Clustering Methods for Real Estate Portfolios

By Goetzmann, William N.; Wachter, Susan M. | Real Estate Economics, Fall 1995 | Go to article overview

Clustering Methods for Real Estate Portfolios


Goetzmann, William N., Wachter, Susan M., Real Estate Economics


William N. Goetzmann [*]

Susan M. Wachter [**]

A clustering algorithm is applied to effective rents for twenty-one metropolitan U.S. office markets, and to twenty-two metropolitan markets using vacancy data. It provides support for the conjecture that there exists a few major "families" of cities: including an oil and gas group and an industrial Northeast group. Unlike other clustering studies, we find strong evidence of bicoastal city associations among cities such as Boston and Los Angeles. We present a bootstrapping methodology for investigating the robustness of the clustering algorithm, and develop a means for testing the significance of city associations. While the analysis is limited to aggregate rent and vacancy data, the results provide a guideline for the further application of cluster analysis to other types of real estate and economic information.

The benefit of geographical diversification across real estate markets is well documented, and has been a guiding feature of portfolio management for some time. See Miles and McCue (1982); Hartzell, Heckman and Miles (1986); and Firstenberg, Ross and Zisler (1987). The reasons for this are not a mystery. As several researchers have pointed out, since fundamentally different economic forces influence the various regions in the United States, real estate values in different regions tend to vary greatly in their behavior, manifesting possibly unique risk factors (see Grissom, Hartzell and Liu 1987). For example, as Hartzell, Schulman and Wurtzbach (1987) hypothesize, and as we demonstrate in this paper, the fortunes of much of the southwest (from Denver to New Orleans) are related to natural resource extraction, such as oil, gas and mining. Diversification across regions can help to reduce the overall risk of the real estate portfolio.

In this paper, a clustering algorithm is applied to effective rents for twenty metropolitan U.S. office markets, and to twenty-two metropolitan markets, using vacancy data. It provides support for the conjecture that there exist a few major families of cities: including an oil and gas group and an industrial northeast group. Unlike other clustering studies, there is strong evidence of bicoastal city associations among cities such as Boston and Los Angeles. We present a new bootstrapping methodology for investigating the robustness of the clustering algorithm, and develop a means for testing the significance of city associations. While the analysis is limited to aggregate rent and vacancy data, the results provide a guideline for the further application of cluster analysis to other types of real estate and economic information. The major benefit of this approach is in reducing the potentially large estimation errors in diversification studies for real estate portfolios.

The Diversification Problem

Mean-variance analysis, developed by Markowitz (1952) has become a widely used method for optimally diversifying real estate portfolios. As originally conceived, mean-variance calculates a set of portfolio weights across assets that result in thc highest expected return for each given level of investor risk. While useful, the procedure is not without drawbacks. A number of authors have pointed out potentially serious problems created by estimation error. Michaud (1989) calls the optimization procedure an error-maximizer because it has the unfortunate feature of overweighting the influence of outlying observations. Jorion (1985, 1986) simulates the effect of estimation error on the composition of portfolios on the efficient frontier by optimizing randomly generated finite-sample means, standard deviations and correlations, rather than population values. His analysis, and similar studies by Broadie (1991) show that seemingly efficient portfolios, determined from historical estimates of means and covariances, ca n lay far from the actual efficient frontier. While the standard error of most statistics decreases in the number of observations, the standard error of the efficient frontier does not. …

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