Evaluating Statistical Models of Mortgage Lending Discrimination: A Bank-Specific Analysis

Posted: 26 Aug 1999

See all articles by Mitchell Stengel

Mitchell Stengel

Government of the United States of America - Office of the Comptroller of the Currency (OCC)

Dennis Glennon

Government of the United States of America - Office of the Comptroller of the Currency (OCC)

Abstract

We present our efforts to develop bank-specific models to test for the presence of mortgage lending discrimination. We discuss the potential for selection and simultaneity biases and delineate the conditions under which a single-equation model is appropriate. The results from three national banks demonstrate that, by incorporating the specific underwriting guidelines of each bank, our alternative approach significantly improves the ability of the model to explain the outcomes of the mortgage lending decision process when compared to a single generic specification applied across all banks. Our results also demonstrate the difficulties encountered in attempting to incorporate the specifics of a bank's underwriting criteria and the remaining potential for omitted-variables problems.

JEL Classification: G21, G28

Suggested Citation

Stengel, Mitchell and Glennon, Dennis, Evaluating Statistical Models of Mortgage Lending Discrimination: A Bank-Specific Analysis. Available at SSRN: https://ssrn.com/abstract=171848

Mitchell Stengel (Contact Author)

Government of the United States of America - Office of the Comptroller of the Currency (OCC) ( email )

250 E Street, SW
Senior Financial Economist; Economics Department Risk Analysis Div. 2-1
Washington, DC 20219-0001
United States
202-874-5431 (Phone)
202-874-5394 (Fax)

Dennis Glennon

Government of the United States of America - Office of the Comptroller of the Currency (OCC) ( email )

250 E Street, SW
Senior Financial Economist; Economics Department
Washington, DC 20219-0001
United States
202-874-4725 (Phone)
202-874-5394 (Fax)

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Abstract Views
992
PlumX Metrics