Using Revisions as a Measure of Price Index Quality in Repeat-Sales Models
Repeat-sales indexes are the most widely used type of transaction based property price indexes. However, such indexes are particularly prone to revision. When a new period of transaction data becomes available and is used to update the repeat-sales model, all past index values can potentially be revised. These revisions are especially problematical for commercial real estate (as compared to housing), due to the relative scarcity of transaction data and the heterogeneity of the underlying properties. From a methodological perspective, the magnitude of the revisions is a particularly useful measure of the index quality, as it directly reflects both the precision of the index and its practical usefulness in economic and business applications. This paper focuses on index revisions in thin, commercial property markets, the type of market that is most challenging. We present multiple specifications of the repeat-sales model (both existing and new), seeking to reduce revisions. We are able to reduce overall index revisions by more than 50%, compared to more traditional repeat-sales models.
KeywordsCommercial real estate Markov chained Monte Carlo Property price indexes State space models
We would like to thank the attendees at the Property Indices session of the 2017 international AREUEA conference in Amsterdam and at the 2016 Hoyt meeting in West Palm Beach. Specifically, we would like to thank Daniel Melser and Martijn Dröes whom both discussed the paper. We also want to thank Real Capital Analytics for providing the data and insights that made this study possible. The comments and observations by Jim Sempere, Elizabeth Szep and Willem Vlaming at Real Capital Analytics were especially appreciated. Finally, we would like to thank the anonymous referee for his comments on this paper.
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