COMMON FACTORS IN INTERNARTIONAL SECURITIZED PROPERTY MARKETS
Kim Hiang LIOW, Department of Real Estate, National University of Singapore
Working Paper to be presented at the ARES 20th annual meeting, April 20-24 2004, South Sea Resort, Florida, USA
Dr Kim Hiang LIOW
Associate Professor and Deputy Head (Academic)
Department of Real Estate
National University of Singapore
4 Architecture Drive
Singapore 117566
Tel: (65)68743420
Fax: (65)67748684
E-mail: rstlkh@nus.edu.sg
*
Please do not quote without permission
Abstract
This paper investigates the presence of common factors in securitized property markets of US, UK, HK and SG. Using
a combination of factor analysis and canonical correlation technique on 10-year monthly return data of 142 real estate
securities of the four markets, we find that there is at least one common securitized property market factor hat is
moderately correlated with the world real estate market, and to a lesser extent, with the world stock market. However,
the degree of linkages across the four securitized property markets is much weaker compared to the strong linkages
present across the four economies. It further appears that the extent to which correlations are found in international
securitized property markets might largely due to the increasing integrated nature of the world real economy rather
than a result of globalization of financial markets. Our results are preliminary but indicative and suggest more studies to
explore how common factors together with the local market portfolio could help explain the return-generating process
of securitized property from the APT perspective.
COMMON FACTORS IN INTERNARTIONAL SECURITIZED PROPERTY MARKETS
LIOW, K.H. (rstlkh@nus.edu.sg, Department of Real Estate, National University of Singapore
INTRODUCTION
There is considerable research devoted to examining international stock market linkages In general these
earlier studies indicate that the correlations among the returns of national stock markets are low and that there are
diversification benefits achieved by investing across different stock markets. International asset pricing and portfolio
diversification have received a great deal of attention in the recent years as the major international stock markets have
experienced rapid deregulation and integration. The liberalization of capital markets and globalization of real estate has
also given reasons to suspect that securitized property returns in different countries might be correlated. Yet there is
very limited literature on the international linkages of securitized property markets. The lack of research in this area is a
peculiar oversight given the increase in indirect global property investment over the last decade and a great opportunity
for further research. With recent studies such as Conover et al. (2002) and Steinert and Crowe (2001) highlighting the
diversification benefits and added value of including international listed real estate in a mixed-asset portfolio,
considerable attention has been given to various aspects of property security performance and their long-run
equilibrium relationship and short-term linkages using co-integration and causality methodologies.
The main objective of this study is to investigate the presence of common factors in securitized property
markets using a combination of factor analysis and canonical correlation techniques. The literature on macroeconomic
influences and stock markets and the work on international stock market integration provide an appropriate empirical
basis on which this study is motivated. In addition, the arbitrage pricing theory (APT) of Ross (1976) suggests that
there is more than one source of common covariation among securitized property returns from the viewpoint of an
international investor. A further point to note is although several recent studies have suggested that stock markets are
becoming more integrated and the evidence for international linkages is reasonably strong, this might not be the case
for securitized property markets as the underlying risk-return performance of securitized property is likely to be
significantly different from the general stock market in the short-, medium- and long-term. Using a monthly dataset that
includes individual securitized property returns of the US, UK, Hong Kong (HK) and Singapore (SG) over the last ten
years, we seek to investigate the presence of common securitized property market factor(s) and further examine
whether any common factor(s) found in one market are correlated with other markets in the macroeconomic context.
The results of this study should prove useful to international property investors and even policy makers who
might be interested in understanding how changes in some local and foreign macroeconomic variables could impact
securitized property returns as a country’s economy is of interest no longer only to its local investors but also to
international investors. For example, a significant relationship between the US economy and the performance of
securitized property markets of UK, HK and SG might be expected given the US-centric nature of the global economy.
One might also expect the degree of correlations between the different securitized property markets to be different due
to the varying extent of integration with the world real economy or a result of globalization of financial markets.
Although it is beyond the objective of this paper to further investigate this issue, we hope to provide some preliminary
but indicative evidence on significant linkages in international securitized markets.
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To establish a background for the study, the next section provides a brief review of relevant literature. This is
followed by presentations of research methodology and data. The empirical results are then reported and discussed.
The final section concludes the study.
LITERATURE REVIEW
The literature on macroeconomic forces and stock market and the studies on international stock market
integration provide a suitable empirical foundation regarding possible linkages in international securitized property
markets.
In general, earlier studies examining the relationship among national stock markets (Granger and
Morgenstern, 1970) reveal that the correlations among the returns of national stock markets are low and that national
factors play an important role in the return generating process. On the contrary, Cheng (1998) produces empirical
results to show that the US and the UK economies are closely related. His results also indicate that the returns of the
UK stocks are significantly positively related to those of the US and that the co-movements between the UK and the
US stocks are fairly high. His findings could have been due to the closer integration of national stock markets in recent
years. Some of the contributing factors might be closer economic integration, financial market deregulation,
liberalization of capital market movements and securitization of national markets. With increasing globalization of
financial markets, Huang and Satchell (1999) observe that domestic investors have become more sensitive to the
world economic situation, the state of development in the financial markets and the behavior of international investors.
Further, globalization in financial markets has possibly resulted in considerable exposure of European and American
markets to significant price movements in Asian markets.
In a different vein, the significance of economic fundamentals using APT on stock market returns has been
well documented. Here, the main objective is to identify possible local and global macroeconomic factors that affect the
local stock markets. Financial economists focus upon systematic factors to explain general stock returns. Their studies
has led them to believe that stock prices move accordingly to ‘surprise’ in macroeconomic factors that explain general
stock price movements. Many empirical studies employing multi-factor models such as APT have tried to resolve the
theoretical question of “which economic factors have significant effects on asset prices”. However, these asset pricing
models do not dictate the number and the identities of these factors even if the central theme is that a few pervasive
factors are the dominant sources of co-variances among asset returns.
So far, three approaches have been used to empirically investigate possible common factors that might
affect common stock returns. The first approach gathers a general pool of common factors before they are eventually
narrowed down. The disadvantage to this approach is that the nature of the underlying factors dreiving asset pricing
cannot be determined. This approach relies on factor analysis techniques to simultaneously estimate the common
factors and factor loadings which might affect security returns (Roll and Ross, 1980). For examples, Titman and
Warga(1986) link factors derived form NYSE and AMEX stocks to returns of REITs. The second approach specifies
before hand, common factors which might affect security returns. For example, Chen Roll and Ross (1986) group US
stocks into size sorted portfolios and find that economic factors that are significantly priced in the general stock market
include; changing state of the economy, inflation, yield curve shifts and a measure of the market risk premium. Clare
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and Thomas(1994) using beta sorted portfolios find that a number of factors have been priced in the UK stock markets;
they are oil prices, two measures of corporate default –“market risk”, UK private sector bank lending, retail price index,
current account balance and the redemption yield on an index of UK corporate debentures and loans. Beenstock and
Chan (1988) however identify a different set of factors to explain variations in UK stock returns—interest rate, money
supply and two inflation measures. Turning to securitized real estate, the bulk of the research relates to US REIT
markets. Examples of this work include McCue and Kling (1984), Ling and Naranjo (1997) and Mei and Hu (2000). The
third empirical approach relies on a combination of factor analysis and canonical correlation technique in different
manners. The main question to be addressed under this line of APT research is “if there are multiple factors affecting
security returns what might they be? “ Fogler et al (1981) , McGowan and Dobson (1993) , Christorfi et al.(1993) and
Cheng (1995) apply these techniques to their dataset and are able to establish at least a significant statistical and
economic relationship between stock return and macroeconomic factors. Finally, Cheng (1998) employs factor analytic
technique and canonical correlation analysis to examine the international transmission mechanism of the UK and US
stock market movements and the relationship between the UK and the US economic indictors.
With the growth of international investing opportunities in securitized property vehicles such as REITs and
listed real estate stocks, considerable attention has also been given to the role of international property stock in an
investment portfolio (Worzala and Sirmans, 2003). In addition, other studies such as Steinert and Crowe (2001) and
Conover et al. (2002) have highlighted the benefits of including international listed real estate in a mixed investment
portfolio. Empirically, Garvey et al (2001) examine the linkage between property stock markets in Australia, Hong
Kong, Japan and Singapore using co-integration and Granger causality techniques, while Stevenson (2002)
investigates volatility spillover effects in USA REITs and other equity and fixed income sectors. Similarly, Liow et al.
(2004) appeals to a combination of co-integration and GARCH methodologies to model the nature and degree of longrun equilibrium and short-term linkages across Asian and European property stock markets. In line with earlier stock
market studies, a research question is thus whether there are common factors found in international securitized
property markets and the degree of integration of the common factors with the world market. This study hopes to
contribute this literature in international real estate.
RESEARCH METHODOLOGY
The principle task in this paper is to investigate whether there are common factors found in securitized
property markets of US, UK, HK, and SG and the degree of integration of the common factors with the world market.
For this purpose, we appeal to a combination of factor analysis and canonical correlation technique to explore the
return-generating process of securitized property in the context of macroeconomic factors.
In stage 1, maximum likelihood factor analysis is conducted on monthly stock returns to identify the number
of factors, their factors loading and factor scores. Lehmann and Modest (1988) compare different methods of factor
loading estimation and find the best is maximum-likelihood factor analysis. To aid factor interpretation, we further
employ varimax method of orthogonal rotation. The Kaiser criterion is used to decide on the common factors which
should be retained. Hence, only those common factors with latent roots greater than or equal to one will be retained
(Kaiser, 1960).
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In stage 2, canonical correlation analysis (CCA) is employed to investigate the relationship between two set
of variables (e.g. factor scores of securitized property returns and macroeconomic variables, factor scores of two sets
of securitized property returns). Briefly, CCA is a multivariate technique between two sets of inter-correlated variables.
Each set may contain several correlated variables, and vectors of weights for each set are derived so that the
correlations between the linear combinations contained by using the derived canonical weights are maximized. The
linear combinations are known as the canonical variables and the composite derived from using these “best possible”
weights are called variate scores. An overall measure of any relation between the two sets of variables is indicated by
the canonical correlation coefficient. Next, canonical loadings are the correlations between the original variables and
canonical scores. Normally loadings greater than some selected lower bound, commonly 0.30 to 0.50 in absolute
value, are used to decided whether a loading coefficient is significant (Fornell and Larcker, 1980). Further, if a
canonical variate pair is significantly associated with certain predictor variable(s) and certain criterion variable(s), it is
inferred that these original variables are related although cross loadings are sometimes used. Finally, Stewart and
Love’s (1968) redundancy index provides a summary index of the average ability of predictor variable to explain the
variability in a set of criterion variables.
Our study is different from that of Cheng (1998) on US and UK stock market movement and their
macroeconomics. Using monthly securitized property returns of the USA, UK, HK, and SG and their key
macroeconomic data, our extended empirical procedures are summarized below:
(a)
Estimate the factor structure of securitized property returns of USA, UK, HK, and SG separately
using maximum likelihood factor analysis with varimax rotation.
(b)
For each market, investigate whether there is a significant relationship between factor scores of
security returns and macroeconomic variables using CCA.
(c)
Investigate the degree of integration between six pairs of economies using CCA (i.e. US-UK, US-
HK, US-SG, UK-HK, UK-SG, and HK-SG).
(d)
Determine the presence (if any) of common factors in securitized property markets by analyzing
the relationship between six pairs of factor scores of stock returns using CCA (i.e. US-UK, US-HK, US-SG, UK-HK,
UK-SG, and HK-SG).
(e)
Examine the cross-correlations between 12 pairs of factor structure of stock returns (P) and
economic indicators (E) (i.e. USP-UKE, USP-HKE, USP-SGE, UKP-USE, UKP-HKE, UKP-SGE, HKP-USE, HKP-UKE,
HKP-SGE, SGP-USE, SGP-UKE, and SGP-HKE).
(f)
Assess the strength of correlations between the common securitized property factors and the world
stock market and world real estate market.
RESEARCH DATA
Four securitized property markets are included in his study: The US, UK, HK, and SG. The two Asian
markets, Hong Kong and Singapore have enjoyed remarkably rapid economic growth in the past decade and have
reasonably long-established track records of listed property investment and development companies in their capital
markets. The sample period is August 1993 to August 2003, giving a maximum of 120 monthly stock returns. The
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sampling frame is all the real estate firms listed on the New York stock Exchange (US), London Stock Exchange (UK),
Hong Kong Stock Exchange (HK) and Singapore Stock Exchange (SG). We thus obtain an initial sample of more than
500 firms.1 However, only those firms with no missing stock return data between August 1993 and August 2003 are
included as required by factor analysis, the initial sample was reduced to 205 companies. The breakdowns are 66(US),
42(UK), 77(HK) and 20 (SG). Hence this selection process might introduce a survivorship bias in that only real estate
firms which are existence for the entire 10-year sample period are included. A further point to note in the US
securitized property market is the dominance of REITs. This is not surprising given a history of more than 40 years of
REIT operations in the US. Out of 66 companies for the US securitized property market, 47 are REITs. For the other
three markets, no REITs are considered either because of non-existence (in the case of UK) or immaturity ( in the case
of HK and SG). For the purpose of this study, no special distinction was made between US REITs and the other
comparables that are largely real estate developers or real estate investors. Finally, the Kolmogorov-Smirnove (KS)
test is applied to this sample of 205 firms to check whether they are drawn from a normal distribution. The final sample
hence includes 142 real estate securities (69.3%) whose return do not have any missing observations and are normally
distributed. The breakdowns are 56(US), 30(UK), 40(HK) and 16(SG)
The macroeconomic variables chosen are based on economic grounds supported by relevant overseas and
local literature and dictated by availability of data. The economic variables considered in this study are those
representing the economic performance, industrial production, money market, stock market, labor market and
international trade. The are represented by gross domestic product (GDP), industrial production index(INPD), inflation
(INF), inter-bank 3-month rate(IB-3M), inter-bank 1 year rate (IB-1Y), money supply(MS), stock market condition
(MKT), unemployment rate (UMR), export price(EXP) and import volume( IMV) . Except for UMR and IB-3M which
experience relatively little movement over the sample period, the remaining eight indicators are measured by the rates
of change rather than absolute values to facilitate comparisons with stock return series and / or render the series
stationary. Changes in GDP, industrial production and unemployment rate have been observed to affect demand for
space and hence the returns of property firms involved in the space market. The rationale behind interest rates and
property stock prices is obvious. Changes in interest rates affect the opportunity cost of holding cash and hence
sustain a substitution effect between property stocks and interest bearing securities. Changes in the money supply and
inflation rates lead to corresponding changes in interest rates and asset prices. Changes in the stock market condition
affect the general investment sentiments. Finally, changes in international trade as measured by export price and
import volume affect the general prosperity of the economy and the aggregate demand for space and related services.
EMPIRICAL RESULTS AND DISCUSSION
Factor structure of securitized property returns
According to UBS Investment Bank research report (2003), the global real estate security market has grown
extensively to an estimated US$265 billion in 2001. In addition, the four securitized property markets included in this
study represent 74% of the global market. The approximate breakdowns are: US (49%), UK (13%), HK (11%) and SG
(1%).
1
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For each market, the monthly returns of the individual stocks are subject to maximum-likelihood extraction
and varimax rotation. Table 1 contains the numbers of factors and the proportions of variance explained by the
identified factors. As can be seen, the first 7 (US), 7 (UK), 6 (HK) and 2 (SG) factors have eigenvalues greater than
one and together they able to explain approximately 47.62%, 61.53%, 69.64% and 72.23% of return variance in US,
UK, HK and SG respectively. Further, the first factor for the respective markets is the most important since it is able to
explain between 26.98% (US) and 66.30% of the total sample variance. A final point to note is that as the number of
significant factors is an increasing function of the size of the group analyzed, it follows that there are fewer factors
present in the securitized property returns of SG and might give rise to some biased results obtained in the subsequent
canonical correlation analysis.
(Table 1 here)
Linkages between securitized property returns and economic indicators
The objective of this section is to analyze the linkages between securitized property returns and economic
indicators for the US, UK, HK and SG markets. The canonical correlations are the association between the factor
scores of securitized returns and selected economic indicators. Table 2 provides the results.
(Table 2 here)
As can be seen, there is one significant canonical pair each for the 4 markets analyzed. The canonical
correlations are 0.629 (US), 0.647 (UK), 0.782 (HK) and 0.518 (SG), representing 39.6% (US), 41.8% (UK), 61.2%
(HK) and 26.9% (SG) each of overlapping variance between the first pair of canonical variate. In addition, canonical
redundancy analysis shows that on average, the macroeconomic variable set could explain between 7.26% (US) and
15.42(%) of the variance in securitized property returns. Additional results on canonical loading show that except for
the US market, the first economic factor is mainly comprised mainly of market indices (the factor loading is between
0.678 and 0.944). Our results hence suggest that similar to stock market (Cheng, 1995), securitized property returns
are influenced by economic forces and that the market return plays a major role in the APT for the UK, HK and SG
securitized property markets.
Relationships between cross-market economic indicators
Here, we are interested in assessing the degree of integration in the four economies. The results will reveal
the canonical structure between a pair of economies. There are a total of six pairs of cross-canonical relationships (USUK, US-HK, US-SG, UK-HK, UK-SG and HK-SG). The canonical correlations are the association between the 10
economic indictors of two economies. Table 3 provides the key results.
(Table 3 here)
As observed, there are five significant pairs of canonical relationships between the economies of US and UK;
US and SG; UK and HK. On the other hand, the numbers of significant pairs of canonical variates are 6 (UK and SG),
7 (US and HK) and 8 (HK and SG) respectively. For the six pairs of cross-economies, the first canonical correlation
ranges between 0.887 and 1.000, representing about 78.6% - 100% of overlapping variance between the first
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canonical variates. The second canonical correlation is between 0.695 and 0.957, accounting for about 48.3% - 91.5%
of overlapping variance for the second pair of canonical variables.
To see whether the US economic factors help to explain the UK economic indicators and the reverse,
canonical redundancy analysis report in Panel A of Table 3 shows that overall, the US economic factors are able to
explain 41.86% of the variance in the UK economic indicators. On the other hand, the proportion of variance in the US
economic factors explained by the UK counterpart is 36.81%. Hence the US economy is more influential than the UK
economy as the US economic indicators have a higher capability of accounting for the variances of the UK economy.
Similar redundancy analyses in Panels B and C of Table 3 also reveals that the US economic indicators are able to
account for a higher proportion of variance in the HK economic factors (HK variance explained :37.32%; US variance
explained: 32.08%) and in the SG economic factors (SG variance explained: 31.26%; US variance explained: 28.59%).
Additionally, canonical redundancy analysis in Panel F shows that the Singapore economic factors are able to explain
a higher proportion of variance in the HK economic indicators (HK variance explained: 45.46%; SG variance explained:
41.59%). Finally, the redundancy results in Panels D and E indicates that UK economy appears to have the least effect
on the other three economies.
Linkages across securitized property markets
The objective of this section is to investigate the extent of linkages between securitized property returns of
US and UK, US and HK, US and SG, UK and HK, UK and SG and SG and UK. The canonical correlations are the
association between the factor scores of market 1’s stock returns and the factor scores of the market 2’s stock returns.
As shown in Table 4, there are two significant canonical pairs each between the factor scores of US and UK,
US and HK, UK and HK, and HK and SG. The first canonical correlation is between 0.498 and 0.796, representing
about 24.8% - 63.4% of the shared variance for the first pair of canonical variables. The second canonical correlation is
between 0.367 and 0.417, explaining between 13.5% and 17.4% of overlapping variance for the second pair of
canonical variables. On the other hand, there is only one significant pair of canonical relationship each between the
factors scores of US and SG (canonical correlation is 0.187), and UK and SG (canonical correlation is 0.357)
(Table 4 here)
For the US-UK pair, canonical redundancy analysis shows that the US factor scores of stock returns are able
to explain 8.61% of the variance in the factor scores of UK returns. On the hand, the factor scores of UK stock returns
have a slightly higher capability of accounting for 9.73% of the factor scores of US stock returns. Similarly, HK property
stock market appears to have more effect on the SG property stock market (SG variance explained: 43.81%, HK
variance explained: 14.66%), presumably HK property stock market has longer history and takes up higher proportion
of the global real estate market.
Taking the results as a whole, the evidence has implied that at least one common securitized property
market factor can be found in the US, UK, HK and SG markets. Further, there are additional linkages between some
pairs of securitized property markets. Our results thus provide some support to the belief that globalization of financial
markets has resulted in increasing correlations between the regional securitized property markets. However, the
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degree of linkages across the four securitized property markets is much weaker compared to the stronger linkages
present across the four economies reported in the previous section.
Relationships between cross-market securitized property market and macroeconomy
This is the highest level of canonical relationship considered in this study. Finding of a significant canonical
relationship in this context implies that the economic forces of one country are able to exert their influences that will
then be transmitted to another securitized property market. This phenomenon could possibly reflect the increasing
integrated nature of the world real economy as represented by co-movements between key macroeconomic variables.
There are 12 pairs of cross securitized property market–macroeconomy relationship. The canonical correlations are
the association between the factor scores of country A’s stock returns and country B’s economic indicators. To the
extent that significant canonical correlations are detected between the two sets, then they imply the presence of
common factor(s) between cross-market securitized property returns and economic forces. Table 5 produces the
results
(Table 5 here)
As can be seen, except for the UK-HK pair, there is one statistically significant (at least at the 10% level)
canonical relationship each for the remaining 11 cross stock return-macroeconomy pairs. The canonical correlation
ranges between 0.488 and 0.601 across the samples, representing approximately 21.9% - 36.1% common variances
between the respective canonical variables.
Canonical redundancy analysis reveals that the US, UK and HK economic forces are able to account for
11.60%, 14.01% and 17.16% of the variance in SG factor scores of securitized property returns. For the other 8 pairs,
the canonical variables are only a moderate overall predictor of securitized property returns, ranging between 4.41%
and 6.89%. Another observation is the canonical variables of the securitized property returns are relatively a poor
predictor of the economic indicators (the proportion of variance explained by the return variates ranges between 2.71%
and 4.25%).
The results imply that there are moderate degree of linkages between factor scores of international
securitized property returns and key economic indicators. However it further appears that any cross-link between the
two systems is mainly in one direction as there are weaker cross-feedback effects from the economies to the financial
markets. Hence, the extent to which correlations are found in international securitized property markets might largely
due to the increasing integrated nature of the world real economy rather than a result of globalization of financial
markets.
Correlations between the common factors and world market indices
Finally, we investigate the strength of relationship between the 10 significant canonical factors in securitized
property (from Table 4) and world market. With increasing globalization of financial markets, it is interesting to assess
to what extent our sample securitized property markets are segmented or integrated with the world stock market and
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world real estate market. Table 6 reports the correlation results. The world indices employed are the MSCI and DJGI
market and real estate indices obtained from the Datastream2.
(Table 6 here)
Panel A of Table 6 reveals that the first canonical factor of the Asian markets (HK-SG1) is significantly
correlated with the first canonical factor of the American / European markets (US-UK1) at the 1% level. The correlation
coefficient is 0.300 which gives shared variance of 9%. Hence, there is some degree of linkages between the regional
securitized property markets. As observed from Panel B of Table 6, the numbers of significant correlation coefficients
between the securitized property market common factors and world indices (up to the 5% level) are 6 (with DJGI real
estate), 5 (with MSCI real estate), 4 (DJGI market) and 1 (MSCI market) respectively. In addition, the correlation
coefficients of the common factors with the world real estate indices are higher (although still on the moderate side)
than those with the world indices. These results indicate that our sample of securitized property markets have common
factors that are moderately correlated with the world real estate market, and to a lesser extent, with the world (stock)
market. Our results also imply that high integration of the world market and securitized property markets might be very
difficult, if not impossible. Hence, investors would benefit from diversifying securitized property investments
internationally
CONCLUSION
In responding to the increasing trend in globalization and securitization of real estate, the main purpose of
this study is to search for the presence of common factors in securitized property markets of US, UK, HK and SG.
Together these four markets represent 74% of listed global real estate market. In addition, we examine whether any
common factors found in the Asian markets (HK and SG) are correlated with those of highly developed markets (US
and UK). The main findings are: (a) the numbers of securitized property market factors are 7, 7, 6 and 2 in the US, UK,
HK and SG respectively; (b) securitized property returns are influenced by economic forces and that the market return
plays a major role in the APT for international securitized property markets; (c) the four economies are closely related.
The US economic indicators have a higher capability of accounting for the variances in SG, HK and UK economies; (d)
at least one common securitized property market factor can be found across the four markets. Hence international
securitized property markets are not fully segmented. Additionally, there are additional links between some pairs of
securitized property markets. However, the degree of linkages across the four securitized property markets is much
weaker compared to the stronger linkages present across the four economies; (e) there is at best moderate degree of
linkage between factor scores of international securitized property returns and key economic indicators. However it
further appears that any cross-links between the two systems are mainly in one direction as there are weaker crossfeedback effect from the economies to the financial markets; (f) Asian securitized property markets have a common
2
The MSCI world index measures global developed market equity performance. The index covers 23 countries that
include US, UK, HK and SG. The DJGI family is Dow Jones benchmark series and covers 34 countries, including both
developed and emerging markets. It includes the US, UK, HK and SG. The MSCI world real estate index is one of the
sub-indices of MSCI world index accordingly to Global Industry Classification Standard (GICS). It covers the listed real
estate stocks in MSCI 23 component countries. Last, the DJGI real estate index covers the listed real estate stocks in
its 34 countries.
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factor that is moderately correlated with the American and European markets; and (g) securitized property markets
have common factors that are moderately correlated with the world real estate market, and to a lesser extent, with the
world stock market.
Overall, support is offered to the finding that common factors display in our sample of securitized property
markets. However the degree of linkages across our four securitized property markets is much weaker compared to
the strong linkages present across the four economies. Hence, the potential for portfolio diversification in international
securitized property remains good. It further appears that the extent to which correlations are found in international
securitized property markets might largely due to the increasing integrated nature of the world real economy rather
than a result of globalization of financial markets. Our results are preliminary but indicative and for which more studies
are definitely required to explore how common securitized property market factors together with the local market
portfolio to explain securitized property market performance from the APT perspective.
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13
Table 1
Proportion (%) of securitized property return variance explained by factors extracted
Factor
1
2
3
4
5
6
7
Total
Note
1.
2.
3.
US
26.98
8.76
3.90
2.32
2.31
1.87
1.48
47.62
UK
33.98
7.58
4.60
4.31
4.00
3.60
3.46
61.53
HK
48.15
8.83
3.69
3.30
2.87
2.80
69.64
SG
66.30
5.93
72.23
The number of property securities included in the factor analysis: 56(US), 30(UK), 40(HK) and 16(SG)
For each market, the monthly returns of all the securities are subject to maximum-likelihood factor analysis.
The rotation method used is varimax with Kaiser normalization
The number of factors extracted are : 7 (US), 7(UK), 6 (HK) and 2(SG)
Table 2
Significant canonical results between securitized property (left set) and macro-economy (right set)1
Canonical pairs
Canonical correlation
Squared canonical
correlation
Significance
US
1
0.629
0.396
0.000
Left set
Right set
7.26
3.79
Left set
(0.734)
(0.533)
(0.439)
Right set
0.638
(o.425)
(0.364)
UK
1
0.647
0.418
0.000
Redundancy (%)
6.52
4.54
Canonical loadings (>0.3)
0.637
0.634
0.401
0.944
HK
1
0.782
0.612
SG
1
0.518
0.269
0.000
0.000
11.28
7.34
15.42
3.59
0.615
0.606
0.441
0.380
0.777
(0.502)
(0.442)
(0.760)
(0.755)
(0.678)
0.638
0.568
The canonical correlations are the association between the factor scores of the country 1’s returns (left set) and its
macroeconomic variables (right set). For each country, a significant canonical relationship each is found between the
left set and right set.
1
14
Table 3
Significant cross-canonical results on macroeconomy 1
Canonical pairs
Canonical correlation
Squared canonical
correlation
Significance
Left set
Right set
Canonical pairs
Canonical correlation
Squared canonical
correlation
Significance
Left set
Right set
Canonical pairs
Canonical correlation
Squared canonical
correlation
Significance
Left set
Right set
Canonical pairs
Canonical correlation
Squared canonical
correlation
Significance
Left set
Right set
Canonical pairs
Canonical correlation
Squared canonical
correlation
Significance
Left set
Right set
Panel A US and UK
2
3
0.857
0.815
0.734
0.664
1
0.956
0.914
4
0.650
0.423
0.000
0.000
0.000
0.000
Redundancy % - Total: 36.81% (left set) and 41.86% (right set)
11.23
8.36
7.88
5.25
14.97
10.30
6.87
5.88
1
0.887
0.786
2
0.811
0.658
Panel B US and HK
3
4
0.772
0.592
0.596
0.351
5
0.560
0.313
6
0.431
0.185
0.000
0.032
2.67
2.26
1.42
1.58
5
0.530
0.280
0.000
0.000
0.000
0.000
0.000
Redundancy % - Total: 32.08% (left set) and 37.32% (right set)
8.99
6.62
6.47
5.02
2.98
12.58
11.18
5.81
3.10
2.62
1
1.000
1.000
Panel C US and SG
2
3
0.890
0.570
0.792
0.325
6
0.411
0.169
7
0.334
0.112
0.010
0.080
1.09
1.30
0.91
0.73
4
0.514
0.264
5
0.482
0.232
0.000
0.000
0.000
0.000
Redundancy % - Total: 28.59% (left set) and 31.26% (right set)
13.37
6.54
3.67
2.59
12.17
10.89
3.16
2.69
0.007
1
1.000
1.000
Panel D UK and HK
2
3
0.957
0.824
0.915
0.679
2.42
2.35
4
0.624
0.389
5
0.468
0.219
0.000
0.000
0.000
0.000
Redundancy % - Total: 45.58% (left set) and 37.96% (right set)
20.13
13.77
6.21
3.66
14.38
11.52
6.38
3.79
0.039
1
0.917
0.840
Panel E UK and SG
2
3
0.695
0.648
0.483
0.419
4
0.558
0.311
0.000
0.000
0.000
0.000
Redundancy % - Total: 34.73% (left set) and 29.55% (right set)
15.03
8.56
4.56
2.67
13.12
5.19
4.02
3.08
1.81
1.89
5
0.485
0.236
6
0.440
0.194
0.000
0.018
2.12
2.26
1.79
1.88
15
Canonical pairs
Canonical correlation
Squared canonical
correlation
Significance
Left set
Right set
1
0.959
0.920
2
0.840
0.706
Panel F HK and SG
3
4
0.778
0.671
0.605
0.451
5
0.593
0.352
6
0.446
0.199
7
0.377
0.142
8
0.324
0.105
0.000
0.000
0.000
0.000
0.000
0.000
Redundancy % - Total: 45.46% (left set) and 41.59% (right set)
16.63
9.22
8.75
4.40
3.26
1.69
15.11
8.40
7.64
3.75
2.55
1.83
0.011
0.090
1.20
1.47
0.31
0.84
1There
are six pairs of cross-canonical relationships on macroeconomy. The canonical correlations are the association
between the 10 economic indicators of country i and country j (i ≠ j )
16
Table 4 Significant cross-canonical results on securitized property (P)1
Canonical pairs
Canonical
correlations
Squared
canonical
correlations
Significance
USP and UKP
1
2
0.612
0.417
USP and HKP
1
2
0.519
0.410
0.374
0.174
0.270
0.168
0.000
0.015
0.000
0.013
Left set
Right set
6.91
6.22
2.82
2.39
4.97
4.84
Left set
(0.719)
(0.602)
(0.316)
0.639
(0.598)
Right set
(0.484)
(0.400)
(0.355)
0.567
0.450
(0.421)
0.648
0.515
0.486
0.391
0.845
0.378
0.345
USP and SGP
1
0.187
0.036
0.100
Redundancy (%)
2,73
3.51
2.80
3.51
Canonical loadings (>0.3)
(0.689)
(0.991)
0.586
0.763
(0.442)
(0.315)
(1.000)
UK and HK
1
2
0.498
0.367
UKP and SGP
1
0.357
HKP and SGP
1
2
0.796
0.417
0.248
0.135
0.128
0.634
0.174
0.002
0.092
0.092
0.000
0.001
4.02
4.28
1.62
2.22
2.83
8.50
11.69
36.40
2.97
7.41
(0.601)
(0.552)
(0.448)
(0.351)
(0.791)
(0.509)
(0.345)
(0.546)
0.449
0.338
0.336
0.762
0.455
(0.341)
(0.634)
(0.573)
(0431)
(0.387)
(0.780)
(0.733)
(0.769)
(0.405)
(0.381)
(0.335)
(0.761)
(0.753)
0.731
(0.542)
0.657
(0.648)
1 There are six pairs of cross-canonical relationships on securitized property. The canonical correlations are the association between the factor scores of the country 1’s returns
(left set) and the factor scores of the country 2 ’s returns (right set).
17
Table 5 Significant canonical results between cross-market securitized property (P: left set) and macro-economy (E: right set) 1
USP – UKE
1
0.501
USP – HKE
1
0.527
UKP- SGE
1
0.534
UKP – USE
1
0.550
UKP-SGE
1
0.549
HKP – USE
1
0.510
HKP – UKE
1
0.552
HKP-SGE
1
0.601
SGP-USE
1
0.468
SGP-UKE
1
0.495
SGP-HKE
1
0.549
0.251
0.278
0.285
0.303
0.300
0.260
0.304
0.361
0.219
0.245
0.302
0.105
0.032
0.018
0.065
0.040
0.000
0.012
0.002
0.000
Left set
Right set
4.41
2.71
5.19
2.96
5.21
2.92
5.27
3.02
6.89
4.25
11.60
2.89
14.01
2.35
17.16
4.15
Left set
(0.597)
(0.527)
(0.485)
(0.856)
(0.460)
(0.336)
0.907
0.572
0.428
(0.634)
(0.859)
(0.670)
(0.477)
0.381
0.322
(0.703)
(0.411)
0.394
0.354
0.599
0.562
0.521
0.364
(0.601)
0.511
0.460
0.384
(0.991)
Right set
(0.838)
(0.376)
(0.346)
(0.343)
(0.699)
(0.429)
0.428
0.074
0.032
Redundancy (%)
5.02
4.95
4.45
2.69
2.97
3.19
Canonical loadings (>0.3)
0.533
0.876
0.814
0.555
0.505
0.471
0.477
0.315
0.457
0.718
0.751
0.930
(0.527)
0.372
0.346
0.337
(0.885)
0.420
(0.402)
0.846
(0.704)
0.591
0.560
Canonical pairs
Canonical
correlation
Squared
canonical
correlation
Significance
0.916
They are 12 pairs of cross-market securitized property - macroeconomy relationship. The canonical correlations are the association between the factor scores of country i’ s
returns (left set) and country j (i ≠ j )’s economy. Of them, only 1 pair (i.e. UKP – HKE) has no significant canonical relationship at the 10% level. The remaining 11 pairs (note
that the USP-UKE pair is significant at the 10.5% level) have 1 significant canonical relationship each and the results are reported on this table.
1
18
Table 6
Correlations results
Panel A: Common factors (HK-SG) and common factors (US-UK) 1
HK-SG1
HK-SG2
US-UK1
US-UK2
HK-SG1
1.000
0.000
0.300**
-0.171
HK-SG2
0.000
1.000
-0.162
-0.181
US-UK1
0.300**
-0.162
1.000
0.000
US-UK2
-0.171
-0.081
0.000
1.000
1 From Table 4, there are two common factors each in Asian securitized property markets (represented by HK-SG1
and HK-SG2) and American/European securitized property markets (represented by US-UK1 and US-UK2).
**
Correlation is significant at the 1% level (two-tailed)
Panel B: Common factors and world indices
Common factors1
US-UK1
US-UK2
US-HK1
US-HK2
US-SG1
UK-HK1
UK-HK2
UK-SG1
HK-SG1
HK-SG2
World Market Indices2
World Real estate Indices2
MSCI world
DJGI
MSCI world
DJGI
0.091
-0.154
0.075
0.094
0.003
-0.215*
0.041
0.095
0.087
0.023
-0.327**
0.182*
-0.131
0.152
-0.195*
0.299**
0.015
-0.276**
-0.177
0.154
-0.298**
0.138
0.011
0.110
-0.152
0.227*
0.023
-0.234*
-0.366**
0.330**
-0.364**
0.161
-0.113
0.099
-0.212*
0.290**
0.004
-0.226*
-0.380**
0.284**
Notes
1 The ten common factors are obtained from Table 4. They are the significant canonical variates of the factor scores of
securitized property returns between 2 markets. For example, US-UK1 refers to the first canonical factor of the US and
UK stock returns; US-UK2 refers to the second canonical factor of the US and UK stock returns etc.
The world indices include the world market indices and world real estate indices. The MSCI world index measures
global developed market equity performance. The DJGI family is Dow Jones benchmark series and covers 34
countries, including both developed and emerging markets. The MSCI world real estate index covers the listed real
estate stocks in MSCI 23 component countries. Last, the DJGI real estate index covers the listed real estate stocks in
its 34 countries.
2
**, * Correlation is significant at the 1% and 5% levels (two-tailed)
19
20