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.
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. In case of further problems read
the IDEAS help
page. Note that these files are not on the IDEAS
site. Please be patient as the files may be large.
Publisher Info
Paper provided by University of Connecticut, Department of Economics in its series Working papers with number
2005-47.
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. Contact details of provider: 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/ More information through EDIRC
For technical questions regarding this item, or to correct its listing, contact: (Christian Zimmermann).
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:
References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
Cited by: (explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)