Historical Bias in Mortgage Lending, Redlining, and Implications for the Uncertain Geographic Context Problem: A Study of Structural Housing Discrimination in Dallas and Boston
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View abstract on PubMed
Summary
This summary is machine-generated.This study reveals persistent racial bias in US mortgage lending, particularly in historically redlined areas. Temporal data analysis shows varying bias trends between 1990-2020 in Boston and Dallas, highlighting structural housing discrimination.
Area Of Science
- Urban studies
- Public health
- Housing policy
Background
- The uncertain geographic context problem highlights how missing temporal data limits bias measurement in mortgage lending.
- Previous methods lacked temporal analysis, hindering the understanding of persistent racial bias in housing.
Purpose Of The Study
- To measure persistent racial bias in mortgage lending for Black Americans by incorporating temporal trends and credit scores.
- To evaluate the continuity of bias in discriminatory areas from 1990 to 2020, creating an indicator of persistent structural housing discrimination.
Main Methods
- Analysis of mortgage denial odds across census tracts in Boston-Cambridge-Newton and Dallas-Fort Worth metropolitan areas (1990-2020).
- Inclusion of temporal trends and credit scores to assess bias persistence.
- Comparison of distinct historical trajectories and urban development impacts on housing discrimination.
Main Results
- Significant changes in mortgage denial bias were observed across all tracts in both metropolitan areas.
- Dallas-Fort Worth showed greater odds of bias over time, while Boston-Cambridge-Newton exhibited lower odds.
- Historically redlined areas demonstrated the strongest persistence of mortgage lending bias.
Conclusions
- Temporal data analysis enhances the identification and measurement of persistent neighborhood bias in mortgage lending.
- Understanding the temporality of residential exposure is crucial for research rigor and policy development.
- Addressing structural housing discrimination is essential to mitigate the health effects of racial bias.
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