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 (su...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Springer Science + Business Media B.V.
2011
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| Online Access: | http://hdl.handle.net/1721.1/64714 https://orcid.org/0000-0003-2865-9475 https://orcid.org/0000-0002-1024-7555 |
| 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. |
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