The Hierarchical Repeat Sales Model for Granular Commercial Real Estate and Residential Price Indices

• Main Authors: Francke, Marc K (Author), van de Minne, Alexander (Contributor)
• Other Authors: Massachusetts Institute of Technology. Center for Real Estate (Contributor), Massachusetts Institute of Technology. Department of Urban Studies and Planning (Contributor)
• Format: Article
• Language: English
• Published: Springer US, 2017-10-13T16:05:23Z.
• Subjects: Article
• Online Access: Get fulltext

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.