A geography of the UK commercial property market

This paper revisits some ideas that were first raised seriously in the mid-90s; that it should be possible to establish linkages (in spatial terms) between local economic factors and sector performance in commercial real estate markets. There have been a number of developments in the quality and quantity of relevant data over the intervening period that make it appropriate to return to have another look at some of these ideas in a more ‘modern’ technological context. Using data from several sources this exploratory paper seeks therefore to look at some of the spatial patterns that can be derived from the data. It examines the extent to which it is possible to make linkages and visualise the geographical structure of those markets and their change over time. Naturally there remain strong limitations on the extent to which it is possible to achieve ‘good’ results in this kind of analysis, and one major intention of the paper is to encourage a debate about how data sets can be developed and improved to allow these methods to be taken further.

Re-thinking commercial real estate market segmentation

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.