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
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19
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182-212
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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