The Impact of Property Condition Disclosure Laws on Housing Prices: Evidence from an Event Study using Propensity Scores

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The Impact of Property Condition Disclosure Laws on Housing Prices: Evidence from an Event Study using Propensity Scores

Author Info

Anupam Nanda (Mumbai)
Stephen L. Ross (University of Connecticut)

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Abstract

We examine the impact of seller's Property Condition Disclosure Law on the residential real estate values. A disclosure law may address the information asymmetry in housing transactions shifting of risk from buyers and brokers to the sellers and raising housing prices as a result. We combine propensity score techniques from the treatment effects literature with a traditional event study approach. We assemble a unique set of economic and institutional attributes for a quarterly panel of 291 US Metropolitan Statistical Areas (MSAs) and 50 US States spanning 21 years from 1984 to 2004 is used to exploit the MSA level variation in house prices. The study finds that the average seller may be able to fetch a higher price (about three to four percent) for the house if she furnishes a state-mandated seller.s property condition disclosure statement to the buyer. When we compare the results from parametric and semi-parametric event analyses, we find that the semi-parametric or the propensity score analysis generals moderately larger estimated effects of the law on housing prices.

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Paper provided by University of Connecticut, Department of Economics in its series Working papers with number 2008-39.

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Length: 33 pages
Date of creation: Sep 2008
Date of revision:
Handle: RePEc:uct:uconnp:2008-39

Note: Authors acknowledge helpful comments from John Clapp, Dennis Heffley, James Davis, Katherine Pancak, Thomas Miceli, and seminar participants at the University of Connecticut, Economics Brownbag Seminar Series, and 2006 AREUEA Doctoral Session in Boston. We would also like to thank Tim Storey (National Conference of State Legislatures), Daniel Conti (Bureau of Labor Statistics) for assistance with data, and Sascha Becker of University of Munich for assistance with STATA module on propensity score matching algorithm (written by Sascha Becker and Andrea Ichino).
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Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
K11 - Law and Economics - - Basic Areas of Law - - - Property Law
L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services
R21 - Urban, Rural, and Regional Economics - - Household Analysis - - - Housing Demand

This paper has been announced in the following NEP Reports:

NEP-ALL-2008-10-13 (All new papers)
NEP-GEO-2008-10-13 (Economic Geography)
NEP-LAW-2008-10-13 (Law & Economics)
NEP-URE-2008-10-13 (Urban & Real Estate Economics)

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This page was last updated on 2009-11-24.

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