DO RETAIL FIRMS BENEFIT FROM REAL ESTATE
OWNERSHIP?
Shi Ming YU
Department of Real Estate
National University of Singapore
4 Architecture Drive
Singapore 117566
Tel: (65)65163469
Fax: (65)67748684
Email: rstyusm@nus.edu.sg
* Kim Hiang LIOW
Department of Real Estate
National University of Singapore
4 Architecture Drive
Singapore 117566
Tel: (65)65161152
Fax: (65)67748684
Email : rstlkh@nus.edu.sg
2009-02-27
* Corresponding author
Acknowledgement
An earlier version of this paper entitled “Corporate real estate’s impact on the stock market performance:
further evidence from international retailers” was presented at the 13th Asian Real Estate Society Annual
Conference, 12-15 July 2008, Shanghai, China. We wish to thank Professor Ko Wang and the conference
participants for their time and constructive comments in improving this paper. The second author wishes to
thank the Singapore Ministry of Education’s ARF Tier 1 funding support for his research project entitled
“corporate real estate performance effects and strategy dynamics of international retail companies”
(research grant number: R-297-000-083-112) upon which this paper was based on.
DO RETAIL FIRMS BENEFIT FROM REAL ESTATE OWNERSHIP? Abstract In this study, we investigate the role of real estate as a contributor to retailers’ corporate wealth. Employing a sample of 556 retail firms and data from 2001‐2006, we first estimate and compare five popular stock market performance measures: medium return, total risk, systematic risk, Sharpe Index and Jensen abnormal performance return index for the “composite” and “business” retail firms across three geographical regions. Then we investigate if there is a statistically significant linear relationship between the relative property levels and incremental stock market performance measures. Overall, the results indicate although higher levels of real estate ownership are associated with better stock market performance, these incremental positive performance benefits are subject to diminishing return to scale. As retail firms do generally hold some form of real estate, these findings are significant for their strategic investment decisions. Keyword: corporate real estate, listed retail firms, incremental stock market performance, diversification, wealth management 1. Introduction Understanding why business firms own real estate is a fundamental question in corporate real estate (CRE) literature.1 First, this is because “given that property is neither the core business nor the only business at many companies, it is surprising that so many firms are hanging on to their property assets.” (Business Times, March 11, 2003). Liow and Nappi –Choulet (2008) note that there are multiple and varying motivations of CRE ownership by non‐real estate corporations, including operational, financial, stock market, social and cultural reasons. Second, what is less understood is that many business firms such as major retailers have very significant property ownership that is up to 80 per cent of net book value in property (Liow and Nappi‐Choulet, 2008), even though investment in real estate requires substantial corporate resources, increases corporate risks as well as their real estate assets have been undervalued by the stock market.2 Although it is beyond the scope of the current paper to provide much 1
CRE refers to the land and buildings owned by business firms not primarily in the real estate business. In conventional economic theory, these organizations (non‐real estate firms) regard their CRE primarily as a “factor of production”, providing space for the production and delivery of goods and services. 2
The extant literature has revealed that, the belief that CRE is undervalued – at least until a company is “put into play”, appears almost universally held by the corporate management and investment bankers. For example, properties that were purchased years ago were carried on the balance sheet for a fraction of their market value ‐ real estate has been categorized as “latent assets” where value of the assets owned by a corporation might not be accurately reflected in its share prices (Brennan, 1990). The extant CRE literature remains inconclusive as to why CRE assets are undervalued. One reason that has been frequently cited is that stock market investors have no idea what the corporate property assets are worth or whether the firm is over‐ or undervalued in property for its current or perspective level of trading. 1
explanation why some firms may find it beneficial or detrimental to own relatively more real estate, these two observations provide motivation and intuition for our study. We contribute by extending the CRE literature in two primary dimensions. First, we address the question: “Do retail firms benefit from real estate ownership in stock market valuation?” Evidence for this question is expected to provide useful insights into the stock market motivations of CRE ownership by listed retailers. A search of the existing literature reveals that this work is most closely related to Liow (2004); however, Liow (2004) only considered the stock market performance effects associated with CRE ownership for Singapore business firms over the 1997‐2001 periods (i.e. immediately after the Asian financial crisis). Liow (2004) further cautioned that the unfavorable impacts of CRE documented for the study period should be generalized as the case that CRE has a negative impact on the stock market performances of non‐real estate firms for other time periods. This was because holding real estate as part of corporate portfolio would lead to lower return and higher risk, especially immediately after the Asian financial crisis,. The analysis in the current study extends the literature by examining the issue for an international dataset of 556 listed retail firms from 15 countries that span across three geographical regions and by analyzing the unexamined sample period of 2001 to 2006. On further reflection, Brounen et al (2005) examined the CRE trend for a sample of 454 international retailers over 1993‐2002. They also conducted an analysis regarding the impacts of CRE on stock market performances using OLS regression methodology over the period 1999‐2002. By using a larger sample of international listed retail firms over different study periods (compared to Liow, 2004 and Brounen et al. 2005) and the “composite” versus “business” return series methodology (compared to the overall return series used by Brounen et al. 2005), more updated and complementary evidence regarding the role of real estate as a contributor to retailers’ corporate wealth is documented from this international study. Second, we address a further question that “Even though the stock market effects of CRE ownership to the retailers may be favorable should their CRE levels be as high as possible?” The extant CRE literature, including Brounen et al. (2005) and Liow (2004), is silent on this question. A positive answer attached to this question may justify why some retailers are also big real estate owners even 2
though the return from holding real estate is (much) lower than the return earned on their core business activities. On the contrary, a negative answer will probably suggest that high CRE ownership levels of retailers are likely unfavorable to marginal shareholders’ wealth maximization; and accordingly each firm should probably determine its optimal CRE level or range of optimal CRE levels that will contribute to maximum shareholders’ wealth on a marginal basis. In addition, our work draws great economic intuitions from Brennan (1990) that real estate is one category of “latent assets” where value of the assets owned by a corporation might not be accurately reflected in its share prices. Since the risk‐return profile of CRE ownership has probably not been fully recognized by the investors due to various reasons,3 one wider implication is that the market valuation of a retail firm’s real estate may become unfavorable when its real estate intensity reaches a limit which is considered too large by the market.4 In consistent with expectation, we find that there is lack of a consistent linear relationship between CRE ownership level and incremental positive stock market benefits. Further, higher CRE ownership levels do not benefit the retail firms from the different economies, implying that these firms have to trade‐off their higher CRE investment against any unfavorable stock market valuation. The optimal proportion of CRE ownership is thus an important strategic investment decision that these retail firms have to carefully commit (Liow and Nappi‐Choulet, 2008). Our work thus contributes and improves corporate management’s understanding of the understanding business economics of real estate investment and corporate wealth and is expected to lead to more research in this area. Methodologically, our simultaneous equations system that accounts for the endogenous relationships among the real estate ownership level, stock performance variables and Fama and French (1992)’s three factors (size, leverage and book/market ratio)5 as well as regional and industry segment differences will result in careful analysis of the underlying economic relationship, 3
It is however not within the scope of this study to discuss/speculate on why CRE assets are undervalued. 4
A related point is that if CRE is undervalued or overvalued at the start of the period examined, and is therefore captured in the stock price to the extent it is undervalued or overvalued. Then, stock market performance may not be related to the relative level of real estate unless the market receives new information about the undervalued or overvalued real estate. Such information my arrive, particularly in times when real estate prices are known to be rising or collapsing or when real estate is sold or purchased. 5
The fourth Fama and French (1992)’s factor is beta. 3
disentangle the endogeneity problems and thereby produce more efficient regression coefficient estimates. Our study is timely when capital markets are in constrained cycles, and with many corporations’ shares selling at or below book value per share – and in some cases, below the market values of their CRE, being vulnerable for takeovers and leveraging on their CRE to raise the required capital for such takeovers. Even some large retailers with a long heritage of freehold property ownership are not immune to this trend. The pressure to divest /outsource their CRE is fuelled by falling stock market valuations, fear of predators and concerns about the long‐term value of property in a world where electronic commerce is becoming increasingly important. The remainder of this paper is organized as follow. First, related past research is reviewed. Next, the CRE sample and data are presented with an analysis of relative CRE ownership levels across retail segments, countries and regions as well some significant changes over 2001‐2006. The methodologies used to examine the stock market performance effects of CRE as well as whether there is a linear relationship between the relative CRE ownership level and incremental stock market benefits follow. The results, conclusions and economic implications of the results are presented. 2. Related Literature Liow and Nappi‐Choulet (2008) discuss CRE ownership and management from three inter‐linked perspectives (business, financial and stock market) because significant amount of capital is locked up in real estate worldwide. In some cases real estate has become the corporations’ largest asset. From an international perspective, the ownership of significant amount of real estate by corporations in the USA is well documented, estimated approximately at about 25% of corporate wealth (Rodriguez and Sirmans, 1996). In the UK, real estate represents on average 30%‐40% of total assets and 100% of capital in the balance sheets of industrial companies (Liow 1999). Further, many of the largest non‐real estate companies control property portfolios that are comparable in value terms with those owned by mainstream real estate companies. Comparatively, European non‐real estate firms own higher percentage of CRE than the US firms, and owner occupation of real estate has historically been part of the corporate culture in the UK and some European countries (Laposa and Charlton 2001). Similarly, Asian non‐real 4
estate firms report higher property holding intensity (i.e. percentage of property held as total tangible assets) than the US and European firms, with many large business firms own their prestigious administrative headquarters in order to boost their corporate image. For example, Singapore business firms invest, on average, at least 40 per cent of their corporate resources in real estate (Liow, 1999). The business perspective of CRE calls for the management CRE as a business function as well as the maximization of CRE contribution to business performance and enterprise strategy. This is even more important for retailers as real estate has always been recognized as a key value diver in the retail industry. In retailing, freehold property is always seen as an asset and is included in the balance sheet, mainly at “open market value” (Guy, 1999). Although the trend to get capital out of the CRE is happening around the globe, many large retailers may still favour CRE freehold ownership particularly when properties house a strategic function or are integral to their retail operations. The financial perspective of CRE highlights the importance of CRE in influencing the profitability and cost of capital of many business firms with significant CRE ownership. It is thus necessary for real estate to move into the mainstream of corporate financial management and its importance analyzed within the context of “whole” firm. Finally the stock market perspective of CRE, which is the focus of this study, links CRE ownership to the maximization of shareholders’ wealth. One of the key benefits of CRE ownership is that it may provide a diversification benefit to non‐
real estate firms, from the modern portfolio theory perspective. If this is the case, then those non‐real estate firms with significant property assets would outperform, on a risk‐adjusted return basis, similar firms (in the same industry) without any or have little real estate in their balance sheets. However, consistent with the findings of Deng and Gyourko (2000) and Seiler et al, (2001) regarding the negative impact of CRE ownership on the US firms; Liow (2004) find that CRE ownership in Asia is associated with lower returns, higher risks, higher systematic risk and poorer abnormal return performance particularly since the 1997 Asian financial crisis using a sample of 75 Singapore “composite” and hypothetical “business” firms from seven non‐real estate industries. This unfavorable (negative) stock market performance impact of CRE is consistent for the non‐real estate firms from different industries and with different real estate holding intensity. The main implication arising from the study is that if there is lack of 5
stock market benefits associated with CRE ownership then it is obvious that non‐real estate firms are likely to own properties for other reasons in addition to seeking improvement in their stock market performance. A case in point is that many Asian firms are still holding on to their real estate assets even though real estate is neither the core business nor the only business at many companies. In contrast, employing OLS regression technique on their sample of 454 retail firms, Brounen et al, (2005) find that CRE ownership for listed retail companies is generally associated with positive relative return and risk‐
adjusted return performances. This is because real estate is closely integrated with the core retail business as many retailers depend to a great extent on the location of their stores for sales and profits; consequently this stronger integration between CRE and retail leads to the creation of shareholder value. Separately, the extant literature has revealed that, the belief that CRE is undervalued – at least until a company is “put into play”, appeared almost universally held by the corporate management and investment bankers. For example, properties that were purchased years ago are carried on the balance sheet for a fraction of their market value ‐ real estate has been categorized as “latent assets” where value of the assets owned by a corporation might not be accurately reflected in its share prices (Brennan, 1990). For publicly listed business firms, their shares are valued in the stock market, whereas the CRE assets are valued by reference to the underlying real estate market. Hence whether the CRE is valued by the stock market on a different basis from its market value is definitely of great concern to corporate management. One obvious implication is that if share prices do not reflect the CRE at current values, there are arbitrage opportunities either for companies or in the stock and real estate markets. The existence of “latent assets” justifies a corporation in “signaling” to the market the value of their CRE assets in order to encourage shareholders to capitalize any potential future value into share prices. Consequently, several non‐real estate firms implement feasible CRE asset strategies that would probably enable investors to explicitly recognize the “hidden” real estate values and enhance the market valuation of the firms. Further, the hostile retail environment characterized by rising costs, price inflation and increasing shareholder scrutiny is prompting more retailers to re‐examine more closely at their property portfolios. As a result, many European retailers such as J. Sainsbury, Tesco, Marks and Spencers, Kingfisher, Carrefour and Metros have been selling their property portfolios over the past few years. The 6
main motivations cited by these retailers are that they hope to rationalize the corporate capital use by focusing all the resources in the core business (i.e. retail) as well as reducing corporate debt. 3. Sample and data characteristics First, an international sample of 658 listed retail firms is derived from the Osiris database6 based on SIC code classification 5200‐5999 as of December 2006. These companies are distributed across three regions (Asia, Europe and North America) and are based in Australia, Austria, Belgium, Canada, China, Denmark, Finland, France, Germany, Hong Kong, India, Ireland, Italy, Japan, Korea, Malaysia, Netherlands, Norway, the Philippines, Portugal, Singapore, Sweden, Switzerland, Taiwan, Thailand, the UK and the USA. To measure the trends in absolute and relative CRE ownership over a period of six years from 2001‐2006, a corporate real estate ratio (CRER), which divides Osiris’s net property, plant and equipment (NPPE)7 by the book value of a firm’s total assets (TA); i.e. CRER = NPPE / TA. We conjecture that the book value of PPE to proxy for the value of real estate assets owned by the firm. The CRER ratio will enable a comparison of relative CRE ownership (i.e. real estate intensity) between the eight retail segments, years and also countries in the sample. Ideally, the percentage of real estate ownership would be a better measure. However, similar to Deng and Gyourko (1999), Seiler et al. (2001), Brounen and Eichholtz (2005) and Brounen et al. (2005) which used PPE from Compustat, we have to use the NPPE variable which offers the best available proxy from Osiris for international comparison in CRE ownership. A further point to bear in mind is that although this measure of real estate concentration, NPPE/TA, does not measure the share of real estate in the firm’s physical capital, but rather the “tangibility” of firm, it is quite unlikely that a larger part of the high CRER ratio for retailers can be attributed to plant and equipment, as most 6
Osiris is a comprehensive database of listed companies, banks and insurance companies around the world. In addition to the income statement, balance sheet, cash flow statement and ratios it contains a wide range of complementary information such as news, ownerships, subsidiaries, M & A activities and ratings. Osiris contains information on 38,000 companies from over 130 countries including 30,000 listed companies and 8,000 unlisted or de‐listed companies. 7
NPPE is included in tangible fixed assets of the firm, having deducted from the historical cost and revaluation of properties, the accumulated depreciation, amortization and depletion. 7
retailers have little need to own significant plant and equipment. If the retail firms own more land and buildings, this should be reflected in higher levels of NPPE. Table 1 reports the 2006’s absolute real estate holding (represented by NPPE) and relative level (represented by CRER) distribution for the 658 retail firms. Aggregate holdings worth approximately USD 702.74 million and real estate comprises about 34% of a retail firm’s total tangible assets. Figure 1 indicates that about 35.8% of the retail firms have an average CRER of between 20 and 40%. In contrast, only 26 retail firms (about 4%) are considered very real estate intensive because they have an average CRER of more than 80%. (Table 1and Figure 1 here) The real estate statistics regarding the sample breakdowns by the SIC codes are presented in Table 2, which reports the sample distribution across nine retail segments by number of firms, average NPPE and average CRER as of financial year 2006. It is noted that all nine retail segments have an average CRER of at least 20%. However, the figures also reveal that the segments have very different CRER levels. Although the “Food stores” and “Materials and home dealers” segments report the highest NPPE of about USD 3.85m and 2.86m respectively, their average CRERs are not the highest among the nine segments. In contrast, with the highest average CRER of 0.50, the “Eating and drinking places” segment owns only about USD 0.69m worth of NPPE, the second lowest among the nine retail segments. The Non‐store segment exhibits the lowest absolute property ownership as well the lowest CRER level (0.20) of all the This suggests that real estate might not be a strategic asset in this retail segment; probably real estate is considered more as a necessary cost to support the core business. Finally, the differences in the absolute and relative levels of CRE ownership among the nine retail segments are statistically significant at the five percent level (Chi‐square values are 75.66 and 162.43, respectively). These differences in real estate ownership across different retail segments are probably expected as businesses in each of these segments have different property needs that eventually translate into different costs of real estate the firms have acquired. Over time, Table 3 reveals that although the dollar value of retailers’ NPPE has increased from USD 0.659m in 2001 to USD 1.068m in 2006, their average CRER decreases from 37% to 34% over the same period. In addition, all nine retail segments report a downward trend in their CRER levels; the 8
decline is between 1% and 6% over the 6–year period. This observation implies a trend towards leasing might be happening over the study period. (Tables 2 and 3 here) The differences in the CRE ownership levels of retail firms, both in absolute and relative terms, across the three geographical regions (i.e. Asia, Europe and North America) are provided in Exhibits 5 and 6. The analysis reveals that the three geographical regions differ significantly at the 5% level at the absolute property level (chi‐square statistic is 17.02); but not at the relative level (CRER) (chi‐square statistic is 5.24). Further, the three regions experience steadily declining CRER over the six‐year periods, possibly in line with the move towards outsourcing of CRE ownership and operations by several European and American retail firms; the decline ranges from 3% (Asia), 3% (North America) to 5% (Europe). (Tables 4 and 5 here) When comparing retail CRE ownership across countries, Table 6 reveals significant differences at both the absolute and relative levels as indicated by the high Chi‐square values (95.47 for absolute level and 81.01 for relative level) from the non‐parametric Kruskal‐Wallis tests. Across countries, Hong Kong and Australia have the lowest average CRER of 0.13 and 0.22 respectively8, while the Korean firms have the highest average CRER of 0.58. Examining the time variation in the CRER data of the countries over time, Table 7 appears to provide some support of the trend towards leasing across countries, as also observed across segments. Specifically we document a decreasing trend at the relative property level for 18 of the 28 countries. While the overall average downward trend in the CRER level decreases steadily from 37% (2001) to 34% (2006), this declining trend is more visible for some countries like Netherlands and Hong Kong that are associated with a decrease of respectively, 13% and 12% in their CRER levels over the six year period (Tables 6 and 7 here) In summary, one main conclusion from the analysis is that, in agreement with Brounen et al. (2005), CRE ownership varies significantly across different retail segments. Also international differences 8
Although Norway has the lowest CRER (0.03), it is considered as not representative as only 1 retail firm was included. 9
are detected at the absolute and relative CRE holding levels. In addition, the relative property level of retail firms (i.e. CRER) is decreasing steadily over time, at an international level as well as across countries and across business segments. However, these findings are viewed with some caution with two caveats: (a) the absolute and relative levels did not only include property, but also plant and equipment, and (d) differences in the national accounting standards, especially with regard to the treatment of “leases” and “depreciation” are likely to contribute to the ways that the NPPE value is reported in corporate balance sheets of the retailers. 4. Methodology Our investigation is conducted through a two‐step process. First we assess the impact of CRE on the stock market performance of the retail firms from 2001 through 2006. Only 556 retail firms (84.5%) are retained as these firms have continuously 72 monthly return data for the full period. Table 8 provides a breakdown of the firms from the 15 national markets. All return index data are downloaded from Datastream. (Table 8 here) One of the key tasks is to employ an appropriate national real estate market index to proxy for CRE performance. Moreover, all 15 national real estate market indices should be consistent in their index construction to facilitate international comparisons. One possibility is to employ a published quarterly direct property index; however, this would leave us with only five markets that we have of their published indices (the USA‐ NCREIF index; the UK‐IPD index; Australia – Property Council of Australia’s composite property index; Hong Kong ‐Valuation and Rating Department’s Office property index and Singapore – Urban Redevelopment Authority’s Office property index). An alternative proxy for commercial real estate returns, monthly traded securitized real estate index, is considered. The use of listed real estate index for the sample retailers is justified on the ground that the performance of listed real estate should reflect the performance of the underlying real estate market (Lizieri and Satchell, 1997). Of the published listed real estate indices (GPR, FTSE/EPRA, Dow Jones, Datastream and S&P/City Group), the only comprehensive source is the Dow Jones (DJ) listed real estate indices that are available for all 15 national markets except 10
China. For consistency purpose the DJ national real estate indices, DJ national stock market indices, DJ world equity index are used to proxy for CRE and stock market performance at the national / global levels (subject to further adjustment and to be discussed below)9. To check whether the results are robust with regard to alternative benchmarks, we also include the five direct property indices as well as the MSCI national stock market indices and MSCI world equity index to analyze the CRE performance of the USA, the UK, Australia, Singapore and Hong Kong. 4.1 Stock market performance effects of CRE ownership Following Liow (2004), the empirical methodology is underpinned by the adoption of two hypothetical return series for each firm; i.e. “composite” and “business” series. For each firm, the “composite” return series is represented by the original stock return series that presumably includes the real estate impact on the stock return series through CRE ownership and the financial effects of changes in property values. In contrast, the hypothetical “business” series represents the returns of the same firm assume that the real estate component is removed from the company’s balance sheet. These “composite” and “business” return series are then compared with regard to five stock market performance indicators, namely, average medium return, total risk (represented by the standard deviation of the return series), time‐varying systematic risk (beta), SI (excess return per unit of total risk) and time‐varying JI. The expectation is that if CRE provides a diversification opportunity in retailers’ portfolio, the benefits of CRE ownership should result in higher return for a given level of risk, lower systematic risk, better SI and JI for the “composite” return series. Conversely, it can be hypothesized that CRE ownership does not benefit retailers because the economic risk of the business might have been incorporated into the common stock returns. CRE ownership would thus not provide a diversification benefit to retailers through a lower systematic risk or higher risk‐adjusted return performance represented by JI and SI. Below is a brief explanation of the empirical procedures which are implemented in five stages: (a) To obtain a “pure” real estate market return series for each country, the monthly DJ real estate returns (RE) are regressed on the DJ stock market series (MKT). This procedure serves to 9
The corresponding performance indices for China are provided by Datastream (DS‐market) 11
separate out the stock market effect from the real estate effect and is similar to that of Lizieri and Satchell (1997). The residuals (  ) from equation (1) then measure the return of the monthly real estate index that is not associated with overall market movements: REt   1  1 * MKTt   t ……………….(1) The monthly stock return series for each of the 556 firms ( Rt ‐“composite” series) is regressed (b) on the monthly “pure” real estate return series (i.e.  t ) to remove the influence of real estate from stock returns. The residual series from (2) is therefore the hypothetical “business” return series (  t ): R jt   j   j *  *  t   t ………………………(2) (c) The “composite” and “business” returns series of each firm are compared using the average medium return,10 total risk, beta, Sharpe Index (SI) and Jensen Abnormal Performance Index (JI). The non‐parametric Wilcoxon Signed Ranks test is conducted to determine if the differences in the five performance measures between the “composite” and “business” return series are statistically significant. The DJ world equity index represents the market portfolio in estimating SI and JI in an international environment; the risk free rate is represented by the yield on the US government three‐month Treasury Bills. (d) The 556 firms are grouped into several portfolios based on five different classification criteria. Within each portfolio, the procedures are repeated for the paired‐sample differences in the five performance measures to assess if the results are robust. The respective paired sample differences are also tested, using non‐parametric Kruskal‐Wallis technique, to determine if there are statistically significant differences across the portfolios. The five portfolio classifications are: i.
Three regional portfolios; namely Asia, Europe and North America. ii.
15 country portfolios iii.
Nine retail‐segment portfolios. 10
“Medium” return is compared instead of the usual “mean” return since all hypothetical “business” return series have zero means by construction. 12
iv.
Five randomized SEG‐CRER portfolios. For companies in each retail segment (SEG), five portfolios of approximate equal number are formed in descending order of CRER. In other words, there will be CRER1, CRER2, CRER3, CRER4 and CRER5 for each retail segment portfolio. Then, the same “CRER” portfolios in the nine SEG groups (i.e. CRER1 in SEG1, CRER1 in SEG2 … CRER1 in SEG9……) are combined to form five randomized SEG‐CRER portfolios. Therefore, in order to study the real estate effect on the five performance measures while controlling for the segment effect, a new set of five SIZE‐CRER portfolios are formed with different CRERs but randomized in term of retail segment. v.
Five randomized SIZE‐CRER portfolios. The 556 companies are first ranked in descending order of their average market capitalization and grouped into five SIZE portfolios. Within each SIZE group, five portfolios of approximate equal number are formed in descending order of CRER. Then, the same “CRER” portfolios in the five SIZE groups (i.e. CRER1 in SIZE1, CRER1 in SIZE2, CRER1 in SIZE3, CRER1 in SIZE4 and CRER1 in SIZE5………) are combined to form five randomized SIZE‐CRER portfolios. (e) The same procedures are repeated for the five direct property performance indices to derive the quarterly “business” return series for each of the 216(USA), 43 (UK), 14 (Australia), 22 (Hong Kong) and 15 (Singapore) firms. Likewise, the five performance indicators are compared between the “composite” and “business” return series of each firm to derive the excess performance metrics. 4.2 Relationship between CRER level and incremental (marginal) stock market performances Next, we assess whether there is a significant relationship between the CRER levels and incremental (marginal) stock market performances derived from the first step (i.e. performance differences between the “composite” and “business” return series); the presence of a significantly positive linear relationship between the two variables suggests there is an increasing return to scale of incremental stock market performance effects with every percentage increase in the CRER levels; conversely a significantly negative relationship implies that the incremental (marginal) stock market 13
performance benefits are subject to diminishing return to scale associated with very percentage increase in the CRER levels. Since financial variables are related in ways that makes it difficult, if not impossible, to determine causality, and that they are often simultaneously determined by each other, we appeal to simultaneous equations system approach, using iterative three‐stage least squares (IT3SLS), to first derive the predicted CRER values ( CRER ) from a regression model using sales turnover, firm leverage, growth opportunities, the sets of two regional dummy variables and the set of seven segment dummy variables as instrumental variables. The natural logarithm of sales turnover (Lnsales) variable is included as proxy for firms’ operating size, and is expected to influence positively the CRER level.11 Debt /total assets (TDEBTR), a proxy for leverage, is included to see if there is a positive leverage influence on the CRER level. The expectation is that higher leverage can buy more real estate particularly if money can be borrowed at lower rate. BV/MV (book‐market value ratio) is designed to capture the extent to which the market perceives the growth opportunities of individual firms with regard to real estate. The influence of BV/MV on CRER level can be either positive (undervaluation of CRE) or negative (overvaluation of CRE).12 DREG r (r = 1,2) are (0,1) dummy variables representing Asia and Europe (relative to North America), DSEG s (s=1,2,3,4,5,6,7) are (0,1) dummy variables representing SIC codes 53, 54 55, 56, 57, 58 and 59 (relative to SIC code 52) and  j is the regression error term. The first regression model looks as follow: Equation 1 2
7
r 1
s 1
CRER j  v 0  v1 LnSales j  v 2 (TDEBTR ) j  v 3 ( BV / MV ) j   wr DREG r   ws DSEG s   j
The simultaneous equation approach further allows for the possibility that the five incremental performance measures (return, risk, beta, SI and JI) have endogenous relationships with the CRER level, with firm decisions affecting the performances and CRE investment, often simultaneously. Consequently, 11
Another related point is the exposure of retailers to internet. A number of retailers now derive significant revenue from internet sales, which do not require a physical size. However, the presence of internet sales were not controlled for in the regression models due to great difficulties involved in assembling a reliable set of data concerning retail internet sales., 12
Although corporate management has always argued that firms with higher BV/MV ratios are associated with higher CRE investment. See Brennan (1990) for a discussion on the concept of “latent assets” that applies to real estate. 14
the five performance metrics are included in this simultaneous equations system that comprises six endogenous variables: (1) CRER (see above), (2) incremental return, (3) incremental risk, (4) incremental beta, (5) incremental SI and (6) incremental JI. Equations (2) to (5) are specified as follow: Equation2 2
7
r 1
s 1
Incremental (Re turn j )  a0  a1 CRER j  a2 Ln( MV j )  a3 (TDEBTR ) j  a4 ( BV / MV ) j   br DREGr   bs DSEGs   j
Equation 3 2
7
r 1
s 1
2
7
r 1
s 1
2
7
r 1
s 1
Incremental ( Risk ) j  c0  c1 CRER j  c2 Ln( MV j )  c3 (TDEBTR) j  c4 ( BV / MV ) j   d r DREGr   d s DSEGs   j
Equation 4 Incremental ( Beta) j  e0  e1 CRER j  e2 Ln( MV j )  e3 (TDEBTR) j  e4 ( BV / MV ) j   f r DREGr   f s DSEGs   j
Equation 5 Incremental ( SI ) j  g 0  g1 CRER j  g 2 Ln( MV j )  g3 (TDEBTR ) j  g 4 ( BV / MV ) j   hr DREGr   hs DSEGs   j
Equation 6 2
7
r 1
s 1
Incremental ( JI ) j  i0  i1 CRER j  i2 Ln( MV j )  i3 (TDEBTR) j  i4 ( BV / MV ) j   kr DREGr   k s DSEGs   j
where incremental (performance) is the average performance difference (over the six‐year period) for each retail firm in their medium return, total risk, beta, SI and JI, respectively, as a result of CRE ownership. Ln (MV), a Fama‐French (1992) factor, represents the natural logarithm of market capitalization (a proxy for firm size). The simultaneous equation estimates are also repeated for a system of incremental performance metrics (derived from using direct property performance indices) from Australia, Hong Kong, Singapore, the UK and the US retail firms. Our interpretation focuses on the influence of the CRER estimates on the excess performance metrics and assesses whether the respective linear estimates are statistically different from zero. Before estimation, we have manually checked the financial data for outliers. They include zero values for variables such as market value and sales turnover, negative values for book‐to‐market value ratio and leverage ratios, and extraordinarily large observations for any of the variables (defined as more than four standard deviations away from the mean). Consequently, a number of firms for which these observations occurred have been removed from the samples, so the remaining samples consists of 508 15
firms (91.4%) (with DJ adjusted property indices) and 290 firms (93.4%) (with direct property indices), respectively. 5. Results and implications The stock market performance results from comparing the “composite” (with CRE) and “business” (without CRE) series are first presented. The estimated relationships between the excess performances and the corresponding CRER levels follow.. 5.1 Performance results with and without CRE ownership Table 9 provides an overall comparison between the “composite” and “business” return series for the 556 retail firms. Several findings are documented. First, retailers’ CRE ownership is associated with higher return accompanied by insignificant increase in business risk. As the numbers indicate, the average medium returns for the “composite” (i.e. with CRE) and “business” (i.e. with no or little CRE) firms are respectively 0.83% and ‐0.34% and their difference (incremental performance) of monthly 1.17% (approximately 14% annually) is statistically significant at the 5% level. This incremental 14 percent per year represents probably the risk premia awarded to 78.8 % of the retailers for owning real estate; with the remaining 118 retail firms remain better off without CRE ownership. In contrast, the additional business risk derived from CRE ownership is only approximately 2.76% per annum and this estimate is not statistically significant. Second, the average time‐varying beta for the “composite” firms (0.7801) is slightly lower than that of the “business” firms (0.7811) suggesting that CRE may be capable of providing some diversification benefits to retailers (Brounen et al. 2005). However, this result is not conclusive as the incremental beta performance estimate is statistically insignificant. Third, the “composite” retail firms outperform the “business” retail firms significantly in the two risk‐adjusted return measures, i.e. JI and SI. In the present context, a positive JI can be interpreted as the firm outperforming the risk‐adjusted expectation after controlling for the “global” market risk. The average time‐varying JI estimates clearly indicate that the “composite” (JI = 0.94%) and “business” (JI = ‐0.04%) firms outperform and under‐
perform the global stock market respectively, with approximately 75% of the “composite” retailers (417) reporting an excess JI performance of 0.97% monthly (equivalent 11.64% p.a.) than their “business” 16
counterpart. The SI measures the risk premium per unit of total risk. The results indicate that 436 (78.4%) retail firms derive higher SI in favor of CRE ownership, and this excess SI performance is highly significant. Overall, CRE ownership appears to contribute positively to the stock market performances in the retail sector. (Table 9 here) Table 10 provides the paired‐sample differences of the five performance indicators for the retail firms grouped into three regional portfolios (Asia, Europe and North America). Overall, retails firms from all three geographical regions derive higher returns (with higher business risk), lower systematic risk, better SI and JI associated with the CRE ownership. Except for the insignificant higher business risk associated with holding real estate in all three regional portfolios as well as the insignificant SI performance for the European “composite” retail firms, all other excess performance estimates in favor of the “composite” retail firms are statistically significant at the 5% level. Hence, the impact of CRE ownership on the stock market performance of retail firms from the three regions are positive, with the North American retail firms appear to benefit most from owing real estate; consistently outperform the Asian and European firms in all performance measures. Finally, chi‐square tests reveal that all five performance differences across the three regional portfolios are statistically significant. (Table 10 here) It is probable that the stock market benefits of CRE ownership to retail firms differ significantly across the 15 countries as real estate is mainly a local business. Table 11 provides the outcomes with several observations worth noting. First, the average excess medium return performance for the China retail firm is a negative 0.66 percent per month (equivalent ‐7.92 percent per annum) suggesting that CRE ownership causes the China retail firms to be disadvantaged in their stock return performance. In contrast, the remaining 14 countries’ “composite” retail firms reporting higher returns, with the excess return performances of firms in favor of CRE ownership from Hong Kong, Australia, Korea, Singapore, Thailand, Canada and Switzerland statistically significant at the 5% level. Second, CRE ownership causes retail firms from all countries to derive higher business risk. This incremental business risk is statistically significant at the 5% level in 60 percent of the countries. Third, retail firms in China, Korea, Thailand and 17
Malaysia achieve higher systematic risk associated with CRE ownership; and that they are probably not able to be compensated with adequate higher returns because the full real estate systematic risk (or part of it) is not priced in the stock market. In addition, the unfavorable excess beta performance for Malaysian firms is statistically significant. Fourth, except for China that derive unfavorable risk‐adjusted performances (i.e. negative SI and JI), CRE ownership appears to have provided positive risk‐adjusted return benefits to retail firms from other 14 countries, with 9 incremental JI and 7 incremental SI statistically significant at the 5% level. Finally, results of chi‐square tests indicate that all five incremental performance metrics in favor of CRE ownership are significantly different across the 15 countries. Hence, the country‐by‐country analysis reveals that the effect of CRE ownership on incremental performances depends partially on where a retail firm mainly operates. (Table 11 here) It might also be the case that CRE ownership is able to benefit some retail segments more than others. To explore this possibility, Table 12 presents the results from analyzing the paired‐sample differences in the five performance indicators. Overall, retail firms from the nine SIC segments derive higher returns, higher volatilities, lower systematic risks and better SI and JI performances associated with holding real estate. Of the nine retail segments, the numbers of retail segments that achieve a statistically significant excess performance in favor of CRE ownership at the 5% level are 7 (excess return), 8 (excess beta), 6 (excess SI) and 9 (excess JI). Further Krusal‐Wallis tests confirm that except for the excess total risk performance, there are significant differences in the incremental return, beta, SI and JI performances in favor of CRE ownership across the segments. Thus, in addition to the earlier finding that the retail segments have different levels of CRE ownership, the effects of CRE ownership on incremental performances are also to a certain extent driven by the segment the retail firm operates in. (Table 12 here) The incremental performance results that are estimated by sorting all 556 retail firms into five portfolios based on their average CRER levels and segment membership are presented in Table 13. Hence, any possible differences in the return, risk, beta, SI and JI performances due to the segment influence are probably controlled. The five portfolios constructed from this randomized segment‐CRER procedure have 18
average CRER levels of, respectively, 60.19%, 44.62%, 34.77%, 25.73% and 13.46%. The “within segment‐
CRER” results are consistent with the earlier findings; i.e. the retail firms derive different levels of higher return, higher risk, lower beta and better risk‐adjusted performance associated with CRE ownership. However, further Kruskal‐Wallis tests are inconclusive in that they have failed to reveal any significant differences in the incremental performance metrics across the five randomized segment‐CRER portfolios. (Table 13 here) Table 14 contains another randomized portfolio incremental performance results, by sorting all the 556 retails firms into five portfolios based on their average capitalizations (a proxy for size) and average CRER levels. Hence any possible differences in the return, risk, beta, SI and JI performances due to the influence of the size factor are probably controlled. The five portfolios constructed from this randomized SIZE‐CRER procedure have average CRER levels of, respectively, 67.53%, 45.47%, 32.21%, 21.99% and 11.60%. The “within SIZE‐CRER” results are in agreement with the earlier findings; i.e. the retail firms derive significant levels of higher return, higher risk, lower beta and better risk‐adjusted performance associated with CRE ownership. Except for two Kruskal‐Wallis tests which reveals the excess medium return and excess risk performance metrics are statistically different at the 10% level across the five portfolios, other similar tests (for beta, JI and SI) are not able to reveal any significant performance differences across the five portfolios. (Table 14here) Finally, the incremental performance results derived from using the direct property performance benchmarks for the USA, the UK, Australia, Hong Kong and Singapore retail firms are presented in Table 15. Overall, they are consistent with the earlier i8ncremental performance metrics obtained from using DJ real estate benchmarks. Except for the USA and UK retailers who derive higher systematic risk due to real estate holdings, the remaining results indicate that the excess performance metrics for each country are probably in favor of CRE ownership. (Table 15 here) 5.2 Relationship between CRER levels and incremental stock market performances 19
Table 16 reports the univariate regression coefficients between the CRER levels and incremental stock performance measures for the retail firms across the three regions and eight SIC segments. As the results indicate, only 9 CRER coefficient estimates (15%) are statistically significant at least at the 10 percent level. Specifically, the CRER coefficient estimate on the incremental JI performance for all retailers is significantly negative (t statistic = ‐1.69). On a regional basis, North American retail firms are associated with two significantly negative CRER coefficients; one for the incremental return and another for the incremental JI performances. For these retailers, higher CRER levels might probably be associated with diminishing incremental stock market performance benefits. Finally, there are six other significant CRER (positive: 4 and negative: 2) effects on the incremental performance measures for retailers from some SIC segments. These results are however regarded as preliminary as they do not control for the effects of Fama and French (1992)’s three factors and the endogenous relationships between incremental stock market performances and real estate ownership. (Table 16 here) Table 17 reports the estimates for equation 1 (dependent variable: CRER) of the simultaneous system for the overall sample and three regional sub‐samples. The sales turnover (Lnsales) variable is statistically significant in predicting the CRER levels for the full sample and the Asian and North American subsamples. The leverage variable (TDEBTR) is able to predict the CRER levels for the overall and Asian firms. Higher BV/MV ratios are significantly related to higher CRER levels for European retail firms only. Finally, the significant coefficients attached to the Asia dummies and some segment dummies indicate that there are probably some variations in the abilities of the three financial variables in predicting the CRER levels of the retailers across the three regions as well as across the eight retail segments. Overall, the results are broadly consistent with prior expectations. The CRER coefficient estimates are weakly and inconsistently related to the incremental stock market performances of the retail firms after controlling for Fama and French (1992)’s three factors, implying that stock market investors are probably unwilling to favor excessive13 CRE ownership of retailers. Table 17 reveals that the CRER coefficient estimates for all five incremental performance 13
A broad definition of excessive CRE ownership is the real estate requirements surplus to the business;
i.e. “surplus” CRE.
20
measures are, except for one regional subsample, very small and are either positive or negative in all regressions. Only a significant CRER coefficient estimate is found to be negative (t = ‐1.99) for North American retailers, and is in agreement with the earlier univariate evidence. For these retailers, one important implication from our results is that excessive CRE ownership is probably discouraged because higher CRER levels impact negatively the firms’ incremental JI performance. In contrast, the CRER impacts of incremental JI and SI are all positive; but statistically insignificant for Asian and European retail firms. It thus appears that CRE ownership may be viewed more favorably by Asian/European retail firms than their North American counterparts. Since the positive incremental performance effects of CRE ownership are not striking, Asian/European retailers still need to trade off their higher CRE investments against the likely increase in the corporate risk profile due to the adverse effect of huge capital investment in real estate on the firms’ leverage and liquidity conditions. Of the Fama and French (1972)’s three factors, the coefficients for firm size are statistically positive on incremental returns, SI and JI and statistically negative on total risk. High gearing leads to lower incremental beta, SI and JI for retail firms, which is inconsistent with the standard relation between leverage and systematic risk. The last factor, the impacts of the BV/MV variable on incremental total risk and beta are significantly negative, which are consistent with prior expectation. The estimation results for the three regions indicates there are some variations in the respective coefficient estimates on the five incremental performance measures of Asian, European and North American retail firms. (Table 17here) The multivariate regression CRER coefficient estimates by eight SIC retail segments are contained in Table 18. The CRER coefficients are statistically insignificant (either positive or negative) in 32 regressions (80%). Thus we uncover no evidence that higher CRE ownership contributes to better incremental performances of retailers from the nine segments. Except for the incremental risk measure, each of the remaining four incremental performance measures yields two significant CRER coefficient estimates (one positive and one negative). Judging from these significant estimates, only SIC segment (57) probably has consistently benefited from higher CRE ownership that contributed to better incremental return, SI and JI performances as well as provided higher diversification benefits of the firms. 21
(Table 18 here) Finally, Table 19 contains the CRER coefficient estimates on the five incremental performance measures derived from using direct property market indices for five national markets (Australia, Hong Kong, Singapore, the UK and the US). As the numbers indicate, the impact of CRER levels on the incremental return performance of Asian firms (Australia/Hong Kong/ Singapore) is statistically positive (t statistic = 1.88). There is also some evidence that CRE ownership may be able to provide diversification benefits for the European retail firms. Although the CRER coefficient estimates on the SI and JI performances for the three regions are all statistically insignificant, the magnitudes are positive and higher for Asian firms; in contrast, the CRER coefficient estimates for the US firms’ incremental SI and JI performances are negative. (Table 19 here) Taking the evidence as a whole, our simultaneous regression results are far from conclusive to suggest a consistent linear relationship between the CRER levels and incremental stock market performances although there appears to have some scattered evidence (especially in the North American context) that higher retail real estate ownership is probably not favorable to their incremental stock market valuation. It is also probable any significant impacts of the CRER levels on incremental stock performances are probably non‐linear and with an optimal level of CRER level that is to be separately determined for each firm. Although it is beyond the scope of this paper to address this question, evidence from Exhibit 2 suggests that the optimal CRER levels for retailers are probably between 20 and 40 percents. Overall, our results have important investment and economic significance for firms in the retail industry. The finding that CRE ownership is associated with favorable stock market performance implies that the importance of retail real estate has probably been recognized by the stock market investors. As Guy (1999) has pointed out, real estate cannot be simply regarded as sunk or negative costs to the retailers. This is because properties have significant values on balance sheets and can usually be disposed of on the open market at substantial financial gains over the original cost of purchase. Consequently, more retailers may be motivated to invest more resources in real estate or probably slow down their 22
existing divesting activities. However, it is also likely that some major retailers own properties for other reasons in addition to seeking improvement in their stock market performances. These reasons may be associated with operational, financial, cultural and institutional factors such as supporting their primary business operations and long‐term business growth, controlling rising business costs due to leasing, boosting corporate image with prestigious administrative headquarters and highly‐valued retail outlets, improving corporate liquidity and reducing corporate debts as well as management’s favorable attitude toward CRE ownership. Although retail freehold ownership might be advantageous, higher CRER levels may not necessarily lead to proportionally higher level of favorable incremental stock market performances, as the relationship between the two is probably non‐linear and is associated with diminishing return scale. Our results imply that there is probably an optimal level of CRE ownership that each firm should aim at. As such, decision concerning the optimal proportion of corporate resources to be invested in real estate has to be carefully weighed in order to balance the likely increase in the corporate risk profile due to the increased investment in properties against any incremental stock market performance benefits. This also means that existing properties would probably be subject to more frequent and rigorous evaluation to justify their continuing inclusion in the firm’s asset portfolio. A case in point is that one of the big questions hanging over retailing is the extent to which consumers will accept an e‐commerce retail format and the resulting implication on the demand for physical space. This reinforces the need for addressing the potential surplus space and its potential absorption by the diversification activity of retailers. Arthur Anderson and Rosen (2000) find that those US retailers with e‐
commerce capabilities (e.g. Wal‐Mart, GAP) are expanding their physical presence at an above average rate of 9.2 percent compared to the 5.8 percent industry average and 2.6 percent for retailers without virtual presence. At the same time, those retailers without virtual presence are closing stores at an above average rate of 3 percent compared to the industry average of 2.1 percent In the final analysis, CRE performance would be benchmarked against the cost of capital and optimized to support retailers’ business and real estate strategies. 6. Conclusion 23
This paper is a contribution to the literature in international CRE ownership and stock market valuation. The main purpose was to investigate comprehensively if CRE ownership benefits international retail firms from the stock market perspective; and further, whether these incremental positive stock market benefits are linearly influenced by the relative level of real estate holdings. Using a sample of 556 retail firms from 2001 to 2006, our approach is implemented into two steps: (a) we estimate and compare five popular stock market performance measures: medium return, total risk, beta, SI and JI for the “composite” and “business” retail firms. The “composite” return series include the real estate impact on the stock market. The “business” return series was derived by removing the influence of the real estate market from the “composite” returns; (b) we investigate if there is a significant linear relationship between the CRER levels and incremental stock market performance measures with the system multivariate regression methodology that control for the effects of firm size, leverage and growth opportunities as well as regional and segment differences. The overall conclusion is that while retailers’ CRE ownership may derive favorable stock market performance benefits, higher ownership levels do not benefit retail firms. There is lack of a consistent linear relationship between the CRER levels and incremental stock market performance benefits. It is likely that any significant impacts of the CRER levels on additional stock performances are probably non‐linear and with an optimal level of CRER level that is to be separately determined for each firm. Finally, as retail firms do generally hold some form of real estate, our findings are helful for their strategic investment decisions. References Arthur Anderson and Rosen (2000), “eReal Estate: A Certainty” Arthur Anderson, Chicago, IL Brennan, MJ. (1990), “Latent assets” Journal of Finance 45(3): 709‐729 Brounen, D., and Eichholtz, P. M. A. (2005), “Corporate real estate ownership implications: International performance evidence” Journal of Real Estate Finance and Economics, 30(4), 429‐445 Brounen, D., Colliander, G., and Eichholtz, P. M. A. (2005), “Corporate real estate and stock performance in the international retail sector” Journal of Corporate Real Estate, 7(4), 287‐299 Deng, Y. and Gyourko, J. (1999), “Real estate ownership by non‐real estate firms: an estimate of the impact of firm return”, Working Paper, Zell/Lurie Real Estate Centre, Wharton Business School, University of Pennsylvania 24
Fama, E.F. and French, K.R. (1992), “The cross‐section of expected stock return” Journal of Finance 47: 427‐465 Guy, C. (1999), “Exit strategies and sunk costs: the implications for multiple retailers”, International Journal of Retail and Distribution Management 6, 237‐245 Laposa, S., and Charlton, M. (2001), “European versus U.S. corporations: A comparison of property holdings” Journal of Corporate Real Estate, 4(1), 34‐47 Liow, K. H. (1999), “Corporate investment and ownership in real estate in Singapore: Some empirical evidence” Journal of Corporate Real Estate, 1(4), 329‐342. Liow, K. H. (2004), “Corporate real estate and stock market performance” Journal of Real Estate Finance and Economics, 29(1), 119‐140. Liow, K.H. and Nappi‐Choulet, I. (2008), “A combined perspective of corporate real estate” Journal of Corporate Real Estate 10(1):54‐67 Lizieri, C., and Satchell, S. (1997), “Interactions between property and equity markets: An investigation of linkages in the United Kingdom 1972‐1992” Journal of Real Estate Finance and Economics, 15(1), 11‐26. Rodriguez, M, and Sirmans, C.F., (1996), “Managing corporate real estate: Evidence from capital markets. Journal of Real Estate Literature” 4(1), 21‐34 Seiler, M. J., Chatrath, A. and Webb, J.R. (2001), “Real asset ownership and the risk and return to stockholders” Journal of Real Estate Research, 22(1), 199‐212 25
Table 1 Corporate real estate ownership of retail companies: FY 2006
Size of NPPE (US$m)
0≤ NPPE≤ 0.2 mil 0.2< NPPE≤ 0.4 mil 0.4< NPPE≤ 0.6 mil 0.6< NPPE≤ 0.8 mil 0.8< NPPE≤ 1 mil NPPE> 1 mil All No. of companies
Mean NPPE
(US$m)
0.058 0.285 0.496 0.690 0.885 7.422 1.068 403 79 40 31 25 80 658 Mean CRER
0.29 0.38 0.42 0.45 0.40 0.44 0.34 Notes: Mean NPPE (US$ m) = net property plant and equipment value reflected on balance sheets (million) Mean CRER = net property plant and equipment value (NPPE)/ total assets value Figure 1
Distribution of corporate real estate ratio (CRER): FY2006
Number of companies
Distribution of CRER
250
194
236
200
137
150
65
100
26
50
0
0≤ CRER≤ 0.2
0.2< CRER≤ 0.4
0.4< CRER≤ 0.6
0.6< CRER≤ 0.8
CRER > 0.8
CRER
26
Table 3
Analysis of CRE ownership of retail companies by retail segment: 2001 – 2006
Year
NPPE
(US$m)
CRER
2001 2002 2003 2004 2005 2006 Mean 0.659 0.717 0.828 0.916 0.992 1.068 0.864 0.37 0.36 0.36 0.35 0.35 0.34 0.36 SIC 5200 –
5271
Materials
and home
dealers
0.37 0.36 0.35 0.35 0.35 0.34 0.35 SIC 5311 –
5399
Departmental
stores
0.42 0.41 0.41 0.40 0.41 0.41 0.41 SIC 5400 5499
Food stores
0.44 0.43 0.43 0.43 0.43 0.42 0.43 Analysis of CRER by segment
SIC 5500 – SIC 5610 SIC 5700 5599
5699
5736
Vehicles
Clothing
Furniture
stores
stores
stores
0.30 0.30 0.30 0.29 0.30 0.29 0.30 0.31 0.31 0.31 0.30 0.29 0.28 0.30 0.27 0.27 0.27 0.27 0.26 0.25 0.26 SIC 5810 5813
Eating and
drinking
places
0.56 0.55 0.54 0.53 0.51 0.50 0.53 SIC 5910 5949
Mixed
stores
SIC 5960 5999
Non store
retailers
0.25 0.23 0.23 0.22 0.22 0.21 0.23 0.24 0.24 0.23 0.21 0.21 0.20 0.22 Notes: NPPE (US$m) = net property plant and equipment value (USD $m) CRER = corporate real estate ratio = (net property, plant and equipment – NPPE) / total asset value 27
Table 2
CRE holdings of retail companies by segment: FY 2006
SIC
SIC 5200 – 5271 SIC 5311 – 5399 SIC 5400 – 5499 SIC 5500 – 5599 SIC 5610 – 5699 SIC 5700 – 5736 SIC 5810 – 5813 SIC 5910 – 5949 SIC 5960 – 5999 Overall Segments
No. of
companies
Materials and home dealers Departmental stores Food stores Vehicles stores Clothing stores Furniture stores Eating and drinking places Mixed stores Non store retailers 16 112 57 54 91 59 126 72 71 658 Chi‐square values Mean
NPPE
(US$m)
2.859 1.755 3.847 1.369 0.260 0.245 0.691 0.461 0.126 1.068 75.66** Mean
CRER
0.34 0.41 0.42 0.29 0.28 0.25 0.50 0.21 0.20 0.34 162.43** Notes: Mean NPPE (US$m) = net property plant and equipment (million) Mean CRER = (net property plant and equipment (NPPE)/ total asset value) The chi‐square values are derived from Non‐parametric Kruskal‐Wallis tests ** ‐ indicates two tailed significance at the 5% level Table 4
Regions
Asia Europe North America Overall CRE holdings of retail companies by region: Financial year 2006
No. of companies
280 113 265 658 Chi‐square values Mean NPPE (US$m)
0.803 1.517 1.157 1.068 17.02 Mean CRER
0.32 0.33 0.36 0.34 5.24 Notes: Mean NPPE (US$m) = net property plant and equipment Mean CRER = (net property plant and equipment (NPPE)/ (total asset value) The chi‐square values are derived from Non‐parametric Kruskal‐Wallis tests 28
Table 5
Year
2001 2002 2003 2004 2005 2006 Mean Analysis of CRE ownership of retail companies by region: 2001 – 2006
NPPE
(US$m)
0.659 0.717 0.828 0.916 0.992 1.068 0.864 CRER
Asia
0.37 0.36 0.36 0.35 0.35 0.34 0.36 0.35 0.35 0.35 0.34 0.33 0.32 0.34 Analysis of CRER by region
Europe
North America
0.38 0.36 0.35 0.34 0.34 0.33 0.35 0.39 0.38 0.37 0.37 0.36 0.36 0.37 Notes: NPPE (US$m) = net property plant and equipment value (million); CRER = corporate real estate ratio Table 6
Countries
Australia Austria Belgium Canada China Denmark Finland France Germany Hong Kong India Ireland Italy Japan Korea Malaysia Netherlands Norway Philippines Portugal Singapore Sweden Switzerland Taiwan Thailand United Kingdom United States Overall CRE holdings of retail companies: FY 2006
No. of companies
15 1 4 16 29 1 1 14 15 27 4 1 1 144 12 15 3 1 1 1 18 7 6 2 11 59 249 658 Chi‐square values Mean NPPE (US$m)
0.426 0.035 1.354 0.613 0.090 0.018 0.464 2.619 1.337 0.106 36.784 0.904 1.012 0.342 0.774 0.125 3.126 0.003 0.144 0.135 0.123 0.274 0.211 0.109 0.082 1.629 1.192 1.068 95.47 Mean CRER
0.22 0.34 0.34 0.34 0.40 0.25 0.46 0.25 0.26 0.13 0.24 0.28 0.30 0.34 0.58 0.36 0.26 0.03 0.37 0.53 0.20 0.23 0.31 0.56 0.28 0.38 0.37 0.34 81.01 Notes: Mean NPPE (US$m) = net property plant and equipment (million);Mean CRER = net property plant and equipment (NPPE)/ gross total asset value);The chi‐square values are derived from non‐parametric Kruskal‐Wallis tests 29
Table 7
Year
2001 2002 2003 2004 2005 2006 Mean Analysis of CRE ownership of retail companies by country: 2001 – 2006
NPPE
(US$m)
0.659 0.717 0.828 0.916 0.992 1.068 0.864 CRER
AUS
AIA
BEL
CAN
CHI
0.37 0.36 0.36 0.35 0.35 0.34 0.36 0.29 0.24 0.26 0.25 0.23 0.22 0.25 0.58 0.57 0.50 0.43 0.40 0.34 0.47 0.40 0.41 0.34 0.33 0.36 0.34 0.36 0.34 0.33 0.34 0.35 0.33 0.34 0.34 0.40 0.40 0.39 0.40 0.41 0.40 0.40 Analysis of CRER by country
DEN
FIN
FR
0.34 0.29 0.31 0.30 0.22 0.25 0.29 0.37 0.31 0.29 0.35 0.40 0.46 0.36 0.28 0.27 0.27 0.25 0.26 0.25 0.26 GER
HK
IND
IRE
ITA
0.28 0.28 0.30 0.30 0.28 0.26 0.28 0.25 0.24 0.18 0.15 0.14 0.13 0.18 0.28 0.27 0.26 0.26 0.27 0.24 0.26 0.33 0.33 0.29 0.29 0.26 0.28 0.30 0.18 0.21 0.23 0.33 0.30 0.30 0.26 Table 7 (Continued)
Year
2001 2002 2003 2004 2005 2006 Mean NPPE
(US$m)
0.659 0.717 0.828 0.916 0.992 1.068 0.864 CRER
JP
KOR
MAL
NET
NOR
Analysis of CRER by country
PH
POR
SG
SWE
0.37 0.36 0.36 0.35 0.35 0.34 0.36 0.36 0.36 0.37 0.37 0.36 0.34 0.36 0.56 0.56 0.60 0.59 0.55 0.58 0.57 0.35 0.35 0.36 0.35 0.35 0.36 0.35 0.39 0.37 0.36 0.32 0.36 0.26 0.34 0.02 0.03 0.04 0.03 0.03 0.03 0.03 0.47 0.44 0.45 0.40 0.38 0.37 0.42 0.55 0.53 0.60 0.62 0.64 0.53 0.58 0.27 0.26 0.25 0.23 0.21 0.20 0.24 0.24 0.23 0.22 0.22 0.23 0.23 0.23 SWI
TW
TH
UK
US
0.34 0.35 0.34 0.33 0.31 0.31 0.33 0.56 0.56 0.57 0.58 0.57 0.56 0.57 0.33 0.33 0.35 0.34 0.34 0.28 0.33 0.45 0.42 0.41 0.39 0.39 0.38 0.41 0.39 0.39 0.38 0.37 0.37 0.37 0.38 Notes: Mean NPPE (US $m) = average net property plant and equipment Mean CRER = average corporate real estate ratio Countries: AUS ‐ Australia, AIA ‐ Austria, BEL ‐ Belgium, CAN ‐ Canada, CHI ‐ China, DEN ‐ Denmark, FIN ‐ Finland, FR ‐ France, GER ‐ Germany, HK ‐ Hong Kong, IND ‐ India, IRE ‐ Ireland, ITA ‐ Italy, JP ‐ Japan, KOR ‐ Korea, MAL ‐ Malaysia, NET ‐ Netherlands, NOR ‐ Norway, PH ‐ Philippines, POR ‐ Portugal, SG ‐ Singapore, SWE ‐ Sweden, SWI ‐ Switzerland, TW ‐ Taiwan, TH ‐ Thailand, UK ‐ United Kingdom, US ‐ United States 30
Table 8
Number of companies included in the final sample
Australia China Hong Kong Japan Korea Malaysia Singapore Thailand France Germany Sweden Switzerland United Kingdom Canada United States Asia Europe North America 14 27 22 137 8 15 15 8 14 13 6 6 43 12 216 Note: 246 82 228 Total : 556 Of the 658 firms, only 556 firms (84.5%) are retained for further study because these retail firms have continuously 72 monthly return observations for the full study period Table 9
Comparison of performance measures between “composite” and “business” return series: 2001-2006
Performance measure
Median return Total risk (Standard deviation) Systematic risk (Beta) Jensen Index (JI) Sharpe Index (SI) Composite
0.0083 0.1094 0.7801 0.0094 0.0976 Monthly return series
Business
Differences
‐0.0034 0.0117 0.1071 0.0023 0.7811 ‐0.0010 ‐0.0004 0.0097 ‐0.0210 0.1186 (z-statistic)
(‐2.39**) (‐0.71) (0.34) (‐8.89**) (‐2.36**) Note: The z‐statistics are derived from non‐parametric Wilcoxon Signed Ranks tests ** ‐ indicates two‐tailed significance at the 5% level 31
Table 10
Comparison of performance measures (“composite” versus “business”) within and
across three regional portfolios: 2001-2006
Performance measure
Asia Median return Total risk (Standard deviation) Systematic risk (Beta) Jensen index (JI) Sharpe index (SI) Europe Median return Total risk (Standard deviation) Systematic risk (Beta) Jensen index (JI) Sharpe index (SI) North America Median return Total risk (Standard deviation) Systematic risk (Beta) Jensen index (JI) Sharpe index (SI) Monthly return series
Composite
Business
Differences
“Within” portfolio 0.0073 ‐0.0050 0.0123 0.1127 0.1090 0.0037 0.6191 0.6302 ‐0.0111 0.0097 0.0001 0.0096 0.0912 ‐0.0206 0.1118 0.0070 ‐0.0012 0.0083 0.1009 0.0974 0.0034 0.9814 1.0045 ‐0.0231 0.0010 ‐0.0057 0.0067 0.0826 ‐0.0218 0.1045 0.0156 ‐0.0025 0.0180 0.1177 0.1163 0.0013 0.9209 0.9466 ‐0.0256 0.0288 0.0111 0.0177 0.1609 ‐0.0202 0.1812 “Across” portfolios z-statistic1
(‐2.46**) (‐1.35) (2.15**) (‐7.39**) (‐2.44**) (‐1.40) (‐0.65) (2.94**) (‐7.65**) (‐1.54) (‐2.69**) (‐0.27) (2.24**) (‐5.79**) (‐2.69**) Difference (median return) Difference (total risk) Difference (beta) Difference (JI) Difference (SI) Chi‐square value2 25.58** 15.42** 12.59** 23.68** 10.52** Notes 1
The z‐statistics are derived from the non‐parametric Wilcoxon Signed Ranks tests; 2 The Chi‐square values are derived from the non‐parametric Kruskal‐Wallis tests; ** ‐ indicates two‐tailed significance at the 5% level. 32
Table 11
Comparison of performance measures (“composite” versus “business”) within and across 15 country portfolios: 2001-2006
Performance
measure
Monthly return series
Composite
Business
Difference
z-statistic
Performance
measure
Monthly return series
Composite
Business
Difference
z-statistic
“Within” country Median return Total risk Beta JI SI ‐0.0094 0.0693 0.1363 ‐0.0161 ‐0.0882 Median return Total risk Beta JI SI 0.0068 0.1697 0.6800 0.0031 0.0901 Median return Total risk Beta JI SI 0.0027 0.0938 0.2792 0.0062 0.0538 Median return Total risk Beta JI SI 0.0096 0.1115 0.8225 0.0110 0.1369 Median return Total risk Beta JI SI 0.0116 0.1461 1.0405 0.0228 0.0906 China ‐0.0028 0.0682 0.0998 ‐0.0025 ‐0.0235 Hong Kong ‐0.0099 0.1675 0.7350 ‐0.0075 ‐0.0144 Japan ‐0.0036 0.0925 0.3851 ‐0.0001 ‐0.0246 UK ‐0.0028 0.1090 0.8490 ‐0.0010 ‐0.0218 US ‐0.0006 0.1444 1.0583 0.0112 ‐0.0182 ‐0.0066 0.0011 0.0366 ‐0.0136 ‐0.0647 (0.67) (‐0.33) (‐1.51) (8.76**) (0.59) Median return Total risk Beta JI SI 0.0122 0.0976 1.0062 0.0134 0.1909 0.0167 0.0022 ‐0.0550 0.0106 0.1045 (‐2.35**) (‐0.19) (3.93**) (‐8.07**) (‐1.61) Median return Total risk Beta JI SI 0.0118 0.1916 1.0411 0.0399 0.1550 0.0062 0.0013 ‐0.1060 0.0063 0.0784 (‐1.17) (‐0.40) (7.84**) (‐5.96**) (‐1.03) Median return Total risk Beta JI SI ‐0.0018 0.1236 0.8189 0.0086 0.0659 0.0124 0.0025 ‐0.0265 0.0120 0.1587 (‐1.76) (‐0.53) (3.86**) (‐9.74**) (‐2.35**) Median return Total risk Beta JI SI 0.0087 0.1126 0.4838 0.0080 0.1542 0.0122 0.0017 ‐0.0177 0.0117 0.1088 (‐1.58) (‐0.36) (0.70) (‐4.59**) (‐1.74) Median return Total risk Beta JI SI 0.0065 0.1114 0.4639 0.0206 0.1077 Australia ‐0.0010 0.0969 1.0089 0.0008 ‐0.0268 Korea ‐0.0148 0.1910 1.0376 0.0145 ‐0.0119 Malaysia ‐0.0051 0.1212 0.5445 0.0005 ‐0.0234 Singapore ‐0.0090 0.1112 0.4934 ‐0.0049 ‐0.0212 Thailand ‐0.0072 0.1108 0.4545 0.0071 ‐0.0194 0.0131 0.0007 ‐0.0027 0.0126 0.2176 (‐2.17**) (‐3.30**) (0.53) (‐2.23**) (‐2.73**) 0.0266 0.0006 0.0035 0.0254 0.1669 (‐2.24**) (‐2.52**) (‐0.98) (‐2.24**) (‐2.24**) 0.0033 0.0023 0.2744 0.0082 0.0892 (‐1.25) (‐3.41**) (‐3.35**) (‐1.16) (‐1.87) 0.0177 0.0014 ‐0.0096 0.0128 0.1754 (‐3.24**) (‐3.41**) (2.10**) (‐2.50**) (‐3.07**) 0.0137 0.0007 0.0093 0.0135 0.1271 (‐1.96**) (‐2.52**) (‐0.28) (‐2.24**) (‐2.24**) 33
Median return Total risk Beta JI SI 0.0022 0.1063 0.8172 ‐0.0047 0.0313 Median return Total risk Beta JI SI 0.0130 0.1127 1.1960 0.0190 0.1791 Median return Total risk Beta JI SI 0.0189 0.1043 0.7756 0.0340 0.2313 France ‐0.0004 0.1046 0.8346 ‐0.0066 ‐0.0235 Sweden 0.0011 0.1121 1.2262 0.0058 ‐0.0211 Canada ‐0.0067 0.1037 0.8111 0.0103 ‐0.0223 0.0025 0.0017 ‐0.0174 0.0019 0.0548 (‐1.04) (‐3.30**) (1.73) (‐0.53) (‐1.41) Medium return Total risk Beta JI SI ‐0.0013 0.1175 1.0608 ‐0.0061 0.0243 0.0119 0.0006 ‐0.0302 0.0132 0.2002 (‐0.94) (‐2.20**) (1.78) (‐0.94) (‐1.99**) 0.0072 0.1117 0.9115 ‐0.0129 0.0416 0.0257 0.0006 ‐0.0355 0.0237 0.2535 (‐2.98**) (‐3.06**) (2.20**) (‐2.82**) (‐2.98**) Median return Total risk Beta JI SI Germany ‐0.0025 0.1164 1.0702 ‐0.0082 ‐0.0208 Switzerland 0.0010 0.1104 0.9475 ‐0.0168 ‐0.0221 0.0013 0.0010 ‐0.0094 0.0021 0.0451 (‐0.52) (‐3.18) (1.15) (‐0.52) (‐1.08) 0.0062 0.0013 ‐0.0360 0.0039 0.0637 (‐1.99**) (‐2.20**) (1.15) (‐1.99**) (‐1.78) “Across” country 2 Chi‐square value
Difference (median return) Difference (total risk) Difference (beta) Difference (JI) Difference (SI) 95.82** 93.22** 137.19** 107.26** 87.17** Notes 1
The z‐statistics are derived from the non‐parametric Wilcoxon Signed Ranks tests; 2 The Chi‐square values are derived from the non‐parametric Kruskal‐Wallis tests; ** ‐ indicates two‐tailed significance at the 5% level. 34
Table 12
Comparison of performance measures (“composite” versus “business”) within and
across nine retail segment portfolios: 2001-2006
Performance measure
Composite
Return series
Business
Differences
(z-statistic)
“Within” retail portfolio P1 (SIC 5200‐5271) Median return Total risk (Standard deviation) Systematic risk (Beta) Jensen index (JI) Sharpe index (SI) Median return Total risk (Standard deviation) Systematic risk (Beta) Jensen index (JI) Sharpe index (SI) Median return Total risk (Standard deviation) Systematic risk (Beta) Jensen index (JI) Sharpe index (SI) Median return Total risk (Standard deviation) Systematic risk (Beta) Jensen index (JI) Sharpe index (SI) Median return Total risk (Standard deviation) Systematic risk (Beta) Jensen index (JI) Sharpe index (SI) Median return Total risk (Standard deviation) Systematic risk (Beta) Jensen index (JI) Sharpe index (SI) Median return Total risk (Standard deviation) Systematic risk (Beta) Jensen index (JI) Sharpe index (SI) 0.0094 ‐0.0059 0.0989 0.0959 0.6606 0.7051 0.0179 0.0041 0.1452 ‐0.0231 P2 (SIC 5311 – 5399) 0.0027 ‐0.0036 0.1109 0.1075 0.5787 0.6017 0.0066 0.0013 0.0527 ‐0.0226 P3 (SIC 5400 – 5499) 0.0079 ‐0.0021 0.0154 0.0030 ‐0.0445 0.0137 0.1683 (‐2.28**) (‐0.64) (3.90**) (‐7.46**) (‐2.39**) 0.0063 0.0034 ‐0.0230 0.0053 0.0753 (‐1.72) (‐0.93) (4.71**) (‐5.52**) (‐1.40) 0.0100 (‐2.26**) 0.0987 0.0967 0.4913 0.5238 0.0083 ‐0.0018 0.1102 ‐0.0268 P4 (SIC 5500 – 5599) 0.0107 ‐0.0027 0.1145 0.1123 0.7266 0.7446 0.0202 0.0079 0.1238 ‐0.0217 P5 (SIC 5610 – 5699) 0.0130 ‐0.0022 0.0020 ‐0.0324 0.0102 0.1370 (‐0.42) (3.44**) (‐9.29**) (‐2.34**) 0.0134 0.0022 ‐0.0180 0.0122 0.1455 (‐2.40**) (‐0.50) (2.73**) (‐6.36**) (‐2.26**) 0.0152 (‐2.30**) 0.1310 0.1288 1.0639 1.0993 0.0193 0.0055 0.1104 ‐0.0176 P6 (SIC 5700 – 5736) 0.0032 ‐0.0008 0.1446 0.1427 0.9822 1.0305 0.0111 0.0084 0.0288 ‐0.0173 P7 (SIC 5810 – 5813) 0.0047 ‐0.0036 0.1346 0.1328 0.5303 0.5629 0.0090 0.0015 0.0719 ‐0.0224 0.0022 ‐0.0354 0.0138 0.1280 (‐0.52) (2.62**) (‐6.09**) (‐2.10**) 0.0041 0.0019 ‐0.0483 0.0027 0.0461 (‐0.63) (‐0.38) (3.29**) (‐2.44**) (‐0.84) 0.0082 0.0018 ‐0.0326 0.0075 0.0943 (‐2.11**) (‐0.45) (4.89**) (‐6.02**) (‐2.08**) 35
Median return Total risk (Standard deviation) Systematic risk (Beta) Jensen index (JI) Sharpe index (SI) Median return Total risk (Standard deviation) Systematic risk (Beta) Jensen index (JI) Sharpe index (SI) Chi‐square value2 Difference (median return) 28.59** P8 (SIC 5910 – 5949) 0.0087 ‐0.0036 0.1204 0.1186 0.6070 0.6555 0.0177 0.0044 0.0977 ‐0.0189 P9 (SIC 5960 – 5999) 0.0038 ‐0.0040 0.1605 0.1582 1.1172 1.1179 0.0132 0.0056 0.0631 ‐0.0186 “Across” retail portfolios Difference (total Difference risk) (beta) 18.13 23.65** 0.0123 0.0017 ‐0.0485 0.0133 0.1166 (‐2.10**) (‐0.25) (5.85**) (‐8.00**) (‐2.06**) 0.0079 0.0023 ‐0.0006 0.0075 0.0817 (‐1.61) (‐0.31) (0.17) (‐5.02**) (‐1.60) Difference (JI) Difference (SI) 34.68** 35.75** Notes 1
The z‐statistics are derived from the non‐parametric Wilcoxon Signed Ranks tests; 2 The Chi‐square values are derived from the non‐parametric Kruskal‐Wallis tests; ** ‐ indicates two‐tailed significance at the 5% level. 36
Table 13
Comparison of performance measures (“composite” versus “business”) in five
randomized SEG-CRER portfolios
Monthly return series
Performance measure
Median return Total risk (Standard deviation) Systematic risk (Beta) Jensen index (JI) Sharpe index (SI) Median return Total risk (Standard deviation) Systematic risk (Beta) Jensen index (JI) Sharpe index (SI) Median return Total risk (Standard deviation) Systematic risk (Beta) Jensen index (JI) Sharpe index (SI) Median return Total risk (Standard deviation) Systematic risk (Beta) Jensen index (JI) Sharpe index (SI) Median return Total risk (Standard deviation) Systematic risk (Beta) Composite
Business
“Within” SEG‐CRER Portfolio P1 0.0065 ‐0.0046 0.1218 0.1197 0.6165 0.6425 0.0131 0.0025 0.0989 ‐0.0220 P2 0.0068 ‐0.0025 0.1276 0.1250 0.7180 0.7600 0.0139 0.0043 0.0790 ‐0.0212 P3 0.0055 ‐0.0030 0.1226 0.1201 0.7532 0.7902 0.0154 0.0063 0.0781 ‐0.0204 P4 0.0064 ‐0.0022 0.1231 0.1207 0.7408 0.7740 0.0083 0.0005 0.0708 ‐0.0215 P5 0.0048 ‐0.0054 0.1491 0.1470 0.8728 0.8778 Differences
z-statistic1
0.0112 0.0021 ‐0.0260 0.0106 0.1210 (‐2.50**) (‐0.63) (5.33**) (‐6.44**) (‐2.54**) 0.0093 0.0026 ‐0.0420 0.0097 0.1002 (‐1.91*) (‐0.63) (6.62**) (‐5.87**) (‐1.91*) 0.0085 0.0025 ‐0.0370 0.0091 0.0985 (‐1.66) (‐0.61) (6.16**) (‐5.57**) (‐1.76) 0.0086 0.0024 ‐0.0332 0.0078 0.0924 (‐1.93) (‐0.57) (6.23**) (‐7.64**) (‐1.87*) 0.0103 0.0021 ‐0.0051 (‐2.32**) (‐0.40) (0.51) Jensen index (JI) 0.0147 0.0048 0.0098 (‐5.93**) Sharpe index (SI) 0.0812 ‐0.0189 0.1000 (‐1.98**) “Across” 5 SEG‐CRER portfolios Chi‐square value2 Difference (median return) 3.19 Difference (total risk) 0.54 Difference (beta) 3.51 Difference (JI) Difference (SI) 2.58 3.21 Notes: For companies in each retail segment (SEG), five portfolios of approximate equal size are formed in descending order of CRER. In other words, there will be CRER1, CRER2, CRER3, CRER4 and CRER5 for each retail segment portfolio. Then, the same “CRER” portfolios in the nine SEG portfolios (i.e. CRER1 in SEG1, CRER1 in SEG2 … CRER1 in SEG9) are combined to form their respective randomized portfolios (i.e. SEG‐CRER1). Therefore, in order to study the real estate effect on the five performance measures while controlling for the segment effect, a new set of five randomised SEG‐CRER portfolios (P1, P2, P3, P4 AND P5) are formed with different CRER but randomized in term of retail segment. The CRER of the five SEG‐CRER portfolios are: P1 (60.19%), P2 (44.62%), P3 (34.77%), P4 (25.73%) and P5 (13.46%). 1 The Z‐statistics are derived from the non‐parametric Wilcoxon Signed Ranks tests; 2 The Chi‐square values are derived from the non‐parametric Kruskal‐Wallis tests. **, * ‐ indicates two‐tailed significance at the 5% and 10% level respectively 37
Table 14
Comparison of performance measures (“composite” versus “business”) in five
randomized SIZE-CRER portfolios
Monthly return series
Performance measure
Differences
z-statistic1
‐0.0040 0.1046 0.5324 0.0036 ‐0.0237 0.0109 0.0014 ‐0.0210 0.0099 0.1203 (‐7.06**) (‐8.82**) (3.18**) (‐6.23**) (‐7.80**) 0.0055 0.1207 0.6599 ‐0.0021 0.1190 0.6891 0.0076 0.0017 ‐0.0351 (‐5.07**) (‐9.14**) (2.93**) 0.0122 0.0735 0.0052 ‐0.0218 0.0070 0.0953 (‐4.87**) (‐6.13**) ‐0.0030 0.1229 0.6860 0.0031 ‐0.0209 0.0077 0.0019 ‐0.0555 0.0069 0.0862 (‐5.17**) (‐9.14**) (4.13**) (‐4.56**) (‐6.42**) ‐0.0025 0.1287 0.8655 0.0075 ‐0.0190 0.0103 0.0015 ‐0.0243 0.0099 0.1024 (‐5.57**) (‐9.14**) (3.71**) (‐5.45**) (‐6.40**) ‐0.0038 0.1352 1.0201 0.0096 0.0116 0.0010 ‐0.0218 0.0105 (‐6.55**) (‐9.19**) (3.54**) (‐6.00**) 0.0891 ‐0.0189 “Across” 5 SIZE‐CRER portfolios 0.1080 (‐6.96**) Composite
Business
“Within” SIZE‐CRER Portfolio P1 Median return Total risk (Standard deviation) Systematic risk (Beta) Jensen index (JI) Sharpe index (SI) 0.0068 0.1060 0.5114 0.0134 0.0966 P2 Median return Total risk (Standard deviation) Systematic risk (Beta) Jensen index (JI) Sharpe index (SI) P3 Median return Total risk (Standard deviation) Systematic risk (Beta) Jensen index (JI) Sharpe index (SI) 0.0047 0.1248 0.6305 0.0100 0.0653 P4 Median return Total risk (Standard deviation) Systematic risk (Beta) Jensen index (JI) Sharpe index (SI) 0.0078 0.1302 0.8412 0.0174 0.0834 Median return Total risk (Standard deviation) Systematic risk (Beta) Jensen index (JI) Sharpe index (SI) 0.0078 0.1362 0.9982 0.0201 P5 Chi‐square value2 Difference (median return) 8.24* Difference (total risk) 8.39* Difference (beta) 3.59 Difference (JI) Difference (SI) 6.08 6.43 Notes: The 556 companies are first ranked in descending order of their average market capitalization and grouped into five SIZE portfolios. Then, within each SIZE group, five portfolios of approximate equal number are formed in descending order of CRER. The same “CRER” portfolios within the five SIZE portfolios (i.e. CRER1 in SIZE1, CRER1 in SIZE2, CRER1 in SIZE3, CRER1 in SIZE4 and CRER1 in SIZE5) are combined to form their respective randomized portfolios (i.e. SIZE‐CRER1). The CRER of the five SIZE‐CRER portfolios are: P1 (67.53%), P2 (45.47%), P3 (32.21%), P4 (21.99%) 1
2
and P5 (11.60%). The Z‐statistics are derived from the non‐parametric Wilcoxon Signed Ranks tests; The Chi‐square values are derived from the non‐parametric Kruskal‐Wallis tests. **, * ‐ indicates two‐tailed significance at the 5% and 10 % level respectively 38
Table 15
Comparison of quarterly performance measures (“composite” versus “business”) in
five country portfolios (CRE performance benchmark: pure direct property index)
Monthly return series
Performance measure
Composite
Business
Differences
z-statistic1
0.0352 0.0043 0.0047 0.0397 0.1876 (‐9.43**) (‐12.74**) (‐6.62**) (‐10.52**) (‐10.94**) 0.0369 0.0049 0.0354 0.0485 0.2734 (‐3.84**) (‐5.71**) (‐2.05**) (‐4.07**) (‐4.67**) “Within” Country Portfolio USA (NCREIF index) Median return Total risk (Standard deviation) Systematic risk (Beta) Jensen index (JI) Sharpe index (SI) Median return Total risk (Standard deviation) Systematic risk (Beta) Jensen index (JI) Sharpe index (SI) 0.0374 0.0022 0.2438 0.2395 0.9751 0.9705 0.0731 0.0334 0.1555 ‐0.0321 UK (IPD index) 0.0391 0.0022 0.2011 0.1962 0.9184 0.8830 0.0337 ‐0.0147 0.2353 ‐0.0381 Australia (Property Council of Australia Index) Median return 0.0429 0.0131 0.0298 Total risk (Standard deviation) 0.1846 0.1823 0.0023 Systematic risk (Beta) 0.9246 0.9082 0.0165 Jensen index (JI) ‐0.0128 ‐0.0567 0.0439 Sharpe index (SI) 0.2944 ‐0.0426 0.3370 Hong Kong (Valuation and Rating Department’s Office Property Index) Median return 0.0009 ‐0.0216 0.0225 Total risk (Standard deviation) 0.3122 0.3046 0.0077 Systematic risk (Beta) 1.1668 1.1931 ‐0.0263 Jensen index (JI) 0.0438 0.0199 0.0240 Sharpe index (SI) 0.1479 ‐0.0248 0.1727 Singapore (Urban Redevelopment Authority’s Office Property Index) Median return 0.0296 ‐0.0172 0.0468 Total risk (Standard deviation) 0.1913 0.1874 0.0039 Systematic risk (Beta) 0.6430 0.6639 ‐0.0208 (‐1.73) (‐3.30**) (‐1.10) (‐2.29**) (‐2.79**) Jensen index (JI) 0.0345 Sharpe index (SI) Chi‐square value2 Difference (median return) 1.40 (‐1.38) (‐4.11**) (1.35) (‐1.61) (‐2.58**) (‐2.90**) (‐3.41**) (1.25) ‐0.0011 0.0357 (‐1.99**) 0.2564 ‐0.0376 “Across” 5 Country portfolios 0.2940 (‐3.07**) Difference (total risk) 4.61 Difference (beta) 14.26** Difference (JI) Difference (SI) 11.40** 3.71 Notes: ** ‐ indicates two‐tailed significance at the 5% level 39
Table 16 Cross‐sectional univariate regression coefficients between incremental stock market performances and CRER: 2001‐2006 Sample Excess return ‐ CRER Excess risk ‐ CRER Excess beta – CRER Excess SI ‐ CRER Excess JI – CRER All retailers Asian firms European firms North American firms Firms (SIC52) Firms (SIC53) Firms (SIC54) Firms (SIC55) Firms (SIC56) Firms (SIC57) Firms (SIC58) Firms (SIC59) ‐0.0044 (t = ‐1.51) ‐0.0071 (t = ‐1.19) 0.0019 (t = 0.27) ‐0.0091 (t = ‐2.57**) ‐0.0024 (t = ‐0.14) ‐0.0155 (t = ‐1.23) 0.0016 (t = 0.23) ‐0.0020 (t = ‐0.17) ‐0.0048 (t = ‐0.33) 0.0376 (t = 2.04**) 0.0051 (t = 0.85) ‐0.0084 (t = ‐1.18) 0.00013 (t = 0.20) 0.0026 (t = 1.49) ‐0.00035 ( t = ‐0.59) ‐0.00049 (t = ‐0.53) ‐0.00084 (t = ‐0.77) 0.00079 (t = 0.31) 0.00007 ( t = 0.05) ‐0.00028 (t = ‐0.22) 0.0019 (t = 0.55) 0.00042 (t = 0.22) ‐0.0029 (t =‐1.50) 0.0012 (t = 1.23) ‐0.0040 (t = ‐0.17) 0.0402 (t = 0.63) ‐0.0329 (t = ‐1.35) ‐0.0370 (t = ‐1.56) 0.0137 (t = 0.26) ‐0.1410 (t = ‐1.86*) 0.0737 (t = 0.74) 0.0995 (t = 0.83) 0.1443 (t = 2.12**) ‐0.2993 (t = ‐2.12**) ‐0.0067 ( t = ‐0.14) ‐0.0252 (t = ‐0.31) 0.0042 (t = 0.16) ‐0.0174 (t = ‐0.29) 0.0502 (t = 0.80) ‐0.0165 (t = ‐0.51) 0.0277 (t = 0.16) ‐0.0709 (t = ‐0.60) 0.0856 (t = 0.94) 0.0326 (t = 0.33) ‐0.0697 (t = ‐0.63) 0.2934 (t = 1.97*) 0.0322 (t = 0.53) 0.0169 (t = 0.27) ‐0.0055 (t = ‐1.69*) ‐0.0042 (t = ‐0.65) ‐0.0013 (t = ‐0.19) ‐0.0122(t = ‐2.73***) ‐0.0031 (t = ‐0.20) ‐0.0175 (t = ‐1.41) 0.0091 (t = 1.27) 0.00057 (t = 0.06) ‐0.0076 (t = 0.51) 0.0290 (t = 1.70*) 0.0044 (t = 0.56) ‐0.0093 (t = ‐1.32) Notes: Excess stock market performances refers to the performance differences (represented by return, total risk, beta, SI and JI) between the “composite” and “business” return series; CRER is the property, plant and equipment divided by total tangible assets. The numbers reported in columns 2 ‐5 are the CRER coefficients and the t‐statistics (in parenthesis); ***, **, * ‐ indicate two tailed significance at the 1, 5 and 10 percent levels. 40
Table 17 Results of Simultaneous Equation Estimation: 2001‐2006 Explanatory variables/ Adjusted R2 Full sample Asian sample European sample 2
7
r 1
s 1
North American sample CRER j  v 0  v1 LnSales j  v 2 (TDEBTR ) j  v 3 ( BV / MV ) j   wr DREG r   ws DSEG s   j
Intercept LnSales Leverage (TDEBTR) Growth (BV/MV) DREG (dummy) – Asia DREG (dummy) –Europe DSGE (dummy) – SIC53 DSGE (dummy) – SIC54 DSGE (dummy) – SIC55 DSGE (dummy) – SIC56 DSGE (dummy) – SIC57 DSGE (dummy) – SIC58 DSGE (dummy) – SIC59 Adjusted R2 0.1630 0.0088(t =1.87*) 0.1206(t =2.74***) 0.0031 ‐0.0538(t = ‐3.03***) ‐0.0309 0.0222 0.0560 ‐0.0657 ‐0.0673 ‐0.0921(t = ‐1.75*) 0.1665(t =3.38***) ‐0.1592(t = ‐3.27***) 32.0% ‐0.1470 0.0175(t =2.29**) 0.1763 (t = 2.77***) ‐0.0044 ‐ ‐ 0.1536(t=1.65*) 0.0949 0.0543 0.0037 0.0032 0.0857 ‐0.0378 16.5% (1)
0.4796 (t = 1.66*) 0.0011 ‐0.0076 0.0637 (t = 2.04**) ‐ ‐ ‐0.1770 ‐0.1080 ‐0.2514(t= ‐1.84*) ‐0.2321(t=‐2.01**) ‐0.3105(t=‐2.62***) 0.0491 ‐0.3408 (t=‐3.11***) 32.3% 0.0029 0.0165 (t = 2.84***) 0.0359 ‐0.0064 ‐ ‐ 0.0030 0.1402 (t = 2.22**) ‐0.1224 (t = ‐1.91*) ‐0.0248 ‐0.0521 0.3199 (t = 5.81***) ‐0.1496 (t = ‐2.75***) 60.1% 2
7
r 1
s 1
Incremental (Re turn j )  a 0  a1 CRER j  a 2 Ln( MV j )  a3 (TDEBTR ) j  a 4 ( BV / MV ) j   br DREGr   bs DSEGs   j
Intercept CRER Size (LnMV) Leverage (TDEBTR) Growth (BV/MV) DREG (dummy) – Asia DREG (dummy) –Europe DSGE (dummy) – SIC53 DSGE (dummy) – SIC54 DSGE (dummy) – SIC55 DSGE (dummy) – SIC56 DSGE (dummy) – SIC57 DSGE (dummy) – SIC58 DSGE (dummy) – SIC59 Adjusted R2 (2)
0.0066 ‐0.0027 ‐0.0227 ‐0.0037 ‐0.0058 0.0066 0.0252 (t = 2.30**) ‐0.0102 0.00060 (t = 1.73*) ‐0.0055 ‐0.00025 ‐0.0048 ( t = ‐ 3.28***) ‐0.0020 ‐0.0052 ‐0.0021 0.0033 0.0019 ‐0.0075 (t = ‐1.70*) ‐0.0031 ‐0.00001 8.0% 0.0017(t=2.39**) ‐0.0017 ‐0.00003 ‐ ‐ ‐0.0029 ‐0.00069 0.0026 0.0074 ‐0.0033 ‐0.0016 0.0033 5.2% 0.0017 (t=1.86*) ‐0.0161 ‐0.0020 ‐ ‐ ‐0.0089 ‐0.0121 0.0176(t=1.74*) ‐0.0007 ‐0.0156(t=1.72*) ‐0.0105 ‐0.0079 12.7% ‐0.00019 ‐0.0124 (t = ‐2.13**) 0.0021 ‐ ‐ ‐0.0058 0.0034 0.0054 0.0009 ‐0.0065 0.0019 0.0008 8.3% 41
Table 17 Results of Simultaneous Equation Estimation: 2001‐2006 (Continued) Explanatory variables /Adjusted R2 Full sample Asian sample European sample North American sample 2
7
r 1
s 1
Incremental ( Risk ) j  c0  c1 CRER j  c 2 Ln( MV j )  c3 (TDEBTR ) j  c 4 ( BV / MV ) j   d r DREGr   d s DSEGs   j
Intercept CRER Size (LnMV) Leverage (TDEBTR) Growth (BV/MV) DREG (dummy) – Asia DREG (dummy) –Europe DSGE (dummy) – SIC53 DSGE (dummy) – SIC54 DSGE (dummy) – SIC55 DSGE (dummy) – SIC56 DSGE (dummy) – SIC57 DSGE (dummy) – SIC58 DSGE (dummy) – SIC59 Adjusted R2 0.0031 ( t = 2.01**) ‐0.000032 0.0131 (t = 3.28***) 0.00093 0.0037 ‐0.0012 ‐0.0002 ‐0.00079 ‐0.00013( t =‐1.98**) 0.00059 ‐0.00044(t=‐2.77***) 0.0013(t=4.47***) 0.00038 0.0019(t=2.34**) 0.00038 ‐0.000021 0.00041 ‐0.00026 0.00013 ‐0.00014 10.6% ‐0.00052(t=‐2.96***) ‐0.00082 ‐0.0010(t=‐3.74***) ‐ ‐ 0.0025 ‐0.00022 ‐0.0011 ‐0.00012 ‐0.00069 ‐0.00095 ‐0.0010 16.4% ‐0.00017(t=‐1.71*) 0.00089 0.000026 ‐ ‐ 0.00053 0.00099 0.0025(t=2.35**) 0.00017 0.00054 0.0016(t=1.82*) 0.00035 10.1% 0.00002 0.00065 0.00067 (t = 2.61***) ‐ ‐ ‐0.0003 0.00048 ‐0.00001 0.00056 ‐0.00029 0.00011 ‐0.00025 5.8% 2
7
r 1
s 1
Incremental ( Beta) j  e0  e1 CRER j  e2 Ln( MV j )  e3 (TDEBTR ) j  e4 ( BV / MV ) j   f r DREGr   f s DSEGs   j
Intercept CRER Size (LnMV) Leverage (TDEBTR) Growth (BV/MV) DREG (dummy) – Asia DREG (dummy) –Europe DSGE (dummy) – SIC53 DSGE (dummy) – SIC54 DSGE (dummy) – SIC55 DSGE (dummy) – SIC56 DSGE (dummy) – SIC57 DSGE (dummy) – SIC58 DSGE (dummy) – SIC59 Adjusted R2 (3)
(4)
0.0728 ‐0.0083 0.3042 0.0640 ‐0.1977 (t = ‐2.36**) ‐0.0236 0.0083 ‐0.0262 ‐0.0033 ‐0.0813 (‐2.48**) ‐0.0127 (t = ‐1.66*) ‐0.0286(t=‐2.21**) 0.0028 0.0401 0.0272 0.0502 0.0156 0.0177 0.0206 0.0180 3.3% ‐0.0188(t=‐2.00**) ‐0.0193(t=‐2.67***) ‐0.0246 (t =1.71*) ‐ ‐ 0.1241 0.1153 0.1717(t=1.73*) 0.0921 0.0770 0.1002 0.0876 7.9% 0.0086(t=2.35**) 0.0201 0.0114 ‐ ‐ ‐0.0289 ‐0.0332 ‐0.0677(t=‐1.75*) 0.0233 0.0300 ‐0.0008 ‐0.0005 8.9% ‐0.0008 ‐0.0051 (t = ‐2.03**) ‐0.0036 ‐ ‐ 0.0127 ‐0.0016 0.0004 ‐0.0108 ‐0.0048 ‐0.0058 0.0032 4.4% 42
Table 17 Results of Simultaneous Equation Estimation: 2001‐2006 (Continued) Explanatory variables /Adjusted R2 Full sample Asian sample European sample North American sample 2
7
r 1
s 1
Incremental ( SI ) j  g 0  g1 CRER j  g 2 Ln( MV j )  g 3 (TDEBTR ) j  g 4 ( BV / MV ) j   hr DREGr   hs DSEGs   j
Intercept CRER Size (LnMV) Leverage (TDEBTR) Growth (BV/MV) DREG (dummy) – Asia DREG (dummy) –Europe DSGE (dummy) – SIC53 DSGE (dummy) – SIC54 DSGE (dummy) – SIC55 DSGE (dummy) – SIC56 DSGE (dummy) – SIC57 DSGE (dummy) – SIC58 DSGE (dummy) – SIC59 Adjusted R2 0.0630 0.0145 ‐0.2816 (t = ‐1.80*) 0.0044 0.0255 0.1164 0.2315 (t = 2.86***) ‐0.0570 0.0075(t=2.49**) ‐0.0824(t=‐2.55**) ‐0.0053 ‐0.0141 0.0206 ‐0.0869(t=‐2.37**) ‐0.0428 0.0124 ‐0.0345 ‐0.1035(t=‐2.67***) ‐0.0719(t=‐1.98**) ‐0.0589(t=‐1.65*) 8.0% 0.0247(t=3.84***) ‐0.0675 0.0031 ‐ ‐ ‐0.1081 ‐0.0732 ‐0.0157 ‐0.0189 ‐0.0116 ‐0.0851 ‐0.0594 10.2% 0.0157 (t = 1.72*) ‐0.2170 (t = ‐2.06**) ‐0.0316 ‐ ‐ ‐0.0949 ‐0.1035 0.1855 (t = 1.81*) ‐0.0460 ‐0.1163 ‐0.1328 (t = ‐1.76*) ‐0.0895 13.5% ‐0.0012 ‐0.0834 ( t = ‐2.24**) 0.0061 ‐ ‐ ‐0.0684 0.0132 0.0186 ‐0.0303 ‐0.0783 (t = ‐2.05**) ‐0.0130 ‐0.0502 7.1% 2
7
r 1
s 1
Incremental ( JI ) j  g 0  g1 CRER j  g 2 Ln( MV j )  g 3 (TDEBTR ) j  g 4 ( BV / MV ) j   hr DREGr   hs DSEG s   j
Intercept CRER Size (LnMV) Leverage (TDEBTR) Growth (BV/MV) DREG (dummy) – Asia DREG (dummy) –Europe DSGE (dummy) – SIC53 DSGE (dummy) – SIC54 DSGE (dummy) – SIC55 DSGE (dummy) – SIC56 DSGE (dummy) – SIC57 DSGE (dummy) – SIC58 DSGE (dummy) – SIC59 Adjusted R2 (5)
(6)
0.0079 ‐0.0031 ‐0.0376 ( t = ‐2.14**) 0.00018 ‐0.0082 0.0057 0.0309 (t = 2.76***) ‐0.0136 (t = ‐1.99**) 0.00064(t =1.82*) ‐0.0076(t =‐1.98**) ‐0.0020 ‐0.0050(t=‐3.32***) ‐0.0023 ‐0.0075(t=‐1.74*) ‐0.0034 0.0028 0.00079 ‐0.0087(t=‐1.80*) ‐0.0056 ‐0.0021 9.2% 0.0024(t=3.14***) ‐0.0034 0.00098 ‐ ‐ ‐0.0055 ‐0.0027 0.0029 0.0066 ‐0.0049 ‐0.0016 0.0022 6.9% 0.0019 (t = 2.03**) ‐0.0204 (t = ‐1.98**) ‐0.0013 ‐ ‐ ‐0.0125 ‐0.0141 (t = ‐1.69*) 0.0139 ‐0.0035 ‐0.0128 ‐0.0142 (t =‐1.71*) ‐0.0089 12.8% ‐0.00032 ‐0.0116 (t = ‐1.94*) 0.00019 ‐ ‐ ‐0.0066 0.0028 0.0047 ‐0.0003 ‐0.0086 ‐0.00028 ‐0.0019 9.1% 43
Notes for Exhibit 18
Notes: This table reports the estimation results for the full sample and three regional (Asian, European and North American) subsamples. The five excess performance indicators (return, risk, beta, SI and JI) and CRER are the endogenous variables. CRER is the predicted value of the percentage of real estate obtained from the first equation of the system estimation. The natural log of sales (lnSales) is used as a proxy for operating size; firm size is represented by the natural log of market capitalization (lnMV) of each firm; leverage is represented as the percentage of debt to total tangible assets (TDEBTR); the ratio of book value to market value (B/M) captures the perceived growth opportunities of the firm. The regional dummies (DREG) and segment dummies (DSEG) controls for cross‐industrial retail variations (North Americas and SIC52 are the respective references). The six equations are estimated via a simultaneous equation framework using iterative 3SLS technique available from E‐view 6. Only significant t‐values are reported in parenthesis. ***, **, * ‐ denotes two‐tailed significance at the 1%, 5% and 10% levels respectively. 44
Table 18 CRER coefficients in the system multivariate regressions (Equations 2 ‐6) Segment (No of valid Equation 2 Equation 3 Equation 4 observations) Dependent variable: Dependent variable: Dependent variable: Incremental RETURN Incremental RISK Incremental BETA SIC52 (13) SIC53 (84) SIC54 (43) SIC55 (40) SIC56 (78) SIC57 (44) SIC58 (96) SIC59 (110) ‐0.0343 (t = ‐2.48***) ‐0.0156 ‐0.0021 ‐0.0026 ‐0.0044 0.0323 (t = 1.89*) ‐0.0074 ‐0.0049 0.00012 0.00053 0.00062 0.00021 0.0018 0.00036 0.00020 0.0011 ‐0.0697 ‐0.0770 0.1115 0.0215 0.1544 (t = 1.95*) ‐0.2594 (t = ‐2.05**) 0.0014 0.0017 Equation 5 Dependent variable: Incremental SI ‐0.2563 (t = ‐2.00*) ‐0.0734 0.0928 ‐0.0194 ‐0.0447 0.2823 (t = 2.08**) ‐0.0621 0.0472 Equation 6 Dependent variable: Incremental JI ‐0.0333 (t = ‐2.84***) ‐0.0181 0.0065 0.0004 ‐0.0068 0.0272 (t = 1.67*) ‐0.0035 ‐0.0049 Notes: This table reports the estimation results (the CRER coefficients) for the relationship between CRER levels and incremental stock market performances (Equations 2 ‐6, see also Table 16) for retails firms from the nine SIC segments. CRER is the predicted value of the percentage of real estate obtained from equation 1 of the system estimation; the six equations are estimated via a simultaneous equation framework using iterative 3SLS technique available from E‐view 6. Only significant t‐values are included in the parenthesis ***, **, * ‐ indicate two‐tailed significance at the 1, 5 and 10 percent levels respectively. Table 19 CRER coefficients in the system multivariate regressions (Equations 2 ‐6): direct property market performance benchmarks System of equations Equation 2 Equation 3 Equation 4 Equation 5 Equation 6 (No of valid Dependent variable: Dependent variable: Dependent variable: Dependent variable: Dependent variable: observations) Incremental RETURN Incremental RISK Incremental BETA Incremental SI Incremental JI Overall (290) Asia (Australia, Hong Kong and Singapore) (48) Europe (UK) (38) North America (US) (204) 0.0019 0.0921 (t = 1.88*) ‐0.0029 ‐0.0095 ‐0.0426 (t = ‐2.24**) 0.0492 0.0312 0.3592 ‐0.0159 0.0929 0.0348 ‐0.0266 ‐0.0149 ‐0.0003 ‐0.2202 (t = ‐2.17**) 0.0028 0.3241 ‐0.1275 ‐0.0560 ‐0.0292 Notes: This table reports the estimate results (the CRER coefficients) for the relationship between CRER levels and incremental stock market performances (Equations 2 ‐
6, see also Table 16) derived using direct property benchmarks (from Table 15). The overall system includes only 5 countries; i.e. from Asia (Australia, Hong Kong and Singapore), Europe (UK) and North America (US). CRER is the predicted value of the percentage of real estate obtained from equation 1 of the system estimation; the six equations are estimated via a simultaneous equation framework using iterative 3SLS technique available from E‐view 6. Only significant t‐values are included in the parenthesis; **, * ‐ indicate two‐tailed significance at the 5 and 10 percent levels respectively. 45