Ayana Littlejohn, Analytical Consultant, SAS
Racial inequalities in NYC housing
Affordable housing as a human right
In seeking out volunteer opportunities to help Black women in tech, I’ve realized that the battle we face isn’t for inclusion alone, but to confront the bias that exists in the data and algorithms used for everyday decisions. Ayana Littlejohn Analytical Consultant SAS
MAKING SENSE OF HOUSING DATA
Public data sets lacking
Initial research indicated a lack of racial attributes in the public data sets available. For the tax lot data set compiled by the NYC Department of City Planning, we used the American Community Survey (ACS Census) to identify race distribution in each of the five boroughs. These distributions were then used to find inequities in the boroughs with higher proportions of Black residents.
Age might be the same, but value is not
Despite there not being any difference in the age of homes, the value varies significantly. When analyzing home values (per square foot), the data revealed that the value of homes is lower in neighborhoods with more minorities. The number of violation reports for two-plus home maintenance deficiencies tends to be higher in minority owned, one-to-three family housing units.
When exploring the differences in closing costs by race in NYC, analysis revealed that the total cost of acquiring a home purchase loan (both conventional and FHA) is higher for Black and Hispanic borrowers than for other races.
Opportunities to explore other inequities
This exploration may inspire new partnerships with financial institutions and other organizations with a wealth of data, including race, to revamp policies and correct bias in algorithms that determine home value and closing costs for Black communities.
Identifying imbalances in healthy housing for Black and Brown communities gets us all closer to removing the biases that have disenfranchised those same communities for centuries.