Estimating Real Estate Price Movements for High Frequency Tradable Indexes in a Scarce Data Environment

Indexes of commercial property prices face much scarcer transactions datathan housing indexes, yet the advent of tradable derivatives on commercial propertyplaces a premium on both high frequency and accuracy of such indexes. Thedilemma is that with scarce data a low-frequency return index (such as annual) isnecessary to accumulate enough sales data in each period. This paper presents anapproach to address this problem using a two-stage frequency conversion procedure,by first estimating lower-frequency indexes staggered in time, and then applying ageneralized inverse estimator to convert from lower to higher frequency returnseries. The two-stage procedure can improve the accuracy of high-frequency indexesin scarce data environments. In this paper the method is demonstrated and analyzedby application to empirical commercial property repeat-sales data.