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Property Condition Disclosure Law: Does 'Seller Tell All' Matter in Property Values?

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  • Anupam Nanda

    (University of Connecticut)

Abstract

At the time when at least two-thirds of the US states have already mandated some form of seller's property condition disclosure statement and there is a movement in this direction nationally, this paper examines the impact of seller's property condition disclosure law on the residential real estate values, the information asymmetry in housing transactions and shift of risk from buyers and brokers to the sellers, and attempts to ascertain the factors that lead to adoption of the disclosur law. The analytical structure employs parametric panel data models, semi-parametric propensity score matching models, and an event study framework using 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. Exploiting 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.

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File URL: http://web2.uconn.edu/economics/working/2005-47.pdf
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Bibliographic Info

Paper provided by University of Connecticut, Department of Economics in its series Working papers with number 2005-47.

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Length: 43 pages
Date of creation: Nov 2005
Date of revision: Jul 2006
Handle: RePEc:uct:uconnp:2005-47

Note: This paper is adapted from the third chapter of my doctoral dissertation. I would like to thank my advisors - Stephen L. Ross, John M. Clapp, and Dennis R. Heffley for their insightful comments on the idea and methodology. I greatly benefited from helpful comments from James Davis and Katherine Pancak. Comments from Dhamika Dharmapala, Thomas Miceli, and seminar participants at the University of Connecticut, Economics Brownbag Seminar Series are acknowledged. I 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). All remaining rrors are mine.
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Postal: University of Connecticut 341 Mansfield Road, Unit 1063 Storrs, CT 06269-1063
Phone: (860) 486-4889
Fax: (860) 486-4463
Web page: http://www.econ.uconn.edu/
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Related research

Keywords: Property Condition Disclosure; Housing Price Index; Propensity Score Matching Event Study;

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References

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  1. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
  2. Marco Caliendo & Sabine Kopeinig, 2005. "Some Practical Guidance for the Implementation of Propensity Score Matching," Discussion Papers of DIW Berlin 485, DIW Berlin, German Institute for Economic Research.
  3. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity score matching methods for non-experimental causal studies," Discussion Papers 0102-14, Columbia University, Department of Economics.
  4. Heckman, James J & Ichimura, Hidehiko & Todd, Petra E, 1997. "Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," Review of Economic Studies, Wiley Blackwell, vol. 64(4), pages 605-54, October.
  5. Rajeev H. Dehejia & Sadek Wahba, 1998. "Causal Effects in Non-Experimental Studies: Re-Evaluating the Evaluation of Training Programs," NBER Working Papers 6586, National Bureau of Economic Research, Inc.
  6. Kiefer, Nicholas M, 1988. "Economic Duration Data and Hazard Functions," Journal of Economic Literature, American Economic Association, vol. 26(2), pages 646-79, June.
  7. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004. "How Much Should We Trust Differences-in-Differences Estimates?," The Quarterly Journal of Economics, MIT Press, vol. 119(1), pages 249-275, February.
  8. Arellano, M. & Honore, B., 2000. "Panel Data Models: Some Recent Developments," Papers 0016, Centro de Estudios Monetarios Y Financieros-.
  9. Heckman, James J & Ichimura, Hidehiko & Todd, Petra, 1998. "Matching as an Econometric Evaluation Estimator," Review of Economic Studies, Wiley Blackwell, vol. 65(2), pages 261-94, April.
  10. Roberta Romano & Sanjai Bhagat, 2001. "Event Studies and the Law - Part I: Technique and Corporate Litigation," Yale School of Management Working Papers ysm182, Yale School of Management.
  11. Arellano, Manuel & Bond, Stephen, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Wiley Blackwell, vol. 58(2), pages 277-97, April.
  12. Sascha O. Becker & Andrea Ichino, 2002. "Estimation of average treatment effects based on propensity scores," Stata Journal, StataCorp LP, vol. 2(4), pages 358-377, November.
  13. Norman Miller & Liang Peng, 2006. "Exploring Metropolitan Housing Price Volatility," The Journal of Real Estate Finance and Economics, Springer, vol. 33(1), pages 5-18, August.
  14. Ahn, Seung C. & Schmidt, Peter, 1995. "Efficient estimation of models for dynamic panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 5-27, July.
  15. Slottje, Daniel J. & Millimet, Daniel L. & Buchanan, Michael J., 2007. "Econometric analysis of copyrights," Journal of Econometrics, Elsevier, vol. 139(2), pages 303-317, August.
  16. Gabor Kezdi, 2005. "Robus Standard Error Estimation in Fixed-Effects Panel Models," Econometrics 0508018, EconWPA.
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Cited by:
  1. Anupam Nanda, 2006. "Property Condition Disclosure Law: Why Did States Mandate 'Seller Tell All'?," Working papers 2006-16, University of Connecticut, Department of Economics.
  2. Jeffrey Zabel, 2007. "The Impact of Imperfect Information on the Transactions of Contaminated Properties," NCEE Working Paper Series 200703, National Center for Environmental Economics, U.S. Environmental Protection Agency, revised Jan 2007.

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