Assessing Fair Lending Risks Using Race/Ethnicity Proxies
评估了多种种族/民族代理变量方法（如地理、姓氏及贝叶斯改进的姓氏地理编码）在非抵押贷款公平风险分析中的准确性，发现BISG最大分类法能更精确估计抵押贷款定价差异。
Fair lending analysis of nonmortgage credit products often involves proxying for race/ethnicity since such information is not required to be reported. Using mortgage data, this paper evaluates a series of proxy approaches (geo, surname, geo-surname, and Bayesian Improved Surname Geocoding (BISG)) as compared with the race/ethnicity reported under the Home Mortgage Disclosure Act (HMDA). The BISG proxy predicts the reported race/ethnicity the best as judged by prediction bias, correlation coefficient, and discriminatory power. In assessing fair lending risks where classification of race/ethnicity is called for, we propose the BISG maximum classification, which produces a more accurate estimation of mortgage pricing disparities than the current practices. The above conclusions withhold various robustness tests. Additional analysis is performed to assess the proxies on nonmortgage credits by leveraging consumer credit bureau data. This paper was accepted by Amit Seru, finance.