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Historical Bias in Mortgage Lending, Redlining, and Implications for the Uncertain Geographic Context Problem: A Study of Structural Housing Discrimination in Dallas and Boston.
Beauchamp, Alaina M; Tiro, Jasmin A; Haas, Jennifer S; Kobrin, Sarah C; Alegria, Margarita; Hughes, Amy E.
Afiliação
  • Beauchamp AM; Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390-9066, USA. Alaina.Beauchamp@utsouthwestern.edu.
  • Tiro JA; Department of Public Health Sciences, University of Chicago Biological Sciences Division, Chicago, IL, USA.
  • Haas JS; Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA.
  • Kobrin SC; Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, USA.
  • Alegria M; Department of Medicine and Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
  • Hughes AE; Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390-9066, USA.
J Urban Health ; 2024 Aug 21.
Article em En | MEDLINE | ID: mdl-39168963
ABSTRACT
According to the uncertain geographic context problem, a lack of temporal information can hinder measures of bias in mortgage lending. This study extends previous methods to (1) measure the persistence of racial bias in mortgage lending for Black Americans by adding temporal trends and credit scores, and (2) evaluate the continuity of bias in discriminatory areas from 1990 to 2020. These additions create an indicator of persistent structural housing discrimination. We studied the Boston-Cambridge-Newton and Dallas-Fort Worth metropolitan statistical areas to examine distinct historical trajectories and urban development. We estimated the odds of mortgage denial for census tracts. Overall, all tracts in Boston-Cambridge-Newton (N = 1003) and Dallas-Fort Worth (N = 1312) displayed significant change, with greater odds of bias over time in Dallas-Fort Worth and lower odds in Boston-Cambridge-Newton. Historically redlined areas displayed the strongest persistence of bias. Results suggest that temporal data can identify persistence and improve sensitivity in measuring neighborhood bias. Understanding the temporality of residential exposure can increase research rigor and inform policy to reduce the health effects of racial bias.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article