IDEAS home Printed from https://ideas.repec.org/p/uct/uconnp/2008-39.html

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

Author

Listed:
  • 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).
    as

    Download full text from publisher

    File URL: https://media.economics.uconn.edu/working/2008-39.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Raffaella Barone, 2025. "Non-Residential Real Estate Prices And Machine Learning: The How And The Why," BAFFI CAREFIN Working Papers 25238, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    2. Daniel Broxterman & Tingyu Zhou, 2023. "Correction to: Information Frictions in Real Estate Markets: Recent Evidence and Issues," The Journal of Real Estate Finance and Economics, Springer, vol. 66(2), pages 299-299, February.
    3. 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.
    4. 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.
    5. Muskan Dugar & Trupti Dandekar Humnekar & V Moovendhan, 2024. "Analyzing Pre- and Post-Pandemic Housing Market Trends in India: A Quasi-Experimental Approach Using ITS and Panel Data Analysis," International Real Estate Review, Global Social Science Institute, vol. 27(3), pages 393-411.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Lee, Sanghoon & Ries, John & Somerville, C. Tsuriel, 2013. "Repairs under imperfect information," Journal of Urban Economics, Elsevier, vol. 73(1), pages 43-56.
    11. Tandel, Vaidehi & Gandhi, Sahil & Nanda, Anupam & Agnihotri, Nandini, 2025. "Do mandatory disclosures squeeze the lemons? The case of housing markets in India," Journal of Public Economics, Elsevier, vol. 247(C).
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.

    More about this item

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:uct:uconnp:2008-39. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mark McConnel (email available below). General contact details of provider: https://edirc.repec.org/data/deuctus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.