M&A in the European Construction Industry [1 ed.] 9783896443199, 9783896733191

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M&A in the European Construction Industry

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M&A in the European Construction Industry Christof Sigl-Griib Dirk Schiereck Christian Voigt (Editors)

2

Preface T h i s is t h e second book of t h e series "Rheingauer M o n o g r a p h i e n " . I t concentrates on one single i n d u s t r y a n d its special regularities, value drivers and merger motives. A p a r t from t h e fact, t h a t there is s t i l l l i t t l e comprehensive literature available on i n d u s t r y specific M & A studies, t h i s book is a d o c u m e n t a t i o n of a project course t h a t brought together research and teaching more closely. T h e book is a c o m p i l a t i o n of articles w h i c h emerged from t h e " P r o j e c t Course M & A - the European C o n s t r u c t i o n I n d u s t r y " i n t h e summer t e r m 2006, a course offered t o 8 t h semester students at t h e European Business School (ebs). T h e design of t h i s project course has a special character. D e p a r t i n g from a single industry, students are asked t o filter and elaborate on relevant aspects of finance related t o M & A activities i n t h i s industry. I n t h e course of t h e i r examinations t h e y do not only have t o b r i n g t h e i r results in paper form b u t also present i t and discuss i t w i t h t h e i r fellows as well. T h e project course gives graduate students t h e o p p o r t u n i t y t o gain insights a n d practical experience i n scientific research. T h e i n d u s t r y w h i c h was chosen for t h e 2006 course is t h e European construction industry. T h e reasons w h y its respective companies are appreciative s t u d y objects are manifold: •

T h e C o n s t r u c t i o n I n d u s t r y used t o be m a i n l y d o m i n a t e d by n a t i o n a l champions a n d segregated markets across t h e world. Recent technological a n d p o l i t i c a l changes, such as t h e creation of a single European market, lead t o an internationalization and consolidation of t h e construction industry.

•

C o n s t r u c t i o n companies of t h e past focused solely on t h e construct i o n of properties itself. D u e t o changing environment a n d increased c o m p e t i t i o n , construction companies change t h e i r business model and become broader based p r o p e r t y companies, also offering an extended line of services.

•

T h e large number of exchange listed firms provide a stable basis for d r a w i n g empirical inferences.

D u e t o t h e u n d e r l y i n g concept, t h e articles i n this book cover a w i d e range of M & A - r e l a t e d topics. T h e y are d i v i d e d i n t o four m a i n areas: I t

3

4 starts w i t h an analysis of t h e performance and v a l u a t i o n of the European construction industry. Secondly, t h e economic i m p o r t a n c e of t h e construct i o n i n d u s t r y a n d its macroeconomic determinants are discussed. T h e t h i r d section of t h e book focuses on t h e announcement effects of M & A s i n great detail. A n d finally, t o r o u n d of t h i s b o o k t w o case studies are presented. B y t h i s m u l t i d i s c i p l i n a r y perspectives a n d methodological m u l t i p l i c i t y , t h i s monography provides insights i n t o t h e construction i n d u s t r y w h i c h can hardly be found i n one single source and i n t h i s compactness. C h r i s t o f Sigl-Grüb D i r k Schiereck Christian Voigt P.S. T h e editors also recommend V o l u m e 1 of t h e Rheingauer Monographien t i t l e d " T h e G e r m a n B r e w i n g I n d u s t r y " .

Contents I

Performance a n d V a l u a t i o n

1 Value Drivers by Fabian Brämisch/Sebastian M a r k o w s k y

7 9

2 Long-term Performance by Sonja B l a n k e n b u r g / A s t r i d M a y

31

3 Multiples Valuation by C h r i s t i a n B ö h m / S t e p h a n Freudl

65

II

Economic Perspectives

4 Economic Importance by B h u p i n d e r S. B r a r / S y l v a i n Fondeur

5 Macroeconomic Determinants by Cowan P h a n Siang F u / L o u i s e D e W a a l

III

Short T e r m Performance of M & A

93 95

111

133

6 A n n o u n c e m e n t Effects B i d d e r s by Alexander S. P e t e r s / P h i l i p p Schäfer

135

7 A n n o u n c e m e n t Effects T a r g e t s by Sebastian Becker/Franz Jaeger

157

5

6

CONTENTS

8 A n n o u n c e m e n t Effects R i v a l s by F l o r i a n J o p e / O t t o von Troschke

191

9 M&A-Advisers by C h r i s t i a n G e ß n e r / M a r t i n Renze-Westendorf

217

IV

Case Studies

251

10 H o c h t i e f / T u r n e r by M a r i e K a t h r i n P l e u s / A n n a Q u i t t

253

11 Abigroup/Bilfinger Berger by B e n j a m i n K r a p f / C o r n e l i u s Vogel

279

Part I

Performance and Valuation

7

Chapter 1

Value Drivers of the European Construction Industry Fabian

t

Bramisci

/Sebastian

Markowskyt

E U R O P E A N B U S I N E S S S C H O O L (ebs), I n t e r n a t i o n a l Schloß Reichartshausen

9

University

CHAPTER 1. VALUE DRIVERS

10

Contents 1 Introduction

10

1.1 Problem and Purpose

10

1.2 Structure of the Analysis

11

2 Fundamentals and Basic Theories

11

2.1 Value Driver

11

2.2 Previous Literature Review

12

3 Rational for W C M

13

3.1 Relevance

13

3.2 Components of W C M

14

4 Empirical Analysis

15

4.1 Research Questions and Hypotheses .

15

4.2 Procedure

16

4.3 Selection of Sample and D a t a

. . . .

4.4 Description of the Analysed Figures .

17 17

4.5 Results

17

4.6 Limitations of the Study

21

5 Conclusion and Future Fields of Research . .

21

References

23

Appendix

27

1 Introduction 1.1 P r o b l e m Statement a n d Purpose of t h e S t u d y A l l companies have t o operate w i t h i n complex environments, b u t those companies belonging t o t h e c o n s t r u c t i o n i n d u s t r y have t o function i n a more d y n a m i c , uncertain, and complex e n v i r o n m e n t . 1 Therefore, i t becomes v i t a l t o identify (a) t h e o p p o r t u n i t i e s t h a t exist i n t h e forces d r i v i n g change, (b) invest i n c o m p e t i t i v e methods t h a t take advantage of these opportunities, and (c) allocate resources t o those t h a t create t h e greatest value i n order t o enhance t h e financial results, and thus the shareholder value. 2 1 2

Cf. T u n g (1979), p. 672. Cf. O l s e n / T s e / W e s t (1998), p. 2.

2 FUNDAMENTALS AND BASIC THEORIES

11

T h i s is consistent w i t h t h e perception of a company's management t o be p r i n c i p a l l y responsible for increasing shareholder value, w h i c h has achieved broad acceptance d u r i n g t h e last decades. 3 Accordingly, managers increasingly t r y t o p r o m o t e t h e value drivers of t h e i r companies. 4 Since value drivers vary w i d e l y among different industries and companies, their p r i m a r y task is t h e firm-specific identification of these factors i n order t o target-value adding economic decisions. 5 W i t h the ongoing change i n t h e European construction industry, a lot of challenges have t o be faced i n t h e near f u t u r e . 6 As m a n y of these factors are external, t h e reaction of management t o be undertaken should a i m at t h e preservation of c o m p e t i t i v e advantages, b y addressing these changes appropriately. Therefore, the a i m of t h e s t u d y is t o identify t h e most relevant value drivers by means of a regression analysis and relate t h e m t o t h e necessary strategic o r i e n t a t i o n of t h e corporations.

1.2 S t r u c t u r e of t h e Analysis T h e next section w i l l provide t h e fundamental framework. Following t h a t , t h e status quo w i l l be investigated by evaluating t h e previous literature and describing t h e research done so far. F i n a l l y Section 2, p o t e n t i a l value drivers w i l l be derived theoretically by differentiating between external and internal value drivers. I n Section 3 t h e rationale for possible value drivers in the construction industry, i n p a r t i c u l a r t h e w o r k i n g capital management ( W C M ) and its m a j o r components, w i l l be o u t l i n e d as a basis for t h e following analysis. A n empirical correlation and regression analysis w i l l be conducted i n order t o verify t h e theoretically derived hypotheses. A f t e r t h a t , t h e results of t h e analysis w i l l be presented a n d discussed w i t h regard t o t h e construction industry. Finally, t h e m a i n findings of this paper w i l l be summed u p and an overview over future fields of research w i l l be provided.

2 Fundamentals a n d Basic Theories 2.1 Value D r i v e r T h e following paragraph w i l l address t h e shareholder value concept as t h e basis for t h e identification of value drivers. Shareholder value i n general can be defined as t h e market value of a company's equity. A company's equity again is defined as enterprise value less market value of debt, i.e. t h e market value of all o u t s t a n d i n g shares. 7 T h e key figures w h i c h are most d o m i n a n t i n enhancing firm values are referred t o as value driver. 3 4 5 6

7

Cf. R a p p a p o r t (1998), p. 1; M i l l e r / M a t h i s e n (2004), p. 70. Cf. Alenchery (2005), p. 3. Cf. M o r i n / J a r r e l l (2001), p. 3. Cf. European F o u n d a t i o n for t h e Improvement of L i v i n g and W o r k i n g Conditions (2005), p. 26. Cf. R a p p a p o r t (1999), p. 39.

12

CHAPTER 1. VALUE DRIVERS

Generally, management is nowadays more a n d more focussing o n generating shareholder value. W i t h i n t h e shareholder oriented managerial approach, decisions have t o be based on those figures w h i c h have t h e most p o t e n t i a l of enhancing t h e market values of t h e companies, i.e. t h e value drivers. 8 I t has t o be distinguished between t w o different types of value drivers - internal a n d external value d r i v e r s . 9 Therefore, t h e following p a r t w i l l o u t l i n e t h e characteristics of external a n d i n t e r n a l value drivers i n order t o provide a clearer p i c t u r e of value drivers and t h e i r differences. E x t e r n a l value drivers are exogenous factors w h i c h cannot be affected directly by company managements, such as c o m m o d i t y prices, w h i c h are influenced by three determinants: scarcity, t a x a n d exchange rates. Moreover, external value drivers are t h e current interest level of an economy, inflation of a country, a n d public i n v e s t m e n t s . 1 0 W i t h respect t o t h e cons t r u c t i o n i n d u s t r y external value drivers have a remarkable influence. T h i s is a t t r i b u t a b l e t o t h e material and labour intense character of the indust r y , 1 1 a n d its low p r o d u c t i v i t y a n d h i g h exposure t o labour r e g u l a t i o n s . 1 2 However, as these factors are not directly manageable, t h i s paper exclusively focuses on i n t e r n a l value drivers. Generally, internal value drivers of a company can be described as figures w h i c h can be directly influenced t h r o u g h managerial decisions w i t h i n the company. A c c o r d i n g t o C o p e l a n d / K o l l e r / M u r r i n (2005), i d e n t i f y i n g value drivers is a creative process, m a i n l y based on t r i a l a n d e r r o r . 1 3 Therefore, value drivers m i g h t vary significantly, depending on t h e strategies and structures of companies and t h e respective industry. A s a result, t h e following p a r t w i l l provide t h e rationale for a construction i n d u s t r y specific approach on t h e definition of value drivers.

2.2 Previous L i t e r a t u r e R e v i e w T h e construction i n d u s t r y is subject t o a persistent change. Accordingly, companies are forced t o adapt t o new challenges, constantly. Furthermore, due t o t h e domestic character of t h e market and its companies, i t is h i g h l y dependent on regulatory changes, labour conditions, as well as public inv e s t m e n t s . 1 4 Concerning those issues, the flexibility of a company t o cont r o l those factors d i r e c t l y is h a r d l y given. A s a result, construction companies t r y t o l i m i t t h e i r exposure t o those factors indirectly. Howell (1999) describes t h e efforts as reaching from m a x i m i s i n g customer value, over o u t 8 9 1 0 11 12

13 1 4

Cf. C o p e l a n d / K o l l e r / M u r r i n (1995), p. 7. Cf. C h u n g (2005), p. 20-21. Cf. C h u n g (2005), p. 3. Cf. L o o s e m o r e / D a i n t y / L i n g a r d (2003), p. 294. Cf. European F o u n d a t i o n for t h e Improvement of L i v i n g a n d W o r k i n g C o n d i t i o n s (2005), p. 19. Cf. C o p e l a n d / K o l l e r / M u r r i n (2005), p. 408. Cf. European F o u n d a t i o n for t h e Improvement of L i v i n g a n d W o r k i n g Conditions (2005), p. 21.

3 RATIONAL FOR WCM

13

sourcing and risk reduction, t o project management by means of integral benchmarking. Thereby, he uses an approach t o reduce overall costs by s t i l l having t h e a i m of satisfying t h e customer need t o a high e x t e n t . 1 5 Focusing on cost savings as a m a j o r interest of a construction company's management, t h e C o n s t r u c t i o n I n d u s t r y C o u n c i l (2000) found o u t t h a t cost saving p o t e n t i a l varies widely, i.e. a m o n g a different alignment of t h e company, customer base a n d technology involved i n t h e b u i l d i n g process. 1 6 H a l p i n / W o o d h e a d (1998) state, t h a t i t is crucial for a construction company t o have cash reserves available i n order t o m a i n t a i n t h e c o n t i n u i t y of operations d u r i n g t h e t i m e w a i t i n g for t h e client's p a y m e n t . 1 7 T h i s gains especially i n importance, as construction projects are generally complex u n d e r l y i n g varying degrees of uncertainty. One such decision is t h e W C M , as V e l l a n k i / H a n n a (2000) s h o w s . 1 8 T h e following section w i l l serve as a v a l i d a t i o n of t h e analysis by accomplishing t h e pertinence of t h e key figures examined.

3 R a t i o n a l e for W o r k i n g C a p i t a l M a n agement i n t h e C o n s t r u c t i o n I n d u s t r y 3.1 Relevance of W o r k i n g C a p i t a l Management as a Measurement o n C o m p a n y Performance I t could be observed t h a t W C M has recently gained higher i m p o r t a n c e t o managers t h a n l o n g - t e r m investments, as well as s p o t t i n g p o t e n t i a l financing sources. 1 9 Generally, a reduction of inventory a n d receivables has a h i g h i m p a c t on t h e free cash flow, w h i c h is crucial for t h e d e t e r m i n a t i o n of t h e value of c o m p a n i e s . 2 0 T h i s gains especially i m p o r t a n c e i n t h e cons t r u c t i o n i n d u s t r y due t o t h e long deferments of payments, a n d t h e usually h i g h amounts o u t s t a n d i n g in t h i s sector. T h i s high amount of savings i n w o r k i n g capital in t h e construction i n d u s t r y could be alternatively used for financing purposes instead of external financing, a n d w o u l d be possible at lower c o s t . 2 1 However, i t needs t o be determined, i f W C M itself m i g h t be sufficient t o reduce t h e external financing necessities. 2 2 Moreover, i t has t o be examined i n how far there is a trade-off between p r o f i t a b i l i t y a n d l i q u i d i t y of t h e company. T h e balancing act is t o decide i n how far holding current assets 15 16 17 18 19 20 21 22

Cf. Cf. Cf. Cf. Cf. Cf. Cf. Cf.

Howell (1999), p. 2. C o n s t r u c t i o n I n d u s t r y C o u n c i l (2000), p. 97. H a l p i n / W o o d h e a d (1998), p. 3. K u m a r / H a n n a / A d a m s (2000), p. 93. G i t m a n / M a x w e l l (1985), p. 59f. D e c h o w / K o t h a r i / W a t t s (1998), p. 134. M a r k s (2005), p. 418. C o l i n a (2002), p. 64.

CHAPTER 1. VALUE DRIVERS

14

is necessary t o be capable of meeting current payments in order t o remain liquid, a n d i n how far these current assets should be rather invested i n t h e company where t h e y m i g h t generate higher returns t h a n w h i l e being hold. Especially considering t h e difficulties i n forecasting cash requirements i n the construction i n d u s t r y 2 3 and t h e h i g h percentage of business failures a t t r i b u t a b l e t o w r o n g f o r e c a s t i n g , 2 4 t h i s aspect becomes v i t a l .

3.2 C o m p o n e n t s of W o r k i n g C a p i t a l Management I n practice as well as i n t h e relevant literature, a huge variety of key figures gained i n i m p o r t a n c e concerning t h e measurement of t h e success of W C M efforts. Therefore, t h e ones focussed on i n this analysis w i l l be explained i n t h e following. Generally, w o r k i n g c a p i t a l can be defined as an absolute figure, w h i c h includes t h e current assets of a c o m p a n y . 2 5 Current assets are a l l assets w h i c h are expected t o be converted i n t o cash w i t h i n one year i n t h e n o r m a l course of business. Current assets include items such as cash, accounts receivable, inventory, marketable securities, prepaid expenses and other l i q u i d assets t h a t can be readily converted t o cash. M o r e precisely, t h e t e r m net w o r k i n g c a p i t a l is defined as t h e difference of t h e amount of inventory and accounts receivable and accounts p a y a b l e . 2 6

Net Working

Capital

= Accounts

Receivable

+

—Accounts

Inventory Payable

Whereas, accounts receivables and inventory are components of t h e current assets, accounts payable are components of current liabilities. A l ternatively, one can express t h e net w o r k i n g c a p i t a l as current assets less current l i a b i l i t i e s . 2 7 Current liabilities are a company's debts or obligations due w i t h i n one year. Besides accounts payable, current liabilities also include short t e r m debt, accounts payable, accrued liabilities and other debts. A l t e r n a t i v e l y , net w o r k i n g c a p i t a l could also be defined as current assets less current liabilities. B u t this figure does not exclusively focus on t h e operating business a n d includes other items such as short t e r m debt. Thus, t h i s paper w i l l focus o n t h e former d e f i n i t i o n . 2 8 I n order t o account for size bias, t h e net w o r k i n g c a p i t a l can be p u t i n t o perspective w i t h several 2 3 2 4 2 5 2 6 2 7

2 8

Cf. K u m a r / H a n n a / A d a m s (2000), p. 1. Cf. Hassim et al. (2003), p. 1. Cf. S m i t h (1980), p. 4. Cf. E i t e m a n / S t o n e h i l l / M o f f e t t (2004), p. 681. Cf. Lamcaster/Stevens/Jennings (1999), p. 39; M c K e e (1992), p. 10; B e r n s t e i n / W i l d (1999), p. 114. Cf. Drukarczyk (2003), p. 83f.

4 EMPIRICAL ANALYSIS

15

positions. T h e Net W o r k i n g C a p i t a l R a t i o ( N W C R ) is one of t h e most comm o n l y used l i q u i d i t y ratios, w h i c h p u t t h e net w o r k i n g capital i n t o relation w i t h t h e T o t a l Assets. Net Working

Capital

Ratio

= Net Working

Capital/Total

Assets

Moreover, a w i d e l y used r a t i o is t h e current ratio. T h e current r a t i o is defined as t h e aggregated current assets i n relation t o t h e current liabilities. Current

Ratio

= Current

Assets/Current

Liabilities

T h e current r a t i o is a l i q u i d i t y r a t i o t h a t measures a company's a b i l i t y t o pay s h o r t - t e r m o b l i g a t i o n s . 2 9 A s t h e a b i l i t y t o meet s h o r t - t e r m obligations is defined as l i q u i d i t y , t h e current r a t i o is one of t h e most frequently used figures t o measure t h e l i q u i d i t y of a company. A low current ratio could lead t o payment difficulties, whereas a very h i g h current r a t i o could mean t h a t t o o much c a p i t a l is b o u n d w i t h i n t h e current assets p o s i t i o n . 3 0 A slight a d a p t a t i o n of t h e current r a t i o is leading t o t h e quick ratio: Quick

Ratio

= Quick

Assets/Current

Liabilities

T h e quick r a t i o is a very conservative l i q u i d i t y r a t i o t h a t measures a company's a b i l i t y t o meet its s h o r t - t e r m obligations w i t h its most l i q u i d assets. 3 1 Thereby, i t p u t s Quick Assets ( Q A ) i n relation t o t h e current liabilities. Q A is defined as current assets less inventory. T h i s measure is especially i m p o r t a n t for companies w i t h p o t e n t i a l difficulties t o t u r n t h e i r inventories i n t o cash, as i n those cases t h e current r a t i o w o u l d overe s t i m a t e 3 2 the company's a b i l i t y t o meet s h o r t - t e r m obligations, i.e. t o mobilise f u n d s . 3 3 T h i s applies i n t h e construction industry, as most inventories are not easily t u r n e d i n t o cash. Therefore, t h i s r a t i o is regarded w i t h special interest d u r i n g the ongoing analysis. W i t h o u t referring t o a particular i n d u s t r y a quick r a t i o of one can be seen as average. 3 4

4 Empirical Analysis 4.1 Research Questions a n d Hypotheses For t h e empirical verification of t h e theoretical framework, t h e following part describes t h e empirical analysis. Accordingly, several hypotheses are stated w h i c h w i l l be examined in t h e following section. 2 9 3 0 3 1 3 2 3 3 3 4

Cf. Cf. Cf. Cf. Cf. Cf.

T o r a l (2000), p. 2. H a m p t o n / W a g n e r (1989), p. 264. Harder (2000), p. 100. D a m b o l e n a / S h u l m a n (1988), p. 74. Hasenfuss (2005), p. 46. T h o m m e n / A c h l e i t n e r (2001), p. 484; Gallinger (1997), p. 22.

CHAPTER 1. VALUE DRIVERS

16

Hypothesis I: Net w o r k i n g c a p i t a l has an influence o n values.

firm

T h e reason for testing t h i s hypothesis is t h e importance of cash management for companies i n t h e c o n s t r u c t i o n industry, as already stated previously. T h i s assumption is based on t h e fact t h a t a reduction i n net w o r k i n g c a p i t a l leads t o higher cash flows a n d lowers a d d i t i o n a l financing demand, w h i c h should enhance t h e market value. Hypothesis I I : There is a trade-off between net w o r k i n g c a p i t a l a n d profitability. T h i s hypothesis examines t h e relation between t h e net w o r k i n g c a p i t a l and t h e Net Profit M a r g i n ( N P M ) as an indicator of a firm's p r o f i t a b i l i t y 3 5 in order t o find out if good W C M has a positive influence o n a company's profits. Hypothesis I I I : Profitable construction companies need liquidity. T h i s hypothesis tests, whether i t is of increased importance for companies i n t h e construction i n d u s t r y t o have high cash reserves i n order t o have t h e a b i l i t y t o continue investing d u r i n g a d o w n t u r n a n d t o avoid financial distress.36 Hypothesis I V : Employee efficiency has an influence on values.

firm

Hypothesis I V is based on t h e evidence t h a t t h e construction i n d u s t r y is not o n l y t h e largest i n d u s t r i a l e m p l o y e r 3 7 b u t can also be characterised by low p r o d u c t i v i t y and high labour c o s t s . 3 8 T h i s should theoretically lead t o a remarkable influence of p r o d u c t i v i t y per employee on the market value.

4.2 P r o c e d u r e T h e procedure of t h e analysis is oriented t o t h e methodology for c o n d u c t i n g q u a n t i t a t i v e analyses w h i c h has been described b y Creswell ( 2 0 0 3 ) . 3 9 First, t h e d a t a sample as well as its assembly w i l l be described. A s there is a wide range of instruments for e m p i r i c a l analyses, t h e following p a r t w i l l firstly describe t h e applied tools before presenting t h e actual analysis and its results. I n particular, t h e focus w i l l be o n t h e regression analysis. However, before any regression can be conducted, a correlation analysis has t o be done i n order t o identify significant relations between variables a n d j u s t i f y 3 5 3 6 3 7

3 8 3 9

Cf. Shin/Soenen (1998), p. 43. Cf. H a r f o r d / M i k k e l s o n (2003), p. 18. Cf. Confederation of I n t e r n a t i o n a l Contractors' Association (2002), p.

8.

Cf. L o o s e m o r e / D a i n t y / L i n g a r d (2003), p. 294. Cf. Creswell (2003), p. 175.

4 EMPIRICAL ANALYSIS

17

ongoing analyses. T h e m a j o r i t y of studies w h i c h have been conducted on W C M use t h e correlation and regression analysis i n order t o identify the relationship between net w o r k i n g c a p i t a l figures a n d other target variables of c o m p a n i e s . 4 0 I f t h e correlation analysis does not show any significance between t w o variables, t h e regression analysis w i l l be o m i t t e d . Concluding t h i s section, possible l i m i t a t i o n s of t h e s t u d y w i l l be presented.

4.3 Selection of Sample a n d D a t a T h e basis for t h e following analysis is a set of 56 listed companies of t h e European construction industry. T h e original number of 172 companies has been reduced due t o t h e lack of data. T h i s cutback seems t o be useful in order t o achieve a h i g h relevancy of t h e d a t a analysed. T h e d a t a has been extracted from Datastream. T h e t i m e p e r i o d for t h e analysis ranges from t h e year 1994 t o 2004. Therefore, t h e t o t a l d a t a sample consists out of 560 company years.

4.4 D e s c r i p t i o n of t h e A n a l y s e d Figures Generally, ratios are used for t h e regression analysis i n order t o exclude any size based bias effects. T h i s is considered t o be i m p o r t a n t because of t h e strongly a l t e r n a t i n g sizes i n t h e construction i n d u s t r y a n d t h e fragmented c h a r a c t e r . 4 1 Thus, instead of t h e net w o r k i n g c a p i t a l as a variable, t h e N W C R w h i c h is t h e r a t i o of net w o r k i n g c a p i t a l t o T o t a l Assets ( T A ) is used. T h e firm value is represented by t h e market value of t h e companies. I n accordance t o Shin/Soenen (1998) t h e N P M is used as a p r o x y for p r o f i t a b i l i t y . 4 2 T h e N P M is defined as net profit i n relation t o sales. Respecting liquidity, t h e current r a t i o and t h e quick r a t i o serve as references. T h e current ratio is chosen, as i t is t h e most prominent l i q u i d i t y ratio. T h e quick ratio is mostly used because i f companies have difficulties i n t u r n i n g t h e i r inventory i n t o cash, t h e current r a t i o m i g h t be overstated, and i t can be expected t h a t t h e quick r a t i o provides more accurate and meaningful results. Lastly, t h e influence of t h e a m o u n t of profit generated b y every single employee on t h e market value of t h e firm is analysed. I n order t o measure this, t h e p r o f i t a b i l i t y per employee is taken as representative figure.

4.5 Results T h e correlation analysis for Hypothesis I results i n a significantly negative correlation of -0.117. T h e significance of at least 99% shows t h a t there i n fact is a relation between net w o r k i n g c a p i t a l a n d t h e value of a construction company. 4 0 4 1 4 2

Cf. Deloof (2003); F i s m a n / L o v e (2003). Cf. Haskell (2004), p. 10. Cf. Shin/Soenen (1998), p. 40.

CHAPTER 1. VALUE DRIVERS

18

Figure I: Model Summary of the Regression Between NWCR and MV

R 0.117

R 2 Adjusted R 2 0.014 0.012

Std. Error of the Estimates 6056670.55

However, t h e regression analysis indicates t h a t W C M has an o n l y very l i m i t e d explanatory power on firm values. Therefore, net w o r k i n g c a p i t a l has t o be rejected as a value d r i v e r . 4 3 For Hypothesis I I , t h e Pearson correlation coefficient for t h e N W C R and t h e N P M shows a significantly negative correlation of 0.107. Figure II: Model Summary of the Regression Between NWCR and the NPM

R 0.107

R 2 Adjusted R 2 0.011 0.01

Std. Error of the Estimates 5.96918

T h e regression analysis shows t h a t t h e dependent variable N P M is explained b y 1.1%. However, t h a t t h e a b i l i t y of t h e N W C R t o explain t h e N P M is very low. T h e reason m i g h t be t h a t there is a trade-off between t h e t w o variables, as shown i n Figure I I I . N W C R values of zero also t e n d t o reduce t h e profitability. T h i s could be explained b y t h e fact t h a t companies, w h i c h pay t h e i r payables earlier receive an a l l o w a n c e . 4 4 T h i s cash discount is often higher t h a n t h e advantage from deferring t h e payment, as t h e deferral is i n most cases incorporated i n t h e final price, leading t o relatively unfavourable payment c o n d i t i o n s . 4 5 Figure III: Relation between NWCR and NPM

40% 35% 30% 25%

Έ 20%

>

Ζ 15% 10% 5% 0% -20%

. • * '

vWJvMr 0%

20%

40%

60%

80% 100%

NWCR 4 3 4 4 4 5

Cf. A p p e n d i x I . Cf. H i l l / R i e n e r (1979), p. 69. Cf. P e t e r s e n / R a j a n (1997), p. 688; Cf. A p p e n d i x I I .

4 EMPIRICAL ANALYSIS

19

Moreover, t h e employment of subcontractors reduces t h e risk and t h e amount of net w o r k i n g c a p i t a l t o be laid o u t , b u t i t also reduces t h e profi t a b i l i t y , 4 6 as t h e subcontractor already incorporates a profit. However, the exact p o i n t of trade-off could not be determined w i t h i n t h i s analysis. Hypothesis I I I analysis, whether profitable construction companies need l i q u i d i t y t o be profitable or not delivered t h e following results: T h e Pearson Correlation Coefficient between those t w o variables is significantly positive w i t h 0.457, as shown i n Figure I V . Figure IV: Model Summary of the Regression between QR and NPM

R 0.457

R2 0.209

Adjusted R 2 0.207

Std. Error of the Estimates 5.34052

Figure V : Relation between Profitability and L i q u i d i t y 40% 35%

y = 0.0361 x +0.0056

30%

OR

Moreover, the conducted regression analysis delivered t h e result, t h a t the quick r a t i o explains 20.9% of t h e dependent variable - the N P M . T h e t r e n d line shows, t h a t i f t h e quick r a t i o increases, t h e N P M increases b y 0.0361. T h i s is leading t o statement t h a t construction companies need t o be l i q u i d i n order t o increase t h e i r N P M . T h e reason for this finding c a n be seen i n t h e character of t h e construction industry, w h i c h is described as being rather cyclical. Therefore, companies t r y t o m a i n t a i n l i q u i d i n order t o avoid financial distress due t o external effects t h e y can h a r d l y incorporate i n t h e i r planning. T h u s , a b i l i t y t o continue investing d u r i n g a d o w n t u r n is beneficial, resulting i n better o p e r a t i n g performance and postd o w n t u r n sales g r o w t h . 4 7 Moreover, t h e forecasting of cash requirements, 4 6 4 7

Cf. H a s s i m / K a d i r / L e w / S i m (2003), p. 3. Cf. H a r f o r d / M i k k e l s o n (2003), p. 18.

CHAPTER 1. VALUE DRIVERS

20

as previously mentioned is rather difficult, as well as the prediction of the accurate time-frame necessary to complete the project. Figure VI: Model Summary of the Regression between CR and NPM R 0.138

R 2 Adjusted R 2 0.019 0.017

Std. Error of the Estimates 5.94664

The regression analysis of the current ratio as independent variable and the NPM shows that there is a significantly positive correlation of 0.38, but the regression analysis shows that the explanatory power of the current ratio on the NPM amounts only to 1.9%. 48 This finding is in accordance with former studies, which state that the quick ratio delivers better results for companies with difficulties in turning their assets into cash. To draw a conclusion: cash reserves have a substantial impact on companies in the construction industry, providing a beneficial source of internal financing for ongoing investments. However this result is only applicable controlled for NPM. Relating it to MV, the correlation and the regression are switched the other way round, as shown in Figure VII. Figure VII: Model Summary of the Regression between QR/CR and MV CR QR

R R2 0.194 0.038 0.085 0.007

Adjusted R 2 0.036 0.006

Std. Error of the Estimates 5982368.95 6076467.22

Here, even though both independent variables are significantly, negatively correlated with the M V with the values of 0.085 and 0.194, the explanatory power is higher for the current ratio by 3.1% in comparison to the explanatory power of the quick ratio with 0.7%. 4 9 The reason can be seen in the market imperfection, as the market does not pay sufficient attention to the particular characteristics of the construction industry when it comes to the liquidation of current assets. It seems that there is not differentiated between different kinds of current assets. Therefore, the inventory seems to be overstated by the market in terms of liquidity. The analysis of the influence of the employee efficiency on the market values delivered to following results: Figure VIII: Model Summary of the Regression between OPE and MV R R 2 Adjusted R 2 Std. Error of the Estimates 0.448 0.200 0.199 5453146.43

As shown in Figure V I I I , the market value of construction companies is significantly, positively correlated with the profitability per employee. The 48 49

Cf. Appendix III-IV. Cf. Appendix V-VI.

5 CONCLUSION AND FUTURE FIELDS OF RESEARCH21 Pearson Correlation analysis results in a correlation of 0.448 between the two variables. The regression analysis results in the independent variable explaining 20% of the market value of construction companies. 50 Even though this figure is comparatively high, it has to be put in context, as outsourcing efforts are not reflected in this study. This is important as many construction companies outsource manufacturing activities to a high extent to subcontractors, where they charge more to their clients than they pay their subcontractors, i.e. make profit. Thus, the profit generated has to be seen in relation to the whole number of workers engaged in the construction process and is not only contributable to the employees of the analysed construction companies. Moreover, as only the number of workers employed directly by the companies is taken into account, the number might not only be attributable to the productivity per employee, but also to good negotiation skills with the subcontractors. 51

4.6 L i m i t a t i o n s o f t h e S t u d y In the following, potential limitations will be outlined. First of all, even though there is a universal accounting standard for Europe, this has only been the case for the most recent years and the data before the introduction of the International Financial Reporting Standards might differ internationally due to different allowances. Moreover, even if the accounting standards are used, they still provide opportunities of manipulating and may vary significantly. Another important factor influencing the data might be the different depreciation policies. This is important for the comparison of construction companies due to the capital extensive business. Additionally, even if all are members of the European Union, the minimum wage level still differs from country to country as well as the period of cancellation, which has an influence on a variety of company figures. Furthermore, due to the availability of data, the sample consisted only of publicly listed companies. As this neglects smaller companies, which do have a significant share within the industry, the sample cannot be seen as representative. Finally, the data selection based on the criteria of having all necessary information for every year analysed reduced the sample and exclude younger firms which have no records right from the starting year of 1994.

5 Conclusion a n d F u t u r e Fields o f Research This paper had the aim to identify the value drivers for the companies operating in European construction industry. Therefore, most of the relevant 50 51

Cf. Appendix VII. Cf. Haskell (2004), p. 10.

22

CHAPTER 1. VALUE DRIVERS

key figures are analysed among each other. The highest correlation has been observed between the market value and the independent variables net working capital ratio, current ratio, quick ratio and operating profit per employee. The net working capital ratio as the dependent variable correlates most with current ratio, quick ratio and NPM. The regression analysis delivered that the net working capital ratio can explain the market value, however only to a slight extent and cannot be considered as a value driver. Another interesting factor is that the liquidity does have a high impact on the profitability of construction companies due to the reasons stated previously. According to the analysis, the profitability per employee can be regarded as a value driver but the data available for this analysis is too limited to deliver more reliable results. It can be expected due to the limited results in the quantitative analysis that the value drivers in this industry are rather of a qualitative nature.

REFERENCES

23

References A l e n c h e r y , J o s e p h J . ( 2 0 0 5 ) : Corporate Performance Measurement Based on Best Principles, in: . Bernstein, Leopold A . / W i l d e John J. (1999): Financial Statements, 5th Edition, New York.

Analysis of

C h u n g , Y e a S. ( 2 0 0 5 ) : Identification of Economic Value Drivers Impacting Operational Cash Flows in the casual Theme Restaurant Industry, in: . C o l i n a , J u a n ( 2 0 0 2 ) : Working Capital Optimization, in: Pulp and Paper Magazine, Vol. 76, No. 7, p. 64. Confederation of International Contractors' Association ( 2 0 0 2 ) : Industry as a partner for sustainable development, 1st Edition, London. Construction Industry Council (2000): tion in PFI Projects, 1st Edition, London.

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C o p e l a n d , T o m / K o l l e r , T i m / M u r r i n , J a c k ( 2 0 0 5 ) : Valuation - Measuring and Man-aging the Value of Companies, 2nd Edition, New Jersey. C r e s w e l l , J o h n ( 2 0 0 3 ) : Research Design - Qualitative Quantitative and Mixed Methods Approaches, 4th Edition, California. D a m b o l e n a , I s m a e l G . / S h u l m a n , J o e l M . ( 1 9 8 8 ) : A Primary Rule for Detecting Bankruptcy - Watch the Cash, in: Financial Analysts Journal, Vol. 44, No. 5, p. 74. D e c h o w , P a t r i c i a M . / K o t h a r i , S . P . / W a t t s Ross L . (1998): The Relation between Earnings and Cash Flows, in: Journal of Accounting and Economics, Vol. 25, No. 2, p. 133-168. D e l o o f , M a r c ( 2 0 0 3 ) : Does Working Capital Management Affect Profitability of Belgian Firms, in: Journal of Business Finance & Accounting, Vol. 30, No. 3-4, p. 573-587. Drukarczyk, Jochen (1993) : 10th Edition, München.

Theorie und Politik der Finanzierung,

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Eiteman, David K./Stonehill, Arthur I./Moffet, Michael H . ( 2 0 0 4 ) : Multinational Business Finance, 10th Edition, Boston. E u r o p e a n F o u n d a t i o n for t h e I m p r o v e m e n t o f L i v i n g a n d W o r k i n g C o n d i t i o n s ( 2 0 0 5 ) : Trends and drivers of change in the European construction sector - Mapping report, in: http://www.emcc. eurofound.eu.int / publications/2005/ef04149en.pdf. F i s m a n , R . / L o v e , I . ( 2 0 0 3 ) : Trade Credit Financial Intermediary Development and Industry Growth, in: Journal of Finance, Vol. 58, No. 1, p. 353-374. G a l l i n g e r , G e o r g e W . ( 1 9 9 7 ) : The Current and Quick Ratios Do they stand up to Scrutiny?, in: Business Credit, Vol. 99, No. 5, p. 22. G i t m a n , L a w r e n c e J . / M a x w e l l , C h a r l e s E . ( 1 9 8 5 ) : Estimating Corporate Liquidity Requirements - A Simplified Approach, in: The Financial Review, Vol. 9, No. 1, p. 79-88. Halpin, Daniel W . / W o o d h e a d , Ronald W . (1998): struction Management, 2nd Edition, New York. H a m p t o n , John J . / W a g n e r , Cecilia L. (1989): Capital Management, 1st Edition, New York. Harder, Ronald R. 171, No. 4, p. 2-3.

(2000):

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Working

Ratio Clarification, in: JCK, Vol.

Harford, Jarrad/Mikkelson, Wayne/Partch, Meagan M . ( 2 0 0 3 ) : Drivers of the Value of the Firm - Profitability Growth and Capital Intensity, in: http://us.badm.washington.edu/harford/papers/hmp _c ash.pdf. Hasenfuss, M a r c 200, No. 6. p. 46-49.

(2005):

Quick Ratio, in: Finance Week, Vol.

H a s k e l l , P r e s t o n H · ( 2 0 0 4 ) : Construction Industry Productivity: Its History and Future Direction, in: . H a s s i m S. e t a l . ( 2 0 0 3 ) : Estimation of Minimum Working Capital for Construction Projects in Malaysia, in: Journal of Construction Engineering & Management, Vol. 129, No. 4, p. 369-374.

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H i l l , N e d C . / R i e n e r , K e n n e t h D . ( 1 9 7 9 ) : Determining the Cash Discount in the Firm's Credit Policy, in: Financial Management, Voi. 8, No. 1, p. 68-73. H o w e l l , G r e g o r y A . ( 1 9 9 9 ) : What is Lean Construction, in: Proceedings Seventh Annual Conference of the International Group for Lean Construction. K u m a r , Vellanki S . / H a n n a , A w a d S . / A d a m s , Teresa ( 2 0 0 0 ) : Assessment of working capital requirements by fuzzy set theory, in: Engineering Construction and Architectural Management, Vol. 7, Iss. 1. p. 93-103. Lancaster, Carol/Stevens, Jerry L./Jennings, Joseph A . ( 1 9 9 9 ) : Corporate Liquidity and the Significance of Earnings versus Cash Flow - An Examination of Industry Effects, Vol. 15, Iss. 3, p. 37-36. Loosemore, M a r t i n / D a i n t y , A n d r e w / L i n g a r d H e l e n (2003): Human Resource Management in Construction Projects - Strategic and Operational Approaches, 1st Edition, London. M c K e e , B r a d f o r d ( 1 9 9 2 ) : Tips to help you check Creditworthiness, in: Nation's Business, Vol. 80, Iss. 10, p. 1-3. M i l l e r , T o m W . / M a t h i s e n , R i c h a r d E . ( 2 0 0 4 ) : Driver of the Value of the Firm - Profitability Growth and Capital Intensity, Journal of Accounting L· Finance Research, Vol. 12, Iss. 6, p. 70-79. M o r i n , R o g e r A . / J a r r e l l , S h e r r y L . ( 2 0 0 1 ) : Driving Shareholder Value - Value-Building Techniques for Creating Shareholder Wealth, 1st Edition, New York. Olsen, M i c h a e l D . / T s e , Eliza C . / W e s t , Joseph J. (1998): Strategic Management in the Hospitality Industry, 1st Edition, New York. P e t e r s e n , M i t c h e l l / R a g h u r a m , R a j a n ( 1 9 9 7 ) : Trade Credit - Theories and Evidence, in: Financial Studies, Vol. 10, Iss. 3, p. 661-691. R a p p a p o r t , A l f r e d ( 1 9 9 8 ) : Creating Shareholder Value - A guide for managers and investors, 2nd Edition, New York. R a p p a p o r t , A l f r e d ( 1 9 9 8 ) : Shareholder value - Ein Handbuch für Manager und Investoren, 2nd Edition, Stuttgart.

26

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S h i n , H y a n - H . / S o e n e n , L u c ( 1 9 9 8 ) : Efficiency of Working Capital Management and Corporate Profitability, in: Financial Practice & Education, Vol. 8, Iss. 2, p. 37-45. S m i t h , K e i t h V . ( 1 9 8 0 ) : An Overview of Working Capital Management, in: Readings on the Management of Working Capital. T h o m m e n , Jean-P./Achleitner, A n n - K . (2003): Betriebswirtschaftslehre, 4th Edition, Wiesbaden

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T o r a l , A l v i n M . ( 2 0 0 0 ) : Fundamentals IX - Current Ratio/Dividends, in: The Pure Fundamentalist, Vol. 9, Iss. 2, p. 2. T u n g , R i c h a r d L . ( 1 9 7 9 ) : Dimensions of Organizational Environments - An Explanatory Study of their Impact, in: Academy of Management Journal, Vol. 22, Iss. 4, p. 672-693.

APPENDIX

27

Appendix

Appendix I: Model Summary of the Regression Analysis of the NWCR and the MV ANOVA Mean F Sum of df Sig. Squares Square Regressions 3.1E+14 1 3.12E+14 8.523 0.004 614 Residual 2.3E+16 3.66E+13 Total 2.3E+16 615 Coefficients Unstandardised Coefficients (Constant) NWCR

Β 3498943 -4613831

Std. Error 467876.7 1580367

Standardised Coefficients Beta t Sig. 7.692 -0.117 -2.919

0.000 0.004

Appendix II: Model Summary of the Regression Analysis of the NWCR and the NPM ANOVA Mean F Sum of df Sig. Squares Square Regressions 253.633 1 253.633 7.118 0.008 Residual 21877.483 614 35.631 Total 22131.116 615 Coefficients Unstandardised Coefficients (Constant) NWCR

Β Std. Error 5.783 0.461 -4.156 1.558

Standardised Coefficients Beta t 12.542 -0.107 -2.668

Sig.

0.000 0.008

28

CHAPTER 1. VALUE DRIVERS

Appendix Ills Model Summary of the Regression Analysis of the QR and the NPM ANOVA Sum of df F Mean Sig. Square Squares Regressions 4619.099 1 4619.099 161.983 0.000 28.521 17512.017 614 Residual Total 22131.116 615 Coefficients Unstandardised Coefficients (Constant) NWCR

Β 0.556 3.613

Std. Error 0.393 0.284

Standardised Coefficients Beta t 1.416 0.457 12.726

Sig. 0.157

0.000

Appendix IV: Model Summary of the Regression Analysis of the CR and the NPM ANOVA Sum of df Mean F Sig. Square Squares Regressions 418.526 1 418.526 11.835 0.001 Residual 21712.590 614 35.363 Total 22131.116 615 Coefficients Unstandardised Coefficients (Constant) NWCR

Β Std. Error 0 6 9 Ö275 0.069 0.020

Standardised Coefficients Beta t Sig. 15.520 ÖÖÖÖ 0.138 3.440 0.001

Appendix V: Model Summary of the Regression Analysis of the QR and the MV ANOVA Sum of Mean F df Sig. Squares Square Regressions 1.7E+014 1 1.65E+14 4.474 0.035 Residual 2.3E+016 614 3.69E+13 Total 2.3E+016 615 Coefficients Unstandardised Coefficients (Constant) NWCR

Β 3223554 -683327

Std. Error 446615.3 323071.0

Standardised Coefficients Beta t 7.218 -0.085 -2.115

Sig.

0.000 0.035

APPENDIX

29

Appendix VI: Model Summary of the Regression Analysis of the CR and the MV ANOVA Mean F Sum of df Sig. Squares Square Regressions 8.6E+014 1 8.619E+14 24.083 0.000 Residual 2.2E+016 614 3.57E+13 Total 2.2E+016 615 Coefficients Unstandardised Coefficients

(Constant) NWCR

Β 3100478 -99680.6

Std. Error 276713.8 20312.156

Standardised Coefficients Beta t 11.205 -0.194 -4.907

Sig. 0.000 0.000

Appendix VII: Model Summary of the Regression Analysis of the OPE and the MV ANOVA Sum of Mean F df Sig. Squares Square Regressions 4.6E+015 1 4.578E+14 153.943 0.000 Residual 1.8E+016 614 2.97E+13 2.3E+016 Total 615 Coefficients Unstandardised Coefficients (Constant) NWCR

Β 244139.9 134335.9

Std. Error 281799.3 10827.089

Standardised Coefficients Beta t 0.866 0.448 12.407

Sig. 0.387 0.000

CHAPTER 1. VALUE DRIVERS

Chapter 2

Long-term Performance of D i f f e r e n t Business Models in the European Construction Industry Sonja Blankenburg^/Astrid

t

Mayî

EUROPEAN BUSINESS SCHOOL (ebs), International University Schloß Reichartshausen

31

32

CHAPTER 2. LONG-TERM PERFORMANCE

Contents 1 Introduction 1.1 Problems and Objectives 1.2 Course of Analysis 2 Basic Concepts 2.1 Construction Industry 2.2 Business Models 3 Performance Analysis 3.1 Different Business Models

32 32 33 34 34 35 36 36

3.2 Comparison of Models

37

3.3 Geographic and Economic Differences

40

3.4 Philipp Holzmann A G

41

3.5 Possible Explanations

42

4 Set of Opportunities

47

4.1 Improvement

47

4.2 Threats

49

5 Conclusion and Outlook

50

References

52

Appendix

56

1 Introduction 1.1 Problems a n d Objectives The European construction industry has experienced significant environmental changes over the last ten years. Since 2003, a slow recovery in terms of demand and investments can be stated. Nevertheless, the construction industry heavily depends on external factors, such as oil prices, steel prices and exchange rates. These factors - as well as consumer behavior - are not controllable by companies but have a decisive impact on their returns. The public sector, constituting a major client of construction companies, faces financial deficits. Many members of the European Union (EU) are encountering difficulties to adhere to the stability threshold of 3%, leading to decreased investments in public infrastructure. 1 The EU enlargement poses further difficulties for smaller companies, since low wage companies Cf. Welfens (2003), p. Iff.

1 INTRODUCTION

33

from the eastern states are entering western markets. 2 Taking all these issues into consideration, construction companies have to find new solutions. For the international players three major strategies can be observed: the first one is a geographical expansion of businesses, which companies can either achieve by organic growth and setting up subsidiaries or by mergers with local partners. 3 Secondly, expansion can be achieved by an extension of offered services again by organic growth or mergers. The third strategy is a specialisation of the companies in niche segments of the construction market as for example petrochemical engineering or dredging. 4 The objective of this paper is the determination of long-term performance of different business models in the European construction sector in order to analyse whether there are certain stereotypes of successful business models in the construction industry. In order to achieve this objective, the following path has been chosen.

1.2 Course of Analysis After a general description of the current situation and the deduced problems for the European construction industry in the Introduction of this paper, the second part will provide an overview over the basic concepts regarding this topic. In this section, scientific definitions of important terms will be given and the analysis of the current state of the European construction sector will be analysed in greater detail. Based on these concepts, the first part of Section 3 provides an introduction of specific business models in the construction industry. In order to compare the long-term performance of a set of European construction firms, these corporates will be divided into different clusters according to their individual business models. Afterwards, the success of these clusters will be compared using stock price performance over a time period of ten years. Since the stock price performance analysis does not include any companies which went bankrupt within the respective time frame, the following sub-section describes the example of Philipp Holzmann AG as a failed company which used a particular business model. The last part of Section 3 is dedicated to the explanations of the deduced performance results. Therefore, the outcomes of several correlation and regression analyses are described in order to identify the relationships between stock price performance of construction companies and general macroeconomic factors, such as the development of the oil price, interest rates or demographic changes. In order to complete the overview of value drivers in the European construction industry, the following section focuses on controllable chances and threats, which are faced by construction firms. A conclusion summarises the main outcomes of this paper and also gives an outlook of possible future developments. 2 3 4

Cf. FIEC (2005), p. 5-9; Schwarz (1997), p. 18. Cf. Janssen (2000), p. 714. Cf. Schwarz (1997), p. 20.

34

CHAPTER

2. LONG-TERM

PERFORMANCE

2 Basic Concepts This section presents and explains basic definitions and ideas of the construction industry. The described concepts are based on common literature and are partly complemented for the purpose of this paper. They form the framework for the further course of this paper and will be referred to in following sections.

2.1 C o n s t r u c t i o n

Industry

2.1.1 Definition Literature reveals that there are several definitions for the term construction industry. Janssen describes it as "that sector of the production chain which, in conformity with the "Eurostat" definition of the sector is tied to the locality, excluding sectors such as the extraction of raw materials, construction material and components, plant and tools production, planning, design, surveying and civil engineering services." 5 Falk, however, divides his definition into two parts, namely construction industry in a broader sense and construction industry in a narrow sense. In a broader sense, his definition includes all companies, which are dedicated to the planning, implementation and usage of construction objects and projects. This also includes manufacturer of raw materials and construction machinery. Since the borderline between the construction sectors in the broader sense is not clearly defined, the definition of the construction industry in the narrow sense seems to be more useful. According to Falk this includes all institutions, which are directly involved in the construction process. This group of companies, however, can also be divided into two parts, namely the main construction industry and the industry dedicated to interior fittings. 6 Appendix I illustrates this concept. For this paper Falk's definition of the construction industry in the narrow sense has been chosen in order to better differentiate between the several business models of the companies in the European construction market. However, companies which conduct project development, concessions and services are also included.

2.1.2 Status quo of the European Construction I n d u s t r y Construction companies today operate in a highly competitive environment. Projects are becoming more and more complex and companies must invest additional time in order to make adequate forecasts for their assignments. 7 External factors also play an important role for the growth of the construction industry. These can be raw material prices like steel and oil prices, but also the general economic development. Some of the main factors shall be examined closer in this paper. The Euro is expected to 5 6 7

Janssen (2000), p. 712, column 1. Cf. Falk (1996), p. 110-111, columns 2-1. Cf. KPMG (2005), p. 1, p. 7.

2 BASIC CONCEPTS

35

stay strong in relation to the US-Dollar. Together with the current account surplus of the Eurozone this would result in a favourable climate for investments outside the EU. 8 Oil prices increased during the last year, which would normally mean an increase in inflation and a reduction of income of the oil-importing countries. But the effect of this rise has been compensated by the influence of the central banks, which are nowadays more independent from the government. 9 Despite a slight increase, interest rates are still low. Furthermore, inflation remained stable although oil prices have been increasing significantly. Therefore, monetary policies do not have to be changed either. Furthermore, due to the Asian competition companies are forced to cut costs wherever possible, leading to higher profits for companies in the Eurozone. 10

2.2 Business M o d e l s Several authors of scientific literature have given different definitions of the term business model". Some of them base their definition on the concept of value added chains by Porter. 1 1 According to this concept a company consists of different activities, which are dedicated to the production and offering of different products and services. By rearrangement of these activities new business models arise. 12 Treacy/Wiersema (1995) define the operating business model of a company as the co-action between operating processes, management systems, organisational structures and the corporate culture, which enable the company to fulfil its purpose and objectives. 13 According to Treacy/Wiersema (1995), a company should focus on specific business segments and specialise on these activities in order to create a successful business model. 1 4 The focus on niche markets has already been mentioned in Section 1.1 as a successful business strategy. In the course of this paper, a business model is understood as a specific combination of several business areas of the construction industry, which is defined by a company in order to achieve a certain market position. Since the construction industry consists of a variety of activities and business segments, many different business models can be created. Some of them even include non-traditional business areas, such as consulting services. The variety of these business models will be further described in Section 3.

8 9 10 11 12 13 14

Cf. IteC (2005), p. 39. Cf. IteC (2005), p. 32-33; w.A. (2006c), p . l l ; Deutscher Bundestag (2005), p. 46ff; Lee/Ni (2002), p. 823. Cf. IteC (2005), p. 36. Cf. Timmers (2002), p. 37;41;45. Cf. Porter (1996), p. 59ff. Cf. Treacy/Wiersema (1995), p. 10. Cf. Treacy/Wiersema (1995), p. 10.

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CHAPTER 2. LONG-TERM PERFORMANCE

3 Performance A n a l y s i s o f Different Business M o d e l s 3.1 I n t r o d u c t i o n of Different Business M o d e l s According to the prior definition of business models, an introduction of the business segments observed is provided in this section. Subsequently, the analysed companies are divided into clusters for simplification purposes. The initial data set consisted of 127 companies that had to be reduced by the building material supplier and construction machinery producers. The companies with missing stock price data are also excluded. The remaining sample includes 45 European construction firms, which still exist today and did not go bankrupt during the last ten years. This fact should be taken into consideration when interpreting the results of the performance comparison. Since the data sample does not include any companies that went bankrupt, a separate section describes an example of an unsuccessful company. This example underlines that certain business models might not work in every case. It has to be stated that in the majority of the 45 companies a clear delineation, as far as certain activities are concerned, is not possible. There are a lot of generalists in the market, offering construction works as well as services. This way they cover the whole value chain and offer a complete portfolio of services to their clients. Therefore, the clusters are separated as follows: Generalists : The companies in this cluster are actively involved in building construction and civil engineering. They also offer comprehensive services in addition to their clients. Generalists with project development: In order to specify some differentiating characteristics, project development has been identified as one of these. Project development is a very complex business model, since the company has to coordinate all different phases of a project, from planning to service and maintenance. 15 The various crafts of the projects can be either done by the company itself or by subcontractors. In both cases, the coordinating procedure is essential for the success of the projects, both in terms of time limits as well as the cost structure. This process requires certain knowledge and experience from the companies. 16 Generalists with concessions: Concessions are also a field of work requiring experience and respective knowledge. Concessions are public services outsourced by the government. 17 This means that companies in this cluster have to design, build and operate public infrastructure. This can also be done in the form of Public Private Partnerships (PPP). 1 8 The projects can range from trams, railroads, streets, dams and power stations 15 16 17 18

Cf. Cf. Cf. Cf. 15.

Isenhöfer/Väth (2000), p. 153. Homann (2000), p. 231. Straub/Laffont/Guasch (2005), p. 2. Went ζ/Bischoff/ Gosewehr (2005), p. 805-806; Kirsch (1997), p.

3 PERFORMANCE ANALYSIS

37

to car parks, bridges and airports. 1 9 This cluster differs from project development because public infrastructure represents a special field of building construction and civil engineering. Most categories, such as power generation facilities, require very specific knowledge which has to be available in the company. Very often the scales of the projects are very large. However, this must not be the case in project development. The concessions business includes the advantage that it is not dependent on building cycles, but rather on public expenditure. Contracts are characterised as longterm, which gives planning stability and allows building companies some independence from the cycle driven construction business. Generalists with concessions and project development: The companies in this cluster are involved in both project development and concessions. It might be expected that there are certain synergies between those two sectors, since concessions is a sort of project development for public infrastructure although it is more complex, as already described above. Advisors: This cluster comprehends the companies that do not conduct construction, but advise construction companies, for example on project management. However, only one company could be identified in this field, namely Arcadis. Pure Construction: These companies do not offer any additional services except for construction. Specialists: This cluster includes construction companies that are specialised on a certain field of work. This can be tunnelling, as in the case of Lemminkainen, or dredging, as in the case of Boskalis Westminster. It should be analysed, whether these companies have any advantage from specialisation effects in these areas. In the next section, the clusters mentioned above are analysed regarding to their performance, both within the clusters and between the different clusters.

3.2 C o m p a r i s o n of M o d e l s Out of the 45 surviving construction companies which have been analysed, the majority is engaged in the building of roads (73.33%), tunnels (55.56%), residential housing (57.78%) and non-residential housing, namely the sectors office (57.78%), commercial (62.22%) and industrial (53.33%). Project Development is conducted by 37.78% of the construction firms. Due to the fact that most of the companies are engaged in more than one business segment, the sum of the percentage figures amounts to more than 100.0%. 20 In order to measure the performance of the different companies within a specific business segment cluster, the relative stock price developments over a time period of ten years, namely from March 14th, 1996 to March 13th, 2006, have been analysed and compared to each other. The relevant stock price data has been extracted from Datastream. The March 14th is 19 20

Cf. w.A. (2006e). Cf. Appendix II.

38

CHAPTER 2. LONG-TERM PERFORMANCE

used as the base date and the stock price development is measured in the percentage development based on the stock price on this particular date. Therefore, the performance development of the company stock prices can easily be compared to each other. The analysis of the percentage increase or decrease of the stock prices does not consider the different stock price levels or the trade currency of the construction companies in the sample. The following results have been observed. The group of generalists consists of seven companies, namely Balfour Beatty, B A M Group, Bouygues, Fomento, Gleeson, Morgan Sindall and Skanska. In this group the French construction company Bouygues shows a significant development as its stock price performance rises strongly within the years 1999 and 2000. In this time period, the stock price rose about 1200.00%, but declined afterwards during the years 2000 to 2002. After hitting its low point in 2003, it slowly recovered during the following three years. Morgan Sindall, B A M Group and Fomento show similar developments but on different levels. The stock prices of the three companies all hit their low point in 2002 and have risen significantly since then. Based on their values in 1996, Morgan Sindall's stock price has increased by 1164.95% in 2006, B A M Group's by 690.62% and Fomento by 640.81%. The cluster of generalists with project development activity consists of the following six companies: Allgemeine Baugesellschaft Porr, Caltagirone, Enka Insaat, Galliford Try, Grontmij and Rok Property Solutions. In this cluster Enka Insaat, a Turkish construction company, shows the most significant stock price development. Based on its value in 1996 the stock price has risen by 44650.0% within the last ten years. The other five companies show more decent results with performance between -36.22% and 1431.29%. The companies ACS, Eiffage, Impregilo, Obrascon and Vinci have been clustered into the group of generalists with activity in the concessions segment. In this cluster ACS shows the highest stock price performance over the last ten years. The price rose by 1704.09 %. Obrascon, Vinci and Eiffage also show positive stock price developments with total inclines of 948.19%, 714.54% and 313.11%, respectively. Compared to these results the development of Impregilo with a stock price increase of 32.19% seems rather low. The fourth group consists of companies, which are categorised as generalists but also conduct project development and concessions. This group consists of Acciona, Bilfinger Berger, Costain Group, Hellenic, Hochtief, Mota Engil, Sacyr Vallehermoso and Strabag. The construction company Acciona shows the strongest stock price development with an increase of 1431.29% over the last ten years. Hellenic shows a significant peak in the years 1999/2000 followed by a decline until 2003 and a slow recovery in the years 2004 and 2005. The German construction company Hochtief, however, shows a negative stock price development during the years 2001 to 2004. Only in 2005 the stock price begins to increase and results in a positive development of 73.68% based on its value in 1996. Hellenic and Mota Engil show decent but positive performance of increases by 261.03% and 226.67%. However, Costain Group's stock price performance results

3 PERFORMANCE ANALYSIS

39

in a decline by 36.22% in 2006 compared to its value in 1996 and Strabag only shows an increase of 3.63 % . 2 1 These results show how volatile the different stock price performance of the companies within one cluster can be. The group of companies, which consists of pure advisors for the construction industry only comprises one company, namely Arcadis - which has already been mentioned in the previous section. The stock price performance of Arcadis was rather volatile during the time period from 1996 to 2003 with several ups and downs in the percentage development. However, since 2003 the stock price has constantly improved and results in a relative increase of 411.95% based on its value in 1996. The cluster of the companies, which conduct pure construction activities, is the largest group of the identified clusters. It consists of ten different construction companies: Abbey, Ballast Nedam, Batenburg Beheer, J&P Avax, NCC Ά ' , NCC ' B \ Peab, Technical Olympic, Veidekke and Yit-Yhtyma. Technical Olympic shows the most significant stock price performance in the time between 1996 and 2006. The stock price rose by 3000.0% in the years 1999 and 2000 and declined afterwards until it reached its lowest point in 2001. Since then it has slowly recovered, but has shown a rather volatile development. Yit-Yhtyma has constantly performed positively and has risen by 1734.04% based on its stock price in 1996. J&P Avax shows a similar performance pattern to Technical Olympic with a significant peak in 1999 and a strong decline in 2000. However, the overall performance level is lower than that of Technical Olympic. Ballast Nedam, Batenburg Beheer and NCC Έ ' show rather negative performance with stock price losses and only weak recoveries within the year 2005. The last group, namely the cluster of specialists, comprises the companies Boskalis Westminster, Condurli, Hanson, Heijmans, Lemminkainen, Sopol, SSZ and Terna. Boskalis Westminster shows a similar stock price development to Technical Olympic and J&P Avax of the group of pure construction companies. The stock price rose strongly in 1999 and 2000, declined afterwards and has recovered since 2001. However, the development also shows a higher volatility. The stock prices of Lemminkainen, Terna, Heijmans and SSZ have slowly risen since 1996, resulting in an overall performance of 579.46%, 346.90%, 328.08% and 273.33%, respectively. However, the most significant stock price performance has been observed with Sopol. The stock price shows a very sharp performance pattern with sudden increases, such as on February 18th, 2000. On the prior day, the stock price had risen by 50.0% since 1996, but on February 18th it suddenly inclined by 622.0% based on its value in March 1996. A similar development can be seen on December 16th, 2005. On December 15th, the stock price had increased by 417.50% since the base year, however, on the following day it showed an increase by 1726.00% based on the same value. Possible explanations for these unusual developments will be given in the following sub-section. 21

Cf. w.A. (2006b), p. 16.

40

CHAPTER 2. LONG-TERM PERFORMANCE

Based on the stock price performance measured by the percentage increase or decrease of the stock price value on March 14th, 1996 a performance index has been calculated for each of the analysed clusters. For this purpose, the stock price performance of each company has been weighted by a certain percentage within the group based on the market capitalisation of the companies in March 2006 in order to consider the particular sizes of the companies included and to stress the outcome of the chosen strategy. Using the market capitalisation of 2006 leads to the fact that the influence of successful companies is over-weighted and the influence of unsuccessful firms is under-weighted. An alternative approach could have been used in order to measure the overall strategy development by using the market capitalisation of March 1996. Taken this approach into consideration the chosen figures dilute the result of the performance comparison. Therefore, the data sample can be considered as biased. The indices now show the overall performance result of the business model segment at the end of the business strategy and underline the most successful companies in the industry. 22 It is noticeable that the group of generalists, which also conduct project development, outperforms the whole group on a very significant level. The second most successful group is the one, which comprises consultants, and the third successful group is the one, which consists of generalists with concessions activities. The remaining four groups show relatively similar performance patterns with a clear increase in 2005. These outcomes result in the conclusion that the companies, which can be categorised as generalists with project development, show the most successful long-term performance as far as the development of stock price performance is concerned. None the less, the data set again has to be reviewed critically because it does not contain any companies that went bankrupt within the last ten years. The following sub-section gives an overview of possible explanations for these outcomes.

3.3 Geographic a n d Economic Differences as Premises for Stock Price Performance The indices of the different business model clusters show the average stock price performance of the entire group of successful firms. However, the results might be strongly influenced by the individual stock price development of one particular company. This, for example, is the case with Enka Insaat, a Turkish construction company, which has a significant influence on the performance of the cluster of generalists with project development. Furthermore, the unusual performance of some companies is a result of individual market developments in their specific home countries. Turkey, for example, has experienced a strong economic growth within the last years. Although its market growth is very volatile during the 1990s 22

Cf. Appendix III.

3 PERFORMANCE ANALYSIS

41

it always averaged 5.0% p.a. 2 3 Therefore, the local construction industry benefits from this economic upturn. The unusual performance of Technical Olympic, J&P Avax and Boskalis Westminster in the years 1999/2000 can be explained by the market boom of the New Economy and the resulting economic upswing. The performance of Technical Olympic, a Greek construction company, might also be based on the preparations for the Olympic Games in Athens in 2004. The economic recession after the reunification in Germany might be a possible explanation for the weak performance of the construction company Hochtief. However, this market development might only explain the performance result to a certain degree because the company operates internationally. A clear explanation for the significant stock price performance of Sopol can not be given either. Business News research shows that Sopol entered into strategic alliances with the Spanish construction firm Dragados in 2001 and that Dragados even offered an acquisition of Sopol on March 9th, 2004. 24 These dates, however, differ from the ones that show Sopol's unusual stock price increases. Since the data sample only includes surviving construction companies, which successfully implemented a certain business model, the following section focuses on the example of an unsuccessful company, which failed implementing the business model, which had been identified as the most successful one in the empirical study.

3.4 P h i l i p p H o l z m a n n A G In order to give an example of an unsuccessful case in a business model, Philipp Holzmann AG has been chosen. The German construction company was founded in 1849 in Frankfurt am Main. Right from the beginning Deutsche Bank owned a considerable stake in the construction company and also kept this stake after the IPO of Philipp Holzmann in 1917. The company started off as a railroad builder, but expanded its businesses into building construction and civil engineering. In the 1980s, the former CEO Lothar Mayer started an attempt to restructure the company and offer also services to the customers. 25 This was done by the acquisition of cooperations in the services sector, but also by the introduction of a project development division within the company. However, by establishing project development services, the company entered a completely unknown, complex and risky market. Only after a short time it became obvious that the expansion strategy had to be considered unsuccessful. The Project Development division had proven to be inefficient, since the risks of the business are higher than the mere construction industry risks. 26 Losses showed up especially in the operation business, as the cases Köln Arena and City-Carré in Magdeburg show. These were the first signs of the financial crises that became obvious two years later. In 1999, the company 23 24 25 26

Cf. Cf. Cf. Cf.

W.A. (2006d). w.A. (2001b), p. 9. w.A. (1999), p. 3. Hauch-Fleck (1999).

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CHAPTER 2. LONG-TERM PERFORMANCE

announced insolvency after a short recovery in the same year. 2 7 At this point of time the company faced financial distress, which amounted to DM Bill. 1.64. This debt was mainly caused by high pre-financing in project developments and by the fact that not cost-covering operators had been employed and that rent guaranties had been agreed o n . 2 8 The Philipp Holzmann AG did not measure the risk of project development adequately. The stock price decreases significantly as shown in Appendix I V . 2 9 After restructuring plans and governmental subventions failed, the company went bankrupt in November 1999. This example shows that the choice of the business model "Generalists with Project Development" does not always represent the right solution. If the risks included in the business of project development are not estimated carefully, it can lead to a great financial loss. Furthermore, the business model of a generalist with project development also includes several construction and real estate services, which require significant market knowledge. This knowledge is essential for the success of development projects. It seems obvious that the inclusion of companies which went bankrupt within the time frame used in the empirical study, such as Philipp Holzmann AG, would definitely have a strong influence on the outcome of the performance comparison. However, this study focuses on the companies which successfully operated according to specific business models. But the fact that bankrupt companies are not included should be taken into consideration when interpreting the results of the study. The following section focuses on possible explanations for the obtained results and describes the outcomes of some correlation and regression analyses which have been conducted in order to measure the relationship between the average stock price performance of a particular business model and the development of several macroeconomic factors.

3.5 P o s s i b l e E x p l a n a t i o n s In this section possible explanations for the development of the indices will be explained. A trial version of the statistical program Winstat has been chosen to conduct correlation and regression analyses. As independent variables the spot oil price, the long-term interest rate of the European Central Bank, the exchange rate of Euro and Dollar, the steel alloy price and the population growth in the European Union have been selected. 30 Oil prices and exchange rates affect the general economic situation and in this sense the situation of the construction industry in terms of order situation, margins and bankruptcies of construction companies and contractors. This is due to the fact that oil is the basis of many building 27 28 29 30

Cf. Cf. Cf. Cf.

Schäfer (2003), p. 67-69. Schäfer (2003), p. 69-71. Appendix IV. Appendices V-IX.

3 PERFORMANCE ANALYSIS

43

materials as for example asphalt. 31 However, as stated in Section 2.1.2, this effect can be smoothened by government intervention. An increase in population signifies an increase in demand for living space and hence the building orders for the construction industry. Interest rates are needed from commercial banks as an indicator for the calculation of interest rates for the financing of building projects. If interest rates increase, the demand for new projects will fall due to the rise in costs for the initiator. The steel price immediately influences the situation of construction companies, since it is needed as raw material in most building projects. The figures have been extracted from Datastream. Due to the availability of necessary figures, a time frame starting from November 11th, 1999 to December 30th, 2005 has been considered. In order to compare these factors to the construction industry indices, the relative development of the data has been calculated. A correlation analysis is a measure to evaluate the strength of a relationship between two factors. The range of possible outcomes lies in between -1 and 1. 1 as a perfect correlation means that the two factors tend to move up and down together. -1 as a negative correlation denotes an opposite movement of the two factors. A regression analysis identifies relationships between one dependent variable and one or more independent variables. It can be studied how much and in which way the dependent variable changes if the independent variables change by one unit. The regression equation illustrates the examined relationships. 32 In this case, linear equations have been used. The majority of the clusters show similar correlations and regressions with the exemption of generalists and specialists. Surprisingly, a high positive correlation for the majority of clusters with steel and oil price development can be stated. The range of correlation lies between 0.94 for oil and 0.96 for steel in the mere construction cluster and 0.86 for oil and 0.79 for steel in the project development cluster. A common result for the effects of oil price inclines is a recession of the economy since businesses and households have less disposable money for other goods than fuel and oil. However, there arguments that this is not the case since governments raise high taxes on oil that could be reduced in the case of an oil price increase. This would weaken the effects of the boost in oil prices. A further explanation could be the conversion of machines to alternative fuels. The industry is no longer dependent on the oil price development to such a high degree. 33 Another possible explanation might be based on the oil price data, which has been used in the study. The data includes overall and general indices without paying attention to individual country developments. Certain countries and also companies in these geographic regions - like Turkey with Enka Insaat - upgrade the index. Germany on the other hand is actually in recession and cannot oppose the development in booming economies. Furthermore, the countries with the highest demand for 31 32 33

Cf. Rosenthal (2005), p. 8. Cf. Backhaus/Erichson/Plinke/Weiber (2003), p. 46-47; Wooldridge (2003), p. 22-23. Cf. Maurer (2005/2006), p. 52.

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CHAPTER 2. LONG-TERM PERFORMANCE

oil and steel, such as the USA and certain Asian economies, are not taken into account in this survey. 34 However, the influence of these countries has only occurred within recent years. If a longer time frame had been taken into consideration, the result might have shown a negative correlation and regression factor as has been expected. This would be due to the missing demand influence of USA and Asia. Nevertheless, this could not be tested because of data constraints. The reasons for the positive correlation with the steel price development can be partially accounted for by the different business models. The only clusters that have a correlation that is not as high as of the other business segments are the groups of specialists and generalists. Particularly specialists are actively involved in heavy engineering, such as bridges and ports, needing high amounts of steel. A l l other clusters have a more diversified portfolio including services as for example waste management. Alternative materials can be a further explanation for the results achieved. Especially with a rise in steel prices many companies will reconsider concrete and brickwork for the supporting structures of the building. 3 5 In the following, we discuss the results of the correlation analyses and regressions. The results are reported in Appendix X-XI. Generalists as the first cluster show some flaring characteristics in the correlation section. The correlation with steel prices and oil prices (0.29 and 0.34) are lower than in the other clusters. Steel alloy is used in almost every building; therefore a high negative correlation could be expected. The oil price is not only a factor that influences the whole economy, but also the fuel prices. The building industry is a sector, which deploys many machines. For that reason oil and fuel are resources. These values supports the hypothesis which states that a rise in prices for oil and steel results in a decrease of performance for construction companies. Correlations with interest rates and exchange rates are positive (0.37 and 0.24), which does not seem to be reasonable because usually the stock price performance should decrease when interest rates increase because the cost of construction is higher. The regression equation shows the following relationships between the dependent variable and the independent variables: Generalists = 0.304 + 0.046 · Crude Oil + 0 · Population + 0.085 · Steel +0.025 · Exchange Rate + 0.312 · Interest

Rate

Although, according to David K. Foot demography explains two thirds of everything 36 the coherence with population equals zero, which can be explained by the data set used. The population in Europe is surveyed only once a year and the changes in population are minimal. Therefore, the relationship between the population development and the indices can be 34 35 36

Cf. Itec (2005), p. 32. Cf. w.A. (2001a), p. 1. Cf. Deutsche Bank Research (2004), p. 3.

3 PERFORMANCE ANALYSIS

45

categorised as insignificant. The R 2 value in this cluster is low with 0.49, and also the F value 3 7 with 344 is base. The cluster of the project developers performs similarly with regard to correlations. Oil price and steel price are also positively correlated with the index (0.86 and 0.79 respectively) but to a higher degree. The population correlation is not striking with 0.05, exchange rates and interest rates are negatively correlated (-0.64 and -0.63). This combination is reasonable, since a rise in interest rates and exchange rates connotes a slowdown in economic development and also in construction. The regression equation shows differences in the level of values: Project Development = 2.75 + 10.24 · Crude Oil + 0 · Population -3.18 · Steel + 19.59 · Exchange Rate - 20.4 · Interest

Rate

This equation shows some interesting factors such as the dependencies of steel price and interest rate. They are as expected clearly negative, but on the other hand the dependency of crude oil and the exchange rate is extremely high. One reason could be that project development represents one of the riskiest activities in the Real Estate and Construction Industry and heavily depends on external factors. 38 Correlations in the concessions cluster are similar to the project development cluster. Oil price and steel price are highly correlated, the importance of the correlation with the population is low and interest rates and exchange rates are negatively connected. The regression equation of the concessions cluster is as follows: Concessions = 0.327 + 0.085 · Crude Oil + 0 • Population + 0.3 · Steel +0.259 · Exchange Rate — 0.15 · Interest

Rate

This equation does not exhibit any remarkably high or outstanding values. The correlations in the cluster of generalists with project development and concessions look similar to the other groups. Oil price and steel price correlations are highly positive (0.82 and 0.94), the exchange rate and the 37

38

The F-Statistic reviews if the model, which has been used, is also valid for the whole population. The empirical F-value is compared to a theoretical value. This value can be obtained from statistical tables for different probabilities of error, e.g. Backhaus/Erichson/Plinke/Weiber (2003), p. 798. The relationship between the variables is significant if the empirical F-value is higher than the theoretical value, which according to the table in Backhaus et. al. is in this case 2.21. However, it is the other way around, the relationship is not significant. Cf. Backhaus/Erichson/Plinek/Weiber (2003), p. 68-72. Cf. Isenhöfer/Väth (2000), p. 175, 176

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CHAPTER 2. LONG-TERM PERFORMANCE

interest rate are again negatively correlated (-0.63 and -0.57). The regression equation however shows some negative values: Project Development and Concessions = 0.307 — 0.021 · Crude Oil +0 - Population + 0.216 · Steel - 0.003 · Exchange Rate - 0.044 Interest

Rate

The R 2 value is at 0.9 and the F value is extremely high, amounting to 3300. The correlations with oil price and steel price in the consultants cluster are 0.88 and 0.95. Population is not momentous, as is the case in every cluster, and interest rates and exchange rates are negatively correlated. The regression equation has the consecutive form: Consultants = -0.008 + 0.098 · Crude Oil + 0 · Population +0.93 · Steel + 1.588 · Exchange Rate - 0.442 · Interest Rate The negative axis intercept is striking in this case. The majority of the values is however similar to those already examined. The cluster of pure construction companies shows significant correlations with the steel alloy performance and the spot oil price performance of 0.96 and 0.94. The high correlation of oil and construction has to be seen against the background of the explanations mentioned above. Exchange rate and interest rates show a negative correlation of -0.57 and -0.52. The correlation between the construction index and the population development amounts to 0.07 and is therefore not significant. The result of the multiple regression analysis in this cluster is the following equation: Construction

Performance

= 0.042 + 0.049 · Spot CrudeOil

+0 · Population + 0.106 · Steel Alloy +0.196 · Exchange Rate — 0.09 · Interest Rate Although there is a relationship between the performance of construction companies and the oil and steel price, it is not very high. The model can be explained in 96.0% of all cases with a value of R 2 of 0.96. The F value is extremely high with 9024 accompanied by a Ρ value of 0. The Specialists index also has relatively low correlations with steel price and oil price developments (0.69 and 0.58). The correlations with interest rate and exchange rate have low negative values (-0.12 and -0.11). The regression equation adopts the following shape: Specialists = 0.057 - 0.007 · Crude Oil + 0 · Population + 0.072 · Steel +0.164 · Exchange Rate — 0.025 · Interest

Rate

The relationship to oil is negative in the regression analysis, reflecting the already explained characteristics of oil price movements. The R 2 value is higher, explaining 66.0% of the values, the F value is still high with 694.

4 SET OF OPPORTUNITIES

47

Despite all these findings, a final explanation for the success of the project development cluster cannot be given as far as macroeconomic factors in the EU are concerned. Therefore, it is most probable that the result is based on the significant development of one company in this cluster, namely Enka Insaat. Highly striking are the values in the regression equation, which show stronger dependencies upon the macroeconomic factors than the other clusters. Dependencies have generally speaking a negative impact on industries, since macroeconomic factors cannot be influenced. 39

4 D e r i v i n g a Set o f O p p o r t u n i t i e s for Performance I m p r o v e m e n t After analysing the types and relationships between the performance development of European construction companies and several macroeconomic factors in Section 3, this part of the paper focuses on different market drivers, which also influence the performance of construction firms. However, in contrast to macroeconomic factors which cannot be changed or influenced by single companies, the market conditions which are identified in this section, can be controlled individually.

4.1 Chances for t h e Positive Influence o n Performance Although globalisation is a world-wide trend which can not be influenced by one single company, firms still have the choice between following this trend and rejecting it. However, globalisation represents a significant opportunity for companies to influence their performance in a positive way because they can exploit new markets and can diversify their risk geographically. Global presence is of great advantage for construction companies because it allows offsetting and smoothening business cycles of national markets. Therefore, cyclical impacts on companies can be lowered due to the balance of international market presence. 40 Furthermore, the innovation of different finance tools and products in the banking sector can be seen as an opportunity for construction companies to become more independent from single investors and to find innovative and alternative finance solutions for their projects. Since the state as financier is no longer able to provide necessary resources for projects 4 1 and private contractors pay more and more attention to reducing their equity, 39

40 41

The Kolmogorov-Smirnov test has been used to verify the statistical model, which has been used. Since the p-values are very low, the Hl-Hypothesis is accepted and the HO-Hypothesis is rejected. Cf. Appendix XII. Cf. KPMG (2005), p. 17; Male/Mitrovic (1999), p. 10. Cf. Kaspereit (2002), p. 5.

48

CHAPTER 2. LONG-TERM PERFORMANCE

innovative project finance solutions with a certain share of equity, mezzanine, junior loan and senior loan finance, offer new solutions for project developers and construction companies. 42 Since construction companies are primarily service providers, they should always observe changing consumer needs and trends in order to meet their customers' expectations. Hence, market research is essential for construction companies. By conducting market studies, trends and threats can be foreseen and the companies can adapt to these changes easily. The improvement of market transparency and data availability, therefore, marks a significant opportunity for construction companies to find out about new market trends and customer needs and to adapt to these changes. This again, has positive effects on companies' businesses and, hence, their stock price performance. 43 Furthermore, innovation of technologies, machines and equipment plays a very important role for the success and the positive stock price performance of construction companies. New technologies can improve efficiency and might substitute manpower in some cases. On the other hand, new technologies are very capital intensive and, therefore, companies face a trade-off between the advantages they gain from new technologies and machines and the cost savings they gain by continuing to use the old equipment. 4 4 However, new technologies tend to improve the work and professionalism of construction companies, which might lead to a better stock price performance. Closing, the essential topic of this paper, namely the creation of different business models, is also identified as a positive driver for the performance of construction companies. The creation of individual business models, which either specialise in the building of particular constructions, such as tunnels or bridges, or diversify a company's product portfolio by offering consultancy services or complementary products, such as concessions or project development, offers companies the opportunity to differentiate themselves from market competitors. 45 Due to changing market environments and customer needs, construction companies are forced to provide more services than just simple construction. Moreover, the placing of building projects depends heavily on factors, such as public finance, interest rates or currency changes. Therefore, the serialisation on certain construction projects, which are financed by governments and are needed in society, can reduce the dependence on these factors. Alternatively, the risk can be reduced by diversifying product portfolios and by offering service packages in order to balance different market cycles. The creation of business models can have significant influences on the stock price performance of construction companies as has been described in section 3.2. However, besides the identified opportunities and chances construction 42

43 44 45

Cf. Male/Mitrovic (1999), p.18; nCrisp (2003), p.5; Rottke (2005), p. 591; Falk (2004), p. 712/713; Iblher (2005), p. 540; Spitzkopf (2002), p. 274ff. Cf. Male/Mitrovic (1999), p. 19. Cf. Male/Mitrovic (1999), p. 19. Cf. Treacy/Wiersema (1995), p. 38-39.

4 SET OF OPPORTUNITIES

49

companies also face certain risks and threats within changing market conditions. These threats can have negative impacts on companies' stock price performance but might be offset by certain actions.

4.2 T h r e a t s for t h e Positive Influence o n Performance As a counterweight to the previous sub-section the challenges to the construction industry shall be examined closer in this part of the paper. One of the main challenges in the construction industry is the successful recruitment of qualified workforce. Skilled workers are rare and most companies do not invest in training because competitors might poach their trained employees.46 Due to changing market environments and aggressive competition, construction companies must focus more and more on risk management. It is important to not only apply risk management in the forefront of a project but also during its conduction. Furthermore, they need to be able to assess the risk of projects in advance and adjust this assessment during the project if necessary. If risks are poorly forecasted they can result in reduced margins for the construction company and, therefore, in lower stock price performance, as has been shown in the case of Philipp Holzmann AG in Section 3.4. 4 7 Globalisation as a driver, which has already been mentioned in the previous sub-section, can also be identified as a threat to European construction companies because it does not only imply the exploitation of new markets but also the risk of foreign competition. Moreover, market entry barriers have to be overcome in order to enter international markets. 48 Since local market knowledge is essential in the real estate and construction industry, companies which enter foreign markets, have to ensure the availability of this data. This can only be achieved either by conducting alliances or mergers and acquisitions with partners in the respective country or by opening up subsidiaries. 49 Therefore, globalisation and the resulting exploitation of foreign markets can have positive and negative impacts on the stock price performance of construction companies. Furthermore, political influence on the construction industry must not be underestimated either. The construction industry is not only dependent on economic factors, such as oil prices and interest rates, but also on government policies. Institutional changes can affect the whole construction industry. Therefore, it is important that construction companies cultivate their communication with governments in order to be prepared for eventual changes. However, not only companies have to face the consequences 46 47 48 49

Cf. Cf. Cf. Cf.

Bosch/Philips(2003), p. 3. KPMG(2005), p. 7. Male/Mitrovic (1999), p. 9. Male/Mitrovic(1999), p. 18.

50

CHAPTER 2. LONG-TERM PERFORMANCE

of changes in regulations but also governments themselves. They also have to consider the impact on the business climate. 5 0 It can be summarised that construction companies have several options of reacting to certain market developments in order to influence their stock price performance in a positive way. Although the drivers and changes themselves can not be influenced, there are several ways of responding to these factors. The creation of certain business models leads to the fact that some companies can adapt easier to macroeconomic changes than others. Hence, the overall stock price performance of these companies might differ significantly, as shown in Section 3.2.

5 Conclusion and Outlook Referring to the purpose of this paper, namely the determination of the long-term performance of different business models in the European construction sector, it can be summarised that the group of construction companies, which offer general construction services including project development, has been identified as the one with the highest long-term performance over the last ten years. This result underlines the outcomes of a survey by KPMG, which shows that 62.0% of executives from European construction companies are of the opinion that construction companies will become broader based property organisations instead of retaining their pure construction focus in the future. 5 1 The creation of business models seems to be essential in the aggressive construction market. However, the significant performance of the group of generalists with project development can be explained by the remarkable performance of one group member, namely Enka Insaat. The survey from K P M G also states that companies, which specialise in transport and infrastructure construction will show the most significant growth within the next years. 52 Nevertheless, the success of the different business models can only partly be explained by the influence of macroeconomic factors, such as the change of oil and steel prices or the development of exchange rates and interest rates. Therefore, the performance of different business units, such as project development, could be compared to each other in a scientific research project in order to identify the influence of particular business units on the overall stock price performance of the company. Such a study could provide detailed information on the success of different business units. Once again it should be stated that the results of the data should be interpreted carefully, since the data does focus on surviving companies and does reflect the result of successfully implemented business models. Nevertheless, it can be summarised that European construction companies face more aggressive market conditions and need to find innovative solutions in order to remain competitive 50 51 52

Cf. nCrisp (2003), p. 3 KPMG (2005), p. 23. KPMG (2005), p. 22.

5 CONCLUSION AND OUTLOOK

51

and to positively influence their stock price performance. Although construction companies cannot influence macroeconomic factors, such as the development of interest rates, they can still affect their own performance by reacting to market changes, such as the innovation of new technologies and the ongoing trend of globalisation. Furthermore, the creation of business models and the resulting diversification of the offered product and service portfolio or the specialisation on certain construction projects might also overcome the effect of macroeconomic factors and might influence the stock price performance in a positive way. Additionally, the European construction industry is supposed to grow by another 1.5% in 2006 and by approximately 2.0% in 2007 and 2008 according to Euroconstruct. This market growth also opens new market opportunities for the companies. 53 Since the construction industry is deemed to be the motor of the overall economic performance, a strong performance development in this particular industry tends to affect the overall economic performance in a positive way. 5 4

53 54

Cf. w.A. (2006a), p. 2. Cf. Schulte/Hupach (2000), p. 5.

52

CHAPTER 2. LONG-TERM PERFORMANCE

References Backhaus, Klaus/Er ichson, B e r n d / P l i n k e , W u l f f / W e i b e r , R o l f ( 2 0 0 3 ) : Multivariate Analysemethoden - Eine anwendungsorientierte Einführung, 10th Edition, Berlin, Heidelberg, New York. B o s c h , G e r h a r d / P h i l i p s , P e t e r ( 2 0 0 3 ) : Building Chaos: An international comparison of deregulation in the construction industry, London. D e u t s c h e B a n k R e s e a r c h ( 2 0 0 4 ) : Aktuelle Themen: Demografie Speziai, in Themen International Economics, No. 294, 28.4.2004. D e u t s c h e r B u n d e s t a g ( 2 0 0 5 ) : Jahresgutachten 2005/2006 des Sachverständigenrates zur Begutachtung der gesamtwirtschaftlichen Entwicklung, Drucksache 16/65, 10.11.2005. Falk, B e r n d (1996):

Fachlexikon Immobilienwirtschaft, Köln.

Falk, B e r n d (2004): Köln.

Fachlexikon Immobilienwirtschaft, 3. Auflage,

F I E C (2005):

Construction activity in Europe.

H a u c h - F l e c k ( 1 9 9 9 ) : Verschleiert, verschwiegen, verraten, in: Die Zeit, Issue 48, 1999, call date: 11.05.2006. H o m a n n , K l a u s ( 2 0 0 0 ) : Bau-Projektmanagement, in: Schulte, Karl-Werner: Immobilienökonomie Band 1: Betriebswirtschaftliche Grundlagen, 2. Edition, München, 2000, p. 229-274. I b l h e r , F e l i x ( 2 0 0 5 ) : Systematisierung der Immobilienfinanzierung, in: Schulte, Karl-Werner: Immobilienökonomie Band 1: Betriebswirtschaftliche Grundlagen, 3. Edition, München, 2005, p. 534-540. I t e C (2005):

Euroconstruct summary report, Barcelona.

I s e n h ö f e r , B j ö r n / V ä t h , A r n o ( 2 0 0 0 ) : Projektentwicklung, in Schulte, Karl-Werner: Immobilienökonomie - Betriebswirtschaftliche Grundlagen, 2. Edition, München, 2000, pp. 151-228. J a n s s e n , J ö r n ( 2 0 0 0 ) : The European construction industry and its competitiveness: A construct of the European commission, in: Construction management and economics, No. 18, p. 711-720.

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K a s p e r e i t , S a b i n e ( 2 0 0 2 ) : Einführung, in: Wirtschafts- und sozialpolitisches Forschungszentrum der Friedrich-Ebert-Stiftung: Public Private Partnership - Mehr Qualität und Effizienz im öffentlichen Güter- und Dienstleistungsangebot, Bonn, 2002, p. 5-7. K i r s c h , D a n i e l a ( 1 9 9 7 ) : Public Private Partnership: Eine empirische Untersuchung der kooperativen Handlungsstrategien in Projekten der Flächenerschließung und Immobilienentwicklung, Köln. KPMG maker.

(2005):

Global construction survey 2005: Risk taker, profit

L e e , K i s e o k / N i , S h a w n ( 2 0 0 2 ) : On the dynamic effects of oil price shocks: A study using industry level data, in: Journal of monetary economics, Vol. 49, Issue 4, p. 823-852. M a l e , S t e v e n / M i t r o v i c , D r a g a n a ( 1 9 9 9 ) : Trends in world markets and the LSE industry, in: Engineering, Construction and Architectural management, No. 1, p. 7-20. M a u r e r , P e t e r ( 2 0 0 5 / 2 0 0 6 ) : Abkehr vom Öl- lohnen sich alternative Energien?, in: Immobilienwirtschaft, No.l2/2005-No. 1/2006, p. 52-56. n C R I S P ( 2 0 0 3 ) : Developing a business model for UK construction, , call date: 20.4.2006. Porter, M . (1996):

Wettbewerbsvorteile, Frankfurt.

R o s e n t h a l , A l a n B . ( 2 0 0 5 ) : Can this profit margin be saved? Inflation in the costs of building materials in: Real Estate Finance, w. No., October 2005, p. 8-10. R o t t k e , N i c o ( 2 0 0 5 ) : Real Estate Private Equity: Finanzierung durch externes Eigenkapital, in: Schulte, Karl-Werner: Immobilienökonomie Band 1: Betriebswirtschaftliche Grundlagen, 3. Edition, München, 2005, p. 591-598. S c h ä f e r , D o r o t h e a ( 2 0 0 3 ) : Die "Geiselhaft" des RelationshipIntermediärs - eine Nachlese zur Beinah-Insolvenz des Holzmann-Konzerns, in: Perspektiven der Wirtschaftspolitik, Vol. 4/Issue 1, p. 65-84.

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S c h u l t e , K a r l - W e r n e r / H u p a c h , I n g o ( 2 0 0 0 ) : Bedeutung der Immobilienwirtschaft, in: Schulte, Karl-Werner: Immobilienökonomie Band 1: Betriebswirtschaftliche Grundlagen, 2. Edition, München, 2000, p. 5-12. S c h w a r z , S t e f f e n ( 1 9 9 7 ) : Zukunftssicherung für die Bauwirtschaft: in vier Schritten aus der Krise, Wiesbaden. S p i t z k o p f , H o r s t A l e x a n d e r ( 2 0 0 2 ) : Finanzierung von Immobilienprojekten, in: Schulte, Karl-Werner /Bone-Winkel, Stephan: Handbuch Immobilien-Projektentwicklung, 2. Edition, Köln, 2002, p. 257-285. Straub, Stephane/Laffont, Jean-Jacques/Guasch, J. Luis ( 2 0 0 5 ) : Infrastructure Concessions in Latin America: government led renegotiations, in: World Bank: Policy Research Working Paper Series, No. 3749. T i m m e r s , P . ( 2 0 0 0 ) : Electronic commerce: strategic and models for business-to-business trading, Chichester. Treacy, M . / W i e r s e m a , zur Spitze, Frankfurt.

F.

(1995):

Marktführerschaft:

Wege

W.A. ( 1 9 9 9 ) : Schwarze Zahlen bei Holzmann, in: Immobilienzeitung, 15.7.1999, News, p. 3. W . A . ( 2 0 0 1 a ) : Wirtschaftliche Lösungen mit Stahl - Geschoßbauten, Chttp: / / w w w . it i. tuwien.ac.at/download/archiv/ss03 / modul/D571-Wirt schaftlicheLoesungen.pdf>, call date: 12.5.2006. W.A. ( 2 0 0 1 b ) : Dragados increase the growth of its Portuguese subsidiary, in: Financial Times, 25.7.2001, p. 9. W.A. ( 2 0 0 6 a ) : Bauwirtschaft in Europa: Deutschland kommt wieder, in: Immobilienzeitung, 16.3.2006, News, p.2. W.A. ( 2 0 0 6 b ) : "Unser Kerngeschäft ist das Bauen", in: Frankfurter Allgemeine Zeitung, 1.4.2006, Unternehmen, p. 16. W.A. ( 2 0 0 6 c ) : Weltwirtschaft in guter Verfassung, in: Frankfurter Allgemeine Zeitung, 20.4.2006, Wirtschaft, p . l l . W.A. ( 2 0 0 6 d ) : Türkei Wirtschaft: Aktuelle wirtschaftliche Lage, , call date: 6.5.2006.

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W.A. ( 2 0 0 6 e ) : Vinci: Unternehmen: Konzessionen, , call date: 12.05.2006. W e i f e n s , P a u l ( 2 0 0 3 ) : Überwindung der Wirtschaftskrise in der Eurozone: Stabilitäts-, Wachstums-, und Strukturpolitik, Wuppertal. Wentz, Martin/BischofF, Thorsten/Gosewehr, Dörthe ( 2 0 0 5 ) : Stadtentwicklung durch Public Private Partnership, in: Schulte, Karl-Werner: Immobilienökonomie Band 3: Stadtplanerische Grundlagen, München, 2005, p. 805-833. W o o l d r i d g e , Jeffrey M . (2003): Modern Approach, Mason, USA.

Introductory Econometrics - A

56

CHAPTER 2. LONG-TERM PERFORMANCE

Appendix

Appendix I: Definition of Construction Industry

Construction Industry Construction Industry in the broader Sense

Construction Industry In the narrow Sense

i

i

Construction conducting Company

Main Construction Trade:

Planning Companies

• Préfabrication and Montage

• Civil Engineering

• Architecture and Engineering Companies

• Chimney and Industrial Furnace • Fettling • Carpenter • Roofing

Construction Incorporations

Finishings: • Construction Installation

Partly Producers of

• Glazier

• Building Materials

• Painter

• Construction Machinery

• Paperhanger

• Construction Materials

• Cabinetmaker • Parquet Reclincr • Dry Construction

Source : Falk (1996), p. 111.

APPENDIX Appendix Ils Business segments 80.00%

Appendix III: Business Model Index

Year Construction Index — Generalists with Concession Index — Generalists Index — Generalists PD & Concess.

Generalists with PD Consultants Index Specialists Index

CHAPTER 2. LONG-TERM PERFORMANCE

58

Appendix IV: Holzmann, Stock Price Performance 40.00%

σ\ o o o o o o o o - - O S ^ C S O S O i O S O O O O O O O O Year

Appendix V: Brent Crude Oil Price

APPENDIX Appendix VI: long-term Lending

1999

2000

2001 2002

2003

2004

2005

Year

Appendix VII: Exchange Rate Euro/Dollar

Year

2006

CHAPTER 2. LONG-TERM PERFORMANCE Appendix VIII: Steel Price Development 250.00% 200.00% „ 150.00% υ S Ë 100.00%

f 50.00% 0.00%

— — g( g g On 0\ © — — ' ( N ( N ( N rs|< -50.00%

N N mgm g T t gT tgt ngi n gf g "( N ( N ( N ( N f N ( N N g 2.50% « > 2.00%

I

1.50% 1.00% 0.50% 0.00% 8s

8s

£ Year

Generalists Index

Consultants Generalists Generalists Index with Conwith PD cession Index 0.89*** 0.92*** 0.86*** 0.09*** 0.07*** 0.05** 0.95*** 0.96*** 0.79*** -0.46*** -0.59*** -0.64*** -0.43*** -0.53*** -0.64***

Appendix X: Pearson Correlation

Generalists PD & Concess.

. SpotCrudeOil 0.58*** 0.82*** 0.34*** Population 0.09*** 0.07*** 0.04* Steel Alloy 0.69*** 0.94*** 0.29*** Euro-Dollar -0.11*** -0.64*** 0.24*** Interest -0.13*** -0.58*** 0.37*** *, **, *** denote significance at 10%, 5 and 1% level.

Specialists Index 0.94*** 0.07*** 0.96*** -0.57*** -0.52***

Construction Index

APPENDIX

Specialists Index

Generalists Index

Appendix XI: Regression Results Generalists PD & Concess.

Consultants Generalists Generalists Construction Index with Conwith PD Index cession • Index Constant 0.06*** 0.31*** 0.3*** ^ÖOl 0.33*** 2.75*** 0.04*** (27.49) (79.10) (30.52) -(0.57) (48.39) (5.63) (19.46) SpotCrudeOil -0.01*** -0.02*** 0.05*** 0.1*** 0.09*** 10.25*** 0.05*** -(6.86) -(9.85) (8.26) (11.90) (22.75) (37.74) (40.64) Population 9.82E-08* 6.46E-08 7.30E-08 1.07E-06** 2.40E-07 1.23E-05 8.33E-08 (1.79) (0.63) (0.28) (2.75) (1.36) (1.00) (1.45) Steel Alloy 0.07*** 0.22*** 0.08*** 0.93*** 0.3*** -3.19*** 0.11*** (34.73) (55.88) (8.55) (63.42) (44.69) -(6.57) (49.08) Euro-Dollar 0.16*** 0 0.02 1.59*** 0.26*** 19.6*** 0.2*** (20.15) -(0.21) (0.63) (27.59) (9.84) (10.28) (23.14) Interest -0.02*** -0.04*** 0.31*** _0.44*** -0.15*** -20.4*** -0.09*** -(6.76) -(6.42) (17.88) -(17.12) -(12.67) -(23.87) -(23.61) F-Value 694.72 3,300.59 344.78 5,705.49 5,487.94 1,727.36 9,024.34 2 R 0.66 0.90 0.49 0.94 094 083 0.96 NB: t-values in parenthesis; *, **, *** denote significance at 10%, 5 and 1% level.

y-variable

62 CHAPTER 2. LONG-TERM PERFORMANCE

APPENDIX Appendix XII: Test of Normal Distribution Construction Index Generalists with P D Generalists with Concession Index Consultants Index Generalists Index Specialists Index Generalists P D L· Concess. SpotCrudeOil Population Steel Alloy Euro-Dollar Interest *, * * , * * * denote significance at 10%

KS-Test 0.23***

Chi-Quadrat 2267.96*** 0.06*** 299.25*** 0.24*** 2092.74*** Q 24*** 5944.47*** 0.1*** 463.83*** 0.09*** 775.52*** 0.21*** 3361.72*** Q 2*** 1242.65*** 0.51*** -1.23E-59*** 0.38*** 3747.9*** 590.45*** Q H*** 1005.45*** 0.18*** 5% and 1% level

CHAPTER 2. LONG-TERM PERFORMANCE

Chapter 3

Multiples Valuation in the European Construction Industry Christian Βöhmfi /Stephan FreudÛ

t

EUROPEAN BUSINESS SCHOOL (ebs), International University Schloß Reichartshausen

65

CHAPTER 3. MULTIPLES VALUATION

66

Contents 1 Introduction

. . .

2 Approach of the Study

66 67

3 Theoretical Review

67

4 Comparable Company Valuation

70

4.1 D a t a Sample and Hypotheses 4.2 Financial Value Driver Analysis

70 ...

70

4.3 M u l t i p l e Valuation

73

5 Precedent Transactions Valuation

75

5.1 D a t a Sample and Hypotheses 5.2 Financial Value Driver Analysis

75 ...

5.3 Transaction Segmentation Analysis

75 .

77

6 Concluding Remarks

80

References

82

Appendix

83

1 Introduction The European construction industry is deeply established in national economies and has a long and fruitful tradition across European countries. Its important economical role becomes obvious with regard to employment aspects and its contribution to national gross domestic products (GDP). Although, the industry is considered to be a rather slow growing and conservative business, major developments and changes have recently occurred and affect the structure of the industry. Over the last ten years construction companies across Europe are constantly seeking to increase and diversify their business scope and activities by tapping into the market of construction related services. As risk and cyclically are industry specific characteristics, the trend towards discovering new sources of revenues has led companies to acquiring services firms to broaden their business scope. Services like facility management or post construction project maintenance represent attractive and profitable opportunities for diversification. For this reason the construction industry is passing through a consolidation process that is characterised by increased M & A activities. Especially the expansion of the European Union opened up new geographic markets offering companies the chance to reposition themselves in major growth markets. Hence, it may be assumed that the industry trends have a substantial influence on the value of construction companies in the capital markets.

2 APPROACH OF THE STUDY

67

Multiples valuation therefore is a powerful, widely acknowledged technique that helps determining companies' value. Within the last two decades multiples valuation techniques have increased in importance and become commonly used and accepted. Thus, it seems reasonable to conduct an analysis that addresses the construction industry by means of multiples valuation to figure out what trends and developments as well as specific value determinants exist in the European industry.

2 Approach of the Study After this general overview of the construction industry and the inherent trends has been illustrated in the introductory part, the further analysis emphasises on multiples valuation techniques which are widely acknowledged and used in practice to examine industry related valuation characteristics. Starting with a summarising section of theoretical aspects of multiples valuation, it is intended to provide a profound understanding of the valuation techniques applied in this study. The analytical approach of the study is based on the comparable company valuation as well as the comparable transactions analysis which are then carried out and further broken down to derive meaningful results for the construction industry. This is done by analysing the effects of key financial value drivers like sales or EBITDA, as well as a country specific examination with regard to the comparable company valuation in Section 3. During the course of the study regression analysis as a statistical tool are applied to analyse how the respective multiple values can be explained. The same course of analysis is then applied to the precedent transactions analysis whereas a further segmentation with regard to the deal type, the ownership form and the target's business activities is carried out. This should answer the question which value is assigned to cross-border M & A activities, private and public targets as well as services or construction related targets before the main results are finally summarised at the end of the study.

3 T h e o r e t i c a l R e v i e w of M u l t i p l e V a l u ation The objective of valuation is to determine the true value of a company. Determining this value a number of different valuation methods is offered. The most common ones in practice are as follows. On the one hand, the cash flow based methods like the discounted cashflow analysis and the leveraged buy-out analysis. And on the other, hand multiples which are a relative valuation method reflecting the value of a company as perceived by the capital markets. 1 1

Cf. Achleitner (2000), p. 174.

68

CHAPTER 3. MULTIPLES VALUATION

In general, multiples can be distinguished in trading and transaction multiples. Trading multiples value the company based on a range of publicly listed companies which show similar characteristics regarding industry, business activities and financials. In contrast, transaction multiples value the company on prices that were paid in historical transactions. For this reason transaction multiples include a premium that was paid in the transactions for expected synergies that could be realised from the combination of complementary product ranges or business models. In the following only the relative valuation concept of multiples valuation will be explained as it is the basis for this study. W i t h regard to multiples valuation the differentiation between equity value and enterprise value is essential as some multiples lead to an equity value whereas others lead to an enterprise value dependent on their level of preference in the capital structure. 2 Multiples relating to an equity value are drawn from operating figures that are only relevant for the equity holders (EBT, net income) whereas operating figures interesting for capital providers (EBITDA, EBIT and sales) are the basis for multiples leading to an enterprise value. Multiples valuation has become very common in practice and is quite familiar to investors, banks and management. The reasons are that the concepts are relatively easy to use as they do not require such a high level of financial information in comparison to other valuation methods. Moreover, multiples valuation allows for receiving a relative value of the company by which the market view on the company is also incorporated. The equity value only represents the value of the ownership interest in a company represented by its market capitalisation and serves as the basis for the calculation of the enterprise value. 3 To derive the enterprise value net debt and minorities at market value have to be added to the equity value. Net debt comprises the sum of all interest bearing short- and long-term debt, pension provisions (if seen as debt), redeemable preferred stocks and financial leases from which excess cash, cash equivalents and marketable securities have to be deducted. 4 In general, the multiples valuation procedure consists of five steps. First of all, adequate comparable multiples have to be selected for which the respective operating figures are considered. The main factors are sector, business model, size, profitability and growth. Extraordinary charges, proceeds and cash flows are then excluded in a second step before minimum and maximum as well as mean and median are calculated. Then, extraordinary multiples will be adjusted to avoid a distortion of the results so that the multiple ranges from minimum to maximum will be finally set up and multiplied with the respective financial figure of the company that is regarded. The most important multiples used in literature are the sales, EBITDA, Cf. Hunt (2003), p. 62. Cf. Ross/Westerfield/ J affé (2002), p. 816ff. Cf. Appendix I.

3 THEORETICAL

REVIEW

69

EBIT and cash flow multiple as well as the Price/Earning ratio (P/E). 5 The Sales Multiple value relates the enterprise value of the firm to its sales. It is very useful when all companies have the same profitability or negative operating figures like for example early stage companies or startups. For this reason it is used as a benchmark for transactions in some industries. This assumption represents the main drawback since the multiple is highly dependent on comparable profitability margins as it contains no implication on it. Sales, on the other hand, are not the best proxy for future profitability. Due to these drawbacks Benninga/Sarig (1997) question the validity and usefulness of the sales multiple as a valuation metric. 6 The EBITDA Multiple value relates the enterprise value of the firm to its earnings before interests, taxes, depreciation, and amortisation. This multiple has become very popular among analysts in recent years. 7 The main requirement for the application of this multiple is the existence of positive EBITDA figures. The multiple has a strong operational focus and is independent of leverage and capital structure as well as diverging depreciation and amortisation (D&A) accounting policies. For this reason it is a good measure for companies in cyclical industries. But, this exclusion may lead to a distortion due to differences in capital expenditures, country specific taxes and leverage. The EBIT Multiple value relates the enterprise value of the firm to the earnings before interests and taxes of the firm. This multiple features similar characteristics as the EBITDA multiple with the exception that it is also influenced by D&A. For this reason an application of the EBIT multiple in comparison with the EBITDA multiple only seems reasonable in cases with low D & A figures due to high leasing or outsourcing ratios. The P / E ratio relates the equity value of the firm to its net earnings. It is one of the most frequently used multiples as it includes the earnings that are distributed to the shareholders. However, some major drawbacks also exist as the earnings can be affected by various aspects. Effects on the multiple can arise from differences in D & A policies, capital structures and taxes. 8 Furthermore, earnings can be affected by earnings generated through non-core businesses and especially for high growth companies the multiples are highly volatile as earnings are very unstable. Finally, the cash flow multiple relates the equity value of a firm to its operating cash flow. As the importance of the firm's cash flows has become more and more critical as a measure for business profits, the application of the cash flow multiple has increased. The advantage is that it neglects any differences in accounting methodologies and focuses only on the cash that a company generates. On the other hand, the calculation of the cash flow requires the most amount of information as there are potentially inconsistent definitions of cash flows across firms. Furthermore, it is a dynamic Cf. Cf. Cf. Cf.

Appendix II. Benninga/Sarig (1997), p. 326. Damodaran (2002), p. 501. Bodie/Kane/Marcus (2002), p. 576-577.

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metric in the sense of a flow-figure and tends to be volatile so that in terms of a static multiple valuation the value loses explanatory power. However, the existence of some major drawbacks cannot be denied. Multiples valuation is generally a static analysis as only data of one point in time is used for the calculation. Besides, another problem arises in finding adequate comparable companies for the valuation as every company has unique characteristics that have to be taken into account. Another difficulty emerges when comparing companies across countries by means of multiples as there are different accounting policies, in particular regarding depreciation, amortisation, and tax policies. Furthermore, value drivers like return on capital employed (ROCE) or cost of capital are not taken into account explicitly. Finally, multiples are not very stable over time but rather volatile as they always include the most recent information and are therefore heavily influenced by volatility of sales and earnings, M & A activity, management changes or result announcements.

4 Comparable Company Valuation 4.1 D a t a Sample a n d Hypotheses For the comparable company valuation a data sample comprising 105 construction companies represents the basis for the analysis. Financial data taken from Datastream in terms of key financial figures like sales, EBITDA, EBIT or net debt are regarded in the time period from 1985 until 2005. The aim of the analysis is to answer the following questions: • What general trend becomes observable with regard to the development in multiple values over the last twenty years? • Which relation exists between a company's size in terms of sales volume and the multiple values? • What is the relation between a company's profitability in terms of EBITDA margin and the respective multiple values? • Which differences exist in multiples valuation across European countries?

4.2 F i n a n c i a l Value D r i v e r Analysis As previously stated, the European construction industry undergoes a consolidation process that is characterised by increased M & A activities. Hence, one may infer that this might have an impact on the valuation as growth gets usually rewarded by the capital markets. Therefore, the development of sales, EBITDA, EBIT and P / E multiples is depicted in Figure I to assess if a general valuation tendency may be observed. In general, a parallel development of the curves over time becomes evident which proves the interrelatedness of the multiples. Even more important is the fact that a slight increase in the multiple values, especially

71

4 COMPARABLE COMPANY VALUATION

Figure I: Valuation Overview of European Construction Companies 1985 - 2 0 0 5

Year I

- - EV/Sales

- - - EV/EBITDA

- -EV/EBIT

—P/E

for the enterprise value multiples, can be observed. In other words, capital markets consider construction companies more valuable in the course of the time. To find an explanation for this fact general industry trends have to be considered. Construction companies are increasingly diversifying their business activities during the last decade by enlarging their business scope. Although the traditional construction activities in residential and non-residential building, civil engineering and renovation are still prevailing, construction companies becomes more and more active in the service sector. Facility management services are gaining in importance for construction companies as service activities after the actual construction process turned out to be an attractive and profitable source of revenues. Another explanation for the slight increase in this overall valuation can be seen in other diversification efforts that are consistently pursued by construction companies. Especially larger companies tend to diversify internationally by mergers and acquisitions when national home markets are considered to be saturated or unattractive due to unfavourable economic developments. Moreover, in comparison to other industries it becomes obvious that the construction industry is characterised by lower multiple values as construction is a rather conservative industry with slow growth opportunities. This is underlined by the fact that the European economy as a whole is growing at a faster rate than the construction industry. 9 To further proceed in the analysis, the impact of financial value drivers on multiples is assessed. Therefore, the question is addressed how the size of a company in terms of its sales volume is decisive for multiple values. To Cf. Euroconstruct (2005), p. 57.

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a t t a i n a meaningful classification, categories are set u p t o achieve an equal allocation of t h e companies. "Small-sized" companies w i t h sales less t h a n € B i l l . 1 were grouped together, t h e remaining were allocated i n categories w i t h sales between € B i l l . 1-5 a n d above € B i l l . 5. I t should be noted t h a t the measures taken t o classify according t o t h e companies' size do not refer t o t h e t r a d i t i o n a l size classification criteria and o n l y serve for o b t a i n i n g a more or less equal allocation of t h e analysed companies. T o see whether changes i n t h e composition of the sample had taken place, the E B I T D A multiples have been calculated for t h e years 2000 a n d 2005 respectively. T h e results i n Figure I I show t h a t substantial v a l u a t i o n differences exist between smaller a n d larger sized companies. W h i l e companies w i t h sales less t h a n € B i l l . 1 have m u l t i p l e values of 3.5x i n 2000 and 2.5x i n 2005, m e d i u m a n d larger sized companies a t t a i n multiples t h a t are almost twice as high. T h u s , c a p i t a l markets seem t o reward larger companies w i t h higher m u l t i p l e values A n o t h e r result t h a t becomes obvious is represented by the fact t h a t a change i n t h e composition of t h e i n d u s t r y towards larger sized companies m i g h t have taken place. W h i l e i n t h e year 2000 fifty six companies belonged t o t h e group w i t h sales less t h a n € B i l l . 1 t h e number decreased t o forty eight i n 2005. O n the other hand, t h e number of the companies i n the segments w i t h sales between € B i l l . 1-5 a n d above € B i l l . 5 increased. T h i s once again underlines t h e consolidation process i n t h e European cons t r u c t i o n industry. T h e same analysis has been carried o u t w i t h regard t o the i m p a c t of p r o f i t a b i l i t y i n terms of E B I T D A margin. Here, t h e respective ranges for the classification have been set t o three categories encompassing E B I T D A

4 COMPARABLE COMPANY VALUATION

73

margins less t h a n 10%, between 10 and 20% and above 20%. T h e results t h a t can be derived are i n line w i t h general findings t h a t more profitable companies a t t a i n higher m u l t i p l e values since c a p i t a l markets reward efficient operations and good profit m a r g i n s . 1 0 I n order t o test, whether a linear relationship between size (sales) and p r o f i t a b i l i t y ( E B I T D A margin) a n d t h e m u l t i p l e value exists, several correl a t i o n analyses and regressions w i t h t h e m u l t i p l e as t h e dependent variable are conducted for t h e year 2000 and 2005. T h e results showed a slight negative correlation (only for p r o f i t a b i l i t y in 2000 correlation is positive) and a m a x i m u m R 2 . T h e coefficient of d e t e r m i n a t i o n t h a t measures how much of t h e variation of t h e dependent variable is caused by changes i n t h e independent variable, is 0.036 (analysis w i t h E B I T D A m a r g i n a n d sales as independent and E B I T D A m u l t i p l e as dependent v a r i a b l e ) . 1 1 These q u i t e low values show a low explanatory power of t h e chosen financials as value drivers. Therefore, various other factors t h a t have an influence o n t h e value of t h e m u l t i p l e must exist and w i l l be assessed i n t h e following.

3.3 M u l t i p l e V a l u a t i o n across E u r o p e a n Countries So far, valuation differences are shown w i t h regard t o different financial value drivers. A s one m i g h t expect from t h e ongoing European integration towards one c o m m o n market t h e question arises whether c o u n t r y specific v a l u a t i o n differences exist. Hence, t h e companies i n t h e d a t a sample are clustered w i t h regard t o t h e i r home c o u n t r y and t h e respective multiples are calculated. A s t h e d a t a sample o n l y provides meaningful results for t h e larger European economies, smaller countries are not considered. Thus, Figure I I I only depicts t h e multiples for Germany, France, U K , Spain, a n d Italy. I t should be noted t h a t t h e most meaningful results are a t t a i n e d b y measuring t h e development of t h e E B I T D A m u l t i p l e since sales multiples are affected by t h e c y c l i c a l l y of revenues. A s shown i n Figure I I I v a l u a t i o n differences i n t h e construction i n d u s t r y m i g h t be observed across t h e m a j o r European economies. W h i l e Germany, U K and France range i n t h e " m i d field" regarding t h e i r multiples, Spain clearly dominates this group since t h e last seven years. T h e I t a l i a n construction companies on t h e other hand are characterised by significantly lower multiples over t h e last fifteen years. T o figure o u t w h y these differences across Europe exist a variety of factors has t o be taken i n t o account. I t could be assumed t h a t t h e m u l t i p l e values are affected by t h e economic development of t h e respective country. However, t h i s holds o n l y p a r t i a l l y t r u e since other factors like t h e movements i n construction prices, t h e gross domestic fixed c a p i t a l formation, unemployment rates or i n d u s t r y specific g r o w t h rates have t o be considered as well. 1 0 11

Cf. A p p e n d i x I I I - V I . Cf. A p p e n d i x V - X .

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CHAPTER 3. MULTIPLES VALUATION Figure III: Geographic Multiple Segmentation by Country

Year France

—Germany

---Italy

UK

—Spain

Hence, Spain's strong position i n t h e multiples v a l u a t i o n m i g h t be explained b y t h e fact t h a t t h e Spanish economy is considered as a " h o t s p o t " due t o h i g h g r o w t h expectations for t h e n a t i o n a l construction industry, a high a n d s t i l l increasing gross domestic fixed c a p i t a l formation as well as increasing construction prices a n d an above average G D P growth. I n contrast, Germany, U K and France have a relative h i g h gross domestic fixed c a p i t a l formation w h i l e i n d u s t r y specific expectations are rather moderate or pessimistic. However, i t should be noted t h a t G D P g r o w t h expectations vary across these three countries. W h i l e Germany's economy is expected t o grow only moderately, g r o w t h rates i n U K are above average a n d go i n line w i t h low unemployment rates. I t a l y ' s weak position m i g h t be explained by under average g r o w t h expectations for t h e construction i n d u s t r y a n d t h e G D P i n general. Negative expectations i n t h e new b u i l d i n g construction sector underline t h e lower valuation. A further weakness of I t a l i a n construct i o n companies becomes evident w h e n regarding t h e i r efforts t o diversify internationally. A s t h e precedent transactions analysis i n t h e next section shows, I t a l i a n construction companies suffer a lack of cross-border M & A activities. W h i l e t h e m a j o r i t y of large, p u b l i c l y listed construction companies i n Europe generate a high percentage of t h e i r revenues abroad, t h i s broad a n d i m p o r t a n t geographical setup is missing for I t a l i a n construction companies.

T h e finding t h a t a variety of factors determine t h e actual m u l t i p l e values is i n accordance w i t h t h e results f r o m t h e performed regression analysis.

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75

5 Precedent Transactions Valuation 5.1 D a t a Sample a n d Hypotheses T h e precedent transaction analysis has been carried out by using a given d a t a sample of 447 transactions i n t h e t i m e period from 1985 t o 2005. T h e transactions t o o k place a l l over Europe w i t h a concentration o n three countries: U n i t e d K i n g d o m ( U K ) , Spain and France. Regarding t h e acquirer, 136 buyers are located i n t h e U K , 72 i n Spain and 63 i n France. W i t h respect t o targets, 148 can be a t t r i b u t e d t o U K , 64 t o Spain a n d 60 t o F r a n c e . 1 2 T h e sample comprises relevant financials of t h e transactions like target sales, E B I T D A , E B I T or transaction values. However, only 128 transactions could be used for t h e multiples v a l u a t i o n since t h e necessary financial d a t a for deriving a m u l t i p l e value is not available for t h e remaining transactions. Further adjustments t o t h e d a t a sample are made similar t o t h e comparable companies v a l u a t i o n analysis as m i n i m u m and m a x i m u m levels were set t o avoid t h a t t h e results were distorted by outliers. T h e respective levels were O.lx - 15.Ox for sales multiples, l.Ox - 40.0x for t h e E B I T D A multiples, a n d 2.0x - 50.0x for t h e E B I T multiples. These q u i t e broad ranges were used t o ensure a representative result w i t h o u t heavily narrowing the number of used transactions. Analogous t o t h e comparable companies analysis, t h e analysis concentrates o n t h e E B I T D A as t h e most adequate multiple. T h e purpose of t h e analysis is generally t h e depiction of transaction specific characteristics a n d encompasses t h e following questions: • W h a t general t r e n d becomes observable w i t h regard t o t h e development in multiples values over t h e last t w e n t y years? • W h a t is t h e i m p a c t of financial analysed transaction multiples?

value drivers on t h e value of t h e

• W h a t differences exist i n multiples between n a t i o n a l and cross-border transactions? • W h a t differences i n multiples become obvious w h e n d i v i d i n g t h e transactions i n t o public and private targets? • W h a t results can be d r a w n by d i v i d i n g t h e targets according t o their business focus i n t o services and construction related transactions?

5.2 F i n a n c i a l Value D r i v e r Analysis T h e m a j o r a i m of t h e s t u d y is t o analyse possible impacts of t h e already described consolidation process w i t h regard t o transaction multiples. One of t h e basic assumptions is t h a t t h e increased M & A activities drive t h e m u l t i p l e and leads t o higher values. T h e development of t h e curve i n Figure I V shows a constant increase i n t h e value of t h e multiples. T h i s increase is characterised b y an u p and d o w n 12

Cf. A p p e n d i x X I .

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Figure I V : Overview of Transaction Multiples and N u m b e r of Transactions

Corresponding

14 12 10 8 6 4

2 0 1988

1990

1992

1994

1996

1998

2000

2002

2004

Year •

Number of values

— Mean

movement having t w o outliers i n 1989 a n d 2004 w h i c h can be explained b y the indicated number of transactions per year. O n l y a single transaction t o o k place i n t h e respective years. A l o n g w i t h t h e increase of t h e curve a similar development is observed for t h e number of transactions as t h e y also increase steadily. I n a second step t h e 5-year average of t h e mean is calculated t o s m o o t h the described up a n d d o w n movement a n d t o illustrate t h e increase i n t h e m u l t i p l e v a l u a t i o n more clearly. Besides t h e E B I T D A m u l t i p l e between 1985-1990, Figure V shows t h e already detected t r e n d even more explicitly. T h e 5-year mean of E B I T D A m u l t i p l e , E B I T m u l t i p l e a n d the sales m u l t i p l e grow significantly over t i m e . Similar t o t h e analysis examined i n t h e comparable company valuation, the impact of financial value drivers on t h e multiples is examined. A g a i n , size i n terms of sales a n d p r o f i t a b i l i t y measured by t h e E B I T D A m a r g i n are considered. W i t h regard t o size t h e transactions are classified by t h e same categories so t h a t a sample of "small-sized", "medium-sized" a n d "largesized" companies is attained. Furthermore, t h e values of t w o time-periods are observed t o analyse p o t e n t i a l developments. I n contrast t o t h e results of the comparable company analysis, t h e multiples of the "large sized" companies do not show t h e highest values. A possible explanation for t h i s is t h e l i m i t e d d a t a t h a t is available as there are o n l y five transactions w i t h companies larger t h a n € B i l l . 5 i n t h e period from 2000-2005 and not a single one between 1994 a n d 1 9 9 9 . 1 3 T h e same analysis is carried o u t w i t h regard t o t h e i m p a c t of profi t a b i l i t y i n terms of E B I T D A margin. Here, t h e respective ranges for t h e classification are set larger t h a n 20%, 10-20% and smaller t h a n 10%. T h e 13

Cf. Appendix XII.

5 PRECEDENT TRANSACTIONS

VALUATION

F i g u r e V : 5 - y e a r A v e r a g e o f M e a n for E B I T D A , Multiple

77

E B I T a n d Sales

20

1985-1990

1991-1995

1996-2000

2001-2005

Years •

EV/EBITDA

ÌEV/EBTT

BEV/Salcs

obtained results are q u i t e d i f f e r e n t . 1 4 D u r i n g t h e period of 1995-1999 t h e v a l u a t i o n of targets w i t h an E B I T D A m a r g i n higher t h a n 20% is t h e highest w i t h a mean of 13.0x compared t o 10.9x (for E B I T D A margin 10-20%) and 9.7x (smaller t h a n 10%). However, significant changes become evident i n t h e period from 2000-2005. M u l t i p l e s for targets w i t h an E B I T D A m a r g i n below 10% grow by over 4.0x whereas t h e respective multiples for t h e t w o other groups heavily decreased, resulting in t h e highest multiples for low m a r g i n firms. I n order t o examine whether a linear relationship between size i n terms of sales, p r o f i t a b i l i t y i n terms of E B I T D A m a r g i n and t h e transaction m u l t i ple exists further correlation analyses a n d regressions have been conducted. A s already seen i n t h e previous analysis, interdependencies between t h e financials and t h e E B I T D A m u l t i p l e are difficult t o prove so t h a t other factors have t o be examined as p o t e n t i a l value drivers.

5.3 Transaction Segmentation Analysis I n order t o derive further meaningful results i t appears appropriate t o analyse t h e precedent transactions w i t h regard t o more detailed segment a t i o n criteria. T h u s , t h e question is addressed t o w h a t degree multiples differentiate when d i v i d i n g t h e transactions i n t o n a t i o n a l and cross-border deals. Therefore, t h e same approach is pursued regarding a segmentation of t h e transactions i n t o public and private targets t o figure out w h a t v a l u a t i o n differences exist. A s t h e construction i n d u s t r y is increasingly characterised by t h e t r e n d of diversification i n t o t h e services sector, a further segmenta14

Cf. Appendix XIII.

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t i o n of t h e targets is finally carried o u t t h a t clusters t h e targets according t o t h e i r business a c t i v i t y i n t o services or construction related companies.

5.3.1 N a t i o n a l vs. Cross-Boarder Transactions B y d i v i d i n g the d a t a sample of 447 transactions i n t o n a t i o n a l a n d crossborder transactions t h e first result t h a t can be observed is t h a t about 1 / 3 of a l l transactions are cross-border deals. W h i l e 295 of t h e regarded transactions remain w i t h i n n a t i o n a l borders, 152 deals t o o k place where t h e acquired target's o r i g i n differed from t h e one of t h e buyer. T h i s large n u m ber of cross-border transactions shows t h a t due t o t h e consolidation process in European construction i n d u s t r y t h e companies are not only focused on their home markets b u t also strengthen t h e i r position abroad. For t h i s reason home markets lose relative i m p o r t a n c e w h a t is underlined b y t h e fact t h a t nowadays large players like H O C H T I E F only generate a relatively small p o r t i o n of t h e i r revenues i n t h e i r home market (e.g. H O C H T I E F approx. 1 7 % ) . 1 5 However, w i t h regard t o t h e v a l u a t i o n differences a clear t r e n d is not observable as from 2000-2005 n a t i o n a l transactions achieved higher multiples whereas i n t h e p e r i o d 1995-1999 t h e opposite result can be seen w i t h higher multiples for cross-border t r a n s a c t i o n s . 1 6 A n explanat i o n for t h i s missing t r e n d i n t h e v a l u a t i o n of cross-border transactions i n comparison w i t h n a t i o n a l transactions could be a t t r i b u t e d t o t h e fact t h a t Europe is more a n d more considered as one c o m m o n market.

5.3.2 Private vs. Public Transactions I n order t o assess whether private or public targets are considered more valuable, a further segmentation according t o t h e ownership t y p e of t h e target companies is i n i t i a t e d . A t t h i s p o i n t i t should be mentioned, t h a t the d a t a sample originally comprising 447 transactions, h a d t o be reduced t o 119 transactions as o n l y for these group an i n d i c a t i o n of at least one m u l t i p l e value is provided. W i t h i n t h i s sub-sample only eleven transactions are contained i n w h i c h t h e acquired firm is a privately owned company. A s this numerical basis is rather unsuitable t o derive meaningful results for t h e considered t i m e period, t h e private target transactions have been consolidated w i t h o u t further t e m p o r a l differentiation. However, t h e public target transactions offered t h e possibility t o group t h e m i n t o five year periods. A l t h o u g h t h e representativeness of t h e private target multiples m i g h t be questionable, Figure V I shows t h a t public transactions are constantly higher valued i n t h e regarded t i m e period t h a n t h e i r private counterparts. A s a reason for t h i s i t can be assumed t h a t , i n general, privately held companies are lower valued due t o t h e unavailability of company specific information, e.g. financial d a t a i n form of published annual reports. T h e higher uncertainty a n d therefore t h e higher risk attached t o t h i s k i n d of transactions m a y serve as a reason for t h e evident discount. Moreover, i t 15 16

Cf. A n n u a l Report H O C H T I E F A G (2005), p. 124. Cf. A p p e n d i x X I V .

5 PRECEDENT TRANSACTIONS

VALUATION

79

Figure VI: Public vs. Private Target Transactions 1985-2005

16

Private 19862005

Public 19861989

Public 19901994

Public 19951999

Public 20002005

is reasonable t o assume t h a t t h e m a j o r i t y of privately held companies are smaller sized t h a n t h e i r p u b l i c l y t r a d e d counterparts w h a t w o u l d also lead t o lower m u l t i p l e values as shown i n t h e comparable company v a l u a t i o n analysis.

5.3.3 Construction vs. Services Transactions A s previously stated t h e construction i n d u s t r y is increasingly characterised by diversification tendencies due t o construction companies' efforts t o diversify t h e i r business activities towards the services sector. Services like facility management or other post construction services are gaining i n importance as companies have identified t h e m as an a t t r a c t i v e a n d profitable source of a d d i t i o n a l revenues and risk diversification. T h i s t r e n d also becomes visible w i t h regard t o t h e multiples v a l u a t i o n depicted i n Figure VII. W h i l e services targets i n t h e decade from 1985 u n t i l 1995 are nearly equally valued, a significant change i n services companies' v a l u a t i o n t o o k place in t h e last t e n years. A s construction companies seek more and more diversification i n t o t h e services sector, t h e services targets v a l u a t i o n has increased significantly. T h i s can be related t o t h e fact t h a t construction companies are w i l l i n g t o pay higher prices for these targets as i n former times t o broaden t h e i r business scope and reap t h e benefits offered by more extensive and more integrated business models.

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6 Concluding Remarks T h e European construction i n d u s t r y is characterised b y an ongoing consoli d a t i o n process t h a t has a substantial i m p a c t o n multiples valuation. T h i s holds t r u e for b o t h comparable company and precedent transaction analysis t h a t have been conducted. I n general, a slight increase in construction companies' valuation over t h e last 20 years can be established. T h i s fact may be explained by t h e diversification efforts t h a t are pursued w i t h regard t o construction related services. Risk and revenue sources are more and more diversified by i n t e g r a t i n g services companies i n t o t h e business model. Concerning t r a d i n g multiples t h e analysis shows t h a t m u l t i p l e values are positively correlated w i t h larger size and higher p r o f i t a b i l i t y as i m p o r t a n t financial value drivers. A d d i t i o n a l l y , t h e regression analysis shows t h a t other factors exist w h i c h have t o be taken i n t o account w h e n explaining the m u l t i p l e values. A s t h e c o u n t r y specific analysis has shown, significant valuation differences are existent across European economies. T o explain these differences a variety of determinants a n d factors i n c l u d i n g general i n d u s t r y expectations, G D P growth, movements i n construction prices or gross domestic fixed c a p i t a l f o r m a t i o n have t o be considered. Moreover, t h e geographical setup of construction companies has also an impact o n t h e i r valuation as i t was shown for I t a l y where construction companies suffered a lack of cross-border M & A activities a n d thus a t t a i n e d lower valuations. T h i s is i n line w i t h the finding t h a t more a n d more revenues are generated abroad and n a t i o n a l home markets lose relative importance. Concerning t h e analysis of t h e transaction multiples an up and d o w n movement is observed w h i c h leads t o t h e assumption t h a t M & A a c t i v i ties are likely t o increase further over t h e c o m i n g years as the market has

6 CONCLUDING REMARKS

81

overcome t h e last d o w n movement. T h i s is i n line w i t h t h e expectation t h a t m u l t i p l e values w i l l increase further i n u p c o m i n g years. I n contrary t o t h e comparable company analysis a certain p a t t e r n regarding t h e financial value drivers could not be detected a n d also t h e regression analysis shows a lower interdependence between t h e multiples a n d t h e financials. T h i s observation shows t h a t for t h e value of t h e transaction multiples t h e i m p a c t of external value drivers is even higher. Further results t h a t were obtained b y segmenting t h e transactions t o different criteria also underline t h e assumption taken so far. W h i l e no precise result could be found i n t h e separate analysis of n a t i o n a l and crossborder deals over t i m e , interesting a n d meaningful outcomes are a t t a i n e d by segmenting t h e transactions according t o the targets' ownership t y p e and business focus. A l t h o u g h the d a t a sample cannot c l a i m representativeness, t h e assumption could be derived t h a t private targets are lower valued due t o a lack of i n f o r m a t i o n available t o t h e c a p i t a l markets and therefore a higher uncertainty or risk. O n t h e other hand, targets t h a t are assigned t o t h e services sector consistently achieved higher valuations i n recent years w h a t underlines t h e mentioned i n d u s t r y trends of diversification effort w i t h regard t o t h e services industry.

82

CHAPTER 3. MULTIPLES VALUATION

References Achleitner, A n n - K r i s t i n (2000):

H a n d b u c h Investment B a n k -

ing, 2nd E d i t i o n , Gabler, Wiesbaden.

Bodie, Z v i / K a n e , A l e x / M a r c u s , A l a n J. (2002):

Investe-

ments, 5 t h E d i t i o n , M c G r a w - H i l I r w i n , New York.

Benninga, Simon/Sarig, O d e d H . (1996):

Corporate F i nance: A V a l u a t i o n Approach, 1st E d i t i o n , M c G r a w H i l l Higher E d u c a t i o n , New York.

D a m o d a r a n , A s w a t h (2002):

Investment V a l u t i o n , 2nd E d i t i o n ,

John W i l e y L· Sons Ine, New York.

Euroconstruct (2005): I T e C - T h e C a t a l o n i a I n s t i t u t e of Cons t r u c t i o n Technology, S u m m a r y R e p o r t , 6 0 t h Euroconstruct Conference, Barcelona 2005. H u n t / P e t e r (2003): S t r u c t u r i n g Mergers & Acquisitions: A G u i d e t o Creating Shareholder Value, 1st E d i t i o n , Aspen Publishers, New York. H O C H T I E F (2005):

A n n u a l Report 2005, h t t p : / / w w w . h o c h t i e f . d e

/hochtief/hochtief?id=432.

Ross, Stephen A . / W e s t e r f i e l d / R a n d o l p h W . / J a f f e , Jeffrey F . (2002): Corporate Finance, 6 t h E d i t i o n , M c G r a w - H i l I r w i n , New York.

83

APPENDIX

Appendix

Appendix I: The Link between Equity and Enterprise Value

Equity Value (market Net debt V ν capitalisation)

„ Minorities

. Value

Appendix II: Multiple Formulas •

Sales M u l t i p l e = E n t e r p r i s e V a l u e / S a l e s

•

E B I T D A M u l t i p l e = Enterprise

•

E B I T M u l t i p l e = Enterprise

•

Cash Flow M u l t i p l e = Market C a p i t a l i s a t i o n / O p e r a t i n g Cash Flow

Value/EBITDA

Value/EBIT

CHAPTER 3. MULTIPLES VALUATION

84

Appendix I I I : Impact of Profitability in Terms of EBITDA Margin on EBITDA Multiple (Mean)

2000 • 0-10%

2005 G310-20%

• >20%

Appendix IV: Impact of Profitability in Terms of EBITDA Margin on EBITDA Multiple (Median) 9 8

2000 • 20%

APPENDIX

85

Appendix V: Profitability (EBITDA margin)/EBITDA multiple Regression Analysis (CCA 2000) Variables Entered/Removed' Variables Variables Method Entered Removed EBITD^ma Enter rginOO a. All requested variables entered. b. RTAIVMVTONT Variahia- F R I T H A M I I I H O I P O O

Model 1

Model Summary Adjusted Std. Error of Model R Square R Square the Estimate R 1 ,183a ,034 ,024 5,30928 a. Predictors: (Constanti. EBITDAmaralnOO ANOVA6 Modal 1

Regression Residual Total

Sum of Squares 100,009 2875,220 2975,229

df 1 102 103

Mean Square 100,009 28,188

F 3,548

Sig. ,062a

a- Predictors: (Constant), EBITDAmarginOO b. Dependent Variable: EBITDAmultlpteOO Coefficients

Model 1

(Constant) EBITDAmarginOO

Unstandardlzed Coefficients Β Std. Error 5,112 ,934 8.647 4,591

a- Dependent Variable: EBITDAmultipleOO

Standardized Coefficients Beta ,183

t 5,476 1,884

Slg. ,000 ,062

86

CHAPTER 3. MULTIPLES VALUATION

Appendix VI: Size (Sales)/EBITDA multiple Regression Analysis (CCA 2000) Variables Entered/Removed* Model 1

Variables Entered SalesOO3

Variables Removed

Method Enter

a. All requested variables entered, b· Dependent Variable: EBITDAmultipleOO Model Summary Model 1

R R Square ,062a .004

Adjusted R Square -.006

Std. Error of the Estimate 5.39059

a. Predictors: (Constant), SalesOO ANOVAf Model 1

Regression Residual Total

Sum of Squares 11,266 2963,963 2975,229

df 1 102 103

Mean Square 11,266 29.058

F .388

Sig. ,535a

a. Predictors: (Constant). SalesOO b. Dependent Variable: EBITDAmultipleOO Coefficients

Model 1

(Constant) SalesOO

Unstandandized Coefficients Std. Error Β 6,697 ,566 -3.8E-008 ,000

a. Dependent Variable: EBITDAmultipleOO

Standardized Coefficients Beta -.062

t 11,842 -,623

Sig. ,000 ,535

APPENDIX Appendix V I I : Profitability (EBITDA margin) and Size (Sales)/EBITDA multiple Regression Analysis (CCA 2000) Variables Entered/Removed Variables Variables Entered Removed Method EBITDAma Enter rginOO, a SalesOO a. All requested variables entered. b. Dftnenrlftnf Variable- FRITDAmultinlftOn

Model 1

Model Summary Adjusted Std. Error of Model R R Square R Square the Estimate 1 ,189e .036 .017 5,32964 a. Predictors: fConstantV EBITDAmarainOO. SalesOO ANO Model 1

Regression Residual Total

Sum of Squares 106,313 2868,916 2975,229

df 2 101 103

Mean Square 53,156 28,405

F 1,871

Sig. ,159s

a Predictors: (Constant), EBITDAmarginOO, SalesOO b. Dependent Variable: EBITDAmultipleOO Coefficient? Unstandardized Coefficients Β Std. Error Model 1 (Constant) .974 5,238 SalesOO -2.9E-008 .000 EBITDAmarginOO 8.461 4.625 a. Dependent Variable: EBITDAmultipleOO

Standardized Coefficients Beta -.046 ,179

t 5,376 -.471 1.829

SiQ. .000 .639 ,070

88

CHAPTER 3. MULTIPLES VALUATION

Appendix V I I I : Size (Sales)/EBITDA multiple Regression Analysis (CCA 2005) Variables Entered/Removed* Mode) 1

Variables Entered Sales05a

Variables Removed

Method Enter

a. All requested variables entered. b. Dependent Variable: EBITDAmultiple05 Model Summary Model 1

R R Square ,102a .010

Adjusted R Square .001

Std. Error of the Estimate 3,16975

a Predictors: (Constant). Sales05 ANOVAP Model 1

Regression Residual Total

Sum of Squares 11,124 1065,019 1076,143

df 1 106 107

Mean Square 11,124 10,047

F 1,107

Sifl. ,295®

a. Predictors: (Constant), Sales05 b Dependent Variable: EBITDAmultiple05 Coefficients

Model 1

(Constant) Sales05

llnstandardized Coefficients Std. Error Β 6,037 ,329 -2.3E-008 ,000

a. Dependent Variable: EBITDAmultiple05

Standardized Coefficients Beta -,102

t 18,375 -1,052

Sig. ,000 ,295

APPENDIX

89

Appendix IX: Profitability (EBITDA margin)/EBITDA multiple Regression Analysis (CCA 2005) Variables Entered/Removed Variables Variables Method Entered Removed EBITD^ma Enter rgin05 a. All requested variables entered.

Model 1

b . NAFTONRFANT V A R I A H L A · Ρ R I Τ Π A r m Ι Ι Η Π Ι Α Ω 5

Model Summary Model 1

R Square R .001 ,036a

Adjusted R Square -.008

Std. Error of the Estimate 3,17174

a- Predictors: (Constant). EBITDAmarainOS AN0VAb Sum of Model Squares df 1 Regression 1,428 1 Residual 1076,410 107 Total 108 1077,839 a· Predictors: (Constant), EBITDAmargin05 b. Dependent Variable: EBITDAmultlple05

Mean Square 1,428 10,060

F .142

Sig. ,707a

Coefficient^

Model 1

(Constant) EBITDAmargin05

Unstandardized Coefficients Std. Error Β 6,063 ,537 -1,063 2,821

a· Dependent Variable: EBITDAmultiple05

Standardized Coefficients Beta -,036

t 11,281 -.377

Sig. ,000 ,707

CHAPTER 3. MULTIPLES VALUATION Appendix X: Profitability (EBITDA margin) and Size (Sales)/EBITDA multiple Regression Analysis (CCA 2005) Variables Entered/Removed Model 1

Variables Entered Sales05, EBITD^ma rgin05

Variables Removed

Method Enter

a· All requested variables entered. b- Dependent Variable: EBITDAmultiple05 Model Summary Adjusted Std. Error of Model R R Square R Square the Estimate e 1 3,17933 ,117 ,014 -.005 a. Predictors: (Constant), Sales05, EBITDAmargin05 ANOV/*» Model 1

Regression Residual Total

Sum of Squares 14,790 1061,353 1076,143

df 2 105 107

F ,732

Mean Square 7,395 10.108

Sifl. ,484e

a· Predictors.· (Constant), Sales05, EBITDAmargin05 b. Dependent Variable: EBITDAmultiple05 Coefficient? Unstandardized Coefficients Std. Error Model Β 1 (Constant) 6,324 ,580 EBITDAmargin05 -1,735 2,882 Sales05 -2.6E-008 ,000 a. Dependent Variable: EBITDAmultiple05

Standardized Coefficients Beta -,059 -.112

t 10,912 -,602 -1,136

Sig. ,000 .548 .259

91

APPENDIX

Appendix XI: Targets and Acquirers by Country Target C ountry

Acquirer Country

34% • France • Spain • Italy D1JK

• Germany • Other E (tob]

Appendix XII: Transaction EBITDA Multiples 2000-2005 and 1995-1999 clustered by size 16

2000-2005

1995-1999 Year

• EBITDA margin >20% Θ EBITDA margin 10-20% • EBITDA margin 5bn

• Size 1 -5bn

9 Size < 1 bn

Appendix XIV: Transaction EBITDA Multiples 2000-2005 and 1995-1999 clustered by size

1995-1999 Year • Crossborder

• National

Part I I

Economic Perspectives

93

Chapter 4

The Economic Importance of European Construction Industry Bhupinder

S. Brart /Sylvain

Fondeur*

Y o r k University, Schulich School of Business, Toronto, Canada Reims Management School, Reims, France

95

CHAPTER 4. ECONOMIC IMPORTANCE

96

Contents 1 Introduction

96

2 Market Analysis

97

3 Market Segmentation

98

3.1 Residential

99

3.2 N o n - R e s i d e n t i a l

100

3.3 C i v i l - E n g i n e e r i n g

100

4 M a r k e t Forecast

101

5 Competitive Environment

101

6 Qualitative Overview

102

6.1 G e r m a n y

102

6.2 F r a n c e

103

6.3 S p a i n

104

6.4 U n i t e d K i n g d o m 7 I m p a c t Factors

104 105

7.1 E c o n o m i c E n v i r o n m e n t

105

7.2 C o m m o d i t y P r i c e s

106

7.3 S t a b i l i t y a n d G r o w t h P a c t

106

7.4 P u b l i c P r i v a t e P a r t n e r s h i p s

107

7.5 E n v i r o n m e n t O b l i g a t i o n s

107

7.6 D e m o g r a p h i c T r e n d s

108

8 Conclusion

108

References

109

Appendix

110

1 Introduction C o n s t r u c t i o n is a m a j o r i n d u s t r y i n t h e world, i t accounts for a significant p o r t i o n of t h e w o r l d G D P . C o n s t r u c t i o n i n m a n y ways indicates t h e economic health of a country. I n most countries from a perspective of G D P proportion, construction is often counted among t h e t o p 2 industries. T h i s paper examines t h e extent of European construction activity, a n d is based on a cross-sectional analysis of published d a t a p e r t a i n i n g t o European cons t r u c t i o n spending. T h e paper first analyses t h e overall economic i m p a c t of construction, t h e n i t i n d i v i d u a l l y examines t h e different sectors of t h e cons t r u c t i o n industry. Finally, m a j o r trends a n d developments i n four m a j o r

2 MARKET ANALYSIS

97

Figure I: Europe Construction Industry Market Value, 2000-2005

I

1,220

2.50%

1,210

2.00%

1,200

1.50%

1,190

1.00%

1,180

0.50%

1,170

0.00% -0.50%

1,160 2001

2002

2003

2004

E3 Market Value in fbill. Source :

2005 ·+-% Growth

E u r o c o n s t r u c t B a r c e l o n a N o v 2005.

countries as well as t h e factors influencing t h e development of t h e construct i o n industry. T h e paper is a q u a n t i t a t i v e analysis of the d a t a available t h r o u g h trade p u b l i c a t i o n and i n d u s t r y reports. I t does not a t t e m p t t o provide a theoretical analysis or q u a l i t a t i v e insights i n t o the construction industry, t h e objective is rather t o familiarise t h e reader w i t h t h e facts and t h e economic i m p o r t a n c e of t h e construction i n d u s t r y i n Europe. T h e construction i n d u s t r y i n t h i s paper refers t o companies involved in residential, non-residential, c i v i l engineering and renovation. Therefore, for t h i s paper, d a t a is used from a l l sectors of t h e construction industry.

2 Market Analysis I n 2005 t h e european construction i n d u s t r y grew by 1.3% t o a constant dollar figure of € B i l l . 1.217. W e present t h e construction i n d u s t r y g r o w t h rate and t o t a l value i n Figure I . T h e g r o w t h rate of t h e construction ind u s t r y was less t h a n t h e overall economic g r o w t h rate of European U n i o n ( + 1 . 5 % ) . T h e g r o w t h rate can be i m p a c t e d b y consumer confidence i n t h e economic recovery and if t h e y do not have confidence t h e y w o u l d not spend i n goods a n d housing w h i c h impacts t h e construction industry. Some of t h e other factors t h a t affect construction i n d u s t r y g r o w t h rate are discussed i n t h e subsequent sections of t h e report. Some of these factors are c o m m o d i t y prices, public deficit, macro economic indicators a n d demographic factors. 1 C o n s t r u c t i o n I n d u s t r y produces 9.9% of t h e European G D P , second o n l y t o t h e m a n u f a c t u r i n g sector. O n a n a t i o n a l basis, construction indusCf. C o n s t r u c t i o n A c t i v i t y i n Europe, p. 8-12.

98

CHAPTER 4. ECONOMIC IMPORTANCE

Figure II: Construction Industry: Market Share of Country (%) Eastern Europe

6% Benelux

Source:

Germany

E u r o c o n s t r u c t B a r c e l o n a N o v 2005.

t r y is t h e largest i n d u s t r y i n Spain (18.1%) and P o r t u g a l (15.7%), b o t h countries have been investing heavily i n t h e i r n a t i o n a l infrastructure such as power, education, health and housing. Furthermore, construction as an i n d u s t r y is responsible for t h e f o r m a t i o n of 50% of Gross fixed c a p i t a l within Europe.2 O n a n a t i o n a l level Germany holds t h e largest share w i t h i n t h e European construction i n d u s t r y a t 19%. T h e five b i g countries (France, Germany, Italy, Spain, a n d U K ) together h o l d 70% of t h e market share. I n a d d i t i o n t o t h e five b i g countries Western Europe c u m u l a t i v e l y holds 94% of the market share. Eastern Europe o n l y accounts for 6% (Figure I I ) o f the market share. Even t h o u g h Eastern Europe as a block of countries has the smallest market share, t h e y have experienced one of t h e highest g r o w t h rates in construction industry. Slovakia, Hungary, Czech Republic, P o l a n d have all posted g r o w t h rate above 3%, i n comparison t o sluggish g r o w t h i n the m a j o r European power house like Germany (-1%) and France (1.7%) C o n s t r u c t i o n I n d u s t r y is t h e single largest employer i n European U n i o n , responsible for t h e creation o f 7.2% of t o t a l employment a n d 28.5% o f i n d u s t r i a l employment. I n absolute figures 13.8 m i l l i o n people are directly employed b y the construction i n d u s t r y a n d a p p r o x i m a t e l y 26 m i l l i o n people depend u p o n i t d i r e c t l y or i n d i r e c t l y . 3

3 M a r k e t Segmentation T h e construction i n d u s t r y can be n o r m a l l y categorised i n t o three sectors residential, non-residential and c i v i l engineering. Each sector is discussed 2 3

Cf. C o n s t r u c t i o n i n Europe: K e y Figures ( w w w . F I E C . o r g ) . Cf. C o n s t r u c t i o n i n Europe: K e y Figures ( w w w . F I E C . o r g ) .

3 MARKET SEGMENTATION

99

Figure I I I : Share of each Sector (%) Shares (%)

New Non residential

18% R & M Non Residential

New Civil Engineering 14% R & M Civil Engineering 7%

13% New residential 24% R&M Residential 24% Source:

E u r o c o n s t r u c t B a r c e l o n a N o v 2005.

i n greater d e p t h i n t h e subsequent sections. I t is i m p o r t a n t t o highlight, t h a t i n t h i s paper we chose t o assume renovation and maintenance as a sub-section of t h e three m a j o r sectors of construction industry. Some i n d u s t r y reports choose t o list renovation and improvements as a separate section because of its sheers size. However, we feel t h a t i t is best t o list i t w i t h i n each sector a n d t h e n analyse i t comparative t o t h e new construction i n t h a t sector. Such a methodology w i l l provide us detailed knowledge of the relative size of new construction a n d maintenance of t h e old lot. Residential sector overall account for 48% of t h e market, non residential sector is 31% and c i v i l engineering is 21%. Renovation and Maintenance accounts for 44% of t h e overall market share and each sector division is shown i n Figure I I I . Renovation and Maintenance is the most significant sector i n residential sector. C i v i l Engineering has had the most steady g r o w t h rate i n t h e past few years, i n 2005 i t grew at 1.8% w h i l e Residential has had t h e most volatile g r o w t h rate, i n 2004 i t grew at 4% and i n 2005 i t grew at 1.64%. Non-residential sector contrary t w o t h e other sectors has experienced t h e least g r o w t h i n European U n i o n 0.47% i n 2004 and 0.52% in 2005.

3.1 Residential T h e t o t a l value of t h e Residential sector is € B i l l . 567.8 in 2005. Resident i a l sector as t h e name suggest is p r i m a r i l y responsible for t h e construction of i n d i v i d u a l dwellings, a p a r t m e n t blocks and subsidised social housing projects. T h e renovation a n d maintenance w i t h i n t h i s sector assumes such significance because Western Europe, w h i c h accounts for 94% of t h e market

CHAPTER 4. ECONOMIC IMPORTANCE

100

share, experienced most explosive c o n s t r u c t i o n g r o w t h t h r o u g h 1960's t o 1970's. T h u s , t h e o l d lot requires consistent repairs, extension a n d m a i n tenance. W h e n we analyse t h e absolute number t h e e n o r m i t y of t h e sector is evident because of € B i l l . 567.8 spent i n 2005 i n residential sector; 50.4% or € B i l l . 287.2 went i n t o renovation a n d maintenance. T h e numbers provide an inadequate p i c t u r e on t h e importance of Cent r a l and Eastern Europe. Currently, C e n t r a l a n d Eastern Europe accounts for 2.7% of t h e new construction a n d 1.3% of renovation and maintenance, b u t the future trends show t h a t t h i s area w i l l experience high and consistent g r o w t h i n t h e coming years. C e n t r a l a n d Eastern Europe has outpaced Western Europe i n annual v o l u m e g r o w t h of new construction by 4% since

2002.4

3.2 Non-Residential N o n residential sector has a value of € B i l l . 383.6 i n 2005. T h e sector prim a r i l y consists of construction of offices, hospitals, i n d u s t r i a l buildings a n d hotels. Basically all forms of b u i l d i n g w h i c h are non- residential a n d do not fall i n t o t h e c i v i l engineering sector, even construction of new shopping malls are p a r t of t h e non-residential sector. There are great variances between countries i n t h e g r o w t h rate of t h i s sector as i t is affected b y public funding and large scale private investment. Private funding has been reluct a n t t o investment i n this sector because of widespread h i g h vacancy rates and s i t u a t i o n is not helped by sluggish economic growth. I n order t o understand t h e effects of public spending affecting t h i s i n d u s t r y we w i l l provide an example of t h e t w o extremes, i n Germany local authorities have reduced c o n s t r u c t i o n spending b y 40% i n t h e last 10 years w h i c h has led t o a decline of t h i s sector b y 5.4% i n 2004 alone. I n Great B r i t a i n t h e sector experienced robust g r o w t h of 6% supported by large scale public spending i n health a n d education. Therefore, we see t h e affects t h a t public spending can have on t h e g r o w t h of a p a r t i c u l a r sector. T h e i m p o r t a n t sub-sectors by v o l u m e are: 5 •

I n d u s t r i a l B u i l d i n g is t h e largest segment: € B i l l . 47 i n 2005

•

Office a n d commercial B u i l d i n g s follow: € B i l l . 39 i n 2005

•

E d u c a t i o n B u i l d i n g : € B i l l . 27 i n 2005

•

Storage B u i l d i n g : € B i l l . 17 i n 2005

3.3 Civil-Engineering T h e value of C i v i l Engineering i n 2005 is € B i l l . 252. C i v i l Engineering t y p i c a l l y entails construction of roads, railways, bridges, tunnels etc. all such projects w h i c h have public benefit or t h e c a p i t a l requirements are extremely high w h i c h makes government involvement a necessity. Cf. Euroconstruct report Barcelona (Nov 2005), p. 91-113. Cf. Euroconstruct report Barcelona (Nov 2005), p. 123-136.

4 MARKET FORECAST

101

T h e g r o w t h rate i n t h i s sector is e x p l i c i t l y dependent u p o n public spending a n d policy, c i v i l engineering has been experiencing a slowdown since 2001 and was intensified i n 2004 w i t h a decline of 0.3%. T h e only bright spot i n t h e European U n i o n has been t h e Eastern European countries or Spain, b o t h are t r y i n g t o b r i n g t h e i r n a t i o n a l infrastructure at par w i t h other European countries like Germany a n d have been investing heavi l y i n t o such activity. T h i s is a h i g h l y volatile sector where a few large scale construction projects can completely alter the g r o w t h rates. For example, France experienced a g r o w t h of 7% i n 2004 a n d o n l y 2.7% i n 2005. T h e cause for t h i s variation was t h e conclusion of few large scale construction projects such as bridge de M i l l a u . U n i t e d K i n g d o m s i m i l a r l y has experienced steady g r o w t h i n 2004 and 2005, supported b y t h e government's i n i t i a t i v e t o modernise t h e antiquated r a i l networks. 6

4 M a r k e t Forecast T h e g r o w t h forecasts for t h e future are not t h e most o p t i m i s t i c , G D P g r o w t h rate is expected t o outpace construction i n d u s t r y g r o w t h rate. M a j o r reasons for t h i s are sluggish g r o w t h i n residential sector i n Western Europe and m i n i m a l g r o w t h i n other sectors p r i m a r i l y i n Western Europe. Residential sector in t h e west is expected t o experience negative g r o w t h rate. However, there are b r i g h t spots. Eastern a n d Central Europe offers a lot of g r o w t h o p p o r t u n i t y for t h e c o m i n g decades as t h e y modernise t h e i r infrastructure. For E x a m p l e C o n s t r u c t i o n I n d u s t r y i n Czech Republic is expected t o grow by 24.5% between 2005 and 2008. For t h e same t i m e period Poland is expecting g r o w t h rate of 34.5%, Slovakia (17.3%) and H u n g a r y (39.3%). T h e o n l y b r i g h t spot among t h e five m a j o r countries i n Europe is Spain, w i t h g r o w t h expectation of 12.6% between 2005 a n d 2008. 7

5 Competitive Environment C o n s t r u c t i o n by nature is a fragmented i n d u s t r y ; a person w i l l never find a company t h a t controls 4% or 5% of t h e market share. T h e same is t r u e unequivocally all over t h e w o r l d as is for Europe. T h e largest company i n Europe also happens t o be t h e largest company i n t h e world; i t is V i n c i from France, w i t h an annual turnover of € B i l l . 19.56 and controls l i t t l e over 1.6% of t h e European market. T h e other four m a j o r firms are Skanska (Sweden), Hoctief (Germany), Bouygues (France) and A C S (Spain). T h e cyclical nature of the construction i n d u s t r y has deeply impacted t h e construction i n d u s t r y i n Europe. I t has created problems for key playCf. Euroconstruct report Barcelona (Nov 2005), p. 147-160. Cf. Euroconstruct report Barcelona (Nov 2005), p. 55-60.

102

CHAPTER 4. ECONOMIC IMPORTANCE Table I: Five Largest Companies in Europe

Company Name Vinci Skanska Hochtief Bouygues ACS Source:

Market (€Bill.) 19.56 17.69 13.1 10.6 10.9

Volume

M a r k e t Share 1.60% 1.50% 1.10% 0.90% 0.90%

Total European Market (€Bill.) 1217 1217 1217 1217 1217

D A T A M O N I T O R report o n E u r o p e a n C o n s t r u c t i o n I n d u s t r y (Published February 2005).

ers such V i n c i a n d A C S because of t h e rising c o m m o d i t y costs a n d t h e relatively weak economic environment. T h e economic o u t l o o k for these companies is bleak because of a stark reality of t h e construction industry, as t h e companies grow i n size; t h e y concentrate t h e i r effort o n large scale projects. Often these large scale projects are public initiatives, as has been discussed before and w i l l be furt h e r explained i n t h e subsequent section t h e governments all over Europe axe reducing c a p i t a l expenditure t o c o n t r o l t h e i r fiscal deficit. O t h e r maj o r projects can be private investment i n housing or i n d u s t r i a l units, even t h e o u t l o o k for those t w o sectors is not promising because t h e declining p o p u l a t i o n g r o w t h , p r i m a r i l y i n Western Europe and also because of weak economic g r o w t h w h i c h dampens d e m a n d for new i n d u s t r i a l u n i t s . 8

6 Qualitative Overview I n t h i s section a q u a l i t a t i v e overview of four m a j o r countries is provided. T h e objective is t o understand some of t h e q u a l i t a t i v e factors t h a t effect t h e g r o w t h of construction i n each c o u n t r y as construction is a heterogeneous industry.

6.1 Germany • T h e economic g r o w t h in Germany has accelerated since 2004, i n 2005 t h e G D P grew by 0.9% a n d i n 2006 G D P is expected t o grow by 1.7% based on first quarter results. 9 T h e p r i m a r y reason for g r o w t h has been growing d e m a n d for G e r m a n good i n i n t e r n a t i o n a l market ( E x p o r t , + 1 4 . 2 % , i n first quarter of 2006). Domestic demand is s t i l l weak, w h i c h w i l l reflect more accurately t h e propensity of consumers t o spend money, u l t i m a t e l y benefiting construction industry. • T h e effect of t h e cuts i n public grants for owner-occupied houses w i l l result i n negative growth. Some i n d u s t r y groups expect a negative 8 9

Cf. Euroconstruct Report Barcelona (Nov 2005), p. 10-25. http://www.tradingmarkets.com/tm.site/news/ECONOMIC%20 NEWS/261863/.

6 QUALITATIVE

OVERVIEW

103

g r o w t h of 5%. •

Germany has consistent v i o l a t e d t h e Maastricht-Criteria, l i m i t i n g t h e public debt t o 3% of G D P . Therefore, Germany had t o reduce public spending o n infrastructure w h i c h negatively impacts t h e cons t r u c t i o n industry. L o c a l governments reduced their construction spending by nearly 40% over t h e last t e n years.

•

A bright spot i n t h e public sector is t h e growing number of P P Partnerships, especially i n renovating and b u i l d i n g new schools.

• A n ongoing p r o b l e m for t h e G e r m a n construction sector is t h e t r e n d i n the manufacturing i n d u s t r y t o invest abroad. Especially G e r m a n a u t o manufacturers and chemical companies are s h i f t i n g p r o d u c t i o n t o low cost countries w h i c h d a m p e n demand for new i n d u s t r i a l units i n Germany. Increasingly G e r m a n contractors are looking at international markets t o follow G e r m a n m a n u f a c t u r e r s . 1 0

6.2 France •

N a t i o n a l Economy has experienced steady g r o w t h of 2.1% i n 2004 a n d 1.6% in 2005. Domestic d e m a n d has been t h e leading factor i n t h e revival of t h e growth. Slight d r o p i n unemployment rate also helped.

•

For construction as whole there was g r o w t h of 3.3% i n 2004. T h e following factors exert an influence on b u i l d i n g a c t i v i t y i n France: — T h e Borloo p l a n aims t o create a m i l l i o n jobs and 500,000 new homes. I t has been enacted as social subsidised housing (reconciliation for t h e recent i m m i g r a n t riots) scheme by t h e labour minister, Jean-Louis Borloo. — T h e c o n t i n u a t i o n of s t i m u l a t i o n measures of "De Robien" ; t h e measure was introduced at t h e start of 2003 t o p r o m o t e new housing starts by allowing taxpayers t o offset c a p i t a l costs against rental income from new property. A l t h o u g h t h e measure is l i m i t e d , its i m p a c t has been significant. — T h e level of V A T on home improvement was reduced from 19.6% t o 5.5%. — G r o w t h w i l l also be driven by many t r a n s p o r t projects i n cities (for example t r a m w a y ) a n d by-works such as t h e H S T East w h i c h w i l l l i n k Paris t o Strasbourg. — Electric infrastructure works and t h e water sector experienced d y n a m i c growth. French companies have experienced robust

European C o n s t r u c t i o n I n d u s t r y Federation, 2005 C o n s t r u c t i o n A c t i v i t y i n Europe, p. 46-49.

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g r o w t h i n M i d d l e East and Asia, where governments axe b u i l d i n g new upgrading o l d i n f r a s t r u c t u r e . 1 1

6.3 S p a i n •

Spanish G D P g r o w t h rate has been steady at around 2.5% since 2004 and is expected t o be 2.6% i n 2006. Therefore, of a l l t h e maj o r European countries Spain is t h e only c o u n t r y w i t h steady h i g h g r o w t h rates. Spanish g r o w t h differential w i t h t h e European U n i o n as a whole (EU-15) increased t o 1.6% points, i n 2004. Some of t h e factors mentioned here, t h a t c o n t r i b u t e d t o t h i s g r o w t h also i m p a c t t h e demand for construction industry. — A n increase i n domestic d e m a n d was c r i t i c a l i n t h e economic dynamism. — Economic environment, a healthy g r o w t h i n employment figures a n d family incomes. — Strong private investments greatly aided t h e construction industry. — A l l t h e i n d u s t r i a l and energy sectors of t h e economy saw a slight acceleration, w h i c h helped t h e construction i n d u s t r y grow positively.

•

C o n s t r u c t i o n Industry, despite experiencing slower g r o w t h t h a n previous years, for t h e fifth year r u n n i n g , remains t h e most d y n a m i c sector of t h e Spanish economy. T h e t r a d i t i o n a l v o l a t i l i t y of t h e sect o r has toned d o w n because of strong d e m a n d from t h e public sector.

•

I m m i g r a t i o n p o p u l a t i o n a n d non-residents have c o n t r i b u t e d signific a n t l y t o t h e housing boom. L o w interest rates have also helped t h e g r o w t h i n t h e construction i n d u s t r y . 1 2

6.4 U n i t e d K i n g d o m •

Economy of U n i t e d K i n g d o m experienced one of t h e strongest growths i n E U i n 2004, G D P increased by around 3.1%, however, i n 2005 t h e g r o w t h slumped t o 1.8% a n d i n 2006 i t is expected t o be 2.2%. T h e economic g r o w t h despite t h e fluctuation is strong compared t o Germany a n d France and t h u s should benefit construction i n d u s t r y positively.

• T h e rate of g r o w t h of construction i n d u s t r y outpaced t h e n a t i o n a l economic g r o w t h i n 2002, 2003 a n d 2004; here are some of t h e quali t a t i v e factors i m p a c t i n g U n i t e d K i n g d o m ' s construction I n d u s t r y : 11

12

European C o n s t r u c t i o n I n d u s t r y Federation, 2005 C o n s t r u c t i o n A c t i v i t y i n Europe, p. 60-64. European C o n s t r u c t i o n I n d u s t r y Federation, 2005 C o n s t r u c t i o n A c t i v i t y i n Europe, p. 54-59.

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— G r o w t h has been i n new construction w h i l e t o t a l repair and maintenance a c t i v i t y has shown no increase. — Engineering o u t p u t has declined; house b u i l d i n g has grown faster t h a n non-residential construction. — W i t h i n t h e t o t a l of non-residential b u i l d i n g work, w o r k for public sector clients has grown faster t h a n private sector work. — T h e U K Government's t w o highest policy priorities are educat i o n and health, and i t is i n these t w o areas t h a t t h e b u l k of the increase i n public sector non-residential b u i l d i n g work is concentrated. — New health and safety regulations have placed t h e construction i n d u s t r y under excessive cost pressures a n d led t o especially t i g h t labour markets. U K is one of t h e few countries i n Western Europe where recent laws have decreased t h e competitiveness of B r i t i s h firms. 13

7 Factors I m p a c t i n g t h e E u r o p e a n Construction Industry There are several factors t h a t i m p a c t t h e g r o w t h of p a r t i c u l a r i n d u s t r y w i t h a region; also other factors can i m p a c t t h e competitiveness of firms globally. These factors can be t h e result of a p o l i t i c a l policy, macroeconomic indicators, and monetary p o l i c y of t h e central b a n k or because of global geo-political conflicts. Here we have o u t l i n e d some of the m a j o r factors and trends t h a t w i l l set t h e direction a n d speed for t h e g r o w t h of the construction i n d u s t r y in t h e c o m i n g decades.

7.1 Economic Environment T h e steady economic g r o w t h i n t h e European U n i o n w i l l definitely have a positive i m p a c t on t h e construction industry. However, the signs or forecasts are not very o p t i m i s t i c , especially i n case of Germany. Recent macro economic numbers from t h e German economy show t h a t t h e steady g r o w t h expected i n 2006 w i l l s l u m p i n 2007 because t h e present g r o w t h is supplemented by budgetary measures. E C B is expected t o raise short t e r m interest rate i n June 2 0 0 6 1 4 t o counter inflationary pressures. A l l these predictions and forecasts for t h e power house of Europe does not reflect positively on the g r o w t h prospects of construction industry. H e a l t h y and growing economy w i l l d e m a n d new construction, thus it is imperative for 13

14

European C o n s t r u c t i o n I n d u s t r y Federation, 2005 C o n s t r u c t i o n A c t i v i t y i n Europe, p. 68-72. http://www.tradingmarkets.com/tm.site/news/EC0N0MIC%20 NEWS/261863/.

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construction i n d u s t r y t o be v i b r a n t , a n d t h e European economy a t least has steady growth. A positive w i t h i n Europe for construction companies is receipt of transfer payments by C e n t r a l a n d Eastern European countries. T h e redistribut i o n of part of t h e European Union's resources t o t h e new member states w i l l produce beneficial effects for t h e construction i n d u s t r y as a whole a n d drive d e m a n d for new construction. Historical low interest rates have fueled t h e housing market i n countries like D e n m a r k , Ireland a n d Sweden. W h i l e low interest rates are b o o n for the construction industry, t h e y by themselves are not sufficient t o boost growth, as is evident i n t h e case of Germany a n d Portugal. T h e strengthening of t h e E u r o relative t o t h e US dollar and other w o r l d countries also could i m p a c t overall economic g r o w t h , as European goods become expensive o n t h e w o r l d stage, b u t i t also impacts the competitiveness of European construction firms t o w i n contracts i n external market.

7.2 Commodity Prices Prices of t w o commodities strongly i m p a c t construction industry, o i l a n d steel. Steel prices have been rising consistently for t h e last few years because of strong d e m a n d from China. A l t h o u g h t h e prices have leveled of i n 2 0 0 5 1 5 and 2006 from t h e highest p o i n t i n 2004, t h e y are relatively high. W i t h I n d i a j o i n i n g t h e race w i t h C h i n a t o modernise its infrastructure t h e future prices of steel appear t o be heading i n t h e w r o n g direction from t h e perspective of European consumers. I n a d d i t i o n t o steel, w h i c h d i r e c t l y impacts t h e construction cost rising o i l prices also c o n t r i b u t e t o t h e higher costs of construction. Rising o i l prices also have a psychological i m p a c t o n t h e consumers as they expect all other prices t o go u p a n d are less likely t o spend on h i g h value items like new homes or cars

7.3 Stability and Growth Pact There is a growing list of countries w h i c h are already - or w h i c h could find themselves t o be - above t h e 3% deficit threshold defined by t h e " S t a b i l i t y and G r o w t h P a c t " ( S G P ) . 1 6 T h i s s i t u a t i o n influences construction activity, some components of w h i c h closely depend on public-sector investment. Public i n s t i t u t i o n s often rely on deficit spending t o fund c i v i l engineering projects, b u t w i t h t h e SGP, t h e i r a b i l i t y t o do so is greatly hampered. Larger countries, especially Germany, w h i c h have been marred by rising costs t o fund social security like health and education, find i t increasingly difficult t o fund infrastructure investments. Here are some of t h e facts concerning the i m p a c t of SGP o n construction investment: http://www.steelonthenet.com/prices.html. European C o n s t r u c t i o n I n d u s t r y Federation, 2005 C o n s t r u c t i o n A c t i v i t y i n Europe, p. 6.

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107

•

German C i v i l engineering investments declined b y - 2 % i n 2004, Deutsche B a h n ( D B ) w h i c h is t h e largest source for public spending o n infrastructure, reduced c a p i t a l spending b y 5 % . 1 7

•

U K and Netherlands's c i v i l engineering sectors have also been declining since 2002 because of lack of funding form t h e government w h i c h themselves are plagued by persistent budgetary constraints.

7.4 Public Private Partnerships P u b l i c sector investments, especially i n t h e form of Public-Private-Partnerships ( P P P ) have proven t o be especially helpful i n t h e recent years. G e r m a n P P P figures recently touched € M i o . 5 0 0 . 1 8 T h e countries w i t h highest public sector expenditure on infrastructure; U K , Netherlands, F i n l a n d , H u n g a r y and Slovakia are increasingly engaging i n PPPs. U K has especially taken t h e lead w i t h i n i t i a t i n g P P P projects t o build, m a i n t a i n a n d repair schools a n d hospitals. P P P s are increasingly being formed where government is unable t o fully provide capital. W e illustrate the scope of P P P projects by showing t h e example of one project i n E n g l a n d w h i c h was undertaken as a P P P between U n i t e d K i n g d o m government, L o n d o n h C o n t i n e n t a l Railways L t d . , and Eurostar. T h e project is w o r t h US$Bill. 7.8, of w h i c h t h e first part was concluded successful and on t i m e i n 2006. R a i l L i n k Engineering c o n s o r t i u m ( R L E ) was also partner on t h e project, R L E includes Bechtel, Ove A r u p a n d Partners, Sir W i l l i a m Halcrow a n d Partners L t d . , and S y s t r a . 1 9

7.5 Environment Obligations Surprisingly, environment obligations offer a bright spot for construction growth. Environment obligations such as K y o t o must be met by 2012; t h e y i m p a c t a l l European countries as t h e y are signatories t o t h e accord. T h e targets set under t h e accord offer renovation a n d maintenance companies a unique o p p o r t u n i t y t o grow, as 40% of E U Energy is used i n buildings a n d E U energy Performance i n B u i l d i n g s Directive ( E P B D ) mandates member countries t o implement measures i n t o n a t i o n a l law b y Jan 2006. I n Western Europe there are 12Bill. m 2 of real estate t h a t was constructed before 1975, w h i c h i f fitted w i t h proper insulation can greatly aid i n European desire t o meet t h e K y o t o standards. A n o t h e r o p p o r t u n i t y i n construction has recently arisen because of env i r o n m e n t ; countries want t o secure t h e i r resources such as water supply infrastructure and want t o adequately use sun a n d w i n d resources, a l l w h i c h offer g r o w t h o p p o r t u n i t y t o construction c o m p a n i e s . 2 0 1 7 18

19 2 0

http://www.nationalcorridors.org/df/dfll282005.shtml . European C o n s t r u c t i o n I n d u s t r y Federation, 2005 C o n s t r u c t i o n A c t i v i t y i n Europe, p. 46-49. http://www.bechtel.com/Briefs/0304/CTRL.htm . Cf. Euroconstruct report Barcelona (Nov 2005), p. 201-208.

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7.6 D e m o g r a p h i c T r e n d s Demographics can i m p a c t construction i n d u s t r y on t w o t i m e horizons, short t e r m and long-term. Short t e r m t r e n d is beneficial as European families increasingly see t h e benefit of o w n i n g a house over renting. There d e m a n d for 1-2 b e d r o o m family homes is expected t o rise, especially i n countries like Prance a n d G e r m a n y . 2 1 T h e other i m p a c t of demographic t r e n d is t h e long-term i m p a c t of declining p o p u l a t i o n i n European countries. M o s t European countries do not have enough children being b o r n t o replace t h e existing population. Replacement f e r t i l i t y rate is considered t o be 2.1 per w o m a n in her life t i m e . F e r t i l i t y rate for Eastern Europe i n most cases is q u i t e d r a m a t i c a l l y below replacement fertility, Western Europe also is below replacement r a t e . 2 2 Therefore, long-term perspective t h i s t r e n d could have dire i m p a c t o n t h e demand for construction. I f t h e p o p u l a t i o n is unable t o replace itself, there w i l l be no demand for e x t r a housing, nor w i l l there be strong d e m a n d for other non-residential buildings as t h e existing p o o l of real estate should be sufficient for t h e societal demands. A t t h e same t i m e this t r e n d offers a perspective i n t o t h e future a n d where p o t e n t i a l g r o w t h m i g h t arise from. I f populations unable t o replace itself, t h e i r w i l l strong d e m a n d for r e h a b i l i t a t i o n , maintenance a n d repair of the existing property.

8 Conclusion We have shown i n t h i s report t h a t t h e construction i n d u s t r y is a corner stone of almost a l l economies of Europe a n d t h e larger European economy itself. T h e size of t h e construction i n d u s t r y is immense; i t is a fragmented i n d u s t r y w i t h homogenous characteristics and g r o w t h p o t e n t i a l for each country. I t is h a r d t o come t o a c o m m o n forecast especially w h e n one considers such facts as Poland is expecting a g r o w t h of 34.4% w h e n Germany, its neighbour is expecting a g r o w t h of 0.4% u n t i l 2008. C o n s t r u c t i o n i n d u s t r y faces some significant challenges from bleak economic o u t l o o k , c o m m o d i t y prices a n d demographic trends.

European C o n s t r u c t i o n I n d u s t r y Federation, 2005 C o n s t r u c t i o n A c t i v i t y i n Europe, p. 14. http://en.wikipedia.org/wiki/NaturaLpopulation_decrease .

REFERENCES

109

References European Construction I n d u s t r y Federation (2002):

Con-

s t r u c t i o n a c t i v i t y in Europe, h t t p : / / w w w w . f i e c . o r g / m a i n . h t m l .

European Construction I n d u s t r y Federation (2005):

Con-

s t r u c t i o n i n Europe: K e y figures, h t t p : / / w w w . f i e c . O r g / u p l o a d / 5 / 1 4 0 7 1 8 0 2 82117162432020198532312068591652f4977v 1 .pdf.

I n s t i t u t de Tecnologia de la construcion de Catalunya (2005) : Euroconstruct R e p o r t , 6 0 t h Euroconstruct Conference Barcelona. I n s t i t u t de Tecnologia de la construcion de Catalunya (2005): Euroconstruct W i n t e r conference press release.

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Appendix A p p e n d i x I : G r o w t h E x p e c t a t i o n s for t h e C o n s t r u c t i o n (2005-2008)

Industry

Chapter 5

Macroeconomic Determinants of Long-term Performance i n European Countries Cowan Phan Siang Puf /Louise

t *

De Waal %

Singapore Management University, Singapore University of Pretoria, Pretoria, Republic of S o u t h A f r i c a

111

112 CHAPTER 5. MACROECONOMIC DETERMINANTS

Contents 1 Introduction

112

1.1 A i m of the Paper

112

1.2 Course of the Analysis

113

2 Links between G D P and Growth 2.1 G D P

114 114

2.2 Governmental Expenditure

115

2.3 Domestic Expenditure

116

2.4 Investment Expenditure

119

2.5 Exchange Rates

122

3 Conclusion

123

References

126

Appendix

130

1 Introduction 1.1 A i m of t h e Paper T h i s report aims t o discover w h e t h e r there is a l i n k between t h e macroeconomic environment w i t h i n European countries and t h e long-term performance of t h e i r construction industries. I t w i l l investigate whether a positive g r o w t h of t h e Gross Domestic P r o d u c t ( G D P ) necessarily leads t o positive performance of domestic construction companies. T h e macroeconomic variables a n d consequent construction i n d u s t r y performance of t h e five European countries w i t h t h e largest construction industries w i l l be used as i t is believed t h a t these countries w i l l give t h e clearest indicat i o n of existing links. T h e macroeconomic variables t h a t w i l l be considered include: governmental expenditure; domestic expenditure; governmental policies; interest rates; and exchange rates. T h e European U n i o n was officially established i n 1992 and t h e E u r o was introduced as c o m m o n currency i n 2002 among twelve of its fifteen members. I n 2004 a further t e n countries j o i n e d t h e U n i o n , b u t d i d not adopt t h e E u r o as currency. T h e a i m of the European U n i o n is t o establish p o l i t i c a l a n d economic u n i t y amongst its member states. T o achieve t h i s aim, developing countries w i t h i n t h e u n i o n greatly depend o n the experience and resources of t h e developed members. Thus, t h e results discovered i n this s t u d y w i l l be valuable t o develop realistic expectations for construction i n d u s t r y g r o w t h i n developing countries.

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Figure I: European Construction M a r k e t Segmentation by Value

Germany 17%

Rest of Europe 29%

United Kingdorr 16%

France 12%

T h e construction i n d u s t r y consists of three m a i n sectors, namely: new construction; refurbishment and maintenance; and c i v i l engineering. A c t i v ities w i t h i n each of these sectors can further be classified as either public or private sector work according t o whether i t is carried o u t b y t h e government or b y private companies respectively. T h i s report w i l l n o t make d i s t i n c t i o n between t h e specific industries w i t h i n w h i c h a company operates. T h i s paper aims t o analyse t h e effect of macroeconomic variables o n long-term performance of European construction companies. T h e s t u d y is l i m i t e d t o t h e European countries w i t h t h e largest construction i n d u s t r y as i t is believed t h a t phenomenon observed i n these countries w i l l be relevant i n subsequent countries. Five countries were identified t o hold t h e pred o m i n a n t market share i n European construction as measured b y o u t p u t volume. These are, i n decreasing order o f market share, Germany (17%), the U n i t e d K i n g d o m (16%), Spain (15%), France (12%), a n d I t a l y (11%). Figure I represents t h e market share of each c o u n t r y i n t h e European cons t r u c t i o n industry.

1.2 Course o f t h e Analysis I n t h i s analysis, a theoretical l i n k w i l l be d r a w n between G D P g r o w t h a n d the long-term performance of construction companies. Events a n d trends in t h e t i m e frame from 1993 u n t i l present w i l l be considered. T h e p r i m a r y determinants w i l l be t h e G D P g r o w t h o f each c o u n t r y a n d t h e stock price g r o w t h o f construction companies w i t h i n t h e said country. I n order t o clearly explain t h e correlation o f G D P t o performance, G D P w i l l be broken

114 CHAPTER 5. MACROECONOMIC DETERMINANTS down into its components. This will facilitate an analysis of the unique characteristics and consequential effects of changes in the components of GDP. Subsequently, this report will consider how disposable income influences home buying. It will consider governmental budget policies and how they have led to the popularity of public private partnerships and consequential legislation. It will also explain the specific requirement of EU membership and how this influences the said governmental policies and budget. It will then consider the influence of interest rates on investment behavior and resultant industry success or failure. Finally, the effect of exchange rates on incoming and outgoing investments will be described.

2 L i n k s between Gross D o m e s t i c P r o duct and G r o w t h of the Construction Market 2.1 G D P GDP is defined by the following equation: GDP = C + J + G + ( X - M )

(5.1)

The items C, I, G, X and M in the above equation are, respectively, domestic expenditure, investment expenditure, government expenditure, export income and expenditure on imports. Most existing literature portrays construction output as the motor that drives GDP. 1 This establishes a positive relationship between the two. Appendix I - V show on two axes GDP growth and stock returns over a period from 1993(the inception of the EU) to 2004. Clearly, the trends do portray that the former indeed is a trailing indicator for the latter. However, what is interesting is that although the trend of the industry returns has a tight link with GDP growth, the magnitude is different for each country. Beyond cross-sectional studies and longitudinal research, the following sub-sections will attempt a qualitative explanation on how each individual component of GDP has a unique impact on the market, and where possible, it will identify particular sectors that are affected. Therefore, beyond the broadly expounded non-linear relationship between construction output and GDP growth, we would like to suggest that as economies further advance, each component will also become more complicated, creating a situation where the effect on outputs may not be easily anticipated. Construction Management and Economics (2000), David Crosthwaite, "The global Construction market: a cross-sectional analysis", p. 619627, p. 620.

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2.2 G o v e r n m e n t a l E x p e n d i t u r e The role of government in the success of the construction industry cannot be understated and we can see clear indications from the movement of government expenditure and the share price returns of the companies. This is predominantly due to the fact that traditionally infrastructure developments depend upon governmental funding. Taking a look at Germany's post-reunification journey, massive subsidies and tax breaks granted by the government, resulted in a construction boom and thus an increase in the stock prices as investors expected this run to continue. However, with subsidies and tax breaks, this immediately has an impact on the government's deficit. In fact, almost all the countries mentioned have been running deficits over the past decade. However, Germany in particular has allocated a good part of its deficit to its infrastructure investments. As a result of this situation, they have now come under the EU microscope 2 due to its repeated flouting of the constraints set out in the Maastricht Criteria, which imposes a limit of 3% of GDP on the public deficit. In actuality, the government has reined in some of its taxes since the mid-1990s. On the graph above, we see negative returns in 1996. This is a result of an expiry of tax incentives given to construction projects in 1995. Further incentives were abolished in 2000, explaining investor sentiment of negative stock returns just prior to 2000 and continuing well into 2003. New ideas are thus being investigated currently. For example, in BadenWürttemberg, austerity measures are being imposed to help cut this budget deficit. Cuts in road construction among other areas worth a total of €Mio. 50 will be used to balance decreased tax revenues by up to €Mio. 135. In order not to compromise further development, the government has also successfully implemented the van-toll to reap some more revenues for road construction. In addition, Public Private Partnerships, already popular in UK are being encouraged. These steps help compensate for the lower incentives that have seen Deutsche Bahn, the biggest civil engineering investor, reduce their investments by 5% in 2005. The actual effect of Chancellor Merkel's new policies are unclear currently, but the yet-unpublished effects of Italy's 2005 decision to insert a 2% ceiling on the growth of public works expenditure, could hint towards answering whether a freeze on investment in infrastructure is indeed a worthy trade-off. Spain's government expenditure is also coming into greater focus, with the government pouring money into cutting edge museums, performing arts spaces and convention centers as part of its 2002/2003 Infrastructure plan. 3 The spillovers to galleries, stores and restaurants can only be good news in the civil works sector. As seen from the graphs again, the sheer magnitude of the 1.5BÌ11. city-wide overhauls shows an immediate impact on the year of 2002 and the following years show great promise to date. At the same BBC News, 14 Mar 2006, "Germany queried on budget deficit". Leigh Newman, Mar 2005, "Spain's New Golden Age", Budget Travel Online, p. 1-3.

116 CHAPTER 5. MACROECONOMIC DETERMINANTS time, the government is also ploughing huge investments into the public sector, 4 which has seen official public tenders rise by 11.2% per year, a great boost for the civil engineering sector. It has been able to circumvent some of the EU budget rules by excluding certain items from its budget accounts, e.g. the development of its high speed network. Even when a certain sector grows due to public investment, not all the companies will benefit from the decisions made by the government. For example, certain sub-sectors such as road works, earth-moving and bridge construction saw a recovery in their fortunes at the end of 2003, but with a decrease in investment in electric infrastructure, the public works showed growth that was not as high as it could have been. Therefore, it is vital that any potential private investors should take a closer look at each company's business lines and establish the link between current trends and the growth of the company. In short, while government expenditures clearly play a big part in the initial development of the construction industry, it cannot be sustained over the long-term, especially since the EU countries are part of a greater network. That said, there will be further stimulation to the EU-wide area as there are plans being made for a high speed rail network. According to current plans, 1700 new miles are due to be developed by 2010 and up to €Bill. 100 requested for a Trans-European Transport Network. Such an action will probably make a great impact on all countries in the area, but it will remain to be seen which country's industry will develop the most based on their companies' ability to tender for the projects.

2.3 D o m e s t i c E x p e n d i t u r e Besides direct government expenditure, domestic expenditure by a country's citizens also plays a vital role in the demand for the services of the construction industry. However, the decisions of individuals are influenced by several factors. The criterion that will be considered is personal disposable income and its interaction with inflation rates and taxes. In this regard, we will also use the aid of the two following charts. Clearly, the past decade has seen personal disposable incomes rise, with each country's increase not necessarily following the trend of GDP. This is immediately an indication that the rise in GDP need not strictly follow the trend of GDP due to the nature of its components. Housing prices have constantly been rising in most EU countries except for Germany, where there is a trend of declining real house prices. 5 One may expect that such a decrease will lead to an increase in activity since people axe theoretically now better able to afford new housing. However, in light of the government deficits mentioned earlier, certain measure taken "Construction Activity in Europe 2005", European Construction Industry Federation, p. 54-57. May 29th, 2003, "A Boom Out of step", The Economist, Survey: Property.

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Figure II: Personal Disposable Income and Inflation Across Countries Personal Disposable Income

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Time -France -"-Germany -

Italy —x- Spain

United Kingdom j

Inflation

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Time —•—France -»-Germany

Italy

Spain —*- United Kingdom

118 CHAPTER 5. MACROECONOMIC DETERMINANTS by the government also affect their decisions. The German parliament is due to end subsidies - the most expensive paid out by the government, costing €Bill. 11.4 last year - to homebuyers. 6 Quantitatively, the cuts in subsidies for home-buyers will weaken demand for new housing since it could normally help fund the purchase of about 100,000 new houses and apartment per year. This will undoubtedly change the perspective of how investments are being made as well. Perhaps it could result in the boom for rental housing and renovations instead, since it might just be more worthwhile to maintain instead of searching for a new place. This can be likened to Italy's situation. 7 Up to 71% of Italy's population stays in their own homes. This is partly because up to 10% of their mortgage interest payments can be deducted from their personal income tax. This tax incentive has empirically been shown to be small as there is little other government intervention in the market for housing finance, with less than 2% of loans to households benefiting from public subsidies for interest rate payments. However, the removal of strict regulations that used to cap rents and excessively protect tenants in the rental market, has spurred new purchases by companies and individuals for investment purposes. In Spain, we see rapid growth in jobs and wages as the economy catches up with the rest of the EU. This bodes well for the construction industry in Spain as the tax relief given to home-buyers is among the largest in the whole of Europe. In addition, the tax incentive is provided for the purchase of a main residence without any means-testing. This might have implications similar to Germany in the future, if Spain is unable to obtain sufficient governmental revenue to sustain its incentive schemes, the government might have to drastically cut spending. Across Europe, we see that inflation has been going down, and this will also lead to lower interest rates, stimulating increased borrowing and more improvements in the construction industry for all sectors. As mentioned in the previous section, the exact nature of lower inflation cannot be compared clearly by country but perhaps, as a later section will show, changing interest rates will induce different types of investments into or out of Europe, introducing yet another facet for the development of the construction industry. Taxes, though high across the different countries, might not at first glance play that important a role, but off-the-cuff, as lower income groups see their personal disposable income rise beyond new brackets, this might ultimately cause them to rein in some of their expenditure.

Claudia Rach, Dec 15 2005, "German Lower House Approves End to Homebuyers' Subsidy in 2006", Bloomberg News. "Housing Finance in Italy", Datamonitor, p. 1-6.

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Expenditure

2.4.1 Public P r i v a t e Partnerships Governmental laws and policies influence investor behavior at various levels. The legislative structure sets the investment sentiment and can either stimulate or suppress investments in the sectors affected by specific laws and policies. The European Union legislation that governments may not have debts exceeding 40% of the country's GDP have stimulated the formulation of Public Private Partnership (PPP) projects and consequent legislation to govern the financing and execution of these project types. These projects provide an alternative financing structure for construction, and more specifically, infrastructure projects. 8 They create a way to take debt off the government's balance sheet, hiding the true level of debt and enabling countries to comply with the Treasury's fiscal rules. 9 This section will discuss PPP policies and how they influence the performance as well as the structure of the construction industry in the selected countries. It will also show why PPP projects may stimulate investment in construction companies. Apart from enabling governments to invest in infrastructure without incurring excessive debt, Public Private Partnerships (PPP) aim to spread risks and benefits of certain public services between the public and private sector. It is a long-term relationship that involves multi sector skills, expertise and financial structures. Public Finance Initiative (PFI) is a subset of PPP and involves the concessions, or franchises, of public sector assets contracted with the private sector to provide long-term services. 10 PPP and PFI change the level of risk, the percentage of required capital investment, the time frame and the nature of cash flow generated by a project. A traditional construction contract has a relatively short time frame. At maximum it stretches from conception to design through to implementation, and at minimum it is limited to physical construction that begins at site occupation and ends at works completion. A PPP project involves these steps, but also includes implementation and facilities management. In the short term PPP means greater risk as it involves a much larger capital investment and greater uncertainty. In the long-term, risk is effectively reduced as stable, long-term cash flows result after implementation. This long life time nature, an average of 30 years, of PPP projects, compared to the relatively short term nature of economic cycles, an average of seven to ten years, means their performance is much less dependent upon economic cycles. 11 Large, multidisciplinary construction companies are able to manage their risks as they have a long-term influence over the whole life span of 2004: 2004: 2004: 2004:

Considerations of the UEAP Construction Forum, p. 1. S&P's Global Project Finance Yearbook, p. 26. S&P Infrastructure Finance, p. 3. S&P Infrastructure Finance, p. 1-2.

120 CHAPTER 5. MACROECONOMIC DETERMINANTS the project. 1 2 This long-term stability may improve the credit strength of the construction company, which result in better credit ratings and thus enhanced lending power, which stimulates further investment. Credit strength is a prerequisite for success of PFI projects as they involve high levels of long-term debt due to project cycles, required prepayments, and lower margins. This means that smaller construction companies that lack the necessary skill to judge the long-term risks involved in PFI projects may perform worse and have lower credit ratings. This leads to much tougher competition and higher entry barriers for smaller companies, as a few powerful, large construction giants, with a wide field of expertise, will dominate the industry. 13 Successful PPP projects have positive results for both companies and investors. These projects are characterised by stable, predictable, longterm income from construction, management, and service activities. This means stable revenues for shareholders and increased trust. PPP and PFI contracts may also result in transactional activity. Construction contractors want to sell their stakes in the service company in order to have more free capital to invest in further construction projects and companies, whose core business is facilities management, or those who want to expand their skill base, in turn want to buy these stakes. 14 The recent popularity of PPP projects may explain the recent, general upturn in the construction industries in the examined countries. This significant upturn is visible from mid 2002 to the beginning of 2003 and continues into 2006. This upturn is further evident when considering the major partners in PPP projects namely: Balfour Beaty and Carillion PLC in the UK; Bilfinger Berger AG and Hochtief AG in Germany; and Grupo Ferrovial in Spain. These companies tend to act very close to the industry average in their country, which may be an indication of their size and characteristic market leadership. 15

2.4.2 L o n g - t e r m Interest Rates Financing in the construction industry is largely debt-driven as the companies see little need to raise and hold a large amount of capital. Therefore, the amount of investment, both domestic and foreign, is largely determined by the cost of holding debt, i.e. interest rates. Intuitively, the more expensive the cost of holding debt, the less inclined companies will be to borrow large amounts to finance new projects and investments. Taking a closer look at the following chart showing the interest rate trends for the United Kingdom, we see that it is an anomaly from the shape of a normal yield curve. Typically, it is upward sloping asymptotically - the longer the maturity, the higher the yield, with diminishing marginal growth. In the current case, it might result in lower 12 13 14 15

2004: S&P 2004: S&P 2004: S&P 2004: S&P

Infrastructure Finance, p. 4. Global Project Finance Yearbook, p. 67. Infrastructure Finance, p. 1-5. Infrastructure Finance, p. 3.

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Figure I I I : Yield Curve as at February 9th, 2006 for GBP

0.00

5.00

10.00

15.00

20.00

25.00

30.00

Borrowing period (years)

Figure IV: Yield Curve as at February 9th, 2005 for the US$

Borrowing period (years)

domestic investment and therefore a constriction of construction investments as companies delay their borrowing, in anticipation of lower future interest rates. Therefore, to a certain extent, we would expect a negative trend in the amount of upcoming domestic investments for the short term. But, if there actually is a reversal in trend, there will almost be a confirmed stimulus for domestic companies to re-commence heavier borrowing for more capital intensive projects once again. The current flattening of the yield curve could however be a positive sign for the companies as they will probably try to refinance their debt at lower cost due to the downward sloping curve. This has implications on the foreign front as well. The United States would not provide a very good example at the current moment as they themselves are also facing an inverted yield curve. But if it were to revert back to its normal shape, like the one below in early 2005, there might be increased investment in foreign countries by the European countries since it would now be cheaper to borrow in US dollars.

122 CHAPTER 5. MACROECONOMIC DETERMINANTS We will not introduce exchange rates into this discussion of interest rates as any possible arbitrage will be eliminated quickly. The following section will thus provide a keener insight on how exchange rates determine the amount of incoming and outgoing international investment.

2.5 E x c h a n g e R a t e s The Euro was introduced in 1991 at an exchange rate of US$ 1.17. The stock market bubble in the United States stimulated the dollar and caused the depreciation of the Euro to its all time low of 83 cents in October 2000. Since then this so-called boom in the US collapsed and the Euro reached new highs in December 2004. It is currently stable around USS 1.20. This is due the price stability strategy followed by the European Central Bank (ECB). The ECB is very committed to the this strategy and pursues it regardless of consequential unemployment, financial distress or organisational problems in the productive system. 16 Construction activity is characterised by high capital investment. This means that a small change in the exchange rate can greatly affect a company's ability to invest abroad. Most of the large construction companies in Europe also conduct business in the United States and therefore the exchange rates will affect their performance. This section will consider how the €/US$ exchange rate influences the stock price performance of construction companies within the European countries. High exchange rates dampen foreign investments, while foreign investors are attracted by lower exchange rates as they can realise better returns. Thus, a weak currency means stronger competition for domestic companies from foreign investors combined with lower competitive power as foreign investors. A strong currency has exactly the opposite effect and leads to better performance of domestic companies as they are less likely to face competition from foreign investors and have stronger investment power abroad. Figure V graphs the movement of the Euro/US$ exchange rate from 1999 until present. Figure V shows the overall performance of the construction industries in France, Germany, Italy, Spain, and the United Kingdom. The United Kingdom does not use the Euro as currency and therefore it will be neglected in this discussion. Looking again at the industry returns in Appendix I-V, we may deduce that the lower performance of companies between 1999 and 2002 can be attributed to tougher competition from foreign investors driven by a weaker domestic currency. This weaker currency can be attributed to the strong dollar and also to uncertainty regarding the new currency. This uncertainty is a very important aspect as the majority of investors is risk averse and therefore favour a stable exchange rate in the country in which they choose to invest. Thus, this uncertainty dampens investment and leads to weaker performance of companies. The Euro started to regain strength in 2002 and continued to increase until the end of 2004. It has remained relatively stable http://mondediplo.com/2005/07/05bank.

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Figure V: The €/US$ Exchange rate from 1999 to 2006

Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan 99 99 00 00 01 01 02 02 03 03 04 04 05 05 06 Time

over the last twelve months. This combination of strength and stability of the currency have stimulated foreign investment and explain the positive performance of companies during this time.

3 Conclusion This report has aimed to explain the influence of macroeconomic variables on the long-term performance of construction companies in Europe. The results will be used to determine whether phenomena in the larger Western European countries can be used to project realistic expectations in newer European Union member countries. In order to do this, the relationship between the Gross Domestic Product and the stock price growth of construction companies in the United Kingdom, Germany, Spain, France and Italy, was analysed. GDP was then broken down into its component parts to determine how specific characteristics and variables interrelate to drive construction company performance. Gross Domestic Product is a strong driver of the domestic construction industry, but the extent of the relationship between these factors differ between countries. This is due to the fact that GDP is made up of various macroeconomic factors. This complexity of GDP requires that it is broken down into its component parts: governmental expenditure, domestic expenditure, investment expenditure, import expenditure and export revenues - in order to describe the macroeconomic environment within a country clearly and to explain the relationship for each country. Governmental expenditure is a great driver of the domestic construction industry. The governmental budget greatly affects the spending policies

124 CHAPTER 5. MACROECONOMIC DETERMINANTS followed within a country. The effect of EU membership and governmental budgetary constraints has influenced governmental spending policies in recent years. There is a clear link between governmental policies and consequent spending and the performance of the construction industry as a whole. Policies affect specific sub-sectors of the construction industry and therefore one sector may see weak performance, while another sector is experiencing a boom. Domestic expenditure drives the demand for construction services. Domestic expenditure in turn is influenced by several factors, such as disposable income, inflation, taxes, and governmental subsidies. The interaction of these factors and their influence on domestic expenditure can be seen in the residential construction market where low tax incentives or subsidies can suppress spending even when disposable income increases. Inflation rates influence borrowing. Lower inflation rates increase borrowing and spending power. The lower inflation rate across Europe has boosted construction. Investment expenditure is influenced by expectations and also interest rates. The decrease in governmental investment, driven by the budgetary constraints discussed above has influenced private investment in recent years. Governments can no longer afford to fund infrastructure projects themselves and has thus created legislation and policies that attract private investors to this sector. The result is Public Private Partnership projects, where the risks and benefits of public projects are shared between the public and the private sector. These projects require multidisciplinary skills and have led to merger and acquisition transactions by construction companies who aim to remain competitive. These projects further lead to stable cash flows and improved trust from shareholders. This leads to improved share price performance of listed companies. The construction industry is largely debt driven and thus performance of companies is driven by interest rates - the cost of holding debt. Construction activity will be suppressed in times where a decrease in interest rates are expected and may increase when a future interest rate hike is expected. This is important as investment is not necessarily driven by the current situation, but rather by expected future levels. Interest rates also drive foreign investment as a lower interest rate in example the US will stimulate US investment by European companies. Exchange rates influence foreign investment as it determines a company's competitive power abroad. A weaker currency suppresses foreign investment and at the same time increases local competition from foreign companies who have greater investment power in your country. This leads to weaker performance of domestic companies in times of weak currency and improved performance when the local currency is strong. Thus, it can be said that macroeconomic variables greatly influence long-term performance of European construction companies. It has been shown that each variable of GDP has a unique effect on company performance and therefore each macroeconomic variable must be considered independently. Keeping the complexity of each factor in mind the newer

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EU member countries can make use of these facts to formulate realistic future expectations of construction company performance.

126 CHAPTER 5 MACROECONOMIC DETERMINANTS

References Backhaus, Klaus/Erichson, B e r n d / P l i n k e , W u l f f / W e i b e r , R o l f ( 2 0 0 3 ) : Multivariate Analysemethoden - Eine anwendungsorientierte Einführung, 10th Edition, Berlin, Heidelberg, New York. B o s c h , G e r h a r d / P h i l i p s , P e t e r ( 2 0 0 3 ) : Building Chaos: An international comparison of deregulation in the construction industry, London. D e u t s c h e B a n k R e s e a r c h ( 2 0 0 4 ) : Aktuelle Themen: Demografie Speziai, in Themen International Economics, No. 294, 28.4.2004. D e u t s c h e r B u n d e s t a g ( 2 0 0 5 ) : Jahresgutachten 2005/2006 des Sachverständigenrates zur Begutachtung der gesamtwirtschaftlichen Entwicklung, Drucksache 16/65, 10.11.2005. Falk, B e r n d (1996):

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H a u c h - F l e c k ( 1 9 9 9 ) : Verschleiert, verschwiegen, verraten, in: Die Zeit, Issue 48, 1999, call date: 11.05.2006. H o m a n n , K l a u s ( 2 0 0 0 ) : Bau-Projektmanagement, in: Schulte, Karl-Werner: Immobilienökonomie Band 1: Betriebswirtschaftliche Grundlagen, 2. Edition, München, 2000, p. 229-274. I b l h e r , F e l i x ( 2 0 0 5 ) : Systematisierung der Immobilienfinanzierung, in: Schulte, Karl-Werner: Immobilienökonomie Band 1: Betriebswirtschaftliche Grundlagen, 3. Edition, München, 2005, p. 534-540. I t e C (2005):

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I s e n h ö f e r , B j ö r n / V ä t h , A r n o ( 2 0 0 0 ) : Projektentwicklung, in Schulte, Karl-Werner: Immobilienökonomie - Betriebswirtschaftliche Grundlagen, 2. Edition, München, 2000, pp. 151-228. J a n s s e n , J ö r n ( 2 0 0 0 ) : The European construction industry and its competitiveness: A construct of the European commission, in: Construction management and economics, No. 18, p. 711-720.

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L e e , K i s e o k / N i , S h a w n ( 2 0 0 2 ) : On the dynamic effects of oil price shocks: A study using industry level data, in: Journal of monetary economics, Vol. 49, Issue 4, p. 823-852. M a l e , S t e v e n / M i t r o v i c , D r a g a n a ( 1 9 9 9 ) : Trends in world markets and the LSE industry, in: Engineering, Construction and Architectural management, No. 1, p. 7-20. M a u r e r , P e t e r ( 2 0 0 5 / 2 0 0 6 ) : Abkehr vom Öl- lohnen sich alternative Energien?, in: Immobilienwirtschaft, No.l2/2005-No. 1/2006, p. 52-56. n C R I S P ( 2 0 0 3 ) : Developing a business model for UK construction, , call date: 20.4.2006. Porter, M . (1996):

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R o s e n t h a l , A l a n B . ( 2 0 0 5 ) : Can this profit margin be saved? Inflation in the costs of building materials in: Real Estate Finance, w. No., October 2005, p. 8-10. R o t t k e , N i c o ( 2 0 0 5 ) : Real Estate Private Equity: Finanzierung durch externes Eigenkapital, in: Schulte, Karl-Werner: Immobilienökonomie Band 1: Betriebswirtschaftliche Grundlagen, 3. Edition, München, 2005, p. 591-598. S c h ä f e r , D o r o t h e a ( 2 0 0 3 ) : Die "Geiselhaft" des RelationshipIntermediärs - eine Nachlese zur Beinah-Insolvenz des Holzmann-Konzerns, in: Perspektiven der Wirtschaftspolitik, Vol. 4/Issue 1, p. 65-84.

128 CHAPTER 5. MACROECONOMIC DETERMINANTS Schulte, K a r l - W e r n e r / H u p a c h ,

Ingo (2000):

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der Immobilienwirtschaft, in: Schulte, Karl-Werner: Immobilienökonomie Band 1: Betriebswirtschaftliche Grundlagen, 2. Edition, München, 2000, p. 5-12. S c h w a r z , S t e f f e n ( 1 9 9 7 ) : Zukunftssicherung für die Bauwirtschaft: in vier Schritten aus der Krise, Wiesbaden. S p i t z k o p f , H o r s t A l e x a n d e r ( 2 0 0 2 ) : Finanzierung von Immobilienprojekten, in: Schulte, Karl-Werner /Bone-Winkel, Stephan: Handbuch Immobilien-Projektentwicklung, 2. Edition, Köln, 2002, p. 257-285.

Straub, Stephane/Laffont, Jean-Jacques/Guasch, J. Luis ( 2 0 0 5 ) : Infrastructure Concessions in Latin America: government led renegotiations, in: World Bank: Policy Research Working Paper Series, No. 3749. T i m m e r s , P . ( 2 0 0 0 ) : Electronic commerce: strategic and models for business-to-business trading, Chichester. Treacy, M . / W i e r s e m a , zur Spitze, Frankfurt.

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W.A. ( 1 9 9 9 ) : Schwarze Zahlen bei Holzmann, in: Immobilienzeitung, 15.7.1999, News, p. 3. W.A. ( 2 0 0 1 a ) : Wirtschaftliche Lösungen mit Stahl - Geschoßbauten, , call date: 12.5.2006. W.A. ( 2 0 0 1 b ) : Dragados increase the growth of its Portuguese subsidiary, in: Financial Times, 25.7.2001, p. 9. W.A. ( 2 0 0 6 a ) : Bauwirtschaft in Europa: Deutschland kommt wieder, in: Immobilienzeitung, 16.3.2006, News, p.2. W.A. ( 2 0 0 6 b ) : "Unser Kerngeschäft ist das Bauen", in: Frankfurter Allgemeine Zeitung, 1.4.2006, Unternehmen, p. 16. W.A. ( 2 0 0 6 c ) : Weltwirtschaft in guter Verfassung, in: Frankfurter Allgemeine Zeitung, 20.4.2006, Wirtschaft, p . l l . W.A. ( 2 0 0 6 d ) : Türkei Wirtschaft: Aktuelle wirtschaftliche Lage, , call date: 6.5.2006.

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W.A. ( 2 0 0 6 e ) : Vinci: Unternehmen: Konzessionen, , call date: 12.05.2006. W e i f e n s , P a u l ( 2 0 0 3 ) : Überwindung der Wirtschaftskrise in der Eurozone: Stabilitäts-, Wachstums-, und Strukturpolitik, Wuppertal. Wentz, Martin/BischofF, Thorsten/Gosewehr, Dörthe ( 2 0 0 5 ) : Stadtentwicklung durch Public Private Partnership, in: Schulte, Karl-Werner: Immobilienökonomie Band 3: Stadtplanerische Grundlagen, München, 2005, p. 805-833. W o o l d r i d g e , Jeffrey M . ( 2 0 0 3 ) : Modern Approach, Mason, USA.

Introductory Econometrics - A

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Appendix

Appendix I: Comparison between GDP Growth and Industry Returns in Germany

Time - · - Industry Returns

—·— GDP Growth

Appendix II: Comparison between GDP Growth and Industry Returns in United Kingdom

Time - · - Industry Returns

—·— GDP Growth

APPENDIX

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Appendix I I I : Comparison between GDP Growth and Industry Returns in Spain

_ <

Ci CO CO TP 1 Isσ> - io

*

P i l i

jj

ω>

Q

Q

+ co

Q

CHAPTER

. ANNOUNCEMENT

EFFECTS

R

S

As the sub-sample t-test as well as the Wilcoxon Rank-Sum test show, that the two samples are not statistically significant from each other. Although CAARs of cross-border transactions are slightly higher than those of domestic ones, the variation cannot be considered to be substantial, and therefore Hypothesis I I has to be rejected. Other (non construction specific) studies investigating the same issue come to the same result. 6 5 This observation can be taken as an indicator that capital markets are efficient, as it is inconsequential whether to acquire a target nationally or in a foreign country. Additionally, with respect to the construction industry it can again be argued with a higher complexity in fragmented foreign markets. 66 Also, since the business functions largely on tender offers, cross-border transactions do not necessarily have to lead to higher abnormal returns due to inefficient networks or mistrust against a foreign owner. Hypothesis I I I postulates that transactions realised with cash yield higher ARs for shareholders as compared to those where target shareholders are compensated by the exchange of stock. Table I I I presents the results of sub-sample analysis between cash (n=75) and stock (n=24) transactions. In contrast to our expectations, the two sub-groups are not significantly different from each other. Although ARs in cash take-overs tend to be higher as in stock take-overs, the deviation remains marginal. Indeed, it is difficult to explain this result, as existing research strongly confirms the method of payment hypothesis. 67 In order to solve this issue, further investigation - especially of the different capital gains tax regulations within the analysed countries - is required. Such an analysis exceeds the scope of this paper, though. According to Hypothesis IV, targets of transactions which are undertaken into a related industry should generate higher returns as compared to targets of unrelated take-overs. The observations of the analysis with 23 related 87 unrelated transactions are shown in Table IV.

65 66 67

Cf. Dunbolt (2004), p. 104. Cf. Eccles (1981), p. 449-451. Cf. Asquith/Bruner/Mullins (1990), p. 43; Wansley/Lane/Yang (1983), p. 22; Huang/Walkling (1987), p. 344; Suk/Sung (1997), p. 595; Heron/Lie (2002), p. 142.

3 EVENT STUDY

171

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Transaction Volume < Transaction Volume Median (Ν = 97) Median (Ν = 97) Ζ PCAR Ζ PCAR Ζ Value Value

Difference

-0.01% -0.72 0.47 -0.26% -0.60 0.55 -0.76% -1.15 0.25 -0.84% -0.72 0.47 -1.57% -0.63 0.53

Difference

0.21% -0.75 0.46 -0.20% -0.06 0.95 -0.23% -0.48 0.63 -0.01% -0.36 0.72 -0.01% -0.06 0.95

Table IV: Transaction Volume - Volume < Median vs. Volume > Median

0.11% 0.19% 0.42%** 0.59%** 0.47%

Cross-Border (Ν = 32) Domestic (Ν = 60) Ζ PCAR Ζ PCAR Ζ Value Value

Table III: Geographie Expansion - Cross-Border vs. Domestic Transactions

Small Sample (Ν = 92) CAR Ζ P- CAR Value

2 EMPIRICAL ANALYSIS

206CHAPTER

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S

For t h e analysis of t h e i m p a c t of t h e transaction volume on t h e rivals' C A R s , t h e large sample containing 194 transactions is used and split i n t o t w o sub-samples of equal size. For t h e entire large sample a n d b o t h subsamples, positive significant C A R s can be observed. E x a m i n i n g t h e C A R s for t h e sample w i t h smaller transactions, highly significant, strongly posit i v e reactions can be observed. These by far o u t r u n t h e a b n o r m a l returns w h i c h competitors of larger transactions experience w i t h i n t h e same event windows. T h e market power hypothesis can be rejected by t h e observations from the entire large sample and its sub-samples. Table I V shows t h a t t h e C A R s from smaller transactions are consistently higher t h a n those of t h e entire large sample. A t t h e same t i m e , t h e l a t t e r ones are constantly lower t h a n those of the rivals i n t h e overall sample. T h i s implies a negative effect from t h e size of t h e transaction o n t h e rivals' C A R s . Yet, t h i s effect cannot be explained by any of t h e given theories. T h e differences i n t h e abnormal returns between the sub-samples are consistent b u t insignificant and therefore Hypothesis I V cannot be rejected or a p p r o v e d . 3 6 F r o m s p l i t t i n g t h e small sample i n t o t w o sub-samples by t h e percentage of stake acquired, p a r t l y significant positive C A R s can be observed i n transactions where more t h a n 50% of t h e interest is transferred. A s explained before, t h e percentage of stake acquired indicates the degree of i n d u s t r y consolidation. Therefore, t h e observed results for t h e sub-sample w i t h an acquired stake above 50% a n d t h e i r significant positive difference from t h e other sub-sample are supported by t h e theory of a n t i c o m p e t i t i v e effects. I t may also be argued t h a t transactions w i t h a higher stake acquired t e n d t o result i n a more extensive public a t t e n t i o n w h i c h increases t h e signalling effects of t h e acquisition p r o b a b i l i t y a n d t h e p r o d u c t i v e efficiency hypotheses. T h e observation of p a r t l y significant positive differences i n the C A R s leads t o t h e approval of Hypothesis V .

2.5 Multivariate Regression & Results I n order t o identify combinations of transaction characteristics, w h i c h help w i t h t h e explanation of t h e variations i n t h e observed C A R s , a crosssectional linear regression analysis is carried o u t i n t h i s section. W h i l e t h e observed C A R is t h e dependant variable, i t can be determined w i t h η regression parameters χ i n a linear regression model. For formal details on the regression analysis see A p p e n d i x V I I . T h e dependent variables i n t h e regression models are t h e C A R s for t h e A n explanation for t h e relationship between transaction v o l u m e a n d t h e rivals' C A R s may be t h a t t h e acquisition p r o b a b i l i t y hypothesis p o t e n t i a l l y has a stronger signalling effect on competitors' returns w h e n t h e transaction volume is lower since t h e n t h e p r o b a b i l i t y of t h e r e p e t i t i o n of such a transaction w i t h another target increases ( i n t h e small a n d i n t h e large sample transactions w i t h a relatively low b i d value are more frequent t h a n deals w i t h a higher volume). B u t yet, no foundation for t h i s assumption could be found i n existing literature.

3 CONCLUSION

207

given event windows. For an explanation of t h e independent variables see Appendix IX. I n t h e model composition i n t h e regression analysis a number of combinations of independent variables a n d dependent variables (i.e. C A R s from different event windows) have been tested. A f t e r extensive model testing, only t w o models w i t h any explanatory power could be derived. T h e first model (Cf. A p p e n d i x X , M o d e l 1) analyses t h e i m p a c t of t h e characteristics of t h e C O N T R O L and V O L U M E variables on t h e C A R observed i n event w i n d o w [ - 2 ; + 2 ] . T h e M o d e l includes the entire large sample of 194 transactions. A s t h e transaction volume is measured i n m i l lions of US$ whereas t h e C A R s are expressed i n percentages, t h e absolute c o n t r i b u t i o n of t h e V O L U M E variable is expectedly low. However, t h e negative sign underlines t h e results derived from t h e univariate analysis of t h e V O L U M E (Cf. Section 2.4, Table I V ) w h i c h s t i l l remain unexplained. T h e gaining of C O N T R O L t h r o u g h t h e acquisition i n t h i s model has a significant negative i m p a c t . A s there is an obvious relation between t h e A C U I R E D S T A K E and t h e p r o b a b i l i t y t h a t t h e bidder gained C O N T R O L of t h e target, this variable was not considered i n t h e model. Nevertheless, w h i l e there is a high significance of t h e overall model, its explanatory power for t h e variation in t h e observed C A R s is q u i t e low, as indicated b y an R 2 of o n l y 0.025. T h e second model (Cf. A p p e n d i x X , M o d e l 2) analyses t h e i m p a c t of t h e characteristics of t h e D I V E R S I F I C A T I O N S T R A T E G Y , G E O G R A P H I C E X P A N S I O N and Transaction V O L U M E variables o n t h e C A R observed i n event w i n d o w [—1; + 1 ] . A g a i n , an insignificant relationship t o t h e transaction V O L U M E can be observed. I n line w i t h t h e univariate analysis of t h e G E O G R A P H I C E X P A N S I O N variable (Cf. Section 2.4, Table I I I ) , M o d e l 2 attests t h e negative effect of domestic transactions of t h e observed C A R s . Furthermore, t h e c o n t r i b u t i o n of related deals is, j u s t as i n t h e univariate analysis (Cf. Section 2.4, Table I ) , positive ( b u t i n t h i s model insignificant) w h i c h could again be explained w i t h t h e consolidation of t h e i n d u s t r y resulting i n c o m p e t i t i v e effects and a higher p r o b a b i l i t y of subsequent merger activity. However, i t should be noted t h a t model 2 is not significant at a sufficient level w h i l e t h e explanatory power is j u s t as low as of model 1. Still, after extensive model testing, these t w o models remain t h e ones w i t h t h e most significant a n d relevant results.

3 Conclusion, Results Classification L· Further Research C o m i n g back t o t h e four basic theories on t h e effects on rivals' returns, t h e results of t h e performed univariate analyses a n d t h e regression models can be classified as either approving or rejecting these theories for t h e European construction industry. S t a r t i n g w i t h t h e t h e o r y of a n t i c o m p e t i t i v e effects (as suggested b y t h e

208CHAPTER

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S

US F T C 3 7 and t h e A n t i t r u s t D i v i s i o n of t h e US D O J 3 8 ) leading t o facilit a t e d unilateral effects a n d coordinated interaction, t h e positive significant relation between related transactions a n d rivals' C A R s constitutes a strong support. A n a d d i t i o n a l i n d i c a t i o n of t h e v a l i d i t y of t h i s theory for t h e European construction i n d u s t r y is t h e positive difference of t h e reaction of competitors' returns between transactions i n w h i c h control of t h e target was gained. These t w o observations are p a r t l y supported by t h e results of t h e m u l t i v a r i a t e analysis a n d provide an approval of t h e a n t i c o m p e t i t i v e effects theory. T h e productive efficiency t h e o r y (as suggested b y Eckbo ( 1 9 8 3 ) 3 9 ) is p r i m a r i l y supported by t h e overall positive competitors' C A R s observed for b o t h , t h e small a n d t h e large s a m p l e . 4 0 I t should be noted t h a t t h e existence of other effects leading t o positive C A R s t o rivals has already been proved. For this reason, i t is not possible t h a t t h e positive C A R s observed for t h e t w o samples are exclusively t h e result of t h e effects suggested by the productive efficiency theory. T h e acquisition p r o b a b i l i t y theory (as suggested by A k h i g b e / M a d u r a (1999) for t h e b a n k i n g i n d u s t r y 4 1 ) can also be proved for t h e European construction i n d u s t r y w h e n looking at t h e finding of t h e univariate analysis t h a t transactions w i t h a lower b i d value i m p l y a higher p r o b a b i l i t y of t h e r e p e t i t i o n of subsequent deals w h i l e t h e likelihood. However, i t must be added t h a t t h i s assumption seems v a l i d b u t has not been derived from any existing evidence i n previous literature. Finally, t h e results of t h e conducted s t u d y lead t o t h e finding t h a t t h e market power theory (as suggested by K n a p p ( 1 9 9 0 ) 4 2 ) cannot be applied for the European construction i n d u s t r y as none of t h e analyses revealed any significant negative share price reactions by i n d u s t r y rivals. I n t h e analysis of t h e impact of t h e volume of t h e merger on t h e returns t o rivals an inverse relationship was found (Cf. Section 2.4, Table I V ) . For t h e large sample, deals w i t h a lower b i d value consistently lead t o higher positive returns t o rivals w h i l e larger transactions result i n lower positive returns t o rivals. There can no e x p l a n a t i o n be found for this relationship i n existing literature. A s a suggestion for further research t h e empirical testing of t h e stated assumption t h a t lower b i d volumes lead t o a higher p r o b a b i l i t y of repeating merger activity, a stronger signalling effect from t h e acquisition p r o b a b i l i t y hypothesis a n d therefore higher returns t o rivals.

3 7 3 8 3 9 4 0

4 1 4 2

Cf. US Federal Trade Commission (1992), Ch. 2. If. Cf. US D e p a r t m e n t of Justice (2002), Ch. I I . Cf. Eckbo (1983), p. 271f. I t should be noted t h a t t h e existence of other effects leading t o positive C A R s t o rivals have already been proved. Therefore, i t is not possible t h a t t h e positive C A R s observed for b o t h , t h e small a n d t h e large sample are t h e result of exclusively effects explained by t h e p r o d u c t i v e efficiency theory. Cf. A k h i g b e / M a d u r a (1999), p. 16. Cf. K n a p p (1990), p. 705.

REFERENCES

209

References A k h i g b e A . / M a d u r a , J. (1999):

T h e I n d u s t r y Effects Regarding t h e P r o b a b i l i t y of Takeovers, in: F i n a n c i a l Review, Vol. 34 Issue 3, p. 193-229.

Bley, J / M a d u r a , J . (2993):

I n t r a - I n d u s t r y and I n t e r - C o u n t r y Effect of European Mergers, in: Journal of F i n a n c i a l Economics a n d Finance, Vol. 27, Issue 3, p. 373-395.

Boehmer, E . / M u s u m e c i , J./Poulsen, A . B . (1991):

EventS t u d y M e t h o d o l o g y under C o n d i t i o n s of Event-Induced Variance, in: Journal of Financial Economics, Vol. 30, p. 253-272.

Brown, S . J . / W a r n e r , J . B . (1980):

Measuring Security Price Performance, in: Journal of F i n a n c i a l Economics, Vol. 8, Issue 3, p. 205258.

Choi J./Russell J· (2004): Economic Gains around mergers and acquisitions in the construction i n d u s t r y of t h e US, in: Canadian. Journal of C i v i l Engineering, Vol. 31, p. 513-525. D e Fusco R . A . / F u e s s S . M . (1991):

T h e Effects of A i r l i n e Strikes on Struck and Non-struck Carriers, in: I n d u s t r i a l and L a b o r Relations Review, Vol. 44, Issue 2, p. 324-333.

Delaney, F . T . / W a m u z i r i S.C. (2004):

T h e i m p a c t of mergers and acquisitions o n shareholder w e a l t h i n t h e U K construction industry, in: Engineering C o n s t r u c t i o n L· A r c h i t e c t u r a l Management, Vol. 11, p. 63-73.

D o d d , P . / W a r n e r , J. B . (1983): O n Corporate Governance A S t u d y of P r o x y Contest, in: Journal of F i n a n c i a l Economics, Vol. 11, p. 401-438. Eckbo, B . E . (1983): Horizontal Mergers, Collusion and Stockholder W e a l t h , in: Journal of F i n a n c i a l Economics, Vol. 11, p. 241-273. Fama, E . (1991):

Efficient c a p i t a l markets: nance, Vol. 46, p. 1575-1617.

I I , in: Journal of Fi-

K n a p p , W . (1990): Event Analysis of A i r Carrier Mergers a n d Acquisitions, Review of Economics Sz Statistics, Vol. 72, Issue 4, p. 703-707. K y l e , R . / S t r i c k l a n d , T . H . / F a y i s s a , B . (1992):

C a p i t a l markets' assessment of airline restructuring following deregulation, in: A p p l i e d Economics, Vol. 24, Issue 10, p. 1097-1102.

210CHAPTER

. ANNOUNCEMENT

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R

S

Lang, L . / S t u l z , R . (1992):

Contagion a n d C o m p e t i t i v e I n t r a I n d u s t r y Effects of B a n k r u p t c y Announcements, in: Journal of Finance Economics, Vol. 32, p. 45-60.

M a c K i n l a y , C . (1997):

Event Studies i n Economics a n d Finance, in: J o u r n a l of Economics, Vol. 35, p. 13-39.

M i t c h e l l , M . L . / M u l h e r n , J . H . (1996): T h e i m p a c t of indust r y shocks on takeover a n d r e s t r u c t u r i n g activity, in: Journal of F i n a n c i a l Economics, Vol. 4 1 Issue 2, p. 193-229. M o r g a n Stanley C a p i t a l I n t e r n a t i o n a l (2005):

M S C I Stan-

d a r d I n d e x Series Methodology, New York.

Scheffman, D . / C o l e m a n M . (2003): Q u a n t i t a t i v e Analysis of P o t e n t i a l C o m p e t i t i v e Effects from a Merger, Federal Trade Commission W o r k i n g Paper, available at www.ftc.gov/be/quantmergeranalysis.pdf . Song, M . H . / W a l k l i n g , R . A . (2000): A b n o r m a l Returns t o R i vals of A c q u i s i t i o n Targets: A Test of t h e " A c q u i s i t i o n P r o b a b i l i t y H y p o t h esis", in: Journal of F i n a n c i a l Economics, Vol. 55, p. 143-171. U S D e p a r t m e n t of Justice (2002):

C o o r d i n a t e d Effects i n Merger Review: F r o m Dead Frenchmen, t o B e a u t i f u l M i n d s and M a v ericks, A n Address by W . J . Kolasky, D e p u t y Assistant A t t o r n e y General of t h e A n t i t r u s t Division, U.S. D e p a r t m e n t of Justice, available at w w w . d o j .gov / a t r / p u b l i c / speeches/11050.htm.

U S Federal Trade Commission (1992): Guidelines, available at: horizJx>ok/hmgl.html.

1992 Horizontal Merger http://www.doj.gov/atr/public/guidelines/

APPENDIX

211

Appendix

A p p e n d i x I: T h e Set of P o t e n t i a l Rivals ( D e s c r i p t i v e Split

by

industry

Split

by

Statistics)

geography

Other C'onstrRelated

28%

Cement. Concrete & ( »lass Products

26%

A p p e n d i x I I : Hierarchical Composition of the S I C code System SIC Code System

IS40: General Building Contractors for NonResidential Bldg.

Source :

http://www.sec.gov/info/edgar/siccodes.htm .

212

CHAPTER 8. ANNOUNCEMENT

EFFECTS RIVALS

A p p e n d i x I I I : D i s t r i b u t i o n H i s t o g r a m of t h e N u m b e r of Rivals in t h e Sets 50x

1

4

6

9

12

14

17

20

23

25

28

31

33

34+

Numbers of Rivals (Classes)

• Frequency

A p p e n d i x I V : Calculation of

CARs

T h e c u m u l a t i v e a b n o r m a l r e t u r n ( G A R ) for a g i v e n s e c u r i t y j d u r i n g a n e v e n t p e r i o d [ — Τ ; + T ] h a s b e e n e s t i m a t e d as f o l l o w s :

t=+T CARjt = Σ (Rjt - {otj + ßj · Rmt )) t=-T where •

R j t = c o n t i n u o u s l y c o m p o u n d e d r a t e o f r e t u r n for a s e c u r i t y j a t t i m e t

•

Rmt

•

otj = intercept regression t e r m estimates for t h e m a r k e t m o d e l for security j

= c o n t i n u o u s l y c o m p o u n d e d r a t e o f r e t u r n for i n d e x m a t t i m e t

•

ß j = slope r e g r e s s i o n c o e f f i c i e n t e s t i m a t e s for t h e m a r k e t m o d e l f o r security j

APPENDIX

213

A p p e n d i x V : Standardisation of observed

SARt = SD AR +

CARs

ARt 2 + ψ + (Rmt-Rm) ^ where

•

S A R t = s t a n d a r d i s e d a b n o r m a l r e t u r n for t h e e v e n t d a t e t

•

A R t = a b n o r m a l r e t u r n for t h e e v e n t d a t e t

•

S D ^ r = estimated standard deviation of abnormal returns during the estimation period

•

Τ = number of days in the estimation period

•

R m t = m a r k e t r e t u r n for t h e e v e n t d a t e t

•

Rm

•

R m d = m a r k e t r e t u r n for d a y d i n t h e e s t i m a t i o n p e r i o d Τ

= average m a r k e t r e t u r n d u r i n g t h e e v e n t p e r i o d

Source :

C f . D O D D / W A R N E R ( 1 9 8 3 ) , p . 436.

A p p e n d i x V I : Derivation of the

Z-Values

jj Σ"=ι SARjt

Ζ -

where •

S A R j t = standardised a b n o r m a l r e t u r n for security j on t h e event date

t •

Ν = number of securities i n t h e sample

Source :

Cf. B O E H M E R / M U S U M E C I / P O U L S E N

( 1 9 9 1 ) , p . 270.

A p p e n d i x V I I : F o r m a l details on the Regression

CAR

=

a + η ßiXi i =

l

+ e

Analysis

w h e r e t h e e x p e c t e d v a l u e for t h e e r r o r t e r m e is:

E(ei tt)

= 0

214

CHAPTER 8. ANNOUNCEMENT

coqiocoqoocsioio c ò d o d d ^ i O H o ò OOOCMINOCOOQOH

j

£ èS ìS èS èS èS èS èS COOOOCOOlOlOlOlO

«3

Ο Ο io IN Ο 3 0 ) . 2 4 Nonparametric tests, w h i c h can be applied for a l l distributions, are therefore used i n small sub-samples ( n < 3 0 ) and for comparing t w o independent subs a m p l e s . 2 5 I n t h e l a t t e r case t h e W i l c o x o n rank s u m test is calculated whose test statistic is based on ranks a n d w h i c h can be applied on independent samples w i t h o u t k n o w i n g t h e v a r i a n c e . 2 6 Lastly, all regressions are controlled for autocorrelation using t h e D u r b i n W a t s o n s t a t i s t i c 2 7 a n d i n case of m u l t i v a r i a t e regressions for multicollineari t y using variance inflation f a c t o r s . 2 8 T h i s assures t h a t t h e regressions are valid i n t h e i r basic assumptions.

2 2 2 3

2 4 2 5

2 6

2 7 2 8

Cf. B o e h m e r / M a s c u m e c i / P o u l s e n (1991), p. 270. Even i n case of event clustering t h e Boehmer test is s t i l l powerful, Cf. B o e h m e r / M a s c u m e c i / P o u l s e n (1991), p. 266-267. Cf. B r o w n / W a r n e r (1985), p. 25. I n relevant cases t h e s t u d y verifies results b y c o n d u c t i n g a Kolmogorov-Smirnov test using a M o n t e - C a r l o S i m u l a t i o n in order t o check the n o r m a l i t y assumption. For further details, Cf. Massey (1951), p. 68-71. Cf. W i l c o x o n (1945), p. 80f; M c C l a v e / B e n s o n / S i n i c h (2001), p. 894899. Cf. D u r b i n / W a t s o n (1951), p. 161. Cf. B a c k h a u s / E r i c h s o n / P l i n k e / W e i b e r (2005), p. 89-92.

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3.3 Empirical Evidence from the European Construction Industry A n a l y s i n g t h e results of t h e 171 transactions under consideration, one can observe generally positive C A R s for b o t h bidder a n d t a r g e t s . 2 9 W h i l e on average t h e targets gain 14.5% over t h e event period, bidder increase t h e i r wealth b y 4.1%. T h i s finding, w h i c h is c o m m o n l y observable for t h e target s i d e , 3 0 surprises for t h e bidder side. 4% w e a l t h generation is significantly higher t h a n returns found i n prior research. 3 1 I n t h e following, t h e advisor impact on these p r e l i m i n a r y results should be i n t h e focus of t h e analysis.

3.3.1 Hypothesis I : Advisor Effect I n Hypothesis I, t h e w e a l t h effect of an advisor involvement is measured. O n the bidder side t h e empirical results reveal t h a t companies w i t h o u t advisor underperform i n longer event w i n d o w s w h e n measured against bidders t h a t hire an advisor (Cf. Table I ) . T h e differences amount up t o 3.89% b u t do not reach significance. Hence, a l t h o u g h t h e involvement of an advisor generally creates value, t h e differences are not statistically significant. Contrarily, i n event periods shortly a r o u n d t h e event date the reaction of bidders w i t h o u t advisors is strongly positive (1.58% at [0]) significantly o u t performing t h e c o n t r o l group. T h i s indicates p o t e n t i a l i n f o r m a t i o n leakages i n t h e market when h i r i n g an advisor as t h e reaction t o t h e new i n f o r m a t i o n is especially strong w i t h o u t an advisor.

2 9 3 0 3 1

Cf. A p p e n d i x V I I . Cf. Servaes (1991), p. 410-418; Agrawal/Jaffe (2003), p. 732-745. Cf. A g r a w a l / J a f f e / M a n d e l k e r (1992), p. 730-745.

0.285

0.83%

0.302

1.033

w/o advisor w/advisor Test statistics = 16 Boehmer η = 45 Boehmer CAR t-test Wilcoxon Z-Value CAR (%) Z-Value differe nce t-value Z-Value 17.35% Σ885 *** Ϊ3Λ2% Σ05Ϊ ** 3M% 07721 ΓΪ31 15.91% 2.939 *** 11.72% 2.856 *** 4.19% 0.821 1.033 14.69% 2.718 *** 10.81% 3.726 *** 3.88% 0.721 1.082 10.86% 2.428 ** 7.39% 3.574 *** 3.46% 0.625 0.656 5.50% 2.095 ** 5.69% 2.944 *** -0.19% 0.005 0 049 15.04% 3.142 *** 11.15% 3.82 *** 3.89% 0.813 118 8.44% 2.612 *** 5.16% 1.828 * 3.29% 0.992 1.033

3A1% ~J °·82^ ._ 2.57% *> **, *** denote significance at 10%, 5% and 1% level

1;2Ql

Event Window η , CAR (%) 1-20; 20] [-10; 10] [-5; 5] [-1;1] [0] [-4; 2] [-20;-1]

Table II: Hypothesis I - Advisor Involvement on Target Side

w/ο advisor w/advisor Test statistics η = 43 Boehmer η = 89 Boehmer CAR t-test Wilcoxon Event Window CAR (%) Z-Value CAR (%) Z-Value difference t-value Z-Value 1-20; 20] 1.52% ^0033 5Λ2% Σ474 ** -3.89% L352 -1 333 [-10; 10] 1.60% 0.502 3.05% 2.403 ** -1.45% 0.682 -0 405 [-5; 5] 2.17% 1.531 1.87% 1.424 0.31% 0.124 0.498 [-Iii] 2.45% 2.748 *** 1.10% 1.638 1.35% 1.159 0.901 [0] 1.58% 2.597 *** 0.15% 0.121 1.43% 2.022 ** 1.245 [-4; 2] 2.98% 2.033 ** 1.19% 1.303 1.79% 1.161 1.017 [-20;-1] -0.24% -1.14 2.56% 1.402 -2.80% 1.236 -1.522 [1;20] 0.18% -0.311 2.70% 2.232 ** -2.52% 1.546 -1.469 *, **, *** denote significance at 10%, 5 and 1% level.

Table I: Hypothesis I - Advisor Involvement on Bidder Side

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O n t h e target side, t h e findings are m a i n l y t h e same (Cf. Table I I ) . Even t h o u g h targets earn more i n transactions w i t h o u t an advisor, t h e i r p r e m i u m (3.93% i n [—20; 20]) is not statistically significant. T h e development of t h e C A R s over t h e event p e r i o d is rather parallel causing no statistical significant differences between t h e t w o groups. A p p a r e n t l y , the bargaining power of an investment b a n k o n l y plays a m i n o r role i n value generation. Success fees for t h e advisor m i g h t even lead t o t h e opposite effect, namely t h a t t h e advisor accepts a lower offer i n order t o complete the deal. I n fact, t h e difference m a i n l y stems from s h o r t l y around t h e announcement date w h i c h indicates a different level i n take-over p r e m i u m . Therefore, the i n i t i a l hypothesis of a positive value i m p a c t for t h e involvement of financial advisors for b o t h bidders and targets cannot be supported i n t h e sample under consideration.

3.3.2 Hypothesis I I : R e p u t a t i o n Effect E x t e n d i n g t h e p r i o r analysis, t h e s t u d y examines t h e w e a l t h gains for bidders a n d targets depending o n t h e r e p u t a t i o n of t h e advisor involved. T h e s t u d y distinguishes T i e r 1 and T i e r 2 advisors by t h e rank volume of advised transactions. Based on annual rank volumes per advisor from t h e S D C database, a three year m o v i n g average for each advisor is calculated w h i c h is t h e n used t o create an annually r a n k i n g of a d v i s o r s . 3 2 A n advisor is defined t o be T i e r 1 i f he ranks among t h e first five advisors for t h e relevant year. A l l other advisors are defined t o be T i e r 2 . 3 3 T h e results for t h e bidder side (Cf. Table I I I ) reveal t h a t acquirers supported by a T i e r 1 advisor earn a C A R of 6.2% compared t o 5.2% C A R for t h e opposite sub-sample i n a [—20; 20] interval. A l t h o u g h t h e i n d i v i d u a l results reach significance i n t h e Boehmer test t h e differences does not. T h e same result holds t r u e for a l l event windows i n d i c a t i n g t h a t there is no systematic and s t a t i s t i c a l l y significant effect of r e p u t a t i o n on t h e value generation. B o t h sub-samples C A R s develop rather parallel. These findings p o i n t o u t , t h a t apparently gains due t o a higher s k i l l level i n T i e r 1 banks are outweighed by higher fees. O n t h e other hand, an advisor charging higher fees i n a complex transaction does not d i m i n i s h value either. Thus, t h e market reaction i n b o t h cases is similar. T h e results are consistent w i t h those of R a u (2000) a n d L o w i n s k i / S c h i e r e c k / T h o m a s ( 2 0 0 4 ) . 3 4

Deals only include those w i t h European p a r t i c i p a t i o n . T h e relevant average for a specific transaction is composed of t h e year of t h e transaction and t h e t w o years before. Same methodology apply B o w e r s / M i l l e r (1990), p. 37f; Beit e l / S c h i e r e c k / U n v e r h a u (2003), p. 17. For an overview of T i e r 1 advisor, Cf. A p p e n d i x V I I I . Cf. R a u (2000), p. 322f; L o w i n s k i / S c h i e r e c k / T h o m a s (2004), p. 326328.

Event Window

Tier 1 advisor Tier 2 advisor Test statistics η = 11 Boehmer η = 34 Boehmer CAR t-test Wilcoxon CAR (%) Z-Value CAR (%) Z-Value difference t-value Z-Value [-20; 20] 11.34% (Ü49 13.98% 2Ä97 ** -2.64% OÖ7 -0.343 [—10; 10] 6.81% 0.487 13.22% 2.976 *** -6.42% 0.684 -0.713 [-5; 5] 8.61% 1.697 * 11.47% 3.296 *** -2.86% 0.306 -0.343 [—1; 1] 8.44% 1.977 ** 7.03% 3.089 *** 1.41% 0.201 -0.264 [0] 8.52% 1.408 4.76% 2.601 *** 3.76% 0.733 0.026 [-4; 2] 9.73% 1.435 11.57% 3.536 *** -1.84% 0.16 -0.66 [-20;-1] 1.65% -0.25 6.23% 2.558 ** -4.57% 0.86 -0.819 [1; 20] 1.17% 0.057 3.00% 0.289 -1.83% 0.142 0.581 *, **, *** denote significance at 10%, 5% and 1% level

Table IV: Hypothesis II - Advisor Reputation Effect on Target Returns

Event Window

Tier 1 advisor Tier 2 advisor Test statistics η = 21 Boehmer η = 68 Boehmer CAR t-test Wilcoxon CAR (%) Z-Value CAR (%) Z-Value difference t-value Z-Value [-20; 20] 6.21% TÖ29 ** 5.19% Γ942 * 1.02% Ö232 0464 [—10; 10] 5.56% 2.191 ** 2.32% 1.561 3.25% 0.996 1.015 [-5; 5] 3.27% 1.3 1.51% 1.385 1.76% 0.666 0.29 [—1; 1] 1.48% 0.998 0.99% 1.629 0.49% 0.238 0.261 [0] 0.81% 0.8 -0.04% 0.072 0.84% 0.646 0.966 [-4; 2] 1.76% 1.131 1.07% 1.524 0.69% 0.369 -0.184 [-20;-1] 2.44% 1.595 2.58% 0.933 -0.14% 0.025 0.56 1 768 [1; 20] 2.96% 1.579 J'64% · * °-32% °·091 -0-367 *, **, *** denote significance at 10%, 5% and 1% level

Table III: Hypothesis II - Advisor Reputation Effect on Bidder Returns

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For t h e target side, t h e empirical results show somewhat t h e same results (Cf. Table I V ) . I n line w i t h t h e prior hypothesis target w e a l t h gains are higher i n transactions w i t h T i e r 2 advisors (14.0% compared t o 11.3% for [—20; 20]). A l t h o u g h t h e results are reversed for t h e shorter event windows, w h i c h favour T i e r 1 advisors, no significant differences are detected for any event w i n d o w . T h e r e p u t a t i o n of t h e advisor does not seem t o influence t h e C A R of t h e target company meaning t h a t p o t e n t i a l higher bargaining skills can not materialise. A g a i n , these findings are consistent w i t h p r i o r research a n d do not support t h e i n i t i a l l y stated h y p o t h e s i s . 3 5

3.3.3 Hypothesis I I I : N e g o t i a t i o n Effect For Hypothesis I I I t h e s t u d y examines t h e different w e a l t h effects for transaction where b o t h parties employed advisors or o n l y one party. T h e effect for t h e bidders is shown i n Table V w h i c h presents nearly no difference between t h e t w o sub-samples. I n transaction where t h e target d i d not employ an advisor t h e bidder gains 5.7% on average whereas i n the other case t h e gains a m o u n t t o 5.3% i n an [—20; 20] interval. Once again, t h e C A R differences over a l l event windows do not reach statistical significance meaning t h a t t h e bidder's r e t u r n is independent from whether the target is advised b y an investment b a n k or not. T h i s finding underlines the results of prior hypotheses a n d shows t h a t t h e bargaining hypothesis has only weak explanatory power for bidder return. Interestingly, t h e target side (Cf. Table V I ) reveals large differences between t h e t w o sub-samples. Targets w i t h advisor bought b y bidders w i t h o u t advisors loose 2.2% of t h e i r value w h i l e i n transactions where b o t h parties are advised targets earn 15.8% on average i n a [—20; 20] interval. T h i s result applies also t o a l l other event w i n d o w s reaching statistical significance i n four cases. A l t h o u g h t h e first sub-sample is small ( n = 6 ) a n d statistical problems m i g h t bias t h e results, t h e finding confutes t h e i n i t i a l expectations. T h e fact t h a t targets even loose value indicates t h a t either the economic s i t u a t i o n of these targets is considerably weak or t h e price was lowered t o complete t h e deal b y a l l means. A s financial advisors are only p a i d for completed deals this could c o n s t i t u t e an incentive t o decrease t h e price demanded for t h e company. O n the other hand, bidder advisors m i g h t have t h e m o t i v a t i o n t o increase t h e deal volume i n order t o charge higher fees. I n conclusion, t h e hypothesis proved t o be insignificant for t h e bidders b u t t o be t h e opposite for t h e targets.

Cf. B e i t e l / S c h i e r e c k / U n v e r h a u (2003), p. 19.

Event Window

Tar. w/ad., bid. w/o ad. Both parties with advisor Test statistics η = 6 Boehmer η = 39 Boehmer CAR t-test Wilcoxon CAR (%) Z-Value CAR (%) Z-Value difference t-value Z-Value [-20; 20] -2.23% ^459 15.78% 2^3 ** -18.00% Γ589 IÖ835 [-10; 10] 3.68% 1.72 * 12.92% 2.746 *** -9.25% 1.219 -0.434 [-5; 5] 0.16% 0.662 12.42% 3.715 *** -12.26% 2.186 ** -1.269 [—1» 1] 2.32% 1.785 * 8.16% 3.386 *** -5.84% 1.893 * -0.735 [0] 2.02% 1.909 * 6.24% 2.707 *** -4.22% 1.644 0.167 [-4; 2] 1.06% 0.951 12.67% 3.757 *** -11.61% 2.309 ** -1.169 [-20;-1] 2.16% 0.48 5.60% 1.754 * -3.44% 0.448 0.301 [1; 20] -6.40% -2.801 *** 3.94% 0.743 -10.34% 2.164 ** -1.636 *, **, *** denote significance at 10%, 5% and 1% level

Table VI: Hypothesis III - Effects on target return through bidder advisor

Event Window

Bid. w/ad., tar. w/o ad. Both parties with advisor Test statistics η = 23 Boehmer η = 66 Boehmer CAR t-test Wilcoxon CAR (%) Z-Value CAR (%) Z-Value difference t-value Z-Value [-20; 20] 5S6% OÖI * 5^2^ 2^29 ** 0381 Ö647 [—10; 10 2.62% 1.236 3.19% 2.114 ** -0.57% 0.133 -0.328 [-5; 5] 1.19% 0.569 2.10% 1.704 * -0.91% 0.375 -0.469 [-1;1] 0.75% 0.415 1.22% 1.946 * -0.47% 0.291 -0.815 [0] -0.01% -0.198 0.21% 0.654 -0.22% 0.243 -0.478 [-4; 2] 0.78% 0.322 1.33% 1.858 * -0.55% 0.297 -0.853 [-20;-1] 3.52% 1.367 2.22% 0.871 1.30% 0.547 0.647 [1; 20] 2.15% 1.597 2.89% 1.902 * -0.74% 0.324 0.141 *, **, *** denote significance at 10%, 5% and 1% level of Boehmer Test

Table V: Hypothesis III - Effects on bidder return through target advisor

3 EMPIRICAL EVIDENCE 231

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232

9. ML· A-AD VISERS

Table V I I : Hypothesis I V - Number of Advisors Effect on Bidder and Target CARs

Target Sid Acquirer Side η = 61 η = 132 Beta t-Value Beta t-value -0.322 1.50% 0.811 [ - 2 0 ; 20 -1.30% 0.384 0.50% [—10; 10 -2.00% -0.586 -0.427 -0.40% [-5; 5 -1.60% -0.588 -0.90% -1.236 -1.90% -1.029 [-1; ι -0.90% -2.079 ** -0.70% -0.47 [o -0.90% [-4; 2 -1.30% -0.519 -1.13 1.10% -0.492 0.793 [-20;-1 -1.10% 1.217 1.30% 0.162 [1; 20 0.40% , * * * denote significance at 10%, 5% and 1% level

Event Window

3.3.4 Hypothesis I V : N u m b e r of Advisor Involved I n Hypothesis I V t h e s t u d y tested whether t h e number of advisors involved i n a transaction can explain different C A R s of bidder an targets. A regression analysis was conducted t o explore a possible coherence of t h e t w o variables (presented i n Table V I I ) . O n t h e bidder side, t h e results do not show a clear consistent relationship over all event windows. Whereas i n longer event windows as well as i n the post-announcement period t h e relation is positive, i t is t h e reverse for the shorter event windows. A p a r t from a very small negative correlation for the event day, none of the coefficients is statistically significant. T h e empirical findings are not strong enough t o support t h e i n i t i a l hypothesis and indicate t h a t especially after t h e announcement date there could be a possible relation between the number of advisors involved and the C A R s of t h e bidders. For the target side t h e analysis yields equally weak significant results. A l l b u t t h e post-announcement w i n d o w show a statistically significant correlation. Hence, o n l y a tendency t o t h e i n i t i a l l y proposed relationship can be found. Thus, t h e results for t h i s hypothesis are statistically not robust enough t o support t h e proposed correlation.

3.3.5 M u l t i v a r i a t e Analysis I n a d d i t i o n t o t h e prior univariate analysis, a m u l t i v a r i a t e analysis is conducted i n order t o examine i f t h e above mentioned findings hold t r u e w h e n tested simultaneously i n c l u d i n g several new control variables. D o i n g so, t h e effect of u n d e r l y i n g factors can be separated from t h e variables o f interest. C o n t r o l variables are t h e following: diversification (measured by a SIC-code comparison, t h e variable receives a one i n case of a three d i g i t m a t c h a n d a zero i n a l l other cases), cash payment (1 equals pure cash payment, 0 all other cases), European company (1 for a b i d d e r / t a r g e t from Europe, 0 i n all other cases), rank v o l u m e of transaction, year of transaction and crossborder transaction ( 1 for a cross-border transaction a n d 0 for a domestic transaction) .

3 EMPIRICAL EVIDENCE

233

A s shown i n Table V I I I six models are c o m p u t e d w i t h a sample 132 bidders. Testing t h e four variables from t h e hypotheses yields no significant results, as neither a variable nor t h e entire model reaches significance. A d d i t i o n a l l y , the model consisting o n l y of control variables explains only very l i t t l e of t h e observed C A R s , too. I n order t o o p t i m i z e t h e model, insignificant variables are left o u t stepwise leading t o o n l y three independent variables i n model three. T h e model is weak significant w i t h a weak significant negative influence of t h e European bidder variable. Thus, European bidders earn smaller C A R s t h a n i n t e r n a t i o n a l bidders. Nevertheless, t h e model's R 2 s t i l l is low w i t h 5.1% i n d i c a t i n g t h a t t h e explanatory value of t h e model is limited.

0.008

(-O.493)

-0.073 -0.073 (-1.566) (-I.473) 0.000 -0.338 (-0.028) -0.007 -0.007 (-I.442) (-1.372) 0.028 0.026 -0.791 -0.889

(-0.606)

-0.007 (-1.373) 0.023 -0.862

-0.07 (-1.487)

(-0.480)

-0.79 6

-0.007 (-1.437)

-0.084 (-I.455)

(-1.376)

(-1.876)*

0.017 0.047 ÖÖ63 0Ό62 (TÖ59 0.051 -0.014 0.002 -0.15 0.001 0.022 0.028 0.696 1.039 0.808 1.018 1.587 2.278* 1.757 1.801 1.755 1.766 1.769 1.768 2.701 1.171 3.874 1.193 1.148 1.013 * * * * * * denote significance at 10%, 5%, 1% level

0.024

-0.007

0.000

-0.076

-1.383

CHAPTER 9.

Model Analysis -R2 Adjusted R2 F-statistic Durbin-Watson statistic Maximum VIF NB: t-values in parenthesis

Cross-Border Transaction

Year

Rank Volume of Transaction

European Bidder

Cash Payment

Diversification

No. of Involved Advisors Hypothesis IV

Target Employs Advisor Hypothesis III

Tier 1 Advisor Involved Hypothesis II

Model 1 2 3 4 5 6~~ η = 132 η = 132 η = 132 η = 132 η = 132 η = 132 14.861 14.799 14.039 14.387 13.737 -0.257 -1.45 -1.378 -1.379 -1.44 0.057 0.029 0.04 0.042 0.04 -1.179 -0.55 -I.245 -1.436 -1.356 0.017 0.012 0.014 -0.405 -0.277 -0.339 -0.007 -0.01 -0.01 (-O.242) (-0.303) (-0.316) -0.015 0.009 (-0.484) -0.236 0.02 0.021 0.02 0.02 -0.697 -0.717 -0.69 -0.721 -0.018 -0.015 -0.014

Table VIII: Multivariate Analysis for the Bidder Sample

Advisor Involvement (y/n) Hypothesis I

Constant

Bidder Sample Independent Variables

234 MA-ADVISERS

0.016

Model Analysis ~~R? adjusted R2 F-statistic Durbin-Watson statistic Maximum VIF NB: t-values in parenthesis

Cross-Border Transaction

Year

Rank Volume of Transaction

European Bidder

Cash Payment

Diversification

0

0.126

0.029

0.248 019 ÔÔ87 0.098 0.047 0.993 1.297 1.653 1.329 1.563 1.609 1.513 1.566 1264 2.484 7.854 2.001 1.139 *,**,*** denote significance at 10%, 5%, 1% level

0.066

No. of Involved Advisors Hypothesis IV

Target Employs Advisor Hypothesis III

Tier 1 Advisor Involved Hypothesis II

0.165 0.097 2.073* 1.566 1.132

0.105 2.762** 1.512

Model 1 2 3 4 5 6 η = 61 η = 61 η = 61 η = 61 η = 61 η = 61 24.91 42.062 13.48 0.374 0.395 -0.154 -0.754 -1.155 -0.393 (1.885)* (2.214)** -0.092 -0.337 -0.087 -0.096 -0.102 (-0.727) (-2.100)** (-0.862) (-1.052) (-1.122) -0.085 -0.109 -0.101 -0.105 -0.102 (-0.689) (-0.824) (-0.746) (-1.005) (-0.978) 0.197 0.173 0.199 0.203 0.176 (1.838)* -1.619 (1.820)* (1.937)* (1.726)* 0.015 0.217 -0.231 (1.971)* -0.052 -0.061 -0.074 -0.071 (-0.592) (-0.706) (-0.836) (-0.857) 0.075 0.113 .0.081 0.08 -0.877 -1.318 (-0.930) -0.976 -0.369 -0.419 -0.356 -0.371 -0.364 (-2.306)** (-2.634)** (-2.223)** (-2.619)** (-2.582)** 0.000 0.000 0.000000399 (-0.294) (-1-427) -0.02 -0.012 -0.021 -0.007 (-0.740) (-1.144) (-0.382) -0.037 -0.008 0.007 (-0.394) (-0.084 ) -0.07 5

Table IX: Multivariate Analysis for the Target Sample

Advisor Involvement (y/n) Hypothesis I

Constant

Target Sample Independent Variables

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MA-ADVISERS

A n a l y s i n g the results for t h e target side, shown i n Table I X , t h e model specifications are generally better. A g a i n t h e model w i t h only control variables describes t h e d i s t r i b u t i o n of C A R s better t h a n t h e variables tested i n t h e hypotheses. However, t h r o u g h t h e o p t i m i z a t i o n of t h e model, i t can be stated t h a t besides the variable European target another factor is significant. A s already found out i n Hypothesis I I I there is a positive correl a t i o n between target returns and t h e employment of a n advisor on bidder side. T h i s holds t r u e i n t h e m u l t i v a r i a t e analysis a n d reaches statistical significance. A g a i n European targets earn less t h a n i n t e r n a t i o n a l targets. W i t h regard t o t h e model analysis, m o d e l six is robust as its F - s t a t i s t i c is significant a n d its adj. R 2 of 10.5% ( R 2 of 16.5%) is relatively c o m m o n for capital market studies. I n conclusion i t can be stated t h a t generally t h e explanatory power of this analysis stays low on t h e bidder side. P r i o r findings for t h e target side, however, are confirmed. I n a d d i t i o n , for b o t h bidders and targets an i n t e r n a t i o n a l company earns more t h r o u g h t h e acquisition t h a n an European company. A l t h o u g h t h i s difference m i g h t be caused t h r o u g h different benchmarks i n t h e event study, i t could also indicate t h a t trans-continental acquisitions are appreciated by the stock market w i t h a p r e m i u m .

3.3.6 Combined E n t i t y Analysis T o r o u n d u p the analysis for t h e present sample, i t may be useful t o give up t h e d i s t i n c t i o n of bidder and target w e a l t h gains b u t t o examine t h e combined e n t i t y C A R s w h i c h should theoretically a good e s t i m a t i o n for the t o t a l synergies of t h e transaction. D u e t o d a t a constraints t h e sample under consideration consists of 40 transactions and shows o n average a value generation of 1.9% over t h e event period. T h e value is relatively small w h i c h can be explained by large bidders compared t o t h e relative small targets i n t h e restricted sample a n d t h e market cap weighted average of t h e C A R s t h a t influences the C A R s . W h e n grouping the C A R s according t o whether no advisor, one advisor or o n b o t h sides advisor p a r t i c i p a t e d i n t h e transaction, i t can be seen i n A p p e n d i x I X t h a t those transactions w i t h o u t advisors have t h e highest average C A R over t h e [—20; 20] interval. Due t o t h e s m a l l sample sizes, a one sample t-test does not reach significance for t h i s event w i n d o w . However, for t h e [—5; 5] t h e same result becomes significant. T o test for significant differences between t h e groups the s t u d y conducted non-parametric and parametric tests (Cf. A p p e n d i x X ) . W h i l e t h e non-parametric test d i d not show any significance, t h e t-test reaches significance for t h e post-announcement period between no advisor a n d advisors o n b o t h sides. I n t h i s interval transactions w i t h o u t advisors strongly underperform (-10.8%). T h i s may indicate t h a t advisors support t h e transaction process i n t h e post announcement p e r i o d better and guarantee a c o m p l e t i o n of t h e deal. T h i s finding is p a r t l y i n line w i t h t h e results form Hypothesis I V where i t was shown t h a t the more advisors are involved t h e b e t t e r t h e post-announcement performance of t h e bidder a n d target.

4 SUMMARY AND CONCLUSION

237

T h e m u l t i v a r i a t e analysis of t h e combined e n t i t y C A R s does not show any significant impact of t h e advisor involvement neither of t h e i r r e p u t a t i o n (Cf. A p p e n d i x X I ) . A s R 2 values for a l l six models stay comparably low t h e explanatory power of t h e presented models is very l i m i t e d . Nevertheless, the variable year is found weak significant i n some of t h e models i n d i c a t i n g a decrease of synergies over t h e t i m e horizon under examination. T h i s may be explained by increasing c o m p e t i t i o n i n t h e construction i n d u s t r y so t h a t managers undergo transactions w i t h less synergies t h a n before or b y the increasing importance of managerial factors such as t h e overestimation of synergies.

4 Summary and Conclusion T h e present study investigates t h e difference i n w e a l t h creation i n M & A transactions w i t h special focus on financial advisors for t h e European cons t r u c t i o n industry. Four hypotheses have been tested by sub-sample comparison for b o t h bidders and targets. T h e results indicate t h a t generally financial advisors are not a value driver i n t h e sample under considerat i o n . Neither the involvement of an advisor, nor its r e p u t a t i o n , nor t h e other p a r t y ' s advisor, nor t h e number of advisors were found significant explanatory factors for t h e value generation i n M & A - t r a n s a c t i o n s . T h e sole significant finding is t h a t target returns are higher i f t h e bidder employs an advisor. These results are validated b y a m u l t i v a r i a t e analysis i n c l u d i n g various control variables a n d an analysis of t h e combined e n t i t y w h i c h do not yield a d d i t i o n a l on t h e role of advisors. T h e empirical evidence of t h e present study, however, is consistent w i t h recent findings from European c a p i t a l markets i n l i t e r a t u r e . 3 6 Possible explanations for t h e observed results may lie in t h e associated costs of an advisor w h i c h outweigh t h e benefits leading t o a "zero-sum game" for the e m p l o y i n g company. However, p u t i n t o different words, t h e involvement of advisors i n complex deals does also not destroy value a n d could be an argument for t h e employment of an advisor. T h e r e p u t a t i o n of t h e advisor has similar implications and causes t h a n t h e employment. Interestingly, t h e superior deal hypothesis may h o l d w h i l e t h e bargaining hypothesis can not be confirmed. N e x t t o this argumentation, t h e results indicate t h a t investment banks m a y have different objectives t h a n t h e i r client as success related fees lead t o w r o n g i n c e n t i v e s . 3 7 T h e payment t o an advisor is generally based on t h e success of t h e transaction, meaning its completion, and t h e deal value. T h i s may lead t o t h e m o t i v a t i o n t o increase t h e deal price or t o lower t h e transaction price i n order t o complete t h e deal. B o t h possibilities c o n s t i t u t e agency problems a n d are not i n t h e interest of t h e client. However, t h i s behaviour is able t o explain t h e findings i n t h e present study. 3 6

3 7

Cf. Beitel/Schiereck/Unverhau (2003), s k i / S c h i e r e c k / T h o m a s (2004), p. 325-237. Cf. K o s n i k / S h a p i r o (1997), p. 8-18.

p.

17f;

Lowin-

238

CHAPTER 9.

MA-ADVISERS

I n t h e light of t h e findings, i t seems t h a t there are no specific factors i n t h e European construction i n d u s t r y w h i c h w o u l d indicate t h e financial advisor have a special value i m p a c t i n t h i s p a r t i c u l a r industry. I n fact, t h e findings suggest t h a t M & A - t r a n s a c t i o n s are similar i n t h e i r nature across industries w h i c h w o u l d explain w h y empirical findings about t h e value crea t i o n t h r o u g h financial advisors do not differ a m o n g different industries. However, a deeper e x a m i n a t i o n of t h e i n d u s t r y specific factors may yield a d d i t i o n a l insights o n t h e role of advisors i n t h e European construction industry. Furthermore, t h e analysis of differences i n n a t i o n a l regulations and t h e solution of statistical l i m i t a t i o n s of t h e present s t u d y c o n s t i t u t e possible extensions. Finally, further research should also investigate t h e fee volume a n d t h e fee s t r u c t u r e of advisors as i t m i g h t be one of t h e core value drivers i n M & A - t r a n s a c t i o n s .

REFERENCES

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Backhaus, Klaus/Erichson, B e r n d / P l i n k e , W u l f f / W e i b e r , R o l f (2005): M u l t i v a r i a t e Analysemethoden, 11th Ed., B e r l i n 2005. Beitel, Derick/Schiereck, D i r k (2003): Value creation of investment bank p a r t i c i p a t i o n i n t h e G e r m a n M & A - B u s i n e s s , University of W i t t e n / H e r d e c k e , W o r k i n g Paper. Beitel, Derick/Schiereck, D i r k (2004):

Investmentbanken i n M & A - T r a n s a k t i o n e n : Stand der empirischen Forschung, in: D i e Unternehmung, Vol. 58, Iss. 6, p. 435-455.

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Bowers, H e l e n M . / M i l l e r , R o b e r t E . (1990):

Choice of Investment Banker and Shareholders' W e a l t h of F i r m s Involved i n A c q u i sitions, in: Financial Management, Vol. 19, Iss. 4, p. 34-44.

Bradley, M i c h a e l / D e s a i , A n a n d / K i m , E . H a n (1988): Synergistic Gains from Corporate Acquisitions and t h e i r D i v i s i o n Between t h e Stockholders of Target and A c q u i r i n g Firms, in: Journal of Financial Economics, Vol. 21, Iss. 1, p. 3-40.

Brown, Stephen J . / W a r n e r , Jerold B . (1980):

Measuring Security Price Performance, in: Journal of F i n a n c i a l Economics, Vol. 8, Iss. 3, p. 205-258.

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Using D a i l y Stock Returns - T h e Case of Event Studies, in: Journal of F i n a n c i a l Economics, Vol. 14, Iss. 1, p. 3-31.

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D o d d , P e t e r / W a r n e r , Jerold B . (1983): O n Corporate Governance - A S t u d y of P r o x y Contests, in: Journal of Financial Economics, Vol. 11, Iss. 1-4, p. 401-438. D u r b i n , J . / W a t s o n , G . S. (1951): Testing for Serial Correlat i o n i n Least Squares Regression I I , in: B i o m e t r i k a , Vol. 38, Iss. 1-2, p. 159-177. Fama, Eugene F . / F i s h e r , Lawrence/Jensen, M i c h a e l / R o l l , Richard (1969): T h e A d j u s t m e n t of Stock Prices t o New Informat i o n , in: I n t e r n a t i o n a l Economic Review, Vol. 10, Iss. 1, p. 1-21.

H u n t e r , W i l l i a m C . / J a g t i a n i , Julapa (2003): A n analysis of advisor choice, fees, and effort i n mergers a n d acquisitions, in: Review of F i n a n c i a l Economics, Vol. 12, Iss. 1, p. 65-81. K a l e , Jayant R . / K i n i , O m e s h / R y a n , H a r l e y E . (2003): Financial Advisors a n d Shareholder W e a l t h Gains in Corporate Takeovers, in: J o u r n a l of F i n a n c i a l a n d Q u a n t i t a t i v e Analysis, Vol. 38, Iss. 3, p. 475-501.

Kosnik, R i t a D . / S h a p i r o , D e b r a L . (1997):

Agency conflicts between investment banks a n d coporate clients i n merger a n d acquisition transactions: Causes and remedies, in: Academy of Management Executive, Vol. 11, Iss. 1, p. 7-20.

Lowinski, Felix/Schiereck, D i r k / T h o m a s , Thomas W . (2004) : T h e Effect of Cross-Border Acquisitions on Shareholder W e a l t h - Evidence from Switzerland, in: Review of Q u a n t i t a t i v e Finance a n d A c counting, Vol. 22, Iss. 4, p. 315-330.

M a c K i n l a y , A . Craig (1997):

Event Studies i n Economics a n d Finance, in: Journal of Economic L i t e r a t u r e , Vol. 35, Iss. 1, p. 13-39.

Massey, Frank J. Jr. (1951):

T h e Kolmogorov-Smirnov Test for Goodness of F i t , in: Journal of t h e A m e r i c a n Statistical Association, Vol. 46, Iss. 253, p. 68-78.

M c C l a v e , James T . / B e n s o n , P. George/Sinich, T e r r y (2001): Statistics for Business a n d Economics, 8 t h Ed., U p p e r Saddle River 2001.

Peterson, P a m e l a P. (1989): Event Studies: A Review of Issues and Methodology, in: Q u a r t e r l y Journal of Business L· Economics, Vol. 28, Iss. 3, p. 36-66.

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R a u , P. Raghavendra (2000): Investment bank market share, contingent fee payments, a n d t h e performance of acquiring firms, in: Journal of Financial Economics, Vol. 56, Iss. 2, p. 293-324. Servaes, H e n r i (1991): T o b i n ' s Q and t h e Gains from Takeovers, in: Journal of Finance, Vol. 46, Iss. 1, p. 409-419. Wilcoxon, Frank (1945) :

I n d i v i d u a l Comparisons by R a n k i n g M e t h ods, in: Biometrics B u l l e t i n , Vol. 1, Iss. 6, p. 80-83.

CHAPTER 9. MA-AD VISERS

242

Appendix Appendix I: Involvement of M&A-advisors in European Transactions,

1995-2005

1995

1996

1997

1998

1999 2000 2001 2002 2003 Time • Number of transactions

Source: Thomson Financial Data.

2004 2005

2000 1997 1996 1996 1996 1994 1992 1991

1990 1990

1990 1989

Hunter, Jagtiani Deiss Servaes, Zenner Blankenburg McLaughlin Kesner, Shapiro, Sharma McLaughlin Michel, Shaked, Lee

Bowers, Miller McLaughlin

Hunter, Walker Carter, Dark, Hanson

Transactions

Examination period Sample 01/90-06/01 114 bidders 01/99-12/00 174 targets, 174 bidders 01/95-12/01 398 bidders 01/81-12/94 390 transactions 01/95-06/00 5337 transactions 01/80/-01/94 438 bidders in tender offers USA 01/95-12/00 488 targets, 495 bidders 01/85-09/98 611 bidders 01/95-12/98 238 targets, 229 bidders 01/80-12/94 bidders in 2638 mergers and 1439 tender offers USA 01/95-06/00 3907 mergers, 1439 tender offers Germany 01/97-11/97 269 M&A advisers USA 01/81-12/92 297 bidders Germany 09/94-10/94 47 bidders USA 01/80-12/86 160 targets in tender offers USA 01/83-12/90 77 targets, 77 bidders USA 01/78-12/86 148 targets, 227 bidders USA 01/81-12/87 81 targets, 122 bidders in tender offers USA 01/01-12/86 114 targets, 114 bidders USA 01/78-12/85 132 targets, 195 bidders in tender offers USA 01/79-12/85 126 mergers USA 01/81-12/87 713 targets and bidders Source: Cf. Beitel/Schiereck (2004), p. 438.

Geogr. Focus Switzerland Europe Germany World USA USA 2001 USA USA USA

Appendix II: Recent Studies on Financial Advisors in M&A

Authors Year Lowinski, Schiereck, Thomas 2004 Beitel, Schiereck, Unverhau 2003 Beitel, Schiereck 2003 Kale, Kini, Ryan 2003 Hunter, Jagtiani 2002 Rau, Rodgers 2002 Allen, Jagtani, Peristiani, Saunders Saunders, Srinivasan 2001 Allen, Jagtiani, Saunders 2000 Rau 2000

APPENDIX 243

244

CHAPTER 9. M&A-ADVISERS Appendix I I I : Data Sample - Advisor Involvement

1 1 7 1 transaction«

Bidder w/ adviser

Target w/ adviser

Appendix IV: Data Sample - Bidder and Target Region large!

196,536.65 237 151.06

• ï-uropc • America • Asia 84,901.68

• Australia

APPENDIX Appendix V: Data Sample -Bidder and Target Industries Bidder

Target

Transportation & Inliastructute 5 Professional Services 5

Other Industrials S

Pipeline» 5

Telecom Equipment 10

Other Real Estate 5

Construction Materials Transportation ^Infrastructure 27

Β ui Idi ngComtr uction & Engineering 99

Building'Coratr uction & tnyiiHxtmg 97 £ 96.536 65

Σ 96,536 65 Computers & Peripherals 3,876 11 Telecommunications Equipment 4,838 52

12,658 73

Building/Construction & tnginccnng 30.832 21 Transportation & Infrastructure 44,376.08

BuildingConstrucuon & Engineering 44,266 55

Appendix VI: Overview - Type of Advisor

Target adviser Auditing company 5

Investment bank 166

Bidder adviser Auditing company 6

Investment bank 165 Σ 171 transactions

Appendix VII: Overall Sample Results

Targets Bidders Test statistics Event Window η = 61 Boehmer η = 132 Boehmer CAR t-test Wilcoxon CAR (%) Ζ-Value CAR (%) Ζ-Value difference t-value Z-Value [-20; 20] 14.48% 3Λ80 *** 4Λ2% L655 * 10.36% T9Ö7 * 0421 [-10; 10] 12.84% 3.975 *** 2.55% 1.875 * 10.29% 2.294 ** 1.020 [-5; 5] 11.84% 4.558 *** 1.96% 1.602 9.88% 2.931 *** 1.610 [—1; 1] 8.31% 4.201 *** 1.54% 2.658 *** 6.77% 3.091 *** 1.591 [0] 5.64% 3.628 *** 0.62% 0.904 5.02% 3.214 *** 0.862 [-4; 2] 12.18% 4.893 *** 1.78% 1.721 * 10.40% 3.385 *** 1.693 * [-20;-1] 6.04% 2.838 *** 1.64% 0.597 4.40% 1.557 0.651 J1;2Q 2.80% 0.727 1.86% 1.462 0.94% 0.021 -2.051 ** *, **, *** denote significance at 10%, 5% and 1% level

246 CHAPTER 9. M&A-ADVISERS

APPENDIX

247 Appendix V I I I : Tier 1 Advisor

Year

1st

2nd

1995

, date of access: 17. M a y 2006. Australian I n d u s t r y Group.

(2003): C o n s t r u c t i o n O u t l o o k , Chttp: //www.constructors.com.au/outlook/downloads/Construction-Outlook _2003_0ctober.pdf>, date of p u b l i c a t i o n : O c t 2003, date of access: 3. M a y 2006. Australian I n d u s t r y Group.

(2005): C o n s t r u c t i o n O u t l o o k , , date of p u b l i c a t i o n : O c t 2005, date of access: 3. M a y 2006. Bilfinger Berger A G (2000):

A n n u a l Report 2000, M a n n h e i m .

Bilfinger Berger A G (2003):

A n n u a l Report 2003, M a n n h e i m .

Bilfinger Berger A G (2004):

A n n u a l Report 2004, M a n n h e i m .

Bilfinger Berger A G (2005):

A n n u a l Report 2005, M a n n h e i m .

Bilfinger Berger A G (2006):

Unternehmensgeschichte, available

at: < h t t p : / / w w w . b i l f i n g e r . d e / b b / d e / b w e b . n s f / f r a m e s e t s / 2 . 1 . 0 ? O p e n D o c u m e n t > , date of access: 2. M a y 2006.

Baulderstone H o r n i b r o o k (2005):

B B A u s t r a l i a n operations not for sale O u r Histroy, available at: < h t t p : / / w w w . b h . c o m . a u / n e w s / V i e w Article.aspx?Art I D = 1 2 4 > , date of access: 17. M a y 2006.

Baulderstone H o r n i b r o o k (2006a):

Corporate Profile, available at: < h t t p : / / w w w . b h . c o m . a u / a b o u t u s / V i e w A r t i c l e . a s p x ? p a g e = 4 > , date of access: 17. M a y 2006, p. If.

Baulderstone H o r n i b r o o k (2006b): available at: < h t t p : / / w w w . b h . c o m . a u / a b o u t u s / V i e w A r t i c l e . a s p x ? p a g e = 2 > , date of publication: 20. Dec 2005, date of access: 17. M a y 2006.

REFERENCES

295

Bilfinger Berger (2004):

Press note on key financial d a t a 2004, O u r Histroy, available at: < h t t p : / / w w w . b i l f i n g e r . d e / b b / d e / b w e b . n s f / o b j e k t e / p d f 0 4 / $ F I L E / v b _ 2 0 0 3 . p d f > , date of publication: 19. Feb 2004, date of access: 20. M a y 2006.

Deutsche B a n k (2006):

Bilfinger Berger A G I n h a b e r - A k t i e n , available at : < h t t ρ : / / w w w . is-asp. pbc. deutsche-bank. d e / p b c / is-asp/oic/ maceOO 4 1 . h t m l ? & w k n = 590900&symbol = G B F . E T R & w o s i d = > , date of access: 22. M a y 2006. Financial.de: Bilfinger Berger e r w i r b t alle A n t e i l e an australischer A b i g r o u p , available at: < h t t p : / / w w w . f i n a n c i a l . d e / n e w s r o o m / n e w s _ d / 1 8 2 4 6 . h t m l > , date of p u b l i c a t i o n : 15. J a n 2004, date of access: 20. M a y 2006.

Handelsblatt (2005a):

Bilfinger Berger erhält Millionenaufträge, available at: < h t t p : / / w w w . h a n d e l s b l a t t . c o m / p s h b ? f n = t t & ; s f n = g o & : i d = 9 4 2 3 0 5 > , date of publication: 27. J a n 2005, date of access: 22. M a y 2006.

Handelsblatt (2005b): Bilfinger Berger hat sich m i t Australienproblemen arrangiert, available at: < h t t p : / / w w w . h a n d e l s b l a t t . c o m / p s h b ? f n = t t & s f n = g o & i d = 1 1 3 9 0 7 4 > , date of publication: 14. N o v 2005, date of access: 22. M a y 2006. IndustrySearch.com: A l l systems go, G e r m a n Bilfinger takeover of A b i g r o u p , available at: < h t t p : / / w w w . i n d u s t r y s e a r c h . c o m . a u / n e w s / v i e w r e cord.asp?id=13462 >, date of p u b l i c a t i o n : 15. Dec 2003, date of access: 20. M a y 2006. Peters, R u t h :

Germans b i d d i n g 186m for A b i g r o u p , available at: , date of p u b l i c a t i o n : 23.Oct 2003, date of access: 22. M a y 2006.

296

CHAPTER IL ABIGROUP/BILFINGER

BERGER

Appendix

Appendix I: Core Markets and Percentage of Total Turnover Germany

Anicrica 2000 13% 2003 9 % 2004 O l Vo 2005 8%

Australia

Africa Middle East

Source :

Bilfinger Berger ( 2 0 0 0 ) , Bilfinger Berger ( 2 0 0 3 ) , Bilfinger Berger (2004).

Appendix II: Structure of Business Lines Bilfinger Berger AG

Civil Knielf ring

Ingenieurbau •Razel

Structural K.nuliirrilnsi

Services

Operating

Environment

•HB Hochbau •BB U.K.

• Rheinhold & Mahla »HSG

•Bilfmger Berger BOT

•Bilfinger Berger Umwelt

•Wlffeits •bebit •OBV

•Hydrobudowa •PassavantRoedn i ger • Bilfinger Beiger Nigeria • Baulderttone lornibrook • Fru-Con (ine!. Ceiteunia!) • Abigruup I.td Source :

Bilfinger Berger ( 2 0 0 4 ) .

APPENDIX

297

Appendix I I I : Bilfìnger Berger's Historical Performance

8000

1999

2000

2001

2002

2003

- Turnover Source:

2004

2005

ΕΒΙΤΑ

Bilfìnger Berger ( 2 0 0 5 ) , p. 129.

Appendix IV: Abigroup's Historical Performance 35

1997

1998 1999 2000

Source :

2001 2002

2003

2004

A b i g r o u p ( 2 0 0 3 ) , p. 44.

Appendix V: Bilfìnger Berger's Acquisition Evaluation Criteria Criteria S t r o n g m a r k e t position Strategic fit Strong management Positive value c o n t r i b u t i o n R e t u r n higher t h a n c a p i t a l costs Source :

Abigroup yes yes n/a yes yes

Bilfìnger Berger ( 2 0 0 2 ) .