by Ayana Littlejohn, Analytical Consultant, SAS
I am a Black woman in tech. Most days I find that I’m the only woman or person of color in the room, and because of that, I have constantly sought ways to use the powerful tools and skills I’ve harnessed over the years – in my education and professional career – to help my community in ways not previously explored.
I’ve volunteered with organizations that hold seminars for young learners interested in coding and I currently serve on the board of another organization to support its ongoing commitment to the young Black and Brown girls venturing into tech education and careers. I’ve realized through these opportunities that the battle we face isn’t for inclusion alone but to confront the bias that exists in the data and the algorithms used for everyday decisions, as well.
When I was introduced to the idea of partnering with the Center for NYC Neighborhoods on its Black Homeownership Project through my job at SAS, I knew I wanted to offer my analytical talents to this important and necessary work. This started by identifying inequities and imbalances in healthy housing for Black and Brown communities, getting us all closer to removing the biases that have disenfranchised those same communities for centuries.
Affordable housing as a human right
Like myself, the Center for NYC Neighborhoods holds on to a firm belief that affordable housing is a human right, even – and especially – in notoriously expensive areas like New York City. Its partnership with SAS, the leader in analytics and advocate for applying analytics to the world’s most pressing needs, was an opportunity to analyze New York housing data and interventions in the decline of Black homeownership.
To do this, SAS pulled together a team of analytics volunteers (Aaren Avery, Portia Exum, Sheri Grice, Ayana Littlejohn, Jacoby Pulley, Sindhu Sevala and Hiwot Tesfaye) to explore two of the Center’s research topics:
- The condition of Black-owned homes in comparison to homes owned by households of other races.
- The closing costs for Black mortgage applicants in comparison to the closing costs for applicants of other races.
Making sense of housing data
One of the early complications of our research was the lack of racial attributes in the public data sets available. For the data sets that did not contain any racial data, like the tax lot data set compiled by the NYC Department of City Planning (PLUTO), we used the American Community Survey (ACS Census) to find the distribution of race in each of the five boroughs. We then used these distributions to find inequities in the boroughs with higher proportions of Black residents.
While exploring the condition of Black-owned homes in comparison to the condition of homes owned by other races, our research found that there was no significant difference in the age of homes across the five boroughs. This observation is important because the increased age of a home tends to affect the home’s condition and increase the home’s maintenance issues. Because the age of a home in each borough is, on average, within the same range as other boroughs, potential disparities found in comparison to other boroughs were not likely due to the normal wear and tear of an aging home, but instead point to other factors, such as race.
Age might be the same, but value is not
Despite there not being any difference in the age of homes, the value of homes varies significantly. When analyzing home values (per square foot), the data revealed the following:
- The value of homes is lower in neighborhoods that have higher proportions of minority homeowners.
- The volume of home maintenance violation reports for two or more home maintenance deficiencies tends to be higher in minority owner-occupied, one-to-three family housing units.
- The presence of roaches was found to be significantly higher in minority-owned dwellings.
Similarly, while exploring the differences in closing costs by race in NYC and controlling for differences in down payment and home value, our analysis uncovered 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.
These insights present the opportunity to explore other areas of inequities. What are the underlining causes of these disparities? Are there other factors that could be explored to give a full picture of what’s happening to Black homeownership in NYC? What other quality of life attributes, such as access to health care and nutrition, are affected by or correlated with the lack of equitable housing in NYC?
While our findings do not automatically challenge the inequities we see in NYC neighborhoods, this exploration, hopefully, inspires 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 things like home value and closing costs for Black communities.