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

Marc K. Francke () and Alex Minne ()
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Marc K. Francke: Amsterdam Business School
Alex Minne: MIT Center for Real Estate

The Journal of Real Estate Finance and Economics, 2017, vol. 55, issue 4, No 6, 532 pages

Abstract: Abstract 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 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (18)

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DOI: 10.1007/s11146-017-9632-1

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