Estimating Loan-to-Value Distributions

57 Pages Posted: 14 Sep 2011 Last revised: 18 Jun 2014

See all articles by Arthur G. Korteweg

Arthur G. Korteweg

University of Southern California - Marshall School of Business

Morten Sorensen

Copenhagen Business School; Columbia Business School; Centre for Economic Policy Research (CEPR)

Multiple version iconThere are 2 versions of this paper

Date Written: April 2014

Abstract

We estimate a model of house prices, combined loan-to-value ratios (CLTVs), and trade and foreclosure behavior. House prices are only observed for traded properties, and trades are endogenous, creating sample-selection problems for existing approaches to estimating CLTVs. We use a Bayesian filtering procedure to recover the price path for individual properties and produce selection-corrected estimates of historical CLTV distributions. Estimating our model with transactions of residential properties in Alameda, CA, we find that 35% of single-family homes are underwater, compared to the 19% estimated by existing approaches. Further, our results reduce the index revision problem and have applications for pricing mortgage-backed securities.

Keywords: Real Estate Prices, Loan-To-Value, Repeat-Sales Price Index, Sample Selection, Bayesian Estimation, Gibbs Sampling, MCMC

Suggested Citation

Korteweg, Arthur G. and Sørensen, Morten, Estimating Loan-to-Value Distributions (April 2014). Columbia Business School Research Paper No. 12-15. Available at SSRN: https://ssrn.com/abstract=1927405 or http://dx.doi.org/10.2139/ssrn.1927405

Arthur G. Korteweg

University of Southern California - Marshall School of Business ( email )

3670 Trousdale Parkway
Los Angeles, CA 90089
United States

HOME PAGE: http://www-bcf.usc.edu/~korteweg/

Morten Sørensen (Contact Author)

Copenhagen Business School ( email )

Solbjerg Plads 3
Frederiksberg C, DK - 2000
Denmark

Columbia Business School ( email )

3022 Broadway
New York, NY 10027
United States

Centre for Economic Policy Research (CEPR) ( email )

London
United Kingdom

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