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Spatial Statistics Applied to Commercial Real Estate

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Abstract

Portfolio theory shows that diversification can enhance the risk-return trade-off. This study uses the absolute location of commercial real estate property along with spatial statistics to address the inherent problem of determining geographical diversification based upon a set of economic and property-specific attributes, some of which are unobservable or must be proxied with noise. We find that commercial real estate portfolios exhibit statistically significant spatial correlation at distances ranging from adjacent zip codes to neighboring metropolitan areas. Given the common structure of dependence found in the data series, we discuss feasible strategies for obtaining diversification within direct-investment real estate portfolios.

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Notes

  1. In support of this hypothesis, Radner and Stiglitz (1984) find that mutual fund managers with higher asset concentrations by industry outperform diversified funds. Van Nieuwerburgh and Veldkamp (2007) develop a rational model of investors who choose to specialize in trading a set of highly-correlated assets because of asset costs. They find that returns to specialization in information acquisition can explain why investors do not hold fully-diversified portfolios.

  2. A lack of spatial diversification may also cause a problem for those portfolio managers that owe a fiduciary responsibility to plan participants and beneficiaries. Endowments, ERISA plans, and foundations may owe a fiduciary duty to decrease risk for a given return, which can lead to geographical diversification.

  3. The term Metropolitan Area (MA) was adopted in 1990 by the U.S. Office of Budget and Management and refers collectively to MSAs (urbanized place of over 50,000 people), Primary Metropolitan Statistical Areas (Contiguous MSAs of over 1,000,000 people), and Consolidated Metropolitan Statistical Areas (Combinations of PMSAs that form a larger, interrelated network).

  4. See Cressie (1993) and LeSage and Pace (2009) for details regarding spatial statistics and econometrics.

  5. We project the locations using the Transverse Mercator map projection (Snyder and Voxland 1989). This allows treating the points from a sphere (the Earth’s surface) as if they were on a plane (a map).

  6. In the spirit of Stambaugh (1982) we also examine using the global equity indices of Russell Global Index and MCSI World Index as a proxy for the market model. Despite the fact that the Russell Global Index is based upon approximately 10,000 global stocks or 98% of the investable global universe, we find that equities are not a significant fit for US commercial real estate properties.

  7. The economic variables control for aspects of the real estate demand curve (Miles et al. 2007, p. 26). Demand for retail space and apartments is a function of household composition and population. Along with the overall population for a specific zip code, we break out population by five age groups with breakpoints at 19, 34, 49, and 65, which is consistent with Cheng and Black (1998). We execute the model with and without the breakpoints and find no difference in the results. We also include the demand-side variables of median income and average house price scaled by median household income. The demand for other commercial property—office buildings, factories, and warehouses—is more closely tied to labor force and employment. To control for these effects along with the economically-based diversification noted in Mueller (1993) we use employment levels using the 2002 North American Industry Classification System codes of 1) mining and agriculture, 2) utilities and construction, 3) manufacturing, 4) wholesale and retail trade along with transportation and warehousing, 5) information, finance, insurance, real estate, and profession, scientific and technical services, 6) education and health care, and 7) arts, entertainment, and food services. Lastly, we account for changes in population and employment using the migration of persons into the zip code.

  8. The MAs referenced here are distinct metropolitan areas and not PMSAs; otherwise, a real estate portfolio manager may experience spatial correlations based upon the CMSA results above.

  9. Additionally, the values do not account for levered cash flows, which will increase portfolio risk.

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Author information

Authors and Affiliations

  1. Department of Finance and Real Estate, University of Texas at Arlington, Box 19449, Arlington, TX, 76019, USA

    Darren K. Hayunga

  2. LREC Endowed Chair of Real Estate, Department of Finance, E.J. Ourso College of Business Administration, Louisiana State University, Baton Rouge, LA, 70803-6308, USA

    R. Kelley Pace

Authors
  1. Darren K. Hayunga
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  2. R. Kelley Pace
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Correspondence to Darren K. Hayunga.

Additional information

Research supported by a grant from the Real Estate Research Institute. We thank an anonymous referee, Richard Buttimer, the editor, Jeff Fisher, David Geltner, Marc Louargand, Glenn Mueller, Tony Sanders, C.F. Sirmans, and seminar participants at the RERI conference, UNC-Charlotte, and UT-Arlington for their suggestions and guidance. Special thanks to Robert White of Real Capital Analytics for data.

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Hayunga, D.K., Pace, R.K. Spatial Statistics Applied to Commercial Real Estate. J Real Estate Finan Econ 41, 103–125 (2010). https://doi.org/10.1007/s11146-009-9190-2

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