Affordable Housing as a Profitable Impact Investment: An International Comparison of Real Estate Strategies 9783031070907, 9783031070914

Citation preview

Contributions to Finance and Accounting

Deepiga Vigneswaran Michael Truebestein Matthias Daniel Aepli

Affordable Housing as a Profitable Impact Investment An International Comparison of Real Estate Strategies

Contributions to Finance and Accounting

The book series ‘Contributions to Finance and Accounting’ features the latest research from research areas like financial management, investment, capital markets, financial institutions, FinTech and financial innovation, accounting methods and standards, reporting, and corporate governance, among others. Books published in this series are primarily monographs and edited volumes that present new research results, both theoretical and empirical, on a clearly defined topic. All books are published in print and digital formats and disseminated globally.

Deepiga Vigneswaran • Michael Truebestein • Matthias Daniel Aepli

Affordable Housing as a Profitable Impact Investment An International Comparison of Real Estate Strategies

Deepiga Vigneswaran EBG Investment Solutions AG Zurich, Switzerland

Michael Truebestein University of Lucerne (HSLU) Lucerne, Switzerland

Matthias Daniel Aepli University of Lucerne (HSLU) Lucerne, Switzerland

ISSN 2730-6038 ISSN 2730-6046 (electronic) Contributions to Finance and Accounting ISBN 978-3-031-07090-7 ISBN 978-3-031-07091-4 (eBook) https://doi.org/10.1007/978-3-031-07091-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 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

Acknowledgement

We would like to express our gratitude to the eight interview participants, who contributed to this research with their knowledge: Dr. Hans-Michael Brey, Chairman of the Berliner Leben Foundation Dr. Marcus Cieleback, Chief Economist and Group Head Research of PATRIZIA Immobilien AG Pierre Jacquot, CEO of Edmond de Rothschild Real Estate Investment Management (REIM) Thomas Jebsen, Member of the Board of Management of Deutsche Kreditbank AG Lars von Lackum, CEO of LEG Immobilien AG Paul Munday, Fund Manager of the Funding Affordable Homes Fund, Edmond the Rothschild REIM Daniel Riedl, Management Board of Vonovia SE Nathan Taft, Partner at Jonathan Rose Companies LLC

v

About This Book

The challenges of affordable housing are manifold. This therefore presents an opportunity to private investors, real estate companies, and developers. With the growing global trend for impact-based investments, many institutional investors have begun to consider the merits of this quasi-real estate-infrastructure asset class. This book examines (1) the profitability of these assets; (2) whether these assets rely on government subsidy; (3) why investors have become more interested in this product; and (4) investment criteria that influence the successful financial performance of these assets. This study employed a mixed methods approach to collect data at two tiers, first through surveys then through interviews of eight firms (three publicly listed companies, three private equity companies, one foundation, and one state bank) across Germany, the UK, and the US. Investment criteria are analysed using inferential statistics, specifically the Hierarchical Algorithm Cluster Analysis. The financial characteristics of the companies are examined and compared using descriptive statistics and the qualitative interview output is explored using the thematic Latent Coding Analysis. This book finds that the bond-like nature of affordable housing is a profitable impact investment option, and that this strategy is particularly worthwhile for institutional investors. It also finds that the profitability of affordable housing products is not dependent on subsidy. Still, affordable housing products supported by government incentives in the UK and the US are most attractive. This study observes six important investment strategies identified by veterans in this field to have an influence on the financial feasibility of affordable housing products.

vii

Contents

1

2

3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Understanding the Teetering Supply and Demand of Affordable Housing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Research Topics and Aims . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Research Question . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Foundational Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Status of Affordable Housing Stock in OECD Countries . . . . . . . . 2.2 A Comparison of Various Real Estate Investment Vehicles and Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Public Versus Private Markets . . . . . . . . . . . . . . . . . . . . . 2.2.2 The Market for Impact Investment . . . . . . . . . . . . . . . . . . 2.2.3 Open- Versus Closed-End Investment Vehicles . . . . . . . . . 2.2.4 Real Estate Investment Trusts . . . . . . . . . . . . . . . . . . . . . . 2.2.5 Strategies Within the Real Estate Asset Class . . . . . . . . . . 2.3 The State of Affairs of Affordable Housing in the Sample Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Historical Conditions and Current Context of Affordable Housing in Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.2 Historical Conditions and Current Context of Affordable Housing in the UK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.3 Historical Conditions and Current Context of Affordable Housing in the US . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Contributions to the Literature . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Identifying Gaps in the Literature . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . .

1 1 2 3 5 7 7 11 11 13 13 15 17 18 18 19 21 22 27 27 29 31

ix

x

Contents

4

Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Research Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Methodology and Research Design . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Population and Sampling Strategy . . . . . . . . . . . . . . . . . . . 4.2.3 Data Description and Collection Method . . . . . . . . . . . . . . 4.2.4 Data Collection Method . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Data Analysis Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Thematic Coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.3 Inferential Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

33 33 35 35 37 38 40 42 42 43 44 52

5

Research Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Description of Selected Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Review of Quantitative Data Output . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Descriptive Statistics of Participant Profiles . . . . . . . . . . . . 5.2.2 Inferential Statistics Using Cluster Analysis on Preferred Investment Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Review of Qualitative Data Output . . . . . . . . . . . . . . . . . . . . . . . 5.4 Data Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.1 Small Data Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.2 Challenging Type of Data . . . . . . . . . . . . . . . . . . . . . . . . 5.4.3 Self-Reported Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.4 Lack of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.5 Lack of Additional Scale . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

55 55 55 55

6

Discussion of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Description of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.1 Investment Strategies that Influence Affordable Housing Return . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Investment Strategies Based on the Different Business Models . . . 6.2.1 Affordable Housing Business Models of Publicly Listed Companies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.2 Affordable Housing Business Models of Open-End Fund Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.3 Affordable Housing Business Model of Closed-End Fund Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Research Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Quality Criteria Test of Quantitative Component . . . . . . . . 6.3.2 Quality Criteria Test of Qualitative Component . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

61 65 67 67 68 68 68 68 69 71 71 71 78 78 91 92 93 94 95 96

Contents

xi

Study Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Affordable Housing as the New Norm for Institutional Investors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.1 Benchmark Comparisons of Affordable Versus Traditional Real Estate . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.2 Why and How Investors Are Acquiring Affordable and Social Housing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.3 Investment Strategies that Enable Financially Feasible Affordable Housing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Critical Reflection of Research Topic and Methods . . . . . . . . . . . . 7.2.1 Study Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.2 Recommendations for Future Research . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

101

Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix A: Survey Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix B: Interview Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix C: Survey Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

113 113 115 117

7

99 99 99

103 109 109 110 111

About the Authors

Deepiga Vigneswaran holds a Bachelor of Environmental Studies in Urban and Regional Planning (Minor in French) from the University of Waterloo, Canada, and a Master of Science in Real Estate from Lucerne University (HSLU), Switzerland. She is an Investment Associate Professional with 2 years of experience in the alternative investments space, including investment analysis, pension asset management, real estate asset management, and conducting due diligence of funds and direct opportunities. She has an additional 4 years of experience in real estate development and urban planning with a demonstrated history of working for both public and private planning sectors. She currently works in the Investment Team at EBG Investment Solutions AG (CH), a globally active boutique firm advising and managing sustainable private market portfolios for institutional investors.

Michael Trübestein MRICS is a professor of real estate management and investments as well as the Head of the Master of Science in Real Estate (MScRE)-programme at Lucerne University (HSLU). Furthermore, he was an elected president of the RICS in Switzerland in 2020. He studied international business administration at the EBS University of Economics and Law (D) and wrote his Ph.D. Thesis at the University of Regensburg (D). He was appointed a professor of real estate management at the University of Kufstein (A) for 5 years (2008–2013). Since 2013, he has been working as a xiii

xiv

About the Authors

professor of real estate investment and management at Lucerne University (HSLU) (CH). Michael Trübestein is an author of various publications in the area of real estate strategies, (international) real estate investments, asset management, real estate markets, and digitization. In 2014, he initiated the annual study on “Real Estate Investments and Asset Management” of institutional investors (in cooperation with the ASIP/Swiss Pension Fund Association) and carried out the world’s first tokenization of a property in 2019, the “Hello World” in Baar (CH).

Matthias Daniel Aepli is a lecturer at Lucerne University (HSLU) and an entrepreneur in the real estate sector. He studied business administration at the University of Northwestern Switzerland (FHNW) where he obtained his Bachelor of Science. At the University of St. Gallen (HSG), he completed the Master of Arts programme in banking and finance and received his Ph.D. with his thesis in quantitative finance. Since 2012, Matthias Daniel Aepli has been working as a lecturer of corporate finance, risk management, and real estate management at Lucerne University (HSLU) (CH). In addition to his work in academia, he is a serial entrepreneur in the field of real estate.

List of Figures

Fig. 2.1 Fig. 2.2 Fig. 2.3 Fig. 2.4 Fig. 4.1 Fig. 4.2 Fig. 4.3 Fig. 4.4 Fig. 4.5 Fig. 4.6 Fig. 4.7 Fig. 4.8 Fig. 4.9 Fig. 4.10 Fig. 5.1 Fig. 5.2 Fig. 5.3 Fig. 5.4 Fig. 5.5

Classic closed-end fund structure despite jurisdiction or legal requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Two examples of financing structures that employ leverage in closed-end real estate investment projects . . . . . . . . . . . . . Classic REIT structure . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . Risk-return profile of various real estate strategies . . . . . . . . . . . . . . . . . Conceptual framework of qualitative decision-making criteria and its influence on quantitative portfolio performance . . . Clustering algorithm and cluster methods overview . . . . . . . . . . . . . . . Single link illustration (Ruiz, 2020 based on Tan et al., 2019) . . . Complete link illustration (Ruiz, 2020 based on Tan et al., 2019) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Centroid link illustration (Ruiz, 2020 based on Tan et al., 2019) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ward’s link illustration (Ruiz, 2020 based on Tan et al., 2019) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Average link illustration (Ruiz, 2020 based on Tan et al., 2019) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example of elbow method plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example of dendrogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example of silhouette method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Financial characteristics of the sample population (Own elaboration via SPSS, 2021) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Criteria affecting net return of affordable housing projects (Own elaboration via SPSS, 2021) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reasons to invest in affordable housing projects (Own elaboration via SPSS, 2021) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Criteria affecting net return of affordable housing projects (Own elaboration via SPSS, 2021) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cluster membership for Question 10 Variables (Own elaboration via SPSS, 2021) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

14 15 16 18 34 45 47 47 47 48 48 50 50 51 57 58 60 62 63 xv

xvi

Fig. 5.6 Fig. 5.7 Fig. 5.8 Fig. 5.9 Fig. 5.10 Fig. 6.1

Fig. 7.1

List of Figures

Validation of cluster numbers for Question 10 Variables (Own elaboration via SPSS, 2021) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 Reasons to invest in affordable housing projects (Own elaboration via SPSS, 2021) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 Cluster membership for Question 12 Variables (Own elaboration via SPSS, 2021) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 Validation of cluster numbers for Question 12 Variables (Own elaboration via SPSS, 2021) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 Percentage of interview data that reflects each of the macro themes (Own elaboration via SPSS, 2021) . . . . . . . . . . . . . . . . . . . . . . . . . 67 Clustered Scatterplot of criteria affecting net return of affordable housing projects (Own elaboration via SPSS, 2021) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 Averaged returns of the actual affordable housing and traditional residential portfolios of the study participants (Own elaboration via Excel, 2021) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

List of Tables

Table 2.1 Table 2.2 Table 2.3 Table 2.4 Table 2.5 Table 2.6 Table 4.1 Table 4.2 Table 4.3 Table 4.4 Table 4.5 Table 5.1 Table 5.2 Table 5.3 Table 5.4 Table 6.1

Social rental housing definitions per countries analysed (OECD Affordable Housing Database, 2019) . . .. .. . .. .. . .. . .. .. . .. Rent setting and increasing systems (OECD Affordable Housing Database, 2019) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Eligibility criteria used to select households for social rental housing (OECD Affordable Housing Database, 2019) . . . . . . . . . . . . Real estate investment schemes as a mix of private, public, equity, and debt (Hudson-Wilson et al., 2005) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Private market fundraising in USD billions in 2018 (McKinsey & Company, 2019a, 2019b) . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparison of real estate investment strategies (Charter Hall Investments Australia, 2014) . . . . . . . . . . . . . . . . . . . . . . . . Hypothesized influence of decision-making criteria on project return (Own elaboration, 2021) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Details of the individual participants within the study sample (Own elaboration, 2021) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparison of data type to analysis methods (Analytics Big Data, 2015) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The three variations of the Likert Scale used in the survey (Own elaboration, 2021) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Categorical data was used to identify and classify the participants’ business model (Own elaboration, 2021) . . . . . . . . . . . . . . . . . . . . . . . . . . . Financial characteristics of study participants (Own elaboration, 2021) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Criteria affecting the net return of affordable housing projects (Own elaboration, 2021) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data points for investing in affordable housing investment products (Own elaboration, 2021) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results of the Latent Coding Analysis (Own elaboration, 2021) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LEG Immobilien AG’s business approach to affordable housing (Own elaboration, 2021) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9 9 10 11 12 17 36 38 39 41 41 56 56 56 66 79 xvii

xviii

Table 6.2 Table 6.3 Table 6.4 Table 6.5 Table 6.6 Table 7.1

List of Tables

Vonovia SE’s business approach to affordable housing (Own elaboration, 2021) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 Patrizia AG’s business approach to affordable housing (Own elaboration, 2021) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 Edmond de Rothschild’s business approach to affordable housing (Own elaboration, 2021) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 Jonathan Rose Companies LLC’s business approach to affordable housing (Own elaboration, 2021) . . . . . . . . . . . . . . . . . . . . . . . 89 Evaluation of cluster analysis (Own elaboration, based on Schwarz, 2020 & Tibshirani, 2012) . . .. . . .. . . .. . . .. . . .. . . .. . . .. . . .. . 94 Summation of similarities and differences of affordable housing strategies analyzed in this study, based on geography (Own elaboration, 2021) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

Chapter 1

Introduction

1.1

Understanding the Teetering Supply and Demand of Affordable Housing

Affordable housing is a global challenge for cities in both developing and advanced economies. If current trends in urbanization and financially overstretched households continue, the affordable housing gap would grow to 440 million households by 2025. This results in 1.6 billion people living in substandard housing or financially stretched by housing costs (McKinsey Global Institute, 2014). In Asia, Africa, and Latin America, about 200 million households live in slums. In the United States (US), European Union, and Japan, more than 60 million households are financially stretched by housing costs. The gap between the cost of acceptable housing and what households can afford is approximately USD 650 billion per year (McKinsey Global Institute, 2014). Good quality affordable housing has not just become out of reach for those with low incomes, but also young people, families with children, and seniors. Housing prices have risen drastically across OECD countries, especially for renters. Housing costs have risen faster than all other expenses from 2005 to 2015 in 20 OECD countries, while the cost of food, clothing, leisure has fallen, and the cost of transport, health, communication, and education has remained stable (OECD, 2019). Housing policy has primarily become a matter of the municipal level, especially since the trend is moving towards downloading of responsibilities from national to lower-tier states, as can be observed internationally in this sector. Cities and regions are strapped with additional concern, such as shortfalls in employment, household incomes, corporate profitability, and tax revenues (European Commission, 2016). Finding employment and making a living in the current environment can prove to be difficult. The global GDP growth rate had declined to 2.9% in 2019 from 3.8% in 2018 (European Central Bank, 2019). Following a sharp deceleration in the second half of 2018, the global economy remained weak in the course of 2019, marking the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 D. Vigneswaran et al., Affordable Housing as a Profitable Impact Investment, Contributions to Finance and Accounting, https://doi.org/10.1007/978-3-031-07091-4_1

1

2

1 Introduction

weakest period of growth momentum since the global financial crisis (European Central Bank, 2019). Global trade has declined significantly amid trade tensions and slowing industrial activity. As of end of 2019, GDP growth rate had already slowed down (European Central Bank, 2019). Trends in the digitalizing economy have further endangered jobs. Artificial intelligence poses a threat to numerous trades from manufacturing to transportation and the service industry. Household income, which was already quite stretched, was further impacted by the unprecedented shock of COVID-19. Accordingly, the global housing market is facing risks not previously considered. The pandemic has affected a variety of areas including home prices, ability to pay mortgages and rents, various housing market indicators such as vacancy rate, and sale-to-listing ratios. Residential real estate investment trusts, for example, have lost much of their value since March 2020, the year-to-date price change dropping by 30% in OECD countries, and 42% in the US (OECD, 2020). The number of unemployed people in the OECD area increased by 18.4–55 million in April 2020. Differences in the pace of unemployment across OECD economies have been observed. In Canada (up to 13.0% in April 2020 from 5.7% in December 2019), the US (up to 14.7% from 3.8%), the European area (up to 6.6% from 6.4%), and Australia (up to 6.2% from 5.2%) (OECD, 2020). The nefariousness of the economic affordability gap highlights why this challenge cannot only be met by one-dimensional solutions such as government subsidies alone. The idea of having to fill the gap of 440 million housing units by 2025 may seem overwhelming to the public sector, but it represents a massive opportunity for the private sector. Affordable housing is a challenge, but also an opportunity.

1.2

Research Topics and Aims

Affordable housing is a sustainable investment that straddles two types of asset classes: real estate and infrastructure. Given the uncertainty in the current environment caused by trade wars, a pandemic, climate change, and urbanization, sustainable assets are most likely to perform well because these assets are providing required services to the public amid insecurity. BlackRock’s third-party research demonstrated that sustainable indices tended to outperform their parent benchmarks during Q1 2020. For example, Morningstar reported 51 out of 57 of their sustainable indices outperformed their broad market counterparts (BlackRock, 2020). Even prior to the pandemic, there has been a persistent and long-lasting shift towards sustainable assets that is not yet fully reflected in market prices (BlackRock, 2020). The trend for sustainable investing was further accelerated by the pandemic, but experts expect for impact and sustainable investing to remain long after. BlackRock, the world’s largest investment management corporation with USD 6.84 trillion AUM, has overhauled its strategy to make sustainability the new standard for investing. This included their launch of new active and passively managed funds that focus on socially responsible investing.

1.3

Research Question

3

In the current financial environment, fund managers are grappling with the challenges of generating sustainable returns. The low interest rate environment has pushed for an increase of investments in alternative assets and has globally steered investments into classes such as private equity or real estate. In Switzerland, for example, pension funds have seen an increase of investments by 45% more in real estate, and by 480% more in alternative investments (Swisscanto Pensions, 2019). It is clear that “investment ratios of pension funds are predominantly shifting towards equities and real estate over time. This trend has continued at the expense of bonds for several years” (Swisscanto Pension, 2019, p. 9). Infrastructure investments have also become attractive, as they offer reliable cashflow as well as capital growth over the long term. There are numerous ways to structure the private financing of affordable housing, as a closed- or open-end private equity fund, through public company development projects, or private real estate developers. This book will explore the different financing methods that are possible. Affordable housing’s high demand, high occupancy, and low turnover rates create a diversified investment profile with non-correlated income stability in market downturns in addition to attractive cash distributions. The research has so far illustrated that there is an extremely high demand for affordable housing, and that there is an opportunity for the private sector to fulfil this demand. Against this background, the research topics for the remainder of this book will deliberate private investment approaches, sustainable investing, affordable housing as a performance driver, opportunities in various markets, and the factors that contribute to successful affordable housing projects. This study aims to understand whether it is profitable for the private sector to provide the service of affordable housing. Does this require the need for a government subsidy programme? Or does it require cheaper construction costs? What are methods to make this investment cheaper (e.g. remove the frill from housing construction)? Is the core of the investment needed in ongoing maintenance? How much do asset management costs differ from affordable housing and general real estate? Where are most profitable locations for affordable housing? What are best practices in the industry thus far? How can this be implemented in countries where the provision of affordable housing is not the mandate of the public government?

1.3

Research Question

This book sets out to identify what criteria influence the success of an affordable housing development project, specifically executed by the private sector and for the purpose of developing returns. For this reason, the following research questions are defined: Is affordable housing a profitable impact investment? What investment criteria influence the financial feasibility of an affordable housing development project executed by the private sector?

4

1 Introduction

For the purpose of this research, the analysis focuses on the context of developed economies. The issue of slums and substandard housing in emerging markets will not be analysed. However, it is intended that the business model for affordable housing in developed nations can also be retrofitted for emerging markets, similar to other private equity ventures. The research will rely on private sector participants from Germany, the United Kingdom (UK), and the US. Participants will make up a variety of investor types and strategies from these countries. Specific funds and development firms from these countries have been sourced because they have a track record of developing affordable housing. Therefore, the data and insights provided from these participants will be of value. The rationale for country and participant selection is further described in Sect. 4.2. To support this research endeavour, the data were collected through a two-tiered approach which involved surveys in the first tier and expert interviews in the second tier. Survey questions focused on extracting information regarding ranking of various criteria that contribute to the current practice in the field of affordable housing development. The interviews followed up on exact performance details of completed development projects within each of the funds or firms. In order to answer the research question, the study applied a cluster analysis of the investment criteria, descriptive statistics analysis of the financial return data, and thematic coding analysis of the qualitative interview data. The overall goal of this book is to understand whether affordable housing can be a profitable investment. Therefore, this book explores the following topics and subtopics: • Existing investment opportunities in affordable housing real estate – Compare and contrast competing investment products – Differences and similarities in the way they are structured • Private investment financing structures of affordable housing projects – Private equity funds – Impact investing – Listed companies • Details regarding private equity funds and how they work – Open-end versus closed-end financing structures • Methodology of building an affordable housing investment product • Factors that contribute to a financially successful affordable housing investment product • Legal analysis for government contribution or subsidy With these topics and subtopics in mind, this analysis delves into details of the type of criteria that either support or prevent return-driven affordable housing investments.

References

5

References BlackRock. (2020). How we are helping our clients achieve their -sustainability objectives. https:// www.blackrock.com/corporate/about-us/sustainability-progress-update European Central Bank. (2019). Europe macroeconomic projections. PDF ISSN 2529-4687, QB-CF-19-002-EN-N. European Commission. (2016). New pressures on cities and regions. https://ec.europa.eu/regional_ policy/en/newsroom/events/2016/11/new-pressures-on-cities-and-regions McKinsey Global Institute. (2014). A blueprint for addressing the global afford-able housing challenge. https://adamsmith.files.wordpress.com/2017/12/mgi_affordable_housing_execu tive-summary_october-2014.pdf Organisation for Economic Cooperation and Development OECD: Affordable Housing Database. (2019). Measures to property developers to finance affordable housing construction. https:// www.oecd.org/els/family/PH5-1-Measures-financing-affordable-housing-development.pdf Organisation for Economic Cooperation and Development OECD. (2020). Record rise in OECD unemployment rate in April 2020. http://www.oecd.org/sdd/labour-stats/unemployment-ratesoecd-06-2020.pdf Swisscanto Pensions. (2019). Swiss pension fund study 2019. https://www.swisscanto.com/media/ pub/1_vorsorgen/pub-107-pks-2019-results-eng.pdf

Chapter 2

Foundational Concepts

2.1

Status of Affordable Housing Stock in OECD Countries

An international consensus on how to define or measure housing affordability does not exist. While there are a number of metrics, there is no single measure that fully captures the range of housing factors. Not to mention that often, determining the affordability of something is subjective. Before continuing, it is important to establish widely used housing metrics, as defined by the OECD (2019) and corroborated by a second source, the UK Collaborative Centre for Housing Evidence (2018): The first common metric is the earnings-to-price ratio which shows the association between prices and income. A faster increase in housing prices would suggest housing is becoming less affordable and a faster increase in income suggests that housing is becoming more affordable. This ratio is straightforward but does not provide the indication of housing quality or housing cost. The second common metric is the expenditure-to-income ratio. A common expenditure-to-income ratio is a 30% affordability threshold. Affordable housing is a housing that a household can pay for, while still having money left over for other necessities like food, transportation, and health care. This method however has limitations and is quite arbitrary. For a low-income household, spending even 20% of their income on housing may leave little finances left for other key consumption items. The third common metric is the residual income measure. This metric captures the amount of income a household has left after paying for housing costs. This measure assesses affordability gaps among vulnerable low- and middle-income households. The definition of affordable housing varies across economies and is based on standard of living, typical household size, and average income of that region. In © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 D. Vigneswaran et al., Affordable Housing as a Profitable Impact Investment, Contributions to Finance and Accounting, https://doi.org/10.1007/978-3-031-07091-4_2

7

8

2 Foundational Concepts

many parts of the world, affordability is defined as housing costs that consume no more than 30–40% of household income (McKinsey Global Institute, 2014). For this reason, this study uses the second metric to define affordable housing. Through this measure, affordable housing is deemed affordable to those with a median household income or below, as rated by the national or local government or by a recognized housing affordability index. Affordable housing is open to a wider range of household incomes than social housing. Social housing is a scheme that allows individuals with little or no income to live in a stable and secure home, often prioritizing families that are legally required to be given reasonable preference (e.g. seniors or people with disability). Meanwhile, affordable housing refers to rental that is offered at less than market value. It is important to note that all the interview participants build and provide a mix of both affordable and social housing. Therefore, this study will cover both terms. In order to do this, this study will adopt the UK Ministry of Housing, Communities and Local Government (MHCLG) definition of affordable housing. According to the MHCLG, affordable housing encompasses social rented, affordable rented, and intermediate housing (2019). These assets are defined as being “provided to eligible households whose needs are not met by the market” (MHCLG, 2019, para. 5). Similar to the study participants, many countries in an OECD Questionnaire on Social and Affordable Housing (QuASH) report having more than one type of rental housing with different providers, target groups, and financing arrangements (2019). According to the QuASH, 34 countries report the existence of some form of affordable and social rental housing. In order to provide a background introduction on the affordable and social housing space, this section relies on the QuASH to compare concepts such as rent determination and eligibility requirements across North American and western European countries. Table 2.1 first sets the background and the definition of social housing in the three geographies of this study: Germany, the UK, and the US. Then, Tables 2.2 and 2.3 will provide details on rent determination and eligibility requirements for all the countries that were brought up in discussion by the interview participants. The rent for social housing is set in different ways with countries using a variety of methods: Market-based: rent is set at least in part with reference to market rent levels for similar properties. Cost-based: rent is set so as to allow long-term recovery of the cost of building. Income-based: rent is set at least in part based on household income levels. Utility-based: rent level is set at least in part by considering the dwelling characteristics. Fixed rent ceilings: can be applied in addition to other criteria. OECD estimates that average social rents are “between 80–90% of -market rents in Austria, Finland, Israel, and Switzerland, and between 25–55% in Australia, France, the United Kingdom, and the United States” (OECD, 2019, p. 6). All countries in the OECD questionnaire have criteria to determine who is eligible to live in social rental housing. Eligibility is determined by local incomes, house

2.1

Status of Affordable Housing Stock in OECD Countries

9

Table 2.1 Social rental housing definitions per countries analysed (OECD Affordable Housing Database, 2019) Country Germany

Term used Subsidized housing or social housing promotion

UK

Social housing

US

Public housing and supportive housing

Definition and overview Housing that is publicly subsidized in order to support households that cannot adequately obtain housing on the market and need assistance through the Act of Housing Promotion, Wohnraumforderungsgesetz. Legally defined in Housing and Regeneration Act (2008) as low-cost rental accommodation and low-cost home ownership accommodation. These forms of housing made available for rent below the market rate, with rules to ensure it serves people whose needs are not adequately met in the commercial housing market. Low-cost home ownership accommodation is made available in accordance with shared ownership arrangements, equity percentage arrangements, or shared ownership trusts, with rules to ensure it serves people whose needs are not adequately met in the commercial housing market. Public housing is direct provision of rental housing by the states and local housing agencies with subsidies from federal government. The federal government provides subsidies to private entities (both for-profit and non-profit) who own and manage supportive housing for elderly and disabled.

Table 2.2 Rent setting and increasing systems (OECD Affordable Housing Database, 2019) Rent setting system

Market Australia Austria Canada Denmark Finland France Germany Netherlands Switzerland UK US

Cost



Income

Utility

Rent increasing system Not Regularly regularly increased increased



       

 



   

  

 

  



 

Social rent as % of market rent 26–33% 80% Not available Not available 82% 55% Not available Not applicable Not available 49% 30%

prices, and authorities (UK MHCLG, 2019). The most common criterion is the income threshold. Income tests are benchmarked against either average incomes or minimum incomes (OECD, 2019).

10

2

Foundational Concepts

Table 2.3 Eligibility criteria used to select households for social rental housing (OECD Affordable Housing Database, 2019)

Australia

Austria Canada Denmark Finland France Germany Netherlands Switzerland UK US

All are eligible No

Income threshold Yes

No No Yes Yes No No No

Yes Yes No No Yes Yes

No No

Yes Yes

Citizenship or permanent residence Yes

Housing composition/ size No

Yes

Yes Yes

No No No No

Yes Yes

Yes No

Housing situation Not owning property No

Other Ability to sustain tenancy

No No

Yes No

Verification on whether household would be a good tenant

The Netherlands (36%), Denmark (23%), Austria (24%), and the UK (19%) have a larger allocation of housing stock to affordable housing whereby over 19% of all dwellings in these countries are social rental housing (European Parliament, 2013; OECD, 2019). The first two countries provide a universal social housing model.1 Austria provides a targeted generalist social housing model.2 For example, Vonovia, one of the interview participants and Austria’s largest developer stated that the minimum threshold for eligibility is a household earning net EUR 70,000. This threshold is comparably high but is the reason why the country is able to produce higher volumes of affordable housing. The UK uses targeted residual housing model.3 Germany and the US both use the latter models but have a lower housing stock, Germany with 5–10%, and the US with less than 5% (European Parliament, 2013; OECD, 2019). The universal models are mostly provided by the public sector. The second and third models are provided by both the public sector and private market. 1 Universal models consider housing to be a primary public responsibility and therefore hold the objective of providing the whole population with affordable housing (European Parliament, 2013, p. 6). 2 Targeted generalist models consider the market to be in charge of allocating housing. The objective is to satisfy only the excess of housing demands. Housing is allocated based on income level (European Parliament, 2013, p. 6). 3 Targeted residual models allocate according to vulnerability indicators, meaning the most vulnerable groups are the primary target of affordable housing (European Parliament, 2013, p. 6).

2.2

A Comparison of Various Real Estate Investment Vehicles and Strategies

11

Table 2.4 Real estate investment schemes as a mix of private, public, equity, and debt (HudsonWilson et al., 2005) Equity

Debt

2.2

Private • Collective investment schemes (open- or closed-end)

• Mezzanine • Bank loans

Public • Listed property funds • REITs • Mortgages • REITs

A Comparison of Various Real Estate Investment Vehicles and Strategies

Over the last two decades, the massive injection of capital into the real estate asset class has pushed for the growth and the variety of the different investment vehicles that exist today. The potential of real estate within a multi-asset portfolio has contributed to the expansion of both public real estate equity market and private real estate equity market. According to the FTSE EPRA NAREIT Global Index,4 the public real estate market is worth USD 1.8 trillion (2021). Meanwhile, the private real estate equity market is worth USD 909 billion (McKinsey & Company, 2019a, 2019b). This section provides an overview of the public versus private real estate investment schemes in Table 2.4 under the equity category, including the operation and current real estate trends within these schemes.

2.2.1

Public Versus Private Markets

Public markets consist of companies that are publicly listed and sell shares to both institutional and retail investors (virtually anyone), who can then buy, sell, or trade these shares on the relevant stock exchange. The public market is an example of a traditional asset class (e.g. stocks and bonds). Retail and accredited investors can access real estate investment opportunities in public markets through real estate exchange-traded funds. Like other ETFs, these trade like stocks on major exchanges. In 2019, the US public equity market is approximately USD 38 trillion, and the US private equity market is approximately USD 5.8 trillion (Morgan Stanley, 2020; McKinsey & Company, 2020). Public equity is still the larger market. However,

4

This is one of the well-known public real estate benchmarks. It includes the European Public Real Estate Association (EPRA) which represents the European public real estate sector and the National Association of Real Estate Investment Trusts (NAREIT) which represents the publicly traded real estate companies in the US property and investment markets.

12

2

Foundational Concepts

Table 2.5 Private market fundraising in USD billions in 2018 (McKinsey & Company, 2019a, 2019b)

North America Europe Asia Rest of World Global

Private equity 212 82 77 14 385

Closed-end real estate 68 28 13 0.5 110

Private debt 67

Natural resources 58

Infrastructure 42

Private markets 448

36 4 1

28 4 4

34 4 2

207 103 21

109

93

82

778

private markets are growing at a faster pace. The private market net asset value has grown by 7.5 times this century, twice as fast as public market capitalization (McKinsey & Company, 2019a, 2019b). Private equity has topped public market returns since 2009 and since 2010, the AUM and number of private equity GPs have more than doubled (McKinsey & Company, 2020). They are now an essential requirement for diversified participation in global growth. Private markets refer to investments in equity and debt of privately owned companies, meaning they are not listed (Credit Suisse, 2020). Private markets are limited only to institutional investors as they are viewed as more sophisticated and better able to protect themselves, given that they face fewer regulatory obstacles. Through investing in private companies, investors hope to increase a company’s value and sell their stake at a later stage either through a trade sale, buyout, recapitalization, or through listing the company on public markets with an Initial Public Offering (IPO). Typically, the lifetime of a fund is on average 10–15 years and is therefore quite illiquid. Pagliari et al. (2003) found that while public versus private market vehicles did not matter in terms of return characteristics, investor preference for one or the other is based on liquidity, governance, transparency, control, and executive compensation. In the private market, real estate accounts for an AUM of USD 909 billion with 60% of these assets in North America, 26% in Europe, 10% in Asia, and 4% in the rest of world (McKinsey & Company, 2019a, 2019b). In the private market, real estate investments account for the second largest vertical and this only includes closed-end funds (so only a partial picture of the asset class) (McKinsey & Company, 2020). Table 2.5 illustrates that private equity assets and real estate assets have raised the most capital of all other asset classes in North America and Asia. Megafunds (funds larger than USD 5 billion) are driving the growth of real assets. For instance, eight infrastructure megafunds worth more than USD 68 billion were raised, and 15 real estate megafunds were raised since 2013 (McKinsey & Company, 2019a, 2019b); this growth has particularly taken place in the North American and European markets.

2.2

A Comparison of Various Real Estate Investment Vehicles and Strategies

2.2.2

13

The Market for Impact Investment

Impact investments are made with the intention to generate positive, measurable social or environmental impact in addition to a financial return (Global Impact Investing Network, 2021). Impact investments exist both in public and private markets. They are investment approaches that go beyond the application of exclusion criteria and the reduction of ESG risks. The total assets in private market impact investing worldwide have been estimated at USD 715 billion (GIIN, 2020), with non-impact private equity amounting to another USD 5.8 trillion (McKinsey & Company, 2020). According to PGIM Real Estate, the real estate asset class accounts for 10–15% of the impact investment AUM (2020). According to the 2020 Annual Impact Investor Survey, 17% of the impact investment space accounts for real assets. Impact real estate typically focuses on either green real estate, affordable housing, or both. Impact real estate funds5 raised USD 4.7 billion in assets between 2004 and 2014 (GIIN, 2017). Similar to the comparative universe, most impact real estate funds pursue a diversified strategy (e.g. residential, retail, industrial, and office). However, there is notably a higher concentration dedicated to residential multifamily properties. About 25% of impact funds focus in this area while only 5% of funds in the comparative universe are in residential multifamily space (GIIN, 2017). Social and affordable impact investments are gradually growing as a number of unlisted funds were launched by asset managers, including CBRE Global Investors, BMO REP, Cheyne Capital, Man Group, and Schroders (Colley, 2021). It is possible to achieve market-competitive and market-beating returns in impact investment. For example, 67% of impact investors observe risk-adjusted market rate returns, and 18% observe below-market rate returns but closer to market rate (GIIN, 2020). Over the period of 1998 and 2004, impact investment benchmark has returned 6.9% to investors versus 8.1% for the comparative universe, but much of the performance in more recent years remains unrealized (GIIN, 2019).

2.2.3

Open- Versus Closed-End Investment Vehicles

Capital has shifted from closed- to open-end funds for core/core-plus real estate strategies; meanwhile, opportunistic strategies continue to be most sought in closedend funds (McKinsey & Company, 2020). The McKinsey Global Private Markets Review suggests this is because there is a shift in investor preference for lower-risk strategies, preference to control timing of their cash flows, and maintenance of longterm exposure to less-correlated asset classes (2020). A collective investment vehicle or collective investment schemes (CIS) can be open-end or closed-end. If open-ended, there is no limit to the number of share or 5 This is based on the count of only 20 impact real estate funds that were willing to participate in the survey organized by Cambridge Associates (CA) and Global Impact Investment Network (GIIN).

14 98% capital 80% return

2

Limited Partner (LP)

Gerneral Partner

Foundational Concepts

2% capital 20% return

(could also be Fund Manager)

Advisor Equity Fund (set up under a structure)

Portfolio Company 1

Portfolio Company 2

Portfolio Company 3

Custodian

Portfolio Company 4

Fig. 2.1 Classic closed-end fund structure despite jurisdiction or legal requirements

units and can be repurchased or redeemed directly or indirectly out of the undertaking’s assets at the shareholder’s request. Open-end funds can be passively or actively managed. Those that are passively managed typically track the performance of a benchmark index. The Vanguard Real Estate Index Fund (VGSIX), for example, is an open-end fund that invests in REITs that buys office buildings, hotels, and other properties. This fund tracks the MSCI US Investable Market Real Estate 25/50 Index. If closed-ended, the number of shares issued is fixed and investors entering or leaving the fund must buy or sell existing shares. Shares can be quoted or unquoted. Collective investments may pay periodic dividends, capitalize the income, or employ a combination of those approaches, depending on the terms. There are three basic legal structures in OECD countries. Through direct investments, investors buy assets or buy securities issued by companies and governments. This is typically the case in public market listings. Through indirect investment, investors give their capital to investment firms, which then invest that capital in a variety of securities and assets on their behalf. Indirect investment vehicles require adherence to many legal entities and regulations. The governance structure of the CIS is based on legal requirements, which operate differently in different countries. As summarized by the OECD Committee on Financial Markets (2001), there are three basic legal structures used among OECD countries: • Corporate form: CIS is a separate corporate entity and investors are shareholders • Trust form: CIS is organized as a “trust”, a concept found in English common law • Contractual form: CIS is a contract under which the investment manager invests funds on behalf of the final investor As illustrated in Fig. 2.1, while legal requirements vary, the general structure and overall setup of these vehicles remain the same globally. Additional roles in the closed-end funds include fund manager (which at times can also be the GP), a custodian, administrator, or auditor. They take side roles to facilitate the transparent and fair operation of the fund. The fund manager will execute their duty to meet the

2.2

A Comparison of Various Real Estate Investment Vehicles and Strategies

Investor A

Investor B

Investor C

Investor A

Investor B

15

Investor C

Seed Investors A Class (USD 5M)

B Class (USD 12.5M)

Investment Fund

Developer

(USD 12.5M)

(USD 5M)

Investment Fund

Investment Portfolio (USD 50M) Equity

Bank Financing

(USD 17.5M)

(USD 32.5M)

Project Company Equity

Bank Financing

(USD 17.5M)

(USD 32.5M)

Fig. 2.2 Two examples of financing structures that employ leverage in closed-end real estate investment projects

mandate of the underlying fund. Real estate funds operate in the same manner as closed-end private equity structures. In Fig. 2.1, the portfolio company would instead be a real asset. There is little standardization to how real estate private equity firms are structured, but they all generally engage in five key activities: capital raising, screening investment opportunities, acquiring or developing properties (fund investment period), managing properties (fund realization period), and selling properties (fund exit). The name or label will indicate the type of scheme and investment orientation. Real estate investments can be made in a variety of structures (Fig. 2.2). Namely through real estate private equity funds (as explained above), public markets, and REITs. Fund of Funds (FoF) are set up to facilitate access by investors to greater asset diversification.

2.2.4

Real Estate Investment Trusts

Real Estate Investment Trusts (REITs) are corporations that pool the capital of its investors and invest directly in income-producing real estate. They also work like a fund allowing shareholders to indirectly own real estate. Most REITs are publicly traded like stocks. Therefore, they are also accessible to retail investors. REITs generate steady income, while real estate funds provide value through appreciation. Public equity REIT opportunities in the UK, for example, are Civitas Social Housing REIT with a market capitalization of GBP 650 million and Triple Point Social Housing REIT with a market capitalization of GBP 440 million. These two REITs have outperformed traditional commercial REITs and wider FTSE All-Share Index during COVID-19 since February 2020 (Colley, 2021). In order to give small-scale investors opportunity to invest in real estate traditionally available only to institutions, the US established regulations for REITs in

16

2

Foundational Concepts

Unitholders Holding of Units

Portfolio Manager

REIT Ownership of Properties

Property Manager

Distributions

Trustees Net Property Income

Acts on behalf of Unitholders

Properties

Fig. 2.3 Classic REIT structure

1960. Germany and the UK enacted REIT-like legislation in 2007. In order to operate as a REIT, a number of legal requirements have to be met; these requirements have shaped the way REITs are structured and operate. REITs are required to pay a minimum of 90% of taxable income (e.g. 90% of profits) back to the shareholders as dividends. There are three types of REITs: • Equity REITs: allow investors to own properties and generate revenues by renting them out – Residential REITs (own and operate multifamily rental apartment buildings and manufactured housing) – Retail REITs – Healthcare REITs – Office REITs • Mortgage REITs: allow investors to own property mortgages and purchase mortgages from lenders, and loan capital for mortgages. Profit from these is earned from interests earned from the mortgages • Hybrid REITs: mix of the above two None of the interviewees in this book discussed real estate investment product in the REIT form. However, it was stated that for companies or funds not wanting to exit their properties, REITs would be a viable structure. REITs must invest at least 75% of its total assets in real estate and derive at least 75% of its income as rents from real property. Therefore, REITs truly fit a long-term rental core-plus real estate strategy. Figure 2.3 illustrates a general REIT structure.

2.2

A Comparison of Various Real Estate Investment Vehicles and Strategies

2.2.5

17

Strategies Within the Real Estate Asset Class

There are numerous real estate investment strategies, especially labelled as per the risk and associated return characteristics (Table 2.6, Fig. 2.4). These strategies, as defined by Origin Investments (2018) are: Core: Core refers to income generating investments. Investors are often conservative and looking to make a stable income with low risk. This can virtually be acquired and held as an alternative to bonds. Core property requires little asset management and is typically occupied by credit tenants on long-term leases. Core Plus: Refers to both income and growth generation. The methodology is to increase cash flows through light property improvements, management efficiencies or by increasing quality of tenants. Therefore, active owner participation is required for this strategy. Value-Add: These properties have little to no cash flow at acquisition but have the potential to produce a significant cash flow once the value has been added. Properties in this case often have occupancy issues, management problems, deferred maintenance, or all of the above. These investments therefore require a deep knowledge of real estate, strategic planning, and daily oversight by owners. Opportunistic: This is the riskiest of all real estate investment strategies. Here, investors take on complex projects that may not see a return on investment for 3 or more years. Ground-up developments, acquiring an empty building, and land development are examples of opportunistic investments. Depending on investment approach, an affordable housing development or fund can be categorized into any of the four strategies. However, the research finds that it is common for affordable housing to be core and core-plus strategies.

Table 2.6 Comparison of real estate investment strategies (Charter Hall Investments Australia, 2014)

Risk Target return (IRR) Sources of earning Financial leverage Building type

Core (income) Low 9–12%

Value-add (growth) Medium 10–15%

Current income (70%) + capital appreciation (30%)

Current income (50%) + capital appreciation (50%)

0–50%

30–70%

Quality assets with long leases and quality tenants, fully or almost fully occupied

Assets with upside potential through refurbishment, releasing, and repositioning

Opportunistic (growth) High 15% +

Current income (80%) 50–80% Development assets, distressed assets

18

2

Foundational Concepts

Fig. 2.4 Risk-return profile of various real estate strategies

Opportunistic

RE

Income

Value Add RE Core Plus RE Core RE

Risk

2.3

The State of Affairs of Affordable Housing in the Sample Countries

The interview participants are based in Germany, the UK, and the US. Section 6.2 of this report discusses their strategies and the use of government subsidies. In order to better understand the different subsidy schemes and various methods of providing affordable housing, this section establishes context of the housing industry in these countries. How have these countries operated affordable housing in the past? What forms of financialization (e.g. state policy, investor activity) have taken place to alter their affordable housing markets? What is the current demand for housing in general as well as affordable housing?

2.3.1

Historical Conditions and Current Context of Affordable Housing in Germany

Subsidized rental housing has always been a cornerstone of German housing policy starting in the late nineteenth century (Harloe, 1995; Kohl, 2015). The demand for affordable housing increased as a result of ruin and damage from World War II. Therefore, unlike any other European country at the time, massive blocks of affordable housing were built and managed by municipal companies. These social housing companies were exempt from taxation as they were serving a public goal. Following reunification, the state support for affordable housing was abolished and “companies were forced to introduce market logics to make the provision of housing possible” (Voigtländer, 2007 as cited in Wijburg et al., 2018, p. 1102). Nonetheless, the German residential market was comparatively stable for several decades and experienced moderate growth rates in sale prices and rents. At the turn of the century, many municipal and industrial housing companies divested their portfolio. Between 1999 and 2011, about 1.4 million residential units were sold (Diamantis, 2013 as cited in Wijburg et al., 2018). This opened the market to international investors, predominantly UK- and US-based private equity and

2.3

The State of Affairs of Affordable Housing in the Sample Countries

19

hedge funds (Aalbers & Holm, 2008; Kaiser, 2008; Voigtländer, 2007). These are, for example, Norges Bank Investment Management, MFS International, Massachusetts Financial Services, Blackrock, Vanguard Group, APG Asset Management, and THEAM. At the turn of the decade, as Germany experienced an increase in housing prices, up 2.5% nationwide in 2010 and up 5.5% nationwide in 2011, individual and institutional investors flooded the market (Richter, 2012) leading to a series of mergers and acquisitions of smaller real estate companies. During the Great Financial Crisis (GFC), the Anglo-Saxon private equity and hedge funds from New York and London exited the German housing market by transforming their housing subsidiaries into independent housing companies and listing these new companies on the Frankfurt Stock Exchange (Truebestein, 2021; Müller, 2012; Scharmanski, 2013 as cited in Wijburg et al., 2018). Given the socio-economic history of the country and the origin of the housing firms, the provision of affordable housing runs in the DNA of the now publicly listed companies such as Vonovia, LEG Wohnen, or Deutsche Wohnen. This history is pertinent to understanding why these publicly listed companies provide affordable housing as part of their mandate. In the past, these companies have relied on subsidized interest rates to build affordable housing. These subsidies are no longer efficient given the low interest rate environment. Germany has a housing stock of approximately 42 million dwelling units—the largest residential market in Europe outside of Russia (PwC, 2019). The German residential market is characterized by a strong rental market. About 47% rent on the private rental market, 7% rent at a subsidized price, and 44% own their dwelling outright or with a mortgage (OECD, 2019). Based on the average price per square metre of freehold apartments, the total housing stock has an approximate value of EUR 4.12 trillion. By way of comparison, the German GDP is approximately EUR 3.36 trillion. However, the investable housing stock has grown marginally in recent years. About 42% of Germany’s housing stock was built during the post-war era in the 1950s–1970s. Since 2000, approximately 59,000 units were built per year. Since 2010, this number declined to 40,000 units per year (Savills Research, 2012). According to the Federal Statistics Office (2021), construction for a total of 196,400 homes was approved in Germany between January and July 2019. This is 3.4% lower than the building permits during the same period of the previous year (PwC, 2019). Slow construction rates paired with population growth have resulted in increasing rent and sale prices.

2.3.2

Historical Conditions and Current Context of Affordable Housing in the UK

At its peak in the 1970s, the social rented sector in the UK accounted for a third of all households (Stephens, 2019). Since 1980, 1.85 million affordable homes have been

20

2

Foundational Concepts

privatized through the Right to Buy scheme.6 The UK’s population is forecasted to grow by 9.02 million from 2018 to 2040 (Eurostat, 2018). There is an undersupply of housing in the UK, one that has accumulated over the past few decades. About 30% of the UK population rent on the private rental market, 4% rent at a subsidized price, and 65% own their dwelling outright or with a mortgage (OECD, 2019). According to the Heriot Watt University, over 345,000 new homes a year are required, of which 145,000 per year need to be affordable (Inside Housing, 2018). A steady year-onyear increase of affordable housing construction has been noted since 2015. Approximately 57,600 units of affordable housing were delivered in 2019–2020 (UK House of Commons, 2021). A declining proportion of affordable housing is social rental housing. In 2011–2012, about 65% of all new affordable housing was allocated for social rent. In 2019–2020, only 11% (6336 units) is allocated to social housing. In March 2020, the UK Parliament Budget slated an additional GBP 9.5 billion to the Affordable Homes Programme (AHP). The programme allocates GBP 12.2 billion of grant funding from 2021 to 2022 to build affordable homes across England. This grant is estimated to bring in a further GBP 38 billion in public and private investment. The funding is expected to support up to 180,000 new affordable home ownership and rental products (UK House of Commons, 2021). In the UK, there are two main types of social housing providers—the local authorities, which own 6.7% of the housing stock in England (1.59 million units), and Housing Associations (HAs) which own 10.5% (2.59 million units) (MHCLG, 2019). HAs are Registered Social Landlords or Private Registered Providers of Social Housing. They play a major role in the current mass entry of private equity firms in the UK affordable housing industry. HAs have greater freedom to strategize their role when compared to local authorities, which are more bound by statutory duties. In total, there are 1900 HAs across the UK, 1200 of which are very small (have below 1000 units in their portfolio). These ones do not generally get in-depth assessments by the regulator of social housing. At the top end, there are about 300 HAs that have more than 2500 homes individually in their portfolios. An amendment to the Housing and Regeneration Act 2008 allowed for-profit social housing providers to enter the affordable housing market for the first time. Since 2010, the Ministry of Housing Communities and Local Government (MHCLG) have observed an increase in For-Profit Registered Providers (FPRPs). Institutional investors and large public equity funds can enter the affordable market by partnering with HAs, creating FPRP subsidiary entities, and/or acquiring affordable housing through Section 106. Section 106 of the Town and Country Planning Act 1990 requires financial contributions to local infrastructure or affordable housing as a condition of planning permission for private development schemes (MHCLG, 2020). Institutional and financial investors then seek to acquire completed and ready-to-rent units from developers through Section 106 agreements (Wijburg &

6

Right to Buy scheme is a policy in the UK (England) which allowed local authorities to sell council houses to their tenants at a discounted rate. This was a policy of Margaret Thatcher’s conservative leadership to increase home ownership (Wilson, 1999).

2.3

The State of Affairs of Affordable Housing in the Sample Countries

21

Waldron, 2020); the risk is alleviated as these acquirers avoid a costly development process. For example, 75% of Legal & General’s affordable housing supply comes from Section 106 units (Simpson, 2019 as cited in Wijburg & Waldron, 2020). Similarly, Sage Housing (owned by Blackstone) has committed GBP 1 billion to affordable housing. Their business model involves buying up the affordable housing private developers are required to build as a percentage of their total homes under Section 106 (Barratt, 2019; Wijburg & Waldron, 2020). Sage Housing (Blackstone) targets an 8% return on these investments (Cross, 2019 as cited in Wijburg & Waldron, 2020). The firm will not manage the stock by itself and has outsourced property management to a company called Places for People.

2.3.3

Historical Conditions and Current Context of Affordable Housing in the US

Government involvement in the affordable housing space began in the 1930s. With each passing decade and the various national issues (e.g. the Great Depression, World War II, racial movements), the many subsidized housing programmes have constantly been reformed. Originally, affordable or subsidized housing was provided through the public sector consisting of concentrated blocks of low-rise and/or highrise apartment buildings. These complexes are operated by state and local housing authorities, funded by the Department of Housing and Urban Development (HUD). The US faces special issues such as concentrated poverty and racial segregation in these subsidized apartment buildings, also referred to as “the projects”. Given these issues and heavy deterioration, many of these public housing agency projects from the earlier years have been demolished. As of the 1970s, the federal government turned to other approaches. The Housing and Community Development Act of 1974 created the Section 8 Housing Programme to encourage the private sector to construct affordable homes. Section 8 authorizes the payment of rental housing assistance to private landlords on behalf of low-income households. Recently, in 2012, HUD initiated a new programme called the Rental Assistance Demonstration (RAD) programme. RAD is available to public housing properties that are being redeveloped in conjunction with private developers and investors. Since 2013, housing agencies have converted about 60,000 public housing units to long-term Section 8 contracts under the RAD. HUD can permit up to 185,000 public housing units to be converted under RAD. An interim evaluation estimated that the conversion of the first 112,000 RAD units will leverage as much as USD 8 billion in private and public investment (Centre on Budget and Policy Priorities, 2017). Today, public housing is one of the three main rental assistance programmes, along with Section 8 Voucher programme and Section 8 Project-Based programme. Public housing developments provide affordable homes to 2.1 million low-income Americans (Centre on Budget and Policy Priorities, 2017).

22

2

Foundational Concepts

Unlike Germany and the UK, the US government has played a minor role in the provision of affordable housing. For example, about 82% of affordable housing is provided by for-profit businesses. The current subsidy programmes, such as Section 8, Freddie Mac and Fannie Mae subsidized loans, or the Low-Income Housing Tax Credits (LIHTC) offer favourable investment opportunities to the private sector. The details of these government subsidies are discussed in Sect. 6.2. New housing construction in the US has declined by 35% from 2006 to 2020 (Blackstone, 2021). Nearly 19 million US households pay more than 50% of their income for housing (Enterprise Community Partners, 2014). The US has a shortage of 6.8 million affordable rental homes (National Low-Income Housing Coalition, 2017). Similar to the UK, the US has a housing tenure that is predominantly ownership-based with 60% of the population owning outright or with a mortgage (OECD, 2018). The remainder of the population rents. Across three primary categories, there are 7 million units of affordable multifamily housing in the US (National Apartment Association, 2017); this increasing demand and restricted supply represent an investment opportunity. There has been a marked entrance of investors in the affordable housing market. For example, CBRE Affordable Housing has managed USD 17.4 billion in affordable market sales (CBRE Affordable Housing, 2021).

References Aalbers, M. B., & Holm, A. (2008). Privatising social housing in Europe: The cases of Amsterdam and Berlin. In K. Adelhof, B. Glock, & J. Lossau (Eds.), Urban trends in Berlin and Amsterdam (Vol. 110, pp. 12–23). Barker, N. (2018). England needs 340,000 new homes a year, says NHF. Inside Housing News. https://www.insidehousing.co.uk/news/news/england-needs-340000-new-homes-a-year-saysnhf-56355 Barratt, L. (2019). Blackstone-owned for-profit provider signs -management agreement with large association. Inside Housing News. https://www.insidehousing.co.uk/news/news/blackstoneowned-for-profit-provider-signs-management-agreement-with-large-association-59800 Blackstone. (2021). Blackstone’s approach to rental housing. https://www.blackstone.com/wpcontent/uploads/sites/2/2021/03/Blackstones-Approach-to-Rental-Housing_2021-1.pdf Braga, M., & Palvarini, M. (2013). Social housing in the EU (report PE 492.469). European Parliament – Policy Department A. https://www.-europarl.europa.eu/RegData/etudes/note/ join/2013/492469/IPOL-EMPL_NT(2013)492469_EN.pdf CBRE Affordable Housing. (2021). Capabilities overview. https://www.cbre.us/real-estateservices/real-estate-industries/affordable-housing Center on Budget and Policy Priorities. (2017). Policy basics: Public housing. https://www.cbpp. org/research/public-housing Charter Hall Investments. (2014). Beyond core real estate investing – What are the opportunities? Retrieved from: https://www.charterhall.com.au/News/news-article/2019/02/11/beyond-corereal-estate-investing-what-are-the-opportunities Colley, N. (2021). UK social housing: Investment case stacks up for institutional investors. IPE Real Assets. https://realassets.ipe.com/real-estate/uk-social-housing-investment-case-stacks-upfor-institutional-investors/10050442.article

References

23

Credit Suisse. (2020). Private market investing explained. https://credit-suisse.com/about-us-news/ en/articles/news-and-expertise/private-market--investing-explained-202010.html Cross, L. (2019). Sage closes in on £1bn affordable portfolio. Social Housing News. https://www. socialhousing.co.uk/news/news/sage-closes-in-on-1bn-affordable-portfolio-60104 Diamantis, C. (2013). Abschlussbericht der Enquetekommission ‘Wohnungswirtschaftlicher Wandel und neue Finanzinvestoren auf den Wohnungsmärkten in NRW’. Landtag NordrheinWestfalen (Drucksache 16/229). https://www.landtag-.-nrw-.-de/Dokumentenservice/portal/ WWW/dokumentenarchiv/Dokument/MMD16-2299.pdf;jsessionid¼73D86804603 A5A5FFE17E2F3814AA30A Enterprise Community. (2014). Impact of affordable housing on families and communities. https:// homeforallsmc.org/wp-content/uploads/2017/05/Impact-of-Affordable-Housing-on-Familiesand-Communities.pdf Eurostat. (2018). United Kingdom population: Demography & migration. European Commission. https://ec.europa.eu/eurostat/web/population-demo-graphy-migration-projections/data/database Federal Statistics Office (Destatis, Statistisches Bundesamt). (2021). Economy – Short-term indicators. Retrieved from: https://www.destatis.de/EN/Themes/Economy/Short-Term-Indicators/_ node.html FTSE Russell. (2021). FTSE EPRA Nareit developed real estate index. https://www.ftserussell. com/products/indices/epra-nareit-green Global Impact Investing Network. (2019). Sizing the impact investing market. https://thegiin.org/ research/publication/impinv-market-size Global Impact Investment Network. (2020). Annual impact investor survey. https://thegiin.org/ research/publication/impinv-survey-2020 Global Impact Investment Network. (2021). About impact investing. https://thegiin.org/impactinvesting/need-to-know/#how-big-is-the-impact-investing-market Global Impact Investment Network, & Cambridge Associates. (2017). The financial performance of real assets impact investments. https://thegiin.org/assets/The%20Financial%20Performance% 20of%20Real%20Assets%20Impact%20Investments_webfile.pdf Hackelberg, F., Conrads, C., & Goller, S. (2019). Investing in German real estate. PricewaterhouseCoopers GmbH. https://www.pwc.de/de/real-estate/investing-in-german-realestate-2019.pdf Harloe, M. (1995). The People’s Home? Social Rented Housing in Europe and America. Wiley Blackwell. Hudson-Wilson, S., Gordon, J., Fabozzi, F., Anson, M., & Giliberto, S. (2005). Why real estate? The Journal of Portfolio Management, 31(5), 12–21. https://doi.org/10.3905/jpm.2005.593883 Kaiser, H. G. (2008). Die Entwicklung der Wohnungswirtschaft von Thyssen/Krupp bis Immeo Wohnen als Mitglied der Fonciere des Regions. https://www.immeo.de/fileadmin/user_upload/ downloads/PDFs/Die_Entwicklung_der_Wohnungswirtschaft_19.02.2009.pdf Kohl, S. (2015). The power of institutional legacies: How nineteenth century housing associations shaped twentieth century housing regime differences between Germany and the United States. European Journal of Sociology, 56(2), 271–306. Mauboussin, M., & Callahan, D. (2020). Public to private equity in the United States: A long-term look. Morgan Stanley Investment Management. https://www.morganstanley.com/im/publica tion/insights/articles/articles_publictoprivateequityintheusalongtermlook_us.pdf?159654 9853128 McKinsey & Company. (2019a). Private markets come of age. McKinsey global private markets review 2019. https://mckinsey.com McKinsey & Company. (2019b). Twenty-five years of digitization: Ten insights into how to play it right. https://www.mckinsey.com/~/media/-mckinsey/-business%20functions/mckinsey%20 digital/our%20insights/twenty-five%20years%20of%20digitization%20ten%20insights%20 into%20how%20to%20play%20it%20right/mgi-briefing-note-twenty-five-years-of-digitiza tion-may-2019.ashx

24

2

Foundational Concepts

McKinsey & Company. (2020). A new decade for private markets. https://www.mckinsey.com/~/ media/mckinsey/industries/private%20equity%20and%20principal%20investors/our%20 insights/mckinseys%20private%20markets%20annual%20review/mckinsey-global-privatemarkets-review-2020-v4.ashx McKinsey Global Institute. (2014). A blueprint for addressing the global affordable housing challenge. https://adamsmith.files.wordpress.com/2017/12/mgi_affordable_housing_execu tive-summary_october-2014.pdf Ministry of Housing, Communities & Local Government. (2019). Housing statistics and English housing survey glossary. UK Parliament. https://www.gov.uk/guidance/housing-statistics-andengland-housing-survey-glossary/a-to-z Ministry of Housing, Communities & Local Government. (2020). White paper: Planning for the future. UK Parliament. https://assets.publishing.service.gov.uk/government/uploads/system/ uploads/attachment_data/file/773079/-Local_Authority_Housing_Statistics_England_year_end ing_March_2018.pdf Muller, S. (2012). Wie Wohnen prekar wird. Finanzinvestoren, Schrottimmobilien und Hartz IV. Zentrale wissenschaftliche Einrichtung der TU Dortmund. http://www.sfs.tu-dortmund.de/sfsReihe/Band%20181.pdf National Apartment Association. (2017). The imbalance of affordable housing: Supply & demand. https://www.naahq.org/news-publications/units/may-2017/article/imbalance-affordable-hous ing-supply-demand.%20May%202017 National Low-Income Housing Coalition. (2017). The gap: A shortage of affordable homes. https:// nlihc.org/sites/default/files/Gap-Report_2017.pdf Organisation for Economic Cooperation and Development OECD: Financial Affairs Division. (2001). Governance systems for collective investment schemes in OECD countries. https:// www.oecd.org/finance/financial-markets/1918211.pdf Organisation for Economic Cooperation and Development OECD. (2018). OECD affordable housing database. http://www.oecd.org/social/-affordable-housing-database/ Organisation for Economic Cooperation and Development OECD: Affordable Housing Database. (2019). Measures to property developers to finance affordable housing construction. https:// www.oecd.org/els/family/PH5-1-Measures-financing-affordable-housing-development.pdf Origin Investments. (2018). What are core, core plus, value-add, and opportunistic investments? https://origininvestments.com/2018/02/21/what-are-core-core-plus-value-added-and-opportu nistic-investments/ Pagliari, J., Scherer, K., & Monopoli, R. (2003). Public versus private real estate equities. The Journal of Portfolio Management Special Real Estate Issue, 29(5), 101–111. https://doi.org/10. 3905/jpm.2003.319911 PGIM Real Estate. (2020). US real estate impact investing. https://www.pgim.com/real-estate/ research/us-real-estate-impact-investing Richter, W. (2012). Now there’s a housing bubble in Germany. Insider. https://www. businessinsider.com/now-a-housing-bubble-in-germany-2012-2?r¼US&IR¼T Savills Research. (2012). Residential markets in Germany: Current developments, prospects, and opportunities. Savills Immobilien Beratungs-GmbH. https://pdf.euro.savills.co.uk/germanyresearch/ger-ger-2015/savills-research-residential-market-report.pdf Scharmanski, A. (2013). Corephorie und Alternativen. Institutionelle Immobilien-inve-stitionen 2013/2014. Quantum Fokus, 4. http://docplayer.org/8154733-Quantum-fokus-4-quartal-2013corephorie-und-alternativen.html Simpson, J. (2019). We are not bidding up prices on affordable housing. Inside Housing UK. https://www.insidehousing.co.uk/news/news/lg-affordable-homes-boss-we-are-not-bid ding-up-prices-on-affordable-housing-63799 Stephens, M. (2019). How housing systems are changing and why: A critique of Kemeny’s theory of housing regimes. Housing, Theory and Society, 32(5), 521–547. https://doi.org/10.1080/ 14036096.2020.1814404

References

25

Truebestein, M. (2021). German real estate markets & strategies. (W.MSCBF_CF04.F2101: Real Estate Investment Banking). Lecture, University of Lucerne (HSLU). UK Collaborative Centre for Housing Evidence. (2018). How should housing affordability be measured? http://housingevidence.ac.uk/wp-content/uploads/2018/09/R2018_02_01_How_ to_measure_affordability.pdf Voigtlande, M. (2007). Die Privatisierung offentlicher Wohnungen. Wirtschaftsdienst, 87(10), 679–686. Wijburg, G., & Waldron, R. (2020). Financialised privatisation, affordable housing and institutional investment: The case of England. Critical Housing Analysis, 7(1), 114–129. https://doi.org/10. 13060/23362839.2020.7.1.508 Wijburg, G., Aalbers, M., & Heeg, S. (2018). The financialisation of rental housing 2.0: Releasing housing into the privatised mainstream of capital accumulation. Antipode, 50(4), 1098–1119. https://doi.org/10.1111/anti.12382 Wilson, W. (1999). The right to buy (Research Paper 99/36). UK Parliament. Retrieved from: House of Comons Library https://researchbriefings.files.parliament.uk/documents/RP99-36/ RP99-36.pdf Wilson, W., & Barton, C. (2021). What is affordable housing? (07747). UK House of Commons Library. https://commonslibrary.parliament.uk/-research-briefings/cbp-7747/

Chapter 3

Literature Review

3.1

Contributions to the Literature

Existing literature in the realm of private investments and affordable housing cover topics such as impact investing, financialization, the involvement of institutional investors, the growth of the alternative investment space, and the shift from state to private finance relations. It is difficult to identify a common pattern among the studies in this space given the disparity in locations and data collection methods. All authors applied a qualitative data analysis, mostly focusing on case studies of single countries. The legal permissions, financing methods, and the standard of affordable housing vary from country to country, making it quite difficult to do a direct comparison among each of the studies. In summary, most of the literature in this space covers how financialization has heightened existing inequalities in housing affordability and stability (Fields & Uffer, 2016; Wijburg & Waldron, 2020). A number of studies have emerged both in theory and practice on rental housing as an investment product through a variety of financing means such as funds, fixed income, REIT, or publicly listed structures. Literature over the last two decades has discussed the theory of financialization and its effects on expanding the asset territories of institutional investors. Through detailed interviews of 18 elite Dutch investors with 10 plus years of experience each, Aalbers et al. (2017) use empirical data to develop and compare real estate investment strategies of 1980 to real estate investment strategies of 2010. They conclude that financialization of common real estate to investment properties has put pressure on various state agencies (e.g. urban planning) to deliver more of such assets, as Dutch pension funds are able to expand geographically. Financialization of real estate has been a common theme among all of the examined literature. Authors Brill and Durant (2021) and Wijburg et al. (2018) corroborate that the financialization of real estate has heightened such that Build to Rent (BTR) models are pivoting from private landlords towards institutional landlords. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 D. Vigneswaran et al., Affordable Housing as a Profitable Impact Investment, Contributions to Finance and Accounting, https://doi.org/10.1007/978-3-031-07091-4_3

27

28

3

Literature Review

The affordable housing space has emerged such that private markets and institutional investors are acquiring, developing, and managing affordable rental homes rather than the state. Wijburg and Waldron (2020) draw on the case of England; these authors study the business models of private sector providers such as Legal & General to describe how affordable housing has emerged into multi-tenure options as a strategy to remain profitable for investors. Similarly, many others in this space have discovered the commercialization of affordable housing in the UK, the US, the Netherlands, France, Italy, or Germany where schemes are emerging between for-profit actors and affordable housing providers (Gimat et al., 2020; Graddy & Bostic, 2010; Belotti, 2017; Wijburg, 2019; Unger, 2013). This commercialization has been supported mostly since the 2008 Global Financial Crisis (GFC) (Tapp, 2019; Ronald et al., 2017; Wainwright & Manville, 2017). For example, in a study on the use of tax credits in the US it was discovered that since 2008, developers layer multiple tax credits (including tax sheltering) to finance redevelopment and deliver affordable housing (Tapp, 2019). The market for tax credits provides insight into the variations of financialization. The literature considers many forms of private financing models. Wainwright and Manville (2017) further contribute evidence to financialization by investigating a new type of real estate bond market in London since 2008, which enables Housing Associations (HAs) to issue social housing bonds. Similarly, Australia is considering the financing of affordable rental supply through fixed-income options (Lawson et al., 2014). Lawson et al. consider a government issued bond scheme managed by a non-profit housing association (2014). The authors suggest the use of a financial intermediary to issue rated bonds for well-targeted rental housing developments, thus attractive investor interest with well-structured government guarantee (2014). The idea of privatization through means other than funds was further considered by Wijburg (2019) through his case study of REIT structures in France. The rise of real estate investment trusts (REITs) in North America, Asia, and Europe has drawn increased attention in the literature. REITs are holding companies that invest in income-producing real estate assets and enable private and institutional investors to hold commercial and/or residential real estate indirectly (Lizieri, 2009). Wijburg focuses on REITs in France since the country’s introduction of REIT-regulations in 2003. He uses a mixed method secondary research design (e.g. thorough reading of economic and industry publications, public policy documents, and academic literature), select interviews with French financial market actors (including REITs), and select interviews with Icade (a REIT subsidiary of the French public bank). The growth of REIT structures in the real estate space is further verified by Wijburg et al. (2018) as they state that following the GFC “many private equity funds were converted into REITs and listed real estate companies, and housing portfolios were sold directly to listed real-estate funds” (p. 1099). REITs and listed real estate companies create a rentier structure to optimize cash flows, rental incomes, and capital gains through the sale of individual housing units (Moreno, 2014). Through these long-term investment structures, ironically short-term investments were enabled by buying and selling shares in these funds on the stock exchange. Unlike the other examples above, the financialization of affordable housing in Germany took place a lot sooner over five

3.2

Identifying Gaps in the Literature

29

phases of mass housing transactions since the 1990s. The paper by Unger (2013) conducted a case study on housing transactions in Germany between 1999 and 2015 to identify the financialization strategies within the history of accumulation regimes through primary and secondary sales. He highlights that financialization takes place through the transition from non-profit housing stock to publicly listed companies (2013). The literature found that in order for private companies to maintain the ability to provide affordable housing, cost optimization strategies (e.g. standardization of services, lowered wages) in facility management, portfolio management, and business accounting were employed (Unger, 2013; Cooper, 2012; Perry & Nölke, 2006). Through the literature review, it became clear that investment strategies and financing methods widely differ from country to country and interestingly has a lot to do with historical context (as large housing portfolios have been put up for sale at during major economic events). The existing literature interrogates the consequences of global institutional investors on rental properties and the renters’ experiences. It conducts comparative research to denote the local and national varieties between countries and cities and more importantly between investment strategies. It also provides a detailed account of how financialization of real estate coincides with newly emerging financing strategies such as social housing bonds, mixed tenure developments, revival of securitization markets, REIT-like schemes across neoliberal systems, special-purpose fund vehicles, and publicly listed companies.

3.2

Identifying Gaps in the Literature

The literature indicates that there is a high degree of financialization of the affordable housing market, supported by both the free market and governments alike. This financialization further indicates that there is a demand and interest among institutional investors to invest in these impact assets. However, the existing literature does not demonstrate the profitability of such investments, and the measures required to attain a financially feasible affordable real estate investment. The authors have evaluated their case studies and investments as evidence of displacing social value creation for economic value creation. The authors have sought to explain the influence of financialization on social housing and its impact on furthering housing inequality (Tang et al., 2017; Wijburg & Waldron, 2020; Wijburg et al., 2018; Unger, 2013; Wainwright & Manville, 2017). Meanwhile, an analysis from the investor’s perspective has not been conducted. For example, the profitability of affordable housing remains understudied in the literature. Other major gaps in the literature occur in research methodology, data collection, and analysis strategies. A majority of authors took a qualitative approach to research and analysis. They mostly focused on comparative case studies between two projects or companies within the same country. For example, Wijburg et al. (2018) conducted a study on two cases of listed real estate companies (namely Immeo Wohnen and Vonovia) that operate in the Ruhr metropolitan region of Germany. A comparative

30

3 Literature Review

analysis was conducted to illustrate the shift from Financialization 1.0 to Financialization 2.0, from speculation to long-term investment (Wijburg et al., 2018). Sometimes, the authors made comparison of social housing models between two different countries. For instance, the study by Aalbers and Holm (2008) analysed the policy formation and the implementation of privatizing social housing in the cities of Berlin and Amsterdam. Here, they quantified the privatization process by counting the number of sales as well as focusing on social composition of the new owners and the urban geography of sales. They mostly looked at the history of housing in these cities and then the current makeup of privatization (Unger, 2013). Similarly, Fields and Uffer (2016) have conducted a secondary analysis of separate primary research projects in Berlin and New York to establish how financialization has impacted tenants. Interestingly, financial managers or development companies have rarely been interviewed or used in the population sampling strategy. In cases where they are included in the data collection method, it is often with the purpose of conducting a qualitative analysis. For example, Brill and Durrant (2021) examined public and private sector actors that have shaped the context of BTR models in the UK. The authors conducted tours and site visits of BTR developments across London and led 45 interviews with investors, developers, brokers, private sector researchers, public and private planning consultants, and charity workers. They used the interviews to inform the study on how these actors have shaped the BTR market through positive and negative narratives. While many authors provided specific case studies on funds and listed companies, the operational figures and accounting procedures of these companies were not assessed. This is observed in studies by Wijburg and Waldron (2020) on their assessment of Blackstone’s strategy of providing affordable housing, Wijburg et al. (2018) on their assessment of Immeo and Vonovia’s strategy of providing affordable housing, or Graddy and Bostic (2010) on their assessment of state governance processes in Massachusetts and New Jersey. These authors still achieve the goal in explaining with high accuracy and reliability the various investment strategies adopted by the private sector to provide affordable housing. Lack of access to private data is, of course, a deterrent. In terms of geography, many of the case studies focus on France, Germany, the Netherlands, the UK, and the US. This is understandable given the amount of activity and data available from these regions. It has been established through the literature’s themes that real estate asset allocation exists, specifically in traditional rental and social housing subcategories. However, the influence of these strategies within investor portfolios is unknown. What are the effects of having global affordable real estate in the portfolio of an institutional investor? What investment criteria is necessary for successful affordable housing projects? This book intends to contribute to the literature gap through the exploration of the relevant criteria for successful affordable real estate investments. The existing literature evaluates best practices in the industry, however, lacks detail when it comes to implementation. It also examines policy documents on housing, and government initiatives in their efforts to support affordable housing development. This study seeks to further fill the gap in the literature by addressing whether government subsidies are necessary for the economic success of affordable

References

31

housing projects. While it is difficult to enable a quantitative approach, as suggested by the studies above, this analysis will attempt to provide a quantitative component. For instance, this book provides quantitative insight by comparing returns between traditional and impact real estate asset classes. Overall, the goal of this book is to substantiate the likelihood of profitability and the investment strategies that enable institutional investors to feasibly provide affordable housing.

References Aalbers, M. B., & Holm, A. (2008). Privatising social housing in Europe: The cases of Amsterdam and Berlin. In K. Adelhof, B. Glock, & J. Lossau (Eds.), Urban trends in Berlin and Amsterdam (Vol. 110, pp. 12–23). Aalbers, M. B., Van Loon, J., & Fernandez, R. (2017). The financialization of a social housing provider. International Journal of Urban and Regional Research, 41(4), 572–587. https://doi. org/10.1111/1468-2427.12520 Belotti, E. (2017). The importation of social mix policy in Italy: A case study from Lombardy. Cities, 71, 41–48. https://doi.org/10.1016/j.cities.2017.06.013 Brill, F., & Durrant, D. (2021). The emergence of a Build to Rent model: The role of narratives and discourses. Economy and Space, 1(18), 1–18. https://www.researchgate.net/publication/348570 618_The_emergence_of_a_Build_to_Rent_model_The_role_of_narratives_and_discourses Cooper, C. (2012). Accounting for the fictitious: Living death by mainstream economics. Interdisciplinary Perspectives on Accounting Conference, Cardiff. Gimat, M., Marot, B., & Le Bon-Vuylsteke, M. (2020). État des connaissances sur la vente de logements sociaux en Europe: Allemagne, France, Pays-Bas et Royaume-Uni. Institut CDC pour la recherche. Fields, D., & Uffer, S. (2016). The financialisation of rental housing: A comparative analysis of New York City and Berlin. Urban Studies, 53(7), 1486–1502. https://doi.org/10.1177/ 0042098014543704 Graddy, E., & Bostic, R. (2010). The role of private agents in affordable housing policy. Journal of Public Administration Research and Theory: J-PART, 20, 81–99. Lawson, J., Berry, M., Hamilton, C., & Pawson, H. (2014). Enhancing affordable rental housing investment via an intermediary and guarantee. AHURI Final Report No. 220, Australian Housing and Urban Research Institute Limited. https://www.ahuri.edu.au/research/finalreports/220 Lizieri, C. (2009). Towers of capital. Office markets & international financial services. WileyBlackwell. Moreno, L. (2014). The urban process under financialised capitalism. City, 18, 244–268. Perry, J., & Nölke, A. (2006). The political economy of International Accounting Standards. Review of International Political Economy, 13(4), 559–586. Ronald, R., Lennartz, C., & Kadi, J. (2017). What ever happened to asset-based welfare? Shifting approaches to Housing Wealth and Welfare Security. Policy & Politics, 45(2), 173–193. https:// doi.org/10.1332/030557316X14786045239560 Tang, C., Oxley, M., & Mekic, D. (2017). Meeting commercial and social goals: Institutional investment in the housing association sector. Housing Studies, 32(4), 411–427. https://doi.org/ 10.1080/02673037.2016.1210098 Tapp, R. (2019). Layers of finance: Historic tax credits and the fiscal geographies of urban redevelopment. Geoforum, 105, 13–22. https://doi.org/10.1016/j.geoforum.2019.06.016 Unger, K. (2013). Der große Ausverkauf. Die Finanzialisierung der ehemals gemeinnützigen Wohnungswirtschaft in Deutschland. Zeitschrift Marxistische Erneuerung, 95, 24–35.

32

3

Literature Review

Wainwright, T., & Manville, G. (2017). Financialization and the third sector: Innovation in social housing bond markets. Environment and Planning A, 49(4), 819–838. https://doi.org/10.1177/ 0308518X16684140 Wijburg, G. (2019). Reasserting state power by remaking markets? The introduction of real estate investment trusts in France and its implications for state-finance -relations in the Greater Paris region. Geoforum, 100, 1–11. https://doi.org/10.1016/j.geoforum.2019.01.012 Wijburg, G., & Waldron, R. (2020). Financialised privatisation, affordable housing and institutional investment: The case of England. Critical Housing Analysis, 7(1), 114–129. https://doi.org/10. 13060/23362839.2020.7.1.508 Wijburg, G., Aalbers, M., & Heeg, S. (2018). The financialisation of rental housing 2.0: Releasing housing into the privatised mainstream of capital accumulation. Antipode, 50(4), 1098–1119. https://doi.org/10.1111/anti.12382

Chapter 4

Research Design

4.1

Research Hypothesis

The literature has established that rental homes are increasingly being managed, produced, or acquired by private equity firms and other institutional investors. What do these fund managers in the public and private industries do to augment return? What development criteria do they focus on? What factors do they consider most important in affordable housing investments? Does their perception and method in which they weight importance of affordable housing investments affect overall portfolio performance? The conceptual framework hypothesizes a number of criteria that answers these questions. The research framework developed below attempts to explore the connections between investment criteria and investment performance of affordable housing impact assets. The two layers of qualitative data (based on preference and practice) and then the final layer of quantitative data (based on performance) are difficult to synchronize. While direct causation will be difficult to prove, there are some criteria that we suppose have a relationship with institutional fund managers’ decision-making practices and in turn, investment performance. Based on the literature review and preliminary research, the following factors have been hypothesized to contribute to a financially feasible affordable housing project: use of an existing property, location of proposed affordable housing, valuation techniques, leverage, financing method, construction cost, speed of planning/building permit approvals process, government subsidy, property management cost, tenant turnover rate, and eventual exit strategy.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 D. Vigneswaran et al., Affordable Housing as a Profitable Impact Investment, Contributions to Finance and Accounting, https://doi.org/10.1007/978-3-031-07091-4_4

33

34

4

• • • • •

Criteria affecting financial feasibility of affordable real estate projects

Use of existing property Location Valuation Financing methodology Leverage

Strategic Planning

• Construction cost • Speedy planning/ building permit approvals • Government subsidy

Production Economics

• Property management cost • Tenants turnover rate • Exit Strategy

Operations

Research Design

Performance

Fig. 4.1 Conceptual framework of qualitative decision-making criteria and its influence on quantitative portfolio performance

Hypothesis 1 The assumed nine criteria (illustrated in Fig. 4.1 and Table 4.1) are correlated to the financial impact as described in the last column of Table 4.1. The relationships are hypothesized in columns 2–6 of Table 4.1. Hypothesis 2 The availability of government subsidy has the largest influence on financial return. It is expected that the variable that will affect performance the most will be the existence and degree of government subsidy. It is hypothesized that where the government subsidy is highest, the return multiple of the affordable housing project is also higher. Government subsidies can come in many forms. The research has indicated that often government subsidies can work in a multitude of ways—through a subsidized loan to the asset owner, through the provision of discounted land to the developer, or through a monthly rent stimulus to the tenant. Government rental stimulus cheques provide a form of “insurance” that monthly rental payments are made to the investment and thus the investor. The goal of the research is to also discover other criteria whereby government subsidies are not the end all and be all of successful affordable housing. Are there other factors that contribute to a high performance, such (continued)

4.2

Methodology and Research Design

35

that these methods can be implemented in countries where government subsidies for affordable housing do not exist? Hypothesis 3 Financial gain is highly likely in affordable housing investments. It is also hypothesized that financial gain is highly likely in affordable housing investments. However, it is hypothesized that the return will be lower than traditional, non-impact real estate investments. Financial gain is still enabled on a number of grounds such as the intrinsic land value of the property or the financing techniques employed by the investor including the leveraging technique. It is suggested that yields are lower than traditional real estate only because rents are capped in affordable housing models, unlike the free market.

4.2

Methodology and Research Design

For the purpose of this study, a comprehensive methodology involving a two-tier data collection process was employed. This subsection outlines the research design, description of data, data collection method, population, sampling strategy, and intended data analysis methods of this book.

4.2.1

Research Design

This study used a mixed methods research design. Mixed methods refer to research that integrates quantitative and qualitative research within a single project. Mixed methods research has “become an increasingly used and accepted approach to conducting business research” (Bryman & Bell, 2015, p. 630). In order to design a mixed methods strategy, qualitative questions and answers that can be assigned quantitative values were considered. There are two types of quantitative research: experimental and descriptive. The quantitative portion of this study conducted its analysis through descriptive statistics; thus, it measured the sample at a moment in time and described the sample’s demography. This was done, for example, through ordinal and ratio data collected from the surveys. Quantitative research establishes statistically significant conclusions about a population by studying a representative sample of the population (Creswell, 2003; Lowhorn, 2003). For the purpose of this study, it was possible to identify a sample that was statistically identical to the population. However, it was not possible for this study to identify a statistically significant1 sample. 1

Statistically significant sample size can vary by the size of the population and intended margin of error. For a population of 5000 and a margin of error of 10%, a sample size of 94 is required (RCSI Sample size Handbook, 2018).

36

4

Research Design

Table 4.1 Hypothesized influence of decision-making criteria on project return (Own elaboration, 2021) Criteria Use of existing property

Movement of criteria Occurrence Return Yes Increases

Movement of criteria No-occurrence Return No Decreases

Location

Central

–

Non-central

–

Property valuation technique

High

Increases

Low

Decreases

Financing methodology

Increases

Less than two methods

Decreases

Degree of leverage

More than two methods High

Increases

Low

Decreases

Cost of construction

High

Decreases

Low

Increases

Speed of planning/building permit approvals process Degree of government subsidy

Fast

Increases

Slow

Decreases

High

Increases

Low

Decreases

Cost of property management

High

Decreases

Low

Increases

Tenant turnover rate

High

Decreases

Low

Increase

Eventual exit strategy

High

Increases

Low

Decreases

Influence Weak positive influence No influence Weak positive influence Weak positive influence Strong positive influence Strong negative influence Strong positive influence Strong positive influence Weak negative influence Weak negative influence Strong positive influence

The qualitative portion of this study took on a similar descriptive approach, as it described the relationship of the analysed variables and its effect on financial outcome based on the direct experiences of the interviewees. It was used to determine relationships between collected data and observations. The hypotheses were then proved and/or disproved using statistical methods. The analysis was natural (not controlled), with a comprehensive and diffuse focus on words. Case studies that “follow the method of structured, focused comparison can even accomplish the important step from description to explanation” (Esser & Vliegenthart, 2017, p. 6). More specifically, this study employed a comparative research design, whereby data collection in two or more cases was sought to make comparisons and use empirical evidence to support theoretical evidence.

4.2

Methodology and Research Design

4.2.2

Population and Sampling Strategy

4.2.2.1

Description of Population

37

The common characteristic of the population is that they have in some form participated in the provision of affordable housing. Therefore, the population group is quite wide and includes fund managers, developers, analysts, and CEOs of public and private companies that have conducted projects in for-profit affordable housing. It was not a requirement that the company’s entire portfolio consisted of impact-only investments. Investments and asset classes in traditional real estate were preferred, so that professionals were able to make comparisons based on their wider set of experiences.

4.2.2.2

Nonprobability: Convenience and Purposive Sampling

This study employed a nonprobability sampling, where “subjective methods are used to decide which elements should be included in the sample” (Lavrakas, 2008, p. 149). Through this strategy, both the convenience and purposive schemes have been used. The sampling strategy was mixed because while access to industry professionals is important, expert judgement was still used to select a representative sample of elements. The framework for research methods was based on an existing professional network and one’s access to industry professionals. This is identified as convenience sampling, whereby the sample is drawn from a population that is close at hand (Battaglia, 2008). It is a type of nonprobability sampling in which the subjects are chosen because they are convenient sources of data for researchers. This technique is used to observe habits, opinions, and viewpoints. Accordingly, the sample is based on the professional network of the authors. In this type of sampling, there are typically no criteria other than the subject’s willingness to participate (Nassiuma, 2001). However, for the purpose of this study, the subjects have been systematically chosen to reflect the characteristics of the elected population described above. It is important to note, this sample includes the current leading companies in the area of private affordable housing development. This involves purposive sampling, also known as judgemental or expert sampling, where the sample is selected based on the researchers’ knowledge about the study and the population (Table 4.2) (Lavrakas, 2008). The target population or sample were not restricted by geography. The subjects are from Germany, Switzerland, the United Kingdom (UK), and the United States (US). These firms have been leading the space in creating for-profit real estate investments either through private equity funds or as publicly operating real estate companies. They are considered leaders in this space in their respective continents. A variety of geographies among the highly developed OECD nations have been chosen to improve the depth of comparative research. Comparative research involves comparisons between a minimum of two macro-level units such as world regions,

38

4

Research Design

Table 4.2 Details of the individual participants within the study sample (Own elaboration, 2021) Participant name Lars Von Lackum Dr. Marcus Cieleback Daniel Riedl Dr. HansMichael Brey Thomas Jebsen Pierre Jacquot Paul Munday Nathan Taft

Position and firm CEO, LEG Immobilien Chief Economist, Patrizia Member of Management Board (CDO) and Head of Development Austria, Vonovia Board Chairman, Berliner Leben Foundation

Member of Board of Directors, Deutsche Kreditbank CEO, Edmond de Rothschild (REIM) Fund Manager, Funding Affordable Homes Partner, Jonathan Rose Companies LLC

Business type Public Real Estate Company Public Real Estate Company Public Real Estate Company Institutional Investor (Foundation) Bank (Financing Agent) Real Estate Private Equity Fund Real Estate Private Equity Fund Real Estate Private Equity Fund

Geography Germany Germany Germany Germany

Germany Switzerland UK US

systems, cultures, and markets at one point or more points in time (Esser & Vliegenthart, 2017). This form of research differs from non-comparative work as “it attempts to reach conclusions beyond single cases and explains differences and similarities between objects of analysis and relations between objects against the backdrop of their contextual conditions” (Esser & Vliegenthart, 2017, p. 2).

4.2.3

Data Description and Collection Method

The data were collected in two tiers; each tier used different methods. The first tier consisted of standardized surveys adhering to the quantitative component of this study. In this phase, the surveys employed self-report scales to measure the criteria of different factors that contribute to the financial feasibility of affordable housing projects. Here, the type of data collected was ordinal data, categorical data, or ratio data. Ordinal data can be classified as both quantitative and qualitative (Cook et al., 1995). Ratio data was collected from questions regarding the company’s performance and track record of historic affordable housing funds. Then, descriptive statistics were used to analyse this data. Descriptive statistics summarize data whereas inferential statistics are used to identify statistically significant differences between groups of data. The latter requires intervention and controlled groups in a randomized controlled study (Frost, 2018). The exact type of data (within the descriptive, ordinal, and ratio measurement categories) that was collected is shaded in Table 4.3.

4.2

Methodology and Research Design

39

Table 4.3 Comparison of data type to analysis methods (Analytics Big Data, 2015) Measurement Scales Nominal

Ordinal

Analysis Methods Categories Arithmetic Operations

Inequality

x

Ordering/Ranking

Order

Interval

Equal intervals Equal intervals with between characteristics true zero point

x

x

x

x

x

x

x

x

Addition/Subtraction Multiplication/Division Descriptive Statistics

Statistical Analysis Techniques Commonly Used

Mode

Ratio

x x

Median

x

x

x

x

x

x

Mean

x

x

Standard Deviation

x

x

x

x

Crosstabs/Chi-Square

x

Rank Order Correlation Analysis of Variance Correlation

x x

x

x

Regression

x

x

Factor Analysis

x

x

The second tier consisted of expert interviews. The interviews followed up on exact performance details of completed development projects within each of the funds or firms. Expert interviews were semi-standardized and based on a highly structured interview guide. Of the various qualitative research methods, expert interviews are best suited to gain expert knowledge regarding specific challenges from the perspective of management practice (Libakova & Sertakova, 2014). In this phase, the goal was to focus on questions relevant to the company and their development experience. The second tier focused on a biographical method of data collection and thus led to a thematic analysis. A Latent Coding analysis was conducted to transform the qualitative data into tangible concepts. While an inferential quantitative analysis would help to draw causality, data from eight participants cannot be projected to make industry-wide conclusions (Table 4.3).

40

4.2.4

4

Research Design

Data Collection Method

Both the survey and interview questions were first drafted based on existing insight and knowledge of the for-profit affordable housing industry, plus gaps identified in the literature review. The questions were then tested and validated. Upon receiving feedback, minor improvements were made to the draft. Final versions of the survey and interview were then released to the participants. As per the convenience and purposive sampling techniques identified above, the eight participants were individually contacted via email. Surveys were sent to all eight participants. Only six had completed the survey. The survey consisted of 13 questions. The surveys were designed on SurveyMonkey, and then sent via email. On average, the survey should take approximately 30 min to complete. However, the analytics function of SurveyMonkey indicated that the participants took an average of 15 min to complete the survey. The survey employed closed-ended questions, which are defined as “question types that ask respondents to choose from a distinct set of pre-defined responses, such as ‘yes/no’ or among a set multiple choice questions” (QuestionPro Survey Software, 2020, p. 1). This type of questioning has the following advantages: They are easy to understand and quick to respond Close-ended questions are quantifiable (easier statistical analysis) The response to the questions is straightforward, so more likely to get an answer As mentioned above, the types of data obtained with the surveys are ordinal, ratio, and descriptive. The first half of the questions were meant to get a background on the respondent, their company, and their portfolio. Most of the questions were structured around understanding respondents’ portfolio. These questions are structured to obtain ratio data, such as the market capitalization of the investments in affordable housing, the percentage of investments in traditional versus impact real estate, or the rate of return of these projects. The next set of questions were structured to obtain ordinal data. As mentioned above, variables have a natural order through a ranking system (Agresti, 2013). Some of the questions employed a Likert Scale. For example, one question used the Likert Scale to test the likelihood of financial gain in affordable housing. A Likert Scale is a psychometric scale, often used in survey-related research. When responding to a Likert element, the respondent defines their level of agreement or disagreement according to a symmetric agree–disagree scale for a series of statements (Allen & Seaman, 2007). Thus, the scale measures the intensity of their feelings for a given item. Likert scaling assumes distances between each choice/ statement are equal. This survey used a variation of the Likert Scale to measure importance, difficulty, possibility, etc. The scales used in the survey are shown in Table 4.4. Categorical data is a type of statistical data which assumes a value as its identifier, so that each individual element can be grouped into a defined group or category according to a common (often qualitative) characteristic. However, there is no

4.2

Methodology and Research Design

41

Table 4.4 The three variations of the Likert Scale used in the survey (Own elaboration, 2021) Importance Likelihood

Very important 5 Very likely 5

Important 4 Likely 4

Neutral 3 Neutral 3

Unimportant 2 Unlikely 2

Very unimportant 1 Very unlikely 1

Table 4.5 Categorical data was used to identify and classify the participants’ business model (Own elaboration, 2021) Real estate developer (publicly listed or private) 1

Private equity fund 2

Real estate investment trust 3

Insurance company 4

Pension fund 5

ordering in relation to the value assumed by the element within the category (Agresti, 2013). An example of categorical data used in this study is presented in Table 4.5. Following the survey, interviews were scheduled and conducted with all eight participants. Due to location constraints, semi-structured interviews were conducted via video or phone conference. Interviews lasted from 30 min to an hour each. The interviews were audio-recorded and then transcribed orthographically. The interview consisted of 13 questions and was structured with three main sections: business model and structure, comparison to market residential, and criteria for success with 7, 2, and 3 questions in each section, respectively. In the business model section, the questions sought to understand the roles that were outsourced, financing strategies, how the project location was determined, how affordability prices were determined, and what the typical exit strategy looked like. In the second section which compared affordable housing to market residential, the questions sought to understand the cost of production and maintenance and whether it is the same, more than, or less than the production of market residential development. The interview questions also attempted to compare returns between the two sub-sectors. In the final section, the questions were structured to follow-up on the participants’ responses to some parts of the survey. For example, the interview questions following the survey further investigated why a certain weight and rank was assigned by the respondents. In this section, the questions also probed to understand whether government subsidies are necessary to achieve profitability. This was also a question in the survey. However, through the interview, the participants were able to explain why government subsidies are or are not important as per their development experiences. The interview responses added value to the survey responses through an anecdotal outlook into the business. The two-tiered approach allowed for triangulation of the data. Triangulation refers to the use of multiple methods or data sources in qualitative research to develop a comprehensive understanding of phenomena (Patton, 1999). By using multiple methods to data collection, the intention is to reduce the deficiencies and biases coming from a single method of data collection. The

42

4 Research Design

results of the interview were used to enhance, augment, and clarify the results of the surveys. See Appendices A and B to view the full set of questions that were designed and asked.

4.3

Data Analysis Methodology

Based on the data obtained in the surveys and interviews, the goal of this study is to understand the features of the data and identify among all collected data, the most relevant investment strategies which guides this study to answer the question: what investment criteria influence the financial feasibility of an affordable housing development project executed by the private sector? As such, this study employed one qualitative data analysis method (thematic coding) and two quantitative data analysis methods (descriptive and inferential statistics).

4.3.1

Thematic Coding

Thematic analysis is a widely used method of qualitative data analysis, which uses processes to code qualitative information and identify patterns of meaning across a qualitative dataset. This form of analysis helps to analyse answers to open-ended questions. The encoding of responses can be based on a set of themes, indicators, and qualifications (Braun et al., 2016). A theme is a pattern found in the information that describes and organizes possible observations or interprets aspects of a given phenomenon.

4.3.1.1

Tools of Thematic Coding

In practice, most thematic analyses include both semantic and latent, and inductive and deductive elements (Braun et al., 2016): Inductive: The data itself is used to derive the structure of analysis, meaning coding occurs without trying to fit the data into a pre-existing theory or framework. Deductive: Analysis is theory driven and a structure or pre-determined framework is used to analyse data. Semantic: A theme is directly observed. Identify and explain the explicit and surface meaning of the data. Latent: Used to capture underlying ideas, patterns, and assumptions. More interpretative and conceptual orientation to the data.

4.3

Data Analysis Methodology

43

The stages to thematic analysis are: Step 1—Familiarization with data: transcription of interview audio Step 2—Coding: deriving short-handed labels to describe highlighted sections of text Step 3—Generating themes: identify patterns among the codes created, and start grouping and creating themes (can discard any codes that do not appear often in the data) Step 4—Reviewing themes: consider if themes are useful and accurate representations of the data. Regroup, combine, split, or discard themes as necessary Step 5—Defining and naming themes: formulate what is meant be each theme and outlining out how it helps to understand the data Step 6—Produce report: communicate findings in table, chart, or graphic format A thematic analysis can be presented in the form of a chart where the columns communicate the analysed theme, assigned code, direct interview quotations, their associated meanings, and the percentage of interview data that had similar meanings. These results can further be illustrated as a graphic to see points of connectivity among the themes. This form of qualitative analysis helped to support the arguments and concepts in Chap. 6 by sourcing evidence-based quotes from the interviews.

4.3.2

Descriptive Statistics

Descriptive statistics are used to summarize and organize numerically and graphically displayed group of data (sample), so that it is possible to achieve an understanding of that group. It allows to present initially raw data from a sample, in a meaningful and easy to interpret way (Frost, 2018). However, descriptive statistics are only meant to describe data of a given sample, so it cannot be used to make inferences from the sample to the whole population. While generalization is not possible, there are at least no uncertainties because only the elements that are part of the sample to be described are measured (Narkhede, 2018).

4.3.2.1

Tools of Descriptive Statistics

Descriptive statistics use the following statistical measurement tools for descriptions of a sample: measure of central tendency, measure of spread, and skewness-kurtosiscorrelation. The properties of the sample in this type of statistic are called parameters (Laerd, 2018). This information can be presented numerically (tables) or graphically (plots and charts) to: Central tendency: Describe the central position of a frequency distribution for a group of data: Mean/Average, Median, Mode.

44

4

Research Design

Dispersion: Summarize a group of data by describing how spread (far from the centre) are and its variability: Standard deviation, Variance, Range, Percentile, Quartile. Skewness: Measure of asymmetry of the probability distribution of a random variable about its mean. Kurtosis: Measure of existence of outlier values in the distribution. Correlation: Measure how strong is the relationship between variables.

4.3.3

Inferential Statistics

Inferential statistics analyses the information from a sample in order to make inferences about the total population. In this type of statistic, the study must take into account the importance of the sampling strategy processes, which define the sample of the study population and which must be representative (Frost, 2018).

4.3.3.1

Tools of Inferential Statistics

Inferential statistics use more complex statistical and mathematical tools, which allow to define trends or relationships based on a sample and project it onto a population (Taylor, 2020). The properties of the sample in this type of statistic are called estimates. Most of the tools used in inferential statistics are part of a family of statistical models known as General Linear Model (Taylor, 2020). This includes: Tests of significance or Hypothesis test (t-test, chi-square test, etc.) Confidence interval Analysis of Variance (ANOVA) Regression analysis Multivariate methods (cluster analysis) The purpose of clustering is for understanding or utility. This study employed the clustering for understanding methodology, which searches for conceptually meaningful groups of items that share common characteristics (e.g. investment criteria) and which can be used to get an insight on how participants analyse and describe phenomena (e.g. impact of investment criteria on financially feasible affordable housing projects).

4.3.3.2

Cluster Analysis

Cluster analysis groups (clusters) objects based only on the information these objects hold, where the data or relationships between them can be founded (Tan et al., 2019). The goal is that “the objects within a group be similar (or related) to one another and different from (or unrelated to) the objects in other groups. The greater the similarity

4.3

Data Analysis Methodology

45

Clustering

Centroid-based Clustering

Connectivitybased Clustering

Density-based Clustering

Distribution-based Clustering

K-Means

Hierarchical

DBSCAN

Gaussian

OPTICS

Agglomerative

Linkage criterion

Between Groups

Nearest neighbour

Further neighbour

Among others

Divisive

Variance

Ward’s Method

Fig. 4.2 Clustering algorithm and cluster methods overview

(or homogeneity) within a group and the greater the difference between groups, the better or more distinct the clustering” (Tan et al., 2019, p. 525). An overview of the different clustering methods and cluster types is presented (Fig. 4.2): The variables on which the cluster analysis is to be done should be selected by previous theory, the hypotheses being tested, and the judgment of the researcher. It is also important to select an appropriate measure of distance or similarity.

4.3.3.3

Types of Clustering

There are four main types of clusters, also known as algorithms (Mandigan, 2013): Centroid-based: Construct various partitions and then evaluate them by some criterion. Connectivity-based (Hierarchical): Create a hierarchical decomposition of the set of data (or objects) using some determined criteria. Density-based: Based on connectivity and density functions. Distribution-based: Based on distributions, such as Gaussian distributions.

46

4.3.3.4

4

Research Design

Measure of Proximity

An important part of the cluster analysis is to define the measure of proximity or similarity to be used. Proximity defines how similar or dissimilar two or more objects are and based on this it is defined in which group they belong and thus form the different clusters (LZP, 2019). When working with the concept of similar or dissimilar, proximity measures the level of similarity, which varies from 0 to 1; the larger the value, the larger the similarity between the objects, with 1 being the maximum possible similarity value (similar). The dissimilarity is measured in a similar way, between 0 and 1, 0 being the minimum value of dissimilarity (not similar). The most used measures of proximity according to LZP (2019) and Black (2019) are: Euclidean Distance: Is a non-negative measure which calculates, based on the Pythagorean Theorem, the distance between two points. Squared Euclidean Distance: Is used in the estimation of parameters in statistical models where regression analyses are performed (not an exact metric measure but can be used as a notion of a statistical distance). The selection of the type of measurement for proximity depends on the type of data with which the analysis is to be performed (Gupta, 2019).

4.3.3.5

The Use of Two Cluster Analysis Methods for the Purpose of This Research

In order to obtain insight about a fund manager’s decision-making investment criteria, a cluster analysis was performed to numerically analyse the information collected in the surveys (inferential statistic) and thus achieve a greater understanding about preferred criteria that contribute to financially feasible affordable housing development. The methods selected for this study’s cluster analysis was Hierarchical and K-means clustering, both of which are defined below.

4.3.3.6

Hierarchical Clustering

Hierarchical clustering is an algorithm that groups similar objects in groups or clusters with the aim of having groups whose elements are similar to each other, and in turn, each cluster formed is completely different from each other (Bock, 2020). There are different methods: from the bottom up, known as Agglomerative and from the top down, known as Divisive. Agglomerative works by forming groups from individual objects (small clusters), joining or agglomerating them into larger groups. Divisive works from a large group

4.3

Data Analysis Methodology

47

Fig. 4.3 Single link illustration (Ruiz, 2020 based on Tan et al., 2019)

Fig. 4.4 Complete link illustration (Ruiz, 2020 based on Tan et al., 2019)

Fig. 4.5 Centroid link illustration (Ruiz, 2020 based on Tan et al., 2019)

(complete data set) and proceeds to divide the observations into smaller groups. As per Tan et al., the basic algorithm comprises the following steps (2019): Step 1: Calculate the proximity of individual points, considering all the data points as individual clusters Step 2: Similar clusters are merged and formed as a single cluster Step 3: Calculate the proximity of new clusters and merge the similar clusters to form new clusters, until there is only one cluster formed Step 4: All the clusters end up merged and form a single cluster The merge process is performed using one of the following linkage methods, which calculate the proximity between the elements within the cluster, joining those that are at a shorter distance from each other (Tibshirani, 2012). Single Linkage (Nearest neighbour): The proximity of two clusters is defined as the shortest (minimum) distance (maximum value of similarity) between any two points within the two different clusters analysed (Fig. 4.3). Complete Linkage (Furthest neighbour): The proximity of two clusters is defined as the longest (maximum) distance (minimum similarity value) between any two points within the two different clusters analysed (Fig. 4.4). Centroid Linkage: The proximity between two clusters is defined as the distance between the centroids of such clusters (Fig. 4.5). Ward’s Linkage: The proximity between two clusters is defined as the increase of the squared error obtained from two clusters when they are joined (Fig. 4.6). Average Linkage (Between groups): The proximity between two clusters is defined as the average pairwise proximity obtained between all pairs of points within the analysed clusters (Fig. 4.7).

48

4

Research Design

Fig. 4.6 Ward’s link illustration (Ruiz, 2020 based on Tan et al., 2019)

Fig. 4.7 Average link illustration (Ruiz, 2020 based on Tan et al., 2019)

The most important result obtained with this algorithm is a tree diagram, called a dendrogram, which graphically presents the different clusters formed as a result of the analysis and the hierarchical relationship between them (Patlolla, 2018).

4.3.3.7

K-Means Clustering

K-means clustering is an algorithm type clustering that groups similar objects in groups or clusters, which are defined based on centroids, the average of a group of points, grouping the elements closest to that centroid (Tan et al., 2019). This process is done in an iterative way, repeating itself to achieve clusters that are as similar as possible while at the same time each group is as different as possible (Dabbura, 2018). The basic algorithm comprises the following steps: Step 1: Specify number of clusters k Step 2: Select a k point and assign it as the centroid (random) Form cluster assigning each data point to the closest centroid Step 3: Compute the centroids for the clusters by taking the average of all data points that belong to each cluster and update the centroid Step 4: Iterate until there is no change to the centroids defined

4.3.3.8

Properties of a Good Cluster Solution

An important part of the cluster analysis is to verify its validity and reliability. As stated by Schwarz (2020), there are characteristics that a cluster analysis must have in order to be of good quality: Efficiency: The analysis uses the minimum number of clusters possible Effectivity: The analysis obtains (forms) all the most relevant clusters High intra-class similarity: Cohesiveness within clusters Low inter-class similarity: Distinctiveness between clusters Generalization: Good clusters make it possible that by adding new data from the same source, the same results are obtained (meaning if the data really do fall into k clusters, new data should fall into the same clusters)

4.3

Data Analysis Methodology

49

Fitting: Good clusters should fit into a theory (part of a valid system of generalizations which allows to make predictions about new conditions and helps us to understand a phenomenon)

4.3.3.9

Cluster Validation

With respect to the validity of the results which is an integral part of the process, there are various approaches to be applied. Cluster validity measures are intended to help in the definition of the degree of “goodness of the clusters” obtained (Tan et al., 2019). These evaluation or validation measures can be classified into three types: Unsupervised (or internal): Are measured for cluster validation without the need to use additional external information for your application (e.g. SSE, Silhouette Coefficient, etc.). These measures allow the estimation of the cohesion cluster (how closely related the objects in a cluster are) and separation cluster (how distinct or well-separated a cluster is from others). Supervised (or external): Measures the degree to which the clustering structure obtained during the analysis fits in with other structures carried out in external or independent research (e.g. entropy, purity, F-measure, etc.). Relative: Allows a comparison between different clustering algorithms or clusters, using a combination of supervised and unsupervised methods. This study used unsupervised measures, in order to facilitate the process by not needing to obtain additional external information. The most used unsupervised methods are the Silhouette Coefficient and the Sum of Squared Error (SSE) Criterion. The value of the Silhouette Coefficient varies between -1.0 and 1.0. The expected value of Silhouette Coefficient as measure of the goodness of a clustering is equal to or close to 1.0. The SSE method considers the values of de-squared distances between the clustering objects and each respective cluster centroid. When comparing two different K-means cluster analyses, the ideal SSE value is the lowest value obtained, which means that the centroids of this clustering are a better representation of the points in their cluster (Tan et al., 2019).

4.3.3.10

Choosing the Right Number of Clusters

According to Tibshirani (2012), it is not clear what is known as “the right number of clusters”. However, this is an important and necessary piece of information, since it allows researchers to obtain more precise conclusions from the proposed model and its generalization. Some guides that could be used to identify the right number of clusters are defined below. General empirical rule of thumb: The upper limit (maximum number of cluster k) is defined by the equation of the square root of n/2, where n is the sample size (Schwarz, 2020).

50

4

Research Design

Fig. 4.8 Example of elbow method plot

Fig. 4.9 Example of dendrogram

Elbow method: This is a type of structural approach based on the sum of squares obtained for each cluster. These are calculated and graphed, and then the variations in the slope of the graph (elbows) are analysed. The number of elbows illustrates the approximate number of clusters (Figs. 4.8, 4.9, and 4.10) (Matt, 2019). Dendrogram: The graphic representation of the clusters is obtained by hierarchical clustering on the horizontal axis. The graph is read from right to left, selecting the longest distance on the x-axis, moving through the dendrogram. Counting the intercepted horizontal lines (the largest increase in heterogeneity), each line (clade) represents a group or cluster, which at its ends (leaf) groups the different points or observations (Schwarz, 2020).

4.3

Data Analysis Methodology

Fig. 4.10 Example of silhouette method

51 0.75 0.7

Silhouette Coefficient

0.65 0.6 0.55 0.5 0.45 0.4 0.35 0.3

0

5

10

15

20

25

30

Number of Clusters

Silhouette Method: This is a visual method to estimate the optimal number of clusters based on the average silhouette of the points or observations for different k-values (Tan et al., 2019). The plot shows the average Silhouette Coefficient (varies between -1.0 and 1.0) versus the number of clusters k for the data. The optimal number of clusters is the one that obtains the maximized average silhouette (a sharp spike or steep slope).

4.3.3.11

Summary of Analysis Design

For the purpose of this study, the Hierarchical and K-means clustering methods were employed. Hierarchical clustering allowed this study to obtain an estimate of the number of existing clusters in the data. It was then later evaluated by K-means clustering. This allowed the investigation to state with certainty the final number of existing clusters. Therefore, this study conducted both methods to complement each other. The parameters used for the algorithm are defined above. This study used the Euclidean Distance as the measure of proximity, as no previous information on the different clusters was needed. As mentioned above, the responses from six surveys were collected. Therefore, the cluster analysis used the input from these six participants. To calculate the optimal number of clusters, this study used the dendrogram method. As a measure for the validation of the clusters, the Silhouette Coefficient was used because this method allowed this study to evaluate the characteristics of a good cluster (high intra-class similarity and low inter-class similarity). Under the cluster algorithm output called Two-Step Cluster, the estimation of the Silhouette Coefficient was also provided.

52

4

Research Design

References Agresti, A. (2013). Categorical data analysis (3rd ed.). Wiley. Allen, E., & Seaman, C. (2007). Likert scales and data analyses. Quality progress. http://asq.org/ quality-progress/2007/07/statistics/likert-scales-and-data-analyses.html Analytics of Big Data. (2015). Making sense of our big data world: Measurement scale. https:// businessoverbroadway.com/2015/08/23/making-sense-of-our-big-data-world--measurementscales/ Battaglia, M. (2008). Purposive sample. Encyclopedia of Survey Research Methods, 645–647. Black, P. E. (2019). Manhattan distance. In Dictionary of algorithms and data structures. https:// xlinux.nist.gov/dads/HTML/manhattanDistance.html Bock, T. (2020). What is hierarchical clustering? https://www.displayr.com/what-is-hierarchicalclustering/ Braun, V., Clarke, V., & Weate, P. (2016). Using thematic analysis in sport and exercise research. In B. Smith & A. C. Sparkes (Eds.), Routledge handbook of qualitative research in sport and exercise (pp. 191–205). Routledge. Bryman, A., & Bell, E. (2015). Business research methods. Oxford University Press. Cook, D., Kress, M., & Seiford, L. (1995). Data envelopment analysis in the presence of both quantitative and qualitative factors. Journal of the Operational Research Society, 47(7), 945–953. https://doi.org/10.1057/jors.1996.120 Conroy, R. (2018). The RCSI sample size handbook. RCSI Data Science Centre Guides. https://doi. org/10.13140/RG.2.2.30497.51043 Creswell, J. (2003). Research design: Qualitative, quantitative, and mixed methods approaches. Sage. Dabbura, I. (2018). K-means clustering: Algorithm, applications, evaluation methods, and drawbacks. Towards Data Science. https://towardsdatascience.com/k-means-clustering-algorithmapplications-evaluation-methods-and-drawbacks-aa03e644b48a Esser, F., & Vliegenthart, R. (2017). Comparative research methods. In The international encyclopedia of communication research methods. https://doi.org/10.1002/9781118901731. iecrm0035 Frost, J. (2018). Difference between descriptive and inferential statistics. Retrieved from: https:// statisticsbyjim.com/basics/descriptive-inferential-statistics/ Gupta, T. (2019). Measures of proximity in data mining & machine learning. Towards Data Science. https://towardsdatascience.com/measures-of-proximity-in-data-mining-machine-learn ing-e9baaed1aafb Laerd Statistics. (2018). Descriptive and inferential statistics. https://statistics.laerd.com/statisticalguides/descriptive-inferential-statistics.php Lavrakas, P. J. (2008). Encyclopedia of survey research methods (Vol. 1). Sage. https://doi.org/10. 4135/9781412963947 Libakova, N., & Sertakova, E. (2014). The method of expert interview as an effective research procedure of studying the indigenous peoples of the North. Journal of Siberian Federal University, 2015(8), 114–129. Lowhorn, G. (2003). Qualitative and quantitative research: How to choose the best design. Academic Business World International. Retrieved from: https://papers.ssrn.com/sol3/papers. cfm?abstract_id=2235986 LZP. (2019). Distance measures and linkage methods in hierarchical clustering. Level Up Coding – Gitconnected. https://levelup.gitconnected.com/distance-measures-and-linkage-methods-inhierarchical-clustering-8b7d488d7ebc Mandigan, D. (2013). Methods in applied statistics. Columbia University in the city of New York. http://www.stat.columbia.edu/~madigan/W2025/notes/clustering.pdf Matt, O. (2019). Choosing the optimal number of clusters. Towards Data Science. https:// towardsdatascience.com/10-tips-for-choosing-the-optimal-number-of-clusters-277e93d72d92

References

53

Narkhede, S. (2018). Understanding descriptive statistics. Towards Data Science. https:// towardsdatascience.com/understanding-descriptive-statistics-c9c2b0641291 Nassiuma, D. (2001). Survey sampling theory & methods. http://erepository.uonbi.ac.ke/bitstream/ handle/11295/63034/SURVEY%20SAMPLING.pdf?sequence¼1 Patlolla, C. (2018). Understanding the concept of hierarchical clustering technique. Towards Data Science. https://towardsdatascience.com/understanding-the-concept-of-hierarchical-clusteringtechnique-c6e8243758ec Patton, M. (1999). Enhancing the quality and credibility of qualitative analysis. Health Services Research, 34(5), 1189–1208. QuestionPro. (2020). Closed ended questions. https://www.questionpro.com/close-ended-ques tions.html Schwarz, J. (2020). Cluster analysis in a nutshell part II. (W.MSCRE_FM02.F2001: Practical Exercises in Research Methods). Lecture, University of Lucerne (HSLU). Tan, P.-N., Steinbach, M., Karpatne, A., & Kumar, V. (2019). Chapter 7: Cluster analysis-basic concepts and algorithms. In Introduction to data mining (2nd ed., pp. 525–612). Pearson. Taylor, C. (2020). The difference between descriptive and inferential statistics. https://www. thoughtco.com/differences-in-descriptive-and-inferential-statistics-3126224 Tibshirani, R. (2012). Distances between clustering, hierarchical clustering. Department of Statistics and Machine Learning Department Carnegie Mellon University. https://www.stat.cmu.edu/ ~cshalizi/350/lectures/08/lecture-08.pdf

Chapter 5

Research Analysis

5.1

Description of Selected Data

Descriptive statistics were used to compute the results for questions 2, 4–6, and 8 of the survey, and the cluster analysis was used to compute questions 10 and 12 of the survey. Tables 5.1, 5.2, and 5.3 describe the data points collected and the number of responses achieved. All of the data that has been collected through the survey and interview have been computed and communicated in both chart and graph formats. Results and outputs for the remaining survey questions are discussed in Chaps. 6 and 7 to support the discussion below and can also be found in “Appendix C: Survey Analysis”.

5.2 5.2.1

Review of Quantitative Data Output Descriptive Statistics of Participant Profiles

Descriptive statistics for all variables regarding the financial characteristics and indicators of the study participants is indicated in Fig. 5.1 below. These variables help to identify the participants’ investor profile, their typical investment strategies (e.g. ratio of affordable housing to traditional real estate, estimated net returns, etc.), and their role in the public and private equity real estate realm. Explanation of Descriptive Statistics of Data Output from Questions 2, 4 to 6, and 8: • Through this, it is identified that on average the participants’ mean AUM is EUR 17.8 billion. The range of EUR 45 billion indicates that there is quite a difference in the participants, the size of their companies, and their business models. Three of the participants identify as publicly listed companies (which naturally have © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 D. Vigneswaran et al., Affordable Housing as a Profitable Impact Investment, Contributions to Finance and Accounting, https://doi.org/10.1007/978-3-031-07091-4_5

55

56

5

Research Analysis

Table 5.1 Financial characteristics of study participants (Own elaboration, 2021) Data regarding study participants Question Data points 2. Firm AuM (billions EUR) 4. Number of affordable housing units in portfolio 5. Market value of affordable housing units (billions EUR) 6. Market value of traditional real estate units (billions EUR) 8. Affordable housing net return 8. Traditional residential net return

N (responded) 6 6 5 7 6 4

Table 5.2 Criteria affecting the net return of affordable housing projects (Own elaboration, 2021) Data of decision-making criteria and its impact on projected return of affordable housing Question Data points N (responded) 10. Use of existing property 8 Location Property valuation Financing methodology Degree of leverage Cost of construction Speed of planning/building approvals Government subsidies Property management costs Tenant turnover rate Eventual guaranteed exit value Table 5.3 Data points for investing in affordable housing investment products (Own elaboration, 2021) Data of rationale to invest in affordable housing real estate investment products Question Data points N (responded) 12. Portfolio diversification 8 Return is comparable to other real estate projects Government subsidy exists To meet ESG/impact requirements High demand and low risk Tax preservation Investor requires these assets

larger AUM), while the other three organizations are private equity fund managers who also deal with high asset volumes, however not to the level of publicly listed entities. • The maximum number of affordable housing units indicated is 105,750 and the minimum is 850 units, with the average participant having about 26,994 units in their portfolio.

5.2

Review of Quantitative Data Output

57

Fig. 5.1 Financial characteristics of the sample population (Own elaboration via SPSS, 2021)

• The average market value of the firms’ affordable housing portfolio is approximately EUR 1.04 billion compared to the average market value of these firms’ complete real estate portfolio of EUR 14.06 billion. This statistic was helpful to understand the average ratio of affordable housing to traditional real estate. However, it is important to note that two of the participants only do affordable housing, so their affordable housing -market value to complete real estate portfolio market value is 1:1. • Both the affordable housing and traditional real estate investments have a minimum net return of about 3.00% and 3.60%, respectively. The maximum net returns are 15.00% and 6.50%, respectively. Interestingly, the return on the affordable housing is largely pulled by a successful private equity fund that focuses only on affordable housing products. This value could be considered an anomaly. • Both the skewness and kurtosis should be close to 0 to indicate a -normal distribution. These values indicate the difference in the participants and the dependability on the data (based on size). However, this is not the case for any of these variables. This is because the sample size of 8 (with an average response rate of 75%) has a big impact on the results. On average, the skewness among the variables is 2 and kurtosis is 4. These values indicate “fat tails”, which implies the strong influence of extreme observations. These values indicate that the sample cannot be used to draw conclusions on the entire financial universe of affordable housing projects versus traditional residential real estate. Two more sets of descriptive statistics were conducted to understand response pattern to the variables in Questions 10 and 12. The SPSS outputs below indicate how the different variables were typically ranked from 1 to 5 (very unimportant to very important). The measures of central tendency and spread aided this study in the process of determining the variables that should be clustered. Further, the standard deviation of the mean allowed this study to understand how reliable and consistent the respondents were with regards to the group responses. The data output is further described below each figure.

58

5

Research Analysis

Descriptive Statistics Mean

N Statistic

Range

Minimum

Maximum

Statistic

Statistic

Statistic

Existing property

8

4

1

5

3.25

.526

1.488

2.214

Location

8

1

4

5

4.50

.189

.535

.286

Property Valuation

8

2

3

5

4.00

.267

.756

.571

Degree of Leverage

8

3

2

5

4.13

.398

1.126

1.265

Financing Method

8

3

2

5

4.13

.398

1.126

1.268

Cost of Construction

8

2

3

5

4.38

.263

.744

.554

Speed of Planning/Building Approvals

8

3

2

5

3.63

.375

1.061

1.125

Government Subsidies

8

2

3

5

3.63

.324

.916

.839

Property Management Costs

8

3

2

5

2.75

.366

1.035

1.071

Tenant Turnover Rate

8

2

1

3

2.00

.267

.756

.571

Eventual Exit Strategy

8

3

1

4

2.63

.532

1.506

2.268

Valid N (listwise)

8

Statistic

Std. Error

Std. Deviation

Variance

Statistic

Statistic

Fig. 5.2 Criteria affecting net return of affordable housing projects (Own elaboration via SPSS, 2021)

Explanation of Descriptive Statistics of Data Output from Question 10 (Fig. 5.2 above): • Variables that are rated as important to very important investment criteria that affect the net return of affordable housing: – Location: Average rating of 4.5. There was extremely high consensus among all participants. It was stated that the location is important as it could make or break the exit value 20 years from investment. – Property valuation: Average rating of 4. There was general consensus that property valuation is neutral to very important. It was stated that valuation strategies are generally important because they are used to tell a story to the investors. The valuation methodology also aids decision makers to manage the business and make strategic judgements when it comes to exiting a property. – Degree of leverage: Average rating of 4.13. Almost all participants rated the ability and degree of leverage (debt to equity ratio) as very important. Only one participant rated this as not important, which skewed the range statistic into being quite wide. The leverage effect was noted as one the most favoured techniques to increase return. – Financing method: Average rating of 4.13. This factor had a similar response pattern to degree of leverage. It was stated when more financing options are available, the company is less dependent on or restricted by government support. It was also stated that having access to investor equity is equally important as having access to leverage. – Cost of construction: Average rating of 4.38. This variable was generally rated as neutral to very important. It was stated that companies had little control over

5.2

Review of Quantitative Data Output

59

construction cost. Nonetheless, it impacts the net return of affordable housing because the same quality of construction is still used for affordable housing units. • Variables that are rated as neutral investment criteria, and therefore may have some to no impact on the net return of affordable housing: – Existing property: Average rating of 3.25. This variable had the largest range and therefore the least consensus among participants. Some firms found it financially effective to redevelop an existing property into affordable housing, while others found it just as costly to demolish and rebuild. – Speed of planning/building approvals: Average rating of 3.63. The responses indicated that the speed of attaining approvals has a neutral or little to no effect on the net return of an affordable housing project. It was stated by all participants that even when proposing affordable housing, the urban planning process consumes the same amount of time and money. – Government subsidies: Average rating of 3.63. There was a low range in these responses indicating consensus among participants. A few felt that government subsidies are necessary, while the majority thought it was entirely possible to develop profitable affordable housing projects without government programmes. – Property management costs: Average rating of 2.75. This criterion was considered not important to neutral, suggesting this variable does not have a significant impact on the return of an affordable housing project. It was noted that while property management plays a role in the asset management phase, the burden consists of mostly administrative tasks. It was also noted that the cost of property management of affordable housing is not more or less than what would be allocated to traditional real estate. • Variables that are rated as unimportant to very unimportant criteria, and therefore have no impact on the net return of affordable housing: – Tenant turnover rate: Average rating of 2. This criterion was noted to have the least amount of effect on affordable housing return. There was generally high consensus among this rating pattern for this variable. It was stated that tenant turnover rates in these projects are consistently low. Therefore, this actually eliminates risk and does not negatively impact the return of affordable housing. – Eventual exit strategy: Average rating of 2.63. There was little consensus with this variable as some participants found the exit not very important and some found it important. This range suggests that a firm’s strategy to buy and hold or buy and sell dictates the importance of this variable.

60

5

Research Analysis

Descriptive Statistics Mean

N Statistic

Range

Minimum

Maximum

Statistic

Statistic

Statistic

Statistic

Std. Error

Portfolio Diversification

8

1

4

5

4.63

.183

.518

.268

Return is comparable to other RE projects

8

4

1

5

3.25

.526

1.488

2.214

Government Subsidy exists

8

3

2

5

3.63

.460

1.302

1.696

To meet ESG/impact requirements

8

2

3

5

4.25

.313

.886

.786

High demand and low risk

8

1

4

5

4.50

.189

.535

.286

Std. Deviation

Variance

Statistic

Statistic

Tax preservation

8

3

1

4

2.25

.366

1.035

1.071

Investor requires these assets

8

3

2

5

3.63

.324

.916

.839

Valid N (listwise)

8

Fig. 5.3 Reasons to invest in affordable housing projects (Own elaboration via SPSS, 2021)

Explanation of Descriptive Statistics of Data Output from Question 12 (Fig. 5.3 above): • Variables that are rated likely or very likely the reason for investment in affordable housing: – Portfolio Diversification: Average rating of 4.63. A high consensus (low range) among participants was noted, as they see portfolio diversification to be the leading reason that their investors chose to invest in their affordable housing products. – Meet ESG/impact requirements: Average rating of 4.25. Participants rated this as being the likely to very likely reason for driving investments in affordable housing. – High demand and low risk: Average rating of 4.5. A high consensus among all participants was noted. The demand to risk ratio was noted as a driver for investment, especially given the stability of affordable housing return in the current global economic and social climates. • Variables that are rated neutral and have no reason for investment in affordable housing: – Return is comparable to other traditional real estate assets: Average rating of 3.25. This variable had the least consensus among participants. About four participants felt that returns were actually quite comparable, while the other four felt this is not true. Again, the ranking of this variable is rooted in the different business models and strategies of each of the companies. – Government subsidy exists: Average rating of 3.63. The participants stated that some of their investors liked that their strategies are based on government subsidy as it provides a form of security. However, it was not a driver for investment.

5.2

Review of Quantitative Data Output

61

– Investor requirement: Average rating of 3.63. None of the participants felt that their investors were forced by any legal requirements to invest in affordable housing, unless it was an internal mandate-based decision. • Variables that are rated unlikely to very unlikely as the reason for investment in affordable housing: – Tax preservation: Average rating of 2.25. A high consensus among participants was noted. It was stated that tax preservation does not drive their investors to invest in affordable housing. They find there are very little to no incentives in terms of tax savings that an institutional investor cannot already access.

5.2.2

Inferential Statistics Using Cluster Analysis on Preferred Investment Criteria

The inferential statistics planned for this study involves cluster analyses. This analysis was performed using SPSS with the methodology and parameters outlined in Sect. 4.3.3. As stated in this section, there are multiple methods to estimate the optimal number of clusters (e.g. Hierarchical dendrogram, Elbow, K-Means, Rule of Thumb). The cluster analysis was performed through the application of the Hierarchical algorithm. The most relevant result when applying the Hierarchical clustering is the dendrogram. Therefore, only the results of dendrograms and cluster memberships obtained for each of the questions are presented below.

5.2.2.1

Results of Data Output from Question 10

The specification of the clustering procedure is centroid-based, and the -measure of proximity used is median-linkage. A line is drawn where the largest increase in heterogeneity (greatest distance between the lines) is observed. The horizontal axis shows the distance between the clusters when they are joined. The number of intercepted horizontal lines is counted. Because each line represents a group, the dendrogram in Fig. 5.4 suggests that three clusters are the ideal number of clusters for this question and response pattern for this set of variables. The results of the related cluster membership are indicated in Fig. 5.5. As mentioned in Sect. 4.3.3.2, the results obtained for the validation of the cluster analysis based on the Two-Step Cluster and Silhouette Coefficient of 0.3 are presented below. The Two-Step algorithm suggests that 3 clusters is “fair”. According to Tan et al. (2019) and Walde (2003), the values obtained are positive (>0), which indicates that the distance between objects is less than the distance between neighbouring clusters. This means there is high intra-class similarity and

62

5

Research Analysis

Fig. 5.4 Criteria affecting net return of affordable housing projects (Own elaboration via SPSS, 2021)

low inter-class similarity. With this in mind, this study considers that the quality of the analysis is sufficient (Fig. 5.6).

5.2.2.2

Results of Data Output from Question 12

Again, applying the Hierarchical algorithm, the dendrogram in Fig. 5.7 determines that 2 clusters are the ideal number of clusters for this question and response pattern for this set of variables. Results of the related cluster membership are indicated in Fig. 5.8. Below are the results of the Two-Step Cluster validation technique. Based on this algorithm, where the Silhouette Coefficient is 0.2, 2 clusters are considered “fair”. To confirm the results of the Silhou-ette Coefficient, 3, 4, and 5 clusters were also tested. The validation of 2 clusters resulted in the highest outcome -attained. With this in mind, this study considers that the quality of the analysis is sufficient (Fig. 5.9).

5.2

Review of Quantitative Data Output

63

Cluster Membership 5 Clusters

4 Clusters

3 Clusters

2 Clusters

1:Existing Property

1

1

1

1

2:Location

2

1

1

1

3:Property Valuation

1

1

1

1

4:Degree of Leverage

1

1

1

1

5:Financing Method

1

1

1

1

6:Cost of Construction

1

1

1

1

7:Speed of Planning/Building Approvals

1

1

1

1

8:Government Subsidies

3

2

2

2

9:Property Management Costs

4

3

3

2

10:Tenant Turnover Rate

4

3

3

2

11:Eventual Guaranteed Exit Value

5

4

1

1

Case

Fig. 5.5 Cluster membership for Question 10 Variables (Own elaboration via SPSS, 2021)

TwoStep Cluster

Model Summary Algorithm TwoStep Inputs

5

Clusters

3

Cluster Quality

Poor

-1.0

-0.5

0.0

Fair

0.5

Good

1.0

Silhouette measure of cohesion and separation

Fig. 5.6 Validation of cluster numbers for Question 10 Variables (Own elaboration via SPSS, 2021)

64

5

Research Analysis

Dendrogram using Median Linkage Rescaled Distance Cluster Combine

0 Portfolio diversification

1

High demand and low risk

5

To meet ESG/Impact requirements

4

5

10

15

20

25

Return is comparable to other RE projects 2

Existence of government subsidy

3

Investor requires these assets

7

Tax preservation

6

Fig. 5.7 Reasons to invest in affordable housing projects (Own elaboration via SPSS, 2021)

Cluster Menbership 5 Clusters

4 Clusters

3 Clusters

2 Clusters

1:Portfolio diversification

1

1

1

1

2:Return is comparable to other RE projects

2

2

1

1

3:Existence of government subsidy

2

2

1

1

4:To meet ESG/Impact requirements

3

1

1

1

5:High demand and low risk

1

1

1

1

6:Tax preservation

4

3

2

2

7:Investor requires these assets

5

4

3

1

Case

Fig. 5.8 Cluster membership for Question 12 Variables (Own elaboration via SPSS, 2021)

5.3

Review of Qualitative Data Output

65

TwoStep Cluster

Model Summary Algorithm TwoStep Inputs

5

Clusters

2

Cluster Quality

Poor

-1.0

-0.5

0.0

Fair

0.5

Good

1.0

Silhouette measure of cohesion and separation

Fig. 5.9 Validation of cluster numbers for Question 12 Variables (Own elaboration via SPSS, 2021)

5.3

Review of Qualitative Data Output

As described in Sect. 4.3.1, this study also codes and analyzes the qualitative data collected through the eight interviews. The -Latent Coding method was applied to capture underlying ideas, patterns, and assump-tions. This method offers a more interpretative and conceptual orientation to the data. Table 5.4 communicates the codes that were assigned during the coding process and the themes that were analyzed as a result of this process; a total of 8 themes, 17 sub-themes, and 34 codes have been discovered. The below results have been further demonstrated as a graphic to illustrate points of connectivity among the themes. The percentage of interview data that had similar meanings and discussions along the same macro themes are illustrated in Fig. 5.10. As suggested by the figure, the most popular themes of discussion among the participants were financing methodology (20.59%), government subsidy (17.65%),
factors having minor impact on net return (17.65%), and exit strategy (14.71%).

66

5

Research Analysis

Table 5.4 Results of the Latent Coding Analysis (Own elaboration, 2021) Theme Government subsidy

Sub-Theme Profitability

Different subsidy types

Affordable housing norms

Historical and cultural influence

Financing methodology

External funding Internal funding

Factors having minor impact on net return

Business strategy funding Other financing structures that have been considered Highly fixed items (no real influence can be made by firm) Typically contract strategic partnerships

Tenant culture

Good behaviour

Bad behaviour Improve efficiency

Platform of scale

Projected return

Return is possible

Exit strategy

Return higher than traditional real estate is possible Long-term exit Shor-term exit Maintenance of affordability

Codes Profitability possible without government subsidy Profitability not possible without government subsidy Rent subsidy Interest rate Land bank Legislation German housing history Austrian housing history Anglo-Saxon housing history Leverage Bank loan Institutional investors Seed funding Shares Sales income from condominiums REIT Construction and development Property management Planning and building approvals Outsourcing construction Outsourcing property management Administrative burden No difference in behaviour between tenants of affordable housing and traditional real estate Better outreach within ethnic enclaves needed Digitalization Prior knowledge of the space Similar return to traditional real estate Bond-like return Niche product paired with smart strategy Exiting after minimum 20 years No exit Block sale Will not remain affordable Will remain affordable

5.4

Data Limitations

67

Fig. 5.10 Percentage of interview data that reflects each of the macro themes (Own elaboration via SPSS, 2021)

5.4

Data Limitations

Overall, between the quantitative and qualitative data gathered, this study had access to information that is sufficient enough to answer the research questions. However, there are some limitations as they relate to the survey and quantitative data analyses portions of this study. Survey research is a non-experimental research approach used to gather information about the “occurrence and distribution of, and the relationships that exist between, variables in a pre-determined population” (Coughlan et al., 2009, p.32). Sample surveys increase the risk of representation and measurement errors. These limitations are outlined below.

5.4.1

Small Data Set

As stated in Sect. 4.2.2, this study has extremely benefited from the contributions of this high-quality sample. The study participants are leaders in the space of public and private real estate equity who have experience with impact investing in their respective continents. Participants brought value to the qualitative data and questions regarding experience, best practices, and suggestions through their roles as either Board Members, Directors, Fund Managers, or CEOs. Despite the quality of the sample, a limitation is that the sample size of 8 is quite small. In all forms of research, the sample size is one of the most important parameters. The limited number of participants affects the reliability of a survey’s results because it leads to a higher variability, which may lead to bias. Furthermore, it is difficult to determine if a particular outcome is a true finding for the entire population.

68

5.4.2

5

Research Analysis

Challenging Type of Data

Given the nature of the data used (e.g. categorical and nominal data where there is not a quantity but a rating), finding solutions for this type of data is less straightforward than when working with numerical (continuous) data. For example, for categorical data, the combination of values is limited because it is discrete (only from 1 to 5).

5.4.3

Self-Reported Data

Self-reported data is subject to various biases such as (1) selective memory, (2) telescoping (recalling events or figures that occurred at one time as if they occurred at another time), (3) attribution (attributing positive outcomes to one’s own agency but attributing negative events and outcomes to external forces), or (4) exaggeration (embellishing events or figures as more or less significant than is actually suggested from the data) (Brutus et al., 2012). In summary, self-reported data cannot be independently verified. Nonetheless, to improve reliability, this study substantiated the evidence across additional materials such as the publicly available quarterly and annual reports of the firms.

5.4.4

Lack of Data

Responses were not provided for some of the survey questions regarding the financial characteristics of a firm’s business operations, such as net operating Income (NOI) or net return on projects. However, it is understandable that not all participants wanted to respond to these questions or that some responded off-record because this type of information is classified and highly sensitive. For this reason, it was difficult to make a quantitative conclusion on the return of affordable housing projects as they compare to traditional real estate. While statistical tests were still done, and comparisons to the Impact Real Estate Benchmark and Traditional Real Estate Benchmark were made, the lower degree of reliability and accuracy of this data must be taken into account.

5.4.5

Lack of Additional Scale

In some cases, the clients (institutional investors or shareholders) drive the way firms design and strategize real estate investments. The interviewed public and private firms are highly involved with their investors, so they were very capable of speaking about their typical experience regarding their investors’ investment needs and

References

69

interests. However, it would be interesting to also learn of preferred investment criteria directly from their clients. This additional scale on the y axis would have allowed this study to cluster data against two inputs on the same matter. It would have been interesting to explore whether the investors or shareholders are satisfied with the real estate firm or fund manager. The last three limitations listed above (self-reported data, lack of data, and lack of additional scale) are consistent with the shortcomings generally faced in this space of public and private equity markets. Benchmarks or data companies who provide financial information or comps of funds, performance, and deal information are also subject to their participants’ willingness to provide thorough and accurate data. Therefore, this is more the rule than the exception (Dong & Peng, 2013). It was necessary to bear this in mind when analyzing the data and acting on it. Additional findings regarding the quality of research and analysis are further discussed in Sect. 6.3. In a perfect world, this study suggests that a more complete and larger sample be identified. A larger sample theoretically helps to obtain greater precision and confidence in the results, thus applying statistical analysis correctly. While self-reported data limitations or the lack of responses to financially sensitive questions may still occur, the larger sample ensures less variability. The degree to which these two limitations occur is much lower, especially if the survey is conducted in a standardized manner by a well-recognized global consulting entity or finance company, for example. The standard deviation of a sample is how far the true results of the survey might be from the results of the sample collected. A large sample size can ensure a lower standard deviation, therefore preventing inconsistencies or anomalies to drive the true results of the population (Creswell, 2003; Lowhorn, 2003).

References Brutus, S., Aguinis, H., & Wassmer, U. (2012). Self-reported limitations and -future directions in scholarly reports: Analysis and recommendations. Journal of Management, 39(1), 48–75. https://doi.org/10.1177/0149206312455245 Coughlan, M., Cronin, P., & Ryan, F. (2009). Survey research: Process and limitations. International Journal of Therapy and Rehabilitation, 16(1), 9–15. https://doi.org/10.12968/ijtr.2009. 16.1.37935 Creswell, J. (2003). Research design: Qualitative, quantitative, and mixed methods approaches. Sage Publications. Dong, Y., & Peng, C.-Y. J. (2013). Principled missing data methods for researchers. Springerplus, 2(1), 222–222. https://doi.org/10.1186/2193-1801-2-222 Lowhorn, G. (2003). Qualitative and quantitative research: How to choose the best design. Academic Business World International. Retrieved from https://papers.ssrn.com/sol3/papers. cfm?abstract_id¼2235986 Tan, P.-N., Steinbach, M., Karpatne, A., & Kumar, V. (2019). Chapter 7: Cluster analysis-basic concepts and algorithms. In Introduction to data mining (2nd ed., pp. 525–612). Pearson. Walde, S. (2003). Chapter 4: Clustering algorithms and evaluations. In Experiments on the automatic induction of german semantic verb classes (Vol. 9, pp. 179–205). Instituts für Maschinelle Sprachverarbeitung AIMS. Retrieved from https://www.ims.uni-stuttgart.de/ document/team/schulte/theses/phd/algorithm.pdf

Chapter 6

Discussion of Results

6.1

Description of Results

This chapter summarizes the results of the data obtained through the cluster analysis in combination with the expert interviews. It implicates these results in the context of the model and empirical observations. The following macro-themes were identified in the Latent Coding analysis: government subsidy, affordable housing norms, financing methodology, factors having an impact on net return, tenant culture, improvements for efficiency, projected return, and exit strategy. These themes align with some of the criteria of the cluster analysis. In tandem with the cluster analyses, the empirical evidence from the interviews is used to state significance of the data, study how these results compare to similar works in the literature, compare results with theoretical expectations, and account for unexpected concepts.

6.1.1

Investment Strategies that Influence Affordable Housing Return

Based on the literature review, this book hypothesized that the occurrence of 11 criteria along the value chain process of planning, production, and operation have an impact on the financial feasibility of affordable housing projects. Of these 11, the research sought to find the most and least influential strategies. Only the most prevalent findings are described below. Direct comparisons are also made with the hypothesized rationale. The clusters are graphically presented below as a scatterplot following the analysis of the results obtained from the dendrogram of the Hierarchical algorithm.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 D. Vigneswaran et al., Affordable Housing as a Profitable Impact Investment, Contributions to Finance and Accounting, https://doi.org/10.1007/978-3-031-07091-4_6

71

72

6

6.1.1.1

Discussion of Results

Cluster Analysis Results: Question 10

The survey found that the most important factors or factors having the largest impact on net return of affordable housing are location and leverage. Factors with neutral to some influence on return are government subsidy and uncontrollable risks/costs (this includes cost of construction, cost of property management, and planning approvals process). The factors that have minor to no effect on the return of affordable housing are tenant turnover rate and exit strategy (Fig. 6.1).

6.1.1.2

Factors with the Largest Influence on Net Return

Location It was hypothesized that the location is not important and has no effect on the return. The basis for this hypothesis is that a high tenant occupation is expected regardless of whether affordable housing exists in the city or in the suburbs because cheaper housing is often absorbed by the market faster. Contrary to the hypothesis, location was rated as either an important or very important factor in determining or affecting the net return of affordable housing. The response pattern had the most consensus among interview participants. “If you choose the location wrong, you cannot move

Cost of Construction

3.00

Median Method

Degree of Leverage Financing Method

1 2 3

2.50

Very Important

2.00

Speed of planning/Building Approvals Property Valuation Existing Property Location

1.50

Government Subsidies

1.00

Property Management Costs

.50

Eventual Guaranteed Exit Value

.00

Tenant Turnover Rate

.00

.50

1.00

1.50

2.00

2.50

3.00

Very Unimportant Fig. 6.1 Clustered Scatterplot of criteria affecting net return of affordable housing projects (Own elaboration via SPSS, 2021)

6.1

Description of Results

73

the building” (Cieleback, Interview, 18 February 2021). Overall, location was determined to be of high importance because it has an impact on the value when it comes time to exiting. It was stated that this also depends on the regulation of how long the property must remain affordable. For example in Germany, the average required time is 25–30 years, after which one is allowed to rent at free market value. Overtime, as the area undergoes gentrification or redevelopment, the quality of the surrounding location can help with the possible rent uplift. While some of the participants stated that it is important to be located in the city for future exit strategies, others stated that the land cost tied to acquisition and operation must be considered. The cost of land can vary drastically from city to city. For example in Hamburg, one can pay up to EUR 700 per square metre for developable land. In Berlin, this is about EUR 500 per square metre, and in Vienna around EUR 250–300 per square metre. In an expensive location such as Hamburg, the cost to develop subsidized housing is just too high. The location and its demography (e.g. unemployment rate, income) also play a role in the ability to leverage. For example banks and lending institutions evaluate these characteristics to determine whether cashflow is feasible. As stated by Jebsen, “banks also conduct due diligence on the location of affordable housing. Locations with poor demographic characteristics of fleeing cities (e.g., locations experiencing an exodus of inhabitants) frighten risk-averse lenders” (Interview, March 26, 2021). Private lenders need a form of security that the capital will be returned. When asked how locations are determined, each firm has different focus criteria, but all have very thorough systems in place. For example one of the interviewed companies, Vonovia, continually monitors 130 cities across Europe on economics, legislation, demography, and environment, to see whether they should invest in these locations for the long-term. If a trusted developer proposes a location outside their pipeline, it is also investigated under the same level of scrutiny. Other participants, such as the US private equity firm, Jonathan Rose Companies, stated that they keep a roster of primary and secondary markets. This firm is in the midst of creating a proprietary data-driven mapping analytic location-intelligence tool. Their -primary markets focus on areas with a high concentration of public amenities. Typically, these high-class markets have the need or demand for affordable housing and tend to also have the best transit infrastructure and access to jobs and schools. Their secondary markets consider climate-risk and mitigation factors among other criteria. In summary, location is an important investment criterion particularly for exit purposes. It is also an important consideration in terms of acquisition costs and demographic characteristics.

Leverage It was hypothesized that the ability and degree of leverage affects the return and is therefore an important investment criterion. This hypothesis is accepted. Leverage is viewed as an extremely important criterion in this kind of investment product. Riedl provides the following example (Interview, 2 March 2021):

74

6

Discussion of Results

Leverage is extremely important in this kind of investment product. For example, let’s say you make a yield, meaning all your investment compared to the annual rental income, is between 2.5–3.5%. For more expensive cities such as Hamburg, let’s use a 2.5% yield. If you have this investment yield plus an expectation of the equity portion of let’s say 5–7%, some shareholders even ask for 10% yield on their equity. You would need a whole lot of debt to lever your equity. With this example, an 80% debt portion can increase to 7–8%.

However, getting a high debt-equity ratio is naturally difficult. All the public and private equity firms in this book have had the experience of being able to access a high degree of leverage. This is because, large firms, such as the publicly listed companies interviewed for this study, have billions in AUM and an accountable shareholder structure. In this case, banks such as the Deutsche KreditBank (DKB) have an existing and strong working rapport with these companies. With smaller development companies or start-up firms, a higher equity portion is expected. In order to give a high leverage to new development partners, a bank “would expect a mixed financing model and a mix of equity versus loan structure” (Jebsen, Interview, March 26, 2021). From the lender’s perspective, it was found that it is difficult to offer a high leverage in exchange for riskier projects such as affordable housing given their exposure to high default risk. Therefore, it was found that it is equally important for businesses to have access to institutional investors or shareholders to provide the equity as much as it is important to have access to efficient debt capital markets at a low cost.

6.1.1.3

Factors with Neutral to Some Influence on Affordable Housing Return

Government Subsidy There were two hypotheses surrounding government subsidies. The first hypothesis is that the existence of government subsidy has an impact on the positive return of affordable housing. The second hypothesis is that government subsidy has the largest impact on the financial return of affordable housing. On the contrary, it was seen as a neutral factor. There was a low range in this response indicating there was consensus among participants. Therefore, the results did not support the hypothesis. “It is possible to be profitable with affordable housing models. . . it is really a question of the investor’s expectations and their appeal for impact” that determines the degree of government subsidy that the business model should rely on (Brey, Interview, 17 February 2021). Only a few of the participants (n ¼ 2 of 8) felt that the existence of government subsidy is required to make a profit on affordable housing projects. Over the last 10 years, construction costs have nearly doubled among OECD countries (OECD, 2019). In West Germany, for example construction costs that were once EUR 1500 per square metre are now EUR 2800 per square metre. This is considered the bare minimum construction cost, which gives a very basic product. “So, if you have on the one side, a huge increase in construction cost, and on the other hand, a squeeze of

6.1

Description of Results

75

benefits, then it is naturally quite difficult to create new and quality affordable housing products” (von Lackum, Interview, 18 February 2021). However, others feel there are additional methods to make a return. One most commonly used method is to mix market rate and subsidized/affordable units in a single development. However, it was noted that mixed tenure options can present challenges of inequality. It was also noted that excluding subsidized housing, other classes of affordable housing that do not rely on government subsidy can be built and maintained, such as controlled rent. In summary, it was repeated that is possible to make a 2.5–3.5% net return on affordable housing without government subsidy. The problem arises when: (a) commercialized mixed tenures compromise the true provision of affordable housing; and (b) more capital or follow-on investment into properties is needed to refresh and maintain the building. In order to diminish these two risks, access to government subsidy is advantageous.

Uncontrollable Risks/Costs There are three criteria which were rated as having neutral effects on the return of affordable housing. These are construction costs, project management costs, and speed of planning and building approvals. Contrary to the hypothesis, all three are considered to have neutral effect, as in, the occurrence of this criterion does not contribute to a better or worse return. It is important to consider the cost or risk that one can influence and those that one cannot. These three criteria are considered risks that firms cannot necessarily influence or manage. “Cost of construction is just embedded in our assessment. The way our model is structured, it is incorporated in our appraisal and we take no risk on it” (Munday, Interview, 10 February 2021). Contrary to what was expected, most of the public and private equity firms (n ¼ 4 of 6) are not developers, nor do they have a construction division as part of their business models. Instead, they use strategic and trusted partners to construct and build planned developments—both for affordable housing and traditional real estate. Two of the public equity companies that also develop only do it to a minor extent and are still not pure developers. This is one of the reasons why it is a neutral to important factor. Construction costs are fixed to a point where they cannot be influenced. It is a portfolio driver, but not a top driver because a firm can very easily tender a partner that meets their cost goals. Firms often seek a development partner on the ground who understands various local nuances such as regulatory requirements or government issues. Property management costs are similar to construction costs, except they were noted to carry even less importance in terms of impact on return. It was hypothesized that property management costs for affordable housing projects are high and therefore negatively impact the return. The results did not support this hypothesis. On the contrary, it was found that because tenant turnover rate is a lot lower in affordable housing compared to the free rental market with young and dynamic renters who continuously move, there is little renovation or maintenance cost per apartment post

76

6 Discussion of Results

move out. One of the publicly listed German companies, LEG Immobilien, found that tenants do not necessarily behave any worse than tenants in the free market. However, this was refuted by another publicly listed German company, Vonovia, who stated that in their experience, because the cost is borne by the state, some renters are using the asset differently to other renters living in a free financed rental apartment. This study has learned that the real property management cost comes from the administrative tasks tied to managing a building of this type. As stated by Jacquot, “there is a lot of administrative burden to actually build, buy, and operate these types of buildings given their status as an impact investment and community asset” (Interview, 14 April 2021). An affordable housing building often involves other stakeholders such as the local or federal governments in terms of adherence to regulation, restrictions, or receiving and coordinating subsidy payments. It is coordination of these tasks which is different in affordable housing more so than the maintenance of deteriorating building or management of leases which are standard tasks in traditional real estate. Some of the participants (n ¼ 3 of 6) outsource property management, while some structure this process as part of their in-house value chain. The three companies that conduct property management in-house have one thing in common. The need for in-house property management was born from them acquiring thousands of units in a single acquisition. Two of these three companies do not property manage every single building acquired or built. In cases where it makes sense to outsource the service (e.g. geographically too far), they have found property management services in the local area. In terms of cost, it is seen as a requirement. It was noted that the cost is not more or less than what is typically allocated to traditional real estate. Therefore, it is simply structured into the financial assessment, similar to cost of construction. The lengthy time of achieving zoning and building approvals was noted as being a constant situation in all three countries. This analysis concludes that the likelihood of permission or timing of permission is a risk that just needs to be calculated and factored in the project. Contrary to the hypothesis, all of the public and private equity firms (n ¼ 6 of 6) found this factor to have no impact on the return of their affordable housing projects. It was hypothesized that affordable housing projects may receive favourable review conditions by the planning and engineering departments. However, it was stated by all participants that this is not the case. The empirical evidence illustrates that even when complete affordable housing projects were proposed, cities handled them with the same speed and cost as traditional development.

6.1.1.4

Factors with Minor Influence on Affordable Housing Return

Tenant Turnover rate It was hypothesized that tenant turnover rate has a weak negative impact on the financial return of affordable housing projects. The results support this hypothesis. As expected, the empirical evidence indicates that the tenant turnover rate in

6.1

Description of Results

77

affordable housing is a lot lower than in free market rental. Therefore, it is not an important consideration for investors or developers. This criterion has the least significant effect on affordable housing return, as participants have rated it between 1 and 3. As previously discussed, there is less movement among tenants in the affordable housing space compared to tenants in the free market. Because these rents are controlled or subsidized, they significantly reduce the investor’s risk of having vacant space. It is intuitively difficult for tenants to forgo cheaper rental options. As Cieleback recounts (Interview, 18 February 2021): We have seen units when a tenant moves out after 30 years, it is like travelling backwards in time because when you look at these -apartments, they are more or less as they have been 30 years ago. They haven’t been massively altered or too damaged.

In summary, it was stated that the tenant turnover is related to location. If a suitable location and living opportunity is provided, the turnover is expected to be low. In cases where turnover is high and companies have systems in place to manage this well, it was stated that this factor does not impact the financial feasibility of affordable housing.

Exit Strategy It was hypothesized that this factor is extremely important and has an impact on the final return. This hypothesis was rejected. On average, this -factor was considered not important to neutral. There was little consensus among participants. Some (n ¼ 2 of 6) found the exit to be important, while most (n ¼ 4 of 6) found it unimportant. Contrary to the study’s expectation, both public and private companies notably prefer not to exit. As stated by Jacquot, “institutional investors do not necessarily want to exit when they are receiving a stable and constant cashflow” (Interview, 14 April 2021). In Germany, for example the publicly listed companies prefer not to exit because after the required 20- to 50-year period of maintaining affordable or subsidized units, landowners have the ability to step into the free finance market. It is at this point that the benefits are reaped and therefore tie back to having an important location that is rentable to future markets. This exit value, even if not formally exited, is certainly influential in the DCF Model. Some fund managers, such as the American private equity firm prefer not to exit, given their desire to continue to maintain the affordability of a property which is not guaranteed after exiting. This company is also the anomaly in the sample with its astounding track record of a stable 15% net return in the subsidized housing industry. So, there is of course no desire to give up this constant cashflow. As Taft states (Interview, 14 April 2021): With the thesis being that these properties are really difficult to buy and that a lot of hard work goes into stabilizing them, and they are also good long-lived assets, the thought is these assets would be -better off in a perpetual vehicle where they couldn’t be bought or sold.

However, like traditional private equity firms, exits are structured for the investors and not necessarily for the benefit of the firm. To manage these differing demands,

78

6 Discussion of Results

this subsidized housing private equity firm has devised an exit strategy that benefits both the GP and LPs. At the moment, this private equity firm “exits” their assets into a Housing Tax Credit Structure which is effectively the same as giving proceeds to the fund investors similar to a method of sale to a third party. For this reason, this company is considering rolling up all assets across all funds and portfolios into a perpetual long-lived vehicle. The exit strategy is driven by different needs. Should exits occur, it was noted that they typically happen as a block sale. A developer might construct affordable housing and then sell it off as block sale to government agencies or management firms. Larger companies such as the ones interviewed prefer not to exit because depending on their mandates, the affordable housing units bring a secure and regular cashflow. The range in responses suggests that a firm’s strategy to buy and hold or buy and sell dictates the importance of this variable. Despite not being ranked important, the general understanding is that the exit is important because it sells a valuation-based story to its investors. It is also vital to have a valuation method that could be approved by a third-party auditor.

6.2

Investment Strategies Based on the Different Business Models

It has been found that affordable housing is profitable. All (n ¼ 6 of 6) public and private entities interviewed for this study stated that they have made a return in this impact investment, ranging from net 3.5% to 15%. The empirical evidence below explains how each of the different business models remains profitable while providing affordable housing. Tables 6.1, 6.2, 6.3, 6.4, and 6.5 build on the findings of Sect. 6.1.1.

6.2.1

Affordable Housing Business Models of Publicly Listed Companies

6.2.1.1

Government Subsidies in Germany

The two main instruments of housing policy in Germany are supply-side -social housing subsidies and direct housing allowances called Wohngeld. Landlords wanting to benefit from social housing subsidies are subject to certain restrictions such as rent ceilings or occupancy control agreements. For this reason, some of the German companies (n ¼ 2 of 3) prefer not to provide affordable housing on the basis of receiving government subsidy. Instead, their business models manage to make return on affordable housing through other means such as company bonds or mixed tenure builds. It is important to note that restrictions, however, only last for a limited period of time. After the 20- to 40-year period, the building becomes part of the privately

6.2

Investment Strategies Based on the Different Business Models

79

Table 6.1 LEG Immobilien AG’s business approach to affordable housing (Own elaboration, 2021) LEG Immobilien AG Company Structure AUM Real Estate Assets

Types of Investors Geography History Differentiation

General Business Approach

Roles in the Value Chain Process

Publicly Listed Company (real estate holding and management company) EUR 14.5 billion • 145,000 rental units, of which 36,000 units are affordable (value of affordable units is EUR 3.6 billion) • Type of real estate: residential 97%, other 3% • Manages 400,000 residents International and local financial investors and institutional investors Germany (west Germany) Founded in 1970 (privatized in 2009) • As of 2016, LEG is a constituent of the MDAX trading index of German mid-cap companies • Prior to privatization, the firm operated as a public housing provider in the North Rhine-Westphalia (NRW) Region Strategy is to provide income-class product: good and safe living conditions at a fair price (affordable for all target groups). • Current housing stock is very much from the 1960s and 1970s (Germany’s active building years) • In order to improve their contribution to Germany’s dire need of new housing stock, LEG adds 500 units to their portfolio on an annual basis. – This includes the purchase of 250 new builds and construction of 250 new builds – Of the 250 units, about 100 is built for restricted rental market and bout 150 for the free rental market Impact: • Attach particular importance to ecological and social commitment • A multitude of programs are provided to tenants through the sub-sidiary Foundation called Your Home Helps, created in 2019 with capital of EUR 16 million – Ex: some programs include giving iPads to low-income families to promote education among children • Strong focus on ecological conservation through updates and retrofits to buildings – Ex: modernized 4800 residential units and saved around 54,000 tonnes of CO2 emissions • Planning to invest EUR 1.1 billion to the maintenance and modernization of existing building stock Fundraising Research Acquisition Urban Planning Construction Property Property Fund Asset Admin Mgmt Mgmt (continued)

80

6

Discussion of Results

Table 6.1 (continued) LEG Immobilien AG

Funding Methodology

How Firm makes a return on Affordable Housing

Risk Management

Exit Strategy

Competitor Strategy

• LEG conducts all roles in the entire value chain process from purchase through to property management • While they construct some apartments, they are not a pure developer. Minor development is done to some extent (e.g. about 250 units per year) • Property management is provided in-house and is therefore fully digitalized to make an efficient process • Use of government subsidy (attained through tenants, not directly deposited) • Equity from shareholders • Leverage (use of government loans through KFW, becoming less of a method now) • Acquire, construct, or renovate new or existing property and units at market rent or affordable/subsidized rent (about EUR 5.10 per square metre) – If the unit is deemed affordable, the rent cannot be marked up for a period of time (can be from 20–40 years depending on the subsidy or city required allocation), after which the unit sale or rental price can be set to market value – Some units are subsidized where tenants receive part of their rent from the government • Return still exists (given experience in this industry and digitalization/efficiency to other roles) – For the first two decades, return acts like a bond for most investors (low but stable and constant), so follows core strategy – Upon value lift, return then follows value-add strategy • LEG also successfully acquires properties on the secondary market from other companies that can no longer manage the subsidization period of a building as they had originally hoped for • Given their history as a formerly state-owned company, they have the know how to make affordable housing profitable • Mostly buy-hold strategy • No exit (what can potentially happen is that firm can wait to bring asset to free finance market, realize valuation uplift and then sell) • Other publicly listed or private equity companies that use government-subsidized loans to provide affordable housing or forego these loans and build mixed tenure housing

6.2

Investment Strategies Based on the Different Business Models

81

Table 6.2 Vonovia SE’s business approach to affordable housing (Own elaboration, 2021) Vonovia SE Company Structure AUM Real Estate Assets

Types of Investors Geography History

Differentiation

General -Business Approach

Roles in the Value Chain Process

Publicly Listed Company (real estate development and management company) EUR 35 billion • 423,000 units in portfolio, of which 105,750 units are affordable (40,000 of those in Germany) • Type of real estate: residential 99%, other 1% • Over a million customers International and local financial investors and institutional investors Germany, Austria, Sweden, the Netherlands, and France Founded in 2001 (History goes back to Deutsche Annington, which merged with GAGFAH) and went public in 2013 • Vonovia is a member of the DAX 30, STOXX Europe, MSCI Germany, and is one of the largest listed companies in Germany and Europe • Vonovia is one of the first representatives of the real estate industry to be included in the DAX, the index of the largest listed stock corporations in Germany Strategy is to provide a mix of housing at market value and affordable rates (lesser focus on subsidized housing). • Produce apartments for own portfolio and produce apartments (condominiums) for sale at approximately a 50:50 ratio • In terms of income, the rental to sale income ratio is approximately 18:1. So, Vonovia’s income is predominantly rental based Impact: • Believes in obligation towards its customers, society, environment, and shareholders • Important ESG considerations: fairly priced housing, neighbourhood development and contribution to infrastructure, sustainable construction and refurbishment, reduction of CO2 in real estate portfolio, renewable energies, appeal as an employer, governance, and compliance, appeal on capital market Fundraising Research Acquisition Urban Planning Construction Property Property Fund Asset Admin Mgmt Mgmt • All roles from acquisition through to property management are conducted in-house • Play a large developer role in Germany and Austria • In the other countries, Vonovia finances projects but uses on ground strategic partners (e.g. local developers) to implement them • Property management is in-house (given the large size of the portfolio) • In some cases, also take on property management roles on behalf of smaller companies as well (continued)

82

6

Discussion of Results

Table 6.2 (continued) Vonovia SE Funding -Methodology

How Firm makes a return on Affordable Housing

Risk Management

Exit Strategy Competitor Strategy

• No use of government subsidy • Equity from shareholders • Leverage (corporate bond at1%) • Bank loans in rare cases • Maintain/refurbish affordable buildings acquired into portfolio • Create new builds that are entirely affordable – Given Vonovia’s strong ability to raise capital and to raise debt (through bonds), the company can employ the leverage effect to make a return on completely affordable buildings • For larger land plots of thousand-unit development, use hybrid-tenure scheme (affordable units, market rental units, condominiums) to provide affordable housing while also making a return for investors – The build and maintenance of affordable housing are financed by the sale of condominiums – Often, this mixed tenure business strategy is required anyway because municipalities such as Vienna, Berlin, or Hamburg have a minimum requirement of 25–30% subsidized housing per new build development • Sometimes, Vonovia also builds-to-sell to municipalities or non-profits who are able to produce subsidized apartments or affordable housing at lower yields • Mix of tenure methods ensures that planning obligations are adhered to, and affordable housing is still financed without taking on restrictive government subsidy/loans • Buy-hold and buy-build-sell strategy • Few exits, mostly exits/sale of new builds occur • Other publicly listed or private equity companies that use government-subsidized loans to provide affordable housing or forego these loans and build mixed tenure housing

financed sector, implying rents can be raised to normal market levels and units can be re-rented without further restrictions. In terms of supply-side social housing subsidies, the government provides a loan through a state-owned bank called Kreditanstalt für Wideraufbau (KFW), a stateowned investment and development bank. The KFW was established so that the government would not have to take on all the debt of funding housing development. The capital that comes out of the program goes back into funding affordable housing. Through the KFW, for example instead of receiving a loan for 1% interest over 10 years, a landlord developing affordable housing may receive the same loan amount at 0.5% for a period of 20 years. However, the company must often have its own source of capital to make the project work. Three decades ago, the advantage of these subsidized rates was by 500–700 basis points, which made building affordable housing feasible. With today’s substantially lower interest rate environment, there is no longer an incentive. It was noted by all three real estate companies

6.2

Investment Strategies Based on the Different Business Models

83

Table 6.3 Patrizia AG’s business approach to affordable housing (Own elaboration, 2021) Patrizia AG Company Structure AUM Real Estate Assets

Types of Investors Geography History Differentiation

General Business Approach

Publicly Listed Company (real assets investment management firm) EUR 46 billion • Type of real estate: office 24%, residential 27%, retail 17%, logistics 12%, infrastructure 4%, other 6% • Number of affordable units: not given • Number of residential real estate funds (open or closed): not given Mostly institutional investors (pension funds, sovereign wealth funds, insurance companies, fund of funds) Germany, Austria, UK, Ireland, France, Belgium, the Netherlands, and the Nordic region Founded in 1984 • Historically, the company has come out of the German residential market and grown into the European commercial real estate space • The company underwent an IPO in 2006, at which time it expanded its horizons to other parts of Europe • It conducted EUR 9 billion of property transactions in 2019 alone Patrizia is a pan-European company that acts as an investment manager delivering a variety of real estate investment services for institutional clients. Approach regarding residential assets only: • Patrizia is continually monitoring 130 cities across Europe on economics and other terms to conclude whether they should invest for the long-term horizon • If a trusted local development partner proposes a location outside their pipeline universe, the firm is open to considering it • Based on this dialogue and their market assessment, Patrizia then decides on creating a Fund with these product ideas • Between 60–80% are renewal investors and between 25–30% are new investors which come out of long-term discussions Impact: • Currently raising for their Sustainable Communities Fund • Provision of affordable housing is not part of impact strategy • Patrizia’s ESG impact focuses on improved environmental output (plus other social benefits are realized through other global initiatives in emerging markets such as through the Patrizia Foundation) (continued)

84

6

Discussion of Results

Table 6.3 (continued) Patrizia AG Roles in the Value Chain Process

Funding -Methodology

How Firm makes a return on Affordable Housing

Risk Management

Exit Strategy

Competitor Strategy

Fundraising

Research

Acquisition

Urban Planning Construction Property Property Fund Asset Admin Mgmt Mgmt • Pipeline development, fundraising, acquisition, and asset management are conducted in-house on its licenced investment platforms • Development services and property management are often outsourced to strategic partners in their respective local areas • No use of government subsidy • Equity from shareholders • Performance fees, management fees, transaction fees • Leverage (bank loans) • Use hybrid-tenure (affordable units, market rental units, condominiums) to provide affordable housing while also making a return for investors – The build and maintenance of affordable housing are financed by the sale of condominiums – Often, this mixed tenure business strategy is required anyway because -municipalities such as Vienna, Berlin, or Hamburg have a minimum requirement of 25–30% subsidized housing per new build development • Mix of tenure methods ensures that planning obligations are adhered to, and affordable housing is still financed without taking on restrictive government subsidy/loans • Buy-hold and buy-build-sell strategy • No exits so far, mostly exits/sale after a holding period of 15 years is expected to occur • Other publicly listed or private equity companies that build mixed tenure housing

that while this scheme has worked for decades, it needs to be changed as it no longer incentivizes private sector development of affordable housing. Interestingly, the KFW has increased the number of subsidized loan programs for buildings with modern energy-efficient standards through the KFW Program 153. However, buildings of higher environmental standards are not affordable to social customers that rent at EUR 5 per square metre. So, the current focus on environmental programs is counterintuitive to the need of affordable housing.

6.2.1.2

Rent Determination in Germany

The rent is determined by the subsidy regulation of the region or city. Within a single jurisdiction exists different subsidy schemes based on household demand. Overall, the average affordable housing rent in Germany is about EUR 6 to EUR 8 per square

6.2

Investment Strategies Based on the Different Business Models

85

Table 6.4 Edmond de Rothschild’s business approach to affordable housing (Own elaboration, 2021) Edmond de Rothschild Real Estate Investment Management (REIM) Private Equity Fund (open-end fund structure) Company Structure AUM EUR 10.4 billion (EUR 1.9 billion residential) Real Estate Assets • Type of real estate: residential 18%, office 41%, retail 12%, other 14% • 747 assets across Europe • 1368 affordable housing or subsidized units across two funds in the UK and Switzerland (both individually explained below) Types of Investors Mostly European institutional investors (pension funds, insurance companies) Social impact investors (foundations) Geography UK, France, Belgium, Luxembourg, Germany, the Netherlands, Switzerland Of these seven countries, residential activity takes place in Switzerland, the UK, and Germany (with affordable housing in the UK and Switzerland) Funding Affordable Homes—Fund I Geography United Kingdom History Vintage 2019 Differentiation • The second fund in the UK to establish a private sector fund for affordable housing 5 years ago Differentiation • FAH was the first fund to partner with Housing Associations (HA) as part of its business model – Since then, there has been quite a growth in private sector affordable housing investments in the UK (at least half, if not three-quarters of those funds use their own set of HAs) General Business Approach Strategy is to support the affordable housing sector in the UK, to increase the number of affordable homes and deliver positive social value while gaining stable returns to investors. A very key factor of the business strategy is to use a subsidiary HA. • Fund currently has 11 projects and 871 units in the portfolio with a Gross Asset Value of EUR 158 million and a pipeline of EUR 800 million • FAH focuses on two main areas of investment: general needs housing which includes social rental housing (at about 50% of market rate), and affordable rental housing (at about 65–80% of market rate), part of which also includes shared ownership homes Impact: • Established as a social impact fund (each project is assessed through a Social Performance Assessment Framework) • 37% general needs housing (includes affordable rent, social rent, shared ownership) and 63% special needs housing (homelessness, extra care) (continued)

86

6

Discussion of Results

Table 6.4 (continued) Edmond de Rothschild Real Estate Investment Management (REIM) Roles in the Value Chain Process Fundraising Research Acquisition

Funding -Methodology

How Firm makes a return on Affordable Housing

Risk Management

Exit Strategy

Competitor Strategy

Urban Planning Construction Property Property Fund Asset Admin Mgmt Mgmt • In terms of its roles in the value chain process, FAH is involved as a fund investment manager • Roles such as planning and construction are led in-house, but are outsourced to strategic partners • The fund is cleverly structured such that the HA owns the underlying freehold. After construction of the asset, the fund enters into a long-term lease, typically for about 20 years with the local HA. Through this model, FAH does not get involved with the individual residents occupying the units because the HA covers this role • The HA is responsible for the maintenance, property management, and social behaviour • Equity from LPs • Because FAH is an individual stand-alone fund, they have found it more arduous to raise the first round of equity (which is quite normal for any new fund in a new space), whereas some of their very large competitors, such as LNG, the Man Group, and Sage under Blackstone, were able to put the seed funding in themselves • Leverage (target 40% debt from lending bank) • Fund has its own HA, which enables it to directly own and manage regulated housing, secure government grant funding, and acquire stock through planning obligations (known as Section 106 requirements) • Based on this model, the Fund then receives a regular payment (FAH builds the asset, and the HA manages it) • Similar to infrastructure investments, combination of subsidized rents and grant funding mean that residual real estate risk is very low • Clever government system in the UK allows the private sector to make the most of government structures while also satisfying a public need • Mostly buy-hold strategy • No exit strategy at the moment, given open-end structure • Prefer not to exit and just renew leases with HAs (the weighted average lease term is 24.2 years) • Typically, 20- to 40-year indexed leases to quasigovernment covenants which are Housing Associations • Other private equity funds (including other funds that focus on providing key worker’s accommodation (for nurses, firemen, policy, army) – The worker accommodation charges 85–90% discount of free market rent (continued)

6.2

Investment Strategies Based on the Different Business Models

87

Table 6.4 (continued) Edmond de Rothschild Real Estate Investment Management (REIM) Edmond De Rothschild Real Estate Sicav—Swiss (ERRES) Geography Switzerland (mostly Geneva area) History Vintage 2011 Differentiation • Listed and traded on SIX Swiss Stock Exchange • Direct property holdings in Switzerland with diversified assets to and yield patterns (core-plus, value-add, opportunistic strategies) General Business Approach • SICAV model (collective investment vehicle) domiciled in Switzerland with gross asset value of CHF 1.7 billion (of which 65% is residential) • Invests in properties throughout Switzerland focusing on both core strategy and value creation through construction, renovation, or repositioning of assets (e.g. conversion of past industrial or commercial spaces into residential units) • Target is two-thirds residential properties to ensure recurring and stable income and one-third commercial properties to boost results • Total number of assets in this fund: 75 buildings, 2296 apartments – Of which 7 buildings, 386 affordable units with a gross asset value of CHF 234 million are affordable Impact: • When possible, includes affordable units in its acquisition, construction, redevelopment of properties in areas required by the zoning • Promotes ecological conservation through updates and retrofits to buildings Roles in the Value Chain Process Fundraising Research Acquisition Urban Planning Construction Property Property Fund Asset Admin Mgmt Mgmt • Tasks such as fundraising and asset management are technically handled by the structure of the SICAV • Formal roles that the asset manager must conduct are acquisition, attaining planning approvals and construction/renovation – The latter two roles are mostly implemented in investment theses that are based on value-add strategies – Roles such as planning and construction are led in-house, but are outsourced to strategic partners • Shareholders Funding -Methodology • Leverage (in terms of affordable housing) – Controlled rent environment controls degree of leverage. In order to maximize a large growth yield on the building, need to go 100% equity. The moment leverage is introduced, the Canton views the investor as making a speculative investment. Therefore, growth yield allowed on the building will be lowered. (continued)

88

6

Discussion of Results

Table 6.4 (continued) Edmond de Rothschild Real Estate Investment Management (REIM)

How Firm makes a return on Affordable Housing

Risk Management

Exit Strategy

Competitor Strategy

– Indirectly, this gives a good advantage to pension funds and institutional investors, because these investors do not use leverage when they invest – The disadvantage is for private investors who like to put very low equity to leverage the yield • No use of government subsidy • Construction and operation of residential buildings are very much regulated particularly from City Planning and allocations by the Canton Geneva of Zone de developments (these development zones are controlled by the Canton in terms of land price, price of construction, rental price, and even yields) • Cannot increase rent for 10, 25, or 50 years depending on the zone categorization – Income just acts as a bond (stable and predictable income)—this often matches the Swiss investor profile • Because rents are controlled, yields are predictable and visible • Very little competition given investor’s lack of understanding of controlled rents • Mostly buy-hold strategy • Prefer not to exit as per the desire of the investor base to continuously receive stable cashflow • In the case of exiting: despite the affordable buildings being controlled, it can still be sold with reasonable marginal profit (controlled does not equate to losing value) • Very little to no competition among other private sector investors in affordable housing in Switzerland • The Swiss government does not subsidize affordable housing – Instead, it is provided through policy requirements (e.g. controlled rental areas) or through non-profit organizations (e.g. Genossenschaften). These cooperatives are non-profit housing associations that buy and construct affordable units to tenants of mixed incomes

metre, compared to the average market rent of about EUR 15 per square metre. In Vienna Austria, depending on the subsidy regulation, the average affordable rent is between EUR 6.50 to EUR 9 per square metre, compared to the market rent of EUR 14.16 per square metre. In Germany, there is no VAT for residential rent, whereas in Austria one must calculate the additional 10% VAT (on the cashflow). It was noted that in cases where rent guidelines are not provided by a regional authority, the affordable rent is defined as 30% of household income. Rental regulation regarding initial rent and future rent increases depends on the rental agreement. As mentioned above, a system of rent control applies to public and private dwellings that are built using public funds. Rents can be increased but only within a certain cap (which is legislated by the respective regional authority). Rent

6.2

Investment Strategies Based on the Different Business Models

89

Table 6.5 Jonathan Rose Companies LLC’s business approach to affordable housing (Own elaboration, 2021) Jonathan Rose Companies LLC (Rose) Company Structure Private Equity Fund (closed-end fund structure) AUM USD 1.1 billion Real Estate Assets • 17,000 units (56 buildings in the fund, 80 within the company) • Type of real estate: residential 99%, other 1% • Track record of 7 Funds (all units and assets are 100% affordable) Types of Investors Some international and mostly American institutional investors (pension funds, insurance companies) Geography United States History Founded in 1989 Differentiation • Award-winning work recognized by the Urban Land Institute, the US Department of Housing and Urban Development, and White House Council for Environmental Quality • One of the largest private sector owners of affordable housing in the US General Business Approach Strategy is to purchase and develop energy-efficient costeffective and safe social and family housing, a combination of core-plus and value-add strategies. • Acquire existing property of buildings to renovate and re-allocate as affordable housing – Rose acquires existing affordable, mixed tenure, and unregulated or unsubsidi-zed property and then voluntarily puts restrictions on these buildings, thus increasing the affordable housing stock • Geographic focus on 18 primary markets and 18 secondary markets across the US Impact: • Rose buys and repositions properties as affordable, renovates them with a consideration for sustainability, green building improvements • Rose benchmarks all properties with an energy score card which is a tool to study the energy profile of the building to then report its carbon footprint – Using this assessment, a renovation strategy is designed for the retrofit of the building • Social impact is also considered and common areas such as kitchens, computer rooms, health and wellness rooms are also designed and built Roles in the Value Chain Process Fundraising Research Acquisition Urban Planning Construction Property Property Fund Asset Admin Mgmt Mgmt • Rose conducts almost all roles of the value chain process in-house (focus of roles are on fund and asset management) (continued)

90

6

Discussion of Results

Table 6.5 (continued) Jonathan Rose Companies LLC (Rose)

Funding -Methodology

How Firm makes a return on Affordable Housing

Risk Management

Exit Strategy

Competitor Strategy

• Additional departments in sustainability and impact management • Fund has in-house design and construction professionals, but bids on multiple contractors for multi-milliondollar renovations • In-house operations conduct minor renovations • Equity from LPs • Fees from LPs help to cover property management/asset management (making money through fees is not the goal) • Leverage (Fannie Mae and Freddie Mac GSE Loan) • Buy existing affordable, mixed tenure, and unregulated or unsubsidized property and then voluntarily puts restrictions on to create new units of affordable housing • Revenue is generated from project-based subsidies offered through Section 8 – Long-term revenue contracts with the federal government to pay the last portions of the rent properties – These rents have the ability to be marked up over time, so creating stable cashflow – Amount of money put into property can impact overall rents even in subsidized contexts because the government takes the quality of the property and services into account • Also generates rent restricted properties developed under the US Low-Income Housing Tax Credit Program – These properties may or may not have subsidies, but they are rent restricted • Construction cost and risks are fully avoided as new developments are not in the context of the fund (models that include development have a different risk profile) • Mostly buy-hold strategy • Most mainstream US institutional investors want a traditional private equity fund structure on a 10-year term • To maintain social status of buildings, Rose exits properties and typically places them into a Housing Tax Credit Structure system (similar to sale to third party) • REITs in the affordable housing industry • Other private equity funds (mostly based on the tax credit and bonds system)

increases work differently from the free finance market in a way that every 3 years, the firm is able to increase rents according to the underlying cost development. For example as cost of maintenance increases, a public index would be published, and the firm can increase rents accordingly.

6.2

Investment Strategies Based on the Different Business Models

6.2.2

Affordable Housing Business Models of Open-End Fund Structures

6.2.2.1

Government Subsidies in the UK

91

In the UK there is less focus on subsidy-based government support. As elaborated in Sect. 2.3, affordable housing is provided by quasi-government -entities called Housing Associations (HAs). It is through HAs that private entities can maximize financial or structural benefits while providing affordable housing. HAs do not necessarily provide grants. However, they have greater freedom to strategize their role when compared to local authorities, which are more bound by statutory duties. HAs act as private registered landlords of social housing. A private firm can work with a for-profit HA or create a subsidiary HA. By doing this, the Fund has its own HA which enables it to directly own and manage regulated housing, secure government grant funding, and acquire stock through Section 106 planning obligations. Companies such as Sage Housing by Blackstone base their business models on the Section 106 incentive. This method is structured such that investors acquire completed and ready-to-rent units from developers through Section 106 agreements. Through this model, the firm invests in the land but avoids a costly development process. Another method for investors to lower cost is by structuring deals such that the HA owns the underlying land. In this case, the fund then builds the affordable housing on behalf of the HA and then collects a rental income. Through this model, the firm avoids land acquisition cost, but takes on the cost of construction. The Funding Affordable Homes Fund (FAH) for example builds the asset and their subsidiary HA manages it. Partnering with HAs is extremely beneficial because arduous tasks such as property management and maintenance are managed by the HA. So far, the FAH Fund has worked with their local HAs to secure government grant (this has been done in five of their investments). The second form of subsidy or assistance achievable through the HA is the provision of either free or discounted land through the local authority. This resource has not yet been used by FAH. While there are some very real benefits in working with HAs, there are also strong regulations. In fact, only a small proportion of the 1900 HAs are for-profit. About 45 HAs operate for-profit, the rest operate on a charitable basis. This is structured as such to ensure that private firms do not use the HAs as a cover to gain benefit. The HAs are regulated by the Regulator of Social Housing (RSH) and are subject to stringent regulatory controls. The RSH annually reviews all HAs with more than 1000 units from a governance and financial viability perspective. The FAH Fund will soon receive its first RSH review as they have 850 units in their portfolio and are rapidly reaching the 1000-unit threshold.

92

6.2.2.2

6

Discussion of Results

Rent Determination in the UK

The rent of these units is determined through the Local Housing Allowance (LHA) system. For tenants that rent privately, their eligible rent amount is either their LHA rate or the actual rent set by the private owner. Because 100% of the Fund’s units are affordable, tenant occupancy, leases, and rental determination are conducted by the LHA. The LHA rate is used to determine the rent. The maximum rent is based on the location, number of bedrooms, and market rate of the broad rental market area. The Housing Benefit is the name of the benefit that UK citizens can claim to help with their rent. LHA is the system for setting the maximum amount that can be claimed, for claimants with private sector tenancies. The UK government dictates -annual rental increases, which were set at CPI+1% in April 2020 for the ensuing 5 years.

6.2.3

Affordable Housing Business Model of Closed-End Fund Structures

6.2.3.1

Government Subsidies in the US

The two main instruments for private sector housing subsidy in the US are Section 8 and the Fannie Mae and Freddie Mac Loan Program. Section 8 offers two types of subsidy programs. These are the Project-Based Rental Assistance (PBRA) program and the Voucher program. The first provides affordable apartment communities that are owned by private landlords with a rental subsidy that helps pay the rent for low-income tenants. This means that a private landowner choosing to re-assign their property to provide only affordable housing units can apply for a project-based subsidy through Section 8. Through this model, the Department of Housing and -Urban Development (HUD) will subsidize each unit irrespective of the tenant. In this instance, the private owner is responsible for maintaining multi-year waiting lists of people wanting to live there. Potential tenants would then be vetted on their qualifications to become legible for Section 8 subsidies. In 2020, a budget of approximately USD 12.02 billion was allocated to the project-based rental assistance program (HUD, 2020). This entails USD 11.68 billion for contract renewals and amendments, as well as up to USD 345 million for PBRA. The Voucher program is a tenant-based subsidy. The tenant is directly issued a voucher that they carry as a badge of proof should they move to other apartments, cities, or states. The Voucher would continue to subsidize the tenant’s rent based on their income and the Area Median Income (AMI). Most private sector firms that provide affordable housing in the US structure their business model to maximize the benefits of the first program, the PBRA. Fannie Mae and Freddie Mac (F&F) are Government Sponsored Entities (GSEs). F&F are public-private entities that provide loans at low cost for the purpose of affordable housing. Following the Great Financial Crisis (GFC), F&F were placed under conservatorship backed by the federal government. Since then, they have been

6.3

Research Quality

93

operating as a hybrid state for over a decade. Through this program, it is possible to leverage as high as 75–80% or to receive a mortgage for 30–40 years at a lowered cost of 3%, for example. The third instrument is the Low-Income Housing Tax Credit (LIHTC) which is more a policy tool to preserve and expand the supply of affordable rental housing. Between 1987 and 2015, 45,905 projects and 2.97 million housing units have been placed in service through this policy (Scally et al., 2018). This tool was created through the federal tax code to give private investors a federal income tax credit as an incentive to make equity investments in affordable rental housing. This tool is highly complex and perhaps the least understood in producing and preserving affordable housing compared to the direct subsidized assistance programs. It requires the involvement of private investors, governments on all levels, real estate finance specialists, and legal and tax experts. In exchange for tax credits, properties must remain affordable for a minimum of 30 years or longer depending on state requirements.

6.2.3.2

Rent Determination in the US

In order for tenants to be eligible for Section 8 benefits, among other things, the household must make less than 80% of the AMI of the region to which they are applying (Centre on Budget and Policy Priorities, 2017). The HUD determines the rent for the entire building. It is their contract that sets the market rent. The HUD methodology to decipher rent involves the comparison of properties in the free market that are unregulated and unsubsidized. How-ever, it is important to note that the program does not change the rent collected by the private landowner. The rent collected by the landowner simulates the free market, to which the tenant contributes 30% of their income. The government pays the difference between the top rent and what the tenant can pay. The government contribution can go up or down depending on the tenant’s income.

6.3

Research Quality

Research quality is a debated topic in the use of mixed methods research. A growing number of authors discuss how quality should be conceptualized and operationalized with the aim of promoting well-designed and properly implemented mixed methods studies. Core quality criteria for mixed methods are not identified in the literature (Fabregues & Molina-Azorin, 2017). In discussions where quality criteria for qualitative research take place, three positions may be distinguished: the use of quantitative criteria for qualitative research, the use of independent criteria for qualitative research, and the postmodern rejection of criteria for qualitative research (Flick et al., 2004). However, based on a two-tiered investigation involving social policy

94

6

Discussion of Results

researchers in the UK, it is suggested that there is “preference for using a combination of quantitative and qualitative research criteria” (Bryman et al., 2007, p. 263).

6.3.1

Quality Criteria Test of Quantitative Component

According to Bryman and Bell (2015), the three quality criteria for quantitative studies are reliability, validity, and objectivity. Therefore, the qualities of this study were tested against the Standards of Quality and Appraisal -Criteria for quantitative research proposed by Curry and Nunez-Smith (2015). Reliability is the extent to which a measurement gives results that are consistent. In order to ensure reliability, the technique requires use of statistical procedure to evaluate quality of analysis methods. This study used multiple methods to analyze the data to ensure consistency of results. This -involves the use of two algorithms for clustering; the main algorithm was the Hier-archical algorithm and secondary algorithm to confirm the first was the K-Means. Prior to using the data, it was completely cleaned, rectified for completeness and reviewed for irrelevant values or duplicate values. These clusters were then confirmed against the codes derived in the Latent Coding method. As per the techniques suggested by Curry and NunezSmith (2015), multiple measures were used to confirm consistency, the study’s data was cleaned, and a statistical procedure was used to evaluate the data.

Table 6.6 Evaluation of cluster analysis (Own elaboration, based on Schwarz, 2020 & Tibshirani, 2012) Quality Criteria Efficiency Effectivity

Generalization

Fitting

Technique • Use of multiple measures to calculate number of clusters • Use of multiple algorithms to check defined number of clusters • Analysis of results and hypothesis verification • Adding new data from the same source with the expectation of seeing consistent results

• Cluster results fit into theory and allows making predictions

Quality Principle Validation Validation

Validation

Validation

Applied Technique • Three measures were used to estimate number of clusters • Two different algorithms were applied satisfactorily • Hypothesis was verified and validated based on results obtained • At theory level, generalization can be achieved (results suit literature review and interview findings) • However, additional data is necessary to actually impose generalizations on the population • Results obtained generally align with conducted literature review

6.3

Research Quality

95

Validity refers to the exactness of the measurements regarding the target and content. Particularly, internal validity indicates whether the results of the study are legitimate based on the ways the sample was selected, data were recorded, and analyses were performed (Bryman & Bell, 2015). Un-fortunately, randomizing research conditions were not used. The sample was not random, as convenience and purposive sampling techniques were used. However, it can be argued that this study sample represents the popula-tion to a high degree. The sample represents diverse business structures in the industry of publicly listed firms, and private equity firms with both open-end and closed-end funding models. Theory can also be sought as -evidence for support (Curry & Nunez-Smith, 2015). As such, validity in this case is confirmed with the detailed literature review that was conducted as a core part of this research. To this regard, the findings represented a true reflection of the relationship between investment criteria or strategies and affordable housing return. However, the true degree of reflection can be argued because the criteria were not tested under controlled circumstances or a simulation. For example the study cannot state the exact basis points that a certain criterion contributes towards the return of an affordable housing project. Testing this would also be difficult. It would require a controlled testing environment as well as access to extremely confidential operational data of the study participants which are not willingly shared. Objectivity means that findings are independent from the research. The researcher can remain distanced from the research findings, thus avoiding bias. To ensure objectivity, Curry and Nunez- Smith suggest that objectivity techniques such as the maintenance of transparency in results and data sets be practiced (2015). This could be achieved through a clear exposition of research aims, design, and analysis. To this regard, the transparency of this study is maintained. An extremely thorough justification regarding research aim, research design, and analysis method are detailed in Chap. 4. There was no bias in which variables were used in the cluster analysis. All variables were analyzed and reported in Chaps. 5 and 6. Therefore, neutrality of results is maintained.

6.3.1.1

Quality Criteria Test of Cluster Analysis

The quality of this study’s cluster analysis was also tested. The results are in Table 6.6 below. The evaluation criteria used against the cluster analysis are based on the standards proposed by Schwarz (2020) and Tibshirani (2012).

6.3.2

Quality Criteria Test of Qualitative Component

Qualitative research entails the investigation of macro and micro topics. It is not a precise science. However, the literature has created parallel versions of reliability and validity in the context of qualitative studies (Lincoln & Guba, 1985). According to Lincoln and Guba (1985), the parallel criteria of reliability for qualitative research

96

6

Discussion of Results

is dependability. The parallel of internal validity is credibility, and the parallel of external validity is transferability. Hammersley states that an additional quality criterion for qualitative research is relevance (1985). Trustworthiness, particularly dependability and credibility were met. As stated by Ritchie et al. (2013), trust in qualitative research depends on the researcher’s ability to translate participant responses and any attached meaning to the analysis and discussion. To ensure that key points of the raw data were correctly understood, an additional 5 min were spent at the end of each interview to review the questions and summarize this study’s interpretation of the participants’ responses. This was to ensure the data had been consistently interpreted. By using the Latent Coding analysis, this study was also able to improve reliability/trustworthiness of the data. The double coding technique of this analysis inherently verified any patterns or inconsistencies in the data. Furthermore, triangulation was used as a validation strategy (Denzin, 1978). To ensure triangulation of data, this study combined data drawn from the literature review and compared it to the quantitative findings of the survey and the empirical findings of the interview. Relevance refers to the importance of a topic in its field and its contributions to the literature in that field (Hammersley, 1985). The tests for this quality criteria validate whether the study is of relevance and whether it has made a contribution to the literature (Flick et al., 2004). Given the pressing and global demand for affordable housing as identified in Sect. 1.1, the provision of affordable real estate is necessary. As proven by this study, there is a market opportunity for the private sector to produce affordable housing as impact investments while also making a financial return. This book contributes to the gap in the literature by conducting a comparative study on the profitability of affordable housing across different business models and geographies. This study does not make new interpretations. Rather, it identifies and explains the most preferred investment strategies and criteria that contribute to the financial feasibility of affordable housing development.

References Bryman, A., Becker, S., & Sempik, J. (2007). Quality criteria for quantitative, qualitative and mixed methods research: A view from social policy. International Journal of Social Research Methodology, 11(4), 261–276. https://doi.org/10.1080/13645570701401644 Bryman, A., & Bell, E. (2015). Business research methods. Oxford University Press. Center on Budget and Policy Priorities. (2017). Policy basics: Public housing. Retrieved from https://www.cbpp.org/research/public-housing Curry, L., & Nunez-Smith, M. (2015). Chapter 6: Assessing quality in mixed methods studies. In Mixed methods in health sciences research: A practical primer (pp. 169–200). Sage Publications. Denzin, N. K. (1978). Sociological methods. McGraw-Hill. Fabregues, S., & Molina-Azorin, J. (2017). Addressing quality in mixed-methods research: a review and recommendations for a future agenda. Quality and Quantity, 51(6), 2847–2863. https://doi. org/10.1007/s11135-016-0449-4

References

97

Flick, W., von Kardoff, E., & Steinke, I. (Eds.). (2004). A companion to qualitative research. Sage Publications. Hammersley, M. (1985). Ethnography: What it is and what it does. In S. Hegarty & P. Evans (Eds.), Research and evaluation methods in special education (pp. 152–163). NFER-Nelson. Lincoln, Y., & Guba, E. (1985). Naturalistic inquiry. Sage Publications. Organisation for Economic Cooperation and Development OECD: Afford-able Housing Database. (2019). Measures to property developers to finance affor-dable housing construction. Retrieved from https://www.oecd.org/els/family/PH5-1-Measures-financing-affordable-housingdevelopment.pdf Ritchie, J., Lewis, J., McNaughton Nicholls, C., & Ormston, R. (2013). Qualitative research practice: A guide for social science students and researchers (2nd ed.). Sage Publications. Scally, C., Gold, A., & DuBois, N. (2018). The low/income housing tax credit. Urban Institute. Retrieved from: https://www.urban.org/sites/default/files/publication/98761/lihtc_past_achieve ments_future_challenges_finalized_1.pdf Schwarz, J. (2020). Cluster analysis in a nutshell part II. (W.MSCRE_FM02.F2001: Practical Exercises in Research Methods). Lecture, University of Lucerne (HSLU). Tibshirani, R. (2012). Distances between clustering, hierarchical clustering. Department of Statistics and Machine Learning Department Carnegie Mellon University. Retrieved from: https:// www.stat.cmu.edu/~cshalizi/350/lectures/08/lecture-08.pdf United States Department of Housing and Urban Development Housing. (2020). Project-based rental assistance 2020 summary of resources. Retrieved from: https://www.hud.gov/sites/dfiles/ CFO/documents/2020CJ-PBRA.pdf

Chapter 7

Study Recommendations

7.1

Affordable Housing as the New Norm for Institutional Investors

This study concludes that affordable housing is a profitable impact investment that offers both concessionary (below market) and risk-adjusted market rate returns. All study participants (n ¼ 8 of 8) state that it is likely or very likely to achieve some form of financial gain in affordable housing investments, and 75% of them believe it is even possible without government subsidy.

7.1.1

Benchmark Comparisons of Affordable Versus Traditional Real Estate

The participants’ return data of their affordable portfolios and traditional portfolios were aggregated and compared. This is illustrated in Fig. 7.1 below. The data indicate that among the study’s firms, the median return of traditional residential real estate is approximately net 5.01%, whereas the median return of affordable housing products is approximately net 3.75%. In 2015, Cambridge Associates (CA) and Global Impact Investment Network (GIIN) introduced the Impact Investing Benchmarks. Together they produce quarterly reports that capture the performance of private equity and venture capital funds in the impact investment space. The impact real estate benchmark includes 21 funds with vintages from 1997 to 2018. According to this benchmark, the quarterly return1 as of Q4 2020 is 4.63%. 1 IRR calculation is based only on that given quarter based on data compiled of real estate funds formed between 1981 and 2020.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 D. Vigneswaran et al., Affordable Housing as a Profitable Impact Investment, Contributions to Finance and Accounting, https://doi.org/10.1007/978-3-031-07091-4_7

99

100

7

Study Recommendations

Distribution of Net Return – Affordable Residential versus Traditional Residential 16.00% 14.00% 12.00% 10.00% 8.00% 6.00% 4.00% 2.00% 0.00% Affordable Residential Net Return

Traditional Residential Net Return

Fig. 7.1 Averaged returns of the actual affordable housing and traditional residential portfolios of the study participants (Own elaboration via Excel, 2021)

The results of this study are compared to the results of both the impact and traditional universe benchmarks, where similar verdicts were found. In a GIIN report regarding the evidence on financial performance of impact investments, GIIN and CA compared funds of vintage years from 1997 to 2014. It was found that the real estate impact funds calculated on an equal-weighted basis returned a net average of 3.8% (GIIN & CA, 2017) compared to 4.9% of conventional real estate funds (CA, 2018). The two-yellow diamonds in Fig. 7.1 represent these return findings of the real estate investment universe. Thus, the results of the average returns found in this study are corroborated by the average returns of their respective universe benchmarks. Only one impact real estate benchmark exists. However, there are a multitude of benchmarks and indices in the universe of traditional real estate. The return distribution of traditional residential is therefore further validated against the returns of other global indices. For example, the MSCI World Real Estate Index annualized gross return since inception is 7.08% (MSCI Publication, 2021). This index has global coverage and consists of 99 constituents. The Dow Jones Global Select Real Estate Index indicates an annualized net return since inception of 5.61% (SPDR Publication, 2021). This index consists of 255 constituents and is comprised of REITs and real estate operating companies traded globally. Unfortunately, both these indices include all types of real estate (e.g. residential as well as industrial, commercial, retail). A fair comparison of the return between affordable real estate and residential real estate would require that global indices be categorized by real estate type. Regardless, this study finds that these annualized returns align with the median value and distribution pattern of the traditional real estate box-whisker-plot above. Remarkably, the top third of impact funds generated net returns in excess of 15% (CA, 2018). Interestingly, this statistic also aligns with this study’s findings that one

7.1

Affordable Housing as the New Norm for Institutional Investors

101

of the impacts of real estate strategies has consistently produced a 15% net return for its LPs. As illustrated by Fig. 7.1, this is an atypical return, and therefore skews the shape of the box-whisker-plot on the left. As with conventional finance, “manager selection and fund due diligence are essential for long term out-performance” (Botha, 2020, para. 9). As stated by Botha, and based on the results of this study, it can be concluded that when investing in affordable housing products, future investors should consider the merits of a Fund Manager’s strategy and the existence of a good government subsidy program. Finally, it is also important to note that given the young and emerging nature of this universe, only a “handful of impact-focused funds have exited their investments” making it difficult to model the full return potential of such investments (Botha, 2020, para. 2).

7.1.2

Why and How Investors Are Acquiring Affordable and Social Housing

There continues to be a significant global demand for affordable housing -further exacerbated by growing urban populations. Supported by cumulative neoliberal policies and the financialization of real estate markets, the affordable housing investment space is increasingly becoming competitive. There has been a rapid growth in social housing property funds. For example in the UK in 2011 this market did not exist as a private sector product. In 2016, there were four funds and GBP 350 million in investment in this space. In 2019, this jumped to about 13 active funds and about GBP 2.2 billion in investment (Big Society Capital, 2021). In Germany, Allianz acquired its first affordable housing product in 2020, a 300-unit affordable housing portfolio for EUR 135 million (IPE Real Assets, 2021). The manager states that the portfolio “is an excellent opportunity to gain traction in affordable housing where (they) see potential to scale up” citing that this asset is well suited for a “long-term approach” with its stable income component (IPE Real Assets, 2021). The Allianz Germany portfolio is currently under development and is due for completion in 2022. The public and private equity participants in this study stated that the top three reasons their investors participate in this type of impact real estate are: portfolio diversification, desire to meet ESG or impact requirements, and the asset’s high demand-low risk profile. Affordable and social housing offers diversity from property markets and economic cycles. This sub-asset class has a much lower correlation to GDP compared to other real estate asset classes (Big Society Capital, 2021). Social and affordable rents are decoupled from market forces resulting in more stable growth. Furthermore, this type of asset acts similar to a fixed income or bond fund. Therefore, it hedges the portfolio against market volatility and uncertainty. Given the risk-return profile of affordable housing and its low correlation to traditional and even alternative markets, it is an effective portfolio diversifier. While impact investing is relatively new, the current market size as of 2020 is USD 715 billion (GIIN, 2019). The current legal environment involving legislation such as the Paris Agreement of 2015 or the European Union Taxonomy of 2020 drives future

102

7 Study Recommendations

investing conditions of public and private markets, committing them to various sustainability compliance requirements. With sustainability rapidly becoming the new investment standard, financial and institutional investors are flooding this market and require the right tools to evolve their portfolios. Aside from regulation, this paper has learned that there is a sense of duty that companies or investors have realized. For example, with regard to Allianz’s recent and first affordable housing acquisition, the fund manager states that as one of the “world’s largest investor in real estate, [Allianz] has a responsibility to grow [their] portfolio through assets that have a positive environmental or social impact” (IPE Real Assets, 2021). When asked, the publicly listed real estate firms of this study also stated that their fundamental reason to continue social and affordable housing is to prove the company’s reliability as a partner to the municipality (von-Lackum, Interview, February 18, 2021). What is needed is a company with a “sustainable, long-term business model whereby social housing is part of that long-term strategy in order to be liked and accepted by the municipalities you are developing in” (Riedl, Interview, 2 March 2021). There is a clear and growing desire for institutional investors and especially publicly listed companies to meet ESG and impact requirements, whether it be based on a newfound epiphany of fiduciary duty or a company’s desire to be socially accepted. Affordable and social housing has a low-risk profile given government involvement and interest. In many countries, affordable housing is backed by government revenues or government loans. Therefore, this sub-asset class is to some extent inflation-linked such that the principal and interest payments rise and fall with the rate of inflation. Government policy would ensure that housing associations generate efficient return and sufficient growth in their income to make it index-linked, so that real estate firms can meet the cost that they borrowed. This is critical from a financial perspective while at the same time giving tenants the assurance that their landlord will continue to keep homes affordable and not start to hike rents at a later date. Housing, especially affordable housing, will always remain in demand. This sub-asset class is truly characterized by a high demand-low risk profile. In the interviews, it was often stated that investing in affordable housing was like investing in a bond. If it is so secure, why not just invest in debt structures that have similar mandates to provide affordable housing? It is equally possible for an investor to finance affordable housing debts on the bond -market instead of through funds. However, this investment choice depends on the investor profile and their choice to do an equity investment. Compared to bonds, equity investments typically seek a higher return. Also, equity-like investments have added benefits to portfolio construction (Meyer & Mathonet, 2005). In summary, affordable housing has an undeniable investment case given its stable flow of income through its long-term operational leases with either a local association or tenants, low tenant turnover rates, and higher degree of diversification. The risk-adjusted return potential, demand in the industry, and growing competition in this sub-asset class suggest that afford-able housing is a worthwhile investment for institutional investors. It is particularly suggested for government pension scheme investors who are susceptible to accepting lower but stable returns so long as the investment derisks the portfolio and more importantly fulfils fiduciary duty.

7.1

Affordable Housing as the New Norm for Institutional Investors

7.1.3

103

Investment Strategies that Enable Financially Feasible Affordable Housing

Since the aim of this paper is to identify investment strategies that contribute to financially feasible affordable housing projects, the questions in the survey and interview were designed to seek empirical-based insight from study participants. This paper concludes that some investment strategies are quite universal despite varying geographies or business models (Table 7.1). The Hierarchical cluster analysis in tandem with the findings of the Latent Coding analysis confirm the four primary strategies most employed by veterans in this field are late exits (buy-hold), Table 7.1 Summation of similarities and differences of affordable housing strategies analyzed in this study, based on geography (Own elaboration, 2021) Strategy

Rent determination

Construction

Property management

Government subsidy Main source of funding

Germany • Buy-hold • Core Plus • Value-add • Set by relevant city jurisdiction or housing association, on average 30% of income • On average EUR 5.50 to EUR 8.50 per square metre depending on city • Sometimes conducted in-house • Sometimes outsourced to strategic partners • Always conducted in-house • Rarely outsourced to strategic partners

Switzerland • Buy-hold • Core plus

United Kingdom United States • Buy-hold • Buy-hold • Core plus • Core plus

• Set by relevant city jurisdiction or housing association, on average 30% of income

• Set by relevant city jurisdiction or housing association, on average 30% of income

Yes and no

No

• Always • Always outsourced to conducted strategic partners in-house • Rarely outsourced to strategic partners Yes Yes

• Investor or -shareholder equity • Leverage

• Investor or shareholder equity • Leverage • Prefer not to exit (longterm of -minimum 20 years) 4%

• Investor or shareholder equity • Leverage • Prefer not to exit (longterm of -minimum 20 years) 4.5%

Exit

• Prefer not to exit (long-term of -minimum 20 years)

Net return

3.5%

• Set by relevant city jurisdiction or housing association, on average 30% of income

• Mostly • Mostly • Mostly outsourced to outsourced to outsourced strategic partners strategic partners through tendered contracts • Always conducted in-house

• Investor or shareholder equity • Leverage • Prefer not to exit (longterm of -minimum 20 years) 15%

104

7 Study Recommendations

on ground strategic partnerships (outsource when possible), mixed financing strategies, and a high degree of leverage. The two secondary strategies are digital integration and use of existing know-how.

7.1.3.1

Buy-Hold Strategy

This paper concludes that there is no loss in holding an affordable housing asset. The residual value is important to explain the asset story to investors while maintaining valuation techniques that a third-party auditor could relate to or approve (Taft, Interview, April 14, 2021). However, outside of this function, attaining the proposed residual value is not important to the players in this industry. As further explained by Jacquot (Interview, April 14, 2021). We are not buying these buildings to create capital appreciation or value creation. We are buying these buildings to create a flow of stable and predictable income. Most of the investors in Switzerland, pension funds, insurance companies, etc., are actually looking for predictable, payable, risk-free yield. So, from that perspective, it’s actually ticking a lot of the boxes.

While the yield is low, the predictability of yield structures these assets like a bond. Therefore, there is no desire to exit a cashflow positive asset. The literature corroborates these findings. There has been a general trend of capital shifting from closed- to open-end funds for core/core-plus real estate strategies; meanwhile, opportunistic strategies continue to be most sought in closed-end funds (McKinsey & Company, 2020).

7.1.3.2

On Ground Strategic Partnerships

Another universal strategy is to handle property management in-house while outsourcing other value chain activities such as development and construction to designated strategic partners. The methodology of core-plus is to increase cash flows through light property improvements, management efficiencies or by increasing quality of tenants. Therefore, active owner participation is required for this strategy. This is why most of the interviewed public and private equity firms (n ¼ 4 of 6) focus their resources on property management rather than on construction or attaining planning approvals. This study concludes that there is no difference in the cost of property management of affordable real estate versus traditional residential real estate. What differs is how the property management tasks are done. Affordable housing involves more administrative tasks given the property’s requirement to maintain a regulated impact status or coordinate subsidy payments. The ability to conduct this task in-house is important. Because core-plus strategies focus on light property improvements, this study concludes that the best way to lower construction cost is by tendering this part of the value chain process to an on ground strategic partner. This study finds that cost of construction of affordable units and traditional

7.1

Affordable Housing as the New Norm for Institutional Investors

105

units are almost identical. Therefore, by outsourcing or minimizing the amount of construction taken on by the business, development risks that naturally carry a higher risk profile are also mitigated.

7.1.3.3

Financing Through Mixed Tenure Versus Government Subsidy

The data indicates that only one major strategy is different among the interviewed firms and countries. Some of the participants (n ¼ 3 of 6) build mixed tenure assets and some (n ¼ 3 of 6) build or acquire/refurbish affordable--only buildings. Alternative tenure forms have grown in popularity since the marketization of affordable housing and the entry of private investors (Wijburg & Waldron, 2020). For example, if a firm proposes to develop a large capital-intensive plot of a thousand units, a mixed tenure strategy is almost a requirement because the sale of the other units at market value finances and sustains the subsidized or affordable units. In order to produce affordable housing but also meet the demands of their shareholders and investors, firms have no choice but to provide mixed tenure options. This investment choice stems from the majority investor profile. However, in the cases of Funding Affordable Homes in the UK and the Jonathan Rose Companies in the US, mixed tenure strategies are not used. This should suggest that their investors have a more philanthropic agenda and are perhaps satisfied with concessionary returns so long as impact is also achieved. Interestingly, these impact funds render a higher return than the other study participants. It could be suggested that this is attributed to their single-minded structure (e.g. focus is only on affordable housing). However, the study finds that their schemes cleverly and effectively use government regulations and programs offered in their respective countries. For example, FAH in the UK relies on their subsidiary Housing Association to attain grants and property management services that is otherwise not accessible to private investors. Rose Funds in the US is able to produce high-quality renovations through a GSE loan or Project-based Section 8 subsidies. These government programs are effective because they financially assist the private sector despite keeping them accountable to restricted lower rent levels. This study concludes that programs available in the UK and the US are better and outdo the ones offered in Germany. This study also concludes that having access to good government programs, structures, or subsidies is advantageous to private affordable real estate business models. However, when these two funds were asked whether it is still possible to make a net positive return on their affordable housing models even without government subsidy, both said yes. This of course, would require an adjustment to their existing approach by reducing property management costs or by focusing on other classes of affordable housing, such as controlled rent instead of subsidized housing.

106

7.1.3.4

7

Study Recommendations

Access to High Degree of Leverage

This study concludes that public and private companies should continue to enhance returns using the leverage effect. For large-cap firms, raising either equity or debt is not a problem. Thereby, employing the leverage effect to build complete affordable buildings is possible. However, this study found that private loans to smaller development companies or start-up firms maybe hard to come by because from the lender’s perspective, offering high leverage on the basis of lower and weaker cashflows of affordable housing exposes the lender to a high default risk. In order to give a high leverage to a small- or mid-cap firm with little rapport, a bank “would expect a mixed financing model and a mix of equity versus loan structure” (Jebsen, Interview, March 26, 2021). Therefore, it is equally important to have access to institutional investors or shareholders to front the equity. However, this study’s expert interviews suggest that loans are also best sought through government or government subsidiary loans (Taft, Interview, 14 April 2021) or company-issued bonds (Riedl, Interview, 2 March 2021). Whether a company chooses to secure a public or private loan depends on their business model. By borrowing from public lenders, the landlord is subjected to certain restrictions. To avoid this, many largecap firms choose to raise their own debt. Leverage is the strategy of using borrowed money to increase return on an investment. If the return on the total value invested in security (own capital plus borrowed funds) is higher than the interest paid on borrowed funds, a significant profit can be made. It was suggested that if a company typically finances at long-term for 40% for its traditional real estate products, subsidized or affordable housing should expect a long-term value of 80% in order to boost equity through the leverage effect and get reasonable returns on said equity. Here is a simplified example. A small affordable housing project is estimated to cost about EUR 20 million. Typically, the affordable housing product can yield about 2.5%. By taking an 80% debt portion at about 1% can boost equity to around 8.5%. The borrowed money plus interest at the end of the payment term is EUR 16.16 million. The value of the investment will be EUR 20.5 million at the end of the year, and after paying the bank back EUR 16 million plus EUR 0.16 million in interest, the firm is left with a total of EUR 4.34 million and a net gain of EUR 0.34 million after subtracting the initial EUR four million equity. This equates to an 8.5% return, which exceeds median return rates in the traditional real estate market. Leverage does not change the percentage rate of return (starting with EUR four million and ending with EUR 4.1 million and starting with EUR 20 million and ending with EUR 20.5 million is still a 2.5% return in both cases). Instead, leverage can increase the total dollar value of return (a return of EUR 100,000 is significantly less than a return of EUR 500,000).

7.1

Affordable Housing as the New Norm for Institutional Investors

7.1.3.5

107

Digitalization of Daily Tasks

In order to promote efficiency and cost reduction, the study results indicate that automation of processes along the value chain are highly relevant. This notion is consistent with the literature. Based on the survey of 1600 firms across nine sectors, McKinsey & Company found that a “successful digital platform can achieve about 10% growth momentum in EBIT in five years” (2019, p.6). In the affordable housing industry, digitalization can be introduced to various processes of the value chain such as customer interaction. Through artificial intelligence many of the mundane manual tasks are reduced and dispensed to the algorithm to consider how to properly classify issues and how to appropriately follow-up with the relevant contractor. This technology is not meant to replace property management services, rather the need for 24/7 concierge or call centres such that affordable housing providers are able to still provide this service and remain financially sustainable. Digitalization helps to achieve scale, which is crucial to achieve both profitability and impact KPIs. Institutionalized processes “require digitalization to develop quickly working organizations, especially along the front lines” (von Lackum, Interview, February 18, 2021). Being digital enables agility, which is important for a business, especially in the affordable real estate industry, where returns are often already concessionary. Digital transformation can be integrated at any point of the value chain process. For example, the Jonathan Rose Companies in the US is currently developing a mapping analytic intelligence tool to continuously evaluate their pipeline of future investment locations. Location intelligence tools can be used to understand and respond to health issues (e.g. concentration of COVID positive areas) or even climate-related activities. Data collection and data analysis connect tasks across the value chain, thus ensuring a company’s ability to scale effectively. Automated processes help to facilitate data collection, eventually resulting in companies being able to predict issues or solutions with real-time data analytics. As cited by all study participants, property maintenance (if not outsourced to a subsidiary or tendered partner) can be an expensive burden. In Germany, for example property management amounted to EUR 398.2 million in 2019 (Brey, Interview, 17 February 2021). For the fiscal year of 2020, almost all companies showed an increase in average maintenance expense compared to the previous year. In this case, an automated customer management tool would be helpful to indicate the concentration and type of complaints or calls, how often contractors are sent, and the satisfaction levels of tenants. Tools such as these can then be used to appropriately budget for maintenance costs based on the type and frequency of reports or issues incurred during an operating year. As stated by Matt, Hess, and Benlian, the exploitation and integration of digital technologies “often affect large parts of companies and even go beyond their borders, by impacting products, business processes, sales channels, and supply chains” as the potential benefits are manifold (2015, p. 339).

108

7

7.1.3.6

Study Recommendations

Existing Know-How of the Affordable Housing Space

This study concludes that the final critical factor to ensuring the affordability and profitability of social housing is the hiring of professionals with existing know-how. This paper demonstrates that it is possible for small- to mid-cap or start-up businesses to build, operate, and sell affordable real estate. However, it is further demonstrated that having existing knowledge of building and maintaining these assets is advantageous. This is why affordable housing currently is mainly built by public housing companies or prestigious large-cap companies that have the knowhow to mix incomes and different product styles to generate cashflow. “If practiced correctly and if approached with a knowledgeable sponsor, it is very likely to make a return with affordable housing” (Taft, Interview, 14 April 2021). In many cases, the mastery of the market stems from the history of the company, such as many of the publicly listed German companies which were formerly state-owned. Sometimes, the knowhow “is in the DNA of the company, and therefore, if you look at the EBITDA margin, what we earn from those 25,000 affordable units are in no way different to those that we are earning in the free finance market” (von Lackum, Interview, 18 February 2021). Existing know-how is important because there are extra considerations that are different from traditional real estate. For example, they may not know the complex requirements behind receiving government subsidies or grants or upholding restricted rental status. As learned through this study, the administrative burden of providing social housing is quite high. When providing this form of asset in varying geographies, it becomes ever more challenging as legislative requirements change from country to country and even between cities in the same province or country.

7.2

Critical Reflection of Research Topic and Methods

The role of institutional investors in providing affordable and social housing has become the object of recent impact real estate interest. With growing global demands for affordable housing, this market represents a massive opportunity for the private sector to invest in this impact asset class. Therefore, the aim of this book was to identify whether affordable housing is a profitable investment opportunity for investors. The goal of this question was to further evaluate whether institutional investors should prioritize this business model. In order to fulfill this question, this book sought to identify the investment criteria that contribute to the financial feasibility of affordable housing. The results are consistent with the hypothesis that affordable housing projects are highly likely to be profitable. Contrary to the study assumption, dependency on government subsidies is not needed for a profitable investment. Through the literature review and more so from the surveys and expert interviews, the authors conclude that there are six investment criteria that positively impact the financial

7.2

Critical Reflection of Research Topic and Methods

109

feasibility of affordable real -estate. These investment criteria are late exit (buy-hold) strategy, on ground strategic partnerships, mixed financing strategies, high degree of leverage, digital integra-tion, and use of existing know-how.

7.2.1

Study Limitations

As illustrated by Ritchie et al. (2013) validity and reliability are “central concepts in any discussion of generalization” as there is a larger opportunity for inference to be made in research (p. 354). The issue with this data is that it prevents this study from being able to generalize given that the sample size consists of eight respondents. This is too small a number to make inferential generalization onto the parent population. However, unlike other studies in the literature, this study conducted a two-tier research method involving surveys and interviews. Therefore, the reliability of the data is high given that the accuracy of responses is heightened in interviews than in surveys. Expert rationale was captured in responses, that otherwise would not be possible only through a survey. Reliability was also heightened as this study sought to compare the average returns of affordable housing projects versus traditional residential real estate among the interview participants, and then against the impact and traditional real estate benchmarks. According to the box-whisker plot that map the participants’ yield quartiles, return patterns were consistent to the universal markets. Furthermore, the figures given by the participants were verified in publicly available quarterly or annual reports. When critically reviewing the research topic and methods, limitations were also found in the method of data collection and method of data analysis. This study was able to strategically identify investment criteria that have the highest impact on affordable housing profitability based on the empirical evidence of the experts. However, in order to statistically prove correlation between two events or variables, this study would need access to pertinent and confidential project information such as the working financial models of an affordable housing asset within each of the participant portfolios, to then test the correlation of each criteria to the project IRR. In doing this, all other variables need to be held constant. All else being equal, the individual variables need to be tested one by one. Then in order to generalize findings, the sample size needs to be statistically significant and stratified such that all regions in question are represented. Furthermore, to conduct an accurate quantitative study, the data should include the financial reports of only the affordable housing projects that share analogous characteristics and -operate under similar business models and company structures. Sample traits, such as the participants’ investment strategies should be similar if not the same in order to repeat tests and argue for or against correlation. Given the sensitivity and confidentiality requirements of company data, participants are not permitted to provide access to individual project information. Therefore, conducting a purely quantitative exercise would be extraneously challenging and next to impossible. This may also explain why almost all the literature in the affordable housing investment universe use qualitative research methods.

110

7.2.2

7

Study Recommendations

Recommendations for Future Research

As identified by the literature review, existing research in the affordable real estate investment space covers topics such as impact investing, financialization, involvement of institutional investors, and the growth of the alternative investment space. Most of the literature focuses on the high degree of financialization of the affordable housing market. Some of the literature compares investment strategies using comparative case studies, at most between two properties. There is, however, limited literature exploring the return -potential and financial contribution of affordable real estate assets to equity investors. This book has contributed to close this gap. However, the gap in the literature is significant enough that there are still opportunities to address topics of profitability or explore affordable housing investments from the perspective of investors. Therefore, it is recommended that future research continue to pursue topics of financial feasibility of affordable real estate. It would be interesting to pursue the same research question regarding most effective investment strategies; however, this time implementing a different research methodology or using a different sample. What does profitability look like if these assets were provided by small- to mid-cap companies? For a study of this nature, it may be prudent to focus within a single geography with a research methodology that uses comparative case studies. Through the literature review, it became clear that investment strategies and financing methods widely differ from country to country and interestingly have a lot to do with historical context. It is recommended that future research compare example portfolios within a single country or even city. This would also allow for a more uniform comparison of investment strategies among smaller firms. Affordable housing is a global challenge. The affordable housing gap would grow to 440 million households by 2025, resulting in 1.6 billion people-living in substandard housing (McKinsey Global Institute, 2014). Therefore, it would be interesting to know how affordable housing investment opportunities look in emerging markets. Would they be on the lower end of profitability and higher end of impact, or would it be the complete opposite given potentially lower barriers to market access? Outside of financial gain, what are impact success stories following implementation? What ESG criteria and KPIs have been fulfilled through affordable housing development and to what degree? These are also some important questions that still need to be explored. Moving forward, there are opportunities for impact investment outside of affordable housing. There is a finite set of government resources and of course finite balance sheets. Regardless of growing competition in the afford-ability space, there are still opportunities for equity investments. Future products could focus on fighting homelessness, the private rental sector for households of lower income, additional needs housing, innovative construction methods, and affordable or social housing in emerging countries. The profitability of these models could and should be assessed. For the time being, public and private equity investors should consider the conclusions of this study and the list of investment strategies that produce financially feasible affordable real estate as suggested by this study.

References

111

References Big Society Capital. (2021). Report and financial statements 2020. Retrieved from: https:// bigsocietycapital.com/latest/report-and-financial-statements-2020/ Botha, F. (2020). What benchmarks reveal about the profitability of -impact investing. Forbes. Retrieved from: https://www.forbes.com/sites/francoisbotha/2020/06/26/what-benchmarksreveal-about-the-profitability-of-impact-investing/?sh¼31eca3b43a64 Cambridge Associates. (2018). Real estate index and selected benchmark-statistics. Retrieved from: https://www.cambridgeassociates.com/wp-content/uploads/2018/05/WEB2017-Q4-Real-Estate-Benchmark-Book.pdf Global Impact Investing Network. (2019). Sizing the impact investing market. Retrieved from: https://thegiin.org/research/publication/impinv-market-size Global Impact Investment Network & Cambridge Associates. (2017). The financial performance of real assets impact investments. Retrieved from: https://thegiin.org/assets/The%20Financial%20 Performance%20of%20Real%20Assets%20Impact%20Investments_webfile.pdf IPE Real Assets. (2021). Allianz acquires EUR 135m affordable housing -portfolio in Germany. Retrieved from: https://realassets.ipe.com/news/allianz-acquires-135m-affordable-housing-port folio-in-germany/10050218.article?adredir¼1 Matt, C., Hess, T., & Benlian, A. (2015). Digital transformation strategies. Business and Information Systems Engineering, 57, 339–343. https://doi.org/10.1007/s12599-015-0401-5 McKinsey & Company. (2019). Twenty-five years of digitization: Ten insights into how to play it right. Retrieved from: https://www.mckinsey.com/~/media/-mckinsey/-business%20functions/ mckinsey%20digital/our%20insights/twenty-five%20years%20of%20digitization%20ten%20 insights%20into%20how%20to%20play%20it%20right/mgi-briefing-note-twenty-five-yearsof-digitization-may-2019.ashx McKinsey & Company. (2020). A new decade for private markets. Retrieved from: https://www. mckinsey.com/~/media/mckinsey/industries/private%20equity%20and%20principal%20inves tors/our%20insights/mckinseys%20private%20markets%20annual%20review/mckinseyglobal-private-markets-review-2020-v4.ashx McKinsey Global Institute. (2014). A blueprint for addressing the global afford-able housing challenge. Retrieved from: https://adamsmith.files.wordpress.com/2017/12/mgi_affordable_ housing_executive-summary_october-2014.pdf Meyer, T., & Mathonet, P. (2005). Beyond the J curve: Managing a portfolio of -venture capital and private equity funds. John Wiley & Sons. MSCI. (2021). MSCI world real estate index (USD) fact sheet. MSCI Publications. Retrieved from: https://www.msci.com/documents/10199/0dc1184b-e692-418a-a181-5a9b8fcfa2a3 Ritchie, J., Lewis, J., McNaughton Nicholls, C., & Ormston, R. (2013). Quali-tative research practice: A guide for social science students and researchers (2nd ed.). Sage Publications. State Street Global Advisors SPDR. (2021, April 30). Dow Jones Global Real Estate UCITS ETF. Retrieved from: https://www.justetf.com/servlet/download?isin¼IE00B8GF1M35& documentType¼MR&country¼CH&lang¼en Wijburg, G., & Waldron, R. (2020). Financialised privatisation, affordable housing and institutional investment: The case of England. Critical Housing Analysis, 7(1), 114–129. https://doi.org/10. 13060/23362839.2020.7.1.508

Appendices

Appendix A: Survey Questions 1. Please state the following: Name of firm: ............................... AUM of firm: ............................... Name of respondent: .................... Title/position of respondent: ........ 2. Which of the following titles apply to your business model? Please mark x. Private real estate developer (real estate service provider) Real estate investment trust (REIT) Private equity fund Pension fund Insurance company Family office Other (if so, please specify):

3. How do you finance your affordable housing projects? Please mark x to all that apply. Institutional investors Bank loan Existing funds (profit from past projects) Government subsidy Other (if so, please specify):

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 D. Vigneswaran et al., Affordable Housing as a Profitable Impact Investment, Contributions to Finance and Accounting, https://doi.org/10.1007/978-3-031-07091-4

113

114

Appendices

4. How many affordable housing only projects have you done or bought? (You may just provide a count of the number of buildings in your portfolio.) 5. What is the market capitalization of your firm’s investments in affordable housing? 3 years earlier ................... Last reporting year ............ 6. What is the market capitalization of your firm’s complete real estate investments? 3 years earlier ................... Last reporting year ............ 7. What percentage of investments do other real estate make of your portfolio? Residential Office Retail Industrial Niche (e.g. student housing or senior home) Other

8. On average, what is the gross and net return for your affordable housing projects? Gross IRR/multiple ........... Net IRR/multiple .............. 9. Based on your portfolios, how likely is it to achieve financial gain in the affordable housing industry? Please mark x. Highly likely Likely Unlikely Highly unlikely

10. Below are 11 criteria that are hypothesized to influence the final return of your affordable housing project. Evaluate the importance of the criteria as they relate to helping you achieve return. Rank the criteria on a scale of 1–5, 1 being least important and 5 being most important. Each criterion must be ranked. Strategic planning phase Use of an existing property instead of new construction Location of proposed affordable housing Property valuation Degree of leverage Financing method

Appendices

115

Production phase Cost of construction Speed of planning/building permit approvals process Government subsidies

Operational phase Property management costs Tenant turnover rate Eventual exit strategy

11. Assuming that government subsidies drive the profitability, would this real estate business model still be profitable without government subsidy? Please mark x. Yes No

12. Below is a list of reasons to build affordable housing. Rank the criteria on a scale of 1–5, 1 being least important and 5 being most important. Each criterion must be ranked. Portfolio diversification Return is comparable to other real estate development projects Government subsidy exists To meet ESG/impact requirements (fiduciary duty) High demand and low risk Tax preservation Investor requires these assets

Appendix B: Interview Questions B.1 Introduction 1. Before beginning, please provide a short introduction about yourself. Explain your role in real estate development/real estate funds management. What is your experience in investing in affordable real estate? What kind of transactions have you done?

116

Appendices

B.2 Business Model and Structure 2. Please take us through your business model (from financing through to exit) and possible competitors. 3. For affordable housing projects, what roles (e.g. planning, building, marketing, property management) does your company execute in-house, and which are outsourced? 4. Elaboration of participant’s response to Question 3 of Survey: How successful are your current financing strategies? Would it be easier to do this as a REIT structure, fund structure, or direct investment (financed through a bank loan or previous profits)? 5. In what locations do you invest and how are these locations decided? Have they changed? 6. How are affordable rental or affordable sales prices determined? 7. What is the typical exit strategy for affordable housing developments? Is this any different from the exit strategy of other real estate types?

B.3 Comparison to Market Residential 8. Can the cost of production and maintenance be considered the same, more than, or less than production of market residential development? 9. Does affordable housing produce same, more than, or less than in IRR as other real estate developments? If less than, why pursue affordable housing as an investment?

B.4 Criteria for Success 10. Elaboration of participant’s response to Question 10 of Survey: In the survey, we went over macro-level priorities that affect the return of investment of affordable housing. Why did you rank x,x,x as the most important criteria? 11. Why did you rank x,x,x as the least important factors? 12. Elaboration of participant’s response to Question 11 of Survey: Is affordable housing an investment profitable without the existence of government subsidy? If yes, how? 13. Maybe: Which countries do you think are attractive for these investments?

Appendices

117

Appendix C: Survey Analysis Question 3 Most used financing methods of Affordable Housing projects

Institutional Investors

75%

Bank Loan

75%

25%

Existing Funds

63%

Government Subsidies

88%

Other

0

10

Institutional investors Publicly listed company 1 Publicly listed company 2 Publicly listed company 3 Private equity fund manager 1 Private equity fund manager 2 Private equity fund manager 3 Bank Institutional investor (Foundation)

20

30

Bank loan Yes

40

Existing funds

50

60

Government subsidies Yes

70

80

90

Other Shareholders

Yes Yes

Bonds, shareholders

Yes Yes

Yes

Yes

Yes

Yes Yes

Yes Yes

Yes Yes

Yes

Family office

Yes

Loans from government sponsored entities Shareholders

Yes Yes

Shareholders Rental income

100

118

Appendices

Question 7 Percentage of Portfolio in Traditional Real Estate Assets Private Equity Fund Manager 1 Private Equity Fund Manager 2 Private Equity Fund Manager 3

18%

99%

1%

99%

0.5% / 0.5%

41%

12%

Publicly Listed Company 1

16%

14%

97%

Publicly Listed Company 2

25%

3%

35%

16%

11%

13%

Publicly Listed Company 3

99%

1%

Institutional Investor (Foundation)

99%

0.5% / 0.5%

0

Private equity fund manager 1 Private equity fund manager 2 Private equity fund manager 3 Publicly listed company 1 Publicly listed company 2 Publicly listed company 3 Institutional investor (Foundation)

10

20

Residential (%) 99 99 18

30

Office (%)

0.5 41

40

Retail (%)

50

60

Industrial (%)

70

Niche (%) 1

35

12

16

100

Other (%) 100% 100%

16

11

99 99

90

0.5

97 25

80

0.5

14

100%

3

100%

13

100%

1

100%

0.5

100%

Appendices

119

Question 8 Distribution of Net Return – Affordable Residential versus Traditional Residential 16.00% 14.00% 12.00% 10.00% 8.00% 6.00% 4.00% 2.00% 0.00% Affordable Residential Net Return

Traditional Residential Net Return

Question 9

Investor Opinion on how likely it is to achieve financial gain in Affordable Housing projects

Very Likely

50%

Likely

50%

Unlikely

0%

Very Unlikely

0% 0

10

20

30

40

50

60

120

Appendices

Publicly listed company 1 Publicly listed company 2 Publicly listed company 3 Private equity fund manager 1 Private equity fund manager 2 Private equity fund manager 3 Bank Institutional investor (Foundation)

Very likely Yes

Likely

Unlikely

Very unlikely

Yes Yes Yes Yes Yes Yes Yes

Question 10 Percentage of Portfolio in Traditional Real Estate Assets Exit Strategy Tenant Turnover Rate Property Management Costs Government Subsidies Speed of Planning / Building Approvals Cost of Construction Finacing Method Leverage (Kredit) Property Valuation Location Existing Property 0

10

20

Not very Important

30

40

Not Important

50

60 Neutral

70

80

Important

90

100

Very Important

Appendices

121

Question 11 Profitability of Affordable Housing without Government Subsidy

25%

75%

Yes

Publicly listed company 1 Publicly listed company 2 Publicly listed company 3 Private equity fund manager 1 Private equity fund manager 2 Private equity fund manager 3 Bank Institutional investor (Foundation)

No

Affordable housing profitable without government subsidy No Yes Yes Yes Yes Yes No Yes

122

Appendices

Question 12 Investor Rationale for Investment in Impact Affordable Real Estate

Required by regulation Mark opportunities/ lack of assets Tax preservation High demand and low risk To meet ESG/ impact requirements Government subsidy exists Return is comparable to traditional RE Portfolio Diversification 0

10 Very Unlikely

20

30 Unlikely

40

50 Neutral

60

70 Likely

80

90 Very Likely

100