Abstract
Using single-family sales data for Louisville, Kentucky, we show the benefits of applying robust methods to down-weight problematic transactions in a repeat sales context. Robust estimators reduce the influence of outliers in repeat sales price changes that are due to data entry errors, quality changes or nonmarket transactions. In addition to comparing conventional and robust indexes, we also use simulated data, where the correct index is known, to show that robust methods control for the impacts of contaminated data. Finally, we demonstrate that robust methods reduce the magnitude and volatility of index revisions.
| Original language | English |
|---|---|
| Pages (from-to) | 517-541 |
| Number of pages | 25 |
| Journal | Real Estate Economics |
| Volume | 41 |
| Issue number | 3 |
| Early online date | 19 Jul 2013 |
| DOIs | |
| Publication status | Published - 2013 |
Bibliographical note
For helpful comments, we thank John Clapp, Gary Engelhardt, Jeff Fisher, Norm Miller, Stephen Ross, Peter Zorn and other seminar participants at the University of Connecticut, University of Memphis, University of Turku (Finland) and Weimer School of Advanced Studies in Real Estate and Land Economics. Two reviewers and Ed Coulson (the editor) also made useful suggestions. The authors are grateful to the Greater Louisville Association of Realtors for providing the MLS data.Fingerprint
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