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

Author

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  • Anupam Nanda

    (Mumbai)

  • Stephen L. Ross

    (University of Connecticut)

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.

Suggested Citation

  • Anupam Nanda & Stephen L. Ross, 2008. "The Impact of Property Condition Disclosure Laws on Housing Prices: Evidence from an Event Study using Propensity Scores," Working papers 2008-39, University of Connecticut, Department of Economics.
    • 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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    2. Saeed Md. Abdullah & Simon Zaby, 2021. "Seasoned Equity Offerings and Differences in Share-Price Impact by Firm Categories," IJFS, MDPI, vol. 9(3), pages 1-10, July.
    3. Nan Liu, 2021. "Market buoyancy, information transparency and pricing strategy in the Scottish housing market," Urban Studies, Urban Studies Journal Limited, vol. 58(16), pages 3388-3406, December.
    4. Jie Chen & Haiyong Zhang & Qian Zhou, 2021. "Rule by Law, Law-Based Governance, and Housing Prices: The Case of China," Land, MDPI, vol. 10(6), pages 1-22, June.
    5. Gong, Cynthia M. & Lizieri, Colin & Bao, Helen X.H., 2019. "“Smarter information, smarter consumers”? Insights into the housing market," Journal of Business Research, Elsevier, vol. 97(C), pages 51-64.
    6. Røed Larsen, Erling, 2018. "Can monetary policy revive the housing market in a crisis? Evidence from high-resolution data on Norwegian transactions," Journal of Housing Economics, Elsevier, vol. 42(C), pages 69-83.
    7. Andres Jauregui & Alan Tidwell & Diane Hite, 2017. "Sample Selection Approaches to Estimating House Price Cash Differentials," The Journal of Real Estate Finance and Economics, Springer, vol. 54(1), pages 117-137, January.
    8. K. W. Chau & Lennon H. T. Choy, 2011. "Let the Buyer or Seller Beware: Measuring Lemons in the Housing Market under Different Doctrines of Law Governing Transactions and Information," Journal of Law and Economics, University of Chicago Press, vol. 54(S4), pages 347-365.
    9. L. Li & K. W. Chau, 2024. "Information Asymmetry with Heterogeneous Buyers and Sellers in the Housing Market," The Journal of Real Estate Finance and Economics, Springer, vol. 68(1), pages 138-159, January.
    10. Seshimo, Hiroyuki, 2014. "Adverse selection versus hold up: Tenure choice, tenancy protection and equilibrium in housing markets," Regional Science and Urban Economics, Elsevier, vol. 48(C), pages 39-55.
    11. Lee, Sanghoon & Ries, John & Somerville, C. Tsuriel, 2013. "Repairs under imperfect information," Journal of Urban Economics, Elsevier, vol. 73(1), pages 43-56.
    12. Walsh, Patrick & Mui, Preston, 2017. "Contaminated sites and information in hedonic models: An analysis of a NJ property disclosure law," Resource and Energy Economics, Elsevier, vol. 50(C), pages 1-14.
    13. Jung, Hosung & Lee, Jieun, 2017. "The effects of macroprudential policies on house prices: Evidence from an event study using Korean real transaction data," Journal of Financial Stability, Elsevier, vol. 31(C), pages 167-185.
    14. Van Vliet, Olaf & Been, Jim & Caminada, Koen & Goudswaard, Kees, 2011. "Pension reform and income inequality among the elderly in 15 European countries," MPRA Paper 32940, University Library of Munich, Germany.

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    More about this item

    Keywords

    Property Condition Disclosure; Housing Price Index; Propensity Score Matching; Event Study;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • K11 - Law and Economics - - Basic Areas of Law - - - Property Law
    • L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand

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