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The Impact of Property Clustering on REIT Operational Efficiency and Firm Value

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Abstract

Conditioned geographical clustering is the strategy of grouping portions of a REIT’s property portfolio within a contiguous region to exploit economies of scale through spatial proximity. This paper examines the impact of conditioned geographical clustering on REIT operational efficiency and value. Our results suggest REITs create value by employing a strategy of property clustering and that operational efficiency is the primary channel through which increases in value are achieved. In addition, results suggest conditioned geographic clustering mitigates the REIT geographical diversification discount. Our findings support an optimal degree of property clustering within the 5th to 35th percentiles of the sample distribution and suggest the optimal cluster size has a radius between 50 and 75 miles.

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Data Availability

Data employed in this paper is available upon request.

Notes

  1. National Association of Real Estate Investment Trusts (NAREIT): https://www.nareit.com/what-reit (last accessed on June 20, 2022).

  2. Agglomeration economies refers to benefits from spatial proximity to concentrations to economic activity (external factors contributing to scale economies).

  3. Examples include: Ambrose et al., 2000; Anderson et al., 2002; Anderson & Springer, 2003; Bers & Springer, 1998; Highfield et al., 2021; Lewis et al., 2003; Miller et al., 2006; Miller & Springer, 2007; Nicholson & Stevens, 2021; Topuz et al., 2005; Feng et al., 2023.

  4. Available through ERSI’s ArcGIS Pro package.

  5. Our analysis relies on accurate spatial location data for individual properties. 98.15% of properties have longitude and latitude data or address information that allows us to geocode their location. To correct for missing location information, we exclude REIT-year observations where more than 50% of the properties are missing coordinate data or a property address. We also exclude all properties without coordinate data and addresses from the clustering calculations.

  6. For a complete discussion on the DBSCAN algorithm see Ester et al. (1996).

  7. S&P Global Market Intelligence data measures REIT property sizes using inconsistent metrics that are dependent on REIT property focus. We overcome this inconsistency issue by employing a measurable unit multiplier (converter) provided by S&P Global Market Intelligence that translates the multiple size metrics (e.g. apartment units, number of beds, hotel rooms, self-storage units) into a consistent square foot measurement for each real property in our sample.

  8. To calculate the missing property sizes, we replace the missing values with the national average of the property size by primary and secondary property types.

  9. We employ the US Census Bureau’s 2010 MSA definitions. When employing the MSA-level classification, if a property is located outside of a formally identified MSA, we place properties in their respective state.

  10. Hartzell, Sun and Titman (2014) suggest that alternative specifications of geographical diversification that allows us to explore for differences when considering the pros and cons of more specific or broad geographical orderings which may provide an indication of the robustness of our findings.

  11. We test various radii and find any radius over 2 miles generated excessively large clusters covering entire regions of the United States.

  12. Distance radii of 5 miles, 12.5 miles, and 100 miles were also examined providing similar results. These figures are displayed in Appendix A (Tobin’s Q) and Appendix B (OER1).

  13. We present a table of mean values for select variables in Appendix Table C as an additional univariate analysis.

  14. Appendix Table D presents these regression results for OER1 while Appendix Table E present results for Tobin’s Q.

  15. For robustness, we examine the impact of dividing the moderate values of clustering into decile bins as well as a final specification of twenty equal bins. The results are quantitively like our main results using terciles and we present these in Appendix F (OER) and G (Tobin’s Q) of the appendix.

  16. The reported specification measures geographic diversification using the National Council of Real Estate Investment Fiduciaries regions. We test geographical diversification at the state and MSA level and find similar results, see Appendix Table H (OER 1) and Appendix Table I (Tobin’s Q).

  17. We thank several anonymous reviewers for identifying potential efficiency and value impacts arising: 1) when REITs locate properties nears other properties within the same asset class; 2) from MSA-specific risk factors; 3) from gateway cities; and 4) year specific effects. We calculate additional control variables representing the percent of properties in a cluster that are of the same asset class as the REIT (Localization Economies), a measure of MSA risk (MSA Risk) following Zhu and Lizieri (2022), and a measure of gateway city concentration (Gateway) following Feng (2022). We use the 2010 MSA definition for the following cities as gateway cities: 1) Boston; 2) Chicago; 3) Los Angeles; 4) New York; 5) San Francisco; and 6) Washington D.C. We also include year fixed effects in addition to the property type and region weights interacted with the year dummy variables. We find the inclusion of the additional controls does not significantly impact our results. Appendix J discusses the calculation of MSA Risk and Localization Economics. We present the results for OER1 in Appendix Table K and the results for Tobin’s Q in Appendix Table L.

  18. For robustness, we also employ OER 2 as the dependent variable for the specification in Eq. (4). We find quantitatively similar results and report these in Appendix Table M.

  19. A complete table of coefficients for Managerial efficiency is presented in Appendix Table N while a complete table of coefficients for Other efficiency is presented in Appendix Table O.

  20. Thresholds of 0%, 1% and 5% represent the 50th percentile, 90th percentile and 95th percentile of the Stand-along change distribution respectively. A complete table of coefficients is presented in Appendix Table P.

  21. For robustness, we also employ Firm Q as the dependent variable for the specification in Eq. (4). We find quantitatively similar results and report these in Appendix Table Q.

  22. In our main model there are 5 bins: 1) below the 5th percentile (mid-point of 2.5); 2) 5th to 35th percentiles (mid-point of 20); 3) 35th to 65th percentiles (mid-point of 50); 4) 65th to 95th percentiles (mid-point of 80); and 5) above the 95th percentile (mid-point of 97.5). In Model 1, the base group consists of below the 5th percentile and above the 95th percentile; there, the estimated coefficients for bins is constant term. For ease of exposition, we calibrate Panels A and B to a baseline of 0.

  23. A complete table of coefficients is presented in Appendix Table R.

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Acknowledgements

We appreciate the thorough review and helpful comments by two anonymous referees. We thank Dr. William Hardin, Dr. Erik Devos, Dr. Jeffrey DiBartolomeo, Dr. Norman Maynard and other participants of the 2022 American Real Estate Society Meeting for their insightful comments. We also acknowledge the support from Dr. H. Shelton Weeks and the Lucas Institute for Real Estate Development & Finance. Authors have no competing interests to declare. All errors are our own.

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  1. Lutgert College of Business, Department of Economics and Finance, Lucas Institute for Real Estate Development & Finance, Florida Gulf Coast University, 10501 FGCU Blvd. S., Lutgert Hall LH-3361, Fort Myers, FL, 33965, USA

    Daniel Huerta

  2. School of Business, Department of Economics, College of Charleston, 5 Liberty Street, Charleston, SC, 29401, USA

    Christopher Mothorpe

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Huerta, D., Mothorpe, C. The Impact of Property Clustering on REIT Operational Efficiency and Firm Value. J Real Estate Finan Econ (2024). https://doi.org/10.1007/s11146-023-09973-w

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