise the potential real estate market price of the properties to be transferred; • The application of a multiple linear regression pattern; • The appraisal of the value of solidarity through the comparison between the real estate market prices and the administered prices carried out by the Municipality according to the current laws. The real estate market survey is carried out to achieve a proper sample of dwellings of which asking prices are reported. The dwellings have been characterized by six primary characteristics, articulated in 17 sub-characteristics [31, 35] (Table 2). In order to provide an early representation of the relation between property value (quality) and asking price, the units belonging to the sample have been characterized by attributing to each of them a score ranging from 1 up to 5, from the points of view of the 17 sub-characteristics k listed in Table 2. Then these scores have been aggregated into an overall score (k∗ ) that provides an early profile of the relation between the current price and value (characteristics). Table 2 Characterization of the sample ke1 Location, urbanization and accessibility

1. Location

1. Settlement quality; 2. Mix of functions

2. Urban facilities

1. Public facilities and services

3. Accessibility

1. Mobility from/to the area with private transportation; 2. Mobility from/to the area with public transportation; 3. Mobility within the neighbourhood

ke2 Neighbourhood characteristics

1. Functional characteristics; 2. Symbolic characteristics

ki Unit location within the building

1. Panoramic quality and view; 2. Brightness; 3. Accessibility within the building

kt Technological characteristics

1. Building overall technological quality; 2. Unit finishes and windows quality; 3. Maintenance levels

ka1 Building architectural quality

1. Overall building decorum

ka2 Unit architectural quality

1. Size, functionality and distribution; 2. Additional surfaces; 3. Quality of finishes

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The multiple regression model is a statistical tool widely used for economic and appraisal purposes [36–38]. It highlights the correlation between market value or income and real estate characteristics such as urban location [39], location within the building, technological conditions, architectural and environmental quality [40], the mutual correlation between the different characteristics and the effect produced by each of them in the formation of the price or income. In the appraisal analysis the multiple regression model provides the equation of price or income and identifies a hyperplane (regression plane) [41]: pj = β0 + β1 xj1 + β2 xj2 + . . . + βn xjn + εj

(4)

where j = 1, 2, . . . , m is the generic observation in a sample of m property; pj is the asking price of the generic j-th property; xij (with i = 1, 2, . . . , n) the property features; β0 is the intercept; β1 is the inclination of p1 with respect to the variable x1 while keeping constant variables x2 , x3 , . . . , xn ; β2 is the inclination of pj with respect to the variable x2 while keeping constant variables x1 , x3 , . . . , xn ; βn is the inclination of pj with respect to the variable xn while keeping constant variables x1 , x2 , . . . , xn−1 ; ε j is error at j-th observation. The market prices of all the sold dwellings are appraised applying the above described multiple regression model which processes the sample of data from the direct real estate market survey. In order to convert ask prices into market prices, a reduction factor has to be applied as effect of the bargaining. The difference between the real estate market price and the administered price measures the degree of solidarity of the social housing policy implemented by the local public administration.

4 Appraisal of the ‘Value of Solidarity’ in the Public Asset Alienation The real estate market survey has been carried out over the Districts V and VI of Palermo, which cover a wide urban area of 41.43 sq.km that is equal to 26% of the total area of the eight districts. These two districts have been chosen as case study since a relevant amount of public housings to be transferred is located there, that is equal to 739 units and to 28.6% of the offered for sale dwellings. A sample of the real estate survey provided a scale of prices and values as benchmarks that, by the elicitation of the marginal prices of the chosen characteristics, allowed us to place all the public housing to be transferred, in the appropriate price range. Figure 3 (left) displays size, prices, aggregated scores of the properties by the main characteristics (kj ) and by the overall quality score (k∗ ). The relation between unit prices (by surface area), and the overall quality score is significantly consistent (Fig. 3, right). The bubble size represents the size of every single property: the smaller real estate matches a lower quality and price, due to

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185 EUR/sq.m

Size Id.

Asking prices total unit/surf. EUR EUR/sq.m 165,000 1500 105,000 1235

B01 B02

surface sq.m 110 85

B03

98

145,000

B04

90

B05

37

B06 B07 B08

€ 3,000

Characterization ke1

ke2

ki

kt

ka1

ka2

k*

€ 2,500

3.0 3.1

3.0 2.0

5.0 1.2

4.1 3.5

2.0 3.5

3.5 2.6

3.6 2.7

€ 2,000

1480

3.6

3.5

4.9

1.6

4.0

2.2

3.0

90,000

1000

2.3

1.5

3.8

2.0

1.5

3.0

2.3

45,000

1216

3.9

4.5

3.8

3.0

1.0

1.8

3.3

52.8 87.5

25,000 139,000

474 1589

1.6 4.0

1.0 2.5

2.4 3.1

1.0 2.7

1.0 1.5

1.0 3.2

1.3 2.9

€ 1,000

110

107,000

973

2.4

2.0

4.6

2.8

2.5

2.6

2.8

€ 500

B09

120

215,000

1792

4.0

3.5

4.4

4.3

3.0

4.8

4.1

B10

100

95,000

950

2.5

1.5

1.5

2.8

2.0

1.9

2.2

€ 1,500

€0

1

2

3

4

5

Fig. 3 Sample of the real estate survey (10 of 98 items) in districts V and VI (left). Relation between unit prices (y axis) and aggregate value quality index k* (x axis)

a local specific characteristic of demand seeking for properties suitable mostly for families especially in the predominantly residential areas. Given the sample of properties of the Districts V and VI, consisting of 98 real estate units characterized by price/sq.m and by real estate characteristics ke1 , ke2 , ki , kt , ka1 , ka2 , the regression model was implemented with the help of SPSS statistical software and is represented by the following equation: pj = β0 + β1 kej1 + β2 kej2 + β3 kij + β4 ktj + β5 ka1j + β6 ka2j + εj

(5)

There are several techniques to select the optimal number of predictors—among the characteristics above mentioned—to be inserted in a multiple regression model, such as the Block techniques, namely Standard regression and hierarchical regression, and Stepwise: Forward (Step-Up) Selection, Backward (Step-Down) Selection, Remove Selection. The Multiple regression was here performed with all the techniques mentioned above, but the best results identified on the basis of the indices and tests validating the regression model, were obtained by applying the Backward (Step-Down) Selection technique. Backward Selection at first involves with all candidate variables, testing the deletion of each variable using a chosen model fit criterion, deleting the variable (if any) whose loss gives the most statistically insignificant deterioration of the model fit, and repeating this process until no further variables can be deleted without a statistically significant loss of fit. The Backward (Step-Down) Selection technique allowed to identify five significant predictors, i.e. ke1 , ke2 , ki , kt , ka2 , with respect to the six previously considered. The architectural characteristic of the building was not significant for this regression model, which is consistent with the general characteristics of the segment of the Palermo real estate market, which the sample belongs to. In fact, in relation to the type of buildings in the sample, the subjects perceive this feature in a less significant way compared to the others. In the case of the model with five variables identified with the Backward (StepDown) Selection technique, the tests provided the following results:

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• The coefficient of determination R 2 , which represents the proportion of variability of p/sq.m explained by the explanatory variables, and which represents a measure of the goodness of the proximity of the model to the original data, in this case is 0.834, which can be considered an acceptable result; • R 2 ad justed takes into account the number of explanatory variables n = 5, i.e. the six real estate characteristics included in the model and the sample size m = 98 has a value of 0.825, which can be considered acceptable; • The test F used to evaluate the statistical significance of the model as a whole is based on the relationship between the variance explained by the model and the residual variance; in this case the p-value observed is less than the theoretical p-value (P < 0.05); • The t-test on the statistical significance of the individual predictors within the meaningful model for the five variables (P < 0.05), i.e. the real estate characteristics ke1 , ke2 , kki , kt , ka2 have a significant influence on the formation of the p/sq.m; • The Variance Inflation Factor (VIF), that helps to quantifies the severity of multicollinearity in an ordinary least squares regression analysis, in this case the is very low, then there are not multicollinearity between the variables; • The analysis of the eigenvalues to identify possible multicollinearity conditions identifies low values of the Condition index, i.e. very low or lack of multicollinearity; • The analysis of the residuals shows that the standardized residue approximates to the distribution to the normal one, i.e. E(εi ) = 0 for each combination of values of the independent variables, the expected residual value is equal to 0 and V A R(εi ) = σ 2 for each i the variance of the residuals is constant for all combinations of the values of the independent variables (Fig. 4, left); • In the Normal P-P graph of standardized residual regression the points tend to be arranged even if with some approximation along a straight line (Fig. 4, middle); • The plots of the residuals standardized with respect to the explanatory variables of the model are characterized by a cloud of points arranged randomly (Fig. 4, right).

Fig. 4 Distribution of the standardized residue (left); normal P-P graph of standardized residual regression (middle) Scatter graph (right)

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The analysis of the residues then highlights the respect of the linearity, additivity and homoschedasticity of the variables of the model. The coefficients of the predictors for the regression model are the following: β1 of ke1 = 135.016; β2 of ke2 = 94.688; β3 of ki = 61.006; β4 of kt = 131.957; β6 of ka2 = 59.136

Once found out the predictors, the multiple regression model was performed to appraise the potential market price of the 32 public dwellings in the Districts V and VI, that were already transferred from the Municipality to the tenants. The 32 dwellings have been characterized by attributing them the scores according to the range which is described in §3, and afterward the resulting appraised prices were reduced of 20% to take into account the bargaining. Afterward, the final comparison between the two types of prices has been carried out in terms of differentials and ratios between administered prices/appraised market values (Table 3) to provide quantitative-monetary elements for the appraisal of the value of solidarity.

5 Conclusions The comparison between the administered prices (P) and the appraised market values (E) suggests the following discussions and some forward-looking considerations. As shown in the Fig. 5, the administered prices vary slightly, as they mainly depend on the cadastral incomes that are almost equivalent over the cadastral zones. These zones are very large areas, especially in the peripheral or suburbs, so the cadastral incomes are unable to take into account the peculiar characteristics of each dwelling for the administered price calculation. The administered prices are very low and range just between 118 and 229 euros/sq.m (Table 4), according to the intention of the legislator to offer affordable housing to low-income households. Instead, the appraised market values vary widely because the regression model is able to implement the peculiar negative or positive characteristics of each dwelling into the market price; as a consequence it ranges from 502 to 1172 euro/sq.m, whereas the Standard Deviation SD is 237 euros/sq.m. Table 4 displays also other indicators that synthetize the overall efficiency and fairness of the housing policy referred to the alienation of 32 public housings. We assume the P/E ratio as the index of the efficiency of the housing policy, the lower the ratio, the higher the housing affordability. The P/E ratios are very low as they ranges from 0.12 to 0.38, while the average ratio is equal to 0.18. As a consequence the current law let the Municipally apply an average reduction of 82% to the market prices, that is higher even than maximum discount set by RTB in the United Kingdom, which is 70%. However, the discount on the market price varies over the sample and, as a consequence, arises disparities among the tenants who purchased the housings. The relative standard deviation (RSD%), which is an index of relative dispersion measures the

VI

VI

VI

VI

14

15

VI

8

13

V

7

12

V

6

VI

VI

5

11

V

4

VI

VI

3

VI

V

2

9

V

1

10

district

id

Rossi

Rossi

Ragusa

Ragusa

Paladini

Centorbe

Centorbe

Calandrucci

Brancato

Erice (IACP-L. 745)

Florio

Erice (IACP-L. 745)

Carreca

Erice (IACP-L. 745)

Petralie (IACP-L. 745)

street/square

Location

155

155

139

139

139

139

139

139

180

229

163

182

139

163

133

EUR/sq.m

Administered price (P)

2.5

2.5

2.5

2.5

3.0

2.5

2.5

2.5

4.0

1.0

4.0

1.0

1.0

1.0

1.0

ke1

2.5

2.5

2.5

2.5

2.5

2.0

2.0

1.5

2.0

1.5

2.0

1.5

2.0

1.5

1.0

ke2

2.0

1.5

3.0

1.5

3.0

2.5

1.5

1.5

2.0

1.5

3.5

2.0

4.0

3.5

1.5

ki

Characterization

2.5

2.5

2.5

2.5

2.0

2.0

2.0

2.0

3.0

2.0

2.5

2.0

2.0

2.0

1.5

kt

2.0

2.0

2.5

2.5

2.5

2.0

2.0

2.0

3.0

2.0

2.0

2.0

3.0

2.0

2.0

ka2

916

891

984

911

989

849

800

763

1140

601

1113

625

875

738

502

EUR/sq.m

Appraised market value (E)

Table 3 Administered prices and appraised market values of the 32 dwellings (Districts V–VI)

761

736

845

772

850

710

661

624

960

372

950

443

736

575

369

EUR/sq.m

Differential (E-P)

17

17

14

15

14

16

17

18

16

38

15

29

16

22

26

%

Ratio (P/E)*100

(continued)

66,968

64,820

74,371

67,929

74,812

62,497

58,202

54,869

84,430

29,735

68,357

31,920

52,965

32,157

17,738

EUR

Differential (E-P)

188 G. Napoli et al.

district

V

V

VI

VI

VI

VI

VI

VI

VI

VI

VI

V

V

V

VI

V

VI

id

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

Paladini

Agostino

Zumbo

Casalini

L’Emiro

Agostino

Zumbo

Paladini

Paladini

Michelangelo

Calandrucci

Alibrandi

Michelangelo

Scaglione

Paladini

Nicosia (IACP-L. 745)

Nicosia (IACP-L. 745)

street/square

Location

Table 3 (continued)

139

169

139

145

163

158

139

139

150

139

118

150

139

164

139

149

161

EUR/sq.m

Administered price (P)

3.0

3.5

2.5

2.0

3.5

3.5

2.5

2.5

2.5

3.0

2.5

3.3

3.0

1.5

3.0

1.0

1.0

ke1

2.5

2.5

2.0

4.0

2.5

2.5

2.0

1.5

1.5

2.5

1.5

1.0

2.5

2.5

2.5

1.5

1.5

ke2

3.5

3.0

1.0

1.5

3.0

2.5

1.5

2.0

2.5

3.5

1.5

2.0

3.5

3.5

1.0

1.5

1.5

ki

Characterization

2.5

3.0

1.5

3.0

3.0

3.0

1.5

2.0

2.0

3.0

2.0

1.5

2.5

1.5

2.0

2.0

2.0

kt

2.0

3.0

2.5

3.0

3.0

3.0

2.5

2.0

2.0

2.5

2.0

1.5

2.0

1.5

2.5

2.0

2.0

ka2

1043

1172

747

1051

1172

1148

771

787

811

1119

763

754

1043

752

892

601

601

EUR/sq.m

Appraised market value (E)

904

1003

608

906

1009

990

632

648

661

980

645

604

904

588

753

452

440

EUR/sq.m

Differential (E-P)

13

14

19

14

14

14

18

18

18

12

15

20

13

22

16

25

27

%

Ratio (P/E)*100

108,440

120,424

68,081

101,511

113,004

110,858

65,756

67,384

68,809

101,931

67,071

62,807

93,981

56,447

72,242

39,772

38,641

EUR

Differential (E-P)

The ‘Value of Solidarity’ in the Public Housing … 189

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EUR 160,000 140,000 120,000 100,000 80,000 60,000 40,000 20,000 0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32

dwellings

Fig. 5 Comparison between administered prices (grey square) and appraised market values (orange circle) of the 32 dwellings

Table 4 Prices, differentials and ratios of the 32 dwellings (Districts V and VI) Statistical index

Administered (P)

Appraised (E)

Differentials (E-P)

EUR/sq.m

EUR/sq.m

EUR/sq.m

Ratio P/E

EUR

Min

118

502

370

17,738

0.12

Average

151

873

722

68,717

0.18

Max

229

1172

1009

120,424

0.38

SD RSD%

20

188

191

25,710

13%

22%

26%

37%

0.06 31%

fairness of the housing policy among the tenants: the lower the RSD%, the higher the fairness. In this case study the RSD% of the ratio P/E is rather high, as it is equal to 31%, and reveals an internal inequality in transferring public housing and “allocating solidarity”. In fact those who paid just the 12.4% of the market price to purchase a dwelling with very good characteristics, e.g. in terms of proximity to urban facilities or panoramic quality and view, gained an additional advantage compared to other households who paid up to 38.1%. The differential “Appraised market value-Administered price” (E-P), instead, is the total amount of the discounts, measuring the “value of solidarity” that the social system recognizes in the public policies aimed at enhancing the social quality of the public housing neighbourhoods. The unit average differential E-P is 722 euro/sq.m, corresponding to an average differential of 68,717 euro ranging up to 120,424 euro, corresponding to a great part of the social wealth given to a household. We appraised that the total value of

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solidarity was nearly 2,200,000 euro for the transfer of 32 dwellings in the Districts V and VI of Palermo. In order to improve the fairness of public housing transfer and reduce disparities between the tenants, some items of the regulation currently in force might be changed by referring the calculation of the administered price to the market price pattern, rather than to the cadastral income and, moreover, by applying a constant coefficient of reduction of the housing price. This coefficient should be greater than the one set for the securitization procedures, and might vary over the years in relation to few national economic and social indicators, such as unemployed rate, index of “material and social vulnerability of households”, etc., in order to increase the social efficiency and fairness of public housing transfer. Future perspectives of the study could focus on the reasons of such a low number of sold dwellings, which might depends on both a very low or zero households’ propensity to save, due to just enough income for basic needs, and an expected capital value even lower than the administered price.

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Management of Maintenance Costs in Cultural Heritage Giovanna Acampa and Claudia Mariaserena Parisi

Abstract The present paper was prompted by the activity carried out within the scope of an EU-funded project (WARMEST) that calls for the analysis of monuments’ degradation due to climate change and growing number of tourists. Taking into account the economic aspects, including the increasingly limited resources, related to the conservation of cultural heritage, our research aims to contribute also to the WARMEST project by developing an optimization model for the management and maintenance costs of cultural heritage. This paper focuses on the Brunelleschi’s Cloister within the church complex of Santa Croce (Florence, Italy) and refers to the preliminary study for the achievement of this objective: an analysis of the intrinsic characteristics of the Pietra Serena, its state of degradation and the causes that generate it. The next step will be the analysis of the maintenance costs necessary to stop the degradation process. The information collected will be used to create an HBIM (Heritage Building Information Modeling) model. Meanwhile we will work on predicting the degradation progression due to the climate change through the analysis of the images collected along a period of time and implementing machine learning processes. Keywords Management · Maintenance cost · Cultural heritage

1 Introduction Cultural Heritage is a limited, unique and vulnerable resource constantly exposed to a large number of natural and anthropic threats. Authorities must ensure its integrity and conservation through the implementation of adequate management and maintenance plans, which nowadays are held as extremely important but rarely accurate. They G. Acampa (B) · C. M. Parisi University Kore of Enna, Viale Delle Olimpiadi, 94100 Enna, EN, Italy e-mail: [email protected] C. M. Parisi e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Morano et al. (eds.), Appraisal and Valuation, Green Energy and Technology, https://doi.org/10.1007/978-3-030-49579-4_14

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require information which is often only partially available and not updated, while current situations cannot be scientifically compared to the past. Moreover, technical and economic analysis are often not linked each other. Sound decisions on options for conservation measures are thus difficult to take, while financial resources are always insufficient and costs are constantly increasing. Cultural heritage sites were created when their usage and environmental conditions were drastically different. Climate change over time is seriously affecting their conservation, so there is a need to implement predictive maintenance plans, which are also by far more efficient and cost-effective than repairing damage or taking hasty decisions. This paper was prompted by the activity carried out within the scope of an EU funded project WARMEST1 whose goal is the analysis of monuments’ degradation due to climate change and growing number of tourists. The project aims is to optimize maintenance procedures in cultural and natural heritage sites through the introduction of new technologies for data collection and new tools to analyse it, creating a novel specific Decision Support System [37]. It investigates three pilot sites: Patio de Los Leones in Alhambra (Granada, Spain), Brunelleschi’s Cloister in Opera Santa Croce (Florence, Italy) and an underwater archaeological site in Marzamemi (Syracuse, Italy). Taking into account the increasingly limited budgets and the climate change, our research aims to further contribute to the WARMEST project by developing an optimization model for the maintenance costs of cultural heritage. This paper focuses on the Brunelleschi’s Cloister within the church complex of Santa Croce (Florence, Italy) and refers to the preliminary study carried out to achieve this objective: the analysis of the intrinsic characteristics of the Pietra Serena, its state of degradation and the causes that generate it. The next step will be the study of the restoration and maintenance interventions for each type of degradation, deemed necessary for the material conservation and relative parametric costs [8]. The information collected will be used to create an HBIM (Heritage Building Information Modeling) model. Meanwhile we will work on predicting the degradation progression due to the climate change through the analysis of the images collected along a period of time and implementing machine learning processes.

1 WARMEST

project—loW Altitude Remote sensing for the Monitoring of the state of cultural hEritage Sites: building an inTegrated model for maintenance. This project has received funding from the European Union´s Horizon 2020 Research and innovation programme under Marie Sklodowska grant agreement Nº 777981.

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1.1 Issues Concerning the Efficient Cultural Heritage Maintenance The literature shows that the main problems affecting the efficient maintenance of cultural heritage are funding, replacement or repair materials, technical issues, human behavior and attitudes, management and administration, education and training. According to Shen [33], there are two main problems associated with the practice of prioritizing maintenance actions. First, decision-making is often subjective (that is, it is difficult to tell why and how a surveyor assigns a certain priority to a particular maintenance item). Second, maintenance budgets in most local authorities are significantly lower than the real maintenance needs. Managers have the difficult task of deciding which components should be included in the maintenance programme (components requiring urgent maintenance) and which should be moved to the “backlog list” (the others) [33]. In addition, the importance of Authorities’ attitude should not be underestimated because they rarely consider maintenance as an important approach to the conservation of historical assets [10]. Private owners of historical assets also have similar views because they do not clearly see the benefits of this activity. Despite the fact that importance and vulnerability of buildings should be key issues in supporting decisions on setting financial priorities and developing maintenance strategies, they rarely seem to be a focal point in decisions taking [15].

1.2 Italian Public Expenditure on Cultural Heritage The Italian Ministry of Cultural Heritage and Cultural Activities and Tourism (MiBACT) is the responsible for protection, management, enhancement of cultural and environmental assets in the country. According to the Code of Cultural Heritage and Landscape,2 public spending for conservation are divided into prevention, maintenance and restoration activities. “Programmed conservation” consists of non-aggressive maintenance activities on cultural heritage. Restoration becomes the last resource after damage has occurred. Italy has a rich cultural heritage which is a very important resource for the community both in social and economic terms.3 The Italian public spending is divided into 34 chapters.4 The “protection and enhancement of cultural heritage and landscape activities” is one of these. Despite 2 Legislative

Degree no. 42 of 22nd January 2004. Code of Cultural Heritage and Landscape.

3 There are 55 Italian cultural assets inscribed on the UNESCO World Heritage List. Italy and China

share the primacy, followed by Spain, Germany and France. Accounting and Public Finance Law no. 196 of 31 December 2009 identifies three levels of aggregation (chapters or “missions”, programmes and actions) in the State Budget in order to allow a greater knowledge of allocative choices. Missions are “the main functions and objectives strategic expenditure”, to which several administrations can contribute. In other words, the missions

4 The

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Fig. 1 Public spending on cultural services and protection of biodiversity and landscape in EU countries. Year 2017. GDP percentage points

the heritage richness and the need for large funds for its conservation, Italy spent for years less than the European average on the protection and enhancement of cultural heritage. Indeed, taking into account the latest National Statistical Institute (ISTAT) data for the year 2017, the expenses attributable to this chapter are equal to 0.3% of GDP. This value puts Italy in 23rd place in the ranking of the European countries. Among the other European countries comparable in size, only in the United Kingdom the indicator of public expenditure on culture is lower than in Italy (0.25%), while Spain and Germany have values close to the EU average5 (0.43 and 0.38%), and France and Poland have much higher values (0.67 and 0.69%) (Fig. 1) [29]. Figure 2 shows a decreasing trend over the last 10 years in the funds allocated to the three-year planning of public works for cultural heritage conservation. This means that authorities have to deal with increasingly limited budgets.

2 Materials and Methods 2.1 Maintenance Plan The “Code of Cultural Heritage and Landscape” defines the maintenance of cultural heritage as “the ensemble of activities and interventions aimed at controlling the represent the major goals pursued through spending public. The State Budget Law for the three-year period 2019–2021 set 34 missions. 5 The European average is 0.44% of GDP.

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Fig. 2 Funding provided over the last 10 years by MiBACT for cultural heritage conservation activities (Data from: http://www.beniculturali.it/mibac/opencms/MiBAC/sito-MiBAC/MenuPrinc ipale/Programmazione/Ordinaria/index.html)

state of the cultural heritage and maintaining the integrity, functional efficiency and identity of the property and its parts” (art. 29, par 3) [27]. According to the Code, maintenance activity is carried out not only through direct interventions on the asset but also through studies, collection and management of information and control of the cultural heritage conditions. It is necessary to define an inspection procedure that provides for observing, assessing and recording the state of decay. According to the UNI 101476 standard, maintenance is divided in: 1. Corrective maintenance, i.e. emergency repair. It is carried out when there are parts, materials and elements of historical evidence are severely degraded 2. Preventive maintenance i.e. routine interventions. It prevents as much as possible the onset of degradation that could compromise the conservation and functioning of the good. 3. Predictive maintenance on condition i.e. identification the a threshold value of a parameter above which the asset has a high probability of failure. Predictive maintenance aims to anticipate problems that may occur through diagnostic techniques. The identification and correction of defects in historical assets allows to prevent damage that could become increasingly serious over time and thus save repair costs.

6 UNI 10147:2013. Maintenance—Additional terms to UNI EN 13306 and definitions. The standard

provides the most commonly used terms in the Maintenance sector that must be read together with those used in UNI EN 13306.

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Presidential Decree No 554 of 19997 introduces for the first time the maintenance plan as a complementary document to the executive project and it should include: User Manual, Maintenance Manual and Maintenance Programme. It is therefore clear that the legislation is still far from defining and imposing predictive maintenance which is widely addressed in literature. Some researchers use probabilistic and statistical approaches to predict material degradation through damage functions [35], fuzzy logic methods [32] and multi regression analysis methods [22, 31, 34].

2.2 Maintenance Costs Financial control and budget management play a central role in heritage maintenance planning. Indeed, the estimation of restoration and maintenance costs is a key factor for taking decisions on maintenance management and on the overall budget. If costs are not correctly estimated and expenses are not controlled, resources may run out causing damage to cultural assets. Cost estimate should be used as a basis for preparing maintenance budgets and budgets should reflect repair/replacement decisions, planned preventive maintenance actions, surveys on the state of the asset and inspection costs [7]. According to Atkin and Brooks, cost estimates for budgeting purposes should calculate, as correctly as possible, the: • • • •

Impact on the capital value of affected facility assets; Costs and benefits accrued from maintenance; Risks and associated costs of deferring maintenance; Costs and benefits of repair against refurbishment and/or replacement.

Unfortunately, however, the lack of a standard method for estimating maintenance costs makes it difficult for Authorities to establish realistic maintenance budgets and develop a good financial plan for maintenance works. Moreover, since a cultural asset is made up of several interconnected technical elements, different in material, size, degradation, function, construction methods, etc., the allocation of maintenance costs is not an easy task. Generally, the life cycle costs of buildings are divided into two categories: – Endogenous variables depending on decisions taken by those who intervene in the building process; – Exogenous variables related to the type of building. These variables affect cost factors such as: physical quantities of the building, single elements and relative cost. Generally, parametric cost of the I component of an e building is [13]: 7 Decree

of the President of the Republic No 554 of 21 December 1999 “Regulation implementing the framework law on public works No 109 of 11 February 1994, as amended”.

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Fig. 3 Degradation level of a technical element over time (a); degradation level with maintenance interventions (b)

ce = qeI ceI qeI = parametric quantity of the I component; ceI = unit cost of the I component. Total cost of the e building is: Ce = Q e



qeI ceI

eI

Q e = total quantity of the e building. Any building element suffers physiological degradation because it is exposed to meteorological factors. Therefore, it will inevitably suffer mechanical, physical, structural and aesthetic degradation. In the best case it can be stopped if the Authorities provide for maintenance activities. On the other hand, a building element suffers pathological degradation when no maintenance is carried out. The degradation then advances to an acceptable threshold beyond which the basic function of the element is jeopardized. Figure 3a shows a probabilistic curve where the x-axis is the time T and the y-axis is the quality of the technical element. Initially the technical element has a very good quality. Over time the quality decreases until it reaches the acceptability threshold at time Tx. The trend of the curve depends on the type of material and the exposure conditions. Figure 3b shows some decreasing probabilistic curves. Maintenance work is carried out to improve the quality of the element that has reached a certain limit of degradation. In order to arrest degradation and prevent quality reaching the acceptable threshold and degradation becoming pathological, adequate maintenance should be carried out. Each maintenance intervention on component I has a certain cost “Cm ”. Its discounted cost is: Cm teI =

Cm teI (1 + t)i

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where: = discounting rate; t = the time when maintenance is carried out; i = current interest rate. 1 (1+t)i

The total cost of all maintenance interventions cm T eI within the time period “T” of the I component of the e building is: Cm teI =



Cm teI =

T eI

 Cm T eI (1 + t)i T eI

The total parametric cost of the I component is:  cT eI = qeI (ceI + Cm T eI ) = qeI

 Cm teI ceI + (1 + t)i T eI



The total cost of the unit quantity of the e building is: cT e =



qeI (ceI + Cm T eI ) =

T eI



 qeI

T eI

 Cm teI ceI + (1 + t)i T eI



The total cost of the e building is: CT e = Qe

 T eI

qeI (ceI + cm T eI ) = Q e

 T eI

 qeI

 Cm teI ceI + (1 + t)i T eI



Maintenance costs depend on the type of degradation, which in turn depends on intrinsic (mineralogical structure of the material) and extrinsic (environmental) factors. Among the extrinsic factors, climate change is the one that most affects the degradation of the material.

2.3 Stone Decay Many world heritage assets are made of stone material which deteriorates over time. Preventing or stopping degradation requires the ability to characterize the many stones involved: describe their decay, understand its causes and mechanisms, measure its extent and severity. A vast literature is devoted to stone characterization and decay. Indeed, each author dealing with stone material maintenance, cannot avoid disregard petrographic description, coupled with measurements of surface hardness, porosity, water

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Fig. 4 Glossary of stone degradation divided into 5 families

absorption, capillarity, pore size distribution, mechanical strength, velocity of sound, resistance to salt crystallization, and so on. Stone decay takes many different forms: detachments, blisters, lost cohesion etc. A long-lasting problem was finding a common language to describe it. Finally, in 2008, ICOMOS published a glossary on stone deterioration patterns, with 5 families of different types of stone degradation (Fig. 4).

2.4 HBIM and Facility Management Building Information Modeling (BIM) is an intelligent 3D model-based process that gives architecture, engineering, and construction (AEC) professionals the insight and tools to plan, design, construct and manage more efficiently buildings and infrastructure. It involves a radical change in the design approach that goes from the graphical representation to the simulation of a process, to criteria to test sustainability [5], both in the field of new constructions (civil engineering, transport infrastructures, etc.) [1, 2, 6] and in the field of Cultural Heritage conservation [4]. BIM goes far beyond simple capturing information about a building [11]. It is a methodology for information sharing and communication between all stakeholders at

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all stages of the building’s lifecycle. BIM is a parametric model well fit for historical buildings especially given the amount of information it can collect and manage. Historic Building Information Modeling (HBIM) has been developed at the Dublin Institute of Technology [28]. BIM process and HBIM process are different: the first is applied to new buildings and is used as a design reference; the second is applied to existing heritage building and consist of collecting and managing a large amount of data [14]. Regarding Facility Management, BIM approach can be a powerful new tool to enhance a building’s performance and manage operations more efficiently throughout a building’s life [24]. The International Organization for Standardization (ISO) defines facility management (FM) as the “organizational function which integrates people, place and process within the built environment with the purpose of improving the quality of life of people and the productivity of the core business” (ISO 41011:2017(en) and Facility management—Vocabulary [21]. Facility Management plays a key role in the efficient management of both the building maintenance and the people management and the functional change of the building. Despite the continuously growing interest in Facility Management and BIM [12, 18, 20], the literature reveals that a seamless information process between BIM and FM systems does not exist yet [25]. To build an HBIM model of a generic historic building, preliminary activities are necessary: 1. Choice of the software supporting BIM methodologies: many software houses provide software that support BIM methodology.8 2. The knowledge phase: data collection consists of graphical documents such as plans, sections and photographs, text documents such as previous diagnostic analyses and reports on previous monitoring, maintenance and restoration activities. This phase is one of the most time-consuming because data has to be collected from technical office, archive, library, etc. 3. Construction of the BIM model: different methods can be used to construct the BIM model of an existing building, such as “CAD to BIM” or “Scan to BIM”, to be chosen according to the type of data collected. With “CAD to BIM”, the BIM model is constructed using plans in CAD format. It requires that actual correspondence between the geometries with reality should be verified. With “Scan to BIM” the 3D model is constructed through a point cloud created by photogrammetry and laser scanner techniques. It implies an almost millimetric precision in the representation, increasing according to the Level of Detail (LOD)9 [3] of the objects. Even today, there is a limit to its use: there is no software able to convert a mesh or point cloud into a parametric 3D model automatically. 8 Autodesk,

Graphisoft, Bentley, etc. Edilizia e opere di ingegneria civile—Gestione digitale dei processi informativi delle costruzioni—Parte 4: Evoluzione e sviluppo informativo di modelli, elaborati e oggetti.

9 UNI 11337-4 2017.

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4. Input of the collected data: the next step after creating the 3D model into the BIM software, is to input the quantitative and qualitative data collected for each element such as the material, degradation, previous control, monitoring, restoration, maintenance, costs.

3 Results 3.1 Case Study: Brunelleschi’s Cloister Opera Santa Croce in Florence (Italy) is a church complex consisting of eight architectural spaces: square, Church, basement, bell tower, sacristy, Pazzi chapel, Cloisters and Opera’s museum. Authorities have always been engaged in the care of the monumental complex with considerable resources and relying on technical-scientific collaborations, especially after the tragic accident of October 2017. Conservation topic has become an absolute priority. Over 23.5 million euros have been committed for the restoration and ordinary and extraordinary maintenance of the monumental complex over the last fifteen years. Most of the economic resources come from ticket revenues, donations and support by Superintendence and Opificio delle Pietre Dure for specific projects [36]. The historical complex consists of several bodies different in size and technology and placed on different levels. The main building material is Pietra Serena stone whose conservation is constantly at risk. Visual inspections and diagnostic analyses detect deterioration in the external and internal claddings due to atmospheric agents and tourists flow; they impact aesthetically (exfoliation, chromatic alterations, biological patina, scabs, dripping) and structurally (cracks, erosion of masonry vestments). Brunelleschi’s Cloister is the second cloister of the Opera Santa Croce. It is called after the architect Filippo Brunelleschi.10 The Cloister is divided into two levels: the lower portico rests on Pietra Serena columns and is covered by cross vaults; the upper portico consists of slender columns with trabeation; the arches are decorated with Spinelli family crests. In both floors, the degradation differs from zone to zone. The roof and the capitals of the columns are in good condition, while the outer shafts of the columns are exfoliated due to the rain. In particular, the cymasium has fractures, cracks and loss of material. Fragments are dangerous because they can eventually fall and hit visitors. Pietra Serena is in bad conservation state due to the lack of rainwater collection channels, to its exposure and to the lack of conservation interventions since long time.

10 He

died in 1453 before the cloister was completed; its completion was probably due to Bernardo Rossellino.

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Columns often affected by exfoliation, chromatic alterations, biological patinas and crusts causing an aesthetic decay, while cracks and erosion of masonry walls cause modest structural decays.

3.2 Pietra Serena: Decay and Causes The conservation of the stone material requires studies on the material itself, on the surrounding environment and the microclimate. Being familiar with the intrinsic and extrinsic factors affecting the material, the processes of decay and the mechanisms governing these processes is pivotal for predicting material behaviour and optimising resources for its conservation. It also enables to avoid emergency restorations to the advantage of predictive maintenance. Pietra Serena is a grey sandstone very popular in Florentine architecture. It was used in architectural ornaments and in domestic, religious and civil buildings, especially during the Renaissance, when large blocks were required for carved columns and capitals [16, 17]. It is easy to work but due to its mineralogical-petrographic characteristics and physical properties (intrinsic factors) has a limited durability and causes serious conservation problems to the city monuments. Temperature change and the rainfall in a polluted urban environment are among the extrinsic factors which degrade the material [9, 23, 26, 30]. Water flows over it and saturate the pores, dissolving the calcium carbonate when it evaporates, causing precipitation of the outer surface. In addition, the clay minerals and the phyllosilicates undergo transformation causing decohesion. Table 1 summarizes the deterioration observable by naked eye in Pietra Serena.11

3.3 HBIM Model of the Brunelleschi’s Cloister As mentioned before, a HBIM model is an information box made of parametric objects which can contain infinite data such as size, materials, construction age, maintenance activities, type of interventions, past future surveys. All information can be upgradable over time. The digitization of the Brunelleschi’s Cloister consists in the creation of a 3D model representing its real copy (digital twins) and storing a database of information. With this BIM model, all the technical elements (such as capitals, trunks) can be monitored. Restoration and maintenance activities can be planned saving times and costs, optimizing Facility Management activities. A HBIM model of the Brunelleschi’s Cloister could be useful for several reasons: information deposit, data source, planned maintenance, monument management, tourist flow management, security plans and budget control. 11 Information

on Pietra Serena decay are taken from literature.

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Table 1 Main Pietra Serena stone degradation [19] Family

Type

Definition

Features induced by material loss

Alveolization

Formation, on the stone surface, of cavities (alveoles) which may be interconnected and may have variable shapes and sizes (generally centimetric, sometimes metric)

Erosion

Loss of original surface, leading to smoothed shapes

Loss of material

Falling and loss of parts of the material

Crust

Generally coherent accumulation of materials on the surface. A crust may include exogenic deposits in combination with materials derived from the stone. A crust is frequently dark coloured (black crust) but light colours can also be found. Crusts may have homogeneous thickness, and thus replicate the stone surface, or have irregular thickness and disturb the reading of the stone surface details

Discolouration

Change in one to three of the colour parameters: hue, value and chroma: hue corresponds to the most prominent characteristic of a colour (blue, red, yellow, orange etc.); value corresponds to its darkness (low hues) or lightness (high hues); chroma, sometimes called saturation, to its purity (high chroma colours look rich and full; low chroma colours look dull and grayish)

Deposit

Accumulation of exogenic material of variable thickness. Examples of deposits: splashes of paint or mortar, sea salt aerosols, atmospheric particles such as soot or dust, remains of conservation materials such as cellulose poultices, blast materials etc.

Efflorescence

Generally whitish, powdery or whisker-like crystals on the surface. Efflorescences are generally poorly cohesive and commonly made of soluble salt crystals

Patina

Chromatic modification of the material, generally resulting from natural or artificial ageing and not involving in most cases visible surface deterioration

Discoloration and deposit

(continued)

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Table 1 (continued) Family

Type

Definition

Moist area

Darkening (lower hue) of a surface due to dampness

Disintegration

Detachment of single grains or aggregates of grains

Exfoliation

Detachment of multiple thin stone layers (cm scale) that are sub-parallel to the stone surface. The layers may bend, twist in a similar way as book pages

Crack and deformation

Crack

Individual fissure, clearly visible by the naked eye, resulting from separation of one part from another

Biological colonization

Vegetation

Vegetal living being, having, when complete, root, stem, and leaves, though consisting sometimes only of a single leafy expansion (e.g. Tree, fern, herb)

Detachment

Currently, we are testing HBIM model for the Patio de Los Leones in Alhambra (Granada, Spain) and then the same process will apply to Brunelleschi’s Cloister. The construction of the HBIM model follows the following steps: 1. Choice of the software supporting BIM methodologies: we chose Autodesk Revit because it is one of the most common and user-friendly software supporting BIM methodologies. 2. The knowledge phase: data, collected in archives and university libraries for both the Patio de Los Leones and the Brunelleschi’s Cloister, include: historical photos, geometrical information (plan and section in CAD), information on the material’s decay and restoration activities carried out over time. As part of the activity scheduled in the WARMEST project, many photos were taken with photogrammetric techniques using a camera for the Patio de Los Leones and a drone for Brunelleschi’s Cloister. 3. Construction of the BIM model: we built a prototype of BIM model for part of a column12 at the Brunelleschi’s cloister. It was built through the “Scan to BIM” process using ContextCapture13 software (Fig. 5). Figure 5 shows a 3D model devoid of any supporting information. In order to create a parametric model of this element we imported 3D model in Revit. As we mentioned before, currently, there is no software that automatically converts a 3D model in BIM model. For this reason, we built the column manually. Scan

12 In

the Patio de Los Leones there are 124 columns. We choose the column number 27 to build the BIM model. 13 ContextCapure is a software developed by Bentley Systems. It allows to automatically generate high-resolution 3D models from simple photographs (or from point cloud), without any human intervention, with any digital camera, including a smartphone or drone.

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Fig. 5 3D model from ContextCapture of a partial column of Patio de Los Leones

to BIM process has the advantage of reconstructing the object in its real state of preservation depending on the level of detail provided. 4. Insertion of the collected data: having imported the model into Revit, the next step was to enter the data collected i.e. the quantitative and qualitative properties of the column such as the identification data, material, degradation, position, previous control, monitoring, restoration, maintenance, etc. (Fig. 6).

Fig. 6 Technical data sheet of the column 27 on Revit software (This figure refers to the base of a column in the Alhambra. The same work will be done for the columns of Brunelleschi’s Cloister.)

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4 Conclusion The research contributes to the goals of the WARMEST project in reference to the optimization of the management and maintenance costs of cultural heritage. This research focuses on the Brunelleschi’s Cloister within the church complex of Santa Croce (Florence, Italy) and shows the preliminary study to achieve the objective. We have here briefly analysed the main economic issues affecting the management of cultural heritage maintenance in Italy, showing the need to increase spending on innovative tools for maintenance, management and enhancement of cultural heritage. Currently, regulations require the drafting of a preventive maintenance plan but we are still far from the concept of predictive maintenance. The next step is to improve Facility Management by providing more information on the restoration and maintenance costs necessary for the conservation of materials like Pietra Serena stone. The aim is to study the prediction of this stone degradation according to the future climate change, in order to contribute with more information to the creation of a predictive maintenance model. The study of the degradation processing will be carried out by means the analysis of images and machine learning processes.

References 1. Acampa G, Bona N, Grasso M, Ticali D (2018) BIM: building information modeling for infrastructures In: AIP conference proceedings, vol 2040, no 1. AIP Publishing LLC, p 140008 2. Acampa G, Contino F, Grasso M, Ticali D (2019) Evaluation of infrastructure: application of TOD to Catania underground metro station In: AIP conference proceedings, vol 2186, no 1. AIP Publishing LLC, p 160010 3. Acampa G, Crespo Cabillo I, Marino G (2019) Representación del dibujo frente a simulación de los sistemas BIM. Oportunidad o amenaza para la arquitectura. ACE Archit City Environ 14(40):111–132 4. Acampa G, Forte F, De Paola P (2020) B.I.M. models and evaluations. In: Mondini G, Oppio A, Stanghellini S, Bottero M, Abastante F (eds) Values and functions for future cities. Springer International Publishing, Cham, pp 351–363 5. Acampa G, Garcìa JO, Grasso M, Diaz-Lopez C (2019) Project sustainability: criteria to be introduced in BIM. Valori E Valutazioni 23:119–128 6. Acampa G, Marino G, Ticali D (2019d) Validation of infrastructures through BIM In: AIP conference proceedings, vol 2186, no 1. AIP Publishing LLC, p 160011 7. Atkin B, Brooks A (2014) Total facility management, 4th edn. Wiley-Blackwell, Southern Gate, Chichester, West Sussex, United Kingdom 8. Campo O, Rocca F (2017) The parameterization of physical quantities in the definition of parametric costs. The Legislative Decree n. 50/2016 on public works design. Valori E Valutazioni 19:3–9 9. Coli M, Livi E, Tanini C (2006) Pietra Serena mining in Fiesole. Part III: structural-mechanical characterization and mining. J Min Sci 42:74–84 10. Dann N, Cantell T (2008) Maintenance in conservation. In: Forsyth M (ed) Understanding historic building conservation. Blackwell Publishing Ltd, Oxford, UK, pp 185–198

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11. Eastman CM (ed) (2011) BIM handbook: a guide to building information modeling for owners, managers, designers, engineers and contractors, 2nd edn. Wiley, Hoboken, NJ 12. Edirisinghe R, London KA, Kalutara P, Aranda-Mena G (2017) Building information modelling for facility management: are we there yet? Eng Constr Archit Manag 24:1119–1154 13. Fattinnanzi E (2012) La valutazione della qualità e dei costi nei progetti residenziali. Il brevetto SISCo. Valori E Valutazioni 9–28 14. Fonnet A, Alves N, Sousa N, Guevara M, Magalhaes L (2017) Heritage BIM integration with mixed reality for building preventive maintenance. 2017 24° Encontro Português de Computação Gráfica e Interação (EPCGI). IEEE, Guimaraes, pp 1–7 15. Forster AM, Kayan B (2009) Maintenance for historic buildings: a current perspective. Struct Surv 27:210–229 16. Fratini F, Pecchioni E, Cantisani E, Rescic S, Vettori S (2015) Pietra Serena: the stone of the Renaissance. Geol Soc Lond Spec Publ 407:173–186 17. Fratini F, Rescic S (2014) The stone materials of the historical architecture of Tuscany. Italy Geol Soc Lond Spec Publ 391:71–92 18. Gao X, Pishdad-Bozorgi P (2019) BIM-enabled facilities operation and maintenance: a review. Adv Eng Inform 39:227–247 19. ICOMOS-ISCS (2008) Glossary of stone deterioration [WWW Document]. http://iscs.icomos. org/glossary.html. Accessed 3 Feb 2020 20. Ilter D, Ergen E (2015) BIM for building refurbishment and maintenance: current status and research directions. Struct Surv 33:228–256 21. ISO 41011:2017(en), Facility management—Vocabulary (2019) ISO 41011:2017(en), Facility management—Vocabulary [WWW Document]. https://www.iso.org/obp/ui/#iso:std: iso:41011:ed-1:v1:en. Accessed 6 Feb 2020 22. Mahmoud S, Khamidi MF, Idrus A, Abdul-Lateef Ashola O (2015) Development of maintenance cost prediction model for heritage buildings. J Teknol 74 23. Malesani PP, Vannucci SA (1974) Decay of Pietra Serena and Pietraforte, Florentine building stones: petrographic observation. Stud Conserv 19:36–50 24. Marmo R, Nicolella M, Polverino F, Tibaut A (2019) A methodology for a performance information model to support facility management. Sustainability 11:7007 25. Matarneh ST, Danso-Amoako M, Al-Bizri S, Gaterell M, Matarneh R (2019) Building information modeling for facilities management: a literature review and future research directions. J Build Eng 24:100755 26. Meda A (2003) Tensile behaviour in natural building stone: Serena sandstone. Mater Struct 36:553–559 27. Ministry of Cultural Heritage and Cultural Activities and Tourism (2014) Il Codice dei beni culturali e del paesaggio [WWW Document]. https://www.beniculturali.it/mibac/exp ort/MiBAC/sitoMiBAC/Contenuti/Norme-e-Pareri/Evidenza/visualizza_asset.html_1095508 472.html. Accessed 18 Jan 2020 28. Murphy M, McGovern E, Pavia S (2009) Historic building information modelling (HBIM). Struct Surv 27:311–327 29. National Statistical Institute—ISTAT (2019) Rapporto Bes 2019: il benessere equo e sostenibile in Italia [WWW Document]. https://www.istat.it/it/archivio/236714. Accessed 1 Feb 2020 30. Pecchioni E, Vettori S, Cantisani E, Fratini F, Ricci M, Garzonio CA (2016) Chemical and mineralogical studies of the red chromatic alteration of Florentine Pietra Serena sandstone. Eur J Mineral 28:449–458 31. Prieto AJ, Silva A, de Brito J, Macías-Bernal JM, Alejandre FJ (2017) Multiple linear regression and fuzzy logic models applied to the functional service life prediction of cultural heritage. J Cult Herit 27:20–35 32. Prieto Ibáñez AJ, Macías Bernal JM, Chávez de Diego MJ, Alejandre Sánchez FJ (2016) Expert system for predicting buildings service life under ISO 31000 standard. Application in architectural heritage. J Cult Herit 18:209–218 33. Shen Q (1997) A comparative study of priority setting methods for planned maintenance of public buildings. Facilities 15:331–339

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The Regional Price Lists for Estimating the Costs of Construction Paolo Rosasco and Leopoldo Sdino

Abstract The forecast of the most probable cost of an intervention in the construction or engineering field developed through a bill of quantities is one of the most critical operations of economic and technical planning [1–4]. Regarding to public works, the recent legislation (Legislative Decree n. 50/2016) has introduced some changes relating the evaluation of costs; the paragraph 7 of article n. 23—which defines the three levels and contents of the design—establishes that the definitive project, in addition to the complete identification of the work and the elements necessary for the authorizations and approvals, must contain “the definitive quantification of the expenditure limit and the related time schedule, through the use, where existing, of the price lists prepared by the regions and autonomous provinces territorially competent”. The reference to regional prices for the estimate of the economic amount has the objective of establishing fair prices for the various operators (designers, construction companies etc.), at least within a territorial area representative of the economic and technological characteristics of the building sector. In many territorial areas, this entails the transition from provincial prices list to regional prices list, with evident difficulties especially for those areas characterized by specific productive and economic realities. In 2018 the Lombardy Region does not yet have a regional price list and only the provincial price lists (published by the local Chambers of Commerce) or that published by the Municipality of Milan were utilized. Precisely in relation to the new requests introduced by national legislation relating to the use of regional price lists for the estimation of public works, this contribution aims to analyze what are the differences in unitary prices for some of building works and verify the possibility of determining “concordance” indices to be considered in the elaboration of a regional price list able to take into account the different functions of P. Rosasco (B) Department Architecture and Design (DAD), Polytechnic School, University of Genoa, Genoa, Italy e-mail: [email protected] L. Sdino Architecture, Built Environment and Construction Engineering—ABC Department, Politecnico di Milano, Milan, Italy e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Morano et al. (eds.), Appraisal and Valuation, Green Energy and Technology, https://doi.org/10.1007/978-3-030-49579-4_15

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formation of the costs (and therefore of the building works prices) of the workings between different provincial areas.

1 Introduction The importance of the correct estimation of the cost is important for the economic sustainability of projects [5–7]; in the field of public works, it must be based on the general principles set out in art. 4 of the Presidential Decree n. 50/2016. The article includes the provisions relating to the determination of the cost of the work and some disposition for ensuring correct management of the contract and the execution of the work. In the article n. 23, paragraph 7, are established the general requirements that the projects of public works must have; the definitive project—necessary for the release of the prescribed authorizations and approvals by the competent public administrations—must contain the definitive cost estimation of the work developed through the use, if existing, of the prices list prepared by the regions and autonomous provinces territorially competing. In the following project phase (executive), each element of the work must be identified by shape, type, quality, size and price (paragraph 8). The economic amount of each individual item in the price list must, therefore, be derived from the regional price list in force at the time of the tender or from specific “price analyzes”, as established by the art. 32, paragraph 2, of Presidential Decree n. 207/2010 (Regulation implementing the previous Legislative Decree n. 163/2006). The price lists are valid until 31 December of each year and are updated, within the following thirty days, by the competent territorial departments of the Ministry of Infrastructure and Transport after hearing the regions concerned (art. 23—paragraph 16—Presidential Decree n. 50/2016). In the public procurement sector, therefore, price lists must be updated with typical administrative procedures, that is to say, specific, not actually substitutable with others market analysis not given in public forms: the updating of the price lists is established by public rules because it serves to make public that the contracting authority has used competitive prices for the costs estimation. This allows for the maximum possible participation in the tender procedure and to allow the entrusting of the companies to the seriousness of the project proposal and contract that the auction base implies. The obligation to ensure the constant adaptation of prices to current market values in public auctions is not a simple element of legitimacy of the procedure, but is a substantial condition of effectiveness and efficiency of the administrative action which is based on the art. 97 of the Italian Constitution and in particular to the principle of “good performance”: the organization of the State Administration must be guarantee the provision of efficient public services that meet an economic criterion (the State Administration must procure the resources needed “with minimal expense”). The main objective of the indication of the use of the price lists, already introduced in the previous versions of the Italian laws, is to prevent public contracting authorities

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from falling below “ordinary” prices which are recorded in the local context where the project is located, in order to favor the correct fulfillment of the successful tenderer and pursue the public interest with a more efficient allocation of economic resources. In other words, with this indication, the legislator wanted to prevent contracting entities from triggering a downward spiral in public tenders, obtaining a saving of money in the award phase of the works which would prove to be somewhat illusory because it would then be discounted with a lower quality of the works carried out or with technical and economic “adjustments” requested by the contractor during the execution of the works in the form of work-in-progress variants or reservations affixed in the accounting documents of the works on site.1 The need to ensure the procurement of the works and the consequent construction in compliance with times, costs, quality, safety, with the minimization of the risks deriving from the introduction of variants and suspensions in terms of contentious, is confirmed by paragraph f—art. 53—of Presidential Decree n. 207/2010 concerning the provisions on project verification and validation. This paragraph establish that the unitary prices taken as a reference are deducted from the price lists of the contracting stations or from the official price lists in force in the area concerned updated annually or—where no data are available in the price lists—from the analyzes of the prices. The contracting stations also have the possibility of adopting tender-based prices lower than those indicated in the reference price list, provided that this deviation is adequately justified. According to a sentence of the Council of State,2 art. 133, paragraph 8, of the Italian Legislative Decree n. 50/2016, provides for the contractors operating on a regional scale the mere possibility of adopting the regional price list, but does not impose an obligation, lacking an express provision that derogates the powers updating of the tariffs of the contracting stations, with the consequence that the regional price list cannot be considered binding, although it can be the basis for having a certain uniformity of the price lists. The aim of defining a price list on a regional basis capable of guaranteeing a representativeness of the unitary prices of the workings and resources found in the different Lombardy provinces for estimating the cost of public works gave, rise to an assignment by ANCE (National Association of Building Constructors) Lombardy to the Polytechnic of Milan—Department of Architecture, Construction Engineering and Built Environment—in order to prepare a “concordance” table for the parametric adaptation—on a provincial basis—of the working costs reported in the Price List of the Municipality of Milan. It is expected that this price list, with the aforementioned “concordance” indices, will therefore be assumed by the Lombardy Region as the price list for construction works as provided for by art. 23, paragraphs 7 and 16, of Italian Legislative Decree n. 50/2016. 1 The

Regional Administrative Court of Sardinia—with the sentence n. 895—16/08/2011—reiterated that “the obligation to base the tender on economic values consistent with market trends finds its reason in the need to avoid shortcomings in the effectiveness of the offers and the effectiveness of the action by the public administration, as well as appreciable changes in competition between the companies”. 2 Sez. V—n. 5702—16/08/2010.

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2 The Construction Sector of the Lombardy Region The size of the Lombardy Region (which has the largest number of inhabitants compared to the other regions of Italy and 12 provinces), its geographical conformation (flat territory in the central part surrounded by mountains to the north) as well as the high concentration and specialization of production activities (distributed within all three economic sectors) determines highly diversified organizational and production models. As regards the construction sector, the size of the Lombard production reality is considerable: according to the data of the Milan MonzaBrianza Lodi Chamber of Commerce, 132 thousand companies are active out of an Italian total of 737 thousand (17.9%); there are 273 thousand employees compared to a total of 374 thousand in Italy (72.9%). The regional business is 32 billion out of 112 billion in Italy, of which 19 billion only in Milan; overall, over 93 thousand companies carry out specialized works, a sector in which Lombardy weighs a fifth of all of Italy which has 481 thousand and 38 thousand of them in the construction of buildings. If we refer only to public works contracts, out of just over 23,000 calls for tenders published in 2018, over 4,600 (20%) concern works located in Lombardy for a total amount of approximately 3.5 billion of euros; the average amount of the tender works is the highest in Italy: around 912 thousand euros. Forty-one thousand companies (and 99 thousand employees) are concentrated in the Municipality of Milan, followed by Bergamo (18 thousand companies), Brescia (16 thousand companies), Monza (12 thousand companies) and Varese (11 thousand companies). The lowest number of companies is registered in Como and Pavia (around 7 thousand companies). There are 10 price lists of construction works published in the Lombardy region, mostly edited by the local chambers of commerce3 ; each reports the unitary prices of works and resources in the construction sector recorded in the different provinces. Due to the different economic, logistical and organizational realities, with the same workings and resources employed, the unitary prices from one provincial reality to another may present differences of up to ±50%.

3 The

price lists are published by: Municipality of Milan; Milan MonzaBrianza Lodi Chamber of Commerce; Confartigianato Imprese Lecco; Varese Chamber of Commerce; Brescia Building Constructors College; Bergamo Chamber of Commerce; Cremona Chamber of Commerce; Pavia Chamber of Commerce; Como Chamber of Commerce; Mantova Chamber of Commerce and ANCE Mantova.

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3 Metodology The assignment from ANCE Lombardy relating to the determination of the “concordance” coefficients to be applied to the unitary prices shown in the various local price lists developed in two different phases. In the first, we selected which of the 10 local price lists were most significant for the purpose of analyzing and identifying the differences in unitary prices between the various provincial areas of the Lombardy Region. The selection was made on the basis of the indications provided by ANCE Lombardy relating to the price lists most commonly used in the estimates of public and private works located in Lombardy; the price lists considered are the following: 1. 2. 3. 4. 5. 6. 7. 8. 9.

Municipality of Milan (MI); Milan MonzaBrianza Lodi Chamber of Commerce (MMBL); Bergamo Chamber of Commerce (BG); College of Building Constructors of Brescia (BS); Como Chamber of Commerce (CO); Cremona Chamber of Commerce (CR); Mantova Chamber of Commerce and ANCE Mantova (MN); Pavia Chamber of Commerce (PV); Varese Chamber of Commerce (VA).

The price list used as a reference for the adjustment on a provincial basis of prices is that published by the Municipality of Milan.4 In the second phase, instead, the categories of works to be taken into consideration for the analysis of the price variances of the relative workings were identified. The selection took place due to the economic impact that these have within a construction work with particular reference to a new construction or refurbishment of an entire residential buildings and infrastructure works (urban roads)5 ; the comparison of the unitary prices of the workings reported in the different price lists was made only for those which are more significant both in terms of frequency and economic relevance (average percentage incidence on the total amount of the works). In particular, the following categories have been selected: – – – –

IC.01—Demolition—removals; IC.02—Excavations—earthworks; IC.04—Reinforced concrete works—injections and restorations; IU.04—Road works.

4 Price

List for the execution of Public Works and Maintenance. This price list will therefore be the one taken as a reference by the Lombardy Region for the preparation of the regional price list. 5 The selection of the workings was made by taking as reference the percentage incidence of the costs of the single workings deduced from 10 bill of quantities relating to new construction and refurbishment of residential buildings [8] as well as construction and maintenance of urban roads [1] provided by ANCE Lombardy.

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The above categories belong to the group “Civil Works and Urbanization” (Volume 1.1—Price List of the Municipality of Milan). Although not subject to analysis, by analogy, the coefficients may also be applied to the following categories: – 2C.01—Removals, traces, small maintenance; – 2C.04—Reinforced concrete works—small maintenance; – 2C.04—Small road maintenance interventions. The aforementioned categories belong to the group “Unitary costs and small civil and urban maintenance” (Volume 2.1—Price List of the Municipality of Milan6 ). From the analysis of the 10 bill of quantities, 25 more significant workings from the economic point of view were selected and each was therefore researched within the 9 price lists and the relative unitary price was compared with that reported in that of the Municipality of Milan. In order to verify how the percentage differences in the unitary prices of the single workings are reflected internally on the cost of the components of a work, the unitary prices were applied to the working related to a residential building built in the Lombardy Region and inserted within the BEST 2.0 functional database of the ABC Department.7 The correlation coefficients were then applied to the costs of the functional elements pertinent to the categories of workings analyzed according to the chronoproduct approach, determining the new amounts. This made it possible to verify the effect generated by the cost differences of the individual workings on the different parts (functional elements) of the working and to determine the percentage difference between one regional area to another. These percentage differences can therefore be taken as a reference for the development of a regional price list capable of taking into account the different territorial realities.

4 Chrono-Product Analysis On the basis of the analysis of ten bill of quantities relating to new construction and urbanization interventions, a panel of about 25 workings was extrapolated which by percentage incidence of the cost on the total amount of the intervention are more representative; they make-up about 80% of the total cost of the interventions analyzed. Each voice of working was therefore searched within the eight price lists by comparing the descriptions; in order to obtain a meaningful comparison of the unitary prices, where the latter showed differences with respect to that reported in the Price List of the Municipality of Milan, the relative unitary prices were “adjusted” 6 Unitary 7 The

Costs and Small Civil Maintenance and Urbanizations. building intervention was carried out in 2016 in the Province of Milan.

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by making—through specific price analyzes—the necessary economic integrations regarding the costs of the resources used. This because, sometimes, the price lists reported for the same working some differences in the use of resources or equipment that, in fact, did not make the values comparable.8 A systematic correction of the unitary price was also necessary for 6 price lists due to the different percentages of general expenses applied on the technical cost compared to the percentage applied in the Price List of the Municipality of Milan (13.5%).9 The workings analyzed for comparison belong to the following categories: – – – –

IC.01—Demolition—removals; IC.02—Excavation—earthworks; IC.04—Reinforced concrete works—injections and restorations; IU.04—Road works.

Following the adjustments, the unitary prices of each of the working was then compared with that reported in the Price List of the Municipality of Milan and the percentage difference calculated by placing the unitary prices of the latter at 100; Table 1 shows, as example, the results of the analysis for the working related to the construction of” reinforced concrete structures”. The values show high differences in unitary prices of workings for contiguous provincial areas: the differences vary from −28.9% (Price List of Milan MonzaBrianza Lodi Chamber of Commerce) to +4.7% (Price List of Brescia Building Constructors College). From the verification of the deviations detected for the individual processes belonging to the 4 categories analyzed, the average percentage difference for each category of processes and works of the Price List of the Municipality of Milan was then determined (Table 2). Even at the level of workings categories, there are high differences in values and big differences, especially for the category of road works (−49.3%) and demolitions—removals (−43.9%). If one considers that within an ordinary construction work, the individual workings relate to parts (or elements) of the building that have different effects on the overall cost due to their characteristics (such as size, specialization of the workings necessary for the realization, quality of the materials used, methods of realization, etc.) these average difference cannot be used for the purpose

8 These

differences were mostly due to complementary processes or supplies.

9 This concerned the processes indicated in the price lists of: Bergamo Chamber of Commerce (inci-

dence varying from 15.0 to 16.0% depending on the importance, nature, duration and particular needs of the individual works); Cremona Chamber of Commerce (incidence: 1.0%); Mantova Chamber of Commerce and ANCE Mantova (incidence: 15.0%); Milan MonzaBrianza Lodi Chamber of Commerce (incidence: 17.0%); Pavia Chamber of Commerce (incidence: 17.0%); Varese Chamber of Commerce (incidence: 12.7%).

BG

A.01.04.0210.a

Supply and installation of durable concrete for reinforced concrete structures: pillars A1.3.30.30.c and walls for stairwells and elevators up to 3.5 meters high, beams, slabs, cast with the help of formwork, steels and formworks accounted for separately, including any internal temporary works up to 3.5 meters, excluding any external scaffolding and the use of a truck-mounted pump packed with suitable aggregates and with a class of:—C25/30 (ex Rck 30 N/mm2 )—exposure X0—consistency S3

MIMBL Supply and installation of durable concrete in accordance with UNI EN 206-1 and UNI 11104 for structural uses, for exposure class XC (corrosion of reinforcement promoted by the carbonation of concrete) and class of fluid consistency S4 at mouth concrete mixer, cast with or without the aid of formwork, iron and formwork accounted for separately, packaged with aggregates with a maximum diameter of up to 32 mm, CE marked and compliant with UNI EN 12620 standards and with a minimum characteristic strength class at 28 days of maturation of: C25/30 (ex Rck 30 N/mm2 )—exposure XC1—consistency S4

Reinforced structures in cement conglomerate (pillars, beams, slabs, walls of C.04.300.0010.b stairwells and elevators) made by casting, with the help of cranes or any other means of handling, of concrete packed in a concrete mixing plant, with aggregates with a granulometric assortment suitable for the particular destination of the casting and maximum diameter of the same equal to 31.5 mm, for thicknesses of not less than 17 cm, including vibration, excluding steel and formworks; resistance—C25/30—exposure XC1 or XC2—consistency S3

MI

Price list code

Working

Price list

Table 1 Analysis of the unitary prices reported in the price lists and percentage difference

cu.m 166.66

cu.m 198.00

cu.m 140.82

(continued)

−15.5

−28.9

−

u.m. Unitary price Difference (%)a

220 P. Rosasco and L. Sdino

cu.m 135.10

Structural concrete with strength class C25/30 (Rck>30 N/mm2 ), consistency class 1.4.2.14 S4/S5, maximum aggregate diameter 32 mm, compliant with UNI EN 206-1, UNI 11104 and D.M. 14/01/2008, under construction for under-masonry works, cast with the help of formworks. Formworks and steel valued separately

Structural concrete with strength class Rck ≥30 N/mm2 consistency class S4/S5, A1.4C.14 maximum aggregate diameter 32 mm, compliant with UNI EN 206-1, UNI 11104 and D.M. 14/01/2008, on site for foundation castings, cast with the help of formworks, these excluded and accounted for separately, cast in the absence of water

Concrete on site for reinforced concrete works: beams, pillars, slabs, walls for stairwells, elevators, balconies and shelters; cast with the help of formworks and steel, these accounted for separately: C25/30 (ex Rck 30 N/mm2 )—exposure XC3—consistency S4

MN

PV

VA

the price shown in the price list of the Municipality of Milan for the similar workings

cu.m 170.00

Concrete in place for reinforced concrete works in general, beams, pillars, etc., (UNI 05.B.10.01 EN 206-1) cast with the help of formwork, excluding iron and formwork: C2/30 (Rck 30)

CR

a Of

cu.m 170.00

Concrete of n. 817 of Chap. 3, consistency class S4, provided with certification concerning the existence and application of a system production control system (FPC), in use for reinforced structures in general (beams, pillars, slabs, rectilinear walls above ground with a thickness of not less than 20 cm, walls of stairwells and elevators etc.), cast with the use of formworks, these and the steel compensated separately: (a) C 25/30 (Rck 30 N/mm2 ), w/c 0.60, XC1 XC2

cu.m 163.68

10.30.10.10.50.10 cu.m 180.00

A.1.4.3.800.a

cu.m 134.50

CO

1.4.2.9.a

Concrete with exposure class XC1, consistency class S4/S5 for reinforced concrete works in general, beams, pillars, slabs, curbs, walls for stairwells and elevators, cast with the aid of formwork, iron and formwork accounted for apart: a) C25/30

−21.8

+4.2

−17.2

−17.2

−14.4

+4.7

u.m. Unitary price Difference (%)a

BS

Price list code

Working

Price list

Table 1 (continued)

The Regional Price Lists for Estimating the Costs of Construction 221

BG

−15.6 −41.9

−27.3 −41.6

IC.04—Reinforced concrete works—injections and restorations

IU.04—Road works

the price shown in the price list of the Municipality of Milan

−8.2

a Of

−27.1

−41.1 −25.4

IC.02—Excavations—earthworks −42.8

3.8

−3.3

−7.1

BS

Average difference (%)a

CCIA MI

Price list

IC.01—Demolitions—removals

Category

Table 2 Percentage differences in prices determined for the four categories of works

−37.3

−12.8

−12.6

−36.4

CO

−49.3

0,7

−15.6

−1,4

PV

−48.6

−11.7

−27.2

−43.9

CR

−49.3

−2.4

−0.7

−12.3

MN

−45.5

−18.9

−33.1

-15.3

VA

222 P. Rosasco and L. Sdino

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of defining a price list on a regional basis except in the face of specific checks of each single unit price to be used in the opera.10 A functional analysis has therefore been developed to verify the possibility of calculating average percentage differences in the prices of the categories of workings, taking into account the impact of the latter on the overall cost of a building intervention.

5 Functional Analysis The BEST 2.0 database of the ABC Department of the Polytechnic of Milan collects the cost data relating to interventions for different uses (residential, commercial, etc.) carried out in a mostly regional context; by the basis of the bill of quantities of the projects of each intervention, the related functional cost analysis models are compiled.11 These models report, for each functional element identified by the scheme of the UNI 8290 standard and the Swedish classification code SfB,12 the following data13 : – location of the intervention, date of implementation, main dimensional and typological data; – SfB codes for identifying the single functional elements of the work, the materials used and the shape; – total cost of construction of each functional element (sum of the costs of the individual processes derived from the bill of quantities relevant to that element); – quantity of the functional element (fq: sqm, cu.m, m, etc.); – unitary cost of the functional element (ratio between total cost and functional quantity); – unitary cost of the element in relation to the overall size of the intervention (total gross area). By the case studies present in the BEST 2.0 database, the workings for each functional elements was then carried out, taking as reference a residential building intervention 10 This would require that—due to the territorial context where the work object of the estimate is located—the prices of the main processes reported in the Regional price list were referred to the local reality through the application of the percentage of difference. 11 The models therefore report the cost of each functional element (both total and unitary) determined by combining the cost of the processes necessary for its realization, the costs of which are reported in the bill of quantities of the executive project. 12 The abbreviation is an acronym for the name of the Swedish organization "Coordination Committee for Building Problems); the system adopted is the Italian version of the Swedish system [8]. 13 The models also report other data and information relating to the intervention: gross and net surface area of the main destination and accessory destinations, volume, type of structure, energy class, location (Region, Province, Municipality), type of customer (public or private), brief description of the intervention (type of finishes adopted, type of plants, etc.).

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(case study) which, by territorial location, size, construction and plant solutions adopted, can be defined “ordinary” (recurring). The main features of the case study are the following: – – – – – – – – – – – – – – – – – – – – –

–

– –

Building use: residential; Type of intervention: new construction; Typology: multi-storey building; Dimensional class: from 1,000 to 2,000 sqm; Building shape: regular type, angles between the walls of 90° (in plan and elevation); Climate Zone: E; Italian seismic zone: 3; Energy class: A; Load-bearing structure: vertical and horizontal frame in reinforced concrete; Roof: flat; Plasters: cement-based premixed, lime-based mineral skim coat; Hydraulic, painting with washable water-based paint; Floors: ceramic and parquet; External frames: in aluminum with high performance double glazing; Internal fixtures: covered with wood; Heating system: autonomous with natural gas boiler; Electrical system: consisting of distribution lines in PVC conduits, and earth line; Gas meters: located outside the building; Water-sanitary system: vertical pipes that bring cold water to the kitchens and bathrooms; Water; Climatic conditions: locations with an altitude of less than 300 meters above sea level with temperatures between 5 and 27 °C and maximum atmospheric rainfall equal to 90 mm/h; Morphological conditions of the site: flat area plot without rocks and artifacts in the subsoil, groundwater lower than the laying surface of the plinths. (permissible soil load: 1 kg/square centimeter); Accessibility to the site: without limits of range, width and time slots with the possibility of placing the crane in any position; Distance from services/supplies: supply of building materials and landfills within 20 km.

The cost of workings used for the construction of the elements of the two interventions were then updated by applying the average percentage difference coefficients determined in the previous chrono-product analysis. In order to make the elaborations developed comparable and representative, the total costs resulting from the application of the average percentage difference coefficients were reduced from the discount (%) offered when the public works contracts were awarded in the various territorial realities, taking as reference the data of the Regional Public Works Observatory of the Lombardy Region.

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Specifically, at the price determined according to the different price lists, the following discount percentages were applied: – – – – – – – – –

MI: −16% MMBL: −19% BG: −18% BS: −16% CO: −17% CR: −23% MN: −16% PV: −18% VA: −18%

Table 3 shows the comparison of the total, unitary costs and the percentage differences that emerged by comparing the Price List of the Municipality of Milan (MI) and that of the Bergamo Chamber of Commerce (BG) for the group of functional elements “Elevation structures—Rustic”.

6 Results On the basis of the application of the percentage difference deriving from the chronoproduct analysis (pertinent to each of the price lists analyzed), the total costs of the case study was then estimated; by the percentage differences calculated, the average difference of each price list were determined by comparing the cost obtained using the Price List of the Municipality of Milan (Table 4). For the purpose of drawing up a regional price list starting from that of the Municipality of Milan, these average percentage differences can therefore be assumed as “adjustment” or “concordance” coefficients of the estimated cost for a works located in the different lombard provinces. Following the parametric analysis of the costs of the specific classes and functional of the standard intervention, a correlation table was drawn up between the different prices of the construction works commonly used in the Lombardy provinces, placing the Price List of the Municipality of Milan. Upon indication of the Technical Table of the Lombardy Region and in order to reduce the possible statistical error, the different provinces (with their respective price lists) were grouped into four homogeneous provincial areas, as defined below: 1. 2. 3. 4.

Milan, MonzaBrianza, Lodi; Varese, Como, Lecco, Sondrio; Bergamo, Brescia; Cremona, Mantova, Pavia.

Following the definition of the territorial areas and taking as reference the deviations determined for each price list used within the four areas (Table 4), the average

Slabs, balconies

Stairs, ramps

Further elements of the class

Balconies, terraces

Roof

Load-bearing structure

Demolitions

23

24

25

26

27

28

29

Total

Interior walls

22

672

1,031

380

55

285

6,225

4,741

External walls 3,382

21

cu.m

sqm

sqm

m

sqm

sqm

sqm

sqm

6,900

6,900

6,900

6,900

6,900

6,900

6,900

6,900

3,098,761

657,434

114,552

90,141

2,677

394,604

827,490

420,309

591,554

(e)

978.32

111.11

237.21

48.67

1,384.58

132.93

88.65

174.91

(e/fq)

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

(%)

449.1

95.28

16.60

13.06

0.39

57.19

119.93

60.91

85.73

(e/sqm)

Cost for sqm of SLP

(sqm)

2-Rustic elevation structures

Diff. MI Price List

Item cost

Unitary cost

Price list of the Municipality of Milan

SLP

Functional quantity of element (qf)

N.

Functional element

Basic data

34.6

7.3

1.3

1.0

0.0

4.4

9.2

4.7

6.6

(%)

% up total cost

3,275,142

695,936

120,498

93,291

2,792

429,732

860,154

447,854

624,885

(e)

Item cost

1,035.61

116.87

245.50

50.76

1,507.83

138.18

94.46

184.77

(e/fq)

Unitary cost

Bergamo price list (BG)

5.7

5.9

5.2

3.5

4.3

8.9

3.9

6.6

5.6

(%)

Diff. MI Price List

474.7

100.86

17.46

13.52

0.40

62.28

124.66

64.91

90.56

(e/sqm)

Cost for sqm of SLP

34.7

7.4

1.3

1.0

0.0

4.6

9.1

4.8

6.6

(%)

% up total cost

Table 3 Rustic elevation structures: cost analysis and percentage difference of cost—comparison between the price list of the Municipality of Milan (MI) and the price list of Bergamo Chamber of Commerce (BG)

226 P. Rosasco and L. Sdino

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Table 4 Average difference (%) for the four categories derived by the functional analysis Category

Price list CCIA MI

BG

BS

CO

PV

CR

MN

VA

+6.7

+6.6

+5.8

+5.9

Average difference (%)a IC.01—Demolitions—removals

+10.0

+5.1

+4.3

+10.5

IC.02—Excavations—earthworks IC.04—Reinforced concrete works—injections and restorations IU.04—Road works a Of

the price shown in the price list of the Municipality of Milan

Table 5 Deviations from the total costs of the intervention detected by the functional analysis Category

Aggregate territorial areas Milan Monza Brianza Lodi

Varese Como Bergamo Lecco Sondrio Brescia

Cremona Mantova Pavia

Concordance index (%)a IC.01—Demolitions—removals

0.0

8.2

5.1

6.4

IC.02—Excavations—earth works IC.04—Reinforced concrete works—injections and restorations IU.04—Road works a Of

the price shown in the price list of the Municipality of Milan

deviation was then calculated with respect to the Price List of the Municipality of Milan.14 The results are showed in the following Table 5.

7 Conclusions The analyzes carried out for the purpose of defining a price list for the Lombardy Region capable of taking into account the peculiarities of the individual territorial areas involved, in a first phase, the comparison of the unitary prices of the main 14 Taking the price list of the Municipality of Milan as reference, the deviation determined for the price list published by the Milan MonzaBrianza Lodi Chamber of Commerce (equal to 10.0%) was not considered; therefore, the “concordance” index is assumed equal to 0.0%.

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processes used in the ordinary building works reported inside. of the price lists used for the estimate of the works in the various provinces of the region. The analysis focused on 25 workings belonging to 4 categories of most recurring works in the construction sector (which together make up about 80% of the cost of construction of residential buildings) and was developed with reference to 9 price lists of as many provincial areas of the Lombardy Region; the comparison highlighted significant differences between the unitary prices of many processes which can be explained by the different structural characteristics and specializations of the companies operating in the construction sector (retailers and construction companies) and by the different costs of some of the resources used (especially the materials). The comparison developed of four chrono-product categories shows average differences of the unitary prices which, compared to the Price List of the Municipality of Milan, reach almost −50% for road works in the Province of Mantova (Table 2). In order to evaluate how the differences in the unitary prices of the individual workings affect the cost of construction, a case study of a residential building was therefore selected from the BEST 2.0 database of the ABC Department of the Polytechnic of Milan located in the Province of Milan and developed an estimate on a functional basis by applying the percentage differences calculated in the chronoproduct analysis to the workings of the building components (functional elements). The percentage differences of the cost compared to the one obtained using the Price List of the Municipality of Milan range from a minimum of +4.3% (Brescia price list) to a maximum of +10.5% (Como price list). On the basis of the indications of the regional Technical Table and ANCE— the Lombardy Region has therefore decided to select the Price List of the Municipality of Milan as a price list to be taken as a Regional Price List for the estimate of the costs of interventions (as indicated by the 23 of Legislative Decree n. 50/2016) and on an experimental basis—and optionally—the possibility of being able to apply the price correction percentages based on the location of the works for the items in Volume 1.1—Works completed “Civil, urbanization and soil protection” referring to the 4 categories of analyzed works (1C.01—Demolitions—removals; 1C.02—Excavations—earthmoving; 1C.04—Reinforced concrete works—injections and restorations; 1U.04—Works road). In particular, for 3 homogeneous provincial areas the “concordance” indices have been defined15 : 1. Varese, Como, Lecco, Sondrio: +8%; 2. Bergamo, Brescia: +5%; 3. Cremona, Mantova, Pavia: +6%. This price list becomes, starting from 2019, the reference price for the estimate of construction works also in relation to the possibility of resorting to the elaboration 15 For the homogeneous territory of the provinces of Milan, MonzaBrianza and Lodi is not considered

any percentage of adjustment.

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of specific prices through the application of the price analysis procedures foreseen by the current regulation (Legislative Decree n. 50/2016). In a first experimental phase, on the basis of the collection and analysis of the reasoned reports of discrepancies (by means of a technical data sheet) by all the subjects who utilise the Regional Price List (Public Administrations, companies, professionals, etc.)—possibly with the coordination by the local Chambers of Commerce—it will therefore be possible to monitor the significance of the regional price list and prepare any corrections to the indicated “concordance” indices. This may also concern the possible modification of the declarations and prices to be completed by the release of the 2019 edition of price list. The adoption of a shared list with unequivocal declarations by the Lombard Chambers of Commerce could be suggested, possibly with the coordination of Unioncamere Lombardia and the assistance of Digicamere, on which to perform price surveys using commonly used tools. At the same time, access to the data of the Regional Observatory of Public Works could be useful, in order to perform a territorial comparative analysis on the average reductions recorded in the individual provinces for the different categories of construction and road works.

References 1. Grosso R, Prizzon F, Rebaudengo M (2019) Monitoring and evaluation of anti-disturbance policy in public procurement: works, services and supply contracts in Piedmont region. Valori e Valutazioni 22:19–33. DEI, Roma 2. Rebaudengo M, Prizzon F (2017) Assessing the investments sustainability after the new code on public contracts. In: Murgante B, Apduhan BO, Borruso G, Cuzzocrea A, Torre CM, Gervasi O, Taniar D, Stankova E, Rocha AMAC, Misra S (eds) Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics, vol 10409 LNCS. Springer, pp 473–484 3. Rebaudengo M, Innocente G, Crisafulli A (2019) PPs palatability to complete unfinished public works in Italy. In: Bevilacqua C, Calabrò F, Della Spina L (eds) Smart innovation, system and technologies, vol 100. Springer Science and Business Media, Deutschland, pp 635–642 4. Sdino L, Rosasco P, Lombardini G (2020) The evaluation of urban regeneration processes. In: Della Torre S, Cattaneo S, Lenzi C, Zanelli A (eds) Regeneration of the built environment from a circular economy perspective. Springer, pp 47–57 5. Capolongo S, Sdino L, Dell’Ovo M, Moioli R, Della Torre S (2019) How to assess urban regeneration proposals by considering conflicting values. Sustainability 11(14) (Article n. 3877). MDPI 6. Guarini MR, Buccarini C, Battisti F (2017) Technical and economic evaluation of a building recovery by public-private partnership in Rome (Italy). In: Green Energy and Technology. Springer, pp 101–115 7. Sdino L, Magoni S (2018) The sharing economy and real estate market: The phenomenon of shared houses. In: Integrated evaluation for the management of contemporary cities—green energy and technology. Springer, pp 241–251 8. Vetriani G, Marolda MC (a cura di) (1983) Piano di classificazione PC/SfB, Dipartimento di Disegno Industriale e Produzione Edilizia, Università degli Studi di Roma—La Sapienza, ITEC Editrice, Milano

Optimal Design in Energy Retrofit Interventions on Building Stocks: A Decision Support System Laura Gabrielli and Aurora Greta Ruggeri

Abstract During the last years, a growing interest has pivoted around strategies and methodologies for energy efficiency in buildings. Nevertheless, the attention has always been primarily directed to single properties, while the scientific research still lacks in solutions for building portfolios. Assets owners, instead, would require reliable decision-support systems in order to implement the most effective retrofit solutions. Hence, this study intends to elaborate a process to identify the optimal allocation of financial resources for energy enhancements in large building portfolios. Some novelties characterize this research. First, the approach developed covers each aspect of energy retrofits, from energy consumption assessment to on-site construction and management. Second, the level of detail requested is not excessively burdensome, ensuring good reliability. Third, the approach is interdisciplinary, connecting energy forecast techniques, economic analyses with operational research. The method developed has been implemented on a portfolio of 25 buildings in North Italy for testing and validation. It was possible to compare several design alternatives and reach for the best outcome, which demonstrated how this decision support system could be successfully used for real applications. Keywords Energy retrofit · Building stocks · Optimization · Life cycle costing · Decision-making

L. Gabrielli (B) Department of Architecture and Arts, University IUAV of Venice, Dorsoduro 2206, 30123 Venice, Italy e-mail: [email protected] A. G. Ruggeri Department of Management and Engineering, University of Padua, Stradella S. Nicola 3, 36100 Vicenza, Italy e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Morano et al. (eds.), Appraisal and Valuation, Green Energy and Technology, https://doi.org/10.1007/978-3-030-49579-4_16

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1 Introduction In this research paper, we present a decision support system aimed at the planning of energy retrofit campaigns in large building stocks. The method here developed can simplify significantly the design and the management of energy refurbishment schemes in wide assets, when a considerable number of buildings and retrofit options needs to be handled simultaneously. We suggest an innovative approach that helps the decision-making process and leads to the best allocation of the available financial resources. The core idea behind this study is the integration of traditional energy/economy analyses with operational research and optimization techniques. We consider the energy retrofit as an optimization problem, including multiple objectives and constraints. The model developed produces the optimal set of retrofit options with the target of improving the overall energy performance of the stock, under the constraint of a monetary budget. The paper contribution in relation to the specific literature is the formulation of a flexible decision support tool that can help asset holders to evaluate different actions in enhancing existing building portfolios and maximizing their cash inflows. With a limited budget, it is necessary to identify the best options both from an energy and economic point of view, and it is also fundamental to understand which retrofit interventions should be implemented with some urgency in the stock, and which ones may be postponed. Therefore, the model presented in this paper could be beneficial for different stakeholders, such as portfolio managers, public or private investors, real estate companies, or Public Administrations, since they manage large real estate assets and wide building stocks. The method designed has also been applied and tested on an interesting portfolio of public office buildings located in North Italy. The results obtained demonstrate how this approach successfully supports the decision-making process and leads to an optimal outcome. The remainder of this article is structured as follows: Sect. 2 presents the theoretical framework adopted in this research. Section 3 illustrates and implements the model developed on a pilot case study. Section 4 discusses the results achieved, outlining the conclusions of the work, and introducing further possible developments.

2 Theoretical Framework The decision support system here developed is based on three cornerstones. The first is the use of a reference building strategy as the fastest technique to estimate the buildings’ energy demand in a large stock. The second pillar is the employment of two methods widely recognized in the field of property investment, i.e., the life cycle

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costing and the discounted cash flow analysis, in order to assess the economic feasibility of the energy retrofit. The third cornerstone is the integration with operational research as a way to sort out a complex decision-making problem. As regards the first point, we rely on a reference building approach, a white-box engineering technique [1], to assess the energy demand for every building in the asset. A reference building (RB) is one single property selected from stock (or from a subset of homogeneous buildings in a stock) due to its emblematic characteristics and its ability to represent an entire building typology. If the building stock taken into consideration is small and homogeneous, the definition of one single RB will be adequate to represent the whole population. In contrast, if the building asset shows heterogeneous properties, it has to be subdivided into smaller buildings groups (or clusters or categories), and one RB is selected per category. Detailed energy analyses are performed only on the selected RBs, and the results are then extended to the other buildings in the same group using common parameters, such as kWh/(sqm year) or others. In particular, the European Authorities have recommended the use of the RB approach to promote buildings’ energy refurbishment cycles at a stock level, when it is clearly unfeasible to reach an intimate knowledge of each individual property in a large and numerous asset [2]. A quite considerable amount of works has already demonstrated the usefulness of a RB strategy to organize and plan comprehensive retrofit programs in wide stocks, such as in [3] for a set of commercial buildings in France and Poland, in [4] for a dwelling stock in Romania, in [5] for a residential portfolio in Ai Ain City (UAE), and in [6] for several building assets in China. Concerning the second issue, when energy retrofit projects are targeted at identifying the most cost-effective option among a set of alternatives, it is essential to define some economic indicators and evaluate the feasibility and profitability of interventions. In this research, we base the economic analysis on two techniques that have been traditionally employed in the field property investment valuation, i.e., the discounted cash flow analysis and the life cycle costing [7]. The inclusion of two techniques based on discount back procedures is fundamental in feasibility analyses for energy investments [8, 9] since we need to compare present monetary amounts (investment costs) with future monetary amounts (operating costs before/after retrofit). The life cycle costing (LCC) is a technique often used in the specific literature to verify the economic feasibility of buildings’ energy retrofits. Some successful application of the LCC can be found in [10], to identify the cost-optimal level for the energy enhancement in historic buildings, in [11], to support a scenario analysis about different energy efficiency designs, and in [12], where the authors combined a LCC analysis with a DCFA. A LCC analysis is a very complete approach to describe energy retrofit processes, because the LCC is equal to the sum of every cost that occurs over a life cycle (or over a period of analysis) for a building, comprising investment costs, operating costs (management and maintenance), and disposal costs (if any). The LCC approach works in accordance with the European Directive 2010/31/EU about the cost-optimal strategy in buildings energy efficiency, which recommends taking into consideration not only the investment costs but also all the future disbursements [13],

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as described by Eq. 1: LCC =

T   t=0

Ci Cop(1 + g)t Rv + + ... ± (1 + r )t (1 + r )t (1 + r )n

 (1)

t ∈ N, 0 ≤ t ≤ T. In this formula, Ci represents the investment cost, while Cop stands for future operating cost due to management and maintenance, Rv is the residual value (if any), r is the discount rate, g is the growth rate on energy price, while t is the time: t varies between 0 and T, and T is the total period of analysis. Furthermore, even the utilization of the discounted cash flow analysis (DCFA) for energy retrofit operations has enormously increased in the latest years [14–16], since this technique is very direct and versatile. The DCFA allows to distribute both the costs and the savings produced by an energy retrofit project along a chronological timeline, and it discounts them to their present value. If discounted back, savings and costs can be summed, which produces the Net Present Value (NPV) of the cash flow. In other words, the NPV can be described as the present value of all incomes and outcomes for a retrofit project after a predetermined period of analysis, and it is a useful index to verify the economic viability of retrofitting actions [17, 18]: if savings overcome costs, the NPV is positive, which means that the operation is financially feasible. The NPV general formulation is as presented in Eq. 2: T   − N PV = t=0

Ci Sv(1 + g)t + . . . + (1 + r )t (1 + r )t

 (2)

t ∈ N, 0 ≤ t ≤ T. Here Ci is the investment cost due to the retrofit actions, Sv stands for the monetary saving on energy consumption. Again, r is the discount rate, g is the growth rate, t is the time and T is the total number of years after which the profitability is valued. Beyond the NPV, there are two other economic indexes traditionally associated with the DCFA that we also include in this study, namely the Payback period (PB) and the Internal Rate of Return (IRR). The PB period indicates the time after which the initial cash outflow, which is, in this case, the investment cost for the energy retrofit, is expected to be recovered from the cash inflows given by the savings on the energy consumption [19]. Instead, the IRR is the discount rate that makes the NPV of all cash flows equal to zero, and it is an indicator of the overall profitability of the investment [20]. Finally, as far as the third issue is concerned, we can state that the two techniques presented above (LCC and DCFA) may be sufficiently complete with supporting decision-making processes if the energy retrofit project is implemented to one single building. However, when the energy retrofit is applied to a portfolio of several buildings, the decision process is not such straightforward, and it may also require the support of operational research (OR) strategies. In fact, different energy retrofit

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operations on a set of numerous buildings may produce conflicting results or create competing objectives. At the same time, financial or technical constraints should also be respected, so the optimal design level may be difficult to be defined. OR is able to identify the optimal solution (or nearly-optimal solution) for complex decision-making problems on the basis of different techniques taken from mathematical modelling, optimization or statistical analysis. A direct application of OR, used in this research paper, is linear programming (LP), also known as linear optimization. LP is that branch of OR that studies algorithms to solve linear optimization problems: a problem is called linear if both the objective function and the constraints can be expressed as linear functions. In other words, LP is a technique used to achieve the optimal outcome for any mathematical model whose requirements can be described as linear relationships. The LP has been seldom used in the field of the buildings energy retrofit, but a few examples can still be found in the dedicated literature. For instance, in [21], the authors applied a LP analysis in Italy to find the optimal distribution of incentives for buildings energy retrofit. Conversely, in [22], the authors employed a mixed integer LP to find the optimal set and sizing of smart building components under the objective of minimizing the whole cost. Besides, in [23], the authors based their study on a mixed-integer LP to determine the optimal design level of building envelopes and building energy system components, under a multi-objective optimization framework.

3 A Pilot Case-Study We consider a pilot case-study to develop, implement, and test the approach proposed in this research. We analyse 25 office buildings in Ferrara (North Italy) that constitute an attractive property stock. There are, in fact, historical buildings as well as new constructions and ex-industrial assets, as presented in Fig. 1. Since this portfolio is heterogeneous, and it shows various building types, sizes, and maintenance conditions, the same energy retrofit measures cannot be suitable for every building in the stock. On the other hand, the large size of the portfolio does not allow each building to be analysed separately and tailor-made solutions to be designed. Therefore, it is chosen to subdivide the portfolio into uniform subsets through partitional clustering, so that to regroup the buildings based on their common characteristics. The attributes defining the subsets are established according to the specific literature. The buildings in the portfolio are defined as Bk , k ∈ N {1, …, K}, K = 25. The clustering algorithm used is the k-means, and the attributes considered for partitioning are, floor area (m2 ) [24], maintenance conditions (0–5) [25] and year of construction [26]. We chose to define six different clusters, as suggested by the graphic elbow method, named Cl1 , Cl2 , Cl3 , Cl4 Cl5 , and Cl6 . Each building belongs to the cluster with the nearest mean: • ∀ Bk , k ∈ N 2 1 ≤ k ≤ 6| Bk ∈ Cl1 , • ∀ Bk , k ∈ N 2 7 ≤ k ≤ 12| Bk ∈ Cl2 , • ∀ Bk , k ∈ N 2 13 ≤ k ≤ 16| Bk ∈ Cl3 ,

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“Palazzo Tro Mos”

“Complesso Macchiavelli”

“Palazzo Tassoni Estense”

“ Polo scienfico Tecnologico ”

“Centro Studi Daccò”

“Ex Zuccherificio”

Fig. 1 A building stock in Ferrara

• ∀ Bk , k ∈ N 2 17 ≤ k ≤ 20| Bk ∈ Cl4 , • ∀ Bk , k ∈ N 2 21 ≤ k ≤ 23| Bk ∈ Cl5 , • ∀ Bk , k ∈ N 2 24 ≤ k ≤ 25| Bk ∈ Cl6 .

3.1 Energy Consumption Assessment Due to their representative characteristics, six buildings are selected as the RBs that represent each cluster. Their structure, technologies, architecture, shape, and maintenance conditions can significantly portray the population of their group of buildings. The six RBs are shown in Fig. 2. We have denominated each building in the stock as Bk . The RBs are: • • • • • •

B1 for Cl1 , B8 for Cl2 , B15 for Cl3 , B19 for Cl4 , B23 for Cl5 , B24 for Cl6 .

A set of M energy efficiency scenarios (S) is designed to improve the overall energy performance of the stock (Table 1). Any design scenario is therefore defined as Sm ,

Fig. 2 Six reference buildings

Cluster 2

Cluster 5

Cluster 1

Cluster 4

Cluster 6

Cluster 3

Optimal Design in Energy Retrofit Interventions … 237

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Table 1 Energy retrofit designs Scenario (m)

Scenario definition

Measure

Energy efficiency measures Unitary prices composing the retrofit scenarios

S0

do-nothing

a

a

Installation of thermostatic valves on heaters and radiators

e each 120

S1 S2

a+b+c

b

a+b+c+d

Installation of mechanical ventilation (Daikin) with heat recovery system, air treatment and humidification

e/mc 600

S3

S4

a+l

c

e/sqm 660

S5

a+l+b+c

Installation of double glazing glasses (filled with Krypton) with low transmittance coating and air tightness

S6

a+l+b+c+d

d

S7

a+d

Internal roof insulation: e/sqm 75 Actis Hybris multylayer heat-reflecting insulation, R = 3.75 m2 K/W

S8

a+d+f

e

e/sqm 100

S9

a+d+f+e+i

External roof insulation: Rockwool Dammkeil 035 stone wool insulation, λ = 0.035 W/(mK)

S10

a+c

f

e/sqm 65

S11

a+c+h

Internal wall insulation: Actis TRISO-SUPER 10+ multylayer heat-reflecting insulation, R = 5.25 m2 K/W

S12

a+c+h+e+i

g

b+c

Internal wall insulation: Rockwool Frontrock stone wool insulation, λ = 0.039 W/(mK)

e/sqm 100

S13

S14

b+c+d

h

e/sqm 100

S15

b+c+d+h

External wall insulation: Rockwool Dammkeil 035 stone wool insulation, λ = 0.035 W/(mK)

S16

a+g

i

e/sqm 65

S17

a+g+b+c+h

Ground floor insulation: Rockwool Ceilingrock stone wool insulation, λ = 0.035 W/(mK)

S18

a+g+b+c+h+f

l

Traditional boiler substitution with a condensing boiler Daikin

e each 5.000

Optimal Design in Energy Retrofit Interventions …

239

with m ∈ N {0, …, M}. When m = 0, we refer to the buildings in the so-called “do-nothing” option (no intervention is applied), when 1 ≤ m ≤ M we refer to an energy retrofit scenario. In particular, we outline three alternative scenarios for each cluster, according to the kind of interventions that are appropriate and compatible with each group of buildings. The three retrofit scenarios are corresponding to, respectively, a minimum, medium or maximum increase in the energy performance of the buildings, leading to a total of 18 retrofit scenarios: • • • • • •

Cl1 | m ∈ N {0, 1,2, 3}, Cl2 | m ∈ N {0, 4, 5, 6}, Cl3 | m ∈ N {0,7, 8, 9}, Cl4 | m ∈ N {0,10, 11, 12}, Cl5 | m ∈ N {0, 13, 14, 15}, Cl6 | m ∈ N {0, 16, 17, 18}.

This produces the complete nomenclature of all (Bk Sm ) elements of the portfolio, and identifies all the possible combination of any mth scenario applied to any kth building as in Table 2. Computer energy simulations are performed for the six RBs to forecast their energy consumption both in the as-is state and in any proposed design option. After accurate investigations have been executed on-site, it was possible to create reliable energy simulation models for each RB, using the software Energy Plus. The energy consumption forecasts are assessed for the six RBs, as in Table 3, and these results are then extended to the other buildings in the same cluster in terms of yearly energy consumption (YECk,m ) in kWh/year.

3.2 Life Cycle Cost Assessment At this stage, we calculate the investment costs, the operating costs, and the consequent monetary savings for all the proposed solutions on the stock. The investment costs are assessed through a linear pricing model, according to the unitary prices presented in Table 1. The operating costs come from the multiplication of the energy consumption (kWh/sqm year) times the price of energy in Ferrara (0.095e/kWh for district heating). The savings are given by the difference in the Cop between the donothing and any retrofit option. In order to account the time preference of money, we assess the discount rate “r” and the growth rate “g”. The discount rate is calculated in this research as the WACC (weighted average cost of capital): r = W ACC = D ∗ K d + E ∗ K e

(3)

S0

Scenario

S1

Cluster 1

Cluster

S2

S3

S0

S4

Cluster 2 S5

S6

S0

S7

Cluster 3

Table 2 Cluster definition according to energy efficiency scenarios S8

S9

S0

S10

Cluster 4 S11

S12

S0

S13

Cluster 5 S14

S15

S0

S16

Cluster 6 S17

S18

240 L. Gabrielli and A. G. Ruggeri

75.38

63.72

47.47

S4

S5

S6

53.70

S3

82.90

95.70

S2

S0

115.00

S1

Cluster 2

126.70

S0

Cluster 1

RB’s energy consumption (kWh/sqm year)

Building-Scenario (Bk Sm)

Cluster

Cluster 4

Cluster 3

Cluster

Table 3 Energy consumption before and after retrofit

S12

S11

S10

S0

S9

S8

S7

S0

Building-scenario (Bk Sm)

13.24

43.83

97.31

153.09

19.29

32.47

72.69

79.96

RB’s energy consumption (kWh/sqm year)

Cluster 6

Cluster 5

Cluster

S18

S17

S16

S0

S15

S14

S13

S0

Building-scenario (Bk Sm)

23.37

50.00

74.91

82.64

38.63

46.81

47.40

61.44

RB’s energy consumption (kWh/sqm year)

Optimal Design in Energy Retrofit Interventions … 241

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In this analysis, we assume 50% Equity and 50% Debt. The Ke (cost of capital on self-financing) is estimated as a risk-free rate (2.765%) plus risk premiums for the construction (1.00%) and illiquidity (0.50%): Ke = 4.26%. The kd (cost of capital on loan) is the Euribor rate (0.89%) plus a 2.00% = bank spread, namely Kd = 2.89%. As a result: WACC = 0.50*4.26% + 0.50*2.89% = 3.58% (nominal rate). Conversely, the growth rate g is estimated from the time series of energy prices in Ferrara. The growth rate assumed is g = 2%. We also take into consideration the financial incentive we can benefit from, which provides public funding for the 50/60/65% (depending on the retrofit measure) of investment costs. The incentive is delivered in instalments during the first ten years of investment. The LCC can, therefore, be calculated for each building-scenario combination (Bk Sm ) as in Eq. 4. The period of the analysis is T = 30 years. ∀Bk Sm , LCCk,m

   30   (Fk,m ) (1 + r )10 − 1 Ci k,m Copk,m (1 + g)t − = + (1 + r )t (1 + r )t r (1 + r )10 t=0 (4)

t ∈ N, 0 ≤ t ≤ 30 k ∈ N, 1 ≤ k ≤ 25 m ∈ N, 1 ≤ m ≤ 18.

3.3 Linear Programming In the decision-making process, two aspects are simultaneously considered: the energy consumption on the one side, and the LCC on the other. Both energy/economy issues are, therefore, accounted for identifying the optimal combination of retrofit options in the stock. To sum up, the building portfolio is composed of 25 elements. Per each building, one out-of-four option can be selected (do nothing, minimum/medium/maximum scenario), which leads to a total of 100 scenarios. Economic and feasibility constraints are also included. The available budget allocated by the Public Administration is 4,000,000.00 e. Since the decision-making problem shows a complex structure, a linear programming algorithm supports the selection. It identifies that particular outcome that maximizes the overall energy and monetary savings, under the budget constraint: max f (x)

K  M 

[0.60(LCCk,0 − LCCk,m ) + 0.40(Y ECk,0 − Y ECk,m )] ∗ (X k,m )

k=1 m=0

(5) K  M  k=1 m=0

Ci k,m X k,m X k,m  4, 000, 000.00e

Optimal Design in Energy Retrofit Interventions …

∀k,

M 

243

X k,m ≤ 1

m=0

k ∈ N, 1 ≤ k ≤ 25 m ∈ N, 1 ≤ m ≤ 18 Xkm ∈ N {0,1}. The multi-attribute optimization combines the two energy/economy attributes in a weighted and scalarized sum. Since the attributes have different units of measurement, they are normalized in the interval [0;100], so their scalarization into one function becomes congruent. The weight 60% is given to the economic attribute, while 40% is given to the energy attribute. The linear optimization algorithm determines the optimal solution, and for each building, one scenario is selected, so that: • • • • • • • •

S0 is applied on B4 , B11 , B18 , B20 , B23 ; S1 is applied on B1 , B2 , B3 , B5 , B6 ; S4 is applied on B7 , B8 , B9 , B10 , B12 ; S7 is applied on B13 , B14 , B15 , B16 ; S10 is applied on B17 ; S12 is applied on B19 , S13 is applied on B21 , B22 ; S16 is applied on B24 , B25 .

3.4 Timing and Economic Assessment By this point, the best option for each building has been identified, and their timing and duration need to be assessed as well, in order to verify the economic feasibility of the whole operation on the stock. Two criteria are considered when prioritizing the actions: emergency and cost-effectiveness. The urgency of interventions is directly provided by the investor himself (the Public Administration in Ferrara) based on buildings’ maintenance conditions and use. The most urgent actions are implemented first. If two (or more) interventions are judged equally critical, the priority is given to the most cost-effective one(s). An effectiveness-index is calculated dividing the monetary savings produced over the capital expenditures (Sv/Ci). Consequently, the higher the index, the more effective the measure applied. We also assess the duration of each scenario’s construction period (time required for on-site construction) considering the hourly labor cost, as in the following equation: T ime f or constr uction k.m =

Ci k,m ∗ I wor kk,m Cwor kk,m ∗ 8 ∗ N Wk,m ∗ U D

(6)

Where Ci are the costs of investment, Iwork is the percentage of investment costs due to workforce, Cwork is the average hourly labour cost (25e/hour), NW is the

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average number of on-site workers, while UD are the useful working days over a year. The economic feasibility is verified through a DCFA. The NPV of the cash flows is the difference between the discounted inward capitals minus the outward capitals, after a predetermined time. In this analysis, we have a 30 year-period of analysis. The discount rate is r = WACC = 3.58%, and the growth rate on energy price is g = 2.00%, as previously calculated. So the NPV of the intervention is estimated as: N PV =

30 K  M   k=1 m=0 t=0

 −

Ci k,m X k,m (1 + r )t

+

Svk,m X k,m (1 + g)t (1 + r )t

 +

(Fk,m )X k,m (1 + r )10 − 1 r (1 + r )10

(7)

t ∈ N {0, …, 30} k ∈ N {1, …, 25} m ∈ N {0, …, 18}. According to Eq. 7, we obtain NPV = 2,856,041 e, the IRR is 11.10%, and the DPB happens between years 10 and 11. Since the NPV is a positive value, this indicates that the operation is financially feasible, while the NPV figure represents the actual savings produced.

3.5 Results We came up to assess the economic profitability of the investment as the NPV of cash flows. However, we should consider that the DCFA developed is based on input values (such as future prices and costs) strongly affected by uncertainty. Several uncertain factors may change the DCFA outcome, such as unpredictable weather [27], energy use [28], occupant behaviour [29], financial and economic conditions [30], energy prices variations [31], alterations in building’s envelope thermal properties [27], or changes in installations efficiency [32]. This means that the value of the NPV calculated can be taken as the “correct” output only if the assessment of all the inputs involved in its forecast is correct in turn. In order to overcome this problem, we can perform a sensitivity analysis on the DCFA result, and complete the economic evaluation with a simple risk management tool so that to provide a much more robust support for decision making [33]. Among risk management approaches, the sensitivity analysis (a.k.a. “what if” analysis) belongs to the one-factor-at-a-time methods [34], in fact in a sensitivity analysis we change only one variable at a time, while we keep constant all the others, so consequent variations in the output can be analysed. A sensitivity analysis can identify how the output changes in correspondence to different percentile variations in the input values of the model. Because every input factor is varied one-by-one, it will be obvious to understand which factors will produce the most significant variation on the outcome.

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Table 4 Uncertain inputs: statistical description Uncertain inputs

Distribution

Sv

Triangular

Best estimate

g

Gaussian

−9.66%

5.45%

debt

Rectangular

50.00%

40.00%

60.00%

Risk free rate

Gaussian

2.76%

1.15%

7.08%

−0.28%

198,115.29 2.00%

Minimum

Maximum

184,568.72

211,661.87

Euribor rate

Gaussian

0.89%

Mortgage spread

Rectangular

2.00%

2.00%

3.00%

F

Rectangular

65.00%

35.00%

70.00%

t

Rectangular

0.00

Ci

Triangular

3,994,578.90

0.00 3,721,440.69

1.82%

2.00 4,267,717.11

According to Eq. 7, there are six input variables directly affecting the assessment of the NPV: “Ci”, “Sv”, “F”, “g”, “r”, “t”. But “r” depends itself on four variables, namely the “debt”, the “Euribor rate”, the “risk-free rate” and the “mortgage spread”. So, in this model, the uncertainty can be represented by a set of nine uncertain input factors. We provide for each uncertain input a statistical definition based on the analysis of historical data or time series. Table 4 collects the statistical descriptions of the input variables. In this research, we use a triangular distribution for “Ci” and “Sv”, as in [30] and [31]. We prefer a bell distribution for “g” (as in [31]), for the “risk-free rate” and the “Euribor rate”, while we assume a uniform distribution for the “mortgage spread”, the “debt”, as well as for “F” and “t” [35]. We set the testing ranges for the sensitivity analysis among the 5th–95th percentiles of the inputs, and the percentiles are calculated from the probability distributions shown in Table 5. We analyse NPV variations for the 5th and 95th percentile of each factor, such that the 5th percentile is the downside case, and the 95th represents the upside case (Table 5). The Tornado Diagram in Fig. 3 graphically represents any variation in the output for each percentile deviation from the best estimate in the inputs. The Tornado Diagram identifies those factors whose uncertainty drives to the most significant impact on the NPV. The “monetary “savings” and the growth rate “g” result to be the most sensitive inputs. Conversely, the inputs that do not strongly influence the model output are the “debt” percentage and the “Euribor rate”.

4 Discussion and Conclusions In this research, different techniques sinergically work to set up a decision-making support system aimed at planning energy retrofit operation in large building stocks.

246 Table 5 NPV variations due to input changes

L. Gabrielli and A. G. Ruggeri Variable

NPV variations (output) 5% (Downside)

95% (Upside)

Range

F

2,667,958.34

2,996,507.90

328,549.56

Sv

2,548,903.05

3,163,179.06

614,276.01

g

2,644,887.80

3,080,270.24

435,382.44

Mortgage spread

2,838,558.05

2,538,041.02

300,517.03

Risk free rate

3,018,779.29

2,700,400.14

318,379.14

t

2,856,041.06

2,608,584.60

247,456..46

debt

2,770,640.14

2,943,535.76

172,895.62

Ci

2,967,890.90

2,744,191.21

223,699.69

Euribor rate

2,907,725.21

2,805,094.73

102,630.48

Fig. 3 Tornado diagram

The core idea of this study is to assist and orientate the decision-making process, managing numerous stock of buildings, comparing different design scenarios and achieving the optimal set of interventions. Since both a reference building approach and a clustering technique are used, only a limited amount of information needs to be gathered to obtain a reliable estimate of buildings’ energy demand. Moreover, the combination of traditional financial techniques, such as the life cycle costing and the discounted cash flow analysis, with operational research provides a direct solution to a complex decision-making problem (optimal allocation of financial resources). It was possible to verify that the model developed helps the decision-making and speeds up the process. It was also possible to compare several design alternatives and reach for the best outcome. Future developments of this research will concern both the enhancement and the extension of this analysis. Different clustering techniques and other, more specific,

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247

grouping criteria could be introduced and tested. We could verify if more tailored attributes for partitioning will produce more accurate results. Besides, we also intend to include analysis of preferences such as Multi-Criteria Analysis and Multidimensional Scaling techniques to introduce qualitative aspects in the selection process, which can be expressed by the decision-maker to strengthen the scenario analysis. Acknowledgments The authors really wish to acknowledge Prof. Pietromaria Davoli and Arch. Marta Calzolari, for the precious help and for the provided database that has been fundamental for this research.

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14. Amstalden RW, Kost M, Nathani C, Imboden DM (2007) Economic potential of energy-efficient retrofitting in the Swiss residential building sector: The effects of policy instruments and energy price expectations. Energ Policy 35(3):1819–1829 15. Brotman BA (2014) Green office construction: a discounted after-tax cash flow analysis. J Property Investment Finance 32(5):474–484 16. Kumbaroglu G, Madlener R (2012) Evaluation of economically optimal retrofit investment options for energy savings in buildings. Energy Build 49:327–334 17. Petersen S, Svendsen S (2012) Method for component-based economical optimisation for use in design of new low-energy buildings. Renew Energ 38(1):173–180 18. Verbeeck G, Hens H (2005) Energy savings in retrofitted dwellings: economically viable? Energy Build 37(7):747–754 19. Bottarelli M, Gabrielli L (2011) Payback period for a ground source heat pump system. Int J Heat Technol 29(2):145–150 20. Kneifel J (2010) Life-cycle carbon and cost analysis of energy efficiency measures in new commercial buildings. Energy Build 42(3):333–340 21. Di Pilla L, Desogus G, Mura S, Ricciu R, Di Francesco M (2016) Optimizing the distribution of Italian building energy retrofit incentives with linear programming. Energy Build 112:21–27 22. Ashouri A, Fux SS, Benz MJ, Guzzella L (2013) Optimal design and operation of building services using mixed-integer linear programming techniques. Energy 59:365–376 23. Schütz T, Schiffer L, Harb H, Fuchs M, Müller D (2017) Optimal design of energy conversion units and envelopes for residential building retrofits using a comprehensive MILP model. Appl Energ 185:1–15 24. Luddeni G, Krarti M, Pernigotto G, Gasparella A (2018) An analysis methodology for largescale deep energy retrofits of existing building stocks: case study of the Italian office building. Sustain Cities Soc 41:296–311 25. Ascione F, Bianco N, Stasio CD, Mauro GM (2017) Addressing large-scale energy retrofit of a building stock via representative building samples: public and private perspectives. Sustain 9(6) 26. Pallis P, Gkonis N, Varvagiannis E, Braimakis K, Karellas S, Katsaros M, Vourliotis P (2018) Cost effectiveness assessment and beyond: A study on energy efficiency interventions in Greek residential building stock. Energy Build 182:1–18 27. Kershaw T, Eames M, Coley D (2010) Comparison of multi-year and reference year building simulations. Build Serv Eng Res Technol 31:357–369 28. Wang L, Mathew P, Pang X (2012) Uncertainties in energy consumption introduced by building operations and weather for a medium-size office building. Energy Build 53:152–158 29. Eguaras-Martínez M, Vidaurre-Arbizu M, Martín-Gómez C (2014) Simulation and evaluation of building information modeling in a real pilot site. Appl Energ 114:475–484 30. Zhu Y, Tao Y, Rayegan R (2012) A comparison of deterministic and probabilistic life cycle cost analyses of ground source heat pump (GSHP) applications in hot and humid climate. Energy Build 55:312–321 31. Kemp AG, Stephen L (1999) Price cost and exploration sensitivities of prospective activity levels in the UKCS: an application of the Monte Carlo technique. Energ Policy 27(13):801–810 32. Smith A, Luck R, Mago PJ (2010) Analysis of a combined cooling. heating and power system model under different operating strategies with input and model data uncertainty. Energy Build 42(11):2231–2240 33. Tian W, Heo Y, de Wilde P, Li Z, Yan D, Park CS, Feng X, Augenbroe G (2018) A review of uncertainty analysis in building energy assessment. Renew Sustain Energy Rev 93:285–301 34. Boussabaine A, Kirkham R (2004) Whole life-cycle costing: risk and risk responses. Blackwell Publishing, Oxford 35. Jeon C, Shin J (2014) Long-term renewable energy technology valuation using system dynamics and Monte Carlo simulation: photovoltaic technology case. Energy 66:447–457

How the Italian State Finances Post-seismic Reconstruction: The 2009 Abruzzo Earthquake Sebastiano Carbonara and Davide Stefano

Abstract This paper examines the development of cost estimates for the renovation of buildings damaged by the earthquake that struck the region of Abruzzo in April 2009. The study begins with experience of the authors as the figures responsible for the “Economic Feasibility of Proposed Works” as part of the activities supporting the reconstruction study and project developed by the Architettura Department at the D’Annunzio University of Chieti-Pescara. The analysis concentrates on the main procedures employed to attain the estimate of financial investments necessary for the reconstruction of eleven towns in the provinces of L’Aquila and Pescara. The objectives of producing a trustworthy cost estimate, a transparency of decision-making and rapid schedule of implementation were forced to confront an inevitably incomplete framework of understanding regarding the nature and form of plans for reconstruction. This situation was further aggravated by an articulated and complex system of normative restrictions governing the distribution of financial contributions. Keywords Preliminary cost estimate · Post-earthquake reconstruction · Abruzzo

1 Introduction A proper starting point for describing the process of reconstruction initiated following the earthquake that hit the region of Abruzzo in 2009 is offered by two pieces of Italian legislation. They are important because they triggered both the system of ordinances motivating procedural aspects and cost estimates, as well as the obligation to implement the different phases of reconstruction in accordance with specific plans. This does not mean that the legislative structure behind post-earthquake interventions S. Carbonara (B) · D. Stefano Department of Architecture, Gabriele D’Annunzio University of Chieti and Pescara, Viale Pindaro 42, 65127 Pescara, Italy e-mail: [email protected] D. Stefano e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Morano et al. (eds.), Appraisal and Valuation, Green Energy and Technology, https://doi.org/10.1007/978-3-030-49579-4_17

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is so simple and exemplified; nevertheless, the content of these two measures has—to date—influenced the phases of reconstruction more than any others. The first is Law n. 225 from 24 February 1992; art. 5 states that following the declaration of a state of emergency in a given territory that has suffered a natural calamity, catastrophe or other event necessitating the use of extraordinary means and powers, it is possible to proceed with measures in derogation to any currently applicable regulation as required to implement the necessary interventions, so long as they continue to respect the general principles of the legal system. The second reference is the conversion into a law, with modifications, of DecreeLaw n. 39 from 28 April 2009. Article 14, comma 5-bis of this law introduced the obligation to prepare Piani di Ricostruzioni, or Reconstruction Plans: “Local mayors… together with the president of the Delegated Commissioner of the Abruzzo Regional Government… in conjunction with the president of the Provincial Government for matters of competence, must prepare Piani di Ricostruzione (PdR) for historic city centres, as determined by article 2, letter a) of Decree n. 1444 issued by the Ministry of Public Works on 2 April 1968, defining strategic guidelines for ensuring the return of socio-economic activities and requalification of inhabited areas, and facilitating the return of populations displaced from homes damaged by the earthquake of 6 April 2009…”. The successive Decree n. 3/2010, issued by the Delegated Commissioner for the Reconstruction, specified the means for defining the perimeters of these Plans and for their preparation and implementation. More in particular, art. 5, comma 3 (Reconstruction Plans—Objectives and Content) requires a survey of the current condition of sites, taking into account, where possible, the pre-earthquake situation, and providing, among other things, an “estimate of planned intervention costs” (point c).

2 The Area of the Earthquake Crater During the phase immediately after the earthquake, a total of 49 towns inside the area of the crater were identified. This number successively grew to 57 based on indications provided by the Civil Protection Department, which confirmed that tremors reached and/or surpassed the sixth degree on the Mercalli intensity scale. The perimeter of the geographic area affected by the earthquake also includes an additional 29 towns that suffered minor damages (Fig. 1).

3 Eligible Subjects and Assets Article 1, comma 2 of the aforementioned DL 39/2009 specifies that subjects eligible for financial aid for reconstruction from the State are limited to physical per-sons residing in the towns inside the crater, as well as businesses and organisations operating and based here on or prior to 6 April 2009.

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Fig. 1 Towns affected by the earthquake

The successive art. 3 specifies that the awarding of financing, for immobile assets, is extended to properties used as primary homes that have been destroyed or declared inaccessible or for the purchase of new homes to substitute destroyed primary homes (comma 1a); however, it also includes buildings other than those used as primary homes, as well as buildings for non-residential uses either destroyed or declared inaccessible (comma 1e).

4 The Damage Surveying Charts The emergency management interventions activated in the wake of an earthquake and, successively, during the different phases of reconstruction, were sub-ordinated to a survey of damages suffered by buildings, which produced an evaluation of their accessibility [1].

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Accessibility defines the boundary between the possibility to return to one’s home and the time spent in a temporary shelter; between the permanence of administrative functions, services, the economy and the slowing of activities in an entire social context [2]. A rapid survey was made during site visits after the earthquake by a team of technicians accredited by the Civil Protection Department. Special surveying charts were developed for ordinary buildings known as AeDES (Agibilità e Danno nell’Emergenza Sismica, Accessibility and Damage during a Seismic Emergency). Models of charts with different structures were instead used for specialised or monumental buildings: for listed buildings the compilation of the charts was man-aged by the Soprintendenza, and religious buildings directly by the Curia. The AeDES charts, each containing specific data and information: 1 Building Identification; 2 Building Description; 3 Typology; 4 Damages to structural elements and emergency interventions carried out; 5 Damages to non-structural elements and emergency interventions carried out; 6 External risks from other constructions and emergency interventions carried out; 7 Ground and Foundations; 8 Accessibility Rating; 9 Other observations. The verification of damages and the consequent accessibility rating was linked to the following categories: – A. Accessible Building: The building can be utilised in all of its parts with no risk to the lives of its occupants, without the need for any emergency measures. – B. Temporarily Inaccessible Building (total or partial) though accessible following emergency intervention measures. – C. Partially Inaccessible Building: the condition of limited portions of the building are considered to pose a serious risk to inhabitants and thus influence the assessment of inaccessibility. – D. Temporarily Inaccessible Building to be studied in more detail: the building presents characteristics sufficient to render uncertain the verdict of accessibility by the technician. An additional and more de-tailed site visit and/or a visit by more expert technicians is request-ed. The building is considered inaccessible until this new visit has been completed… – E/F. Inaccessible Building: for organisational purposes a distinction is made between the effective inaccessibility of the building owing to structural, nonstructural or geotechnical risks (E), and inaccessibility due to serious external risks (F), even in the absence of consistent damages to the building. The building cannot be utilised in some of its parts, even after the completion of emergency interventions. Leaving aside those cases in which the verdict on accessibility remains uncertain after the first site visit (rating D) and those in which the situations of risk are not linked to damages suffered by the inspected building, but derive from situations determined by adjacent buildings or constructions (rating F), the range of possible situations varies substantially from A to E, passing through the intermediate results B and C.

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5 The Methodology In extreme synthesis, the work can be summarised in three principal phases: – identification of the area of each unità immobiliare urbana (u.i.u., urban real estate unit) inside the perimeter of the plan; – assignment of an area to each accessibility rating; – application of parametric costs to areas in relation to their accessibility rating to quantify the costs of recovering the buildings, as defined in the OPCM1 and opportunely modulated in accordance with specific situations and the guidelines issued by the Delegated Commissioner for Reconstruction.

5.1 Association of the Area of Each U.I.U. with the Results of the Accessibility Survey Given these conditions, it is evident that the methods for calculating building areas was decisive to the estimate, as it could signify a variation in the value based on the approach adopted [3]. Four approaches were potentially feasible. In theory, the area of the u.i.u.’s, and thus of the buildings, could have been inferred from the AeDES charts, under Sect. 2 containing a description of the building. The dimensions found in the charts were characterised by a more or less elevated degree of imprecision that, for the most part, was justified by the approximation—rather important—allowed during the surveying of damages. In fact, point 2.5 Building Description, found in the manual for filling out the 1st level charts,2 suggested surveying the average values of heights and floor areas in groups and the guideline criteria for making a selection, in the case of important variations between floors, was to consider average values that best describe the total volume (for height indicate that closest to the average height of all floors; for floor areas indicate the range that best describes the average area of all levels). This meant it was not a question of whether or not to consider eventual errors in the surveys (highly probable), but instead one of evaluating their order of magnitude. A second hypothesis could have involved the building surveying operations, effectively capable of providing specific information about floor areas. In this specific case, it was merely a theoretical possibility, considering that the time allowed to complete the surveys, in the majority of the towns involved, was far longer than that during which the estimates were developed. A third expeditious hypothesis, apparently without any difficulty, could have considered the “footprint” of a building and its number of floors. This method would hot have been without glaring imprecisions, given the topography and building 1 In

particular 3778/2009, 3779/2009, 3790/2009. of the Council of Ministers and Civil Protection Department [2] (op. cit.).

2 Presidency

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S. Carbonara and D. Stefano

Fig. 2 Survey of a building damaged by the earthquake

typologies of the different towns. As an example, Figs. 2 and 3 illustrate frequently encountered situations. The comparison between the footprint of a building and the dimensions of its façade clearly demonstrates that—at least in the example considered—the calculation of the area produced by multiplying the footprint by the number of floors would produce an evident distortion (Fig. 2). The situation was repeated also for entire structural aggregates such as those illustrated in Fig. 3. In any case, all three hypotheses are characterised by a common defect that is impossible to ignore: they provide no indications about the use of the spaces inside the building; instead they indistinctly consider primary and accessory spaces, producing a gross floor area that, for buildings with an E rating, cannot be considered in this manner when calculating the financing available, which is subject to a maximum limit. This is anything but a small problem, considering that it refers to buildings with seriously compromised structures, of which there are e great many in the areas affected by the earthquake,3 and thus with a greater incidence on the total cost of reconstruction. 3 The

total costs of reconstruction will depend for the most part on E rated properties: in the towns analysed in the province of Pescara, the percentage ratios between the number of sub-parcels distinguished by accessibility rating were slightly less than 40% for rating A, 20% for B/C, and slightly more than 40% for rating E. In terms of area, the gap between results B/C and E grows. In the towns analysed in the province of L’Aquila, closer to the epicentres of the earthquake, in some cases these ratios were 2% for ratings B/C and 90% for rating E.

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255

Fig. 3 Example of a structural aggregation: representation of floor levels

Regarding this issue, it is worth recalling a few sections of legislation related to the concept of the maximum limit. Art. 5, comma 4, of OPCM 3881/2010 establishes a ceiling on financing for interventions to recover and repair buildings with an accessibility rating of E; the reference is represented by the cost forecast for subsidised housing: Admissible financing for the reconstruction of primary residences and the common areas of condominiums must not exceed the cost of construction of a building of equal volume calculated using the cost of construction for subsidised housing determined by the Abruzzo Regional Government, increased by 20% to account for the costs allowed for in applicable legislation governing energy efficiency and acoustic insulation…. This means that, for the Abruzzo earthquake, the State Government offered financing for the recovery of significantly damaged buildings up to but not in excess of the hypothetical cost of their substitution, based on the structure outlined in the Deliberation of the Abruzzo Regional Government n. 615/2010 in matters of Updates to the cost of subsidised residential construction. In light of the specific items expressed with regards to Decree 27/2010 issued by the Delegated Commissioner for the Reconstruction, the following line items were to be considered (Table 1). This calculation provides the maximum admissible unit cost for the recovery and/or substitution of buildings with an E rating, net of charges and technical fees. That said, art. 6 of the aforementioned Abruzzo Regional Government Deliberation

256

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Table 1 Maximum admissible unit cost for the recovery and/or substitution of buildings with an E rating Basic cost for new construction (CNB) e/sq. m

808,00

Health and Safety costs pursuant to D.Lgs 494/96, n. 81/2008 (up to 5% CNB) e/sq. m

40,40

Application of D.Lgs. 192/2005 and D.Lgs. 311/2006 on matters of energy 161,60 efficiency (up to 20% CNB) e/sq. m Incorporation of earthquake design legislation (up to 7% CNB) e/sq. m

56,56

Total Basic Technical Cost (per square metre of total area (T.A.)

1.066,56

Insurance policies (3% CNB)) e/sq. m

24,24

Improvements to environmental comfort offered by acoustic and temperature-humidity conditions (5% CNB)) e/sq. m

40,40

Use of earthquake protection devices (base isolators) (3% CNB) e/sq. m

24,24

Particular local conditions (3% CNB) e/sq. m

24,24

For building typologies up to or equal to 4 floors (8% CNB) e/sq. m

64,64

For a prevalent number of units with an net floor area that does not exceed 65 sq. m (4% CNB) e/sq. m

32.32

Increased cost differential for particular technical conditions (e/sq. m)

210,80

CNR base tender cost (A + B) per unit of total area

1.276,64

n. 615/2010 specifies that that the Total Area (Superficie Complessiva SC ) to be used to verify the congruity of residential construction costs is defined as follows:   Sc = Su + Snr + S p × 0, 6 where: – S c Total Area (Superficie Complessiva); – Su represents the useful inhabitable area, in other words the paved surface of a home, net of the perimeter and interior walls, thresholds, splayed door and window jambs, internal stairs and built-in closets; – Snr indicates instead the non-residential area to be intended as the sum of the areas annexed to a home (loggias, balconies, small cellars, attics) and areas annexed to a building (foyers, lobbies, open porticoes, technical volumes, heating plants and other spaces serving a home), net of perimeter and internal walls; in addition, the area of stairwells connecting different residential units, including stair landings, is calculated as a horizontal projection only once; Snr does not consider inspectiononly attic spaces and/or attics with a ceiling height inferior to 2.40 m; the Snr of an elevator shaft is calculation in projection only once; – S p indicates parking areas, in other words garages or covered parking spaces annexed to residential buildings.

How the Italian State Finances …

257

For example, we can consider the case of a structural aggregate consisting of 3 u.i.u. with an E rating, and with a total inhabitable floor area of 233.60 sq. m, plus 206.30 sq. m of storage areas and cellars, considered accessory spaces indirectly serving principal spaces. The total area is thus 439.90 sq. m that, net of accessory charges and technical fees, considered as primary homes, produces a total estimated value of e561,312. If, instead, we more correctly evaluate the total area (useful area plus opportunely reduced non-residential area), we arrive at an area of 285.90 sq. m, with an estimated value of e364,813. A considerable difference. To introduce a fourth hypothesis, we can compare the area calculations in the example with the corresponding values from cadastral maps for the three residential units: – gross area: 439.90 sq. m – total area (SC): 285.90 sq. m – cadastral area: 285.18 sq. m. The example presents an almost total correlation between the SC and the cadastral area; this is not always the case, though the two values are generally very similar. With the intention of obviating these problems, the area was inferred from the cadastral maps, whose calculation appears to be in line with the criteria listed in Deliberation n. 615/2010, which produced a substantial equivalency between the two values.

5.2 Assignment to Each Area of the Accessibility Ratings Having defined the area of each u.i.u., it was possible to assign the accessibility ratings found in the AeDES charts, though only after verifying and resolving dubious situations. For u.i.u. without an AeDES chart, in some specific cases an “intrinsic rating” was assigned after conferring with the structural engineering team working on the PdR. The product of this first phase concluded with the creation of a list of all u.i.u. inside the PdR perimeter, each with its own area for each sub-parcel, parcel, aggregate or zone in which it was located and its accessibility rating.

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5.3 Application of Parametric Costs to Surfaces in Relationship to Damage The cost value, per u.i.u. (parcel or sub-parcel) and expressed by zone, aggregate, use and accessibility rating, was calculated in accordance with the instructions contained in specific ordinances and—in the event of a lack of direct normative and/or design indications—using parametric costs indicated/agreed upon by/with the STM. The first step was to identify primary homes using the indications received from municipal offices. This was followed by a summary examination of specific situations relative to each of the accessibility ratings and a description of the criteria used in the estimate.

5.3.1

Buildings with an Accessibility Rating A

OPCM 3778/09 considers a maximum contribution of e10,000 for the repair of damages only for unità immobiliari urbane (u.i.u.) used as “primary homes”, to which it is possible to add an additional maximum sum of e2,500/u.i.u. for repairs to common areas. The framework defined for A rated buildings is completed by the content of art. 7 of OPCM 3820/09, which deals specifically with buildings that are part of aggregates. Comma 6 foresees that interventions of reinforcement or seismic retrofit are financed up to a maximum sum for the entire aggregate equivalent to the eligible sum for each building. In the case of buildings with different ratings, including those with an E rating, financing for buildings with B and C ratings may be increased by 30% and those for A rated buildings may be considered as B rated, however without the… increase of 30%. This means it is possible to foresee an additional contribution of up to 150 e/sq. m also for A rated buildings4 [4]. In general, all u.i.u. with an A rating/primary homes were assigned the maximum allowable sum, also in consideration of the fact that financing which had already provided, in the majority of cases, was just under the threshold of e10,000. Following indications from the STM, the additional sum of e2,500 for common areas was always assigned. Furthermore, all u.i.u. with an A rating (primary homes and non-primary homes) were always assigned a sum of e150/sq. m when they were part of an aggregate containing E rated buildings.

4 Civil

Protection Department—Delegated Commissioner [4] (op cit.).

How the Italian State Finances … Table 2 Comparison between the two methods

5.3.2

259

Floor area

sq. m

Unit cost (e/sq. m)

Estimated Financing (e)

Gross total

439.90

200–290

87,980–127,571

Cadastral

285.18

450 + 150

171,105

Buildings with an Accessibility Rating B/C5

OPCM 3779/09 and the successive guideline measure connected with it, foresees the awarding of financing to cover all repair costs for “primary homes”.6 For nonresidential properties or homes that are not primary homes, coverage is guaranteed up to 80% and in any case capped at e80,000; all the same, unlike E ratings, there is no indication of any reference unit cost. Admissible costs include design fees and technical assistance by licensed professionals, including VAT. Unit costs for B/C rated u.i.u. were calculated beginning with a base unit cost of e450/sq. m (net of fees and technical expenses), and an eventual additional contribution of e150/sq. m for local reinforcement works of structural elements7 extendable to e195/sq. m (in the presence of E ratings). The base unit cost of e450/sq. m + e150/195/sq. m, while still related to cadastral areas, provides ample guarantees supporting the hypothesis that excludes the underestimation of costs for these ratings [5–7]; if we return to the previously proposed example of the aggregate, with a hypothetical rating of B/C for three homes, the comparison between the two methods gives the following results (Table 2). For primary homes, the applied cost integrally covers interventions of local reinforcement (e150/sq. m or 195), and repairs (e450/sq. m). The total cost of properties not used as primary homes or used for other purposes, was instead obtained by integrally recognising a rate of e150–195/sq. m for local reinforcement works, while repairs were evaluated at e450/sq. m and reducing the product with the area calculated at 20%,8 in any case never exceeding the limit of e80,000.

5 All

costs indicated are net of accessory fees and technical expenses. order to favour the rapid return to homes in the territories of towns identified pursuant to art. 1 of Decree-Law 39/2009, that had suffered damages sufficient to make them temporarily inaccessible, either totally or partially (with a B rating) and whose accessibility can be restored following emergency measures, of those that are partially inaccessible (C rating), are eligible for direct financing of the costs of costs relative to the repair of non-structural elements and building systems, as well as repairs or local interventions to single structural elements or parts thereof, in any case suitable to ensuring increased conditions of safety… (OPCM 3779/09, art. 1, comma 1). 7 Civil Protection Department, Delegated Commissioner [4], Guidelines for the implementation of interventions pursuant to OPCM n. 3779 dated 6.6.2009 (repairs to damaged non structural elements and building systems, repair or local reinforcement of structural elements or parts thereof). 8 The corresponding 20% is the responsibility of the property owner, who is obliged to offer proof of the total effective cost. 6 In

260

5.3.3

S. Carbonara and D. Stefano

Buildings with an Accessibility Rating E9

OPCM 3790/09 provides financing for repairs, including seismic retrofit, of damaged buildings or the reconstruction of collapsed buildings that have been declared totally inaccessible (E). Financing covers all costs for primary homes, but must not exceed 80% for non-primary homes or non-residential properties with a ceiling of e80,000. Admissible costs include design fees and technical assistance by licensed professionals, including VAT. An increase in this limit is allowed up to a maximum of 60% for buildings “of particular historic artistic value” and up to 100% for listed buildings (OPCM 3917/10). The level of safety of buildings can be increased by up to 80% of seismic retrofit works and in any case beyond 60%. More in detail, the part of each intervention relative solely to structural components, in relation to the damage surveyed, activates a specific quota of unit costs that vary from e250/sq. m (light damages to less than one third of the structure, in the presence of a safety level of 60% of seismic retrofit works) solely for local reinforcement; up to e600/sq. m when it is possible to demonstrate the need to use particular technical solutions to achieve a level of safety of at least 60% through seismic retrofit works. That said, it is necessary to consider that retrofit and repair works involving common areas, following the constitution of consortia between property owners, for obvious reasons create a different flow of financing with respect to the distinction between primary homes and non-primary homes considered singularly. For this reason, ordinary u.i.u. with an E rating were always associated with a max unit cost of e1,267/sq. m (net of fees and technical expenses) referred only to primary homes. For non-primary homes and properties with other uses, the estimated value was obtained by integrally recognising a cost of e700/sq. m for interventions involving common areas (including additional financing for energy efficiency), while those for non-structural interior works were calculated at e576/sq. m, with a reduction of 20%, within the limit of e80,000. Finally, the increases foreseen for buildings “of particular historic artistic value” (up to + 60% of the “base” cost used for ordinary u.i.u. E: e2,041/sq. m), and for listed buildings (up to +100%: e2,553/sq. m), were applied only to primary homes. On the contrary, the scheme developed for ordinary non-primary homes was applied.10

9 Also in this case, costs are to be considered net of all accessory expenses and technical fees, which

are instead considered in the QTE. 10 OPCM 3917/2010, art. 21. Regarding this issue, the STM adopted the binding position of limiting

additional financing to primary homes.

How the Italian State Finances …

5.3.4

261

Specialised or Monumental Buildings

Public buildings (strategic and non strategic, listed or not), religious buildings, as well as the network of underground services, were treated based on interventions already completed and/or underway, suggested by the STM and in any case within the interval listed in Table 3. Special mention must be reserved for religious buildings, whose accessibility ratings were classified in different terms than those used in the AeDES charts: light damage (D1); medium-serious damage (D2–D3); serious damage D4–D5). Below is a summary table of the cost ranges considered in the estimate, distinguished by building typology, nature and accessibility rating.

5.3.5

Expenses and Technical Fees

The incidence of expenses and technical fees—including those to be paid to Presidents of Consortia and the costs of geotechnical services (studies and professional fees), both subject to specific modulations—was estimated at between 35.7% of the cost of works for aggregates with E rated buildings, and 28.8% for single B rated buildings outside of an aggregate.

6 Summary Table of Estimated Costs The estimated costs for the eleven PdR contained in the QTE developed and submitted to municipal governments are summarised in Table 4.11 The total value of approximately 444 million Euro12 is relative to an area that, solely for A, B/C and E rated buildings, is slightly less than 372,000 square metres. The total number of cadastral sub-parcels was 5,441: more than 42.6% characterised by E ratings; B/C ratings account for 12.6%, A ratings for 24.7%, F ratings for 1.2%. The situation is completed by properties with no rating (8.4%) those with recovery projects that had already been financed (6.2%) and, finally, linked, suppressed or uninhabitable properties that, together, account for 4.2%. One element worthy of emphasis, and which emerges clearly from the numbers in the table, regards the ratio between the costs of private construction/total cost of 11 The approval of the PdR for Brittoli, Bussi, Civitella, Cugnoli, Montebello, Ofena and Popoli, as per Decree 3/2010 issued by the Delegated Commissioner for Reconstruction, was completed in the summer of 2012, when the municipal governments formalised an agreement with the President of the Region of Abruzzo and the specific Provinces. More recently, the PdR for Goriano Sicoli was also approved, according to the methods outlined in applicable ordinary reconstruction management. The towns of Castelvecchio Subequo, Poggio Picenze and Caporciano, to date, have only adopted their respective plans. 12 This value does not include projects financed during the period preceding the procedure to develop the PdR.

262

S. Carbonara and D. Stefano

Table 3 Indicative costs for public and religious buildings and underground utilities Typology

Result/intervention

Unit cost

Public buildings of strategic interest

A

Ordinary

300–400

e/sq. m

Public buildings of strategic interest

A

Listed

500–1400

e/sq. m

Public buildings of strategic interest

B/C

Ordinary

700–1100

e/sq. m

Public buildings of strategic interest

B/C

Listed

1800–2100

e/sq. m

Public buildings of strategic interest

E

Ordinary

1500–2000

e/sq. m

Public buildings of strategic interest

E

Listed

2100–3000

e/sq. m

Public buildings not of strategic interest

A

Ordinary

150–250

e/sq. m

Public buildings not of strategic interest

A

Listed

400–1000

e/sq. m

Public buildings not of strategic interest

B/C

Ordinary

550–750

e/sq. m

Public buildings not of strategic interest

B/C

Listed

1300–1500

e/sq. m

Public buildings not of strategic interest

E

Ordinary

1100–1400

e/sq. m

Public buildings not of strategic interest

E

Listed

1800–2500

e/sq. m

Religious buildings

D1

Up to 200

e/cu. m

Religious buildings

D2–D3

200–300

e/cu. m

Religious buildings

D4–D5

300–500

e/mc

Water network—independent path

Maintenance

42–54

e/m

Water network—independent path

Total substitution

140–180

e/m

Gas network—independent path

Maintenance

36–45

e/m

Gas network—independent path

Total substitution

120–150

e/m

Sewer network—independent path

Maintenance

90–108

e/m

Sewer network—independent path

Total substitution

300–360

e/m

Electrical network—independent path

Maintenance

24–33

e/m (continued)

How the Italian State Finances …

263

Table 3 (continued) Typology

Result/intervention

Unit cost

Public buildings of strategic interest

A

Ordinary

300–400

e/sq. m

Electrical network—independent path

Total substitution

80–110

e/m

Telephone network—independent path

maintenance

21–30

e/m

Telephone network—independent path

Total substitution

70–100

e/m

Pub. light. network—independent path

Maintenance

48–60

e/m

Pub. light. network—independent path

Total substitution

160–200

e/m

Excavations and reinf. conc. works—inaccessible tunnel

1000–1800

e/m

Water network—inaccessible tunnel

110–150

e/m

Gas network—inaccessible tunnel

90–120

e/m

Sewer network—inaccessible tunnel

150–190

e/m

Electrical network—inaccessible tunnel

50–80

e/m

Telephone network—inaccessible tunnel

40–70

e/m

Pub. light. network—inaccessible tunnel

130–170

e/m

Paved public spaces

230–280

e/sq. m

Public landscaping

70–85

e/sq. m

the PdR, which is approximately 78%, ranging from the minimum value in Ofena of just under 51% (the only town among those considered where the estimated cost for religious buildings has a notable impact) and more than 84% in the town of Bussi. Thus the costs of recovering private buildings, and above all E rated units, have a very significant impact on the overall costs of reconstruction; this consideration can be extended to all of the towns in the area of the crater, including the city of L’Aquila, as clearly demonstrated by the PdR presented thus far. Something that is not shown in the tables, but found among the information requested of the documentation accompanying the QTE, regards the indicator that expresses the relationship between the overall cost of each PdR and the resident population of the Ambiti (zones) located inside the perimeter of the plans: from the minimum value in the towns of Civitella C. and Montebello di B., with a cost per inhabitant of roughly e50,000, we reach the maximum cost in Ofena, which

PP-A-B-C3

PP-B

115.250

Technological networks and public spaces of which

Technological services network

2.153.625

2.079.885

Religious buildings

1.878.647

4.873.737

3.011.821

3.942.568

Public buildings not of strategic interest

1.824.268

820.000

202.905

4.034.726

Public buildings of strategic interest

6.096.193

1.693.259

587.872

2.281.130

45.000

2.332.750

192.360

2.525.110

11.255.796

PP-B-C

Zone

Montebello di Bertona

558.714

7.135.405

4.894.375

12.029.781

13.300.087

PP-1-2-3-4-6-7-8

Zone

Ofena

118.020

3.604.454

7.188.585

10.793.038

50.887.568

PP-A-B-C

Zone

Popoli

1.333.530

7.115.480

11.084.327

3.385.777

660.488

15.130.592

91.293.841

1-2-3-4-5

Zone

Poggio Picenze

946.741

2.523.329

4.856.511

305.280

5.161.791

22.627.947

PP-Le Pagliare, Nucleo Centro Storico

Zone

Goriano Sicoli

26.963.018

1-2-3-4-5-6-7-8-9-10

Zone

Castelvecchio Subequo

5.727.750

3.345.940

112.010

91.170

3.549.120

2.079.885

9.950.983

unico e PP

Zone

Cugnoli

Public buildings and religious buildings of which

16.121.513

PP-A-B

Zone

Civitella Casanova

155.363

57.576.343

Zone

Zone

5.430.897

Bussi sul Tirino

Brittoli

Public housing

Private constructions

Typology

Table 4 Summary of economic and technical conditions in the eleven towns

1.350.609

2.293.609

7.564.534

200.207

7.764.741

41.784.365

PP-A-B-C-Bominaco

Zone

Caporciano

(continued)

5.509.526

25.195.156

44.977.430

24.926.243

1.542.435

71.446.109

155.363

347.192.358

TOTAL of 11 MUNICIPA -LITIES

264 S. Carbonara and D. Stefano

Zone

294.355

494.578

661.020

Escape route safety

Other network of technological services

Public spaces

Other interventions

Landslide areas

Road netwok

1.449.954

Other costs of which

380.000

883.244

3.033.669

4.296.913

2.565.000

265.200

940.324

3.770.524

21.980.506

7.626.032

TOTAL AMOUNTS

68.546.273

124.000

PP-A-B

1.700.268

2.995.090

PP-A-B-C3

PP-B

115.250

Zone

Zone

Civitella Casanova

Public spaces

Bussi sul Tirino

Brittoli

Road network

Typology

Table 4 (continued)

124.000

756.300

817.120

774.806

2.472.226

12.232.113

unico e PP

Zone

Cugnoli

1.280.000

2.977.000

1.498.865

1.930.080

7.685.945

13.825.906

45.000

PP-B-C

Zone

Montebello di Bertona

9.784.580

349.978

17.206

483.468

10.635.233

25.888.581

558.714

PP-1-2-3-4-6-7-8

Zone

Ofena

890.344

890.344

61.798.626

118.020

PP-A-B-C

Zone

Popoli

2.584.710

2.584.710

113.539.913

5.781.950

1-2-3-4-5

Zone

Poggio Picenze

462.658

1.414.800

1.877.458

30.313.067

1.576.589

PP-Le Pagliare, Nucleo Centro Storico

Zone

Goriano Sicoli

918.950

918.950

36.395.251

5.727.750

1-2-3-4-5-6-7-8-9-10

Zone

Castelvecchio Subequo

1.222.848

930.572

2.153.420

51.842.715

943.000

PP-A-B-C-Bominaco

Zone

Caporciano

9.784.580

4.225.000

124.000

4.744.298

9.165.380

10.692.418

38.735676

443.988.985

19.561.630

124.000

TOTAL of 11 MUNICIPA -LITIES

How the Italian State Finances … 265

266

S. Carbonara and D. Stefano

exceeds e1.4 million/res. Between these two extremes are the values for Castelvecchio Subequo (over e600,000/res.) and Goriano Sicoli, Caporciano and Brittoli (slightly more than e400,000/res.). For all of the other towns, a range of between e100,000 and e225.000/ residents was calculated. While this is a simple indicator, it nonetheless lends itself to different interpretations and must be comparatively analysed not only in terms of pure accounting, but also in relationship to the restoration of a stable urban structure for these settlements, prospects of development for local manufacturing systems, the importance of architectural heritage and, more in general, the meaning that can be attributed to the notion of the cultural good that, tangible or intangible, a testimonial to the historic or artistic or environmental value, the cell of the suitable space or fabric of a territory, is always and also an economic good [8].

7 Conclusions The structure of the summary calculation of the cost of the PdR began with the objective of defining the maximum theoretical costs admissible based on the criteria expressed in the Ordinances issued by the President of the Council of Ministers. This means that, with respect to the variability of possible, or simply imaginable situations, choices that generated the highest financial coverage were always preferred. In other words, within the “cage” of restrictions imposed by legislation, the hypotheses assumed always tended toward the highest values. All the same, two points remained fixed: naturally the accessibility ratings (acquired) and the areas to which the costing structure was applied. The entire project was based on the cadastral areas and pursued the result of reconstructing the estimate based on individual urban properties. This made it possible to provide municipal governments and controlling institutional bodies a set of documentation that contains a highly detailed and transparent recounting of the genesis of the potential costs for each sub-parcel, parcel, aggregate and zone. That said, a question was raised in any case about the entity of financing effectively necessary for the reconstruction. In other words, it is justifiable to ponder the percentage shift between the estimated costs and the actual financing that will be provided: while hypotheses can be advanced, they would require further study before being presented. From this point of view, it would have been preferable to utilise other procedures of mass appraisal, for example multi-parameter linear regression, based on the observation of costs inferred from interventions that had already been financed. What is more, an approach of this type could have been further improved and offer a higher level of rigour and transparency, if the Technical Offices working on behalf of the Government had prepared the final recovery projects with the same rapidity as the site visits used to define the physical conditions of properties. Costs could have been analytically estimated based on a sample of buildings (150–200), opportunely

How the Italian State Finances …

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defined based on their accessibility ratings, location, building and structural typology, function and dimensional class, to define a more robust regression model. Estimates defined in this way would have referred to the effective characteristics of different buildings, both in terms of their construction and level and percentage of damage, based on relations between the two categories of aspects. In any case, once the QTE had been developed and the PdR approved, this initial idea was never again considered. Not without some difficulty, little more than one hundred estimates of private recovery projects that had already been financed (results B, C and E) were acquired. They made it possible to define a model of multiple linear regression that allowed for the use of the mass of information contained in the AeDES charts, transformed into independent variables (building typology, level and percentage of damage to vertical structures, infill and stairs, floor slab typology, etc.) [9]. The results of this calculation, now complete, are extremely comforting, even if, as one would hope, it would be opportune to test the model against a more important number of observations, expanded to the towns closer to the epicentres of the seismic events.

References 1. Milano L, Marchetti L, Marsili C, Fontana G, Mannella A, Nola F (2011) Prime analisi dei costi di ripristino post-sisma del 6 aprile 2009 in Abruzzo e problematiche connesse ai rilievi di agibilità e danno. XIV Convegno ANIDIS L’ingegneria sismica in Italia, Bari, pp 18–22 2. Presidenza del Consiglio dei Ministri—Civil Protection Department (2009) Manuale per la compilazione della scheda di 1° livello di rilevamento danno, pronto intervento e agibilità per edifici ordinari nell’emergenza post-sismica (AeDES). Editrice Italiani nel Mondo, Roma 3. Carbonara S (2014) La stima dei costi del patrimonio edilizio privato nella ricostruzione postsismica abruzzese: un’analisi critica delle procedure utilizzate. In: TERRITORIO, vol 70. Franco Angeli, pp 119–125. https://doi.org/10.3280/tr2017-070019 4. Civil Protection Department—Commissario Dele-gato (2009) Indirizzi per l’esecuzione degli inter-venti di cui all’Ordinanza del Presidente del Consi-glio dei Ministri n. 3779 del 6.6.2009 5. DEI (2010) Prezzi informativi dell’edilizia, recupe-ro, ristrutturazione, manutenzione. DEI tipografia del genio civile, Roma 6. Abruzzo Regione (2008) Prezzi informativi Opere Edili. DEI tipografia del genio civile, Roma 7. Umbria Regione (2007) Manuale per la riabilita-zione e la ricostruzione post-sismica degli edifici. DEI, Roma 8. Forte C (1977) Metodologie di valutazione del danno subito dal patrimonio dei beni culturali del Friuli in conseguenza degli eventi sismici, Atti del Congresso Regione autonoma Friuli Venezia Giu-lia, “L’esperienza internazionale nella conservazio-ne dei beni culturali nelle zone terremotate”, Udine 9. Carbonara S, Cerasa D, Sclocco T, Spacone E (2015) A Preliminary estimate of the rebuilding costs for the towns of the abruzzo region affected by the april 2009 earth-quake: an alternate approach to current legislative procedures, Lecture Notes in Com-puter Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9157:pp 269–283

Is Investing in Safety Worthwhile? A Methodology for Assessing the Costs and Benefits of Accidents in the Construction Sector Maria Rosaria Guarini and Rossana Ranieri

Abstract According to the International Labor Organization accident at work and occupational diseases caused globally around 2.3 million deaths in 2014. The costs attributable to accidents at work and occupational diseases represent a loss of wealth amounting to 3.9% of global gross domestic product and 3.1% of that of all EU Member States. Accidents represent a significant social and business cost in all production processes, particularly in the construction sector, which in Europe and Italy has the highest rate of fatal accidents. Starting from an analysis of existing models used for the calculation of accident costs, this contribution proposes a methodology to evaluate the costs occurred both in case and in absence of accident, by some stakeholders (Worker, Enterprise, Society, State) with reference to the construction process, in four scenarios that can be foreshadowed in the mention context. The model developed could be applied in different sectors; in this first phase there are some examples of compilation of the tools and matrices of the methodology using input data from the construction sector in Italy. Keywords Building process · Accidents at work · Occupational safety and health

M. R. Guarini, R. Ranieri have conceived, structured and written the article in equal part, as well as they have deepened review and editing the proposed article. In particular. R. Ranieri has made the resources; M. R. Guarini has made work supervision. M. R. Guarini (B) · R. Ranieri Department of Architecture and Design, Architecture Faculty, Sapienza University of Rome, Rome, Italy e-mail: [email protected] R. Ranieri e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Morano et al. (eds.), Appraisal and Valuation, Green Energy and Technology, https://doi.org/10.1007/978-3-030-49579-4_18

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1 Introduction Due to the consequences of an accident at work or an occupational disease, around 2.3 million people died in 2014 [11]. In the Member Countries of the European Union (EU) with 27 states, between 2008 and 2016 the number of accidents at work has decreased in all productive sectors (non-fatal −2.3%; fatal −3.5%). The construction sector has the highest number of fatal accidents: 20.54% of the total [7]. The downward trend in the EU is due to the increased attention paid to Occupational Safety and Health (OSH), followed by the promulgation, since 1989, of specific rules harmonized at European level on OSH, both for all workplaces and specifically in the management of public works contracts [15, 30]. In 2014, the costs of occupational accidents and occupational diseases in the world represented a significant loss of wealth (3.9% of global GDP—approximately e 2,680 billion; 3.1% of the GDP of all EU Member States—approximately e 476 billion) [11]. According to the European Agency for Occupational Safety and Health at Work (EU-OSHA) a safe workplace contributes to labor productivity and promotes economic growth by reducing the costs of accidents that the community has to bear [5, 6]. Accident costs are difficult to identify and are often underestimated [1]; Waehrer et al. [19, 32]. In the literature, there are contributions that propose the determination of the: • cost of occupational accidents and/or illnesses, using methods such as the Human Capital Approach, the Opportunity Cost Method [3–6, 9, 16, 17, 22]; • cost-benefit of OSH investments by calculating synthetic indicators such as the Return of Prevention (ROP), Social Return of Investiment (SROI) [2, 25, 33]; • costs of the non-application of safety standards in places of work (costs of nonsecurity) [31, 32]. Non-security costs are “the monetizable consequences of nonsecurity events” [19]. The methods and approaches are heterogeneous in terms of: type of costs considered, estimation methods adopted and perspective of stakeholders involved in the Accident Phenomenon (AP) (including: worker, enterprise, state, family members of the injured workers, social security agencies, insurance agencies, etc.) [13, 25]. The objective of this contribution is to present a methodology to calculate and evaluate the costs incurred by the main stakeholders, in the event and in the absence of an accident, in scenarios that can be foreshadowed combining certain conditions that represent compliance or non-compliance with the OSH legislative framework. In the chapter below is reported: (2) analysis of the literature on cost calculation methods related to the AP, highlighting recurring elements (stakeholders, cost categories, calculation methods, accident consequences) (EU-OSHA [3–6] and more widely adopted methodologies (3) methodology proposed and its main components, its application to the construction sector in the Province of Rome in Italy and results obtained; (4) conclusions and prospects for expanding the research.

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2 Materials 2.1 Review of Methods for Calculating Accident Costs In the literature, different methods are described [16, 17, 21, 22, 26, 29, 31, 32], to calculate and estimate the financial and economic effects of an AP. Because of the complexity of the AP, it is difficult to identify an exhaustive and shared list of the cost items to be considered, as some of them are immediately evident and quantifiable in monetary terms (cost related to lost days of work, lack of productivity, etc.), while others are more difficult to be individuated and monetized (reduction of quality of life; damage related to the corporate image increase, etc.) [3]. Two documents produced by EU-OSHA [3, 5] present a critical review of the international studies produced on this topic. In particular, EU-OSHA’s: • 2002, with reference to the AP and related prevention measures contains guidelines and an overview of tools, calculation methods and application cases. • 2014, systematically compares the methods identified in the literature and summarizes a conceptual framework for OSH costs, which highlights: – four high-level potential outcomes of AP; – the main methods that can be used for each cost category; – five main cost categories summarizing the consequences of accidents in economic terms; – distribution of the specific costs within each of the five categories among four types of stakeholders bearing the effects of AP. In order to verify (December 2019) the state of the art on AP cost calculation methods, using the same methodology used in the EU-OSHA [5] report, a keyword search (OSH Costs, HS Costs-benefits) was carried out on Scopus. Subsequently, 12 studies (out of 89 that emerged from the research) were selected on the basis of at least two of the following three criteria: (i) “covers a broad range of industries or a key industry for OSH”; (ii) “not focused on a specific type of injury or illness”; (iii) “related to one of the EU Member States”. From this review, it is possible to highlight some innovative contributions of the analyzed theme, concerning: • the assessment of the safety risk and for prioritizing the risks in worksites use a Fuzzy Extended Analytic Hierarchy Process (FEAHP) [14]; • the relationship between accidents at work and climatic condition [20]; • the economic outcome of implementing an OSH management system [28]; • the efficacy of a fire department proactive risk management program aimed at reducing firefighter injuries and their associated costs [27]; • integrated management strategies based on the evaluation and calculation of OSHbenefits [2].

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Although there has been progress in the use of AP costing methods, they can still be traced back to the EU-OSHA [5] framework, which is therefore taken as a reference for the methodology proposed in this work.

2.2 Framework EU-OSHA The EU-OSHA framework illustrate: (i) the consequences of the AP; (ii) the stakeholders; (iii) the cost categories; (iv) the calculation methods and their relationships. The AP consequences are distinguished into occupational disease (pathology whose cause is slowly and progressively acting on the body) and accident (violent cause), distinct [5] between events leading to fatal outcome (IM) and events not leading to fatal outcome (NMI). In the AP, multiple stakeholders are involved both directly (accident victim, entrepreneur) and indirectly (state social security agencies, family members of injured workers, other workers, the community). At an international level, it is agreed that the calculation of the costs related to the AP is carried out primarily for four stakeholders, as they are the receivers of the main physical and socioeconomic effects of an Accident Event (AE): the Worker (W), a person affected by the accident or his family members; the Enterprise (E), a company or organization where the individual works; the State (C), those public authority responsible for Social Security and Insurance Institution and the Society (So), meaning the effect on society in general. In brief according to the EU-OSHA [5] report: • • • • • • •

The costs of each accident can be broken down into five cost categories: Productivity (P): decrease in yield or production; Healthcare (S): both direct and indirect medical services; Administrative (Am): social security benefits; Insurance (As): insurance benefits; Quality of Life (Q): related to the decrease in quality of life The main methods used to calculate the cost categories are (Table 1): the Human Capital Approach; the Friction Cost Method; the Opportunity Cost Method; the Quality of Life Approach. • The types of costs that each stakeholder has to bear during an accident are obtained from the relationship between stakeholders and cost categories (Table 2). The EU-OSHA [5] report, in its conclusions, highlights which are the most used calculation methods to associate to each cost category for each stakeholder (Table 3).

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Table 1 Purpose and procedures of the calculation methods used for the calculation of AP costs Calculation methods Human capital approach

Friction cost method

Opportunity cost method

Quality of life approach

Purpose of calculation; measure

Productivity of every possible stakeholder

Productivity friction period

Incidence of the accident on costs (additional)

Incidence of accident on quality lifestyle

Costs

– Pay – Income

– Needed for restore productivity at the level pre-injury

– New recruitments – Training – Interruption activity working

Procedure of calculation

CP = Tp × S

CF = Tl × S

CO = Tm × Sm

CP = Productivity costs Tp = workingtime job lost due to (depending on descriptors): injury; disability, early retirement

CF = Friction costs Tl = average time average used to request compensations, legal practices, internal investigations, etc.

CO = Opportunity costs Tm = average time for compensations, legal paperwork, internal investigations etc.

Willingness to pay

Legend S = labour cost (or loss of work) by prospect of stakeholder concerned Source Processing of data EU-OSHA [5]

3 Methodology Proposed and Application to the Construction Sector in the Province of Rome 3.1 Structure Starting from the EU-OSHA framework [5], in the structure of the proposed methodology, the following elements are introduced: • • • • • •

accident categories consequences and their connotations; scenarios; alternatives to assess; stakeholder characteristics; time horizons and reference periods; list of macro-cost items and their declination.

In order to calculate the cash flows related to the AE costs over time, it is necessary to define the categories of accident consequences and the scenarios, identified by whether or not the conditions for compliance with the OSH obligations for W and E

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Table 2 Stakeholder cost types and cost categories Cost categories

Stakeholder Worker (W)

Enterprise (E)

State (C)

Society’ (So)

– Production lost – Damages to Equipment – Damage to corporate image – Payments diseases

– Payments diseases – Loss contribution fiscal – Expenses state benefits

– Loss gross domestic product – Variations negative labour market

Healthcare (S)

Costs: Sickness costs of – medical the direct worker indirect – rehabilitation

Costs: – medical direct indirect – rehabilitation

Administrative (Am)

Cost of time employed for: – requests indemnity – attorneys’ fees

Costs: – administrative – Legal – reintegration – re-training workers

Costs: – inspections – expenses court cases

Insurance (As)

Compensation

Insurance premiums

Quality of life (Q)

Physical and moral pain and suffering

Productivity (P) Loss revenue present and futures

Source Processing of data EU-OSHA [5]

occur. Together they make it possible to construct the framework of the alternating scenarios to be assessed. The evaluation of the costs related to the correct management of OSH is expressed on the basis of “ordinary” behavior of the operators; to this end, it is necessary to identify the characteristics that allow to describe the “standard figure” of each of the four stakeholders on the basis of statistical data (using the latest available data), relating to the production sector to be examined [23]. In relation to the association between cost types and calculation methods proposed in the EUOSHA framework (Table 3), it is necessary to compose a list macro-costs items and identify their articulation and the respective methods of calculation in order to proceed with the quantification of cash flows for each “standard” stakeholder. These must be identified with reference to data and information coming from the application context, and separated into: • • • •

closely related to AE, sustained or perceived: once (Ku , una tantum costs); regularly (Kr , recurrent costs); not referred to AE (Kb , basic costs).

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Table 3 Association of cost calculation methods to cost categories for each stakeholder (types) Cost categories

Stakeholders Worker (W)

Productivity (P) Human capital approach

Enterprise (E)

State (C)

Society’ (So)

Friction cost method opportunity cost method

Calculation procedures of prevention payments Revenue calculation tax

Human capital approach Friction cost method Opportunity cost method

Healthcare (S)

Medical direct indirect costs

Medical direct indirect costs

Administrative (Am )

Opportunity cost method

Insurance (As )

Direct calculation of expenses and annuities

Quality of life (Q)

Quality of life approach

Insurance premium calculation

Direct calculation of expenses and annuities

Source Processing of data EU-OSHA [5]

A different status of the standard worker (in service: able to work; injured; unfit for work; retired; after his or her death) implies a change in the cost items to be calculated in order to assess the financial and economic effects in the presence or absence of an AE over time and their impact on the worker’s earning capacity, on the productivity of the Enterprise, or on the Society and the State. Once the alternatives to assess have been identified and the characteristics of each “standard” stakeholder in the production sector to be examined have been defined, the cost items (for the determination of cash flows) must be calculated with reference to the years included in three defined periods with respect to the time horizon of the year of: • the occurrence of the accident, from which the consequent disbursements are made: Ku , Kr , Kb, to be counted for this whole year; • retirement, when the remuneration payable to the worker for the years between the year following the accident and the year of retirement changes (Kr , Kb steady and steady performance); • life expectancy of the worker stakeholder, when the annuity from the pension to be paid to the worker’s death. Once the time horizons have been defined and the years of the reference periods have been considered, three monetary flows are calculated for each of the alternatives to assess. The sum of those three flows allows to quantify the total amount (total flow) of the costs resulting from the AE for each alternative in the years between the year in which the accident in the production sector in question occurs on average and the year in which the worker’s life is expected to end. Then, taking as reference

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the value (“maximum”) obtained for the alternative that corresponds to the Scenario in which all the OSH compliance are respected and the non-occurrence of the AE, the difference between this “maximum” value and the values obtained in all the other alternatives will be calculated. This will allow quantifying and highlighting the existing differences. Figure 1 summarizes the logical structure of the proposed methodology and the elements of which it is composed, described in detail below in this chapter, at the same time as its application to a case study referring to the construction sector of the Province of Rome. This choice was made with a perspective to promote the OSH culture, in collaboration with the CEFMECTP—Organismo Paritetico per la Formazione e la Sicurezza in edilizia—of Rome and Province. Indeed, it is essential to estimate the costs of OSH and non-OSH, during the evaluation of an investment in settlement transformation processes, because: (i) they are a non-reductive burden in the tendering phase; (ii) they can have a significant impact on the success of an investment in the case of an AE; (iii) the benefits of OSH are not yet sufficiently perceived by all stakeholders.

3.2 Categories of Accident Consequences and Their Connotations The methodology does not consider occupational diseases, but only the consequences related to accidents, because it is difficult to highlight the cause-effect correlation between work and illness and to associate exact consequential costs. Between accidents, a further distinction is introduced: with permanent disability (IP) and with temporary disability (IT) and is also considered the case of non-occurrence of the accident (NI), as it also produces costs. The four categories of accident consequences can be described (connotations) by examining the values present in the databases of the social security and/or Social Security and Insurance Institutions of the reference context, assuming the most frequent ones, related to: location (example: hand) and nature (example: injury) of the injury. By relating the categories of accident consequences and their connotations, a matrix is compiled with reference to the production sector in question (Table 4), identifying the prevalent type of impairment suffered by the injured worker for each category of accident consequence, and making it possible to determine, in relation to the type of compensation to be paid, the breakdown of the cost items to be considered.

3.3 Determining Scenarios In order to define the articulation of the different OSH cost items to be computed and their breakdown by type, scenarios are defined, relating the compliance or

Fig. 1 Structure of the proposed methodology

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Table 4 Matrix of accident consequence categories and their connotations Consequences accident Connotations Types

Categories

INM

NI

IM

Lesion location

Type of lesion

Type disability

Type compensation

IT

Hand

Contusion and wound

– Impairment: absent Indemnity Daily – Grade disability: Temporary none (IGTA) – Prognosis and healing: 23 days

IP

Track thoracic and column spinal

Fracture

– Impairment: macropermanent – Grade disability: 100% – Prognosis and healing: more than 40 days

Inability permanent Pension (Rp)

M

Skull

Fracture

Death

Survivors Pensions (Rs)

Source INAIL database (years 2015/2018) Construction sector; Province of Rome—https://bancad aticsa.inail.it/bancadaticsa/bancastatistica.asp?cod=2

non-compliance with the different conditions that have a significant impact on the approach and management of OSH by the two stakeholders (Worker and Enterprise) who, with their decisions and behaviors, have a greater influence on the probability of the occurrence or not of an AE. These conditions are referred one to the worker conditions and three to the enterprise, such as the possession of an Occupational Health and Safety Management System (HMS) or Registration with a pension fund. Given the presence of harmonized rules for the management of the labor market and OSH in the EU, the significant conditions to refer to are present in a similar and comparable way in all the Member States. From the combination of the occurrence or non-occurrence of the conditions considered, four scenarios are obtained (Table 5): from Scenario 1 (S1) where all conditions are non-compliant, to Scenario 4 (S4) where they are compliant. In non-EU countries, must be found factors with the same significance of those in the EU context. In the application phase, for the sector examined in a given national text, it is necessary to identify the OSH’s specific legislative and regulatory framework of reference, in order to deduce the conditions of compliance and the related information that allow to compose, decline and compile the list of cost items for each scenario. Figure 2 shows this compilation referred to the legislative apparatus in force in Italy in the construction sector.

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Table 5 Definition of scenarios Conditions Position of the worker respecting the conditions provided for in the sector’s national collective bargaining agreement

Scenarios S1

S2

Not regular

Regular

S3

Compliance with mandatory requirements Violations under current OSH legislation

Compliance

Possession of an Occupational Health and No Safety Management System (HMS) or an Organizational and Management Model (MOG)

Yes

Registration with a pension fund in the sector in which the enterprise operates

Registered

Not registered

S4

Fig. 2 Contextualization of case study scenarios

3.4 Alternatives to Be Assessed With reference to the European context, the relationship between accident categories and the four scenarios defined (Table 5) will lead to sixteen alternatives to be assessed (Table 6) which will be used to outline the framework of the calculation operations. They make it possible to estimate the extent to which costs may vary in relation to: the same consequences of accidents occurring in different scenarios; different categories of accident consequences in the same scenario.

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Table 6 Alternatives to be assessed Consequences accident

Scenarios

Types

Categories

S1

S2

S3

S4

INM

NA

A1

A2

A3

A4

IT

A5

A6

A7

A8

IP

A9

A10

A11

A12

D

A13

A14

A15

A16

IM

3.5 Description of the “Standard Figure” for Each Stakeholder As already expressed, it is necessary to consider “ordinary” behavior of the stakeholders involved in AP in order to identify those characteristics whose variability could have the greatest impact on the calculation of costs. This makes it necessary to construct and compile tables describing the significant characteristics of each stakeholder (stakeholder “standard”). For the compilation of these tables must be used data or information from statistical institutes or pension funds for the production sector examined. Table 7 shows the significant characteristics to be considered at European level and the data relating to the construction sector in the province of Rome. Table 7 Characteristics of standard stakeholder: Worker (a) and Enterprise (b), data from the construction sector in the province of Rome (2015) Characteristics (a) Worker Working conditiona

Regular

Sex

Male

Familiar composition

Wife and two kids

Classification workplace

Common worker (I level) None

Not regular

Type of contract

Indefinite

None

Theoretical annual working hours

2088 (ht)

1500 (hnr)

Average annual working hours worked

1567 (hl)

1500 (hnr)

(b) Enterprise Average number of workers in the enterprise

5

Average annual revenue

e 1.000.000

Number of accidents in the two years prior the accident None occurred Source direct survey, a Data bank Cassa Edile di Roma e Provincia (year 2015) https://www.cassae dilediroma.it/dati-statistici/

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For the State, the significant characteristics are to be found in the State organization of the Social Security and Insurance Institutions (in Italy Istituto Nazionale Previdenza Sociale–INPS and Istituto Nazionale per l’Assicurazione contro gli Infortuni sul Lavoro–INAIL) that manage the costs related to the AP. For the Society, the main indicators are those that refer to the quality of life of citizens and how the AP could affect it. For the State and the Society, reference should be made to a subsequent phase of the research.

3.6 Definition of Times, Composition and Declination of the List of Macro-Cost and Cost Items One In relation to the association between methods and cost types (Table 2) from the EU-OSHA framework it is necessary to define macro-cost items, organized in categories, declined, compiled and computed, according to each of the alternatives to be assessed identified, on the basis of data and information related to the specific context and sector of reference (input data) already used to compile the scenarios, the connotations of the accident consequence categories, the characteristics of the typical stakeholders. These (and consequently the value to be used in the calculation of monetary flows) are to be found in data: • tables, consulting: the national collective labor agreements of the sector in which the methodology is applied or ministerial tables (e.g. to know the annual working hours, the maximum daily working hours); the databases of the sector-specific or general social security institutions of the State (to know, for example, the tax rates to be applied to the worker’s pay for holiday pay, or the tax rates of personal income taxes, or provisions for severance pay); • calculated using formulae indicated by the relevant legislation in force (e.g. the worker’s annual salary or the amount of post-accident compensation); • available through synthetic market surveys, which can be carried out by comparing the cost of services or similar materials (e.g. expenses relating to legal proceedings, or funeral proceedings, etc.). In order to proceed with the calculation, the years to be included in each significant time period must be defined in advance on the basis of three reference time horizons, as shown for the case study in Table 8. In relation to the determination of the cost items for the different periods, it should be noted that Rp or Rs are calculated up to the year of the worker’s life expectancy and should be considered if the worker is involved in an accident with a fatal outcome. The costs relating to the survivor after the date of the worker’s presumed life expectancy are not taken into account because the variables to be considered, being closely linked to family circumstances, cannot be traced back to an ordinary condition. Starting from the list of macro-cost items, all the items to be used for the calculation of costs must be taken into account. As an example, Table 9 shows the breakdown

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Table 8 Definition of horizons and time periods Horizon time

Period/years

Milestones

Calculation years mode of the period

Average age at year of:

Case study

Data

Calculation

Ea = 37

Ya = 1

Retirement year Yr = Er − Ea (Er )

Er = 67

Yr = 67 − 37 = 30

Yd = Ed − Er

Ed = 82

Yd = 82 − 67 = 15

Accident

First period (Ya ) Accident (Ea ) = Accident’s year

Retirement

Second Period (Yr ) = From second year to the year of retirement

Life expectancy

Third period Perspective (Yd ) = Since the living (Ed ) year of pension to year of prospective life

Ya = 1

Source INAIL (accident year) 2015, CCNL (average retirement age) 2015; ISTAT (average age of life expectancy) 2015. http://dati.istat.it/index.aspx?queryid=7283

and calculation methods of the cost items declined and compiled in relation to the productivity category of the stakeholder “standard” worker in the case study.

3.7 Cash Flows by Time Horizons Once the list of cost items has been drawn up and calculated, the calculation must be made for the three reference periods (Ya , Yr , Yd ) determining the cash flows relating to: first (F1 ), second (F2 ), third (F3 ) time period and then calculate their sum (Ftotal) for each of the alternatives to be assess An (with n from 1 to 16 in the case of application). Operationally, for each stakeholder (indicated generically with z) and for each alternative to evaluate (An ) is calculated: • F1 , adding up for each cost category (P, S, Am, As, Q) the values of the cost items Ku , Kr , Kb relating to the year the accident occurred. Generalizing this sum with Kya you get (Formula 1): F1 f (z; An ) = Σya f (z; An ) [Kya(P) + Kya(S) + Kya(Am) + Kya(As) + Kya(Q) ] (1) • F2 , first adding Kr and Kb for the year immediately following (yir ) to the year of the accident and then multiplying this sum (Kir ) by the number of years between the two time horizons (yr ) (Formula 2):

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Table 9 Stakeholder “worker standard” list of cost items by productivity Cost category

Macro-cost Breakdown of Computation items cost items factors

Productivity Wage charges

Tax and social security charges

Insurance deductions

Computation data

Source

7 e/h

Market investigation

Hourly wage base non-regular worker

Ron (in S1)

Hourly wage base regular worker

Roa (in S2; S3 S4) 15e/h

CCNL

Annual salary R S1 = hl*Ron for non-regular workers (in S1)

R

Annual salary R (S2; S3; S4) = for regular h*Ro(SS2; S3; S4) workers (in S2; S3; S4)

R (S2; S3; S4) = 2088 h*15e

S1= 1500

Market h*7e/h investigation

CCNL

Taxable income

If = Ro + B+Dm + Df

Bonus DL66/14

B

e 960,00

Dependent spouse

Dm

e 690.00

Dependent kids

Df

If = 1900*(110.000 − 18.771)/110.000

Employee’s responsibility

Incd = If*9,19/100

IRPEF

Irpef = If*27/100

Severance indemnity

Tfrn = [Tfr–(Tfr*0,23)]

CCNL

Employee’s construction cash account

Ceo = Ro(S2; S3;S4 *0,919

Cassa Edile

Aliquote Contributive INPS

Source Own calculations on INPS data, construction CCNL (year 2018), and market surveys

  F2 f (z; An ) = {Σyir f (z; An ) [Kir (P) + Kir (S) + Kir (Am) + Kir (As) + Kir (Q) ]} ∗ yr

(2)

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• F3 , adding up the cost items (Kr ) related to pension contribution (K3 ) and then multiplying them by the years of this period (yd ) (Formula 3)   F3 f (z; An ) = {Σy3 f (z; An ) [K3(P) + K3(S) + K3(Am) + K3(As) + K3(Q) ]} ∗ yd (3) Figure 3 shows the synoptic picture of the F1 calculation methods for the sixteen alternatives and for each of the stakeholders. For each of the alternatives to assess and for each stakeholder, the total amount of OSH-related costs (Ftotal) is obtained by summing the three cash flows described above (Formula 4) Ftotal (z)∀An = F1(z;An) + F2(z;An) + F3(z;An)

(4)

Finally, in order to be able to quantify and evaluate how much investment in OSH can be convenient, all the results obtained for each alternative are compared. This is done by referring to the combination of the conditions of the scenarios and the “best” accident consequence categories—which in the case study corresponds to A4 (S4-NA)—as the maximum positive value obtained, against which the  for each of the alternatives must be calculated. Figure 4 shows the summary of the results obtained in the application to the case study, calculating the four cash flows (F1, F2, F3, Ftotal) and the  (A4) with respect to each of the alternatives and according to the data collected in the list of cost items for the Worker and Enterprise standard stakeholder. The application of the methodology allows to quantify the costs resulting from the AP, confirming that non-compliance with OSH regulations leads to significant losses for both the Worker and the Enterprise as a consequence of an accident. On the other hand, having an HMS does not lead to additional costs for the Enterprise even in cases of fatal accidents.

4 Conclusions and Outlook The proposed methodology enables to compare the costs of OSH and non-OSH for each stakeholder and shows whether, and to what extent, the investment costs, to comply with mandatory requirements and to implement HMS for workers, are lower than those that would be present because of an AE. This highlights that in OSH management the active collaboration of all operators can lead to positive results [18]. Currently, the results of applying the methodology could be used to raise awareness: employers in companies who become aware of the benefits of investing in OSH; workers who may be aware of how much a serious accident and an irregular working position can ‘cost’ to themselves. The proposed methodology, applied for the first time to the construction sector in Italy in the Province of Rome, could be extended

Fig. 3 Synoptic panel of calculation for F1

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Fig. 4 Summary of cash flows for the operator: F1, F2, F3, Total and  compared to A4

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at national and European level and to all stakeholders. Moreover, it could be applied to heterogeneous contexts (any sector, contexts with different legislative references, different accident dynamics, etc.), modifying the input data used, or it can be partially used to investigate the consequences of a specific type of accident or scenario [8, 24]. Using data and values referred to a wider reference context (state, European or multi-sectoral), the methodology could be functional to the analysis of which are the sectors, or the types of injury that most affect the State budget and support decision makers for a more effective strategic planning of State investments in prevention, health, training.

References 1. Bartolomei C, Felicioni S, Ganapini S (2013) Progetto S&cante: stima dei costi della non sicurezza nel settore delle costruzioni, Decreto Direttoriale del 23/12/2009 prot. 22496 del Ministero del Lavoro e delle Politiche Sociali, Direzione generale della tutela delle condizioni di lavoro Tema C “Progettazione e sperimentazione di modelli statistico-economici di valutazione dei costi della mancata organizzazione e gestione della salute e sicurezza in azienda” 2. Chountalas PT, Tepaskoualos FA (2017) Implementing an integrated health. Safety and environmental management system: the case of a construction company. Int J Qual Res 3. European Agency for health and Safety at Work (2002) Inventory of socioeconomic costs of work accidents. Publication Office of European Union, Luxembourg 4. European Agency for health and Safety at Work (2011) Socioeconomic costs of accidents at work and work related ill health. Publication Office of European Union, Luxembourg 5. European Agency for health and Safety at Work (2014) Estimating the cost of accidents and ill health at work: a review of methodologies. Publication Office of European Union, Luxembourg 6. European Agency for health and Safety at Work (2014) The business case for safety and health at work: cost-benefit analyses of intervention in small and medium size enterprises. Publication Office of European Union, Luxembourg 7. EUROSTAT. Accidents at work by sex, age and NACE Rev. 2 activity (A. C-N), https://app sso.eurostat.ec.europa.eu/nui/show.do?dataset=hsw_mi01&lang=en. Accessed 10 Nov 2019) 8. Guarini MR, Morano P, Sica F (2019) Integrated ecosystem design: an evaluation model to support the choice of eco-compatible technological solutions for residential building. Energies 12(14):2659 9. Health and Safety Executive (2013) Costs to Britain of workplace fatalities and self-reported injuries and ill health 2012/13, London 10. INAIL, BANCA DATI STATISTICA, Tavola: IL_DN_IS_AS_ATE_TEM - Analisi per attività economica e anno di accadimento. https://bancadaticsa.inail.it/bancadaticsa/bancastatistica. asp?cod=2. Accessed 10 Nov 2019 11. International Labour Orgnization (2014) Safety and health at work: a vision for sustainable prevention: XX World Congress on Safety and Health at Work 2014: Global Forum for Prevention, 24–27 August 2014, Frankfurt, Germany/International Labour Office, Geneva 12. International Social Security Association (2013) Calculating the international return on prevention for companies: cost and benefits of investments in occupational safety and health, Geneva 13. International Social Security Association (2013) Social security and a culture of prevention: a three-dimensional approach to safety and health at work, Geneva 14. Koulinas GK, Marhavilas PK, Demesouka OE, Vavatsikos AP, Koulouriotis DE (2019) Risk analysis and assessment in the worksites using the fuzzy-analytical hierarchy process and a quantitative technique—a case study for the Greek construction sector. Saf Sci 112:96–104

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15. Legislative Decree (2008) No 81/2008 implementation of Article 1 of Law No 123 of 3 August 2007 on the protection of health and safety at work. https://www.bosettiegatti.eu/info/norme/ statali/2008_0081.html. Accessed 10 Nov 2019 16. Leigh JP, Miller T (1997) Ranking occupations based upon the costs of job-related injuries and diseases. J Occup Environ, Med 17. Leigh JP, Waehrer G, Miller TR, Keenan C (2004) Costs of occupational injury and illness across industries. Scand J Work Environ Health 18. Leipziger D (2002) The definitive guide to the New Social Standard. Financial Times Prentice Hall, Upper Saddle River (USA) 19. Leppink N, (2015) Costi socioeconomici degli infortuni e delle malattie connessi al lavoro: Stabilire sinergie tra salute e sicurezza sul lavoro e produttività, atti del Seminario INAIL «I costi della non sicurezza», Bologna 20. Ma R, Zhong S, Morabito M, Hajat S, Xu Z, He Y, Bao J, Sheng R, Li C, Fu C, Huang C (2019) (Estimation of work-related injury and economic burden attributable to heat stress in Guangzhou, vol 666. China Science of the Total Environment, pp 147–154 21. Miller P, Whynes D (2000) An economic evaluation of occupational health. Occup Med 50(3):159–163 22. Miller T, Waehrer G, Leigh J, Lawrence B, Sheppard M (2002) Costs of occupational hazards: a microdata approach. National Institute of Occupational Safety and Health, Washington DC 23. Morano P, Tajani F, Di Liddo F, Guarnaccia C (2018) The value of the energy retrofit in the Italian housing market: two case-studies compared. WSEAS Trans Bus Econ 15:249–258 24. Morano P, Rosato P, Tajani F, Manganelli B, Di Liddo F (2019) Contextualized property market models vs. Generalized mass appraisals: an innovative approach. Sustainability 11(18):4896 25. Mossink J, de Greef M (2002) For Topic Centre on Research—Work and Health, ‘Inventory of socioeconomic costs of work accidents’, 2002. https://osha.europa.eu/en/publications/rep orts/207. Accessed 10 Dec 2019 26. National Institute for Occupational Safety and Health (2011) The economic burden of occupational fatal injuries to civilian workers in the United States based on the census of fatal occupational injuries 1992–2002 27. Poplin GS, Griffin S, Pollack Porter K, Mallett J, Hu C, Day-Nash V, Burgess JL (2018) Efficacy of a proactive health and safety risk management system in the fire service. Inj Epidemiol 5(1), art. no. 18 28. Rzepecki J (2012) Cost and benefits of implementing an occupational safety and health management system (OSH MS) in enterprises in Poland. Int J Occup Saf Ergon 18(2):181–193 29. Safe Work Australia (2012) The cost of work related injury and illness for Australian employers, workers and the community: 2008–09, Canberra 30. Tajani F, Morano P, Paz Saez-Perez M, Di Liddo F, Locurcio M (2019) Multivariate dynamic analysis and forecasting models of future property bubbles: empirical applications to the housing markets of Spanish metropolitan cities. Sustainability 11(13):3575 31. Waehrer G, Dong XS, Miller T, Haile E, Men Y (2004) Costs of occupational injuries in construction in the United States 32. Waehrer GM, Dong XD, Miller T, Haile E (2007) Men Y (2007) Costs of occupational injuries in construction in the United States. Accid Anal Prev 39(6):1258–1266 33. Zainal Abidin M, Rusli R, Khan F, Shariff M (2018) A development of inherent safety benefits index to analyse the impact of inherent safety implementation. Process Saf Environ Prot 117:454–472

Real Estate Finance and Property Management

To Buy or Rent to Buy? Appraisal Questions Francesca Salvo, Pierluigi Morano, Francesco Tajani, and Manuela De Ruggiero

Abstract The paper aims to discuss the new typology of the Rent to Buy contract from an appraisal perspective. Starting from the analysis of the contractual typologies alternative to the traditional property sale, the paper analyzes the Rent to Buy formula, with particular reference to the definition of the Discounted Cash Flow terms. The study prefigures the use of the real estate market cycles when defining the terms of the contract, in order to increase confidence in the use of this interesting contractual form. Keywords Rent to buy · Market cycle · Discounted cash flow analysis · Risk analysis

1 Introduction The increasingly pressing economic crisis, the low dynamism of the real estate market and the difficulties in accessing bank credit have generated the emergence of new types of real estate transactions such as the preliminary sale agreement with anticipated effects, the hire purchase with retention of title, the rent with purchase obligation. F. Salvo (B) · M. De Ruggiero Department of Environmental Engineering, University of Calabria, Rende, Italy e-mail: [email protected] M. De Ruggiero e-mail: [email protected] P. Morano Department of Civil Engineering Science and Architecture, Polytechinc University of Bari, Bari, Italy e-mail: [email protected] F. Tajani Department of Architecture and Design, Sapienza University of Rome, Rome, Italy e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Morano et al. (eds.), Appraisal and Valuation, Green Energy and Technology, https://doi.org/10.1007/978-3-030-49579-4_19

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This kind of transactions can provide attractive advantages to both the tenant and the owner, significant civil and fiscal advantages, moreover in a phase of general criticality of real estate investments. Among these new types of interesting contract, an important role is played by the rent to buy one, which consists of the sale of the property, by granting the buyer a rental period and the right (and not the obligation) to purchase the property definitively within a period (up to ten years), at a fixed selling price, which will be subtracted from the additional monthly rents already paid. It is clear that the appraisal discipline, which by its nature deals with regulating real estate sale and rent transactions, cannot exempt from investigating these new types of contract, offering scientific and not only practical answers to the objective uncertainties connected to these new type of transactions. This paper intends to investigate the rent to buy contract from an appraisal perspective, in order to provide a reference scheme for the construction of the contract’s cash flows.

2 Alternative Property Investments. The Rent to Buy Contract The deep crisis that has hit the real estate market in the Western countries in the last decade has impoverished the demand for real estate, producing an imbalance on the supply side. This condition, which continues to persist, has led to the emergence of new types of property investments, aimed at favoring deferred real estate investments. There are numerous types of contracts, which differ in the terms of the contract, but they are all intended to postpone the real estate sale with respect to the signing of the contract. The preliminary sale agreement with anticipated effects represents a first typology. This is a preliminary contract, with mandatory and non-real effects, which provides for the immediate delivery to the purchaser of benefits and obligation for the parties to enter into the sale at a later time. This typology, therefore, anticipates some of the effects of the final contract. Usually, there is also a penalty that can be variable based on the duration of use, which will be due by the buyer in the event of default or withdrawal. The hire purchase with retention of title is another type of contract. It is an immediate sale with a deferred price. The seller is guaranteed in the event of default by the buyer. It favors the sale in the event that there is a deferred payment over time. It is useful for those who must immediately sell the property but they want guarantees on the balance of the deferred amount and buyers who want to benefit from a staggering of the amount over time. This deed can be constructed as a sale subject to termination conditions. Taxes are paid according to the applicable laws as if it were a normal sale.

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Among the various alternative property investment contracts, there is the rent with purchase obligation, which allows to redeem the rental fees paid during a certain period of time, after which the property is transferred to the tenant. In practice, the seller finances the buyer by allowing him to pay the property in installments. It is an atypical contractual form, in which two contracts are entered into, that of the lease and that of the option to purchase; the buyer rents the property by paying a higher fee intended to cover a portion of the advance payment, in the event of an option for the purchase. Starting from the idea of combining the main advantages of rent with redemption for both parties involved, it was established with Legislative Decree No. 133/2014 another contractual form, the so-called rent to buy. Unlike what happens in the other mentioned contractual forms, which in some way replace the banking system to finance the purchase of real estate, in the rent to buy the seller rents the property to the buyer and freezes the price until it is able to access bank financing. In practice, the potential seller remains a lessee until he can get a mortgage loan in order to buy the leased property. It is not a typical contract, but the combination of two contracts, the lease and the preliminary sale cones. The conditions are determined on the basis of the needs and will of the parties, the two contracts can be joined into a single contract or they can be kept separate, but connected. In the case of a single contract, the advantages are undoubtedly related to the clarity of the agreement (due fees, purpose and will of the parties) and the disadvantages are due moreover to uncertainty about the legal regime applicable in the event of default and absence/rarity of precedents. In the case of two separate contracts, there are, on the one hand, the lease contract and, on the other hand, the preliminary sale. The advantages are undoubtedly linked to a clear discipline for each individual contract and the foreseeability of the legal consequences in the event of default. The disadvantages, on the other hand, are due to the less clarity of the actual will of the parties. The terms of the contract generally include: – an initial payment as a deposit, which usually is not less than 10% of the agreed final sum; – the lease payments that become price advances on future sales. They are normally slightly higher than the market ones, at least 10/20%; – a final amount, given by the difference between the agreed price of the future sale and the entire deposit (consisting of the initial payment and the sum of all the paid down payments). The expected duration of the lease is between 3 and 5 years, up to a maximum of 10 years. At the natural end, the contract is expected to be redeemed and the buyer must purchase the property through a notarial deed. The full payment of all the rents is an essential condition for the purchase. Furthermore, the lessee has the right to choose to purchase the property before the natural

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expiry of the lease. Of course, the initial payment of the deposit and the rental fees hitherto paid will be discounted. It is a fairly flexible formula, which produces mutual benefits for contractors. The tenant has the advantage of taking possession of the property from the date of signing the contract even if he does not have a sufficient amount to pay the purchase price, at the same time accumulating the rental credits as a down payment on the future purchase price, so that he can request a mortgage payment lower than the agreed purchase price. The advantage is even more evident if market prices tend to rise since in this case the buyer benefits from a price already blocked at the time of signing. On the other hand, the owner has a very high probability of completing the purchase transaction, at the same time perceiving a monthly fee generally higher than that of traditional rent, leaving the management and use costs of the property to the tenant/buyer. The contractual typology is obviously more complex and the tax charges are generally higher. For the buyer there is also an obligation to purchase both in the event of a subsequent loss of interest and in the case of lack of funds for the final redemption, while for the seller disputes are possible on the exact and complete fulfillment of the contract and/or in the event of default of the contract. To complete a rent to buy contract, the tenant buys the house at the end of the rental period at the price established in the contract with a maxi installment which normally represents most of the agreed price. As it is evident, the buyer will be able to access bank credit only if the value of the house at the request of the mortgage loan will be higher than or equal to the sum requested, under penalty of not being able to conclude the contract, with the consequent loss of the paid sums, which are higher than the ordinary rental rates. Given the frequent fluctuations in the real estate prices, as well as the progressive wear and tear of the property following use, the possibility that real estate values decline over time is not so remote, as on the other hand past trends have shown, so that the rent to buy contracts include caution clauses such as the possibility of extending the lease contract, of modifying the price initially agreed upon or even the return of part of the fees paid.

3 Literature Review The contractual form of the rent to buy has its origin in the Anglo-Saxon model of the rent to own. The concept of rent to own first emerged in the United Kingdom, while in the United States it appears in the applications of such forms of sale in the 1950s and 1960s. The rent to own formula was initially more commonly associated with the dispositions of consumer goods, and only later it began to be used with reference to the real estate sector [12]. In the periods in which there was a crisis in the real estate market, such as that of the subprime crisis in the United States in 2007, the rent to own has become the most widespread contractual form of real estate

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exchange, acquiring the possibility of accessing the purchase of a case by postponing the disbursement of bank credit [28]. The rent to buy contract was introduced in Italy by Legislative Decree no. 133/2014. The introduction of rent to buy as a contractual form in the Italian real estate market was not easy and had to face the skepticism of real estate operators [31]. The diffusion of this institution was very limited, despite the main purpose of the rent to buy was precisely the reactivation of the real estate market [1, 5, 7, 18]. The main diffusion limits of this contractual form are essentially linked to the flexible contractual nature of the rent to buy which leads to a free and excessive law interpretation [9–11, 15, 32]. The rent to buy is characterized by a form of randomness, linked to the possibility of completing the contract in relation to the current economic crisis and the volatility of the real estate markets [29]. The “time” factor constitutes a determining factor in the institution of the rent to buy, so that the risk plays a key role on the future evolution of the market value of the property, being the variations in the property market value over time influenced by changes: in the macroeconomic system; in the demand and the supply of properties; and in the positional factors of the area where the property is located. These factors contribute to the formation of the real estate “cycles,” i.e. they determine the classic sinusoidal trend that—at least in the previous decades—has characterized the evolution of property market values. The study of property cycles has a long history. The first one was carried out by Hoyt [16] about the real estate market in Chicago. Even prior to Hoyt’s work, Wesley Mitchell established the theoretical foundation for and empirical evidence of cyclical economic activity in the United States [21], while in the RICS report it’s declared that studies about property market cycles go back to 1921. Despite its early importance in the general business and land economics literature, the real estate cycles have been largely ignored or undervalued by real estate academics and practitioners until recent years when studies about real estate cycles have then received increasing attention over the years, because of the importance of commercial real estate in the economy [13] and according to the market evidence that have usually shown periods of boom and slump [17]. According to RICS definition, property cycles are “international and global forces” constituted by “a logical sequence of recurrent events reflected in factors such as fluctuating prices, vacancies, rentals and demand in the property market” [14]. Pyhrr et al. [26] define real estate cycles as the interaction between the physical cycles of the supply and the demand. It should be pointed out that “a cycle is a sequence of events that repeat,” [20]. Consequently, the capacity to perceive events as susceptible to repetition as opposed to being isolated, random and non-recurring is crucial to coping with nature, political economy, business and investing. Mueller and Laposa [23] identify four phases in the cycle: recession, recovery, expansion, and contraction. Investors indicate recovery and expansion as the up-cycle or the cycle upside, because the occupancy rate increases in these phases, whereas they label contraction and recession the down-cycle or the cycle downside, due to the fall of the occupancy rate. The equilibrium-rate line intercepts the curve at the

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inflection points where the curve changes its concavity and it marks the different positions and directions of the cycle [26]. However, the periodicity of the cycle is not always the same and Barras [3] listed four types of property cycles identified by the literature: short cycles lasting for four to five years, long cycles lasting for nine to ten years, long swings lasting up to 20 years, and long waves of 50 years. The conceptual model elaborated by Barras [3] shows how the property cycle is influenced by the business cycle in the real economy and by the credit cycle in the money economy. Indeed, real estate prices, and consequently total property returns, are mainly determined by capital flows. In addition, interest rates have an impact on prices as well [22]. Mueller [22] refers to these movements as financial cycles. The distinction between physical and financial cycles is useful to understand the lag between market movements and real estate price [24]. When discussing cycles, real estate academics and practitioners identify three markets in which they occur: use, investment and development. In this way, they distinguish the rental cycles, the yield cycles and the construction cycles [30]. In this context, another useful definition considers cycles as the “tendency or property demand, supply, price and returns to fluctuate around their long term trends or averages” [2]. A review of the current literature has revealed that most of researchers attempted to answer different questions about property market cycles: existence, causes, phases, relationship with macro and microeconomic factors, predictability [19]. Some authors used conventional spectral analysis techniques to examine property and financial assets for evidence of cycles and co-cycles [8, 25]. Other authors examined cycles in the frequency domain and employs spectral analysis as its tool too [33]. Other studies have been drawn in order to build cycle model [4] or to study and interpret cyclical relationship between commercial real estate and property stock price [8] and their implications in managing the global economy [26]. Some studies have aimed at assessing the impact of supply and demand cycles, equilibrium prices, inflation rates, etc. on real estate cash flows in the Discounted Cash Flow Analysis [6].

4 Dynamics of Real Estate Cycles for the Appraisal of the Contractual Terms The main issue in a rent to buy contract is to define the terms of the contract so that this can actually be completed, basically making sure that the value to be paid on balance is not higher than that of the house at the time of the loan, i.e. at the end of the lease. Assuming that the sale price is known at the date of signing the contract, the question is to define the installment payment, which must not exceed the value of

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the property at the end of the contract, under penalty of failure to disburse the bank loan, and the amounts to be paid as down payments on the sale price. It is therefore necessary to establish a safety ratio between the initial down payment and the balance, trying to investigate the dynamics of the real estate cycles. Normally, the assessment of the discounted cash flows, such as those applicable to rent to buy transactions, are based on assumptions regarding changes in the cash flow variables related to the trends (upwards or downwards) rather than market cycles, and the links among the critical variables are often omitted or incorrectly specified. In particular, these assessments define a set of current market conditions and economic trends which are usually assumed to remain relatively stable in the future. Therefore, valuation projections often show little change from current operational appraisals other than an annual adjustment of inflation revenue and expenditure. The studies of real estate cycles and the discounted cash flow analysis can provide a measure of the proportions and the relationships between the monetary terms of the contract (sums on account, monthly installments and balance), such that the interests of the contractors are mutually protected. The evaluation model and process used to generate forecasting economic expectations and behaviors usually therefore do not provide an appropriate analysis of the market cycles. The distorted results often produced by these trend models can lead to overestimates when market demand exceeds supply close to the peak of the real estate cycle; vice versa, when market supply exceeds demand (soft market) close to the minimum of the real estate cycle, the capitalization of the income stream leads to underestimations of the value. Due to this prejudice in the methodology, the appraisal process tends to overestimate/underestimate the property at or near the extremes of the real estate cycle (with varying degrees of injury). It therefore seems important to include the effects of market cycles in the assessment of the cash flow terms. In general terms, the cash flows corresponding to a rent to buy transaction can be represented as reported in Fig. 1. In analytical terms, the discounted cash flows of the rent to buy contract can be expressed as:

Fig. 1 Discounted cash flows in the rent to buy contract

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V = A+

n 

R · (1 + i)−t + S · (1 + i)−n ,

i=1

where V is the agreed sale price, A is the down payment installment, R are the annual net flows (revenues and costs), S is the final payment. The cash flow equation could be set as an inequality, such as to guarantee the sustainability of the bank loan for the final payment, namely: S < Vn , where V n is the value of the property at the end of the lease. The question is to understand how the market variables of real estate cycles can intervene to reasonably predict the value of the property at the n time, in order to be able to define safety proportions in relation to the amounts on account and the annual installments. Some studies have been conducted in this direction [6], aimed at assessing the impact of the supply and the demand cycles, the equilibrium prices, the inflation rates, etc. on the real estate cash flows. There are many drivers of the residential real estate market: the country’s economic situation, the expectations about the future of both economy and family incomes, the change in money sale power, the inflation, the possibility of obtaining financing, the maximum percentage (on the price) in money requested as a mortgage for the real estate purchase, the local property taxes, the taxes on income generated by real estate, the variation in the number of families present in the territory, the number of new (and/or used) properties offered on the market, and many others. The analysis of all possible drivers/variables allows higher precision in analyzing the cycles of the real estate market. At the same time, an excess of variables prevents from quickly understanding what the fundamental drivers are. The fundamental drivers for understanding the performance of the real estate market and for making specific forecasts (with reasonable reliability) are: – – – –

the number of sales of real estate units; the sale prices of the properties; the annual inflation change; the annual change in the average employee’s purchasing power.

Important drivers are also: – the possibility (more or less high) of obtaining purchase financing from banks; – the percentage (more or less high on the price) of the maximum loan obtainable by the buyer. In the recessionary phases of the market, banks less easily grant loans for the purchase of properties and they agree to finance low percentages (of the purchase price) while the opposite occurs in the expansionary phases of the market.

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Fig. 2 Forecast on market cycle

According to recent studies, hypotheses and forecasts on market cycles can be formulated according to the following procedure: – connect the minimum values of the various cycles together to draw a straight line (support line); – connect the maximum values of the various cycles together to draw a straight line (resistance line); – by prolonging the support/resistance lines in the future and assuming the duration of the next cycle (if the duration of the previous cycles is kept as an information base) it is possible to make forecasts about the market trend for the next few years (Fig. 2). If the market has had a drop in prices, it could occur that the market value of the property at the n is lower than the balance payment, under penalty of not being granted the mortgage loan. It is therefore important that the installment and periodic installments cover this hypothetical discrepancy with a safety margin, namely:  S = V − A−

n 

 −t

R · (1 + i)

· (1 + i)n < Vn · (1 + i)−n

i=1

where V n can be estimated using market cycles. Solving the inequality then means quantitatively defining the installment and periodic installments, such as to guarantee the solvency of the contract: n 

R · (1 + i)−t + A > V − Vn

i=1

The contractual terms may provide for different possibilities for quantifying the installment on account and the periodic installments, provided that overall they

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exceed the difference between the agreed price and the final value, suitably discounted at the current time. If, on the other hand, the market is growing, the question does not arise because the value at the n time will be greater than the agreed price.

5 Conclusions In a generalized situation of deep economic crisis, the use of alternative forms of real estate investment to the traditional real estate purchase can represent an interesting operational possibility. In particular, the rent to buy formula could favor the revitalization of the real estate market, allowing the postponement of the access to the bank credit. However, in order to make this contractual form concretely applicable, it is necessary that indications are provided regarding the definition of the contractual terms, under penalty of the final non-conclusiveness of the contract and the skepticism connected with the approach to new types of investment. In this direction, a research opportunity refers to the analysis of the real estate cycles, intended to define whether the market has a future tendency to increase or vice versa to decline. Future research intends to actually work on the construction of real estate cycles and on the analysis of the volatility and the uncertainty related to the construction of the cash flows.

References 1. Atzeni C, Cirillo A (2015) Il trattamento fiscale dei contratti rent to buy degli immobili. Amministrazione & Finanza 30:5 2. Baum A (2001) Evidence of cycles in european commercial real estate markets and some hypotheses. A global perspective on real estate cycles. Springer, Boston, MA, pp 103–115 3. Barras R (1994) Property and the economic cycle: building cycles revisited. J Property Res 11(3):183–197 4. Barras R (2005) A building cycle model for an imperfect world. J Property Res 22(2–3):63–96 5. Belotti R, Cavalli F (2014) Rent to buy fra problematiche fiscali e dubbi vantaggi economici. Il Fisco 45 6. Born W, Pyhrr S (1994) Real estate valuation: the effect of market and property cycles. J Real Estate Res 9(4):455–485 7. Bottero S (2015) Rent to buy: disciplina fiscale applicata al contratto. PMI 21:5 8. Brown G, Liow KH (2001) Cyclical relationship between commercial real estate and property stock prices. J Property Res 18(4):309–320 9. Bulgarelli A (2015) Luci ed ombre del rent to buy italiano. Archivio delle locazioni e del condominio 1 10. Busani A (2015) Il rent to buy alla prova della prassi degli operatori. Il Sole 24 Ore, 26 Gennaio 2015, 12

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11. Castellano P (2015) Il rent to buy: un fenomeno sociale in cerca di definizione giuridica. Rivista del Notariato 67(1):45–65 12. Clark B, Dow DR, Smith SP (1994) Rent-to-own agreements in banktrupcy: sales or leases. Am Bankruptcy Inst Law Rev 115(2):123–124 13. Currie DA, Scott A (1991) The place of commercial property in the UK economy. London Business School, London 14. French N (2003) The RICS valuation and appraisal standards. J Property Investment Finance 21(6):495–501 15. Furlani F, Pellegrino S (2014) Il rent to buy tra disciplina civilistica e problematiche fiscali. Bilancio, Vigilanza e Controlli, p 10 16. Hoyt H (1933) 100 years of land values in Chicago: the relationship of the growth of Chicago to the rise in its land values, 1830–1933. University of Chicago Press, Chicago 17. Key T, MacGregor BD, Nanthakumaran N, Zarkesh F (1994) Economic cycles and property cycles. Main report for understanding the property cycle. RICS, London 18. Lomonaco A (2015) Rent to buy: profili fiscali. Rivista del Notariato 4:446–453 19. Man KF, Chau KW (2005) Hong Kong property cycle–a frequency domain analysis. In: Eleventh Pacific rim real estate society conference. Melbourne, Australia:[sn] 20. Miller M (1997) The future of futures. Pacific-Basin Finance J 5 21. Mitchell WC (1927) Introductory pages to “Business Cycles: The Problem and Its Setting”. In: Business cycles: the problem and its setting. NBER, p 23 22. Mueller GR (1995) Understanding real estate’s physical and financial market cycles. Real Estate Finance 12:47–52 23. Mueller GR, Laposa SP (1994) Evaluating real estate markets using cycles analyses. In: Annual meeting of the American real estate society 24. Mueller GR, Pevnev A (1997) An analysis of rental growth rates during different points in the real estate market cycle. In: The American real estate society meetings 25. Okunev J, Wilson P (1999) What is an appropriate value of the equity risk premium? J Investing 8(3):74–79 26. Pyhrr S, Roulac S, Born W (1999) Real estate cycles and their strategic implications for investors and portfolio managers in the global economy. J Real Estate Res 18(1):7–68 27. Reddy’s group. http://www.milano.federmanager.it/wp-content/uploads/2019/03/Aldai-16-52019.pdf. Accessed 12 Dec 2019 28. Rosenblum G (2008) In a down market, rent to own option becoming popular. Providence J 9 29. Tajani F, Morano P, Salvo F, De Ruggiero M (2019) An evaluation model for an effective risk assessment in the rent to buy property market. Property Manag (vol ahead-of-print No. ahead-of-print) 30. Tiwari P, White M (2014) Real estate finance in the new economy. Wiley 31. Vaira M (2015) I Contratti di godimento in funzione della successiva alienazione. Rivista del Notariato 69(1):223–234 32. Vv.Aa. (2016). Il rent to buy. Wolters Kluwer, Milan 33. Wang P (2003) A frequency domain analysis of common cycles in property and related sectors. J Real Estate Res 25(3):325–346

A Model for Determining a Discount Rate in Market Value Assessment of Buildable Areas Subject to Restrictions Fabrizio Battisti and Orazio Campo

Abstract At the European level, the exercise of building rights on areas that general planning tools have considered to be buildable is allowed only upon the issuance of a specific building permit. The legal procedures used to obtain the above-mentioned building permit can be largely affected by some constrains able to limit—and occasionally prevent—the exercise of the building rights (even if they are established by a general planning tool). Therefore, in the evaluation of buildable areas without building permit, the uncertainty relating to the presence of constraints/limitations that require a specific authorization issued by the competent authorities need to be considered as a part of the urban risk. This article is thus based on the assumption that to assess buildable area without building permits must be considered as the actual condition it is found in, and therefore its limitations and the time necessary for the building to be built actually and not just “potential”. To implement this approach, a model for determining a discount rate in market value of buildable areas subject to restrictions (limitations and constraints) has been structured. Keywords Appraisal · Buildable land · Real estate · Discount rate · Market value

1 Introduction and Aims of the Work In European spatial planning system, the assignment of buildability can be officially granted to an area through different levels of planning tools. Usually, it is a municipallevel urban planning instrument to declare whether a specific area is buildable. But, sometimes, operative instruments more circumscribed in scope can make variations to the municipal instruments in force [2]. The approval of a general urban planning F. Battisti (B) · O. Campo Department of Planning, Design, Architecture Technology (PDTA), Faculty of Architecture, Sapienza University of Rome, Via Flaminia 72, 00196 Rome (RM), Italy e-mail: [email protected] O. Campo e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Morano et al. (eds.), Appraisal and Valuation, Green Energy and Technology, https://doi.org/10.1007/978-3-030-49579-4_20

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instrument (or its variances) consists of a number of phases, of which the essential ones are adoption by the municipality and subsequent approval by the higher administrative authority, having obtained all the opinions and/or clearances from the various sectoral authorities that have responsibility in urban planning proceedings [4]; it is then possible for building to be subordinated to the prior approval of an implementation instrument (detailed plan, development plan, public housing construction plan, etc.). From the urban planning standpoint, land may generally be referred to as “buildable” only after the general urban planning instrument has gone into effect and, if necessary, also after the general urban implementation planning instrument has gone into effect; in fact, only after that time can the building permit actually be obtained; this articulated process may have a considerable duration, even of several years, such as the Italian experiences testify. The building right are usually assigned by local authorities taking into account the general needs of the community to whom the planning is addressed; therefore, in general urban planning quite often the adaptation with the superordinate constraints is performed on the basis of general verification on large area cartographies [6, 18]. In summary, in areas with constraints and limitations, buildability established by a general planning tool is only potential until the building permit is obtained. Usually, the issuance of this building permit is subjected to the authorization/positive opinion given by the competent authorities, which occurs evaluating the projects (individually) for which building permits is requested, after the approval of general urban plan. However, according to the international evaluation practices, a buildable area may be considered as such only after the obtaining the authorization/building permit to start building construction. Therefore, in the evaluation of market value of buildable area without building title it is necessary taking into account the limitations entailed by the operating constraints [8, 10, 13–15, 22]. This article, which is centered mainly on appraisal/assessment-related studies and research, is thus based on the assumption that responding to the market value assessment of area subject to constraints and limitations must be considered as the actual condition it is found in considering all the operating restrictions, and therefore its limitations and the time necessary for the building right to be actually exercised and not just “potential” [1]. To implement this approach, the analytical/indirect estimate method of transformation value (hereinafter, simply “transformation value”) may be used; however, this methodology must be operatively articulated in such a way that consideration may be made of the components characterizing the areas by means of appropriate assessment parameters that go towards forming these areas’ value: these are the market value discount rate of the area in relation to the uncertainty and risk of reaching effective and concrete buildability, and the estimated time needed to complete the procedural path for making the area actually buildable. In light of the above, this article is focused on the main concept and on the specific evaluating process of the discount rate component characterizing those areas that are considered buildable by the general regulatory plan but at the moment have not yet obtained the final building permit. In order to estimate the discount rate, this

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paper aims to structure a mathematical model applicable in the evaluation of the transformation value of buildable area without building permit. The results expected by this article, therefore, consist of the proposed approach’s operative applicability, that makes it possible to appraise an area in consideration of its actual condition; the result of the estimate represents the market value. Below, Sect. 2 carries out a context analysis relating to the main issues related the attributing building potential, with general reference to the European situation and specifically to the Italian situation. Section 3 analyzes, shortly, the methodology for the assessment of buildable area market value. Section 4 structures a model that will allow us to estimate the parameters characterizing the discount rate, thus making the analytic/indirect method of transformation value implementable. Section 4 draws the conclusions of this article.

2 Overview on the Development of Planning Tools in Europe and Focus on Constraints Operating in Italy Analysis of the constitutions of a significant sample of European Union member states (Italy, France, Portugal, Austria, Germany, Netherlands, Spain, Greece), and the United Kingdom (the latter in the phase of ending its membership in the European Union) has cast light on how none of them have outlined a full-blown spatial planning system, the configuration of which is therefore usually devolved upon ordinary lawmaking. The European Commission, in The EU compendium of spatial planning systems and policies [9], categorized the Member States’ various approaches in the matter of urban planning and consequent attribution of building rights: a. The regional economic planning approach, in which spatial issues are compared with economic ones and planning takes account of and integrates both aspects. This system is typical of France and Portugal. b. The comprehensive integrated approach, marked by a hierarchy of plans (topdown) and aiming more towards spatial coordination than towards economic development. This system is typical of Austria, Germany, and Netherlands. c. Land-use management, aimed mainly at controlling and regulating land use, with regard to a sustainability parameter. This approach is typical of the United Kingdom. d. Urbanism, circumscribed to the urban and building dimension. This approach is widespread in the Mediterranean area, and is typical of such countries as Italy, Spain, and Greece. Although European planning follows several different approaches, the procedural paths by which the areas subject to planning are zoned and, where necessary, attributed building rights, is, in general, the same in a European setting: responsibilities relating to spatial (and economic and social) planning are divided between the state and local authorities. The decision as to an area’s buildability, on the other

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hand, is generally the responsibility of local authorities/bodies; the formal attribution of building rights is vested with bodies/authorities at the supra-local level [17, 24]. With this approach, the process of attributing building rights to an area subject to planning does not depend on instantaneous administrative action yielding juridical effects from the moment it is taken, but is the outcome of an administrative process extended over time. It bears pointing out that, with specific reference to the Italian case, the phase of adoption of general/implementation planning instruments with which the process of attributing a building potential to an area is initiated, can be considered apart from the in-depth examination of all the limitations and/or constraints (competence for which is assigned to specific public authorities) that act upon the chosen area; a first general verification of consistency with higher-order juridical/administrative provisions and with the consequent constraint provisions, the prerogative of the process within the procedure, comes after the adoption phase and before the approval phase enshrining the building right. In general, in the adoption phase, the choices and provisions based on “local” reasons prevail. These include, for example, satisfying residential and non-residential demand, completion of partially urbanized areas regardless of a more complex analysis of the nature of the landscape and of the environment, political consensus; and desire to decentralize residential functions and services to strip down urban centers [7, 20]. Consequently, building provisions adopted, considering the buildable area de facto (even when it is not), during the complex process of approving the urban planning instruments, might be reviewed and even canceled by competent public authorities on limitations and constraints. In other cases, even if the first (general) verification does not “exclude” some buildable areas, the building rights can always be limited during the approval phase—mandatory to the issue of the building permit—if it is necessary for the protection of the asset. In fact, in the process of attributing building potential to an area, the juridical/administrative characteristics that are manifested in restrictions limitations and constraints (restrictions) connoting particular local sensitivities that could place uncertainties and risk on the positive completion of the process connected with attributing building potentials, which ends with the release of the title/building permit that enables construction. Taking the Italian case as a reference, an analysis was done on the regulations in force, and the following constraints and limitations were identified: • Landscape constraints (Clc), regulated by Legislative Decree no. 42/2004, in turn, classified as landscape constraints by law [Clc(l)], declaration constraints [Clc(d)], constraints as per the landscape plan [Clc(p)]. • Archaeological constraints (Cac), also regulated by Legislative Decree no. 42/2004, direct in type [Cac(d)]. • Geological protection measures under art. 89 of Presidential Decree no. 380/2001 (Cgm), in turn, dependent upon the seismic zone where the intervention takes place [Cgm(h)], and mechanical properties of the soil [Cgm(s)]. • Hydro-geological protection limitations and measures (Chm), provided for in the hydro-geological structure instruments prepared pursuant to Law no. 183/1989,

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in turn, classified as measures and restrictions due to flood hazard [Chm(f)], and measures and restrictions due to landslide hazard [Chm(l)]. • Protection measures for vulnerable environmental elements pursuant to Legislative Decree no. 152/2006 (Cem), in turn, classified as measures and restrictions in reserves and protected areas or natural park instituted by Law no. 394/1991 [Cem(p)] or Sites of Community Importance (SCI) instituted with directive 92/43/EEC or Special Protected Areas (SPA) instituted with directive 79/409/EEC [Cem(s)]. Considering once again the Italian apparatus, during the endo-procedural phase of development of general planning tools, the authorities with jurisdiction over the aforementioned constraints express their own assumption about the “general” compatibility between the provision expressed in planning and the constraint discipline. However, the expressed viewpoint does not authorize the construction phase as fore-seen by the general planning tool. In order to proceed with the transformation of the area subject to building planning, it is necessary a specific building permit. If there are restrictions as constraints or limits, the permit can be released only after a specific authorization by the authorities with jurisdiction over the constraint itself. Consequently, an area subject to constraint, even if buildable in accordance with the general planning tool and—at a general level—verified under a binding profile, before obtaining the qualification title for its construction it must be promptly checked by the entity with jurisdiction on the constraint itself. It will analyze in detail the compatibility of the transformation with the constraint discipline. It is evident that during the detailed analysis of the constraint discipline, conditions may emerge that limit the development of building potential and the timescales for obtaining endprocedural opinions aimed at issuing building permits. Hence the decrease in the value of a buildable area subject to restrictions compared to an area without restrictions. In this sense, each constraint “discounts” a specific percentage of the value of the area. To respond to this problem, in this article it is proposed a model to determine these percentages (differentials or influences) of discount, to be applied in the assessment of buildable area market value.

3 Methodology for Buildable Area Assessment In accordance with international assessment standards [15, 22]; European Group of Valuers’ Associations [10, 14], a property’s market value, including the buildable areas, must be estimated considering its highest and best use (hereafter, HBU). The HBU considers a real estate resource’s most profitable use, taking note of the fact that potential purchasers are willing to pay a price that reflects their expectations as to the most fruitful use of the resource, chosen in the context of the uses that are possible and permitted by regulations. The HBU thus reflects a current (market) value of a property, with a view to its best use—current or attainable upon adequate

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transformation. To implement the assessment of a property’s HBU, it is therefore necessary to estimate its (related to the direct use of the asset), the different transformation values (as many as there are the eligible transformations), and any subrogation values. The highest of these values corresponds to the HBU. The most profitable use may also be the current one if the market value (direct utility) is greater than the alternative uses (indirect utility). Considering that EU planning practice ordinarily assigns to an area susceptible to being urbanized a univocal intended use and a building potential index, it follows that the possibility of transformation, at least in the planning phase itself, is only one, and that its market value thus coincides with the transformation value (TV) with regard to the only admitted transformation option. With reference to the above, the specific aim of this article is to provide elements (r , the discount rate) useful to determine the market value (or transformation value) of a buildable area. In order to implement the HBU in accordance with the most widespread current scientific literature, it is therefore necessary to estimate the transformation value (TV), using the following formula which represents the transformation value method indirect-analytic equation (TVM) [11, 14]: TV =

 MV(bl) − Kp (1 + r )n

To implement the formula that allows a buildable area to be estimated, the MV (bl) (which is to say the MV of the buildable area, provided by the total of MVs of the buildings erected there) and Kp (or the total costs necessary for the transformation) estimate may be resolved via the collection—from sources of information—of known prices of similar real property. An area’s physical characteristics impact both the MV (bl) and the kp; moreover, if the kp includes the calculation of the promoter’s profit and the initiative’s financial interests, the parameter r’ becomes a discount coefficient with respect to the area‘s juridical/administrative characteristics: in sum, it reflects the difficulty and/or uncertainty of completing the area’s transformation process due to constraints and limitations requiring more verifications and various authorizations. This discount therefore corresponds with an extra profit for the promoter, commensurate with the risks taken on. Lastly, the parameter n represents the time coefficient connected with the intervention’s duration, which becomes the exponential parameter of the discount. Therefore, in appraising the value of a buildable area, particular attention must be given to estimating the parameters r and n, the former depending on the juridical/administrative characteristics and the latter on the times connected with the procedural path (general urban planning instrument, or the implementation plan) attributing the building right. In order to grant practical applicability to the transformation value method, the two variables must be transformed into parameters usable in the context of assessment: variable 1 is translated into a discount rate, the sum of various coefficients correlated

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with the different “juridical/administrative” characteristics; variable 2 is translated into the discount rate’s exponent. In this article the focus is on variable 1: in order to obtain discount coefficients related to the “juridical/administrative” characteristics of buildable areas, a model has been structured that, through the use of inverse power functions, elaborates the price differentials found in the market of areas with different juridical/administrative characteristics, and yields discount differentials specific for the individual characteristics. The use of the indirect-analytical TVM, implemented considering the two variables above-mentioned, allows us to consider the specificity of each building area. Even in the framework of an active market, the several juridical-administrative characteristics concerning a buildable area (the nine different variables connected to constraints) generate thousands of possible overlapping constraints; it strongly reduces the possibility of intercepting data referring to comparable assets characterized by the same juridical-administrative conditions. In other words, buildable areas, even if belonging to the same real estate market, are strongly differentiated, in term of value, from the juridical-administrative condition that derives from the imposition of constraints underlying the administrative activity of land management and the environmental-and-landscape protection.

4 A Model for Determining the Discount Rate in the Market Value Assessment of Buildable Area Without Building Permit The model was constructed in order to determine a set of parameters, “differentials”, or “influences”, through which to be able to estimate the discount rate “r ” for a buildable area (without building permit) being appraised. This is, in sum, a matter of estimating the impact of juridical/administrative characteristics on the rate, or the constraints and limitations, that characterize the area itself. The differentials estimated hereafter vary the industrial performance rate which, generally, for territorial transformation interventions, is on the order of 10–25% [3, 5]; in other words, the overall incidence of the constraints and limitations that may be encountered in this types of area represents what is commonly understood as the “urban planning risk” of a territorial transformation intervention [16, 21]. The estimate of the overall incidence (iCO ) of these constraints and limitations on the rate is provided by the following formula: iCO = iClc + iCac + iCgm + iChm + iCem

(1)

where: iCO = overall incidence of constraints and limitations on the rate; iClc = incidence of constraint/limitation Clc; iCac = incidence of constraint/limitation Cac; iCgm

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= incidence of constraint/limitation Cgm; iChm = incidence of constraint/limitation Chm; iCem = incidence of constraint/limitation Cem. The incidence of each constraint/limitation depends on the partial incidence ip of the various components of the constraint/limitation. The incidence of constraint/limitation Clc (landscape constraints) equals: iClc = dClc(l) + dClc(d) + dClc(p)

(2)

where: dClc(l) = differential due to the component Clc(l) “landscape constraints by law”; dCcl(d) = differential due to the component Clc(d) “declaration constraints”; dClc(p) = differential due to the component Clc(p) “constraints of landscape plan”. The incidence of constraint/limitation Cac (archaeological constraints) equals: iCac = dCac(d)

(3)

where: dCac(d) = differential due to the component Cac(d) “direct archaeological constraints”. The incidence of constraint/limitation Cgm (geological limitations) equals: iCgm = dCgm(h) + dCgm(s)

(4)

where: dCgm(h) = differential due to the component Cgm(h) “geological limitations due to seismic hazard”; dCgm(s) = differential due to the component Cgm(s) “geological limitations due to mechanical properties of poor soil”. The incidence of constraint/limitation Chm (hydro-geological limitations) equals: iChm = dChm(f) + dChm(l)

(5)

where: dChm(f) = differential due to the component Chm(f) “hydro-geological limitations due to flood hazard”; dChm(l) = differential due to the component Chm(l) “hydro-geological limitations due to landslide hazard”. The incidence of constraint/limitation Cem (elements of environmental vulnerability) equals: iCem = dCem(p) + dCem(s)

(6)

where: dCem(p) = differential due to the component Cem(p) “park element present”; dCem(s) = differential due to the component Cem(s) “SPA/SCI (special protected area/site of community importance) element present”. The overall incidence is therefore provided by the sum of the incidence of each of the constraints/limitations which, in their turn, are provided by the sum of the differentials of each of their components. It therefore becomes necessary to estimate, for each constraint/limitation, the differential of each of its components through the following inverse power function in Equation:

A Model for Determining a Discount Rate …

 dCx(x) = t [Cx(x) ]n − 1

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(7)

where: dCx(x) = differential of the component x of constraint/limitation x. [(Cx(x) ]n = difference of value relating to survey sample n, between a buildable area without the limitation as per component x of constraint/limitation Cx and a buildable area with the limitation as per component x of the same constraint/limitation. t = the presumed number of years for the completion of the settlement transformation. The survey sample therefore consists of data related to the value (expressed via the known price) of two similar areas, in a homogeneous territorial context, that are differentiated only by a component of a constraint/limitation. It is therefore possible to estimate [Cx(x) ]n using the following Equation: [Cx(x) ]n = Pnl[Cx(x) ]n /Pwl[Cx(x) ]n

(8)

where: Pnln = is the known price of a buildable area without limitations. Pwln = is the known price of a buildable area with the limitation as per the component Cx(x) . The condition for being able to consider the survey sample valid is for Pnln to be less than Pwln ; otherwise, the solution is out of the ordinary and the datum is not considered. Considering a non-defined number of survey samples, Eq. (8) becomes Eq. (10):

dCx(x)

  

   Pnl Cx(x) Pnl Cx(x) 2 Pnl Cx(x) n t 1  + + ... + /n) − 1.    = Pwl Cx(x) 1 Pwl Cx(x) 2 Pwl Cx(x) n

(9)

For the model’s implementation, it therefore becomes necessary: • to obtain a significant number of survey samples, or known prices of homogenous buildable areas. • to apply Eq. (10) for the estimate of the differentials of each component of each relevant variable. Lastly, considering, then, that the industrial performance rate is provided by the sum of the differentials due to the component of the constraints/limitations in Equation: r = iCO = d1 + d2 + . . . + dn ,

(10)

it is possible, having implemented the model and obtained the results relating to the incidences, to estimate r as follows from Equation:

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r = dClc(l) + dClc(d) + dClc(p) + dCac(d) + dCgm(h) + dCgm(s) + dChm(f) + dChm(l) + dCem(p) + dCem(s) .

(11)

Equation (12) represents nothing more than the sum of the different incidences in Equation: r =

n 

d

(12)

k=0

From the sum of Eq. (13), r , an element essential for estimating the value of the area, may then be estimated. This parameter can be effectively used within the Transformation Value Method; it must be also estimated the time needed to complete the required conversion, also taking into account the increase in authorization times for obtaining opinions related to any specific constraints operating in the area [19, 21, 23].

5 Conclusions The use of the transformation value method, along with the proposed model to assess a discount rate related to restrictions (constraints and limits), allows a buildable area to be appraised as it is, contemplating presumable risk before the actual condition of buildability is juridically enshrined with the obtaining of building permit. In sum, the proposed innovation consists of estimating the “urban planning risk” component related to constraints in real estate initiative. The model, starting from simple data referring to specific situations, made it possible to determine influences in the rate of industrial profitability rate correlated with the urban planning risk of buildable areas without permits; a strength consist in the possibility to use data obtained in a extended territorial context: these influences may have a broader validity in territorial terms since, from one territorial environment to another, it is held that the “effects” produced by the so/called “limitations”—in terms of reducing the areas’ value—are substantially analogous: in this sense, the incidences, once defined, have a broad field of action. Further examination may be structured on linear or multiple regression models, being able in this way to process data referring to areas that present several constraints/limitations at the same time. Acknowledgments This work is an extract of a Research funded by Sapienza University of Rome, granted by “Research Project”, 2019. The title of Research is: “Real estate taxation: periodic taxation of of building areas with planning tools adopted but not yet approved. An EU matter particularly relevant for Italy”. Scientific Director: Prof. Orazio Campo.

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23. Simonotti M (2006) Metodologia estimativa. Flaccovio D. Metodi di stima immobiliare. Applicazione Degli Standard Internazionali Palermo, Italy, pp 1–423 24. Stanghellini S (2012) Il Negoziato Pubblico Privato nei Progetti Urbani. DeI Rome, Italy, Principi, Metodi e Tecniche di Valutazione

A Rational Assessment Procedure of Long-Term Sustainable Values for Bank Lending Purposes Francesco Tajani, Pierluigi Morano, Vincenzo Del Giudice, and Pierfrancesco De Paola

Abstract Even if the mortgage lending value is not included among the bases of valuation recognized by the International Valuation Standards, the consequences of the global financial crisis have pointed out the peremptoriness for the banking institutions to adopt, in the transactions that involve properties as credit exposure guarantees, sustainable value judgments in the long term. In the present work, with reference to the Italian context, a rational method for estimating the mortgage lending value is proposed and tested. By borrowing the operating logic of the Value at Risk and introducing appropriate assumptions, the model is based on a time series analysis of the property values recorded in the Italian regional capitals, and assesses the abatement coefficients of the market value according to the location of the urban area (central, semi-central or peripheral) of the property. The coefficients obtained satisfy the need for a rational assessment of the property risk and for an appropriate spatial contextualization of the risk components as regards the local demand and supply. Keywords Long-term sustainable value · Value at risk · Mortgage lending value · Market value · Italian property market F. Tajani (B) Department of Architecture and Design, Sapienza University of Rome, Via Flamina 359, 00196 Rome, Italy e-mail: [email protected] P. Morano Department of Civil Engineering Sciences and Architecture, Polytechnic University of Bari, Via E. Orabona 4, 70126 Bari, Italy e-mail: [email protected] V. Del Giudice · P. De Paola Department of Industrial Engineering, University of Naples Federico II, Piazzale V. Tecchio, 80125 Naples, Italy e-mail: [email protected] P. De Paola e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Morano et al. (eds.), Appraisal and Valuation, Green Energy and Technology, https://doi.org/10.1007/978-3-030-49579-4_21

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1 Introduction In a recent editorial, French [10] has highlighted that “[…] if I had to determine the most used word in real estate and business for the last ten years, it has to be uncertainty”. The latest financial—the emergence of substantial non-performing loans in the bank balance sheets—and political events—Brexit, duty war on import products, government instability in several EU countries—have further accentuated the market volatility, confirming the urgency of defining assessment techniques able to effectively take into account the uncertainty [12]. The notion of sustainable valuation emerged in the real estate industry in the aftermath of the most recent global downturn, when it became obvious that some properties had been overvalued [1]. Within the definitions of value for bank lending purposes, although the market value (MV) remains the most widely used basis of valuation, in several countries the determination of long-term sustainable value is provided, to be assessed in conjunction with the market value, and its most wellknown application is the mortgage lending value (MLV). The European Union Capital Requirements Regulation defines the MLV as “the value of immovable property as determined by a prudent assessment of the future marketability of the property taking into account long-term sustainable aspects of the property, the normal and local market conditions, the current use and alternative appropriate uses of the property” (art. 4(74)). The European Banking Authority (EBA) has also pointed out the need to apply the MLV in the context of secured lending valuations, and to achieve a harmonization of rigorous criteria for the respective assessment, in order to “provide long-term, sustainable value as a stable basis for judging the suitability of a property as a security for a mortgage which will continue through potential market fluctuations” [7]. Therefore, the pro-cyclical nature of the market value has prompted a search for a counter-cyclical value, i.e. the MLV, that can be defined as the lowest market value that will characterize the property during the loan amortization period. In Table 1 the European countries that currently rely on market value to set loan to value limits and the European countries that include an assessment of a long-term value, or more specifically the MLV, for secured lending purposes [19] have been reported. It should be noted that not all European countries have a system of MLV or another form of long-term value in place: currently, about in 1/3 of the European countries the assessment of a long-term sustainable value is provided. In particular, Germany has provided the most well-established set of guidelines for the assessment of a “time-sustainable value”, that is the German Beleihungswert [24], usually translated as “mortgage lending value”, which dates from as long as 1900. The reformation of the German Pfandbriefgesetz [23] establishes that the assessment of the mortgage lending value should not be developed through a derivative approach, i.e. the preliminary valuation of the property market value and then the application of an abatement coefficient—that is the generally applied approach by the banking institutions—, but it requires the determination of an independent value, assessed through indirect procedures as the income approach and the cost approach, with

A Rational Assessment Procedure of Long-Term Sustainable Values … Table 1 Classification of the European countries in relation to the “value” to be assessed for bank lending purposes

Country

Market value

Long-term value ٧

Austria Belgium

٧

Bulgaria

٧

Cyprus

٧ ٧

Czech Republic Denmark

٧

Finland

٧ ٧

France

٧

Germany Greece

317

٧ ٧

Hungary Iceland

٧

Ireland

٧

Italy

٧

Latvia

٧ ٧

Luxembourg The Netherlands

٧

Norway

٧ ٧

Poland Portugal

٧

Romania

٧

Russia

٧

Slovakia

٧

Slovenia

٧ ٧

Spain Sweden

٧

Turkey

٧

United Kingdom

٧

appropriate assumptions on the parameter values. These methodologies, however, have been strongly criticized [13]. In Spain, the Ministry of Economics Order ECO/805/2003 established that the properties as credit exposure guarantees had to be assessed through “a prudent valuation of the future possibility of trading […], taking into account the long-term durable aspects”. In France, properties financed by the loans are assessed according to the Regulation No. 99-10, establishing that “the valuation is conducted on the basis of the long-term lasting characteristics of the building […]. This mortgage value, at the most, is equal to the market value”.

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In most of the Anglo-American countries the MLV concept did not exist and there was a significant lack of understanding of the German methods for the assessment of the MLV [20].

2 Aim The recent global downturn has highlighted that, in the valuations for bank lending purposes many properties had been overestimated, in part due to overly optimistic assumptions about the future market trends [1]. The extreme volatility of the market have pointed out that the property prices can change unexpectedly, and the risks and the opportunities created by possible future price movements could be major market drivers for the investors. Taking into account the directives of the EBA concerning the definition of univocal and rational criteria for the assessment of the MLV, the Italian Banking Association has supported the inconsistency and the danger of determining the MLV through a simple and lump-sum percentage abatement of the market value. According to Quentin [17], the MLV calculation should be “unattached by temporary, e.g. economically induced, fluctuations in value on the relevant property market and excluding speculative elements […] by taking into account the long-term sustainable aspects of the property, the normal and local market conditions, the current use and alternative appropriate uses of the property”. In this framework, the research carried out by Bienert and Brunauer [3] has outlined the best performance of the classical valuation approach of the MLV (i.e. first, the market value is determined and then, the mortgage lending value is obtained from this market value through the application of appropriate security reduction coefficients), if the ordinary practice of lump-sum discounts is replaced by the reasonable assessment of the investment risks. In this work, with reference to the Italian context, a rational methodology for the assessment of the mortgage lending value is proposed and tested. Borrowing the logic of the Value at Risk [16] and introducing appropriate assumptions for the assessment of the variables, the model provides for a time series analysis of the property values detected in the Italian regional capitals for the residential and commercial intended uses and assesses the abatement coefficients of the market value considering the location of the property (central, semi-central and peripheral urban area). The paper is divided into three sections. In paragraph 3 the developed model is described: the assumptions are formulated and the procedure for the determination of the abatement coefficients of the market value is explained. In paragraph 4 the application of the model is presented: as regards the Italian regional capitals, for each urban area and for each intended use considered, the abatement coefficients of the market value for the assessment of the MLV are valued. Finally, in paragraph 5 the conclusions of the work are discussed.

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3 The Model The model proposed for the assessment of the mortgage lending value borrows the logic of the Value at Risk (VaR) and, by providing suitable assumptions, allows a rational and reliable valuation of the property investment risks. Specifically, the model hypotheses can be summarized as follows.

3.1 Determination of the Property Investment Risk In general terms, the VaR can be defined as the measure of the maximum potential loss that an investment may incur, within a future time period and under the assumption of a certain confidence level. The mean/variance approach is undoubtedly the most widespread procedure for estimating the VaR, due to its simplicity as regards the underlying concepts and the mathematical formulas for the calculation. Assuming that the time series of the analyzed investment yields are characterized by a statistical normal distribution, the mean/variance approach determines the VaR of an investment through the product of four parameters: (a) the market value of the investment considered (MV ); (b) the volatility of the time series of the investment yields (σ i ); (c) a scalar factor (β) that, due to the hypothesis of normal distribution of the the past investment yields, allows to obtain a measure of the risk corresponding to the expected level of confidence; (d) the amplitude (n) of the time horizon on which the potential loss is to be measured. Through these four parameters, the VaR of an investment can be determined by Eq. (1). VaR = M V · σi · β ·

√ n = M V · σi · α

(1)

It is assumed that the risk related to a specific intended use is equal to the volatility of the time series of the real (i.e. adjusted for inflation) annual revaluation rates of the selling prices collected for that intended use, in a significant time period and in the urban area in which the property to be assessed is located [22]. The annual revaluation rates are obtained by the geometrical method of Eq. (2): Rt = ln where:

Pt Pt−1

(2)

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Rt = real annual revaluation rate of the selling prices detected, relating to the year t; Pt = selling price recorded in the year t; Pt − 1 = selling price collected in the year t − 1. The volatility of each intended use is then determined through the Exponentially Weighted Moving Average (EWMA) method [14, 21], that allows to assign higher weights to the revaluation rates “closer” to the valuation moment. Therefore, the standard deviation is assessed by Eq. (3):   T   σi = (1 − λ) · λt−1 · (Rt − R)2

(3)

t=1

where: σ i = standard deviation of the time series of the real annual revaluation rates; λ = decay factor, usually set equal to 0.94 [2]; R = mean value of the time series of the real annual revaluation rates; T = temporal amplitude of the time series of the real annual revaluation rates. The EWMA procedure allows, on the one hand, to avoid leptokurtosis phenomena (“fat-tailness”) which often concern the distribution of investment yields, on the other hand, to overcome the higher complexity of econometric models, such as the generalized autoregressive conditional heteroskedasticity (GARCH) models [4], which, although more precise than the EWMA models, require a large amount of data to be developed. Furthermore, the empirical evidence does not provide a precise indication of the best forecasting capacity of a GARCH model compared to a simpler model based on the exponential moving average technique [18].

3.2 Choice of the Reference Year for the Valuation of the MLV and of the Observation Period of the Selling Price Time Series The selling price time series have been detected for the period 1967–2015, as regards the central, semi-central and peripheral urban areas of the Italian regional capitals1 and the residential and commercial intended uses. The reference year for the valuation of the MLV is 2008, i.e. the starting year of the subprime crisis and consequently of the consistent reductions of the national (and international) property values. The observation period of the selling price time series for the assessment of the volatility 1 In

the research carried out, the regional capital of L’Aquila (in the Abruzzo region) has not been considered, due to the impossibility to collect reliable selling price time series in the period following the earthquake in 2009.

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(σ i ) is 1967–2008. The selling price time series for the following years (2009–2015) have been implemented in the estimation of the safety factor (α).

3.3 Definition of the Reliability Period of the Estimated MLV The reliability period constitutes the timeframe in which it is considered probable that there will be no depreciation [15] of the property higher than the estimated one. It is undoubtedly the most delicate phase in the valuations of properties as credit exposure guarantees. In this study, the model operates the assessment of the MLV in 2008 (year of evaluation) starting from the subsequent selling prices, recorded in the period 2009–2015. In this sense, the operating logic of the model verifies the permanence of conditions, a principle derived from economic theory [5] and that represents a milestone of the Italian appraisal approaches [6, 8, 9]. Therefore, the reliability period is set equal to seven years, and the maximum depreciation that the property may bear in this period is known a priori, as it is equal to the lowest selling price recorded on the market in the seven years following the reference year for the valuation (2009–2015). The period of validity has been fixed in order to coincide with the average duration of a property cycle [3].

4 Application of the Model The time series of the selling prices detected for the years 1967–2015 in the central, semi-central and peripheral urban areas of the Italian regional capitals and for the residential and commercial intended uses, have been obtained through elaborations on the data published by institutional Entities (Italian Revenue Agency, Scenari Immobiliari and Nomisma) and private research institutes. Taking into account the assumptions of the model, for each urban area and intended use of the Italian regional capitals it is necessary: (a) to determine the volatility σ i of the revaluation rates in the year 2008; (b) to collect the selling price in the year 2008 (MV 2008 ) and to identify the lowest selling price in the period 2009–2015 (MV min(2009–2015) ), i.e. the MLV of the property in the year 2008 (MLV2008 ). Starting from Eqs. (1) and (4) can be obtained: MLV2008 = M Vmin(2009−2015) = M V2008 · (1 − σi · α)

(4)

In Eq. (4) the unique unknown variable is the safety factor α, therefore:  1− α=

MV

min(2009−2015)



M V2008

σi

(5)

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Fig. 1 Abatement coefficients [%] for the Italian regional capitals (residential intended use)

The parameter α represents the safety factor for the assessment of the MLV in the year 2015 and for the reliability period 2016–2022, i.e. the following seven years. In general terms, taking into account that n is the reliability period for the assessment of the potential loss of value, Eq. (6) allows the valuation of the MLV in a generic i-th year: M L Vi = M Vmin(n) = M Vi · (1 − σi · α)

(6)

In Figs. 1 and 2 the abatement coefficients of the market value for the Italian regional capitals and respectively the residential and commercial intended uses are represented (σi · α), to be applied for the assessment of the mortgage lending value, and relating to the central, semicentral and peripheral urban areas considered. The results obtained generate interesting considerations. For the residential intended use, the average values of the abatement coefficient for the sample analyzed are 10.12% for the central urban areas, 12.31% for the semicentral areas, 10.80% for the peripheral areas. The maximum abatement factor value is recorded for the semi-central area of the city of Rome (=28.59%). In 58% of the cases analyzed the abatement coefficient value is higher than 10%, whereas in seven areas—the semi-central area of the city of Cagliari (=22.90%), the three macro-zone of the city of Milan (=20.27% for the center, 23.48% for the semi-center, 20.24% for the periphery), the peripheral area of the city of Naples (=25.27%), the central (=21.16%) and semi-central areas of the city of Rome—there is a difference between

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Fig. 2 Abatement coefficients [%] for the Italian regional capitals (commercial intended use)

the market value and the mortgage lending value higher than 20%. In three cases— central area of the city of Aosta, central area of the city of Turin, peripheral area of the city of Trieste—the abatement coefficient value is zero, i.e. the mortgage lending value is equal to the market value: this phenomenon occurs when the lowest market value in the period 2009–2015 is higher than the market value recorded for the year 2008, i.e. the value that, according to the logic of the model, already represents the mortgage lending value of the property. For the commercial intended use, the average values of the abatement coefficients for the analyzed cities increase compared to the residential intended use (=11.42% for the central areas, 13.33% for the semi-central areas, 14.19% for the peripheral areas): this phenomenon confirms the empirical evidence of the higher volatility of the commercial property market compared to the residential one, and therefore a higher market appreciation of the riskiness of this investment compared to the classic residential segment. The maximum abatement factor value is recorded for the peripheral area of the city of Campobasso (=29.84%). In 62% of the cases analyzed the abatement coefficient value is higher than 10%, whereas in eleven areas - the peripheral area of the city of Cagliari (=20.67%), the three macro-zones of the city of Campobasso (=22.45% for the center, 22.58% for the semi-center, 29.84% for the periphery), the three areas of the city of Naples (=25.30% for the center, 24.04% for the semi-center, 24.80% for the periphery), the central area of the city of Turin (=21.91%), the central (=22.52%) and peripheral areas (=22.53%) of the city of Venice—there is a discrepancy between the market value and the mortgage lending value higher than 20%. In a single case—peripheral area of the city of Turin—the

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abatement coefficient value is zero, whereas in three cases—central area of the city of Cagliari (=0.73%), central area of the city of Florence (=0.72%), central area of the city of Trieste (=0.75%)—it is less than one.

5 Conclusions In the post-global financial crisis, the regulatory authorities are placing an increasing attention on the real estate valuations. The subprime phenomenon has highlighted that the over-lending secured on real estate market, generated by unreliable valuations and detached from the real market conditions, has been the main cause of the economic instability in the recent years. Since then, a broad reflection has taken shape on macro-prudential policies, with particular reference to the financial-credit sector for the purchase of houses and for the construction companies. The logic of these policies is to intervene with tools that avoid, in the presence of an overheating of the market, credit practices that exacerbate the risks of overexposure of the financialcredit sector towards the property market. It is evident that there is the cogency of high professional skills in the real estate sector: in this context, the formalization of shared and uniform rules and principles in the International Valuation Standards has become peremptory, in order to guarantee a common language among professionals and the public interest in the integrity of the valuation process [11]. In fact, it should be highlighted that increasing price trends imply higher disbursements of debt capital and, consequently, higher risks on the value variations of the properties as guarantees for the credit exposures, if the property prices change the positive trend and begin to decline. Therefore, appropriate assessment procedures, that could intercept the actual trends of the property market, are strongly required. The concept of a longterm value, able to incorporate the uncertainties on the future scenario evolutions and to monitor the loan performance, is currently the main reference of the central banks. Within the methods for the assessment of the mortgage lending value, the present work proposes a rational approach which attempts to refine, in the context of the classical derivative approach for the estimation of the MLV, the valuation of the abatement coefficients to be applied to the market value. The permanence of the conditions principle—a fundamental concept of the Italian appraisal theory—constitutes the basic logic underlying the methodology proposed: according to this principle, the economic decisions are developed by considering the information generally available on the market in the reference time of the valuation and taking into account the normally expected changes. According to this principle, the abatement coefficients determined are not fixed, but they will be annually updated taking into account the market time series that will be available in the subsequent years. Similarly, the future evolution of the selling prices can indicate possible modifications in the property cycle duration considered, and consequently of the reliability period assumed in the model.

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References 1. Askew J (2016) When market value is not enough. RICS Property J 2. Best P (2000) Implementing value at risk. Wiley, England 3. Bienert S, Brunauer W (2007) The mortgage lending value: prospects for development within Europe. J Property Investment Finance 25(6):542–578 4. Bollerslev T (1986) Generalized autoregressive conditional heteroskedasticity. J Econometrics 31(3):307–327 5. Cozzi S, Zamagni T (1992) Economia Politica. Il Mulino, Bologna (Italy) 6. Di Cocco E (1960) La Valutazione Dei Beni Economici. Calderini, Bologna (Italy) 7. European Banking Authority (2015) Opinion of the European banking authority on mortgage lending value (MLV). www.eba.europa.eu 8. Famularo N (1963) Teoria e Pratica Delle Stime. Unione tipografico-editrice torinese (Italy) 9. Forte C, De Rossi B (1974) Principi Di, Economia edn. Estimo, Etas Libri, Milan (Italy) 10. French N (2018) Will markets ever be normal again? J Property Investment Finance 36(2):134 11. Gilbertson B, Preston D (2005) A vision for valuation. J Property Valuation Investment 23(2):123–140 12. Joslin A (2005) An investigation into the expression of uncertainty in property valuations. J Property Investment Finance 23(3):269–285 13. Kleiber W (2005) Babylon schreitet voran. Grundstucksmarkt und Grundstuckswert, Koln 6:1 14. Lowry CA, Woodall WH, Champ CW, Rigdon SE (1992) A multivariate exponentially weighted moving average control chart. Technometrics 34(1):46–53 15. Mansfield JR, Pinder JA (2008) Economic and functional obsolescence. Property Manag 26(3):191–206 16. Marshall C, Siegel M (1997) Value at risk: implementing a risk measurement standard. J Deriv 4(3):91–111 17. Quentin J (2009) The subprime crisis: implications for property valuation. Deutsche Hypothekenbank (Germany) 18. Resti A, Sironi A (2008) Rischio e Valore Nelle Banche. Egea, Milan (Italy) 19. RICS (2017) Bank lending valuations and mortgage lending value (Europe). www.rics.org 20. Shea-Joyce S (2001) The appraisal of real estate, publication No. 12. Appraisal Institute, Chicago, IL 21. Tajani F, Morano P (2018) An empirical-deductive model for the assessment of the mortgage lending value of properties as securities for credit exposures. J Eur Real Estate Res 11(1):44–70 22. Tajani F, Morano P, Salvo F, De Ruggiero M (2019) An evaluation model for an effective risk assessment in the rent to buy property market. Property Manag 38(1):124–141 23. VDH (2004) Musterwertermittlungsanweisung. Verband deuscher Hypothekenbanken (Berlin) 24. Werth A (1998) Vom Verkehrswert unabhangige Beleihungswerte im Blcikfeld der Europaischen Union. Grundstucksmarkt- und Grundstuckswert 5:S257–S266

A Multi-criteria Decision Analysis for the Assessment of the Real Estate Credit Risks Marco Locurcio, Francesco Tajani, Pierluigi Morano, and Debora Anelli

Abstract Following the approval of the Basel capital adequacy framework, the credit institutions had to adapt their skills to determine regulatory capital. In this regard, the methodologies defined by the Third Basel Accord [2] have some limits concerning the subjectivity of some aspects and the complexity of the valuation models, especially for the smaller credit institutions that lack suitable corporate structures capable of efficiently applying the established procedures. In order to overcome these issues, the Income Producing Real Estate Risk Index (I I P R E,risk ) is proposed in this work to provide a synthetic index of multi-criteria derivation as a useful reference in the decision-making processes relating to restructuring and debt relief operations. Keywords Credit risk analysis · Mortgage loan risk analysis · MCDA · AHP · TOPSIS

1 Introduction The economic crisis, which began in 2007, has also worsened due to the excessive financial leverage possessed by credit institutions, the low quality of the assets and the insufficient liquidity reserves. The effects of the crisis have thus led the banking M. Locurcio (B) · P. Morano Department of Civil Engineering Sciences and Architecture, Polytechnic University of Bari, Via E. Orabona 4, 70126 Bari, Italy e-mail: [email protected] P. Morano e-mail: [email protected] F. Tajani · D. Anelli Department of Architecture and Design, Sapienza University of Rome, Via Flamina 359, 00196 Rome, Italy e-mail: [email protected] D. Anelli e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Morano et al. (eds.), Appraisal and Valuation, Green Energy and Technology, https://doi.org/10.1007/978-3-030-49579-4_22

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system to a series of difficulties linked to the absorption of systemic losses on trading activity and on loans and the renegotiation of debt positions. Moreover, the market has lost confidence in the solvency of many banks, producing a contraction in available credit that the public sector has tried to counteract, at least in part, through liquidity injections and recapitalizations. This situation has caused significant negative impacts on loans for the purchase of the properties, leading to the crisis in the whole construction and real estate sector, with its effect on other sectors of the economy. In order to sort out the weaknesses of the banking system, the Basel Committee has introduced a series of reforms aimed at increasing the assets stability of the banks. By considering the overall dimensions assumed by the financial system, the birth of the intermediaries deemed Too Big to Fail (TBTF), their excessive financial leverage, in 2011 the set of rules known as Basel III has been adopted. Basel III is part of the Committee’s continuous effort to improve the banking regulatory framework and refers to some issues of the “International Convergence of Capital Measurement and Capital Standards” document, commonly known as Basel II. Specifically, the measures adopted with the Basel III Accord are based on three pillars already outlined with the previous Basel II: (i) minimum capital requirements; (ii) prudential control; (iii) public disclosure. In order to implement the Basel capital adequacy framework, the credit institutions had to adapt their skills to the required measures aimed at determining the regulatory capital and, for this purpose, a significant role is played by the assessment of credit risk. The most used methodologies (Standard methodology, Internal Ratings-Based Foundation and Internal Ratings-Based Advanced) are often difficult to apply, mainly for the smaller credit institutions that lack suitable corporate structures and for the diversity of the situations with which the credit managers have to deal with. In order to overcome these limits, a multi-criteria derivation index is proposed in this research, as a useful reference in the analysis of credit risk and debt relief operations. The paper is organized as follows: in paragraph 2 the main multi-criteria methodologies adopted for credit risk analysis are presented, in paragraph 3 the proposed index for the credit risk assessment is described and in paragraph 4 the conclusions and the future developments of the research are drawn.

2 The Multicriteria Models for Credit Risk Assessment The development and the employment of quantitative analysis techniques increasingly sophisticated are due to the progressively complexity of financial problems. In this way, the connections between financial theory and the tools of mathematical modelling have become more entwined. For this reason, such observations and findings have motivated researchers to explore the potentiality of the multi-criteria techniques in addressing financial decision-making problems. Many Authors have highlighted the weakness of the system currently adopted for the credit-scoring [4], because it does not allow an effective consideration of the

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occurrence of a failure by the debtors to meet the obligations relating to the loan contract [6], and the various financing aspects [18]. The Multi-Criteria Decision Analysis (MCDA) by applying flexible mathematical methods can be well-suited to overcome the ranking problems: Peng et al. [17] have developed a Multicriteria Convex Quadric Programming (MCQP) model for the credit classifications, whereas Gineviˇcius and Podvezko [9] have implemented a multi-criteria evaluation approach in the banks’ ranking. In particular, the assessment of the credit risk has been one of the major research field in finance for the last four decades [3, 21] have used a Multi Criteria Credit Rating (MCCR) for assessing credit risk of industrial companies by using financial statement data and specific industrial information; Yu et al. [23] have applied an intelligent-agent-based fuzzy MCDA model for credit risk assessment; Li et al. [12] have proposed a multiple kernels multi-criteria programming approach based on evolution strategy for credit risk analysis; Zhang et al. [24], thanks to a Multi-Criteria Optimization Classifier (MCOC), have identified the bad creditors and, consequently, estimated the specific credit risk. A more qualitative approach has been improved by Wang and Lin [22] through the development of an analytic hierarchy framework for supporting the banks in the selection of merged strategies: surveying approximately 150 stakeholders of the Bank of Kaohsiung (Southern Taiwan), they have identified the bank’s priorities based on specific goals and criteria. Furthermore, the MCDA include other numerous types of flexible assessment tools that are highly performing in the contexts characterized by great uncertainty and by the presence of criteria with different units of measure and, therefore, difficult to aggregate such as the land take [1], the regeneration of the urban peripheries [13], the enhancement of cultural heritage [15] and the analysis of urban redevelopment projects [14, 16].

3 The Income Producing Real Estate Index In the following subparagraphs is described the construction of the Income Producing Real Estate Risk Index (I I P R E,risk ) with particular attention to the role of the different subjects involved in the real estate credit sector and to the potential application fields of the proposed index.

3.1 Aim The main methodologies used by the credit institutions to assess the credit risk have the following qualifications: (i) the subjective parameters and the high discretion in assigning the rating; (ii) the formation of possible conflict of interest; (iii) the complex and not always easily understandable and applicable procedures; (iv) the

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need for an articulated corporate structure as a requirement for the application of the methodologies. Furthermore, when the entrepreneur is unable to meet the debt obligations, the Non-Performing Loan (NPL) credit manager has to define the new debt and the new debt repayment period by minimizing the credit institution’s losses and by reducing the payment period depending on the effective financial capacity of the debtor. For this type of situations, the present research aims at establishing a multicriteria based methodology, for the determination of a synthetic risk index—called I I P R E,risk —, able to support the NPL credit manager evaluations when different debt relief scenarios occur for the so called Income Producing Real Estate (IPRE) investment typology. Although in the past many scholars have focused on the analysis of the default rates on home mortgages [5], in the present work the IPRE is analyzed since, in recent decades, in many countries the loans secured by the mortgage on commercial properties have often caused the deterioration of the assets quality of the banking sector. In order to achieve the described aim, the proposed methodology for determining the Income Producing Real Estate Index is structured through six phases, and for each of them the different subjects potentially involved during the entire decision-making process—respectively in column 2 and 3 of the Table 1—are described below: – the decision-maker (DM), i.e. the NPL credit manager who is responsible for the debt relief operations; – the analyst team that, by exerting skills acquired in the sector of the MCDA and the consultancy for the Investment Management and the Advisory, Valuation and Real Estate Services companies, are able to advise the DM in several stages of the decision-making process; – the panel of experts, operating in the real estate finance and the NPL sector with underlying property, that plays a significant role in the determination procedure Table 1 The synoptic framework of the methodology’s phases, of the subjects involved and of the MCDA tools implemented Phase

Description

Subject

Analysis

I

Goal setting

Analyst team and DM

Brainstorming

II

Construction of the criteria matrix

Analyst team

Brainstorming and AHP

III

Determination of the local weights of the criteria

Panel of experts

AHP

IV

Construction of the matrix of the alternatives and normalization of the weights

DM, panel of experts and analyst team

PROMETHEE and TOPSIS

V

Calculation of the global weights

Analyst team

TOPSIS

VI

Aggregation of the weights Analyst team and determination of the best alternative

TOPSIS

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of the importance of each criterion. In particular, the requirements related to the skills of the panel members must cover all the phases of the real estate finance, from the determination of the market value of the property to the management of the Unlikely To Pay (UTP) and the NPL, including the granting of the financing and the restructuring of the debt. In the definition of the Income Producing Real Estate Risk Index (I I P R E,risk ) different MCDA tools correspond to each stage of the procedure, as reported in the last column of Table 1. First of all, the analyst team sets the goal (phase I) which will have to be discussed later in cooperation with the DM. Following, the analyst team proceeds to identify the most suitable criteria (phase II) capable of describing the goal defined in the previous phase. The determination of the importance of each criterion (phase III), by implementing the Analytical Hierarchy Process (AHP), is performed by the panel of experts and the normalization of the weights assigned to some criteria (phase IV) takes place through the use of the Preference Ranking Organization Method for Enriched Evaluation (PROMETHEE). At a later time, the normalizations carried out must be validated by the DM with the support of the panel of experts. Finally, the results are aggregated thanks to the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS), in order to obtain the synthetic index researched (I I P R E,risk ), that represents the best alternative (phase V and VI).

3.2 Criteria and Weights Considered After the definition of the goal, the analyst team proceeds—with appropriate brainstorming sessions—to identify the five criteria which best define the I I P R E,risk : the criteria 1, 2 and 3 consist of the main debt covenants -i.e. the Interest Cover Ratio (ICR), the Debt Service Cover Ratio (DSCR) and the Loan to Value Ratio (LTV)— according to Duke and Hunt [7]; whereas the criteria 4 and 5 -i.e. the Debt relief (s) and the Variation of Debt Repayment Period (t)—are specifically selected according to the predefined goal (see Table 2). With reference to the Table 2, E B I T Table 2 Description of the criteria considered

n.

Criteria

Formula

1

Interest Cover Ratio (ICR)

IC R =

2

Debt Service Cover Ratio (DSCR)

DSC R

3

Loan to Value Ratio (LTV)

LT V =

4

Debt relief

s =1−

5

Variation of debt repayment period

t = tnew − told

EBIT I nter est E x pense EBIT = T otal Debt Ser vice MA APV Old debt N ew debt

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is the Earnings Before Interest and Taxes, M A is the Mortgage Amount, A P V is the Appraised Property Value, tnew is the New Debt Repayment Period and told is the Old Debt Repayment Period. In this phase, the determination of the A P V requires some considerations: the variations in the security value of the properties lead the banks to rectify the credit component, known as the Credit Valuation Adjustment, caused by the deterioration in the creditworthiness of the counterparties. For this reason, the mortgage lending value is the base value that must be considered, representative of the value of the property determined by the appraiser through a prudential assessment of the future marketability of the property itself, taking into account the long-term lasting aspects, the normal conditions of the local market and the current and future function that the property will be able to host. By implementing the AHP the weight wi and, therefore, the importance of each criterion can be defined. This multi-criteria technique assumes that during its choice the DM develops, more or less consciously, a hierarchy of the different elements involved in the decisional process [20]. The use of the hierarchical structures allows to gain a detailed awareness of the complex phenomenon through its division into gradually smaller “units” [10]. The fundamental question that is submitted to the stakeholders in order to define the weights wi of each criterion is: “For the purpose of financing, refinancing and restructuring the debt relating to investment properties, between the criterion a and the criterion b which of them is the most important?” where a, b = 1, 2, 3, 4, 5 are the five criteria considered. The verbal expressions deriving from the pairwise comparisons between the criteria made by the experts are translated into mathematical terms by adopting the Saaty scale [19]. The verification of the internal consistency of the answers (transitive property) and, consequently, of the effective significance of the weight of each criterion with respect to the preferences expressed by the experts, is carried out through the calculation of the Consistency Ratio (CR). At the end of this phase, a specific weight for each stakeholder and for each criterion is thus determined: the final weight associated with each criterion is obtained by the average of the weights assigned by each stakeholder. Subsequently, as each criterion must respect a specific goal of the DM, by using a mathematical operator, the preferences logic, the possible presence of a range for the allowed values for such criteria, the presence of the absolute preference/indifference thresholds or the unacceptability thresholds are reconstructed. In particular, the absolute preference/indifference threshold represents a limit value assigned to the criterion, beyond which the preference is respectively maximum or minimum. The unacceptability threshold, instead, represents a limit value which, if it exceeded, determines the exclusion of the alternative from the comparison: this is equal to impose the principle of non-comparability (typical of MCDA with partial aggregation) for which, under certain conditions, the compensatory effect between criteria no longer exists. In Table 3 there are the main limits that allow to delineate the domain of the admissible solutions according to the hypothetical decisions that the DM will take. Indeed, in the “Operator” column “MAX” or “MIN” are reported for the criteria that have to be respectively maximized or minimized; in the “Range” column can be observed the range of eligibility for each criterion or the acronym NA (not available)

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Table 3 Range and limits of the criteria n.

Criteria

u.m.

Operator

Range

Preference threshold

Veto threshold

1

IC R

%

MAX

NA

NA

>170%

2

DSC R

%

MAX

NA

NA

>100%

3

LT V

%

MIN

20–90%

90%

4

s

%

MIN

NA

NA

>40%

5

t

years

MIN

0–15

15 years

if it cannot be defined; finally, in the last two columns there are the preference and the veto thresholds. The standard limits indicated for the I C R and the DSC R criteria represent their veto thresholds, below which credit institutions should activate safeguard measures; alongside these thresholds, the analyst team has identified the max debt relief (s) equal to 40%. If the veto thresholds are exceeded, the default of the debt is decreed, therefore the corresponding alternative is considered unacceptable. For the L T V and t criteria the analyst team detects a specific range within which a linear preference function is defined with the support of the PROMETHEE: in this way a preference degree ( p) can be assigned, by assuming the logical reasoning of the DM. The two preference functions are respectively: p(t) = 10 −

2 · t 3

p(L T V ) = 12.9 − 14.3 · L T V Taking into account what has been previously described regarding the preference and the veto threshold, it can be observed that if t > 15 years or L T V > 90% then the alternative will be automatically rejected, whereas if L T V < 20% then p(L T V ) = 10.

3.3 Normalization and Determination of the Best Alternative The normalization of the scores assigned to the various criteria for each alternative with the AHP, in the event that the pairwise comparisons does not occur, takes place according to a simplified method represented by the following mathematical expression: pi, j pn i, j =  j pi, j

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where pn i, j is the normalized score that refers to the j-th alternative in relation to the i-th criterion, while pi, j is the value or the preference assigned to the j-th alternative linked to the i-th criterion. The pi, j values for the I C R, DSC R and s criteria derive directly from the specific characteristics of the analyzed alternative, whereas for the L T V and t criteria is necessary to translate the input values into preferences using the functions for determining p(t) and p(L T V ). In order to make the final result as independent as possible from the specific type of standardization and from the worst and best comparison alternative, the TOPSIS technique is applied, as allows to overcome the normalizations’ limits belonging to the AHP. Therefore, the values associated with each alternative are normalized by adopting the following methods: – distributive normalization pn i, j = 

pi, j j=1÷m

xi,2 j

– ideal normalization pn i, j =

pi, j   operator xi, j

  where operator xi, j represents the mathematical operator previously described in column 4 of the Table 3. Hence, the weighted normalized decision matrix is determined, and each element is given by multiplying the score by the weight of the criterion: ri, j = pn i, j · wi Later can be determined the positive ideal solution ( A+ ) and the negative ideal one (A ) collecting the best and worst performance on each criterion and assuming an absolute ideal and anti-ideal point. Then, the Euclidean distances from the positive (A+ ) and the negative (A− ) ideal solution [11] are calculated: −

d+ j =

  + 2 ri − ri, j i=1÷5

  − 2 − dj = ri − ri, j i=1÷5

Finally, the relative closeness C j for each alternative A j is calculated with respect to the positive ideal solution as given by:

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Table 4 Synthesis of the models A+ /A− Best and worst performance

A+ /A− Absolute ideal and anti-ideal point

Distributive normalization

Model 1

Model 2

Ideal normalization

Model 3

Model 4

I I P R E,risk, j = C j =

d− j − d+ j + dj

In this way, for each alternative, four possible C j are obtained associated with as many models characterized by different systems according to the chosen normalization and the approach used to identify the positive (A+ ) and the negative (A− ) ideal comparison solution. In Table 4, the Model 1 is given by the positive and negative ideal solution through the best and worst performance and the distributive normalization, whereas the Model 4 results from the positive and negative ideal solution through the absolute ideal and anti-ideal point and the ideal normalization, and the same goes respectively for the Model 3 and 4.

4 Conclusions The economic crisis that began in 2007 has led banks to acquire high quality regulatory capital in order to reduce the risk exposure; in fact, the crisis has demonstrated that the credit losses and related provisions reduce profit reserves, that constitute an essential part of banks’ primary quality assets. This contingence has also concerned the credit exposures with underlying property, for which a high level of attention is required due to the illiquidity connected with the property investment. The credit managers very often have to analyze the complex debt situations in which the multi-criteria approaches are useful, in order to define a credit-scoring to assist the decision process [8]. Starting from these considerations, the research has analyzed the restructuring and debt reduction operations where the DM is often supported by various subjects who intervene with different roles. In these complex contexts it is necessary to analyze the alternative scenarios by creating a decision-making platform shared among the various stakeholders, that are often characterized by diverging interests. The decisionmaking complexity clashes with operating structures, that are typical of the smaller credit institutions, which are not adequately sized to analyze the various aspects of these transactions: in order to support the credit manager in these circumstances, a synthetic index has been proposed, i.e. the Income Producing Real Estate Risk Index (I I P R E,risk ), which provides a first indication of the reliability of the proposed financial plan.

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The involvement of the stakeholders in the definition of the criteria and the associated weights, helps to achieve a widely shared decision that pursues the predefined goal, i.e. the analysis of the credit risk against different scenarios related to the debt relief operations. The presence of preference and veto threshold prevents the compensatory aspect, that is typical of multi-criteria analysis with a full aggregation approach, from the risk of rewarding scenarios that maximize only the criteria with higher importance, making irrelevant the other ones. The use of different MCDA techniques in an integrated approach has allowed to customize the I I P R E,risk on the reasoning logic of the DM and on the specific issues of the problem analyzed. A future development of the proposed approach could concern the implementation of the I I P R E,risk on a real world credit application involving: (i) the panel of experts (suitably defined) for giving the weights to each criterion; (ii) the credit manager (as the DM) in the validation of the preference functions and the preference and veto thresholds.

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