Using Revisions as a Measure of Price Index Quality in Repeat-Sales Models

Alex Minne (), Marc Francke, David Geltner and Robert White
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Alex Minne: MIT Center for Real Estate
Marc Francke: Amsterdam Business School
David Geltner: MIT Center for Real Estate
Robert White: Real Capital Analytics

The Journal of Real Estate Finance and Economics, 2020, vol. 60, issue 4, No 3, 514-553

Abstract: Abstract 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.

Keywords: Commercial real estate; Markov chained Monte Carlo; Property price indexes; State space models (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (9)

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DOI: 10.1007/s11146-018-9692-x

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