Risk Premiums for International Real Estate Investing in the Pan-Asia Region

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2007 ARES Annual Meeting, 11-14 April 2007, San Francisco, California
Risk Premiums for International Real Estate Investing in the Pan-Asia
Region
HO, Kim Hin / David *,
CHUA, Leslie Su Peng***,
SING, Tien Foo*,
GLASCOCK, John L.**
*Associate Professor, Department of Real Estate, School of Design and Environment; National University
of Singapore; **Grosvenor Professor of Real Estate Finance, Department of Land Economy, University of
Cambridge; ***Director of Research, ISTITHMAR, Level 38, Shatha Tower, Dubai Media City, PO Box
262080, Dubai, UAE.
This paper investigates the merits in the estimation and comparative analysis of direct real estate
risk premiums by direct real estate sector, city and regional cluster, utilizing the quarterly direct
real estate data from the JLL REIS-Asia Returns Index, 1999Q1 to 2004Q2. A significant
contribution is that this paper has rigorously estimated the risk premiums for the pan-Asia direct
real estate sectors. Another is that these regional markets are classified into 2 groups under the 2step cluster analysis. The direct real estate risk premium of the cluster dummy is estimated and
significant. Risk premiums of the direct real estate sectors under Cluster 1, mostly comprising the
fast growing sectors, are found to be higher than those direct real estate sectors under Cluster 2.
This paper offers additional insights into the direct real estate risk premium model estimation,
within the context of the pan-Asia regional market classification. The time-series CAPM (capital
asset pricing model) model reveals that most of the sensitivities (betas) of the direct real estate
total returns to the market risk factors are significant for the residential and retail sectors. Less
than half of the betas for the office sector are significant and most is positively signed. This
paper’s multi-factor arbitrage pricing theory model specification is consistent with that of Ling
and Naranjo (1998), having conditional and unconditional factors for each moving window time
period and is expressed in eq (4).
Ri , t = Ri ,f t + β1, i X 1,Ci , t + β 2, i X 2, i , t + β3, i X 3, i , t + ε i , t
(4)
, where subscript i indicates the ith direct real estate sector and t indicates time t. Eq (4) is estimated on the investment
time horizon of 3 years (12 calendar quarters). Apart from the length constraint of the JLL data set, the investment time
horizon is consistent with the common institutional real estate investment management practice of reporting rolling 3year returns for the performance of real estate portfolios. The entire period of the moving window estimation is from
1999Q1 to 2004Q2. 1 The superscript “C” represents the conditional factor. Ri,t = total returns of a direct real estate
sector, Rfi t = risk-free rate, XC1,i,t = conditional variable, GDP lag 1; X2,i,t = unconditional variable, unexpected
inflation rate; X3,i,t = unconditional variable, vacancy rate; βk,i = risk of the total returns of a direct real estate sector i
to kth economic variable (k = 1, 2, 3); εi,t = error term that captures risks that cannot be explained by the three
variables.
After estimating the moving window betas, the average betas from 1999Q1 to 2004Q2 of each
real estate sector are used to estimate the cross-sectional risk premiums. The risk premiums are
represented by the coefficients of the betas in the eq (5)’s cross-sectional model.
Ri = C + λ1β1, i + λ2 β 2, i + λ3 β3, i + λ4 DUMS1i + λ5 DUMS 2i + ε i
, where subscript i indicates the ith direct real estate sector.
direct real estate sector i; C = intercept;
Ri
(5)
= average total returns from 1999Q1 to 2004Q2 of the
β k , i (where k = 1, 2, 3)
= average value of betas that are derived from eq
1
Owing to the data availability of the vacancy rates for the Hong Kong retail, Tokyo and Seoul office
sectors, these three sectors are not included in the moving window estimates. However, they are included
in the estimation of the betas from 2001Q1 to 2004Q2.
1
(4); λk (where k =1, 2, 3) = cross sectional risk premium to risk factor k; DUMS1 = dummy variable of a direct real
estate sector with DUMS1=1: residential; DUMS2 = dummy variable of a direct real estate sector. DUMS2=1: retail;
DUMS1=DUMS2=0: the office sector; εi = error term that captures the direct real estate risk premiums that are not
explained by
β k (where k = 1, 2, 3) .
Under the 2-step cluster analysis methodology, 2 clusters for the pan-Asia direct real estate
sectors are established. The analysis shows that a dummy variable indicating the direct real estate
market transparency level, GDP growth rate and vacancy rate are significant in ascertaining the
clusters. Those direct real estate sectors, which are growing fast, are found to have high vacancy
rates and total returns, and they are classified under Cluster 1. There is additional return premium
to be gained for the direct real estate sectors under Cluster 1. In study period between 1999Q1
and 2004Q2, the direct real estate premium can be as high as 6.3%. Furthermore, the retail sector
may well have positive risk premiums beyond those of the other sectors in study period between
2001Q1 and 2004Q2. The associated direct real estate risk premiums are rigorously estimated and
summarized below:
Table 4.5 Direct Real Estate Risk Premium Estimates (1999Q1 to 2004Q2)
Variable
GDP growth rate lagged by 1-quarter
Risk Premium, %
-0.5%
Unexpected Inflation Rate
0.6%
Vacancy Rate
1.1%
Residential Sector (DUMS1=1)
1.5%
Retail Sector (DUMS2=1)
4.7%
Cluster 1 (DUMC=1)
6.3%
NB. DUMS1=DUMS2=0 is the Office Sector, DUMC=0 is the Cluster 2.
Cluster 1 - Beijing, Bangkok, Jakarta, Manila and Shanghai real estate markets.
Cluster 2 - Hong Kong, Kuala Lumpur and Singapore real estate markets.
Table 4.6 Direct Real Estate Risk Premium Estimates (2001Q1 to 2004Q2)
Variable
Risk Premium, %
GDP growth rate lagged by 1-quarter
-0.3%
Unexpected Inflation Rate
0.8%
Vacancy Rate
0.3%
Residential Sector (DUMS1=1)
5.0%
Retail Sector (DUMS2=1)
8.1%
Cluster 1 (DUMC=1)
6.2%
The risk premium for the retail sector is very high at 4.7%-8.1%, primarily owing to the high
time-series beta estimates of the retail sector relative to the residential and office sectors. Upon
closer scrutiny of the JLL REIS-Asia data sources and its real estate market analysis, it is found
that the retail sector’s high-risk premium in the pan-Asia region is attributable to the institutional
environment, in terms of market structural behavior and institutions, that lead to relatively high
direct real estate market uncertainty:
Direct Real Estate Market Structural Behavior
• Very high or the ‘best’ retail rents that are recorded for the prime ground floor retail
accommodation in the enclosed retail centers, under the JLL REIS-Asia data set, leading
to the persistence for the over- or under-valuation of retail rents – a temporal lagging
error (bias).
• Frequent mismatch between supply and demand in the retail accommodation. This can be
adversely affected by an excess supply of new competitive retail accommodation, and an
excess of competitive retail stock. It may well lead to relatively high volatility in the retail
real estate returns.
• Low transparency of market information that lead to high real estate transaction cost.
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Market Institutions
• Access to easy financing for speculative retail developments during an economic boom.
This may well lead to relatively high volatility in the retail real estate returns.
• Expertise building to resolve much more difficult and complex retail-centre asset and
investment management relative to that for the prime office and residential sectors.
The next highest risk premiums are for the Cluster 1 direct real estate sectors (i.e. Beijing,
Bangkok, Jakarta, Manila and Shanghai) in the narrow range between 6.2% and 6.3%, followed
by the residential-sector risk premium in the range between 1.5% and 5.0%. Then follows the
much lower risk premiums: the vacancy-rate risk premium in the range between 0.3% and 1.1%,
and the unexpected-inflation-rate risk premium in the range between 0.6% and 0.8%.
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