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Corporate Real Estate Usage and Firm Valuation

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Abstract

The trade-off between the potential benefits and costs of using corporate real estate (CorRE) in the production process creates an optimal level of CorRE that varies over time and across firms. We document the importance of conditioning on a firm’s optimal CorRE usage when analyzing the relation between CorRE and firm valuations. Controlling for year and firm fixed effects and using rolling-window regressions, we estimate differences in firms’ actual CorRE usage from predicted levels by industry and examine how the difference affects firm value. We find a nonlinear relation between firm value and the deviation of CorRE usage from predicted levels: investors tend to punish the valuations of companies when CorRE usage deviates from predicted levels, especially for the companies that use more CorRE than predicted. This result is robust to instrumental variable regressions. We uncover several channels through which deviations in the use of CorRE can affect firm value: firm profitability, the cost of debt, sales growth, and investment in non-real estate assets.

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Notes

  1. https://www.reit.com/news/blog/market-commentary/total-size-of-us-commercial-real-estate-estimated-between-14-and-17-trillion

  2. Flow of Funds Accounts of the United States Federal Reserve, March 10, 2022, Tables B.101, B.103, and B.104.

  3. We use Fama–French 12 industry classification. However, utility industry is omitted because of lack of observations. As a result, we have 11 instead of 12 industries in our sample.

  4. The use of book value in place of unobservable true market values may understate the market value of firms that have a larger percentage of older properties (with low book values) relative to firms that have a higher percentage of new assets. It may also understate the market value of firms' real estate assets that have a larger percentage of their properties in cities that have experienced more rapid price appreciation relative to firms that have a higher percentage of their properties in markets that have experienced lower price appreciation.

  5. This evidence is from our baseline regressions in Table 3. We find both too little and too much CorRE usage have significant negative impact on firm values when using instrumental variable regressions (see our Table 4).

  6. A negative relation between the level of CorRE and Tobin’s Q may be mechanical because operating real estate assets have limited growth potential, leading to lower market-to-book ratios, all else equal. Thus, a firm with more CorRE may mechanically have a lower Tobin’s Q because its balance sheet contains more assets that individually have low market-to-book ratios. We thank a referee for pointing out this potential mechanical relationship between RER and Tobin’s Q. We avoid this potential bias by focusing on RER deviations instead of raw RER level. Also, if a negative mechanical relationship between RER deviations and Tobin’s Q still exists after subtracting predicted CorRE usage from actual CorRE holdings, we should observe a significantly higher Tobin’s Q among firms with too little CorRE. However, this is not what we find. We also control for a firm’s growth potential in our Tobin’s Q regressions.

  7. Bokhari and Geltner (2018) find an overall average depreciation rate of just 1.5% per year for income-producing real estate, ranging from 1.82% per year for properties with new buildings to 1.12% per year for properties with 50‐year‐old buildings.

  8. COMPUSTAT also provides CorRE usage data measured at the net (of depreciation) level; however, these data are not available after 1997.

  9. The variable names in COMPUSTAT for our real estate ratio components are: Buildings, FATB; Land, FATP; and Construction, FATC.

  10. REOCs include publicly traded construction and development firms as well as brokerage and real estate advisory firms. A “qualified” equity REIT may deduct dividends paid from corporate taxable income if it satisfies a set of restrictive conditions on an ongoing basis. Among other things, these requirements ensure that REITs invest primarily in real estate.

  11. The figure reveals a clear break in the level of RER in early 1990s. This break is the result of a change in accounting compliance in the 1990s that formalized the recognition of Property, Plant and Equipment. IAS 16 Property, Plant and Equipment was issued by the International Accounting Standards Committee in December 1993. It replaced IAS 16 Accounting for Property, Plant and Equipment (issued in March 1982). See the following link: https://www.ifrs.org/issued-standards/list-of-standards/ias-16-property-plant-and-equipment/. IAS 16 requires that firms recognize their property, plant, and equipment using either the cost method or the revaluation method.

  12. See: https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/det_12_ind_port.html. Fama and French assign each NYSE, AMEX, and NASDAQ stock to an industry portfolio at the end of June of year t based on its four-digit SIC code at that time. COMPUSTAT SIC codes for the fiscal year ending in calendar year t-1 are used. If COMPUSTAT SIC codes are not available, CRSP SIC codes for June of year t are used.

  13. The Utilities industry (Fama- French industry #8) is omitted because of a lack of observations in our sample.

  14. Plots of the equally-weighted annual mean value of RER for each of our 11 industries along with the 95% confidence interval are contained in Appendix Fig. 4.

  15. These summary statistics are generally comparable with previous studies that use COMPUSTAT, such as Cremers and Ferrell (2014) and Duchin (2010).

  16. If not otherwise specified, economic significance is calculated as the coefficient estimate times the standard deviation of the independent variable of interest scaled by the sample average of the dependent variable. Thus, -2% = -0.261 * 0.149 / 2.0.

  17. The economic significance of a dummy is calculated as the coefficient estimate scaled by the average of the dependent variable. -3.2% = -0.064 / 2.0.

  18. -18.6% = -0.104 * 0.116 / 0.065. -9.2% = -1.000 * 0.060 / 0.065.

  19. 6.9% = 0.149 * 0.060 / 0.130.

  20. -22.3% = -0.405 * 0.116 / 0.211.

  21. -22% = -0.074 * 0.116 / 0.039.

  22. 14.5% = 0.094 * 0.060 / 0.039.

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Authors and Affiliations

  1. Finance, Insurance and Real Estate Department, Warrington College of Business, University of Florida, Gainesville, USA

    Qing Li

  2. McGurn Professor of Real Estate, Hough Graduate School of Business, University of Florida, Gainesville, USA

    David C. Ling

  3. Department of Accountancy, Economics, and Finance, School of Business, Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Qie Ellie Yin

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  1. Qing Li
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  2. David C. Ling
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  3. Qie Ellie Yin
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Correspondence to Qing Li.

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Appendix 1

Appendix 1

Figure 

Fig. 4
figure 4

RER Confidence Intervals by Industry. This appendix depicts the time trend of cross-sectional average RER and its 95% confidence intervals by Fama–French industries. Industry #8 Utilities is omitted for the lack of observations. X-axis (year 1984–2020) labeling is suppressed in each figure for a clear look

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Li, Q., Ling, D.C. & Yin, Q.E. Corporate Real Estate Usage and Firm Valuation. J Real Estate Finan Econ (2023). https://doi.org/10.1007/s11146-023-09948-x

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