Sticky Valuations, Aggregation Effects, and Property Indices

Abstract

Previous studies on real estate smoothing have generally focused on the second moment of returns for individual properties. Although this body of research has developed plausible reasons for explaining the observed lower risk associated with real estate, no explanation has, however, been offered to account for the large difference in serial correlation at the individual property level compared with the index level. This article addresses this issue and also offers an explanation for the difference in serial correlation observed with different frequency real estate indices. Employing the framework developed by Holbrook Working (1960), we argue that the high levels of serial correlation typically observed in real estate indices results from a combination of random and sticky appraisals that induce cross-correlations between the component returns. Using the concept of sticky values we question the results of Lai and Wang (1998) in which they argue that the variance of appraisal-based returns should always be greater than true returns. We argue that a pragmatic conclusion regarding volatility should be conditioned on the underlying stochastic processes. We draw a distinction between serial cross-sectional and temporal sticky appraisal processes that influence smoothing at the index and individual property levels. Our results indicate that smoothing does not appear to be a serious issue at the individual property level. However, when different appraisal processes are aggregated into an index the underlying cross-correlation between those processes can induce high levels of smoothing.