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Distance to Headquarter and Real Estate Equity Performance


We study the effect of geographic portfolio diversification of real estate firms on their investment performance before and after the global financial crisis (GFC). In addition to previously used dispersion metrics, we also account for the distance of the properties to the corporate headquarters. We document a notable shift in the non-market performance of real estate companies after the crisis. Pre-GFC, we do not find a difference in non-market performance across equities based on geographic diversification. Post-GFC, equities with high geographic dispersion significantly outperform the market, while firms with concentrated property holdings do not deliver a significant alpha. Increased real estate equity market sophistication and strong institutional presence can explain why this effect is only observed for dispersed small firms, those invested outside gateway metro areas, or companies with low institutional ownership.

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  1. Real estate companies have considerable decreased their leverage following the GFC.

  2. At the property level, we exclude properties located outside mainland U.S. – that means properties located in states like Alaska or Hawaii, for example, are not included to enable a reasonable calculation of the average distance between property pairs.

  3. None of the firms exits the sample period once they have entered. This is a standard way of constructing a data sample in asset pricing model.

  4. Alternatively, we also used NCRIEF total return indicator as additional measure for real estate market performance, the results remain robust. However, the beta for NCREIF total return index is significantly negative, which might be caused by the multicollinearity between NCRIEF return, NAREIT return, and stock market return.

  5. We have performed additional robustness for our result of Table 4: (1) There were 41 firms in our sample of 223 firms that were acquired during our sample period. The concern arises that the acquirer may not purchase all the properties, even though SNL keeps the records of the firms and the changes in the headquarter/assets. We rerun Table 4 excluding the 41 firms, and the findings are robust as shown in Appendix Table 10; (2) To address the concern of couple firms driving our findings and causing the results being biased, we winsorize the returns at 95 percentile. The results are still robust as documented in Appendix Table 11; (3) Lastly, we use the adjusted-cost weighted distance measured by is the maximum of the reported book value, the initial cost of the property, and the historical cost of the property, including capital expenditures and tax depreciation. The results are relatively robust, as in Appendix Table 12.

  6. The period between 2007 to 2009 is too short to run a Fama-MacBech regression.


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We thank David Shulman, Charles-Olivier Amédée-Manesme, Bertram Steininger, Alex Moss for helpful comments and suggestions and the seminar participants at Baruch College, Laval University, RWTH Aachen, the 2018 ERES, and the 2019 EFMA meeting in Portugal.

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Correspondence to Stanimira Milcheva.

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Table 10 Portfolios based on average distance to headquarters excluding REITs that have been part of a merger or accusation
Table 11 Portfolios based on average distance to headquarters using winsorized returns
Table 12 Portfolios based on average distance to headquarters using the adjusted cost weighted distance

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Milcheva, S., Yildirim, Y. & Zhu, B. Distance to Headquarter and Real Estate Equity Performance. J Real Estate Finan Econ 63, 327–353 (2021).

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  • Distance to headquarter
  • Real estate returns

JEL Classification

  • G12
  • R3