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An analysis of foreclosure rate differentials in soft markets

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  • Francisca Richter
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    Abstract

    A quantile regression model is used to identify the main neighborhood characteristics associated with high foreclosure rates in weak market neighborhoods, specifically for two counties in Ohio and one in Pennsylvania. A decomposition technique by Machado and Mata (2005) allows separating foreclosure filing rate differentials across counties into two components: the first due to differences in the levels of neighborhood characteristics and the second due to differences in the model parameters. At higher than median rates, foreclosure rate differentials between counties in Ohio are mainly explained by the levels of these characteristics. However, foreclosure rate differences between counties across states are mainly explained by the parameter component, suggesting that state level effects might have contributed to shape foreclosure rate outcomes.

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    File URL: http://www.clevelandfed.org/research/workpaper/2008/wp0811.pdf
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    Bibliographic Info

    Paper provided by Federal Reserve Bank of Cleveland in its series Working Paper with number 0811.

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    Date of creation: 2008
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    Handle: RePEc:fip:fedcwp:0811

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    Keywords: Foreclosure ; Federal Reserve District; 4th ; Economic conditions - Ohio ; Economic conditions - Pennsylvania;

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    1. Morris M. Kleiner & Richard M. Todd, 2009. "Mortgage Broker Regulations That Matter: Analyzing Earnings, Employment, and Outcomes for Consumers," NBER Chapters, in: Studies of Labor Market Intermediation, pages 183-231 National Bureau of Economic Research, Inc.
    2. Chris Mayer & Karen Pence, 2008. "Subprime mortgages: what, where, and to whom?," Finance and Economics Discussion Series 2008-29, Board of Governors of the Federal Reserve System (U.S.).
    3. Bostic, Raphael W. & Engel, Kathleen C. & McCoy, Patricia A. & Pennington-Cross, Anthony & Wachter, Susan M., 2008. "State and local anti-predatory lending laws: The effect of legal enforcement mechanisms," Journal of Economics and Business, Elsevier, vol. 60(1-2), pages 47-66.
    4. Grover, Michael & Smith, Laura & Todd, Richard M., 2008. "Targeting foreclosure interventions: An analysis of neighborhood characteristics associated with high foreclosure rates in two Minnesota counties," Journal of Economics and Business, Elsevier, vol. 60(1-2), pages 91-109.
    5. Morris M. Kleiner & Richard M. Todd, 2007. "Mortgage Broker Regulations That Matter: Analyzing Earnings, Employment, and Outcomes for Consumers," NBER Working Papers 13684, National Bureau of Economic Research, Inc.
    6. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731.
    7. Atif Mian & Amir Sufi, 2008. "The Consequences of Mortgage Credit Expansion: Evidence from the 2007 Mortgage Default Crisis," NBER Working Papers 13936, National Bureau of Economic Research, Inc.
    8. José Mata & José A. F. Machado, 2005. "Counterfactual decomposition of changes in wage distributions using quantile regression," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(4), pages 445-465.
    9. Giang Ho & Anthony Pennington-Cross, 2005. "The impact of local predatory lending laws," Working Papers 2005-049, Federal Reserve Bank of St. Louis.
    10. Christopher J. Mayer & Karen Pence, 2008. "Subprime Mortgages: What, Where, and to Whom?," NBER Working Papers 14083, National Bureau of Economic Research, Inc.
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    Cited by:
    1. Blackburn, McKinley L. & Vermilyea, Todd, 2012. "The prevalence and impact of misstated incomes on mortgage loan applications," Journal of Housing Economics, Elsevier, vol. 21(2), pages 151-168.

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