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Neighborhood Information Externalities and the Provision of Mortgage Credit

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  • Xiaoming Li

    (University of Connecticut)

  • AKM Rezaul Hossain

    (St. Mary's College)

  • Stephen L. Ross

    (University of Connecticut)

Abstract

Recent theoretical models and empirical analyses argue that mortgage market activity creates information and lowers the costs of underwriting mortgages. We re-examine this question using models that control for neighborhood-lender fixed effects and address the potential endogeneity of market volume using information on lagged volumes. We find that the omission of neighborhood fixed effects substantially biases analyses of the effect of neighborhood volume on underwriting, and our tests imply that the one or two period lags of volume typically used in cross-sectional studies cannot be treated as exogenous due to the persistence of neighborhood economic shocks. In our preferred specification, we cannot rule out the possibility that overall market activity has a small positive influence on mortgage underwriting, but the statistical evidence for the existence of such effects is weak. On the other hand, we find that lender-specific activity in a neighborhood has strong positive effects on the mortgage approval decision, and this effect is underestimated in traditional cross-sectional models.

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Bibliographic Info

Paper provided by University of Connecticut, Department of Economics in its series Working papers with number 2010-10.

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Length: 27 pages
Date of creation: May 2010
Date of revision:
Handle: RePEc:uct:uconnp:2010-10

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  1. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-26, November.
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Cited by:
  1. AKM Rezaul Hossain, 2010. "Branch location choice: Do lenders discriminate?," Economía, Instituto de Investigaciones Económicas y Sociales (IIES). Facultad de Ciencias Económicas y Sociales. Universidad de Los Andes. Mérida, Venezuela, vol. 35(30), pages 11-55, July-Dece.
  2. Xiaoming Li, 2011. "Fixed Effects Estimation in Panel Nonlinear Fractional Response Models," Working papers 2011-11, University of Connecticut, Department of Economics.

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