Correlation Shifts and Real Estate Portfolio Management

By Lee, Stephen L. | Journal of Real Estate Portfolio Management, January-April 2003 | Go to article overview
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Correlation Shifts and Real Estate Portfolio Management


Lee, Stephen L., Journal of Real Estate Portfolio Management


Executive Summary. The success of any diversification strategy depends on the quality of the estimated correlation between assets. It is well known, however, that there is a tendency for the average correlation among assets to increase when the market falls and vice-versa. This suggests that correlation shifts can be modeled as a function of the market return. This is the idea behind the model Spurgin, Martin and Schneeweis (2000), which models the systematic risk, of an asset as a function of the returns in the market. In this paper the Spurgin et al. model is applied to monthly data in the United Kingdom over the period 1987:1 to 2000:12. The results show that a number of market segments display significant negative correlation shifts, while others show significantly positive correlation shifts.

This article is the winner of the Real Estate Investment/Portfolio Management manuscript prize (sponsored by RREEF) presented at the 2002 American Real Estate Society Annual Meeting.

Introduction

The benefits of diversification within real estate portfolios are well known [see Hamelink, Hoesli, Lizieri and MacGregor (2000) and Viezer (2000) for comprehensive reviews]. These benefits accrue from the less than perfect correlation between the various market segments (i.e., if the correlation between market segments is low, spreading the portfolio across these segments should lead to a decrease in total risk and allows fund managers more opportunities to find properties with higher returns). In other words, the lower the level of correlation between assets, the greater the potential for portfolio risk reduction and increased returns. The success of a particular diversification strategy consequently depends on the quality of the estimated correlation between assets. It is well known, however, that there is a tendency for the average correlation among assets to change as markets rise and fall. Assuming that the correlation between assets is constant over time therefore seems unrealistic. The better the estimation of the change in the correlation coefficients over time, the greater the potential benefits to the management of the real estate portfolio.

The traditional approach to estimating the correlation between assets is to use historic data over a fixed time period. Such an approach is poorly suited to studying changes in correlation over time, as a large number of observations are required just to estimate one correlation coefficient. Alternative estimation methods have been suggested that either have severe limitations or are not easy to implement (see Solnik and Roulet, 2000). Recently, Spurgin, Martin and Schneeweis (2000) have proposed a simple way to estimate the changes in the correlation of an asset as a function of the general level of the market. This is an approach that offers particular attractions to fund managers as it suggest ways by which they can adjust their portfolios to benefit from changes in overall market conditions.

The remainder of the paper is organized as follows: the next section discusses the model proposed by Spurgin, Martin and Schneeweis (2000). The sections that follow describe the data, and present the estimation results and the model. Next, the asset returns characteristics are discussed, along with the implications for portfolio asset allocation. The final section is the conclusion.

Estimation of Correlation Coefficients

The traditional approach to the estimation of the correlation coefficient between assets is to use a fixed number of time series observations with a sufficiently large number of data points to provide statistically significant estimates. However, such an estimation method is deficient in at least two areas. First, each pairwise correlation coefficient is computed separately; consequently the overall correlation between each asset has to be estimated from say the average of all pairwise coefficients. Secondly, the time series method provides only an unconditional estimate and so changes in correlation coefficients are difficult to judge.

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