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

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 1 Introduction Real estate is the largest single class of investment in the world and the biggest source of household wealth in most industrialized countries. Estimating the risk and return

Riskiness of Real Estate Development: A Perspective from Urban Economics and Option Value Theory

“…Note that this is similar to estimating the Bailey, Muth and Nourse () repeat sales model in “levels” (Francke and Minne ). One known issue with these types of repeat sales models is that they can be affected by selection bias.…”

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An Improved Approach for Constructing the Real Estate Price Index with Bargaining Effect

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