Robust Repeat Sales Indexes

Steven C. Bourassa; Eva Cantoni; Martin Hoesli

doi:10.1111/reec.12013

Robust Repeat Sales Indexes

Steven C. Bourassa, Eva Cantoni, Martin Hoesli

Accountancy & Finance, Accountancy

Research output: Contribution to journal › Article › peer-review

19 Citations (Scopus)

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	https://doi.org/10.1111/reec.12013
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.

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Cite this

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title = "Robust Repeat Sales Indexes",

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.",

author = "Bourassa, {Steven C.} and Eva Cantoni and Martin Hoesli",

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.",

year = "2013",

doi = "10.1111/reec.12013",

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PY - 2013

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AB - 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.

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JO - Real Estate Economics

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Robust Repeat Sales Indexes

Abstract

Bibliographical note

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