Re-Thinking Commercial Real Estate Market Segmentation

Abstract

Identifying groups of comparable individual assets for a relative comparison of investment performance presents a major difficulty for direct real estate investors. The old adage ‘no two properties are exactly the same’ expresses this problem, yet investment managers require reliable this information to assess the performance of individual assets. The most common segmentation currently used in real estate investment analysis combines property sector and geographical region. In this paper, we compare the predictive power of existing industry classifications with a new type of segmentation using cluster analysis on a number of relevant property attributes, including the equivalent yield and size of the property, as well as information on lease terms, number of tenants and tenant concentration. The new segments are shown to be distinct and relatively stable over time. In a second stage of the analysis, we test whether the newly generated segments are also able to better predict the resulting financial performance of the assets than the old two-dimensional segments. Applying both discriminant and neural network analysis we find mixed evidence for this hypothesis. Overall, we conclude that each of the two approaches is valid depending on the specific task at hand. While our new clusters are more suitable for identifying investment opportunities and risks, the old sector-region classification is sufficient for describing the broad characteristics of a real estate portfolio.

Keywords: market segmentation, commercial real estate, financial performance measurement, cluster analysis, neural network analysis, risk diversification

JEL Classification: C45, D4, G11, R33