THE DYNAMIC RELATIONSHIP BETWEEN COMMERCIAL REAL ESTATE AND STOCK MARKETS YANG HAISHAN (B.Eng. (Hons), M.Sc., Chongqing) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF REAL ESTATE SCHOOL OF DESIGN AND ENVIRONMENT NATIONAL UNIVERSITY OF SINGAPORE 2003 ACKNOWLEDGEMENTS I appreciate the dedication of the many individuals who helped me to complete this dissertation. First and most foremost, I like to express my deepest gratitude to my academic advisor, Professor Liow Kim Hiang, for his tremendous guidance, encouragement, and extraordinary support throughout the whole process of this dissertation. I am also grateful to the rest of my dissertation committee, Dr. Joseph Ooi Thian Leong and Dr. Lum Sau Kim for their thoughtful input and guidance during the process of completing this dissertation. In addition to my dissertation committee, I owe many thanks to Dr. Tu Yong, who provided me the prompt feedback through emails and calls. I am also grateful to Dr. Zhang Xibin for his support as a good friend and as a mentor. Moreover, I wish to extend to profound appreciation to my parents who have taught me the importance of education and who have devoted their lives to supporting their children. Finally, and most importantly, I am indebted to my girlfriend, Miss Sun Yang. Without her constant encouragement, unceasing sacrifices, and emotional support, the completion of this dissertation could not have been possible. i To my family, in gratitude for their confidence and patience—I relied on both when my own wavered, and to the memory of my beloved mother whose influence on my life has become clearer since she was called home to be with the Lord during my Ph.D. study. ii TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES CHAPTER INTRODUCTION 1.1 Background 1.2 Research Objectives 14 1.3 Research Questions 14 1.4 Significance of the Research . 15 1.5 Research Data . 17 1.6 Research Framework and Methodology - Commercial Real Estate Market, Stock Market, and Macroeconomy 18 1.7 Empirical Research Framework and Methodology 26 1.8 Scope of the Research . 30 1.9 Outline of the Research 31 CHAPTER LITERATURE REVIEW 2.1 Introduction 35 2.2 Relationships between Direct and Indirect Real Estate 35 2.2.1 Presence of a Pure Real Estate Factor in Indirect Real Estate 36 2.2.2 Causal Relationship Between Direct and Indirect Real Estate . 43 2.2.3 Long-run Relationship Between Direct and Indirect Real Estate . 50 2.3 Relationships between Real Estate and Stock Markets 53 2.3.1 Cross-correlation Analysis 54 2.3.2 Market Integration/Segmentation Analysis 55 2.3.3 Causal Relationship Analysis . 60 2.3.4 Measurement Issues in Real Estate Return and Risk . 62 2.4 The Macroeconomic Factors Influencing Real Estate and Stock Markets 65 2.4.1 Stock and the Macroeconomy . 66 2.4.2 Real Estate and the Macroeconomy 72 2.5 Conclusion . 75 iii CHAPTER MARKET STRUCTURE AND HISTORICAL REVIEW 3.1 Introduction 80 3.2 Overview of Singapore . 80 3.3 The Singapore Commercial Real Estate Market 82 3.4 The Singapore Stock Market . 88 3.5 Listed Property Companies in Singapore 90 3.6 Review of the Market Performance . 95 3.7 Review of Related Singapore Property Research – Commercial Real Estate and Property Companies 104 3.8 Concluding Remark 105 CHAPTER UNIVARIATE LINEAR AND NONLINEAR ANALYSIS 4.1 Introduction . 107 4.2 Descriptive Statistics 107 4.3 Univariate Linear Analysis . 112 4.4 Univariate Nonlinear Analysis . 116 4.5 Summary 121 CHAPTER MARKET INTEGRATION BETWEEN COMMERCIAL REAL ESTATE AND STOCK MARKETS 5.1 Introduction 124 5.2 Research Scope and Hypotheses . 125 5.3 Research Data . 126 5.3.1 Office Price Index (PPIO) 126 5.3.2 Stock Market Index (SGXA) 127 5.3.3 Data Treatment for Synchronization . 130 5.4 Research Methodology . 130 5.4.1 Unit Root Test 133 5.4.2 Engle-Granger’s Cointegration Technique 140 5.4.3 Johansen Cointegration Technique . 141 5.4.4 Fractional Cointegration Technique 143 iv 5.5 Results and Discussion 151 5.5.1 Results of Unit Root Test . 151 5.5.2 Results of Cointegration Analysis . 153 5.6 Summary 162 CHAPTER CAUSAL RELATIONSHIP BETWEEN COMMERCIAL REAL ESTATE AND STOCK MARKETS 6.1 Introduction 165 6.2 Research Scope and Hypotheses . 165 6.3 Research Data . 167 6.4 Research Methodology . 167 6.4.1 A Testing Procedure for Linear Granger Causality . 167 6.4.2 A Testing Procedure for Nonlinear Granger Causality 169 6.4.2.1 Modified Baek and Brock Nonparametric Method 169 6.4.2.2 Bivariate Fractionally Integrated Error Correction Model . 173 6.5 Results and Discussion 175 6.5.1 Results of Linear Granger Causality Tests 175 6.5.2 Results of Nonlinear Granger Causality Tests . 178 6.6 Summary 183 CHAPTER RELATIONSHIP BETWEEN COMMERCIAL REAL ESTATE AND STOCK MARKETS IN THE MACROECONOMY 7.1 Introduction 186 7.2 Research Scope and Hypotheses . 187 7.3 Research Data . 191 7.3.1 Gross Domestic Product . 192 7.3.2 Interest Rate . 193 7.3.3 Inflation . 194 7.3.4 Exchange Rate . 194 7.3.5 Data Treatment for Synchronization . 194 7.4 Research Methodology . 195 7.4.1 Cointegration Analysis . 196 7.4.2 Vector Error Correction Model and Fractionally Integrated Error Correction Model . 198 v 7.5 Results and Discussion . 200 7.5.1 Long-run Relationship in a Macroeconomic Setting . 200 7.5.1.1 Results of Linear Cointegration Analysis 202 7.5.1.2 Results of Fractional Cointegration Analysis 208 7.5.2 Short-run Dynamics in a Macroeconomic Setting . 210 7.5.2.1 Results of Vector Error Correction Model . 210 7.5.2.2 Results of Fractionally Integrated Error Correction Model . 213 7.5.3 Forecasting Performance 217 7.6 Summary 218 CHAPTER CONCLUSION 8.1 Summary of Major Findings . 222 8.2 Implication for Real Estate Investment . 227 8.3 Research Contributions . 229 8.4 Limitations of the Research 230 8.5 Recommendations for Further Study . 231 BIBLIOGRAPHY vi SUMMARY This thesis explores three major issues regarding dynamic relationship between commercial real estate and common stock markets: market integration, causal relationship, and long-run and short-run relations in the macroeconomic setting. Various linear and nonlinear, parametric and nonparametric testing methodologies are applied to a Singapore data set over the 1975-2001 period in an integrated procedure. Before the relationship between the two markets is examined, we provide a comprehensive description of the distributional traits for each of the series throughout this dissertation. In general, certain degree of nonlinear structure, other than linear fabric, is present in commercial real estate and some macroeconomic variables. Thus, existing linear models would need to be enhanced to ensure that these nonlinear structures are accounted for in order to uncover the complex interrelation between commercial real estate and stock markets. The dynamic relationship between commercial real estate and stock markets is then addressed in three ways. First, we examine integration/segmentation between the two markets. While evidence of a long-run relationship for the two asset classes is found with Johansen cointegration tests, the standard residual-based cointegration techniques fail to provide any similar evidence. We attribute the inconsistencies between the cointegration results to the possibility of fractional cointegration which allows for fractionally integrated equilibrium errors and thus encompasses a wide range of meanreversion behavior. Based on the parametric Geweke-Porter-Hudak (GPH) test, the empirical results show that the equilibrium errors could be characterized by the fractionally integrated processes. Therefore, the commercial real estate market is vii nonlinearly related to the stock market, but the mean reversion between the two markets is quite slow. Second, we consider linear and nonlinear causal relationships between the two markets. In a linear Granger causality framework, the error-correction augmented VAR model shows that the commercial real estate market seems to informationally lead the stock market. The results imply that the private real estate market contains information that can be impounded in the securitized market through insider trading. However, the nonlinear Granger causality test based on the fractional cointegration indicates a significant bi-directional nonlinear causal relationship between the two markets. The strong evidence of the feedback effect is consistent with the fractional cointegration results and provides support for the prior finding that the commercial real estate market does have an influence on the stock market, rather than simply acting as a secondary absorber of information flow. Third, we examine the long-run and short-run relations between the two markets within the economic system including GDP, interest rate, inflation and exchange rate. If both corporate profits and rents were driven by economic fundamentals, then commercial real estate and stock prices should be expected to move together within the macroeconomic framework. The results show that the two markets are linearly in longrun equilibrium within the economic system, and that after controlling for changes in the macroeconomy, the observed positive relation between the two markets weakens. This implied that the correlation between the two markets is mainly due to their common reactions to fundamental economic activities. In addition, the results indicate the presence of nonlinear fractional cointegration among commercial real estate, stock viii and key macroeconomic factors. In the short-term dynamics, the macroeconomic variables are found to exert stronger influence on the commercial real estate market than the general stock market. Hence the commercial real estate market is significantly related to the key macroeconomic factors. To unveil the implication of the nature of long-run relation for the short-term dynamics, we further investigate the forecasting performance of the fractionally integrated error correction model (FIECM) against the conventional vector error correction model (VECM) for the prediction of the commercial real estate price index. Consistent with our expectation, incorporating fractional cointegration (if present) in an ECM does improve forecasting performance over the traditional ECMs. We not intend to uncover the whole spectrum of the three selected issues in making inferences about the dynamic interactions between the commercial real estate and the common stock markets. Within the sphere of mixed-asset allocation and corporate real estate management, the methods and evidence reported in this research can form the basis for further study in empirical literature related to the synthesis of modern economic and finance theories with real estate investment and management in the context of increasing interactions between real estate and broader capital markets. ix Chapter 8: Conclusion Secondly, our research results contribute to the empirical literature on the relationship between real estate and financial markets by indicating the presence of significant nonlinear effects in the dynamics between the commercial real estate and the common stock markets, both in the long term and the short term. This finding may prove helpful to further theoretical and empirical research on the commercial real estate market. It implies that researchers should consider the nonlinear theoretical mechanisms and empirical regularities when designing and evaluating models of the joint dynamics of the commercial real estate and the stock market. In addition, the complex and dynamic relationships between the two dominant asset classes uncovered by this study will help investors and financial analysts to properly assess the commercial real estate in a mixed-asset portfolio, forecast its future performance with more accuracy, and thus to formulate the optimal asset allocation strategy. More specifically, the fractional cointegration between the two markets suggests that diversification across these two asset classes may not produce the optimal risk reductions in the long run, though they may be held together in the short and medium terms, depending on the adjustment speed of mean reversion. In addition, the nonlinear bi-directional causal relationship between the two markets implies that besides the conventional wisdom to predict commercial real estate returns from stock market behavior, private commercial real estate market does contain useful information of future movement of stock market, and therefore can be used to forecast the future performance of stock market. Furthermore, incorporating the nonlinearity component in the long-term joint dynamics of commercial real estate price and stock price within the macroeconomic framework in the short-term dynamics could improve the forecasting performance of the relevant asset markets over the conventional linear error correction models. 8.4 Limitations of the Research Our research inquiries are exposed to the limitations of most empirical studies. In social 230 Chapter 8: Conclusion science, the conclusions of various researches are ultimately no better than the quality of our operationalization of abstract concepts in question through proper processing of values of observable proxy variables (Judd, Smith and Kidder, 1991). For the office price index in Singapore, although it has the advantage of being generated from the transaction prices rather than appraisal values, the construction of the index has two main shortcomings (Lee, 1995). First, the use of the transaction price by property type does not control for differences in the quality and characteristics of the sample of properties transacted across time. The second deficiency results from the relatively small stock of transacting office space plus the fact that most of the office space traded is secondary-grade office buildings, since most of the primegrade properties remain under the same ownership by institutional investors and property companies for several years at a time. Hence, the office price index may not be reflective especially of the underlying value of institutional-grade office buildings. Furthermore, the epistemological implications arising from our positivist approach to research might raise a problem. As Ryan (1982) argues, the positivist account for science has led the academic finance community to place an unwarranted reliance on empirical testing as a means to settle theoretical disputes. 8.5 Recommendations for Further Study While interpreting our evidence with the above caveats, we have nevertheless opened a promising and an exciting path in explicitly conducting an economic and econometric analysis of commercial real estate market behavior and investment in a broad capital market context. To suggest the least, there are several follow-ups from this research. The findings of nonlinear (fractional) cointegration and nonlinear dependence between the commercial real estate and the stock markets invite questions concerning the source of such nonlinearities. We suggest that these sophisticated properties can be accounted for by possible interactions among the underlying macroeconomic factors. It is possible that the long-memory 231 Chapter 8: Conclusion behavior of the deviations between the two markets and nonlinear causal relationship may in general mirror the influences of economic fundamentals, such as the level of aggregate output, inflation and interest rates. For example, empirical studies on the dynamic properties on the US macroeconomic time series – output, interest rate and money-supply data – show that these macroeconomic series may be characterized by fractionally integrated processes. (Diebold and Rudebusch, 1989, 1991b; Porter-Hudak, 1990; Shea, 1991). Of course, more in-depth research is needed to determine the source of the long-memory deviation from equilibrium and bidirectional nonlinear causal relationship between the two significant asset markets. This work is the first step towards developing further tests to capture specific types of nonlinear relationship between the commercial real estate and the stock markets such as the fractional cointegration. Other forms of nonlinearity could exist as well. For instance, Dijk and Franses (2000) advise that if the presence of an equilibrium relationship has been established, there is a possibility of asymmetric adjustment process to equilibrium that can be modeled by smooth-transition error-correction models due to such market frictions as short-selling restrictions and transaction costs. Various forms of nonlinear models can be compared in a “horse-race” of out-of-sample forecasting power. Furthermore, tests allowing for the coexistence and interaction of many types of nonlinearity can be developed as well. Finally, various econometric tests employed in this study could potentially be applied to examine the dynamic relationships between the other real estate sectors and the stock market locally and internationally; for example the residential real estate and the stock markets. 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Question 3: Is there a long-run relationship between the commercial real estate (office) market, the stock market and key macroeconomic variables? What are the implications of the nature of the long-run relationship, if found, for the short-term dynamics 16 Hong... Relationship Between the Direct and Indirect Real Estate Markets 52 2.4 Summary of Studies on the Integration between Real Estate and Stock Markets with Asset-pricing Framework 57 2.5 Summary of Studies on the Integration between Real Estate and Stock Markets by Modeling the Relations Between Asset Prices 59 2.6 Summary of Studies on the Relationships Between Stock Market and Macroeconomic... (Sing, 2001) Furthermore, the interaction between the general asset market and macroeconomic conditions compounds the perplexity To have a holistic understanding of our theme – the dynamic relationship between the commercial real estate asset market and stock market – it is important to take into consideration the interaction among the commercial real estate space market, commercial real estate asset... the demand for commercial real estate space The dual functions of commercial real estate thus suggest that there are two distinct but interrelated real estate markets: the market for tenant space (space market) and the market for investment capital (the asset market) (Dipasquale and Wheaton, 1992) The demand for commercial real estate space as a factor of production is linked to the conditions of the. .. interaction between commercial real estate market and financial sectors, especially the stock market, increasing attention has been focused on the relationship between the commercial real estate and stock markets As an asset, commercial real estate has received renewed attraction from many investment programs, especially at a time when the high-return boom years fade In the past decade, commercial real estate, ... more about the commercial real estate market through studying the process of price discovery between the commercial real estate and the general stock markets For practitioners, the dynamic relationship between these two asset markets has important significance for portfolio diversification, strategic management of corporate commercial real estate, and market structure and policy-making From the portfolio... reflecting real estate fundamental; 8 More details about the relationships between the securitized and unsecuritized real estate markets are available in the Chapter Two 6 Chapter 1 Introduction b) Securitized real estate performance leads the underlying real estate performance; c) There is a long-term equilibrium relationship between securitized real estate and its underlying real estate market in the context... the economy and as an important component in the mixed-asset portfolio, direct commercial real estate is less understood than stock and fixed income securities, especially where its relationship with other two asset markets is concerned While sporadic studies appeared in exploring the relationship between the commercial real estate and stock markets by using securitized real estate as a proxy for the. .. the relationship between the commercial real estate and stock markets would provide useful insights into the underlying mechanism in which the commercial real estate market is correlated with the common stock market, which therefore allows us to capture more economic implications Finally, the examination of the causal relationship between the two asset markets may prove useful in learning more about the . order to uncover the complex interrelation between commercial real estate and stock markets. The dynamic relationship between commercial real estate and stock markets is then addressed in. Issues in Real Estate Return and Risk 62 2.4 The Macroeconomic Factors Influencing Real Estate and Stock Markets 65 2.4.1 Stock and the Macroeconomy 66 2.4.2 Real Estate and the Macroeconomy. Pure Real Estate Factor in Indirect Real Estate 36 2.2.2 Causal Relationship Between Direct and Indirect Real Estate 43 2.2.3 Long-run Relationship Between Direct and Indirect Real Estate