Re-thinking Commercial Real Estate Market Segmentation

Franz Fuerst and Gianluca Marcato ()

Real Estate & Planning Working Papers from Henley Business School, University of Reading

Abstract: Investments in direct real estate are inherently difficult to segment compared to other asset classes due to the complex and heterogeneous nature of the asset. The most common segmentation in real estate investment analysis relies on 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 able to better predict the resulting financial performance of the assets than the old dichotomous segments. Applying both discriminant and neural network analysis we find mixed evidence for this hypothesis. Overall, we conclude from our analysis that each of the two approaches to segmenting the market has its strengths and weaknesses so that both might be applied gainfully in real estate investment analysis and fund management.

Keywords: market segmentation; commercial real estate; financial performance measurement; cluster analysis; neural network analysis; risk diversification (search for similar items in EconPapers)
JEL-codes: C45 D4 G11 R33 (search for similar items in EconPapers)
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