Estimating Real Estate Price Movements for High Frequency Tradable Indexes in a Scarce Data Environment
Posted: 31 Aug 2012 Last revised: 31 Jan 2018
Date Written: 2012
Indexes of commercial property prices face much scarcer transactions data than housing indexes, yet the advent of tradable derivatives on commercial property places a premium on both high frequency and accuracy of such indexes. The dilemma is that with scarce data a low-frequency return index (such as annual) is necessary to accumulate enough sales data in each period. This paper presents an approach to address this problem using a two-stage frequency conversion procedure, by first estimating lower-frequency indexes staggered in time, and then applying a generalized inverse estimator to convert from lower to higher frequency return series. The two-stage procedure can improve the accuracy of high-frequency indexes in scarce data environments. In this paper the method is demonstrated and analyzed by application to empirical commercial property repeat-sales data.
Keywords: Real estate price indexes, Frequency-conversion, Transactions-based-index estimation, Derivatives, Noise filter
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