The Hierarchical Repeat Sales Model for Granular Commercial Real Estate and Residential Price Indices
Posted: 27 Nov 2017
Date Written: November 20, 2017
This paper concerns the estimation of granular property price indices in commercial real estate and residential markets. We specify and apply a repeat sales model with multiple stochastic log price trends having a hierarchical additive structure: One common log price trend and cluster specific log price trends in deviation from the common trend. Moreover, we assume that the error terms potentially have a heavy tailed (t) distribution to effectively deal with outliers. We apply the hierarchical repeat sales model on commercial properties in the Philadelphia/Baltimore region and on residential properties in a small part of Amsterdam. the results show that the hierarchical repeat sales model provides reliable indices on a very detailed level based on a small number of observations. The estimated degrees of freedom for the t-distribution is small, largely rejecting the commonly made assumption of normality of the error term.
Keywords: commercial real estate; Residential real estate; property price indices; Thin markets; Bayesian inference; Kalman filter; State space models
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