Daniel E Huerta

Conditional geographical clustering is the strategy of grouping real estate properties within a contiguous region to exploit economies of scale through spatial proximity. We expect significant benefits from this strategy as a result of gains in local market expertise and cost reductions associated with improved operational performance from the efficient management of a portion of a Real Estate Investment Trust (REIT) property portfolio. This strategy differs from both geographical diversification and agglomeration strategies. Geographical diversification is the strategy of acquiring properties in distinct geographical markets as to take advantage of the diversification effect of the differing economic conditions in the multiple markets. However, managing a property portfolio that is geographically disperse may pose challenges such as lack of expertise in the multiple markets, difficulty in property monitoring, lower management efficiency, and higher agency costs. Prior literature finds REIT geographical diversification either destroys firm value or has little to no benefit. Ambrose, Ehrlich, Hughes, and Wachter (2000), Capozza and Sequin (1998, 1999), Gyourko and Nelling (1996), and Demirci, Eichholtz, and Yonder (2020) find either no, or limited, evidence of economic benefits. Whereas, Campbell, Petrova, and Sirmans (2003), Cici, Corgel, and Gibson (2011), Cronqvist, Hogfeldt, and Nilsson (2001), and Hartzell, Sun, and Titman (2014) present results that indicate discounts in value for geographically diversified REITs. More recently, Feng, Pattanapanchai, Price and Sirmans (2019) find geographical diversification benefits arise for REITs with high levels of institutional ownership and which invest in core property types. Agglomeration, on the other hand, refers to the strategy of locating properties near concentrations of economic activity such as in areas of fast economic growth or areas where similar properties owned by other firms are located. Prior literature explains agglomeration economies benefits firm productivity and provides positive externalities (Henderson 1986; Henderson 2003; Rosenthal and Stranges 2008; Melo et al., 2009; Greenstone et al. 2010; Koster et al. 2014) which may explain the concentration of REIT properties in certain U.S. markets. However, agglomeration generally refers to the location of properties neighboring other properties that are not owned by the REIT.

In this paper, we examine the impact of conditional geographical clustering on REIT operations and firm value. Specifically, we test whether a strategy of property clustering translates into improved efficiency and performance that may impact REIT firm value and stock returns. That is, we explore channels through which conditioned geographical clustering contributes to REIT shareholder wealth. Such channels include operational efficiency, operational performance, and credit risk.

We contribute to the literature by measuring the optimal REIT cluster size (in terms of number of property units) and distance (in terms of amplitude of radii) by property-type specialization. This analysis provides REIT managers with indications of if property clustering is an effective strategy for all REIT specializations. Moreover, for those property-types for which clustering matters, our results provide guidance on the optimal proportion of the portfolio that should be clustered and the size of the cluster that will provide most benefit. The analysis by property-type specialization is of particular importance since each property sector has unique characteristics, distinct demand and supply drivers, and responds to economic factors in different ways. Each REIT asset class signifies a distinct business line with different economic sensitivities and which calls for a particular investment strategy that corresponds to the idiosyncrasies of the property type. Prior literature highlights the importance of property-type specialization segmentation in REIT studies finding, for example, that specialized REITs show varying degree of business cycle exposure, tend to have distinct levels of correlation with the economy, show markedly dissimilar capital structures, varying risk-return characteristics and deviations from net asset value, and are prone to different pricing anomalies (Wheaton, 1999; Reddy and Cho, 2018; Van Nieuwerburgh, 2019; Huerta et al., 2020).