Estimating Real Estate Price Movements for High Frequency Tradable Indexes in a Scarce Data Environment
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...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer Science + Business Media B.V.,
2011-06-29T21:11:09Z.
|
| Subjects: | |
| Online Access: | Get fulltext |
| Summary: | 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. Real Capital Analytics (Firm) Real Estate Analysts Limited |
|---|