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Activity Number:
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410
- Social Issues, Trends, Inequality, and Employment
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Type:
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Contributed
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Date/Time:
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Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
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Sponsor:
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Social Statistics Section
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Abstract #324733
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Title:
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Statistical Tools for Missing Data Inference in Home Mortgage Applications
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Author(s):
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Andrew Porter*
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Companies:
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Office of the Comptroller of the Currency
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Keywords:
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Missing Data ;
Spatial Statistics ;
HMDA ;
Mortgage ;
Regulation ;
Fair Lending
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Abstract:
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The emergence of online banking and the burgeoning Fintech industry pose new challenges for banking supervision. One of these challenges applies to the Home Mortgage Disclosure Act (HMDA) which mandates that financial institutions publicly report protected class information, such as race and ethnicity, for each mortgage applicant. A significant proportion of these data can be missing, particularly from online applications. The growth of the Fintech industry will likely exacerbate the incidence of missing data further. We impute missing race and ethnicity by analyzing spatially aggregated HMDA applicant data for some large banks and online lenders. Leveraging a hierarchical modeling framework, we adopt a spatial regression model to estimate the (unknown) proportion of online applications in HMDA data in order to estimate the impact of the emergence of Fintech and online lending on fair lending, ultimately providing regulators with an enhanced information set for decision-making.
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Authors who are presenting talks have a * after their name.
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