STRATA: A Name-and-Geography Race Inference Model for Fair Lending and Housing Equity Applications
Accurate imputation of race and ethnicity (R&E) is essential for fair lending compliance under ECOA, HMDA, and the Community Reinvestment Act, where up to 15% of mortgage applications carry missing race data and regulated institutions bear responsibility for identifying disparities on those records. Existing proxy methods, including Bayesian Improved Surname Geocoding (BISG), exhibit systematic misclassification biases linked to socioeconomic status that cause measured disparities to understate true levels. This paper introduces STRATA (Socioeconomic and Tract-Referenced Attribution for Algorithmic analysis), a race and ethnicity inference model that integrates character-level name sequences with census tract geolocation via stacked Bidirectional LSTM networks and XGBoost post-filtering. A central goal is reducing the socioeconomically correlated bias that causes non-White individuals to be misclassified as White: STRATA reduces this White False Positive Rate from 41.8% under BISG to 17.8% for the STRATA ensemble. On a held-out voter registration validation dataset, the STRATA base model achieves 88.7% accuracy, outperforming standalone LSTM (86.4%), BISG (82.9%), BIFSG (86.8%), and ZRP (85.8%); the STRATA ensemble (LSTM+XGBoost) reaches 89.2% accuracy. On a national Paycheck Protection Program loan validation dataset covering all 50 states, the STRATA ensemble achieves 84.8% accuracy versus 76.6% for name-only LSTM, confirming cross-state generalizability. A companion paper applies STRATA to 2.26 million New York City residential deed transactions. The authors caution that these models are appropriate for aggregate, population-level analysis and should not be used for individual-level transactional decisions.