Corporate Real Estate and Stock Market Performance - Springer Link

Journal of Real Estate Finance and Economics, 29:1, 119±140, 2004 # 2004 Kluwer Academic Publishers. Manufactured in The Netherlands.

Corporate Real Estate and Stock Market Performance KIM HIANG LIOW Department of Real Estate, National University of Singapore, Singapore 117566 E-mail: [email protected]

Abstract An interesting question in corporate real estate literature is whether real estate can improve the stock market performance of ``property-intensive'' non-real estate ®rms. Using a data set comprising 75 non-real estate corporations that own at least 20 percent properties, this paper empirically assesses and compares the pair-wise return, total risk, systematic risk and Jensen abnormal return performance of ``composite'' (with real estate) and hypothetical ``business'' (without real estate) ®rms. We employed Morgan Stanley Capital International world equity index instead of a local market index to provide some insights into the performance of the local market relative to the ``global'' market during the 1997±2001 volatile periods experienced by many Asian countries. Our results suggest the inclusion of real estate in a corporate portfolio appears to be associated with lower return, higher total risk, higher systematic risk and poorer abnormal return performance. It is therefore likely that nonreal estate ®rms own properties for other reasons in addition to seeking improvement in their stock market performance. Further research is needed to explore the main factors contributing to corporate real estate ownership by non-real estate ®rms. Key Words: corporate real estate, composite returns, business returns, stock market performance, Singapore

1. Introduction Corporate real estate (CRE) refers to the land and buildings owned by companies not primarily in the real estate business. In today's business environment, many non-real estate ®rms are investing signi®cantly in properties which are used for operational, investment or development purposes. In some cases, real estate has become the corporations' largest asset. From an international perspective, the ownership of signi®cant amounts of real estate by corporations in the United States is well documented (Rodriguez and Sirmans, 1996; Seiler et al., 2001). In the United Kingdom, many of the largest nonreal estate companies control property portfolios that are comparable in value terms with those owned by mainstream property companies (Debenham Tewson and Chinnock, 1992; Liow, 1995). Similarly, Singapore business ®rms invest, on average, at least 40 percent of their corporate resources in real estate (Liow, 1999). With such a signi®cant concentration of corporate wealth in real estate, one interesting question that is worth to take a closer look is why non-real estate ®rms own instead of leasing properties. There are several possible explanations. In the ®rst instance it depends on the corporate strategy and on the type of real estate assets involved. Second, these companies must have clearly bene®ted from the properties that are left in their balance sheets. For example, if much is held primarily for the operational purposes of a company,

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and so long as its value increases, then property will be important for the part it plays in enabling the ®rm to be ef®cient and make pro®ts (Scarrett, 1991). Other main reasons reported in the United States literature (Machlica and Borunch, 1989) include: (a) CRE is perceived as a ®gure on the annual balance sheets that re¯ects organization growth; (b) CRE is viewed as a necessity for achieving the ®rm's operational mission; (c) CRE is regarded as a source of cash in bad time; (d) CRE ownership provides a source of capital growth, investment income, and disposal and development pro®ts, and (e) CRE is an asset capable of improving the ®rm's market performance. Bruggeman et al. (1990) further reported that as a consequence of corporate restructuring activities involving real estate in the United States since the 1980s, the traditional notion that non-real estate corporations have a comparative advantage in owing real estate had increasingly been questioned by corporate managements. Today, there is some evidence that European ®rms own a higher percentage of real estate on the balance sheets than by U.S. ®rms, and that the owner occupation of real estate has historically been part of the corporate culture in the United Kingdom and some European countries (Laposa and Charlton, 2001). Similar observations can be made on many Asian countries such as Singapore where many large business ®rms own their own prestigious administrative headquarters in order to boost their corporate image. However, to our knowledge, there is very limited research as to why business ®rms invest in real estate outside of the major developed countries such as the United States and United Kingdom. This paper did not pretend to have a conclusive answer as to why some non-real estate corporations chose to own properties and the changing attitudes of some corporate managers and investors toward real estate ownership. Instead the paper aimed to examine point (e) above empirically on Asian, and in particular, Singapore CRE from the perspective of modern portfolio theory. The selection of Singapore for this study was based on two major factors. First, the availability of data and practical reason dominated the choice. This was mainly because information on CRE is not publicly available and has to be extracted from listed company accounts available in the University library. In addition, data on real estate assets for some companies are missing and sometimes of poor quality. Second, Singapore is one of the strongest Asian economies and the leading regional ®nancial center in Asia. The in¯ow of foreign investment from Europe, North America and Asian countries has contributed signi®cantly to the rate of economic development. In particular, the real estate sector has played an important role in enhancing the Republic's status and attracting multi-national companies to establish their regional headquarters. Hence this study, although based on speci®c country's (i.e., Singapore) CRE, is expected to contribute to the growing knowledge base in CRE literature with regard to understanding the main factors and attitudes toward owning real estate by Asian business ®rms. Another institutional factor that renders this study interesting is, compared with the real estate market in the United States, Asia is characterized by land scarcity and high population density, and thus real estate values are relatively high (Glascock et al., 2002). This has made real estate the most favored investment target within Asia. There is a strong desire by individuals and corporations to own real estate in the belief that the Paci®c±Asia region is potentially a hot spot in international real estate market because its rapid

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urbanization and economic growth have brought about enormous opportunities for land and property development. Of the many countries in the region, Singapore real estate has a great potential to be one of the key investment targets in the future in view of increasing globalization in Asian property and stock markets. There are many contributing factors. First, there is a strong government intervention in the economy and in the urban growth. Second, the high density of population and scarcity of land is representative of many cities in the region. Third, the in¯ow of foreign investment has contributed signi®cantly to the rate of real estate development (Lim et al., 2002). Finally, Singapore has been regarded as a model city in urban development, one from which many Asian countries are keen to learn. Using a data set on Singapore CRE, we examined the impact of CRE ownership on the stock market performance of non-real estate ®rms. Speci®cally, if real estate assets are a good diversi®er then non-real estate ®rms with signi®cant property endowment should command a higher rate of return for a given level of risk or a lower level of risk for a given rate of return. Conversely, since real estate business is a risky venture and that economic risk of the business may be incorporated into the common stock returns, then the ownership of real estate assets will not provide a diversi®cation bene®t to business ®rms through a lower systematic risk or a higher risk-adjusted return. With the use of Morgan Stanley Capital international world equity index instead of a local market index, our results provide some insights into the performance of the local market relative to the ``global'' market. Additionally, there is some evidence of lower return, higher risk, higher systematic risk and poorer abnormal return performance associated with CRE asset ownership in the period 1997±2001. During this period, many Asian markets experienced severe recession following the ®nancial crisis experienced in the region, which started with the devaluation of the Thai batt in July 1997. Additionally, the unfavorable stock market performance impact of CRE appears to be consistent for all the non-real estate ®rms regardless of industry membership and real estate asset intensity. With the increasing signi®cance of real estate in Asian countries and in Singapore business ®rms' asset structure, the results of this paper are expected to encourage similar CRE research comprising country comparison within Asia and in other regions such as the United Kingdom, Europe and United States. The remainder of this paper consists of six parts. Section 2 reviews the relevant empirical literature. The research data and methodology are described in Sections 3 and 4. Section 5 presents and discusses the empirical results. In Section 6 there is a summary plus some implications. Section 7 concludes the paper. 2. Brief review of previous work There has been much concern especially in the United States that the potential of CRE to earn a sound return on investment beyond its operational use and that its value may appreciate have often been overlooked. For example, about one-half of the ®rms in Farragher's (1984) survey did not assess the risk associated with their real estate assets. This was mainly because traditionally real estate had been considered as a necessary overhead expense to the ®rm as opposed to an asset that had potential to contribute to the

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pro®tability and success of the ®rm. Veale (1989) reported that about two-thirds of the ®rms in his survey did not provide for continuing management and control of their real estate assets by a separate management information system. Similar observations were also made by Zeckhauser and Silverman (1983), Miles et al. (1989), Nourse (1994) and Rodriguez and Sirmans (1996) although real estate plays an important role on U.S. corporations' balance sheets (Johnson and Kealser, 1993). Miles et al. (1989) examined the role of CRE within the overall ®nancial structure of the ®rm. They pointed out that real estate could affect many corporate ®nancial parameters such as cost of equity, debt, ®nancial leverage, systematic risk and market-to-book value ratio of a ®rm. As such, real estate has to move into the mainstream of corporate ®nancial management, and its importance must be analyzed within the context of the ``whole'' ®rm. Their ®ndings were echoed by Liow (1995) in his study of United Kingdom industrial ®rms that owned signi®cant real estate. Rodriguez and Sirmans (1996) provided a comprehensive review of the literature on the impact of real estate management decisions using evidence from the capital markets. The real estate decisions reviewed are leasing, acquisitions, mergers and purchases, jointventures, dispositions, sell-offs, liquidations, sales-and-leasebacks, spin-offs and CRE unit formation. Overall, the evidence shows that decisions concerning CRE have signi®cant impact on ®rm value. To empirically assess the effect of real asset ownership on the risk and return for a ®rm's stockholders, Seiler et al. (2001) estimated a set of two-stage least square equations to examine whether or not real assets (include plant and equipment) provided a diversi®cation bene®t to U.S. corporations. Their results failed to provide evidence in support of a diversi®cation bene®t due to corporate real asset ownership, both in terms of systematic risk (beta) and risk-adjusted returns. They suggested further research is necessary to draw any generalizations. In the developing world, Cheong and Kim (1997) investigated a yearly cross-sectional model on Korean CRE during 1987±1991. Their study examined the relationship between common stock returns, systematic risk (beta) of stock returns and ratio of equity to real estate holdings of non-real estate companies. Their results suggested that the ratio of real estate holdings did not affect stock returns of the ®rms. In addition, they found that the higher the debt ratio, the larger the loss of growth opportunity value due to real estate. An initial research on the Singapore CRE was reported by Liow (1999). Covering a 10year period between 1987 and 1996, there was strong evidence to suggest that CRE affected the asset structure, capital structure and stock market valuation of non-real estate ®rms that owned at least 20 percent property in their asset structure. From the stock market perspective, Liow's (2001a) ``three-index'' model found that the market risk for CRE was a factor in capital asset pricing, and that property market risk was re¯ected in an ex ante premium in the stock market. However, this risk premium was only signi®cant in periods characterized by a high risk±high return market pro®le. Additionally, Liow (2001b) found that the proportion of CRE assets positively affected common stock returns due to the growth opportunities presented by real estate. However, it remained unclear in the study as to whether the real estate impact was highly signi®cant. This is because the in¯uence of

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other real estate related factors such as debt ratio and ®rm size has to be jointly considered in stock market valuation.

3. Research sample and data characteristics The sampling frame used in this study was the Singapore Stock Exchange's (SGX) mainboard SGD$ non-real estate sectors as of December 2001. Due to the peculiar nature of business operations, companies from the ®nance sector were excluded. The total number of companies from the remaining seven sectors was 239. The total gross real estate holdings of the companies amounted to about $145.72 billion and property constituted about 24.5 percent of these non-real estate companies' total tangible assets. A sample of ``real estate intensive'' companies was derived from this population. A focus on ``real estate intensive'' non-real estate ®rms would pro®le the companies better and re¯ect the growing strong feature of real estate (if any) in corporate ®nancial management. As there was no prior agreement in the literature as to the level of property asset holdings (in both absolute and relative terms) that could be considered as ``property intensive'', as in Liow (1999) we de®ned real estate asset intensity (PAI%) as the proportion of total tangible assets represented by property in a non-real estate company's asset structure and applied a 20 percent cut-off point to identify ``property intensive'' nonreal estate ®rms. The choice of the cut-off point was guided by several research studies to the effect that a benchmark portfolio should hold 20 percent real estate (Firstenburg et al., 1988). We thus derived a sample of 109 listed companies which represented about 45.6 percent of the population.1 Table 1 reports the distribution of the gross real estate asset holdings (PTYABS) and real estate asset intensity (PAI%) of the companies for the ®nancial year 2001.2 Total CRE holdings were approximately S$34.22 billion, and property made up about 43.24 percent of a non-real estate ®rm's total tangible assets. Additionally, Figure 1 further reveals that 55 percent of the ``property intensive'' companies had PAI% of between 20 and 40 percent. Only eight companies were extremely ``property intensive'' with at least 80 percent of their resources invested in properties. Table 1. Property asset holdings of the ``property intensive'' companies: Financial year 2001. Size of PTYABS (S$m)

Number of Companies

Mean PTYABS (S$m)*

Mean PAI%{

PTYABS 4 1,000 100 PTYABS 1,000 50 PTYABS 100 0 PTYABS 50 All

7 34 18 50 109

3,012.19 311.44 66.36 27.03 313.95

61.71 54.69 40.10 34.00 43.24

* PTYABS (S$m) gross property asset value re¯ected on balance sheets (million). { PAI percent (gross property asset value (PTYABS)/gross total asset value) 6 100 percent. Source. Derived from company balance sheets.

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Figure 1. Distribution of property asset intensity (PAI%). Table 2. CRE asset holdings of SGX non-real estate segments.* Segments

Number of Companies

Average PTY ABS($m){

Average PAI%{

Multi-industry Manufacturing Commerce Transportation/storage/communication Construction Hotel/restaurant Services Overall

10 45 17 7 14 11 5 109 Chi-square value}

1,431.72 204.30 212.61 67.64 78.97 430.19 156.80 313.95 35.25 k

46.77 32.45 47.30 36.86 48.67 73.35 47.05 43.24 31.75

* For the ®nancial year 2001. { Average PTYABS ($m) gross property asset value (million). { Average PAI percent (gross property asset value (PTYABS)/gross total asset value) 6 100 percent. } Derived from non-parametric Kruskal±Wallis tests. k Indicates two-tailed signi®cance at the 1 percent level. Source. Derived from company balance sheets and datastream.

Table 2 shows that the proportion of total tangible assets made up by properties varied considerably between different business segments. Speci®cally, the role of CRE varied from around 32.5 percent for manufacturing companies to around 73.4 percent for hotels and restaurants in year 2001. Statistically the Chi-square values of 35.35 (absolute real estate holdings) and 31.75 (real estate intensity) (both signi®cant at the 1 percent level)

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Table 3. Analysis of CRE asset holdings of SGX industrial and commerce companies: 1997±2001. Analysis of PAI% by Business Segment{ Year

PTYABS ($m)*

PAI% (%){

MI

MU

CO

TR

CN

HO

SE

1997 1998 1999 2000 2001 Average

410.46 368.10 349.73 302.90 313.95 342.48

0.403 0.407 0.401 0.418 0.432 0.414

0.539 0.484 0.482 0.450 0.468 0.484

0.281 0.300 0.329 0.321 0.325 0.313

0.437 0.449 0.451 0.483 0.473 0.462

0.295 0.273 0.266 0.330 0.369 0.313

0.624 0.537 0.407 0.443 0.487 0.476

0.561 0.617 0.608 0.708 0.734 0.651

0.608 0.770 0.373 0.436 0.470 0.471

* PTYABS ($m) gross real estate value (in million). { PAI percent gross real estate asset intensity. { Business segments: MIÐMulti-industry, MUÐManufacturing, COÐCommerce, TRÐTransportation/service/ communication, CNÐConstruction, HOÐHotel/restaurant, SEÐServices. Source. Derived from company balance sheets.

derived from non-parametric Kruskal±Wallis tests con®rmed that there were marked differences in the absolute and relative levels of CRE ownership and investment among the seven non-real estate business sectors. Hence CRE ownership is a function of industry.3 Table 3 contains the absolute and relative real estate holdings of the 109 companies for each of the 5-year periods 1997±2001. In absolute term, CRE holdings reported a decrease from S$410.46 million in 1997 to S$313.95 million in 2001. However, the average PAI% increased from about 40.3 to 43.2 percent over the same period. On average, the hotel/ restaurant group was ranked the most ``property intensive'' with its PAI% ranged from 56.1 (1997) to 73.4 percent (2001). The multi-industry group was ranked the second most ``property intensive'' with an average PAI% of 48.4 percent over the ®ve-year period. Consistently at the lowest ends were the manufacturing and the transportation/storage/ communication groups, with annual PAI% ranged between 26.6 and 36.9 percent. 4. Research methodology The principal task in this research was to assess the possible impact of CRE on the stock market performance of ``property intensive'' non-real estate ®rms over the 1997±2001 periods. Datastream was relied on to extract nominal monthly total return data for each of the 109 companies. The ®nal sample comprised 75 ®rms that had continuous ®ve-year monthly total return data (60 months), and this was about 69 percent of the 109 ``property intensive'' non-real estate ®rms. The groups selected were: 9 (multi-industries), 34 (manufacturing), 10 (commerce), 4 (transport/storage/communication), 4 (construction), 10 (hotel/restaurants) and 4 (services). The 75 ®rms in the sample are listed by industry in Table 4. An important methodological issue relevant to this study is the choice of an appropriate ``real estate'' market index to proxy for CRE performance. First, the published Singapore

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Table 4. List of the 75 companies included in the sample by industry. Multi-industry (9) HAW PAR CROP LTD HOTEL PROPERTIES LTD KEPPEL CORPORATION LTD SINGATRONICS LTD STRAITS TRADING CO LTD TUAN SING HOLDINGS LTD UNITED ENGINEERS LTD UNITED INDUSTRIAL CROP LTD WBL CORPORATION LTD Manufacturing (34) ACE DYNAMICS LTD ARMSTRONG INDUSTRIAL CORPORATION LTD ASIA PACIFIC BREWERIES LTD BEYONICS TECHNOLOGY LTD BROADWAY INDUSTRIAL GROUP LTD COMPACT METAL INDUSTRIES DOVECHEM STOLTHAVEN LTD ELEC & ELTEK INTERNATIONAL CO LTD FIRST ENGINEERING LTD FRASER & NEAVE LTD FU YU MANUFACTURING LTD GB HOLDINGS LTD HTL INTERNATIONAL HOLDINGS LTD IPC CORPORATION LTD JURONG CEMENT LTD LIANG HUAT ALUMINIUM LTD LINDETEVES-JACOBERG LTD MEIBAN GROUP LTD METALOCK (S) LTD NETWORK FOODS INTL LTD PENTEX-SCHWEIZER CIRCUITS QAF LTD ROTOL SINGAPORE LTD SANTEH LTD SEATOWN CORPORATION LTD SINGAPORE PRESS HOLDING LTD SM SUMMIT HOLDINGS LTD SMB UNITED LTD SUPER COFFEEMIX MANUFACTURING TRI-M TECHNOLOGIES (S) LTD UNITED PULP & PAPER CO. LTD YEO HIAP SENG LTD CSH LTD G. MAGNETICS LTD

Commerce (10) C K TANG LTD CYCLE & CARRIAGE LTD GRP LTD GUTHRIE GTS LTD ISETAN (SINGAPORE) LTD KIAN HO BEARINGS LTD LION TECK CHIANG LTD METRO HOLDINGS PROVISION SUPPLIERS CROP LTD FHTK HOLDINGS Transport/storage/communications (4) COSCO INVESTMENT (S) LTD CWT DISTRIBUTION LTD FREIGHT LINKS EXPRESS HOLDINGS LABROY MARINE LTD Construction (4) CHEVALIER SPORE HLDGS LTD KOH BROTHERS GROUP LTD LEE KIM TAH HLDGS LTD ROTARY ENGINEERING LTD Hotel/restaurants (10) APOLLO ENTERPRISES LTD GOODWOOD PARK HOTEL LTD HIND HOTELS INTERNATIONAL HOTEL GRAND CENTRAL LTD HOTEL MALAYSIA LTD HOTEL NEGARA LTD HOTEL PLAZA LTD OVERSEA UNION ENTERPRISE REPUBLIC HOTELS & RESORTS LTD SEA VIEW HOTEL LTD Services (4) ENG WAH ORGANIZATION LTD KEPPEL TELECOMMUNICATION & TRANSPORTATION LTD SUPERBOWL HOLDINGS LTD VICOM LTD


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private property price index published by the Urban Redevelopment Authority (URA) is presently the only index available to track the performance of local property market.4 As the URA has discontinued the publication of the overall property index since 1999, one possibility is to employ its of®ce property index (PPIO) to proxy for CRE performance. From a practical viewpoint, however, the PPIO series is only available on a quarterly basis with full period data of 20 points. An alternative data proxyÐmonthly traded property stock index (SGXPTY) was considered to proxy for real estate market performance. Speci®cally the market-based SGXPTY is a value-weighted index and is a standard market portfolio benchmark for property stock investors. The use of SGXPTY as a ``property'' proxy can be justi®ed, that in the longer term, the performance of property company shares should re¯ect the performance of the underlying real estate market (Lizieri and Satchell, 1997). Hence the SGXPTY was used to calculate the monthly CRE returns (RSGXPt ). Finally, the SGX All-share index (SGXALL) is the standard market portfolio benchmark for investors. It provides a good measure of the overall price movements in the stock market because of its 100 percent coverage of all the listed companies traded on the SGX. The All-share index was therefore used to calculate the monthly market returns (RSGXAt ). To check whether our results are similar, we also include the of®ce property returns (RPPIO) as a quarterly proxy for CRE performance. The empirical procedures comprised four stages. They are brie¯y explained below.

4.1. A ``pure'' property return series To the extent that the property stock performance is affected by general market conditions, RSGXP may contain information about general market conditions. To separate out the market effect from the real estate effect, we regressed RSGXP on the overall market measure RSGXA. The residuals from equation (1) then measure the return on the monthly property index that is not associated with overall market movement. Our procedure was similar to that of Lizieri and Satchell (1997) that sought to remove the in¯uence of property from equity movements. Additionally, we repeated the same procedure for the quarterly of®ce returns (RPPIO). RSGXPt aI b1 RSGXAt mt :

1

RPPIOt aI b1 RSGXAt mt :

2

where mt is the monthly ``pure'' normalized indirect real estate return series in (1) (PRSGXP) and quarterly ``pure'' normalized direct real estate return series in (2) (PRPPIO) respectively, and has zero mean by construction in each case. The assumptions on the error term are imposed: a. The mean value for the error term is zero, i.e., Emt 0. b. The variance of the error term is constant. c. The errors are uncorrelated with Rit , i.e., Covmt ; Rt 0.

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d. The errors are serially uncorrelated, i.e., Covmt ; mt n 0. e. The error terms between assets are uncorrelated.

4.2. Hypothetical ``business'' return series The monthly stock return series for each of the 75 companies (termed as ``composite'' series) was regressed on monthly PRSGXP to separate the in¯uence of real estate from stock returns. The residual series from regression 3 was termed the hypothetical ``business'' return series (i.e., no real estate in¯uence). In other words, for each company, its ``composite'' returns include the real estate impact on the stock market returns through CRE asset ownership and the ®nancial effects of changes in property values; its hypothetical ``business'' series proxy the returns of the same ®rm assume that the real estate is removed from the company's balance sheet. Hence, this regression method isolated a ``real estate'' effect from the overall ®rm (with real estate) effect. The same procedure was repeated for PRPPIO to derive the quarterly ``business'' return series for each of the 75 non-real estate ®rms (equation (4)). Likewise, the same analysis was performed for all the 75 in¯ation-adjusted return series (i.e., in¯ation-adjusted return nominal return in¯ation rate). Rit aI b1 PRSGXPt mit :

3

Rit aI b1 PRPPIOt mit :

4

where mt is the monthly ``business'' normalized return series in (1) and quarterly ``business'' normalized return series in (2), respectively and has zero mean by construction.

4.3. Comparison of performance measures Each stock would thus have four pairs of return series. The four pairs are as follows: (a) nominal monthly returns (``composite'' and ``business''); (b) in¯ation-adjusted monthly returns (``composite'' and ``business''); (c) nominal quarterly returns (``composite'' and ``business'') and (d) in¯ation-adjusted quarterly returns (``composite'' and ``business''). We computed the average medium return,5 total risk (standard deviation of the return series), systematic risk (time-varying beta) and abnormal return (time-varying Jensen index (JI)) for each series. For each ®rm, the ``composite'' and ``business'' series were compared using the four performance measures. The non-parametric Wilcoxon Signed Ranks tests were conducted on each pair to evaluate if the differences in the four performance measures between the ``composite'' and ``business'' return series were statistically signi®cant.

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4.3.1. Time-varying Jensen index and systematic risk (Beta) From the wealth maximization perspective, the long-term bene®t of CRE asset ownership is whether it is able to create economic value for shareholders, as measured by the traditional JI of abnormal stock returns (JI). A time-varying Jensen measure would provide a more appropriate pro®le of abnormal performance that is likely to be uneven in the equity market. The abnormal performance results obtained from using the ®xed parameter approach, as is typically the case in previous studies, may therefore be misleading and biased (Matysiak and Brown, 1997). The single index market model is usually employed to estimate abnormal performance although multiple index methodology might be another alternative. In our context, the main objective is to determine, on a time-varying basis, whether the ``composite'' and ``business'' return series will earn a rate of return in excess of the ``normal'' currently expected return from the market. Under the rational expectation hypothesis, the JI (1968) is estimated from the following equation: Ri;t

Rf ai bi Rm;t

Rf e t ;

5

where Ri;t and Rm;t are the actual return on security i and market portfolio in period t, respectively; Rf is the risk-free rate observed at the beginning of the period; Ri;t Rf is the excess return of the security; bi is the market risk of the security and ai is the Jensen's measure of abnormal return. In essence, a portfolio outperforms the market if ai > 0. However, equation (5) assumes that the market risk is constant over the estimation period and the estimated abnormal return is also ®xed. In the current study, we considered a world equity index instead of the local market index (SGXALL). The choice of a world equity index is a function of to what extent the Singapore capital markets are segmented or integrated with the world capital markets. We used the MSC world equity index to proxy for the market Rm . The MSCI total return index (with dividend reinvestment) is valueweighted and consists of stocks that broadly represent 23 markets (including Singapore) as of April 2002.6 The risk-free rate Rf was represented by the yield on the U.S. government three-month treasury bills. By using the MSCI world equity index (instead of a local market index) as a benchmark portfolio, the present study brings closer Singapore capital markets into the mainstream of world capital markets. To account for possible changes in both the market risk and the abnormal return estimation, the following stochastic behavior is subscribed to the coef®cients in equation (5): Measurement equation : Ri;t Transition equations : ai;t ai;t bi;t bi;t

1

ot ;

Rf ai;t bi;t Rm;t 1

Zt ;

Rf e t :

6 7 8

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where Eet EZt Eot 0:

9

Equations (7) and (8) assume that the abnormal return and market risk follow a random walk process. This assumption is consistent with the ef®cient market hypothesis whereby one knows nothing about the level of the state variables, ai;t and bi;t , in the next period. The respective parameters in equations (7)±(9) can be collectively estimated via Kalman Filter maximum likelihood technique in a state space formulation. The unique feature of the state space model is that it allows the updating of coef®cients (abnormal returns and betas) each time a new observation is available.7 The JI is given by ai;t, ai;t > 0 implies that the stock has outperformed the market or it has earned an abnormal return. Finally, the systematic risk (beta) is given by bi;t, which is also time-varying. 4.4. Further comparison of performance measures The 75 companies were grouped into several portfolios based on three different classi®cation criteria. Within each portfolio, the procedures were repeated to examine the paired-sample difference in the four performance measures in order to assess if the individual portfolio results were consistent with the overall sample results. In addition, the respective paired-sample differences were tested to determine if there were statistically signi®cant differences between the portfolios. The 75 companies were classi®ed into: a. Seven industry portfolios based on the SGX classi®cation; b. Three corporate portfolios based on their average real estate asset intensity (PAI%) and using the 20 percent cut-off point. This was obtained by sorting the 75 companies into three portfolios in descending order of average PAI% over the ®ve-year period.8 The number of ®rms included in the three portfolios were 20, 12 and 43, respectively; and c. Two randomized corporate portfolios. For the companies in each industry (IND), two portfolios of approximate equal size were formed in descending order of PAI%. In other words, we would have PAI%1 and PAI%2 for each industry portfolio. Then the same ``PAI%'' portfolios in the seven industry portfolios (i.e., PAI%1 in IND1, PAI%1 in IND2 . . . PAI%1 in IND7 and PAI%2 in IND1 . . . PAI%2 in IND7) were combined to form PIND1 and PIND2. Hence, to study the real estate effect on the four performance measures while controlling for the industry effect, a new set of two PIND portfolios (PIND1 and PIND2) was formed with different PAI% but randomized in term of industry. 5. Results and discussion Table 5 provides a summary of the comparative median return, total risk, time-varying systematic risk and time-varying JI for the four pairs of ``composite'' and ``business''

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Table 5. Comparison of performance measures between ``composite'' and ``business'' return series. Return Series Performance measure

Composite

Business

Difference (z-statistic*)

Panel A: Nominal monthly stock returns (real estate proxy: Property stock returns) Median return 0.0184 0.0166 Total risk (standard deviation) 0.1714 0.1615 Systematic risk (beta) 1.3799 1.1096 Jensen index (JI) 0.0327 0.0258

0.0018 ( 1.42) 0.0099 (6.78{) 0.2703 (7.48{) 0.0069 ( 5.99{)

Panel B: In¯ation-adjusted monthly stock returns (real estate proxy: Property stock Median return 0.0185 0.0174 Total risk (standard deviation) 0.1713 0.1613 Systematic risk (beta) 1.3934 1.1374 Jensen index (JI) 0.0324 0.0257

returns) 0.0011( 0.75) 0.0100 (6.80{) 0.2560 (7.43{) 0.0067 ( 5.64{)

Panel C: Nominal quarterly stock returns (real estate proxy: Of®ce property returns) Median return 0.0700 0.0654 0.0046 ( 1.13) Total risk (standard deviation) 0.2855 0.2737 0.0118 (6.98{) Systematic risk (beta) 2.0795 1.9560 0.1235 (4.68{) Jensen index (JI) 0.1022 0.0896 0.0126 ( 4.44{) Panel D: In¯ation-adjusted quarterly stock returns (real estate proxy: Of®ce property returns) Median return 0.0711 0.0674 0.0037 ( 1.02) Total risk (standard deviation) 0.2847 0.2734 0.0113 (6.97{) Systematic risk (beta) 2.0310 1.8761 0.1549 (5.60{) Jensen index (JI) 0.1037 0.0935 0.0102 ( 4.03{) * The z-statistics were derived from non-parametric Wilcoxon Signed Ranks tests. { Indicates two-tailed signi®cance at the 1 percent level.

return series. By comparing the ``composite'' return series with the hypothetical ``business'' series for each ®rm, the main objective was to assess if CRE asset ownership could help improve the stock market performance of the non-real estate ®rms and hence increase shareholders' wealth. 5.1. Risk and return It appears that CRE causes a non-real estate ®rm to be disadvantaged in terms of risk and return performance as ®rms with real estate achieved a lower average return and higher risk. Panel A of Table 5 shows the average monthly (nominal) median returns were negative 1.84 percent and negative 1.66 percent for the ``composite'' and ``business'' series, respectively. About 46.7 percent of the ®rms (35 numbers) reported higher returns associated with CRE ownership, the remaining 40 ®rms were better off without real estate. Nevertheless the average lower return performance of the 75 companies was statistically insigni®cant z 1.42. On the other hand, a high proportion of the ``real estate

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intensive'' ®rms (69 ®rmsÐ92 percent) derived additional business risks from investing real estate (17.14 percent for ``composite'' and 16.15 percent for ``business'') presumably the economic risk of real estate ownership might have been incorporated into the stock returns. Finally, the average higher risk of the 75 companies was statistically signi®cant at the 1 percent level z 6.78. The results for the in¯ation-adjusted monthly returns (panel B), nominal quarterly returns (panel C) and in¯ation-adjusted quarterly returns (panel D) were consistent in that the ®rms derived lower returns (although statistically insigni®cant) and higher risks from investing in CRE. With regard to the systematic risk, the results indicated that the average time-varying betas for the ``composite'' ®rms (betas 1.3799, 1.3934, 2.0795 and 2.0310) were signi®cantly higher than those of the ``business'' ®rms (betas 1.1096, 1.1374, 1.9560 and 1.8761). The higher systematic risk may imply CRE ownership increase the systematic risk pro®le of the ``real estate intensive'' ®rms with higher business risk. However, these ®rms might not be able to be compensated with adequate higher returns possibly because the full real estate systematic risk (or part of it) was not priced in the stock market (Liow, 2001a). Once again, the results suggest that CRE asset ownership may not provide a diversi®cation bene®t in regard to lowering the systematic risk of stock returns for the non-real estate ®rms. 5.2. Abnormal performance Time-varying excess returns (JI) were derived using the market model with the MSCI as the benchmark portfolio in equation (5). A positive JI can be interpreted as the ®rm outperforming its risk-adjusted expectation after controlling for the ``global'' market risk. With the nominal monthly return series, the average time-varying abnormal returns were 3.27 and 2.58 percent for the ``composite'' and ``business'' ®rms, respectively over the periods 1997±2001, with only seven ®rms (9.3 percent) derived better JI for the ``composite'' ®rms. The average difference in the JIs for the ``composite'' and ``pure'' series was negative 0.69 percent and was statistically signi®cant at the 1 percent level z 5.59. Similarly, the JI differences derived from other three return series were 0.67 percent (in¯ation-adjusted monthly), 1.26 percent (nominal quarterly) and 1.02 percent (in¯ation-adjusted quarterly). Once again, the results suggest that CRE ownership failed to provide a positive bene®t in regard to the abnormal returns for non-real estate ®rms. 5.3. Further performance results by industry and property asset intensity It may be possible that CRE asset ownership is able to bene®t certain industries. To explore this possibility, Table 6 presents the results from analyzing the paired-sample differences in the four performance indicators (i.e., return, risk, beta and JI) of ®rms in the seven industries. Overall, non-real estate ®rms in the seven business segments derived lower returns (although with some variations), higher volatilities, higher systematic risks

133


Table 6. Comparison of performance measures (``composite'' versus ``business'') in/between seven industry portfolios. Paired-Sample Difference (z-Statistic) in the Performance Measures* Industry

Median Return

Total Risk

Beta

JI

Panel A: Nominal monthly stock returns (real estate proxy: Property stock returns) ``Within'' Industry Construction 0.0065 (1.10) 0.0079 (1.83c) Commerce 0.0036 ( 1.17) 0.0083 (2.40b) Hotel/restaurant 0.0003 ( 0.26) 0.0164 (2.81a) Multi-industry 0.0069 ( 1.72c) 0.0068 (1.83c) Manufacturing 0.0006 ( 0.44) 0.0089 (4.49a) Services 0.0059 (0.73) 0.0094 (1.83c) Transportation/storage/communication 0.0146 ( 1.83c) 0.0159 (1.83c) ``Between'' Industry Chi-square value{ 12.45c 3.62

0.3043 (1.83c) 0.2834 (2.80a) 0.2227 (2.81a) 0.3066 (2.66a) 0.2574 (4.97a) 0.3201(1.83c) 0.2966 (1.83c)

0.0044 ( 1.46) 0.0067 ( 2.71a) 0.0092 ( 2.81a) 0.0066 (1.84c) 0.0064 ( 3.53a) 0.0079 ( .183c) 0.0088 (1.83c)

6.43

8.49

Panel B: In¯ation-adjusted monthly stock returns (real estate proxy: Property stock returns) ``Within'' Industry Construction 0.0075 ( 1.46) 0.0079 (1.83c) Commerce 0.0027( 0.76) 0.0086 (2.59a) Hotel/restaurant 0.0001( 0.56) 0.0164 (2.81a) Multi-industry 0.0068 ( 1.60) 0.0067 (1.84c) Manufacturing 0.0001( 0.01) 0.0090 (4.54a) Services 0.0073 ( 1.46) 0.0094 (1.83c) Transportation/storage/communication 0.0134 ( 1.83c) 0.0159 (1.83c) ``Between'' Industry Chi-square value{ 14.38b 3.55

0.2982 (1.83c) 0.2647 (2.80a) 0.2864 (2.81a) 0.2783 (2.67a) 0.2365 (4.95a) 0.3021(1.83c) 0.1856 (1.46)

0.0083 ( 0.0044 ( 0.0055 ( 0.0085 ( 0.0068 ( 0.0086 ( 0.0062 (

1.26

1.92

Panel C: Nominal quarterly stock returns (real estate proxy: Of®ce property returns) ``Within'' Industry Construction 0.0045 ( 1.10) 0.0141 (1.83a) Commerce 0.0002 ( 0.36) 0.0101 (2.40b) Hotel/restaurant 0.0004 ( 0.26) 0.0142 (2.19b) Multi-industry 0.0143 ( 1.52) 0.0068 (2.19b) Manufacturing 0.0059 ( 1.27) 0.0121(4.90a) Services 0.0061(0.72) 0.0154 (1.83c) Transportation/storage/communication 0.0055 ( 0.73) 0.0121(1.83c) ``Between'' Industry Chi-square value{ 5.66 4.32

0.1035 (0.37) 0.2698 (1.27) 0.1178 (2.81a) 0.1123 (1.48) 0.1251(3.24a) 0.0835 (1.83c) 0.0499 (0.73)

0.0076 ( 0.0004 ( 0.0103 ( 0.0257 ( 0.0128 ( 0.0104 ( 0.0275 (

1.85

5.23

Panel D: In¯ation-adjusted quarterly stock returns (real estate proxy: Of®ce property returns) ``Within'' Industry Construction 0.0042 (0.73) 0.0137(1.82a) 0.2803(1.82a) Commerce 0.0009 ( 0.05) 0.0097 (2.40b) 0.2667 (2.09b) Hotel/restaurant 0.0012 ( 0.26) 0.0143 (2.40b) 0.1050 (1.80c) Multi-industry 0.0132 (1.22) 0.0063 (2.07b) 0.0969 (1.59) Manufacturing 0.0052 (1.43) 0.0115 (4.89a) 0.1419 (3.72a) Services 0.0032 (0.37) 0.0146 (1.83c) 0.1761(1.83c) Transportation/storage/communication 0.0070 ( 1.83c) 0.0114 (1.83c) 0.0933 (1.46) ``Between'' Industry Chi-square value{ 5.99 4.54 3.09

* The z-statistics were derived from non-parametric Wilcoxon Signed Ranks tests. { The chi-square values were derived from non-parametric Kruskal±Wallis Tests. a, b, c Indicates two-tailed signi®cance at the 1, 5 and 10 percent levels, respectively.

1.83c) 1.79c) 1.79c) 2.67a) 3.48a) .1.83c) 1.46)

1.46) 0.57) 1.89c) 1.72c) 2.66a) 1.47) 1.46)

0.0257 ( 1.46) 0.0094 ( 1.79c) 0.0003 (0.26) 0.0073 ( 2.03b) 0.0095 ( 2.13b) 0.0153(1.46) 0.0114 ( 1.47) 2.80

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and poorer abnormal return performance associated with holding real estate. Once again, the results suggest that the impact of CRE asset ownership on the stock market performance of non-real estate ®rms may be unfavorable. A non-parametric Kruskal±Wallis test was conducted to determine if there were statistically signi®cant differences between the seven industry portfolios. The only two signi®cant differences were in the monthly nominal median returns (chi-square value 12.45) and in¯ation-adjusted monthly returns (chi-square value 14.38) which were statistically signi®cant at the 10 and 5 percent levels, respectively. There were no signi®cant differences in total risk, systematic risk and abnormal return performance between the seven industry portfolios. Hence the results suggest while the PAI% does vary signi®cantly between industries, the unfavorable impact of CRE ownership on stock market performance were similar across non-real estate ®rms in different industries. Using the 20 percent cut-off rule, the 75 non-real estate ®rms were grouped into three PAI% portfolios (20, 12 and 43 ®rms) with average PAI% of 70.44, 49.06 and 27.49 percent, respectively. Within each portfolio, Table 7 provides further indications that the impact of CRE ownership on the ®rms' risk, systematic risk and abnormal return performance were signi®cantly unfavorable regardless of type of returns used (nominal monthly, in¯ation-adjusted monthly, nominal quarterly and in¯ation-adjusted quarterly). On the other hand, the results for the return comparison were inconclusive. Additional non-parametric Kruskal±Wallis tests failed to reveal any statistical differences in the four performance indicators between the three PAI% portfolios (Chi-square value ranged from 0.54 to 5.19). However, these results did not take into account of the possible differences in the performance measures driven by industry factors. Finally, Table 8 contains the results that were derived by sorting all the 75 non-real estate ®rms into two portfolios based on their PAI% and industry membership. Hence any possible differences in risk, return, beta and JI due to industry factors were controlled. The two portfolios derived from this procedure had average PAI% of 54.70 percent (PIND1 group) and 29.76 percent (PIND2 group), respectively and were statistically signi®cantly different at the 1 percent level. The ``within-industry'' results were consistent with the overall results. Between the two portfolios, the PIND1 group derived lower median return, higher total risk, higher systematic risk and poorer abnormal return performance. However, further non-parametric Mann±Whitney tests failed to reveal any signi®cant differences in the four performance indicators between the two portfolios (z statistic ranged between 1.22 and 0.005). 6. Summary and implications In all, the empirical results appear to suggest that a negative relationship between CRE asset ownership and stock market performance of non-real estate ®rms cannot be precluded. Speci®cally, the inclusion of real estate assets in a corporate portfolio is likely to result in lower return, higher risk, higher systematic risk and lower abnormal return performance. The results are broadly consistent with those of Seiler et al. (2001) on U.S. ®rms.

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Table 7. Comparison of performance measures (``composite'' versus ``business'') in/between three PAI portfolios. Paired-Sample Difference (z-Statistic) in the Performance Measures{ Portfolio*

Average PAI (%)

Median Return

Total Risk

Beta

JI

Panel A: Nominal monthly stock returns (real estate proxy: Property stock returns) ``Within'' Portfolio P1 0.7044 0.0009 ( 0.22) 0.0122 (3.85a) P2 0.4906 0.0060 ( 1.96b) 0.0110 (3.06a) P3 0.2749 0.0010 ( 0.81) 0.0085 (4.72a) ``Between'' Portfolio Chi-square value{ 58.36a 2.09 1.20

0.2525 (3.92a) 0.2651(3.06a) 0.2801(5.64a)

0.0081 ( 3.81a) 0.0080 ( 2.28b) 0.0061( 4.22a)

3.05

5.19c

Panel B: In¯ation-adjusted monthly stock returns (real estate proxy: Property stock returns) ``Within'' Portfolio P1 0.7044 0.0012 ( 0.30) 0.0122 (3.85a) P2 0.4906 0.0043 ( 1.26) 0.0110 (3.06a) P3 0.2749 0.0002 ( 0.02) 0.0087(4.77a) ``Between'' Portfolio Chi-square value{ 58.34a 1.14 1.01

0.2751(3.92a) 0.2508 (3.06a) 0.2486 (5.53a)

0.0051( 2.46b) 0.0088 ( 2.98a) 0.0068 ( 4.24a)

0.54

0.80

Panel C: Nominal quarterly stock returns (real estate proxy: Of®ce property returns) ``Within'' Portfolio P1 0.7044 0.0079 (1.57) 0.0138 (3.92a) P2 0.4906 0.0012 (0.01) 0.0069 (2.04b) P3 0.2749 0.0048 (1.58) 0.0121( 5.41a) ``Between'' Portfolio Chi-square value{ 58.36a 1.56 3.06

0.2102 (2.65a) 0.1224 (2.20b) 0.0835(3.26a)

0.0172 ( 0.0119 ( 0.0108 (

1.06

0.68

Panel D: In¯ation-adjusted quarterly stock returns (real estate proxy: Of®ce property returns) ``Within'' Portfolio P1 0.7044 0.0069 (1.30) 0.0136 (3.92a) P2 0.4906 0.0020 ( 0.16) 0.0068 (2.12b) P3 0.2749 0.0037 (1.50) 0.0115 (5.39a) ``Between'' Portfolio Chi-square value{ 58.36a 1.27 2.76

0.1668 (2.13b) 0.1480 (2.27b) 0.1512 (4.88a)

0.0140 ( 2.39b) 0.0001( 0.47) 0.0112 ( 3.31a)

0.96

2.61

2.88a) 1.02) 3.24a)

* The portfolios were constructed based on individual ®rms' average PAI percent over the ®ve-year period; P1: 60 percent PAI percent 100 percent (20 ®rms); P2: 40 percent PAI percent 60 percent (12 ®rms); P3: 0 percent PAI percent 40 percent (43 ®rms). { The z-statistics were derived from non-parametric Wilcoxon Signed Ranks tests { The chi-square values were derived from non-parametric Kruskal±Wallis tests a, b, c Indicates two-tailed signi®cance at the 1, 5 and 10 percent levels, respectively.

One immediate concern arising from the study is ``Why do non-real estate ®rms invest in properties?'' If there is lack of stock market bene®ts associated with CRE ownership, it appears reasonable to argue that non-real estate ®rms are likely to own properties for other reasons in addition to seeking improvement in their stock market performance. We can only speculate about some possible reasons. First, a high proportion of non-real estate ®rms still regard properties similar to other corporate ®xed and current assets, i.e., as a factor of production. However, instead of leasing properties from a third party, some non-real estate ®rms purchased freehold/leasehold properties to reduce business cost,

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Table 8. Comparison of performance measures (``composite'' versus ``business'') in/between two industryPAI% portfolios. Paired-Sample Difference (z-Statistic) in the Performance Measures{ Portfolio*

Average PAI%

Median Return

Total Risk

Panel A: Nominal monthly stock returns (real estate proxy: Property stock returns) ``Within'' Portfolio PIND1 0.5470 0.0021( 1.09) 0.0114 (4.55a) PIND2 0.2976 0.0014( 1.02) 0.0084 (5.20a) ``Between'' Portfolio z-statistic{ 5.83a 0.15 1.19 Panel B: In¯ation-adjusted monthly stock returns (real estate proxy: Property stock returns) ``Within'' Portfolio PIND1 0.5470 0.0015 ( 0.29) 0.0116 (4.56a) PIND2 0.2976 0.0008 ( 0.50) 0.0085 (5.21a) ``Between'' Portfolio z-statistic{ 5.83a 0.06 1.22 Panel C: Nominal quarterly stock returns (real estate proxy: Of®ce property returns) ``Within'' Portfolio PIND1 0.5470 0.0044(1.09) 0.0129 (4.70a) PIND2 0.2976 0.0049 (1.35) 0.0106 (5.19a) ``Between'' Portfolio z-statistic{ 5.83a 0.005 0.83

Beta

JI

0.2776 (5.30a) 0.2632 (5.33a)

0.0073( 0.0066(

0.26

0.41

0.2620 (5.22a) 0.2501 (5.30a)

0.0077( 0.0057(

0.74

0.19

0.1474 (3.11a) 0.0989 (4.93a)

0.0131( 0.0122(

0.41

0.42

Panel D: In¯ation-adjusted quarterly stock returns (real estate proxy: Of®ce property returns) ``Within'' Portfolio PIND1 0.5470 0.0036 (1.34) 0.0125(4.73a) 0.1622 (3.61a) PIND2 0.2976 0.0037(1.45) 0.0101(5.20a) 0.1477 (3.04a) ``Between'' Portfolio z-statistic{ 5.83a 0.11 0.76 0.63

0.0151( 0.0054(

4.67a) 3.76a)

4.75a) 3.33a)

3.44a) 2.26b)

3.51a) 2.78a)

0.64

* For the 75 companies in each industry (IND), two portfolios of approximate equal size were formed based on descending order of PAI percent. In other words, we would have PAI% 1 and PAI% 2 for each industry portfolio. Then the same ``PAI percent'' portfolios in the seven industry portfolios (i.e., PAI percent 1 in IND1, PAI percent 1 in IND2 . . . PAI percent 1 in IND7 and PAI percent 2 in IND1 . . . PAI percent 2 in IND7) were combined to form PIND1 and PIND2. Thus, to study the real estate effect on the four performance measures while controlling for the industry effect, a new set of two PIND portfolios (PIND1: 37 companies; PIND2: 38 companies) was formed with different PAI percent but randomized in term of industry. { The z-statistics (``within'') were derived from non-parametric Wilcoxon Signed Ranks tests. { The z-statistics (``between'') were derived from non-parametric Mann±Whitney tests. a, b Indicates two-tailed signi®cance at the 1 and 5 percent levels, respectively.

maintain ¯exibility in their primary business operations and support their long-term business growth. There are other business ®rms who disposed their real estate assets in bad times (e.g., during the 1997±1999 periods) and realized cash to improve liquidity and reduce corporate debts. For some non-real estate ®rms in certain industries, it is also possible that CRE ownership may have helped the ®rms improve their stock market performance during certain periods. In all, the reasons of investing and owing properties by non-real estate corporations are likely to vary and remain inconclusive in the CRE literature. Further research is needed before drawing any generalizations. A wider question


137

raised by Laposa and Charlton (2001) is why European ®rms own higher percentage of property than U.S. ®rms. Similar questions can also be asked as why Asian non-real estate ®rms are likely to report higher PAI% than U.S. ®rms, and further comparisons can be studied between the Asian and European ®rms. There are some reservations that we need to note regarding the results of this study. First, we recognize that changes in CRE holdings are likely to be accompanied by changes in gearing ratios and the resulting impact on stock returns should be jointly considered (Liow, 2001b). However, there were dif®culties in assembling a reliable data set on the ®rms' gearing ratios that matched the frequency of our data series. As such, we did not control for the effects of ®nancial leverage and our conclusion regarding the impact of CRE on systematic risk was based on levered betas. Second, our evidence that the ownership of CRE underperformed the MSCI market index should not be taken as conclusive that CRE had a negative impact on non-real estate ®rms' stock market performance. This was because we used an overall SGXPTY and PPIO to proxy for CRE performance and therefore did not account for non-homogenous CRE performance which should be expected given the heterogeneous nature of CRE ownership. Additionally, the use of ``modi®ed historical cost accounting'' for CRE by some ®rms raise some questions as to whether changes in real estate values have more to do with their impact on balance sheet value than the ®rms' economic exposure to CRE. Finally, the fact that CRE may have been unfavorable during the study period (1997±2001) should again not to be taken as the case that CRE has a negative impact on the stock market performance of non-real estate ®rms for other time periods. Since the Asian ®nancial crisis it would appear that holding real estate as part of investment portfolio would lead to lower return and higher risk. This was in great contrast with the situations in the late 1980s and early 1990s where many Asian countries (including Singapore) reported remarkable growth in their real estate markets. In consequence many non-real estate ®rms made `èxtremely healthy pro®ts'' from their CRE and the ownership of CRE on the ®rm's stock performance might have been a positive one. 7. Conclusion Given the signi®cant commercial real estate component in some business ®rms' corporate asset base, there is an a priori reason to argue that real estate affects signi®cantly the stock market performance of these ®rms. Speci®cally, we seek to ascertain the impact of CRE ownership on the return, total risk, systematic risk and Jensen abnormal return performance to shareholders. In the context of modern portfolio theory, the inclusion of real estate in a ®rm's asset portfolio would provide a diversi®cation bene®t to the ®rms. If this were the case, then ®rms with signi®cant real estate would outperform, on a riskadjusted return basis, similar ®rms (in the same industry) without any or have little real estate in their balance sheets. A sample of 75 ``property intensive'' ®rms from seven Singapore non-real estate industries was included in the study. For each ®rm, we estimated and compared four performance measures: medium return, standard deviation (a proxy for total risk), time-

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varying systematic risk (beta) and time-varying Jensen abnormal return index (JI) for the ``composite'' and ``business'' return series. The ``composite'' return series included the real estate impact on the equity market. The ``business'' return series was derived by removing the in¯uence of the real estate market from the ``composite'' returns. We used the MSCI world equity index to proxy for the ``market''. The results for both the nominal returns and in¯ation-adjusted returns are reported. There is some evidence of lower returns, higher risks, higher systematic risk and poorer abnormal return performance associated with CRE asset ownership. Additionally, the unfavorable (negative) stock market performance impact of CRE appeared to be consistent for all the non-real estate ®rms in the seven industries and in different PAI% portfolios. These conclusions hold for the two alternative CRE performance benchmarks used, namely ``pure'' property stock market index and ``pure'' of®ce market index. The research presented here contributes to understanding the relationship between CRE and stock market performance. At the same time, it points to possible research avenues that will lead to deeper insights into the major motivations behind CRE asset ownership. This is because if CRE asset ownership does not bene®t non-real estate ®rms in terms of risk-return reward, then it is likely that these companies owned properties for other reasons in addition to seeking improvement in their stock market performance. These reasons may be broadly associated with cultural, institutional and ®nancial factors. Therefore, comparative work across different economies would provide evidence as to why nonreal estate ®rms own properties in Asia, Europe and United States.

Acknowledgment An initial version of this paper was presented at the Asian Real Estate Society Conference, 3±6 July, 2002, Seoul. The author wishes to thank Prof. C.F. Sirmans, Prof. Ko Wang and conference participants for their helpful comments and suggestions. Ms. Huang Qiong also provided valuable research assistance with the data analysis.

Notes 1. As our main objective was to examine and compare the impact of CRE on the risk-return of the ``property intensive'' ®rms in different PAI% categories, we speci®cally exclude non-real estate ®rms that were not ``property intensive'' (i.e., PAI% 5 20 percent). Additionally, for the latter group of companies, their insigni®cant amounts of real estate holdings may not have any noticeable impact on the ®rm's risk-return performance (Firstenburg et al., 1988). 2. In the United States, corporations are not permitted to revalue their real estate assets. However, Singapore, as in many other countries such as the United Kingdom, The Netherlands, Australia and New Zealand, does not have a strict historical cost accounting system but one in which assets may be revalued. Companies are hence permitted to revalue their real estate assets if they so wish. The vast majority of revaluations of real estate assets therefore appear in corporate balance sheets ``prepared under the historical cost convention as modi®ed by the revaluations of certain properties''. Whilst some non-real estate companies carry their real estate at full historical cost, others include revalued properties. Hence real estate value reported in company accounts


3. 4.

5. 6. 7. 8.

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suffers from lack of comparability. Any interpretation of the evidence would need to take this limitation into consideration. Rudolph (1979) analyzed industry effects on the balance sheet structure of manufacturers. His results provide evidence that for ®rms in speci®c industries, there exist optimum or ranges of optimum asset structure which maximize share prices. The URA property index is a transaction-based index for Singapore direct real estate, with capital return performance reported on quarterly basis from 1975Q1 onwards. It is computed from information obtained in caveats lodged with the Singapore Land Registry. The property index covers: all property (discontinued after 1998), residential property (sub-indices for detached house, semi-detached house, terrace house, apartment and condominium are also available, of®ce property index, retail property index, ¯atted factory property index and warehouse property index. Prior to 1998Q4, the price indices were computed based on the Laspeyres method where the weights assigned to the various property types in each locality are based on the mix of properties in a ®xed year called the base year ( 1990). From 1988Q4 onwards, the price indices were computed based on the Moving Average Method. This means the weights are computed based on the moving average of transactions over the last quarters, instead of being based on the transactions in a particular year. The weights in the price indices are therefore updated quarterly so that they are as current as possible. For each series, we computed its ``median'' return rather than ``mean'' return as all the hypothetical ``business'' return series have zero mean by construction. The 23 markets are: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Hong Kong, Ireland, Italy, Japan, The Netherlands, New Zealand, Norway, Portugal, Singapore, Spain, Sweden, Switzerland, the United Kingdom and the United States. Please refer to Harvey (1989) for mathematical details of the Kalman Filter and state space model. Based on the 20 percent cut-off rule, we should have ®ve PAI% portfolios. However, only three companies had PAI% above 80 percent. We therefore merged this group with the 60±80 percent group to obtain 20 companies. Another six ®rms had PAI% of less than 20 percent. We combined them with the 20 percent±40 percent group to obtain 43 ®rms.

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