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.

An analysis of commercial real estate returns: an anatomy of smoothing in asset and index returns

In this article, we investigate the commonly used autoregressive filter method of adjusting appraisal-based real estate returns to correct for the perceived biases induced in the appraisal process. Many articles have been written on appraisal smoothing but remarkably few have considered the relationship between smoothing at the individual property level and the amount of persistence in the aggregate appraisal-based index. To investigate this issue we analyze a large sample of appraisal data at the individual property level from the Investment Property Databank. We find that commonly used unsmoothing estimates at the index level overstate the extent of smoothing that takes place at the individual property level. There is also strong support for an ARFIMA representation of appraisal returns at the index level and an ARMA model at the individual property level.

Cycles and steps in British commercial property values

This paper examines cyclical behaviour in commercial property values over the period 1956 to 1996, using a structural times series (unobserved components) approach. The influence of the transition to short rent reviews during the late 1960s and the short and long-term impacts of the 1974 and 1990 property crashes are also incorporated into the analysis, via dummy variables. It is found that once these variables are taken into account a fairly regular cyclical pattern can be discerned, with a period of about 7.8 years. Furthermore, the 1974 and 1990 property crashes are shown to have had a major long-term impact on property value growth (presumably via their influence on investors' expectations).

Why do research on commercial property management? Somebody HAS to!

This paper identifies some significant gaps in our knowledge of the configuration and performance of the property asset management sector. It is argued that, as many leading academic property researchers have focussed on financial vehicles and modelling, in-depth analysis of property assets and their management has been neglected. In terms of potential for future in-depth research, three key broad preliminary research themes or questions are identified. First, how do the active management opportunities presented, costs of management and the key management tasks vary with market conditions, asset type and life-cycle stage? Second, how is property asset management delivered and what are the main costs and benefits of different models of procurement? Finally, what are the appropriate metrics for measuring the performance of different property managers and approaches to property management? It is concluded that the lack of published materials addressing these issues has implications for educating property students.