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Property portfolios are traditionally constructed by diversifying across geographical areas, property types, or a combination of both. In the United Kingdom it is normal practice to use regions rather than towns or local market areas as the geographical divisions. The authors use cluster analysis to construct homogeneous groups from 157 UK local markets, by means of commercial property returns. The results show strong property-type dimensions and only very broad geographical dimensions in the clusters. These clusters are found, in general, to have temporal stability with changes in cluster membership being explained by the changing economic geography of the United Kingdom. The cluster-derived groupings are used to derive efficient investment frontiers and are compared with frontiers based on conventional heuristic groupings. It is shown that strategies based on parsimonious cluster-based groupings, appropriate for smaller investors, generate results that are comparable with those of conventional groupings and capture the main drivers of property performance.

In this paper, we use constrained cross-section regressions to disentangle the effects of various factors on real estate security returns in 21 countries. A better knowledge of the risk factors driving real estate returns is crucial, whether a pure real estate portfolio is constructed, or whether real estate is considered as an altemative asset class within the traditional stock portfolio. Besides a common factor, "pure" country, size, and value/growth factors are considered. The value/growth measure that is used in this paper is a unique indicator developed by Salomon Smith Bamey (SSB). It provides for each stock the relative importance of the value and growth components, rather than using a binary classification. The value/growth factor is fotmd to have a substantial ,and increasing effect on returns over the analyzed period February 1990-April2002. Country factors are important determinants of real estate security returns also. Statistical analysis of the residuals indicates that additional "hidden" factors most likely exist. These statistical factors are shown to explain about one third of specific returns on intemational real estate securities. Nevertheless, as is the case for traditional stock portfolios, stock picking keeps al1 its importante for real estate stocks as well.:~Lombard Odier & Cie (Geneva), Vrije Universiteit (Amsterdam) and FAME,

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The role of real estate in a mixed-asset portfolio is investigated when the maximum drawdown (hereafter MaxDD), rather than the standard deviation, is used as the measure of risk. In particular, it is analysed whether the discrepancy between the optimal allocation to real estate and the actual allocation by institutional investors is less when a Return/MaxDD framework is used. The empirical analysis is conducted from the perspective of a Swiss investor using international data for the period 1979-2002. It is shown that most portfolios optimized in Return/ MaxDD space, rather than in Return/Standard Deviation space, yield a much lower MaxDD, while usually only a slightly higher standard deviation (for the same level of return). Also, the reported weights for real estate are much more in line with the actual weights to real estate by institutional investors.