Estimating Real Estate Price Movements for High Frequency Tradable Indexes in a Scarce Data Environment
Name
Geltner Estimating Real Estate Price Movements.pdf
Size
573.32 KB
Format
Adobe PDF
Checksum (MD5)
1acb7ccd100a6b2d8c345b002527c2ca
Author(s)
Bokhari, Sheharyar
Geltner, David M.
Date Issued
July 2010
Journal
Journal of Real Estate Finance and Economics
Publisher
Springer Science + Business Media B.V.
Citation
Bokhari, Sheharyar and David Geltner. “Estimating Real Estate Price Movements for High Frequency Tradable Indexes in a Scarce Data Environment.” The Journal of Real Estate Finance and Economics (2010) : 1-22.
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
http://www.springerlink.com/content/uxl087168u781086/
