The Hierarchical Repeat Sales Model for Granular Commercial Real Estate and Residential Price Indices
Author(s)Francke, Marc K; van de Minne, Alexander
MetadataShow full item record
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.
DepartmentMassachusetts Institute of Technology. Center for Real Estate; Massachusetts Institute of Technology. Department of Urban Studies and Planning
The Journal of Real Estate Finance and Economics
Francke, Marc K., and van de Minne, Alex. “The Hierarchical Repeat Sales Model for Granular Commercial Real Estate and Residential Price Indices.” The Journal of Real Estate Finance and Economics 55, 4 (August 2017): 511–532 © 2017 Springer Science+Business Media, LLC
Author's final manuscript