Abstract
Little is known about the effects of real estate ownership and leasing on the stock return characteristics of public firms. In this study, we first examine the sensitivity of retail firm returns to a real estate factor over the period 1998–2008. The retail industry is chosen because of the significant use of real estate in a typical retail firm’s production function. Consistent with our expectations, retail stocks exhibit positive real estate risk exposure, even after controlling for sensitivity to general market risk as well as other standard risk factors. The second part of our analysis examines whether the intensity of real estate ownership and the use of off-balance operating leases to finance real property holdings are reflected in the market and real estate betas of retail stocks. We find that greater use of off-balance sheet operating leases is associated with higher market betas. In fact, the use of operating leases appears to have a larger impact on sensitivity to market risk than does the use of on-balance sheet debt. Our findings also confirm our hypothesis that real estate intensive firms display significantly greater exposure to a real estate factor. Moreover, our results strongly suggest that investors are fully aware of the risk associated with off-balance sheet operating leases.
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Notes
Using property, plant, and equipment (PP&E) from Compustat as a proxy for real estate can be problematic if the industries analyzed are heavy in non-real estate property like in trucking (trucks) or auto manufacturing (capital goods equipment).
The following two-digit SIC codes constitute the retail trade division: Group 52, building materials, hardware, garden supply, and mobile home dealers; Group 53, general merchandise stores; Group 54, food stores; Group 55, automotive dealers and gasoline service stations; Group 56; apparel and accessory stores; Group 57, home furniture, furnishings, and equipment stores; Group 58, eating and drinking places; and Group 59, miscellaneous retail.
Returns are annualized by multiplying the mean weekly return by 52 and the standard deviation of returns by the square root of 52.
We also estimate the model using various alternative specifications, including estimating the results by year.
Using Compustat data item number classifications, the present value of operating leases is calculated as follows: \( \begin{gathered} OPLEASES = \left( {{\text{Compustat}}\,{\text{Item}}\,{96}\,{\text{discounted}}\,{\text{back}}\,{\text{1 - year}}} \right) + \left( {{\text{Compustat}}\,{\text{Item}}\,{164}\,{\text{discounted}}\,{\text{back}}\,{\text{2 - years}}} \right) \hfill \\ + \left( {{\text{Compustat}}\,{\text{Item}}\,{165}\,{\text{discounted}}\,{\text{back \, 3 \!- years}}} \right) + \left( {{\text{Compustat}}\,{\text{Item}}\,{166}\,{\text{discounted}}\,{\text{back}}\,{\text{4 - years}}} \right) \hfill \\ + \left( {{\text{Compustat}}\,{\text{Item}}\,{167}\,{\text{discounted}}\,{\text{back}}\,{\text{5 - years}}} \right) + {\text{adjustment}}\,{\text{factor}} \hfill \\ \end{gathered} \). The discount rate employed is the annual BAA rate from the H15 data (Fed Reserve Data). The adjustment factor requires multiple steps: (1) Take Compustat Item 389, the value of all minimum rents after year 5, and divide by Compustat Item 167, the last minimum rent payments for year 5; (2) Round this number up to next integer (so if ratio is 5.3, round up to 6); (3) Take Compustat Item 389 and divide by the ratio. This gives the present value of leases that will persist for the next 6 years (in this example); (4) Take this annuity and discount it back for the adjustment factor. For example, if the rounded up integer is 6, the adjustment factor would equal: \( \begin{gathered} {\text{Adjustment}}\,{\text{factor}} = \left( {\left. {{\text{Compustat}}\,{\text{Item \, 389\!}}/{6}} \right\}{\text{discounted}}\,{\text{back}}\,{\text{6 - years}}} \right. + \left( {\left. {{\text{Compustat}}\,{\text{Item}}\,{389}/{6}} \right\}{\text{discounted}}\,{\text{back}}\,{\text{7 - years}}} \right.{ } \hfill \\ + \left( {\left. {{\text{Compustat}}\,{\text{Item}}\,{389}/{6}} \right\}{\text{discounted}}\,{\text{back}}\,{\text{8 - years}}} \right. + \left( {\left. {{\text{Compustat}}\,{\text{Item}}\,{389}/{6}} \right\}{\text{discounted}}\,{\text{back}}\,{\text{9 - years}}} \right. \hfill \\ + \left( {\left. {{\text{Compustat}}\,{\text{Item}}\,{389}/{6}} \right\}{\text{discounted}}\,{\text{back}}\,{1}0{\text{ - years}}} \right. + \left( {\left. {{\text{Compustat}}\,{\text{Item}}\,{389}/6} \right\}{\text{discounted}}\,{\text{back}}\,{\text{11 - years}}} \right. \hfill \\ \end{gathered} \). Note: Because Compustat Item 389 is missing for many firms, we also experimented with several variations of the adjustment factor, including setting it equal to zero if missing. Also note that if the rounded up integer is 8 in our above example, then we discount Compustat Item 389/8 for years 6–13. Compustat data items numbers 96, 164, 165, 166, 167, and 389 correspond to Compustat items MRC1-MRC5 and MRCTA, respectively.
In some cases, there are missing values for gross land and gross building. This is usually because the firm leased all its properties or the firm combined the two figures. Because we combine gross land and building in our alternative measure of real estate intensity, we again use the firm’s 10-k filings on the SEC Edgar database to obtain the information for the missing values when possible. In 27 cases, the data are not broken out sufficiently to make an adjustment. This results in 2,632 final observations for our alternative real estate intensity measure versus 2,659 final observations when using our first real estate intensity measure.
For the explanatory variables there are a total of 2,659 observations across the 11 years from 1998–2008, which is 24 fewer observations than those reported in Tables 1, 2 and 3 (i.e., 2,683 versus 2,659 observations). 19 of the 24 missing observations are due to the firm missing all operating lease data both in Compustat (i.e., missing all MRC1-MRC5) and in our additional search of the firm’s 10-k data on the SEC Edgar database. The other five missing observations are missing a D/E long term debt component. Note also that our alternative real estate intensity measure has 27 fewer observations since the data are not broken out sufficiently to make an adjustment in these 27 cases.
The significance of this negative relation is, however, sensitive to additional time-varying definitions of the D/E ratio. In particular, if we allow the equity component of the D/E ratio to vary monthly and define the annual ratio as an average of the monthly varying D/E ratios, the estimated coefficient is no longer statistically different from zero. In this specification, market risk is more related to off-balance sheet leverage as opposed to on-balance sheet leverage.
We thank Lynn Fisher for this insight.
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Acknowledgements
The authors gratefully acknowledge the assistance provided by Dean Gatzlaff and Stacy Sirmans, the editors of the special issue, the helpful suggestions of Lynn Fisher and other participants in the FSU Critical Issues in Real Estate Symposium, as well as the research assistance provided by Leming Lin, Benjamin Scheick and, particularly, Matthew Souther.
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Ling, D.C., Naranjo, A. & Ryngaert, M. Real Estate Ownership, Leasing Intensity, and Value: Do Stock Returns Reflect a Firm’s Real Estate Holdings?. J Real Estate Finan Econ 44, 184–202 (2012). https://doi.org/10.1007/s11146-010-9271-2
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DOI: https://doi.org/10.1007/s11146-010-9271-2