ARE CORPORATE PROPERTIES UNDERVALUED? EVIDENCE
FROM INTERNATIONAL RETAIL COMPANIES1
Working paper by Kim Hiang Liow, National University of Singapore; to be presented at the 25th Annual ARES Meeting, April 1‐4 2009, Monterey Marriott, Monterey, California Abstract This paper examines whether properties owned by retailers are undervalued by the stock market. We propose a theoretical CRE valuation framework to place properties in the context of core business and use the model to assess whether the values of CRE assets are fully reflected in earnings (firm) valuation. Our results from a simultaneous equations model document the empirical validity of the “latent assets” hypothesis in CRE literature and highlight the importance of the joint earnings and property valuation in corporate environment. 1. Introduction The use of land, also known as real estate, as part of business operations and associated activities, is referred to as corporate real estate (CRE). Capital markets today are putting tremendous pressure on corporate management to maximize shareholders’ value. As CRE is a major component in some business firms’ financial statements, one question of interest is: given that management is committed to increasing shareholders’ wealth, what role, if any, can CRE play in achieving that aim? 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 1
The 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. 1
CRE at current values, there are arbitrage opportunities either for companies or in the stock and real estate markets. The Arthur Anderson and Rosen (2000) surveys suggest that CRE may be a sub‐optimally managed asset. This notion has been supported by Brennan (1990) who has used the term “latent assets” to describe situations where the stock price of a firm does not reflect the true value of all the assets it owns. Real estate holding as a class of corporate assets is a good example. Brueggeman, Fisher and Porter (1990) have reviewed real estate’s role in the corporate restructuring of the 1980’s, and they have shown that rational investors may value companies with large real estate holdings at prices well below break‐up value, even when stock markets are efficient. They state that “……..the value of CRE is “hidden” from investors and therefore not fully reflected in share prices.” The market‐to‐book value ratio (M/B) measures the relative market valuation of a firm’s assets. To the extent that its property assets are undervalued by the stock market, the disparity (gap) between the market value and the book value of the firm’s assets is indicated by its higher B/M ratio or even M/B ratio of less than one. This measure has been popularly discussed in the USA CRE literature as well as in corporate finance. This paper will examine whether the values of properties owned by retailers are fully reflected in share prices (i.e. firm valuation). No formal examination of this issue has been reported although corporate management generally agrees that there is some extent of market undervaluation in corporate properties. We contribute to this scanty literature by focusing on listed retail firms because real estate forms a significant proportion of the total assets in the balance sheet of many leading retailers. Further, real estate has always been recognized as a key value driver in the retail industry. Separately, Guy (1999) has pointed out that many retailers’ 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. Nevertheless, it should be noted that it is probably very 2
difficult (if not impossible) to separate the performance of the retail firms’ core business from the contribution of the real estate owned by the firm. That is, whether the firm is more (or less) valuable because it merely owns real estate; or is the real estate more (or less) valuable because of the real estate’s synergy (or lack thereof) with the firm? This study will not make a distinction between these two groups of firms. Our contributions are two folds. First we propose a theoretical CRE valuation framework to place property in the context of core business on the belief that property value in consumer‐led sectors such as retailing is largely driven by earnings and alternative use. By positioning the retail firms in one of the four valuation segments depicted by the framework, investors will be able to assess whether corporate properties are undervalued in firm valuation. We use this framework to position a sample of retail firms in the four valuation segments and to provide a basis for the empirical examination. Second, we appeal to a simultaneous equations model to consider the relationship between CRE ownership and firm valuation (i.e. book‐to‐market valuation ratios ‐ B/M) The simultaneous equations approach is appropriate because CRE investment decisions, firms’ key financial characteristics (such as firm size, gearing and profitability), earnings and asset valuation and country/segment differences are intertwined and are considered in the context of “whole” firm (Miles et al. 1989). Because of their inter‐relationships, examination of the components in isolation misses feedback effects and may result in erroneous inferences. Using a sample of listed retail firms from the world three developed economies, our results document the empirical validity of the “latent assets” hypothesis in CRE literature and highlight the importance of the joint earnings and property valuation in corporate environment This research is timely when capital markets are in constrained cycles, and with many firms’ 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 3
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. This paper is organized as follows. Section 2 presents a brief literature review. The third section describes a theoretical framework for corporate property valuation. Section 4 describes the retail sample and their real estate ownership characteristics. In the fifth section, the main empirical procedures are explained. Thereafter, we discuss the empirical results and implications in Section 6. The paper ends with a summary of the key findings in Section 7. 2. Related Literature The concept of shareholder value provides a direct capital market indicator to demonstrate to management how real estate could affect the health of the company (Louargand 1999). There are two ways in which real estate might affect firm valuation. Firstly, occupancy costs play a very important role in determining the cost base and hence the net operating profit of the firm. The proportion of fixed occupancy cost to total business cost affects the profitability and return of the firm through the simple equation: Profit = revenue – cost. The second way is through its cost of capital. The presence of real estate on balance sheet could mean a high cost of capital that includes a substantial risk premium to account for higher operating leverage arising from the ownership of real estate. Another source of risk comes from the increased financial leverage as a result of financing CRE using debt. Financial theory postulates that cost of capital is the weighted average of cost of equity and cost of debt of the firm. It is able to influence the systematic risk and hence the pricing of the firm in the stock market. Higher CRE ownership normally suggests that the firm is likely to have a higher debt (i.e. high‐geared). As debt financing has the effect of leveraging (positively or negatively) any changes in the company’s returns, a higher CRE / higher‐geared firm may be riskier than a lower CRE / lower‐geared firm, and consequently, results in unfavorable stock market valuation. 4
Corporate management has long argued that there is significant “hidden value” in real estate that is not reflected in a company’s share price. One main reason is that properties that were purchased years ago are carried on balance sheets for a fraction of their market value. In Brennan (1990)’s terminology, real estate would be categorized as “latent assets” where value of assets owned by a corporation might not be accurately reflected in its share prices. Brueggeman et al (1990) have pointed out that the market value of property assets are typically governed by factors very different from those that drive the value of a firm’s operating business. For example, a unique quality of real estate, as a corporate asset, is that it can function as either a production factor or a profit centre and provides flexibility not common to other corporate assets. Other reasons for the perceived undervaluation of corporate properties include differences in the valuation methods between stock and properties, high information cost due to thin trading, uncertainty as to the true property asset values due to historical cost convention or inefficiency in the management of CRE. However, the efficient market hypothesis (EMH) argues that if the stock market is efficient, then current stock prices should reflect all available public information about a company’s future profitability. Accordingly, current values should be unbiased estimates of the present value of future cash flows which derive the values of CRE assets. Finally, there is also evidence in the literature (Rodriguez and Sirmans, 1996) that decisions concerning CRE such as acquisitions, leasing, dispositions, sell‐offs, sales‐and‐leasebacks and spin‐offs could have significant effects on firm value. As shareholders are concerned with the net present value of the firm’s current and future investment opportunities in order to create value, it follows that non‐real estate firms need to seek and implement feasible CRE asset strategies that would enable investors to explicitly recognize “hidden” real estate values and enhance market valuation of the firms. 3. A theoretical framework of CRE asset valuation 5
For many non‐real estate firms such as retailers, corporate management generally agrees that the significance of their real estate to the stock market might not be so much of the property value effect per se, but rather more of the investors’ expectations on the performance of the company closely tied to the profitability of the trading business. From the valuation perspective, it is however very difficult, if not impossible, to separate the market valuation of properties (
(
BV property
MV property
BVearnings
MVearnings
) from the market valuation of the firm (represented by earnings valuation) ). This valuation task is made even more complicated because the market values of properties are often unobservable. To overcome this problem, we propose a CRE valuation framework in the context of core business, as shown in Exhibit 1. The framework is designed as a plane formed by two decision parameters. The X‐axis represents the earnings valuation of a retail firm and the Y‐axis indicates the CRE valuation line. To the extent that the Net Present Values of the marginal dollar invested in its business and its CRE assets are equal to one, then the two axes might separate the plane into four valuation segments (A, B, C and D) where the relative valuation “premium” and “discount” of firm’s CRE assets against its earnings could be assessed and investigated. (Exhibit 1 here) The significance of the four valuation segments is briefly highlighted below: Segment A – a retail firm in this segment is in an unfavorable position as both its earnings and CRE holdings are not fully reflected in its share prices. In this situation, the value of an asset to a business is its net realizable value (or break‐up value). As the market prices of properties are low, a comprehensive restructured business strategy is likely required with the hope to turn the company around with property policy being a component of the restructuring process if property is a sizable asset in its balance sheet 6
Segment B ‐ A retail firm in this segment faces slower growth prospects and falling margins which depress market valuation of earnings (i.e. at discount) and on the property side too much property for a tired retail formula. Faced with this situation, a plausible strategy for an ailing multiple retailer typical of this segment is to reduce its property holdings through a program of rationalization in order to reduce heavy borrowing and raise cash to inject into the business. For example, a property disposal program will involve selling off property sites and those outlets where the firm no longer sees an adequate return on capital. In addition, those “high‐price” properties might be written down in order to produce surpluses on disposal, thereby boosting the firm’s operating profits to a point where its wishes to signal a major shift in strategy or earnings trends. Exhibit 1 Four Strategic CRE and Earnings Valuation Segments Property valuation ( BV property MV property )
Segment C
(property valuation>1, earnings valuation<=1)
Segment D
(property valuation<=1, earning valuation<=1)
Segment A (property valuation>1, earnings valuation>1)
Earnings (firm) valuation ( BVeaenings MVearnings ) Segment B (property valuation<=1, earnings valuation>1)
7
Segment C ‐ a retail firm in this segment has its CRE holdings undervalued by the stock market. Compared with its good earning performance, one plausible strategy is to create a separate real estate subsidiary to exploit the undervalued CRE assets. In so doing, the firm lets the stock market puts its own value on the properties as a separate business. Segment D – A firm in this segment is in a favorable position as both its earnings and property assets are valued at a premium. This suggests that the firm has a successful trading formula and can access extra property holdings as a strong platform for growth. However, one possible problem with this firm is that some of its “high‐price” stores were not able to achieve the high return on capital expected. Hence the firm has to re‐align use of its properties in the hope of “squeezing” higher profitability from its space portfolio and improving its relative property ratios. Finally, since the market values of properties for individual firms are often not directly observable, we use the market value of firm (i.e. earnings) to proxy for the market value of CRE. This is based on the assumption that for retailers, their MVearnings should usually be greater than the MV property if property ownership is viewed as subsidiary to core business. We apply this theoretical framework as a basis to assess the empirical relationship between firm valuation (proxied by BV
MV )
and CRE ownership for our sample of retail firms. 4. Sample and Data A sample of 326 listed retailers from Japan, the USA and the UK was derived from the Osiris database2 based on SIC primary code classification 52 to 59 3 as of December 2007. The stocks of 2
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. 8
these firms must be continuously traded over the study period from January 2002 through December 2007. Our sampling procedure has survivorship bias as well as liquidity restrictions; but has the advantage of maintaining the identity of the firms throughout the period. Following literature, a corporate real estate ratio (CRER) is derived to measure the trend in relative CRE ownership over a period of six years from 2002‐2007. This CRER divides Osiris’s net property, plant and equipment (NPPE)4 by the book value of a firm’s total tangible 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.5 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 Ambrose (1990), Deng and Gyourko (1999), Seiler et al. (2001) and Brounen et al. (2005) which derived CRER from Compustat, we have to use the NPPE variable which offers the best available proxy from Osiris for an international comparison in the 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 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. In addition, we use the natural log of NPPE (LnNPPE) (book value) of each firm over the same period as an 3
The eight primary SIC retail segments are: SIC52 (Building materials. Hardware, garden supply and mobile home dealers), SIC53 (General merchandise stores), SIC54 (Food stores), SIC55 (Automotive dealers and gasoline service stations), SIC56 (Apparel and accessory stores), SIC57 (Home furniture, furnishings and equipment stores), SIC58 (Eating and drinking places) and SIC59 (Miscellaneous retail) 4
NPPE is equivalent to tangible fixed assets of the firm, having deducted from the historical cost and revaluation of properties, the accumulated depreciation, amortization and depletion. 5
This CRER specification ignores the impact of leasing (i.e. only include owned properties). Many large retailers have long term lease contracts that effectively give them control of (and thus exposure to) real estate; however, this will not appear on the firms’ balance sheets. As our study focuses on the relative market valuation of owned real estate, we do not capture the impact of long‐term leases. 9
alternative CRE indicator. This is because the significance of real estate to a firm can be measured by its relative real estate level (CRER) or absolute dollar level (LnCRE). Further, the CRE literature remains silent as to which indicator (i.e. relative or absolute) is a better measure of a firm’s real estate investment activities. Exhibit 2 reports the sample distribution of average CRE holdings and CRER levels across countries over the six‐year period. Average real estate holdings were between US$95.8m (Japan) and US$183.4m (the USA), and property made up about 38 percent of the 326 retail firms’ total tangible assets. The ANOVA evaluations indicate that country differences are displayed in the absolute; but not the relative CRE levels. However, these results could probably be time or sample specific. In addition, Exhibit 3 compares the CRER levels of the retail firms for the three countries for each of the six‐year period, 2002‐2007. On average, only the UK retail firms reported a decrease in their CRER from 45.2% (2002) to 42.2% (2007) (Exhibits 2 and 3 here) Exhibit 4 shows that the average CRE holdings and the CRER levels varied considerably between different retail segments, even within the retail industry itself. Specifically, the role of CRE varied from around 21.7% for mixed stores to around 57.1% for eating and drinking places (and yet with the smallest CRE holdings of about US$81.67m). The significant ANOVA F‐statistics confirmed that there were marked differences in the absolute and (especially) relative levels of CRE ownership and investment among the eight retail segments. This implies that CRE ownership is a function of retail segment. (Exhibit 4 here) Finally, Exhibit 5 provides a breakdown of the sample retail firms’ CRE portfolios by the usual 20% CRER criterion. Overall, only 12 retail firms (about 3.7%) were extremely “real estate intensive” (CRER Group 1) with at least more than 80% of their resources invested in properties. In contrast, a total of 205 retail firms (about 62.8%) were moderately property intensive with their CRER levels 10
range between 20% and 60% (CRER Groups 3 and 4). The breakdowns are 84 (77.1%), 105 (57.3%) and 16 (47.1%) for Japan, the USA and the UK respectively. (Exhibit 5 here) 5. Methodology Empirically, as real estate and firm size (market value) are correlated, the effect of property on book‐market ratio might be due to the proxy effect of size. To reduce this possibility, we employ the randomization process of Basu (1983).6 Firstly, in each year, three "size" portfolios (MV1, MV2, MV3) were formed, and each of them was further sorted in descending order of CRER (relative) and LnRE (absolute) respectively and divided into four portfolios of approximately equal size (P1, P2, P3 and P4). In other words, we would have MV1P1, MV1P2, MV1P3, MV1P4, MV2P1, MV2P2, MV2P3, MV2P4, MV3P1, MV3P2, MV3P3 and MV3P4, Finally the same "P" portfolios in the three "MV" portfolios (i.e. P1 in MV1, P1 in MV2, P1 in MV3 and so on) were combined to form P*1, P*2, P*3 and P*4. Thus, to study the real estate effect on B/M while controlling for the size effect, a new set of four P* portfolios was formed with different CRER and LnRE levels but randomized in terms of size as measured by market value. The analysis of variance (ANOVA) technique is used to test for the equality of mean B/M across the four sets of randomized property portfolios. This portfolio grouping method did not consider the country and segment differences. 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 consider a simultaneous equations model, using iterative three‐stage least squares (IT3SLS), to investigate whether there is a significant relationship between the B/M valuation of the retail firms and their CRER (and LNCRE) ownership levels. In our context, a simultaneous equations model is appropriate because real estate ownership decisions and corporate financial management are intertwined. A 6
Basu (1983) conducted a study on the relationship between earnings' yield, market value and return for New York Stock Exchange common stocks. The terms 'randomized' and 'unrandomized' are his sorting definition.
11
significant positive relationship between the B/M ratios and real estate variables suggests that the property holdings of retailers might be undervalued by the stock market as higher CRE ownership leads to higher B/M (or lower M/B) valuation. To implement this approach, we first derive the predicted real estate variables ( CRER / LNCRE ) from a regression model using lagged one‐year real estate, a set of two country dummy variables and another set of seven segment dummy variables as instrumental variables. The two equations of the system are specified as: 2
7
r 1
s 1
CRER j  e0  e1 (lagCRER) t 1, j   f i DNATr   f s DSEG s   j
2
7
r 1
s 1
( B / M ) j  a 0  a1 CRER j  a 2 LnMV j  a 3 (TDEBTR) j  a 4 ( ROA) j   c i DNATr   c s DSEG s   j
Where CRER
is the predicted value of percentage of real estate holdings at year t, lagCRER is the one‐period lagged value of CRER7 and B/M is the book/market value ratio. LnMV represents the natural logarithm of market capitalization (proxy for size), ROA is the return on asset (proxy for profitability) and TDEBTR is the percentage of debt to total assets (proxy for financial leverage). DNATr (r = 1,2) are (0,1) dummy variables representing Japan and the USA (relative to the UK); the national dummy is included to account for country fixed‐effect and would provide a good control for differences in accounting methods and lease structure across countries. DSEG s (s=1,2,3,4,5,6,7) are (0,1) dummy variables representing SIC codes 52, 53, 54, 55, 56, 57 and 58 (relative to SIC code 59); this segment dummy variable allows for a determination of possible differences in the CRE ownership by retail segments. Finally  j is the regression error term. We repeat another system estimation using LnCRE (absolute CRE ownership) to search for possible evidence of undervaluation in absolute property holdings. 7
Some CRE investment decisions may also take on a longer planning horizon. Although, we could only afford to consider one‐year lag because of the shorter sample period (only 6‐years) with the study. 12
6. Results Results of the randomization tests are provided in Exhibits 6(a) (CRER) and 6(b) (LnCRE). Over the 6‐year period, there is some evidence of a significant positive relationship between the B/M valuation ratio and average LnCRE across the four property portfolios; i.e. the larger the average LnCRE values, the higher the B/M values. A wider implication from this evidence is that the market valuation of a non‐real estate firm’s property assets may become unfavorable when its real estate ownership (in absolute term) reaches a limit that is considered too high by the market. This situation happens either because the stock market is of the opinion that the firm’s earnings growth is not strong enough to sustain a larger property base or the market does not believe the firm can manage its enlarged property holdings in their highest and best use. Nevertheless, we were unable to derive the same finding for the relationship between B/M and CRER levels. (Exhibits 6a and 6b here) Following the theoretical CRE valuation framework depicted in Exhibit 1, the 326 retail firms were positioned in the four valuation segments based on their six year averages of ( BV property MVearnings ) and ( BVearnings MVearnings ). A point of clarification is needed here. Since the market values of properties for individual firms are not available, we use the market value of firm (i.e. earnings valuation) to proxy for the market value of CRE. This is based on the assumption that for retailers, their MVearnings should usually be greater than the MV property if property ownership is viewed as subsidiary to core business. The results reported in Exhibit 7 indicate that the numbers of retail firms in each valuation segment are: 36 (Segment A), 57 (Segment B), 37 (Segment C) and 196 (Segment D). For our sample of retail firms, it thus appears that the property assets of at least 73 firms (22.4% ‐ from Segments A and C) were clearly undervalued by the stock market. On average, these firms’ shares were selling at approximately between 49.1 percent and 61.8 percent of their book values of properties. The market values of these firms were too heavily dependent on their property values! 13
(Exhibit 7 here) Exhibit 8 reports the simultaneous equations estimates of the relationship between B/M ratios and CRER levels for the overall sample and three national sub‐samples. As the numbers of Equation (1) indicate, all lagged one‐year CRER values are highly significant in predicting the current year CRER levels implying that CRE investment decisions were stretched for at least a year. Except for the USA sub‐samples, the remaining three CRER coefficients are significantly positively related to the B/M ratios in equation 2, implying that the higher the CRER levels, the higher the B/M valuation. The coefficient for the USA’s CRER level is insignificantly positive. As for the three financial variables, firm size (LnMV) is significantly negative in all four cases; higher debt ratios (TDEBTR) are associated with significantly lower B/M values (higher M/B values) in the full sample and two national sub‐samples and profitability (ROA) has a significantly negative impact on B/M except for one national sub‐
samples. The coefficients on the two national dummies and seven segment dummy variables indicate that there are some significant variations in the B/M valuation across Japan, the USA and the UK as well as across the eight retail segments. The adjusted R2 for the four cross‐sectional B/M regressions range between 0.329 and 0.406 suggesting that a moderate portion of the variations in the four cross‐sectional endogenous variables is accounted for with the chosen set of explanatory variables that include the CRE ownership variable (i.e. CRER level). (Exhibit 8 here) The 3SLS estimates, by retail segments, are reported in Exhibit 9. Focusing on the estimated CRER coefficients, they are significantly positive for three retail segments (SIC 53, 54 and 56), insignificantly positive for 4 segments (SIC 52, 53, 58 and 59) and significantly negative for one retail segment (SIC 57). In accordance with the earlier results that different retail segments have different CRER levels, our results here imply that the impact of CRE ownership on B/M ratios is also a function of retail segment membership. Thus it is possible while retail firms in the majority of the retail segments are likely to experience some degree of market undervaluation of properties, firms in one 14
or two other retail segments could have their properties fairly valued or even valued at a premium by the stock market. (Exhibit 9 here) Using an absolute CRE proxy (LnCRE), Exhibit 10 indicates CRE ownership influences significantly retail firms’ B/M valuation positively for the full sample and three national sub‐samples. Similar estimates reported in Exhibit 11 indicate that the coefficients for LNCRE are significantly positive for six retail segments (SICs 54, 55, 56, 57, 58 and 59). Hence, the positive impact of corporate properties on B/M valuation is affirmed. (Exhibits 10 and 11 here) Finally, Exhibit 12 reports the real estate impact of B/M valuation for the four firm portfolios classified using Exhibit 1. As the numbers indicate, the coefficients for LNCRE are significantly positive in all four portfolios. Thus our results imply not only that property values were “discounted” by the stock market in all three countries and in some retail segments, the market undervaluation of properties could happen even to some retailers whose earnings and properties are valued at a premium. As highlighted above, a possible scenario is that these firms’ property portfolios could be highly valuable relative to the current level of earnings achieved by the firm (Segment D ‐ i.e. real estate was too expensive for the business!). If they fail to realign use of their properties to “squeeze” higher profitability from the space portfolio, then its full property values are unlikely to be recognized by stock market investors and reflected in firm valuation. Finally, the results using the CRER levels support the market undervaluation argument for retail firms in Segments A and C. (Exhibit 12 here) In summary, the simultaneous equations results are reasonably conclusive to support the “latent assets” hypothesis for retailers’ CRE. Higher CRE ownership (whether measured in absolute or relative levels) causes larger B/M valuation implying that stock market investors are unlikely to recognize the true value of the corporate properties the business own. While the reasons for this 15
market undervaluation might be different for different firms, it is important for those affected firms to identify their relative valuation premiums or discounts (i.e. property valuation and earnings valuation) at the firm level. In this regard, our Exhibit 1 provides a useful analytical tool. Another lesson to learn from our analysis is that even real estate is a value driver of the retail industry, stock market investors would still penalize those retailers who own significant properties that are probably in excess of their business requirements. The optimal proportion of CRE ownership remains an important strategic decision that these retail firms have to regularly review with the hope to achieve higher stock market recognition in property valuation in the wider context of firm valuation. 7. Conclusion This paper is a contribution to the literature in CRE ownership in an international environment. Motivated by Brenann (1990)’s argument that labeled corporate properties as “latent assets” and lack of academic evidence regarding the relative valuation of corporate properties, we investigate whether the property holdings of a sample of listed retail firms have been and are adequately valued by the stock markets. With an understanding that retailers’ property values and business performance are often inseparable, we develop a theoretical framework that depicts for four corporate property valuation segments and identify the characteristics of property valuation and earnings (firm) valuation in each valuation segment. Our empirical investigation appealed to a simultaneous equations model that placed CRE in the context of “whole” firm and further considered the country and retail segment differences. Overall, a reasonable conclusion emerged from our analysis is the “latent assets” hypothesis put forward by Brennan (1990) is supported at least for our sample of retail firms across country, across retail segment and within the four valuation segments. For those retailers that have a significant CRE portfolio, our evidence indicates since stock market investors are unlikely (not able and not willing) to recognize the true value of corporate properties in firm valuation and in some cases create 16
“disequilibrium” in firm and property valuations, it is important for them to realign their CRE ownership with the hope of maximizing the property contribution to business performance as well as improving the property valuation ratio. Finally, it should be noted that our results could simply be time or sample specific. All else equal, investors might expect better (inferior) property valuation ratio for firms with relatively large real estate holdings during periods of booming (collapsing) real estate markets. Either result should not be blindly assumed to apply out of sample. With longer periods of data available, our analysis can well be extended to other non‐real estate sectors to uncover more empirical evidence to validate the “latent assets” hypothesis in CRE investment. References Ambrose, B.W. (1990), “Corporate real estate’s impact on the takeover market” Journal of Real Estate Finance and Economics 13(4): 307‐324 Anderson and Rosen (2000), “eReal Estate: A Certainty” Arthur Anderson, Chicago, IL Brennan, M. J. (1990) Latent assets, Journal of Finance 45(3), pp. 709‐729 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 Brueggman, W.B., Fisher, J.D. and Porter, D.M. (1990) Rethinking corporate real estate, Journal of Applied Corporate Finance 3(1), pp. 39‐50 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 Fama, E.F. and French, K.R. (1992), “The cross‐section of expected stock returns”, 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 27(6): 237‐245 He, L.T. (2002), “Excess returns of industrial stocks and the real estate factor”, Southern Economic Journal 68(3): 632‐645 Liow, K. H. (1999) Corporate investment and ownership of real estate in Singapore – some empirical evidence, Journal of Corporate Real Estate 1:4, 329‐42 17
Liow, K.H. and Nappi‐Choulet, I. (2008), “A combined perspective of corporate real estate” Journal of Corporate Real Estate 10(1):54‐67 Louargand, M. (1999), Real estate’s influence on enterprise value, Journal of Corporate Real Estate (3): 254‐261 Miles, M., Pringle, J. and Webb, B. (1989) Modeling the corporate real estate decision, Journal of Corporate Real Estate 4(3), 47‐66 Rodriguez, M and Sirmans, CF (1996) Managing corporate real estate: evidence from the capital markets, Journal of Real Estate Literature 4, 13‐33 Seiler, M., A. Chatrath and J. Webb (2001) Real asset ownership and the risk and return to stockholders, Journal of Real Estate Research 22(1/2), 199‐212 Zeckhauser, S. and R. Silverman (1983), "Rediscover your company's real estate" Harvard Business Review, January/February, pp. 111‐117 18
Exhibit 2 Country
Real estate ownership of retail companies: 2002‐2007 No. of companies
Japan USA UK Total Average NPPE
(US$m)
95.72 183.36 128.64 142.18 4.125** 109 183 34 326 ANOVA F Average CRER
0.352 0.387 0.429 0.380 2.109 Notes: Mean NPPE (US$m) = net property plant and equipment Mean CRER = (net property plant and equipment (NPPE)/ (total asset value) ANOVA F – derived from SPSS One‐Way Analysis of Variance Test ** ‐ indicates significance at the 5% level Exhibit 3 Real estate ownership (CRER) of companies by years: 2002 – 2007 Year
2002 2003 2004 2005 2006 2007 Mean ANOVA F Overall
0.388 0.386 0.382 0.378 0.375 0.371 0.380 0.314 Japan
0.351 0.350 0.359 0.356 0.355 0.342 0.352 0.157 USA
0.398 0.398 0.387 0.384 0.378 0.379 0.388 0.272 UK
0.452 0.436 0.424 0.421 0.418 0.422 0.429 0.090 Notes: CRER = corporate real estate ratio ANOVA F – derived from SPSS One‐Way Analysis of Variance Test 19
Exhibit 4 SIC
SIC 5200 – 5271 SIC 5311 – 5399 SIC 5400 – 5499 SIC 5500 – 5599 SIC 5610 – 5699 SIC 5700 – 5736 SIC 5810 – 5813 SIC 5910 – 5949 Overall ANOVA F Real (estate) ownership of retail companies by segment: 2002‐2007 Segments
No. of
companies
Materials and home dealers Departmental stores Food stores Vehicles stores Clothing stores Furniture stores Eating and drinking places Mixed stores ‐ 10 37 27 24 52 28 76 72 326 236 Mean
NPPE
(US$m)
244.15 291.47 328.79 143.21 172.28 136.62 81.67 105.48 142.18 2.999*** Mean
CRER
0.405 0.437 0.483 0.304 0.310 0.289 0.571 0.217 0.380 32.610*** Notes: Mean NPPE (US$m) = net property plant and equipment (million) Mean CRER = (net property plant and equipment (NPPE)/ total asset value) The chi‐square values are derived from Non‐parametric Kruskal‐Wallis tests ANOVA F – derived from SPSS One‐Way Analysis of Variance Test *** ‐ indicates significance at the 1% level Exhibit 5 Country Japan USA UK Overall Distribution of real (estate) ownership profile: 2002‐2007 CRER CRER range No of Mean CRER group companies 1 80%<CRER<=100% ‐ ‐ 2 60%<CRER<=80% 7 0.658 3 40%<CRER<=60% 35 0.476 4 20%<CRER<=40% 49 0.304 5 0% <=CRER<=20% 18 0.123 1 80%<CRER<=100% 8 0.864 2 60%<CRER<=80% 29 0.698 3 40%<CRER<=60% 43 0.481 4 20%<CRER<=40% 62 0.289 5 0% <=CRER<=20% 41 0.126 1 80%<CRER<=100% 4 0.845 2 60%<CRER<=80% 6 0.671 3 40%<CRER<=60% 6 0.498 4 20%<CRER<=40% 10 0.303 5 0% <=CRER<=20% 8 0.144 1 80%<CRER<=100% 12 0.858 2 60%<CRER<=80% 42 0.688 3 40%<CRER<=60% 84 0.480 4 20%<CRER<=40% 121 0.296 5 0% <=CRER<=20% 67 0.127 Mean NPPE(US$m) 173.52 96.01 117.19 43.51 156.61 147.04 216.38 267.81 104.77 212.89 298.60 86.01 158.34 55.58 173.27 167.25 144.40 183.49 76.71 20
Exhibit 6(a) Book‐to‐market values ratio (BV/MV) of four Property Portfolios (CRER) Randomized over Size A Property Portfolio
Mean CRER (%)
0.669
0.431
0.289
0.141
4485.46***
1804.55***
*1
P*2
P*3
P*4
F-stat 1
Chi-sq 2
Mean BV / MV
0.7718
0.8551
0.8758
0.8189
1.62
9.56**
Exhibit 6(b) Book‐to‐market values ratio (BV/MV) of four property Portfolios (LnCRE) randomized over Size A Property Portfolio
Mean LNCRE
Mean BV / MV
P*1
20.360
0.9059
P*2
19.275
0.8398
P*3
18.433
0.8287
P*4
17.031
0.7466
F-stat 1
387.05***
3.298**
Chi-sq 2
678.89***
18.422***
A
The main objective is investigate the real estate effect of BV/MV while controlling for the firm size effect. A new set of four P* portfolios was formed with different CRER (Exhibit 5a) and LnCRE (Exhibit 5b) but randomized in term of firm size as measured by market capitalization. 1 Obtained from ANOVA test (parametric); 2 Obtained from Kruskal‐Wallis test (non‐parametric) ***, ** Indicates two‐tailed significance at the 1% and 5% levels respectively Exhibit 7 Average results of four retail portfolios 1 (Segment A) RE/MV>1 and BV/MV>1 36 0.383 131.74 PORTFOLIO GROUP 2 (Segment C) 3 (Segment B) RE/MV>1 and RE/MV<=1 and BV/MV<=1 BV/MV>1 37 57 0.432 0.352 511.69 34.69 4 (Segment D) RE/MV<=1 and BV/MV<=1 196 0.375 170.66 Number of firms Average CRER Average RE (‘USD million) Average BV/MV 1.324 0.805 1.109 0.465 Average RE/MV 2.041 1.618 0.508 0.358 Average RE/BV 1.542 2.010 0.458 0.770 Notes: Based on the four valuation segments shown in Exhibit 1, the 326 firms are grouped into four portfolios based on their 6‐year average RE/MV and BV/MV values. RE is the book value of CRE, BV and MV are the book value and market value of the firm. This portfolio grouping method does not consider the country and segment differences 21
Exhibit 8 Results of Simultaneous Equation Estimation: 2002‐2007 (real estate variable: CRER) Explanatory variables/ Adjusted R2 Full sample Japan USA 2
7
r 1
s 1
UK CRER j  e0  e1 (lagCRER) t 1, j   f i DNATr   f s DSEG s   j
Intercept LagCRER DNAT (dummy) – Japan DNAT (dummy) –USA DSGE (dummy) – SIC52 DSGE (dummy) – SIC53 DSGE (dummy) – SIC54 DSGE (dummy) – SIC55 DSGE (dummy) – SIC56 DSGE (dummy) – SIC57 DSGE (dummy) – SIC58 Adjusted R2 0.0061 0.9532*** ‐0.0006 0.0011 0.0097 0.0169*** 0.0134*** 0.0061 0.0009 0.0077 0.0146 0.953 0.0009 0.9714*** ‐ ‐ 0.0317*** 0.0137*** 0.0101 0.0068 0.0005 0.0037 0.0110*** 0.950 0.0101*** 0.9390*** ‐ ‐ 0.0093 0.0159*** 0.0189*** 0.0055 0.0029 0.0085 0.0226*** 0.954 0.0038 0.9600*** ‐ ‐ 0.0065 0.0327 0.0127 ‐0.0047 ‐0.0008 0.0160 0.0057 0.945 2
7
r 1
s 1
( B / M ) j  a 0  a1 CRER j  a 2 LnMV j  a 3 (TDEBTR) j  a 4 ( ROA) j   c i DNATr   c s DSEG s   j
Intercept CRER Size (LnMV) Leverage (TDEBTR) Profitability (ROA) DNAT (dummy) – Japan DNAT (dummy) –USA DSGE (dummy) – SIC52 DSGE (dummy) – SIC53 DSGE (dummy) – SIC54 DSGE (dummy) – SIC55 DSGE (dummy) – SIC56 DSGE (dummy) – SIC57 DSGE (dummy) – SIC58 Adjusted R2 4.805*** 0.3671*** 7.7260*** 0.4952*** 4.4501*** 0.1411 3.4564*** 0.8347*** ‐0.2032*** ‐0.4244*** ‐0.6006*** 0.2746*** 0.0988*** 0.0312 0.2658*** ‐0.0054 0.1506*** ‐0.0672 0.3107*** ‐0.3558*** 0.329 ‐0.3152*** ‐1.5344*** ‐3.6843*** ‐ ‐ 0.2055 0.4466*** 0.1261 0.2820*** 0.3360*** 0.8038*** ‐0.4110*** 0.378 ‐0.1827*** ‐0.0727 ‐0.4265*** ‐ ‐ 0.0068 0.2889*** ‐0.1768 ‐0.0427 ‐0.1847*** 0.0711 ‐0.2747*** 0.343 ‐0.1417*** ‐0.5952*** ‐0.3158 ‐ ‐ ‐0.1490 0.2565 0.1326 0.3897*** 0.4825*** ‐0.0503 ‐0.4134*** 0.406 Notes: This table reports the estimation results for the full sample and three countries (Japan, USA and UK) subsamples. CRER is the predicted value of the percentage of real estate obtained from the first equation of the system estimation and B/M is the ratio of book value to market value. 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 country dummies (DNAT) and segment dummies (DSEG) controls for cross‐country retail variations (UK and SIC59 are the respective references). The two equations are estimated via a simultaneous equation framework using iterative 3SLS technique available from E‐view 6. *** ‐ denotes two‐tailed significance at the 5% level 22
Exhibit 9 Results of Simultaneous Equation Estimation – By retail segments: 2002‐2007 (real estate variable: CRER) Explanatory variables /Adj R2 SIC52 (N=13) SIC53 (N=80) SIC54 (N=43) SIC55 (N=37) SIC56 (N=75) 2
7
r 1
s 1
SIC57 (N=41) SIC58 (N=91) SIC59 (N=100) 0.0338*** 0.9092*** ‐0.0116 ‐0.0064 0.867 0.0210 0.9430*** 0.0021 0.0090 0.913 0.0033 0.9264*** ‐0.00008 0.0020 0.933 CRER j  e0  e1 (lagCRER) t 1, j   f i DNATr   f s DSEG s   j
Intercept LagCRER DNAT (dummy) – Japan DNAT (dummy) –USA Adjusted R2 ‐0.0292 1.0276*** 0.0434*** 0.0157 0.974 0.0419*** 0.9490*** ‐0.0169 ‐0.0199*** 0.896 0.0223 0.9521*** ‐0.0038 ‐0.0006 0.944 ‐0.0196 1.0095*** 0.0125 0.0217*** 0.951 0.0176 0.9156*** ‐0.0030 0.0030 0.898 2
7
r 1
s 1
( B / M ) j  a 0  a1 CRER j  a 2 LnMV j  a 3 (TDEBTR) j  a 4 ( ROA) j   c i DNATr   c s DSEG s   j
Intercept CRER Size (LnMV) Leverage (TDEBTR) Profitability (ROA) DNAT (dummy) – Japan DNAT (dummy) –USA Adjusted R2 4.2760*** 0.1144 5.7858*** 1.9241*** 2.8833*** 0.7016*** 4.6429*** 0.0102 5.5924*** 1.1755*** 8.3343*** ‐1.5879*** 4.1042*** 0.2149 4.6046*** 0.1817 ‐0.1496*** ‐1.4064*** ‐0.2364 0.1521 0.1565 0.704 ‐0.2192*** ‐1.9989*** ‐6.2126*** 0.1436 0.2334 0.389 ‐0.1196*** 0.1651 ‐2.6735*** 0.1495 ‐0.2491*** 0.446 ‐0.1924*** 0.2038 ‐2.7587*** 0.3024*** ‐0.0130 0.509 ‐0.2561*** 0.0789 0.0500 0.2148 ‐0.2624*** 0.444 ‐0.3453*** ‐0.8399 ‐0.9467 0.8783*** 0.4314*** 0.446 ‐0.1835*** ‐0.3447*** ‐0.1986 0.1894*** 0.1607*** 0.365 ‐0.1850*** ‐0.8140*** ‐1.0209*** 0.2849*** 0.2510*** 0.269 Notes: This table reports the estimation results for eight SIC retail segments. CRER is the predicted value of the percentage of real estate obtained from the first equation of the system estimation and B/M is the ratio of book value to market value. 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 country dummies (DNAT) and segment dummies (DSEG) controls for cross‐country retail variations (UK and SIC59 are the respective references). The two equations are estimated via a simultaneous equation framework using iterative 3SLS technique available from E‐view 6. *** ‐ denotes two‐tailed significance at the 5% level 23
Exhibit 10 Results of Simultaneous Equation Estimation: 2002‐2007 (real estate variable: LNCRE) Explanatory variables/ Adjusted R2 Full sample Japan USA 2
7
r 1
s 1
UK LnCRE j  e0  e1 * lag ( LnCRE ) t 1, j   f i DNATr   f s DSEG s   j
Intercept 1.0278*** 1.4758*** 0.9193*** 1.3253*** Lag(lnCRE) DNAT (dummy) – Japan DNAT (dummy) –USA DSGE (dummy) – SIC52 DSGE (dummy) – SIC53 DSGE (dummy) – SIC54 DSGE (dummy) – SIC55 DSGE (dummy) – SIC56 DSGE (dummy) – SIC57 DSGE (dummy) – SIC58 Adjusted R2 0.9507*** ‐0.0716 ‐0.0202 0.0157 ‐0.0084 ‐0.0089 0.0448 0.1248*** ‐0.1234*** ‐0.0424 0.899 0.9203*** ‐ ‐ 0.1182 0.1252 0.0025 0.0847 0.2700*** ‐0.1803 ‐0.0295 0.857 0.9571*** ‐ ‐ 0.0240 ‐0.0960 ‐0.0402 ‐0.0122 0.0789 ‐0.1518 ‐0.0738 0.900 0.9271*** ‐ ‐ ‐0.0639 0.1371 0.4189*** 0.2199 0.2451 0.1269 0.1188 0.931 2
7
r 1
s 1
( B / M ) j  a 0  a1 LnCRE j  a 2 LnMV j  a 3 (TDEBTR ) j  a 4 ( ROA) j   c i DNATr   c s DSEG s   j
Intercept 4.6093*** 0.1266*** 7.2790*** 0.1708*** 4.2824*** 0.0643*** 3.9729 0.1995*** LnCRE Size (LnMV) ‐0.3066*** ‐0.4491*** ‐0.2329*** ‐0.3480*** Leverage (TDEBTR) ‐0.4506*** ‐1.4426*** ‐0.0998 ‐0.4802*** Profitability (ROA) ‐0.5425*** ‐3.0084*** ‐0.4226*** ‐0.2615 DNAT (dummy) – Japan 0.2335*** ‐ ‐ ‐ DNAT (dummy) –USA 0.0850 ‐ ‐ ‐ DSGE (dummy) – SIC52 0.0290 ‐0.0009 ‐0.0013 0.0683 DSGE (dummy) – SIC53 0.2996*** 0.3799*** 0.3360*** 0.2805*** DSGE (dummy) – SIC54 0.0140 0.0621 ‐0.1585 0.5156*** DSGE (dummy) – SIC55 0.1269*** 0.2430*** ‐0.0534 0.2278 DSGE (dummy) – SIC56 ‐0.0699 0.1795 ‐0.1681*** 0.3189*** DSGE (dummy) – SIC57 0.3011*** 0.7768*** 0.0535 0.0362 DSGE (dummy) – SIC58 ‐0.2540*** ‐0.3485*** ‐0.2241*** ‐0.1480 Adjusted R2 0.354 0.407 0.351 0.474 Notes: This table reports the estimation results for the full sample and three countries (Japan, USA and UK) subsamples. LnCRE is the predicted value of the percentage of real estate obtained from the first equation of the system estimation and B/M is the ratio of book value to market value. 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 country dummies (DNAT) and segment dummies (DSEG) controls for cross‐country retail variations (UK and SIC59 are the respective references). The two equations are estimated via a simultaneous equation framework using iterative 3SLS technique available from E‐view 6. *** ‐ denotes two‐tailed significance at the 5% level 24
Exhibit 11 Results of Simultaneous Equation Estimation – By retail segments: 2001‐2006 (real estate variable: LNCRE) Explanatory variables /Adj R2 SIC52 (N=13) SIC53 (N=80) SIC54 (N=43) SIC55 (N=37) SIC56 (N=75) 2
7
r 1
s 1
SIC57 (N=41) SIC58 (N=91) SIC59 (N=100) 0.6563*** 0.9712*** ‐0.1141 ‐0.0708 0.923 1.1619*** 0.9361*** 0.0376 0.1545 0.874 LnCRE j  e0  e1 * lag ( LnCRE ) t 1, j   f i DNATr   f s DSEG s   j
Intercept Lag(lnCRE) DNAT (dummy) – Japan DNAT (dummy) –USA Adjusted R2 0.7767 0.9574*** 0.0512 0.1619 0.956 0.9206 0.9529*** 0.0490 ‐0.0159 0.891 1.5082*** 0.9372*** ‐0.3450 ‐0.2336 0.891 0.6642 0.9744*** ‐0.0795 ‐0.0912 0.892 1.8708*** 0.9107*** 0.0625 0.0041 0.857 1.1519*** 0.9233*** ‐0.2736 ‐0.0821 0.886 2
7
r 1
s 1
( B / M ) j  a 0  a1 LnCRE j  a 2 LnMV j  a 3 (TDEBTR ) j  a 4 ( ROA) j   c i DNATr   c s DSEG s   j
Intercept LnCRE Size (LnMV) Leverage (TDEBTR) Profitability (ROA) DNAT (dummy) – Japan DNAT (dummy) –USA Adjusted R2 4.3704*** ‐0.0232 6.0500*** 0.0595 3.9364*** 0.1158*** 4.4773*** 0.1717*** 4.9936*** 0.1488*** 6.8964*** 0.2532*** 4.0804*** 0.0630*** 4.6111*** 0.1288*** ‐0.1306*** ‐1.3606*** ‐0.3198 0.1774 0.1554 0.703 ‐0.2554*** ‐1.6619*** ‐5.3507*** 0.0972 0.1378 0.350 ‐0.2547*** 0.2274 ‐2.9158*** ‐0.2294 ‐0.4544*** 0.450 ‐0.3555*** 0.1635 ‐1.6375 0.3807*** 0.1180 0.558 ‐0.3426*** ‐0.2500 0.0293 0.0928 ‐0.2353*** 0.454 ‐0.5343*** ‐0.7225 ‐0.4257 0.8010*** 0.1954 0.469 ‐0.2352*** ‐0.3290*** ‐0.2255 0.1427 0.1404 0.373 ‐0.3011*** ‐0.8725*** ‐0.9550*** 0.2576*** 0.2007 0.296 Notes: This table reports the estimation results for eight SIC retail segments. LnCRE is the predicted value of the percentage of real estate obtained from the first equation of the system estimation and B/M is the ratio of book value to market value. 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 country dummies (DNAT) and segment dummies (DSEG) controls for cross‐country retail variations (UK and SIC59 are the respective references). The two equations are estimated via a simultaneous equation framework using iterative 3SLS technique available from E‐view 6. *** ‐ denotes two‐tailed significance at the 5% level 25
Exhibit 12 Results of Simultaneous Equation Estimation by Portfolio group ‐ 2002‐2007 (real estate coefficients only)( BV/MV Real estate variable: CRER (relative) RE/MV >1 <=1 >1 2.2112*** 0.1525 <=1 2.9531*** ‐0.0684 BV/MV Real estate variable: LnCRE (absolute) RE/MV >1 <=1 >1 0.3063*** 0.0826** <=1 0.1145** 0.0563*** Notes: This table reports the estimation results (to save space, only for the coefficients of CRER and LnRE in equation 2 of the simultaneous equation system) between BV/MV and real estate values for the 4 portfolios of 326 retail firms (refer to Exhibit 1 also), controlled for country and retail segment differences. ***, and ** ‐ indicate two‐tailed significance at the 1% and 5% levels respectively. 26