The New York Times: Is an Algorithm Less Racist Than a Loan Officer?

The New York Times, September 18, 2020, Is an Algorithm Less Racist Than a Loan Officer?

Digital mortgage platforms provide an improvement to the discrimination plaguing the housing sector; however individuals must be vigilant in this process. Fair housing advocates say government regulators and banks in the secondary mortgage market must rethink risk assessment: accept alternative credit scoring models, consider factors like rental history payment and ferret out algorithmic bias.

Getting a mortgage can be a harrowing experience for anyone, but for those who don’t fit the middle-of-last-century stereotype of homeownership — white, married, heterosexual — the stress is amplified by the heightened probability of getting an unfair deal. In 2019, African Americans were denied mortgages at a rate of 16 percent and Hispanics were denied at 11.6 percent, compared with just 7 percent for white Americans, according to data from the Consumer Finance Protection Bureau. An Iowa State University study published the same year found that L.G.B.T.Q. couples were 73 percent more likely to be denied a mortgage than heterosexual couples with comparable financial credentials.

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Redlining and Neighborhood Health

Before the pandemic devastated minority communities, banks and government officials starved them of capital.

Lower-income and minority neighborhoods that were intentionally cut off from lending and investment decades ago today suffer not only from reduced wealth and greater poverty, but from lower life expectancy and higher prevalence of chronic diseases that are risk factors for poor outcomes from COVID-19, a new study shows.

The new study, from the National Community Reinvestment Coalition (NCRC) with researchers from the University of Wisconsin–Milwaukee Joseph J. Zilber School of Public Health and the University of Richmond’s Digital Scholarship Lab, compared 1930’s maps of government-sanctioned lending discrimination zones with current census and public health data.

Table of Content

  • Executive Summary
  • Introduction
  • Redlining, the HOLC Maps and Segregation
  • Segregation, Public Health and COVID-19
  • Methods
  • Results
  • Discussion
  • Conclusion and Policy Recommendations
  • Citations
  • Appendix

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