COMMON RISK FACTORS AND RISK PREMIA IN DIRECT AND SECURITIZED REAL ESTATE MARKETS Sing, Tien Foo Department of Real Estate School of Design & Environment National University of Singapore Date: 17 September 2002 Revised: 17May 2004 Abstract: This study empirically examined the effects of systematic market and common risk factors in explaining the variations in excess returns of securitized and direct real estate using multi-factor asset pricing models (MAP). The homogeneity of risk premia associated with the economic risk factors was also tested to determine whether the two real estate markets were integrated. By constraining the risk premia to be constant within each of the real estate market using the seemingly unrelated regression (SUR) technique, we found that risk factors were priced differently in the two real estate markets. Credit risk, unexpected inflation and spread between government and commercial bonds were significantly priced in the securitized real estate market, whereas real T-bill yields and unexpected inflation were the two risk factors affecting the excess returns of direct real estate. The time-varying risk premia were also estimated using the standard FamaMacBeth two-pass regression technique. Credit risk factor remained significant in the pricing of excess securitized real estate returns, whereas term structure risk and unexpected inflation were the two factors significantly priced in direct real estate returns. We also tested the significance of the homogeneity of various risk premia across the two markets. The tests rejected the null hypothesis of integration of the two real estate markets in both fixed and time-varying MAP frameworks. Keywords: * Common Risk Factors, Risk Premia, Market Integration Hypothesis Correspondence please forward to the first author by email at rststf@nus.edu.sg, or by mail at Department of Real Estate, School of Design & Environment, National University of Singapore, 4 Architecture Drive, Singapore 117566. The author wishes to thank E.P Chong for her research assistance; and L.Fisher for her comments at the AREUEA conference, Washington D.C., 2003, and the anonymous referees for constructive suggestions and comments. 1 COMMON RISK FACTORS AND RISK PREMIA IN DIRECT AND SECURITIZED REAL ESTATE MARKETS 1. Introduction Exchange-traded or securitized real estate market has been found to provide leading signal to the price generating processes in direct real estate market (Gyourko and Keim, 1992; Ong, 1994; Barkham and Geltner, 1995). Prices of securitized real estate also reflect market values of underlying real estate assets (Martin and Cook, 1991). The results imply that information in one market can be used to efficiently predict the price changes in another market, despite a time lag between the responses in the two markets. In the cointegration term, we can technically say that the price changes for the two markets contain a long-run contemporaneous relationship (Ong, 1994 and 1995; Okunev and Wilson, 1997; Liow, 1998; Wilson, Okunev and Webb, 1998). The securitized real estate prices are then expected to move in line with direct real estate prices. The conintegration relationship between the two markets is enriched with arbitrage rationality in the asset pricing framework by Liu, Hartzell, Greig and Grissom (1990). Based on their definition, we can say that two markets are deemed to be integrated, if systematic market risk is the only risk undiversified by holding assets in both securitized and direct real estate markets, investor should then earn the same risk adjusted expected returns on the two assets. There should be no variations in the risk factor premia for the two markets. If undiversified market risks affecting the returns of both securitized and direct real estate markets are priced differently, the two markets are likely to be segmented. Information flows to one market does not flow to another market in the same way, which results in differences in the pricing of risk factors. Arbitrage opportunity may then exist for investor who possesses information that is privileged to him. The applicability of the asset pricing theory in explaining the fundamental pricing behaviors of different asset classes: stocks, bonds, securitized real estate, and 2 unsecuritized real estate, have been widely tested in the real estate literature using predominantly empirical data in the US markets. The studies invariably focus on two main propositions of the asset pricing theory: the predictability of the asset prices (Karolyi and Sanders, 1996; Ling and Naranjo, 1997; Ling, Naranjo, Ryngaert, 2000, and others) and the integration or segmentation of different asset markets (Liu, Hartzell, Greig and Grissom, 1990; Liu and Mei, 1992; Mei and Lee, 1994; Li and Wang, 1995; Ling and Naranjo, 1999; and others). The same empirical evidence in other markets like Singapore has not been available, although some statistical evidence of contemporaneous relationships between different forms of real estate markets were reported (Ong, 1994 and 1995; Liow, 1998; Sing and Sng, 2003). However, the results of these earlier studies do not provide direct answers to the questions of predictability and integration of different real estate markets in Singapore. This study is therefore undertaken to empirically test the market and common risk factors that affect both the securitized and indirect real estate markets in Singapore. The second objective of the study is to test whether the premia associated with the market and common risk factors are priced equally in the two real estate markets. Compared to the earlier studies in the US, which found significant evidence that equity market was integrated with real estate investment trusts (REITs), but was segmented with indirect /private real estate markets (Liu, Hartzell, Greig and Grissom, 1990; Ling and Naranjo, 1999), this study seeks to empirically test the common factors and premia associated with these risk factors in the securitized and unsecuritized real estate markets. Many empirical models ignored the correlations of the error terms in the price generating processes between securitized and direct real estate models. Like Ling and Naranjo (1999), this study adopts a Seemingly Unrelated Regression (SUR) technique to recursively estimate the conditional heteroskedasticity and contemporaneous correlations across the two excess return equations for securitized and direct real estates. In the SUR model, the risk factors are fixed within each asset class, but the pricing of the risk factors are allowed to vary across different asset classes. They are estimated simultaneously in a system of asset pricing equations for the two real estate markets. The test of market 3 integration is a joint test of the specification of the asset pricing model (Ling and Naranjo, 1999). Due to differences in the market structure and institutional features, different sets of macroeconomic risk factors will be specified in this study to explain the price generating processes of Singapore’s real estate assets. For the two real estate asset classes, we use real estate companies1 listed on Singapore Exchange to represent the securitized real estate claims, and the transaction based direct property market indices published by the Urban Redevelopment Authority (URA)2 of Singapore as proxy for direct real estate market movements. The REIT market in Singapore is still at an infancy stage of development.3 They are not included as one of the sample assets in the tests. This paper is organized into six sections. Section 1 gives background and objectives of the study. Section 2 reviews the literature on predictability and market integration tests in the real estate literature. Section 3 describes the conceptual framework, which is mainly underpinned on the Capital Asset Pricing Model (CAPM) and the multi-factor asset pricing models. Empirical methodology, which includes data analysis, testable hypotheses, and SUR regression models, is explained in Section 4. The empirical results of the common risk factors that explain the price generating processes of the securitized and the direct real estate market returns and also the tests of risk premia for common risk factors are discussed in Section 5. Section 6 concludes the paper with highlights of the implication of the empirical findings. 2. 1 2 3 Literature Review Unlike the “pure” real estate play normally associated with REITs, listed real estate companies though generate a large proportion of their revenues from real estate related activities. The companies are less restrictive in terms of dividend distributions, capital structure, and also their real estate development activities. They are, however, not granted any tax break at the corporate level. The Urban Redevelopment Authority (URA) is the national planning authority of Singapore, which is entrusted with the responsibilities of planning the physical development and land resources in Singapore. The URA publishes performance indicators on different real estate sub-sectors based on caveats lodged with the Singapore Land Registry. REIT market was relatively new in Singapore with a trading history of less than 2 years. The first REIT was a retail-focused REIT listed in July 2002, called CapitaMall Trust (CMT) sponsored by CapitaLand Limited, one the largest listed real estate companies on Singapore Exchange. There are currently three REITs traded in Singapore markets. The data of these REITs are too short to justify their inclusion in our sample assets. 4 2.1 Predictability of Securitized Real Estate Returns Gyourko and Keim (1992) empirically tested the relationship between equity REIT returns and returns on a standard appraisal-based index4 in the US. They found no significant contemporaneous correlation between equity Real Estate Investment Trusts (REITs) and appraisal-based portfolio. However, they showed that traded REIT portfolios contain information that can be used to make forward prediction of returns of the appraisal-based index four quarters ahead. The one-year lagged period Granger-causality from securitized to unsecuritized commercial properties was also evidenced in the UK market (Barkham and Geltner, 1995). The positive price discovery between securitized and direct real estate markets in the US and UK, despite the lagged effects, suggests that there may be common factors that drive the price changes in the two markets. Securitized real estate and direct real estate markets are expected to respond consistently to common market and macroeconomic factors like interest rates, anticipated inflation and expectations of future growth (Giliberto, 1990). In equity markets, Chan, Chen and Hsieh (1985) and Chen, Roll and Ross (1986) specified a multi-factor asset pricing models that include macroeconomic variables like inflations, term structure and spread of corporate bond yields, industrial production and stock capitalization and empirically tested the models were significant in explaining variations in the stock market returns. The systematic risks of these variables were significantly priced, ex-ante, in the US stock markets. Karolyi and Sanders (1998) extended the multi-asset factor model to test whether stocks, bonds and REITs can be significantly predicted with the same set of macroeconomic explanatory variables. They found that while the stock and bond market risk factors are significantly priced in the returns of the two asset portfolios, the explanatory relationships of these risk premia on REIT returns was relatively weaker. They concluded that there is an important real estate risk premium that is not reflected in the multi-asset factor asset pricing models. Lind and Naranjo (1997) used a fixed coefficient non-linear SUR 4 The standard appraisal-based index used in this study is the Frank Russell Company (FRC) and the National Council of Real Estate Investment Fiduciaries (NCREIF) property index. 5 technique and the time varying approach of Fama-Macbeth (1973), and found that growth rate in real per capita consumption was the additional variable that are significantly priced in the real estate returns in their multi-factor asset pricing models. What are the implications of the predictability of real estate returns for investment and trading strategy in real estate assets? Could we exploit the information inefficiency in the market with various tactical and timing strategies to realize abnormal portfolio returns over a buy-and hold strategy? Mei and Liu (1994) found that the level of predictability is positively correlated with the adoption of active timing strategies in real estate portfolios. They found moderate success in employing market timing strategies to generate higher risk-adjusted excess returns in real estate portfolios than the passive buy and hold strategy. Ling, Naranjo and Ryngaert (2000), however, with an expanded time-series of REIT returns data in 1990s, found that the abnormal profits created by active trading of REIT portfolios dissipated when transaction costs were included in the tests. 2.2. Integration of Real Estate and Equity Markets If two markets are integrated, investors will not be able to achieve risk reduction through holding well-diversified portfolios in these two markets. There will also be no additional premium associated with real estate market risks. This market integration hypothesis was proposed and empirically tested by Liu, Hartzell, Greig and Grisssom (1990) using equity REITs and commercial non-farm real estate data in the US. Their empirical results supported the integration between equity REIT and the stock market, but rejected the integration between commercial real estate market and stock market (Liu, Hartzell, Greig nd Grissom, 1990). They used the Jorion and Schwartz (1986) framework in the tests that follow the standard Capital Asset Pricing Model (CAPM) assumption, where systematic risk was the sole market factor priced in the returns of both real estate markets. The same empirical results were obtained by Ling and Naranjo (1999) using a more robust multifactor asset pricing models. They also estimated two versions of the risk premia. The first one was based on the non-linear SUR technique, and the second one allows for timevarying risk premia in the standard two-pass methodology of Fama-McBeth (1973). 6 Another group of literature provides indirect evidence of market integration by examining the commonality of the predictive components in the price generating processes of real estate and stock assets. Liu and Mei (1992), Mei and Lee (1994) and Li and Wang (1995) applied the Generalized Method of Moments (GMM) methodology, which corrects for heteroskedascity and serial correlation in the residual terms, and also allows correlated residual terms across securities, in their tests for common risk factors in predicting the excess real estate portfolio returns. The “true” estimation of the market returns was also not strictly imposed in the models. Two latent factors that can be represented by stock and bond market factors were found to be sufficient in significantly explaining the expected excess of REIT and other asset returns. Mei and Lee (1994) interpreted these results as evidence that reject the market segmentation proposition found by Liu, Hartzell, Greig and Grissom (1990). Mei and Lee (1994) in another separately study found the third latent factor associated with real estate market when they include the appraisal-based real estate returns in their tests. Li and Wang (1995) also found that three common factors: dividend yield, term premium and default premium, with no real estate factor, can be relied on to predict the returns of REIT and other stocks. The three GMM models confirmed that the predictability of REIT returns was no different from other stock and bond returns. Therefore, there was no reason to reject the hypothesis that the securitized real estate and stock markets are integrated. 2.3. Market Evidence in Other Countries Evidence of cointegration and Granger-causality relationships have been widely used as an indirect way to define integration between securitized and direct real estate markets by the studies in other countries, such as the UK (Lizieri and Satchell, 1997), Australia (Wilson, Okunev and Ta, 1996) and Singapore (Ong, 1994 and 1995; Liow, 1998, 2001; Sing and Sng, 2003). In Singapore, Ong (1994) discovered a contemporaneous long-term relationship between property stock price, real estate price and 3-month Treasury bills interest rate employing a structural vector autoregressive model. In another study by Ong (1995) covering the 7 returns of securitised real estate and direct real estate for the period 1977 to 1992, his results contradicted those in his previous paper, which revealed that securitized real estate and direct real estate prices have no significant cointegration relationships. Using the cointegration methodology, Liow (1998) also rejected the hypotheses that securitized and direct real estate markets are co-integrated. In another study by Liow (2001), he examine time-varying Jensen’s abnormal return performance and their temporal cross-correlation patterns of direct real estate and securitized real estate from the first quarter of 1975 to the fourth quarter of 1999. He confirmed his earlier findings that there were no statistical significant contemporaneous relationships in the Jensen’s indices between the two sectors. Sing and Sng (2003) used the Generalized Autoregressive Conditional Heteroscedasticity in Mean (GARCH-M) model and found evidence that incremental information flowed from conditional volatility of the unsecuritized market to the securitized property market. The two markets were, therefore, not segmented. 3. Conceptual Framework The capital asset pricing model (CAPM) provides the basic framework for the tests of the impact of systematic market risks on the excess returns of securitized and direct real estate markets. However, the single factor specification of CAPM may not be able to fully capture the market and other economic risk factors affecting asset returns. The multi-factor asset pricing framework as proposed by Chan, Chen and Hsieh (1985), Chen, Roll and Ross (1986) and subsequently adopted in other studies (Liu and Mei, 1992; Mei and Lee, 1994; Li and Wang, 1995; Ling and Naranjo, 1997 and 1999; Karolyi and Sanders, 1998; Ling, Naranjo and Ryngaert, 2000; and others) is used as a more generalized framework for testing the significance of common risk premia among asset classes in this study. The SUR technique is used to simultaneously estimate the common risk factors and risk premia specified in the multi-factor asset pricing equation systems. In the SUR model, the coefficients are constrained to be fixed across asset classes within the respective real estate markets. We also use the Fama-MacBeth two pass procedures to estimate the time-varying price of market risks for the two real estate assets. 8 If the two markets are integrated, returns from both markets should reflect fully the information of the common risk factors. There should be no variations in premia for different risk factors in the two integrated markets. 3.1 Multi-Factor Asset Pricing Model In the standard Sharpe-Lintner-Mossin CAPM model, the excess return of an individual asset is linearly related to the excess return of a benchmark market portfolio. The multifactor asset pricing model (MAP) underpinned with the Arbitrage Pricing Theory (APT) is a more general model that includes additional market and economic variables to explain the variations in excess returns of real estate assets. In addition to the systematic market risk factor, the theoretical structure of the k-factor MAP for the excess return processes of real estate asset i, either a securitized real estate (sre) or a direct real estate (dre), which includes (k-1) number of economic risk factors, δk, can be written as follows, k −1 ∆Ri ,t = α i + β im,t ∆Rm ,t + ∑ β ij ,t .δ ij ,t + ε i ,t (1) j =1 k −1 ∆Ri ,t = λ0 + β im,t λm,t + ∑ β ij ,t λ j ,t + ξ i ,t (2) j =1 where ∆Ri,t ∆Rm,t Rm and Ri Rf βij βim δij,t λj λm αi and λ0 εi and ξi = Excess return of direct and securitized real estate respectively at period t, calculated as [Ri – Rf] = Excess overall market return at period t, calculated as [Rm – Rf] = Returns of the overall market portfolio and i-th risky real estate assets respectively , where [i = (PPI and SESP)] = Risk-free rate of return = Sensitivity coefficient of asset i return to risk factor j = Measure of systematic market risk factor = Conditional macroeconomic risk factor j = Risk premium for j-risk factor = Market risk premium = Constant term and zero-beta risk premium respectively = Random error terms 9 Equation (1) of the MAP model defines the price generating processes of the real estate assets, which are assumed to be directly related to the systematic risk, βmi, and the k-th macro-economics risk factors. The second-pass Equation (2) estimates the market pricing of the systematic and macroeconomic risk factors, λm and λj, where [j = (1,2..k)]. This implies that if integration exists, the beta for various risk factors should be priced equally in both the securitized and direct real estate markets. In other words, investors should expect the same premia for systematic market risk and common economic risks on both the direct and indirect real estate assets. Let set δj,t equal to [Fj,t = E(Fj)], the empirical model for the above k-factor MAP can be rewritten following Sweeney and Warga (1986) and Ling and Naranjo (1999) as follows, [ ] ∆Ri ,t = λ0 + β im,t λm ,t + β im,t [∆Rm,t − E (∆Rm ,t )] + ∑ β ij ,t .λ j ,t + ∑ β ij ,t F j ,t − E ( F j ,t ) + ε i ,t k k j =1 j =1 (3) where E(.) is the expectation operator. Given that the beta factor for the market portfolio is [βmk ≅0, ∀k], and the price of market risk premium is [λm,t = E(∆Rm,t) - λ0], the E(∆Rm,t) term in equation (3) can be cancelled out, the system of equations can be rearranged as follows, k k j =1 j =1 [ ] ∆Ri ,t = λ0 (1 − β im ,t ) + β im,t ∆Rm,t + ∑ β ij ,t .F j ,t + ∑ β ij ,t λ j ,t − E ( F j ,t ) + ε i ,t (4) where [λ*j,t = λj,t – E(Fj,t)] is the gross risk premium. For a n-sample securitized and unsecuritized real estate asset portfolios over T-period, where [i=(1, 2,…,n)], and [t = (1, 2…,T)], the gross premia and the sensitivity coefficients of the k risk factors in system Equation (4) will be simultaneously estimated using the SUR estimation technique on the condition that [k < n]. 10 4. Empirical Methodology Based on the multi-factor conceptual framework defined earlier, the study will empirically test whether common risk premia for the economic risk factors are observed in both the securitized and direct real estate markets. Two regression estimation methodologies are used in this study to estimate both the time-varying and the constant versions of the risk premia associated with the risk factors. The standard two-pass FamaMacBeth (1973) regression approach is used to estimate the beta coefficients and time varying risk premia in the MAP models, whereas SUR technique is used to estimate the constant risk premia in the MAP model. Then, the two-sample t-tests and the Wald tests are then applied to test the market integration hypothesis, which imposes homogeneity of risk premia in different k-factors for assets across the two real estate markets. 4.1. Data Sources Two sets of real estate markets data are used in this study, which have a sample time period of 56 quarters starting from the second quarter of 1990 (2Q1990) to the first quarter of 2004 (1Q2004). The first set of data consists of securitized real estate returns made up of stocks listed on the Singapore Exchange’s (SES) property section. The quarterly stock price data, (Pi,t), are collected from Datastream, and the stock i return is computed as the simple one-period lagged return, i.e. [Ri,t = (Pi,t - Pi,t-1)/ Pi,t-1], which is not adjusted for dividend payouts. After removing non-Singapore dollar denominated property stocks and also those stocks with listing history of less than 2 years, 20 sample stocks are selected for our analysis (Appendix 1). Based on the market capitalization, the 20-sample real estate stocks are divided into seven value-weighted portfolios, (Ri,t), where the portfolio returns are computed as: N Ri ,t = ∑w i ,t i =1 * ri ,t N ∑w i =1 (5) i ,t where wi,t is the market capitalization and ri,t is the quarterly return of the stock i at time t, and N is the number of real estate stocks included in the portfolio. 11 The second set of real estate data consists of sub-sectors property price indices, which are compiled by the Urban Redevelopment Authority (URA) of Singapore based on the actual caveats lodged with the Singapore Land Registry. Twelve sub-market transaction indices that proxy the transaction price trends in four main direct real estate markets in Singapore, namely industry, office, shop and residential, are collected for our analysis, and they are summarized in Table 1. The quarterly direct real estate returns are also computed as a simple one-period lagged change in indices. The excess returns of the two real estate asset classes are computed as the difference between the simple asset returns and the quarterly yields of the 3-month Treasury bills (T-bill). The quarterly T-bill yield, (Rqf,t), is a compounded rate, which is computed from the annualized yield, (Raf,t), following the equation: ⎡ R = (1 + R ) 1 4 − 1⎤ . The af ,t ⎢⎣ qf ,t ⎦⎥ descriptive statistics in Table 1 show that the mid-size securitized real estate portfolio (i=4) has the highest mean quarter excess returns of 0.0298 and the highest risk of 0.2661. In comparison, the performance of the central region shopping malls was poor with the lowest excess return of -0.0105. The retail sector of the direct real estate market was also the least risky asset class with a standard deviation of 0.0446. We also include a systematic market and five macro-economics variables as the predictive factors for the proposed multi-factor asset pricing models for the real estate market excess returns. The Singapore Exchange All-share index (SESA) is used as proxy of the systematic market factor, and the excess returns of the SESA index is computed. The five macro-economics factors include the real quarterly 3-month T-bill yield (rtbr), the yield spread (ysprd), terms structure premium (terms), credit risk premium (crdr), and the unexpected inflation (urcpi). The sources and the derivations of the macro-economic variables are summarized in Table 1. The government bond yield data are obtained from the Monetary Authority of Singapore (MAS) and the remainders are collected from the Datastream. The unexpected inflation rate is computed using Fama and Gibbons (1984) methodology by regressing the real 3-month T-bill rate in a Autoregressive Integrated 12 Moving Average, ARIMA(0,1,1) process, which gives an estimate of [rtbrt-1 = - 0.6043*ut-1], where ut-1 is the lagged-period moving average errors. [Insert Table 1] 4.2. Seemingly Unrelated Regression (SUR) Model Seemingly Unrelated Regression (SUR) model, also known as Zellner’s model, is used to address the problem relating to cross-equation correlated residuals in the ordinary least squares (OLS) models. SUR is a recursive regression model, which consists of a series of endogenous variables that bear a close conceptual relationship with each other. It is used to obtain more efficient estimates when the error terms of the equations are correlated or heteroskedastic. SUR estimates the parameters for a system of equations simultaneously. The simultaneous approach allows for constraints to be placed on coefficients across equations and to account for cross-equation correlation in the residuals. This helps to account for any predetermined variables omitted from the equations. Through the use of the SUR method, the common risk factors and the integration of the two markets can be tested subject to homogeneity and symmetric restrictions across the two excess return equations. 4.3. Testable Hypothesis Based on the MAP model defined in equations (1) to (4), the hypotheses of the existence of common risk premiums for the securitized and direct real estate markets can be tested using the Wald-test and the two-sample t-test statistics. The restrictions of the homogeneity of different risk factor premia imposed on the MAP models (1) and (4) can be tested in the null (H0) and competing (H1) hypotheses specified as follows: dre H 0 : λ sre =0 j − λj sre H 1 : λsre j − λj ≠ 0 (6) where [j = (1,2,…,k)] and the superscripts (sre) and (dre) represent the securitized real estate and direct real estate assets respectively. The Wald-test and pool variance t-test 13 statistics are estimated to test how close the unrestricted coefficients for the five macroeconomic variables satisfy the homogeneity restrictions imposed under the null hypothesis. If the null hypothesis restrictions are not rejected, then the premia associated with the selected risk factors are statistically indistinguishable for the two real estate markets. In other words, same premia will be reflected in the securitized and direct real estate prices for common risk factors. There are no arbitrage opportunities for trading on the differences in the risk premia in the two real estate markets. The homogeneity in the risk premiums also indicates that the two markets are fully integrated. Information flows from one real estate market to another real estate market is efficient. 5. Analysis of Results The MAP models are estimated with two different estimation techniques using EViews Quantitative Micro Software Package. The first method uses the SUR estimation technique that imposes cross-equation correlated residuals across different real estate asset classes. In the SUR framework, we estimate the beta risk factors and risk premia simultaneously by pooling the cross-section and the time-series data together for the two real estate markets. The regression coefficients in these pooled regressions are allowed to vary across different real estate assets, but the respective risk factor premia are constrained to be constant within the real estate market. In the second estimation approach, we relax the fixed coefficients assumption by using the standard two-pass process proposed by Fama and MacBeth (1973). The hypothesis tests of homogeneity of the fixed and time-varying coefficients are performed using the Wald-test and the pooled variance t-test respectively. If the equality of the risk premia is not rejected in the tests, the two real estate markets can be said to be integrated, and the pricing of the risk factors is deemed to be efficient across the two markets. 5.1. Constant Risk Premia Table 2 shows the results of the SUR regressions and the Wald hypothesis test statistics for MAP in equation (4). Different macroeconomic risk factors were significantly priced in the two real estate markets. The unexpected inflation risk premia were significantly priced in both markets at 10% level, but the magnitude and the sign of the coefficients 14 differ across the two markets. The direct real estate market responded negatively with an 11.04% reduction in the excess return for every 1% increase in real rate of return in the risk-free 3-month government T-bill. For the securitized real estate markets, the variations in the bond-market (ysprd) and bank’s interest rate (crdr) were two risk factors that were significantly priced. The excess returns of securitized real estate responded negatively to yield spread risk, but were positively related to the spread between the prime and the interbank interest rates. The term structure variable, which reflects the yield curve risks of benchmark government securities, was not statistically significant in both real estate markets. The economic risk factors were priced differently in the excess returns of real estate assets in the two markets. The findings, however, were preliminary evidence to suggest the segmentation between the two markets. We further test the market integration hypothesis by examining whether there is significant evidence on the homogeneity of risk premia associated with the beta-risk factors in the two markets. The chi-square statistics of the Wald tests rejected the null hypothesis at 10% level, and the results indicated that the two markets have significantly different risk premia for the five economic factors. The results point to the conclusion that the direct and the indirect real estate markets are not integrated. Information inefficiency may exist in the two markets. Investors should not consider the two assets as mutually substitutable, and diversification benefits can be obtained by holding the two real estate asset classes in the same portfolio. [Insert Table 2] 5.2. Time-varying Risk Premia The second estimation approach allows the risk premia associated with the five economic factors to vary across different sample periods. We estimate the first-pass excess returns process for different real estate asset classes as specified in equation (1) using time-series data over a rolling window period of 20 quarters. The systematic market risk and five macroeconomic risk variables were included in the models. The beta coefficients for the risk factors vary across different window periods, and the coefficients were stored for the second pass cross-section regression as specified in equation (2). The period-by-period 15 cross-sectional risk premia for different risk factors were estimated for the two real estate markets separately, and the results were summarized in Table 3. There were 37 period-specific coefficients (1995Q2- 2004Q1) estimated for each risk factor in the second pass regressions. The means of the beta coefficients and the standard errors were computed in Table 3. The number of risk factors that were significantly priced in each real estate market and the proportion of the total period-specific risk premia were computed. The results showed that terms structure risk (terms) (51.35%), real 3-month T-bill yields (rtbr) (37.84%) and unexpected inflation (urcpi) (45.95%) were the three risk factors that were priced most frequently in the direct real estate market, with at least 14 times across the 37 window periods. In comparison, the risk premia were not as frequently captured in the securitized real estate excess returns vis-à-vis the asset classes in the direct real estate market. Consistent with the results in the early fixed risk premia analysis, the bank’s lending rate spread appeared to be an important risk factor that was significant in 11 out of the 37 quarters in the securitized real estate market. Next, we use the same market integration hypothesis test set-up as in equation (6) with an additional subscript t to differentiate the tests on the quarter-by-quarter basis, dre H 0 : λ sre j ,t − λ j , t = 0 sre H 1 : λsre j ,t − λ j ,t ≠ 0 (6’) The pool-variance t-statistics were computed for different pairs of risk premia coefficients over the 37 estimation periods, and the results were summarized in Table 3 in terms of number of tests that do not reject the null hypothesis and the respective proportions of the total tests. The results showed that the models do not reject the null hypothesis in 14.29% of the regressions for the term structure risk premium (terms) and the 11.43% for the real 3-month T-bill yield (rtbr). There were still considerable variations in the risk premia associated with different risk factors in the two markets. The rejection of the market integration hypothesis was most clearly shown in the tests when the zero-beta term was excluded. 16 In general, the results were consistent with the findings of the earlier fixed risk premia cases, which indicated that the two real estate markets were not well integrated. The reaction of the two real estate markets towards macroeconomic risk shocks, despite similarities in the underlying real estate asset holdings in their portfolios, was to some extent not perfectly correlated. Significant differences in risk premia were found in the two real estate markets, and macroeconomic shocks were perceived differently by investors in the two markets. The results were not inconsistent with the earlier studies by Ong (1995) and Liow (1998 and 2001), which found no long-term contemporaneous relationships between the securitized and direct real estate markets. The two markets are segmented. [Insert Table 3] 6. Conclusion The debate of whether the share price performance of securitized real estate companies, whose cash flows are highly dependent on their direct real estate market activities, is dependent on the up-and-down movements in the direct real estate market was examined in this study using listed property stocks and direct property assets in Singapore. Real estate investment trust market is still at an infancy stage of development in Singapore. We, therefore, use the listed property companies in Singapore as a proxy of the securitized real estate market in our analysis. The transaction based direct real estate market performance indicators with different sub-sector indices published by the URA were used as the proxy of the direct real estate market performance. Are the inter-temporal flows of information efficiently conducted across and between the two real estate markets? Based on the proposition by Liu, Hartzell, Greig and Grissom (1990), if the two real estate markets were integrated, investors should expect homogeneous premia associated different market and economic risk factors. There were evidence that REIT market, but not the direct real estate market, was integrated with the equity markets in the US (Liu, Hartzell, Greig and Grissom, 1990; Ling and Naranjo, 1999). In this study, we use the SUR estimation technique and the standard Fama and MacBeth (1973) two-pass regression technique to estimate the risk premia specified in 17 the proposed MAP models. The predictive components in the MAP models include systematic market risks, real T-bill rate, term structure risk, credit risk, yield spreads between commercial and government bonds, and unexpected inflation risk. Our results showed that the fixed risk premia for credit risk, yield spread and unexpected inflation factors were significant in explaining the variations in excess returns of securitized real estate. The direct real estate asset excess returns were, however, negatively related to the fixed risk premia of real T-bill yield and unexpected inflation. In the time varying risk premia model, the credit risk appeared to be significantly priced in the excess returns of securitized real estate assets. For the direct real estate assets, the term structure risk premium was significant in additional to the premia associated with real T-bill yield and unexpected inflation. With the exception of unexpected inflation risk, there were clearly differences in the risk premia captured by the excess asset returns in the two real estate markets. This study further tested the homogeneity of macroeconomic risk premia in the two markets using the Wald test and pooled variance t-tests. The results of the wald tests, when applied to jointly test the five fixed risk premia jointly in the two markets, rejected the null hypothesis that the two markets were integrated. The same conclusion was derived in the tests of differences in the risk premia for economic factors in the timevarying framework. The time-varying risk premia for most of the economic factors were significantly different over the 37-quarter window. Term structure risk premium was the factor that was priced equally in the two markets in 5 out of the 37 periods. The results rejected the null hypothesis of integration between the two real estate markets. The presence of different premia associated with different macroeconomic risk factors suggests that the two markets are segmented. Real estate investors, who seek portfolio diversification, can no longer assume that property stocks move in line with property prices. There are risk reduction benefits through diversifying the holdings in both direct and indirect real estate assets because their price changes react differently to various economic shocks. 18 Reference: Barkhan, R. and Geltner, D. (1995) Price Discovery in American and British Property Markets, Real Estate Economics, 23(1), 21-44. Chan, K., Chen, N. and Hsieh, D. (1985) An Exploratory Investigation of the Firm Size Effect, Journal of Financial Economics, 14, 451-471. Chen, N., Roll, R. and Ross, S. (1986) Economic Forcees and the Stock Market, Journal of Business, 59, 383-403. Fama, E. and Gibbons, M. (1984) A Comparison of Inflation Forecasts, Journal of Monetary Economics, 13, 327-348. Fama, E. and MacBeth, J. (1973) Risk,Return and Equilibrium: Empirical Tests, Journal of Political Economy, 81, 607-636. Giliberto, S.M. (1990) Equity Real Estate Investment Trusts & Real Estate Returns, Journal of Real Estate Research, 5(2), 259-264. Gyourko, J. and Keim, D. 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(2000) The Predictability of Equity REIT Returns: time Variation and Economic Significance, Journal of Real Estate Finance and Economics, 20(2), 117-136. Liow, K.H. (1998) Singapore Commercial Real Estate and Property Equity Markets: Close Relations? Real Estate Finance, 41,603-616. 19 Liow K.H. (2001) The Long-term Investment Performance of Singapore Real Estate and Property Stocks, Journal of Property Investment and Finance, 19(2), 156-174. Liu, C.H., Hartzell, D.J., Greig, W. and Grissom, T.V. (1990) Integration of the Real Estate Market and the Stock Market: Some Preliminary Evidence, Journal of Real Estate Finance & Economics, 3, 261-282. Liu, C. and Mei, J. (1992) The Predicatability of Returns on Equity REITs and Their Comovement with Other Assets, Journal of Real Estate Finance and Economics, 5, 401418. Lizieri, C. and Satchell, S. 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(2003) Conditional Variance Tests of Integration Between Direct and Indirect Real Estate Markets, Journal of Property Investment & Finance, 21(4), 366-382. Sweeney, R. and Warga, A. (1986) The Pricing of Interest Rate Risk: Evidence from the Stock Market, Journal of Finance, 41(2), 393-410. Wilson, P., Okunev, J. and Ta, G. (1996) Are Real Estate and Securities Markets Integrated? Some Australian Evidence, Journal of Property Valuation & Investment, 14(5), 7-24. 20 Table 2: Estimation of Constant Risk Premia using SUR Technique Variable: Zero-beta CRDR RTBR TERMS A) Securitized Real Estate Assets (No of asset class: 7) URCPI YSPRD Coefficient Std. Error t-Statistic 2.0672 5.2434 0.3942 2.2023* 20.8587 0.1056 -0.0757** 2.4074 -0.0314 -0.4152 1.0697 -0.3882 -14.3382** 4.2555 -3.3693 0.0959 0.4911 0.1954 -0.1116 0.0922 -1.2102 3.4186* 2.0452 1.6715 -2.9687 10.6199 -0.2795 B) Direct Real Estate Assets (No of asset class: 12) Coefficient Std. Error t-Statistic 0.0406** 0.0188 2.1604 -0.1837 0.4173 -0.4403 -11.0370** 2.1666 -5.0941 ** 5% significance level * 10% significance level Wald Test: dre sre H 0 : λsre = 0; H 1 : λsre (6) j − λj j − λj ≠ 0 No cross-section fixed effect with cross-section fixed effect Test Statistic Value Probability Value Probability Chi-square 97.7319 0.0000 9.3834 0.0947 Hypotheses: 21 Table 3: Time varying Risk Premia Estimates for Two Real Estate Markets Average Zero-beta CRDR RTBR A) Securitized Real Estate Assets (No of asset class: 7) Coefficient -0.0897 -0.0008 0.0015 Standard errors 0.2084 0.0280 0.0101 No of Significant 7 11 5 Coefficients Proportion 18.92% 29.73% 13.51% B) Direct Real Estate Assets (No of asset class: 12) Coefficient -0.0110 -0.0042 -0.0016 Standard errors 0.0397 0.0186 0.0050 No of Significant 13 12 14 Coefficients Proportion 35.14% 32.43% 37.84% TERMS URCPI YSPRD -0.0006 0.0172 7 -0.0009 0.0062 5 -0.0002 0.0172 5 18.92% 13.51% 13.51% 0.0016 0.0058 19 0.0011 0.0030 17 0.0009 0.0080 12 51.35% 45.95% 32.43% Pooled Variance t-Tests: Hypotheses: dre H 0 : λsre j ,t − λ j ,t = 0; Variable: Zero-beta CRDR a) MAP models with zero-beta term Do not reject H0 3 2 Proportion 8.11% 5.41% b) MAP models without zero-beta term Do not reject H0 1 Proportion 2.70% sre H 1 : λsre j ,t − λ j ,t ≠ 0 RTBR (6’) TERMS URCPI YSPRD 4 10.81% 5 13.51% 3 8.11% 2 5.41% 1 2.70% 1 2.70% 1 2.70% 1 8.11% 22 Table 1: Descriptive of Sample Real Estate Asset Classes and Macroeconomic Variables Description of Variable Real Estate Stock Portfolio i=1 (largest Mkt Cap) Real Estate Stock Portfolio i=2 Real Estate Stock Portfolio i=3 Real Estate Stock Portfolio i=4 Real Estate Stock Portfolio i=5 Real Estate Stock Portfolio i=6 Real Estate Stock Portfolio i=7 (smallest Mkt Cap) Property Price Index of Industrial Property Property Price Index of Multiple-user Factory Property Price Index of Multiple-user Warehouse Property Price Index of Office Space Property Price Index of Office Space in Central Area Property Price Index of Office Space in Fringe Area Property Price Index of Residential Properties Property Price Index of Non-Landed Residential Properties Property Price Index of Landed Residential Properties Property Price Index of Shop Space Property Price Index of Shop Space in Central Area Property Price Index of Shop Space in Fringe Area Singapore Exchange All-Equities index Actual quarterly inflation rate Unexpected Inflation rate Quarterly yield for 3-month Treasury bill (compounding rate) Code* ERESP1 ERESP2 ERESP3 ERESP4 ERESP5 ERESP6 ERESP7 ERPPII ERPPIIF ERPPIIW ERPPIO ERPPIOC ERPPIOF ERPPIR ERPPIRC Derivation of variable =(Ri-Q3TBR) =(Ri-Q3TBR) =(Ri-Q3TBR) =(Ri-Q3TBR) =(Ri-Q3TBR) =(Ri-Q3TBR) =(Ri-Q3TBR) =(Ri-Q3TBR) =(Ri-Q3TBR) =(Ri-Q3TBR) =(Ri-Q3TBR) =(Ri-Q3TBR) =(Ri-Q3TBR) =(Ri-Q3TBR) =(Ri-Q3TBR) Mean 0.0224 -0.0004 0.0160 0.0298 0.0110 0.0096 0.0039 0.0030 0.0031 0.0046 -0.0082 -0.0077 -0.0096 0.0092 0.0085 Std. Dev. 0.2026 0.1742 0.1923 0.2661 0.2250 0.2224 0.1791 0.0552 0.0548 0.0768 0.0589 0.0628 0.0601 0.0553 0.0518 Source Datastream Datastream Datastream Datastream Datastream Datastream Datastream URA URA URA URA URA URA URA URA ERPPIRL ERPPIS ERPPISC ERPPISF ERSESA ARCPI URCPI Q3TBR =(Ri-Q3TBR) =(Ri-Q3TBR) =(Ri-Q3TBR) =(Ri-Q3TBR) =(Ri-Q3TBR) =[log(CPIt) - log(CPIt-1)] ARIMA(0,1,1) (Fama & Gibbons, 1984) 0.0105 -0.0086 -0.0105 -0.0016 0.0046 0.0016 0.0001 0.0044 0.0645 0.0446 0.0560 0.0649 0.1182 0.0018 0.0015 0.0024 URA URA URA URA Datastream Datastream Datastream Datastream = ⎡ Rqf ,t = (1 + Raf ,t ) ⎢⎣ 1 4 − 1⎤ ⎥⎦ Real quarterly 3-month T-bill yield RTBR =(Q3TBR – ARCPI) 0.0028 0.0026 Datastream Credit Risk Premium CRDR =(Prime Lending Rate - 3-month Interbank Rate) 0.0326 0.0117 Datastream 0.0107 0.0038 MAS Term structure of government bond TERMS# =(5-year bond yield - 2 year bond yield) Yield spread YSPRD =(3mth commercial bill yield - 3mth T-bill yield) 0.0071 0.0091 Datastream *E? denotes the excess of return of the respective real estate classes, e.g. ERESP1 indicates the excess return of the securitized real estate portfolio i=1. # Term structure premium for government bonds is computed based on the government 5-year and 2-year bond yields obtained from the Monetary Authority of Singapore (MAS). 23 Appendix 1: List of Real Estate Companies on Property Section of Singapore Exchange (SES) Name CITY DEVELOPMENTS CAPITALAND SINGAPORE LAND KEPPEL LAND ALLGREEN PROPERTIES UNITED OVERSEAS LAND WING TAI HOLDINGS MARCO POLO DEVELOPMENT MCL LAND GUOCOLAND BUKIT SEMBAWANG ESTATE THE ASCOTT GROUP ORCHARD PARADE HOLDING HONG FOK CORPORATION BONVEST HOLDINGS HO BEE INVESTMENT SC GLOBAL DEVELOPMENT L C DEVELOPMENT CHEMICAL INDUSTRIES (FE) HIAP HOE LIMITED Code Date of Listing on SES CITY CPTL SLND KEPL ALLG UOLD WING MARC MCLL GUCL BUKT SCOT OPHH HONG RONV HOBE SCGD LCDV CHMI HHOE November-1963 November-2000# June-1963 May-1905 May-1999 Before 1989 February-1989 June-1981 November-1967 November-1978 July-1968 September-1999 September-1968 July-1981 October-1973 December-1999 December-1982 August-1973 December-1963 September-1998 Average Market Capitalization (19902004) (S$ million) 4942.59 3154.41 1698.16 1498.74 1190.10 959.44 847.83 720.62 629.16 615.65 367.79 301.60 259.00 219.55 177.53 155.42 109.84 88.74 87.83 57.11 Ranking by Market Capitalization 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Securitized Real Estate Portfolio i 1 1 2 2 2 3 3 3 4 4 4 5 5 5 6 6 6 7 7 7 Average Stock Price (S$) (19902004) 4.85 2.74 5.34 2.65 1.13 1.81 1.55 1.87 1.82 2.33 15.32 0.73 1.05 0.39 0.70 0.25 1.15 0.52 1.70 0.13 # Capitaland Limited was created from the merger of Pidemco Land and DBS Land in November 2000, and the stock prices of CPTL prior to November 2000 were based on the former listed DBS Land stock prices. 24