Spatial Inequality in NYC

Spatial Inequality; Statistical Analysis of Housing Values, Race, Income, and Poverty of NYC Community Districts

Naomi Tinga (Lowe) Income Inequality and Poverty in the US & NYC Prof. Noguchi 5.26.19

Introduction New York City is a contested space between private financial powers and the livelihood of working people, particularly, low income residents by which the city’s local economy, formal and informal, greatly rely on. These residents today experience displacement, rent and local price gouging (from predatory equity and private developments), and high levels of poverty that can be traced back to historically discriminatory housing policies have targeted black and people of color, that has not only resulted in segregation, stratification of resources, infrastructure and investment, but also its’ consequences are solidified on the build environment.

This study will analyze the socioeconomic, housing, and racial composition of the city by New York City Community Districts, by examining the change in property values, rent, income, population in poverty and population of racial groups from 2010 to 2016.

Literature Review The article “Housing Crisis Is a Displacement Crisis” highlights the history of the housing crisis in America and how its’ consequences still permeate the current housing struggle. Historically, people of color experienced discrimination from accessing affordable and quality housing. Therefor housing is political as much as it is economical. During the great depression, President Roosevelt’s New Deal offered federal funding for housing and affordable subsidized housing. This largely accelerated Americans into the middle class, but excluded African Americans from accessing these low interest loans, preventing them from buying property and thus generating wealth. The Federal Housing Authority at the time was also practicing inequitable policies like redlining which valued white occupied units higher and identifying areas where majority people of color lived as a risk for granting loans to. Even in the recent 2008 housing crash released systemic racial discrimination against black homeowners in their mortgages compared to white homeowners of similar housing and economic profile. This concentrated poor and

people of color in cities and white populations in the suburbs. Today, generational wealth of whites in suburbia amounts to almost $1 trillion. Affordable housing is possible with government support. It’s lasting effect of allowing white folks to generate and pass on their wealth shows how successful Roosevelt’s New Deal was. The value of a property and of a place is very much related to who resides there and has been socially engineered through racially charged policies, which means it can be socially reengineered for equity using participatory policy and zoning laws. Furthermore, researching and quantifying gentrification, housing, and financial investment or disinvesting is difficult. It requires analysis of historic policies, tracking their consequences, and identifying the role of governments, fiscal policy, and neoliberal markets play in shaping the current conditions. Then testing these variables against each other to identify relationships and trends. Applying this logic to the research at hand, measuring population in poverty according to community districts in relation to redlined areas could give strong insight into generationally accumulated economic affects of discriminatory policy.

Figure 1

Data and Methods The data used for this study, is of the 55 Community Districts of New York City, conducted by the the American Community Survey (see figure 1 for spatial unit of analysis).

Using data sets from 2010 and 2016, both containing estimates of the socioeconomic profiles in the given Census Districts. The variables used and their description are listed in the following table:

Empirical Strategy The goal of the study is to explore how rent, and property values have changed in relation to population in poverty, incomes, and race. Percentage change between all variables from 2010 and 2016 data sets were calculated to make for percentage change variables. From this, tests for association will be executed to analyze if there is a correlation among the variables and the percent change variables. Next, tests for significance will be executed to measure the relationship between dependent and independent variables. Using regression to describe percentage change in median rent with percentage change of: racial subgroups, property values, incomes, and

population in poverty as regressors to identify the strongest variable that impacts the change in rent variable. Another regression to describe percentage change of population in poverty. Finally, separate regressions for 2010 and 2016 variables on median rent, population in poverty, and median property value.

Results Form follows finance. Starting with income, figure 2 is a bar graph of the top six and bottom six community districts that saw the highest change percent change in income from 2010 to 2016. Figure 3 is a map of the data created using ArcMap. Notice, the community district that saw the largest percentage change was Greenpoint & Williamsburg, Brooklyn: 44.6% increase median income.

Percentage Changes in Income by Community Districts (2010-2016)

% Figure 2

Figure 3

The next graph is a sample of community districts that saw significant percent changes among race, income, property value, rent, and population in poverty (Figure 4).

Percentage Changes in Race, Income, Property Values, Rent, Population in Poverty by Community Districts (2010-2016)

Figure 4

Notice Bushwick, Brooklyn saw one of the highest percent increase in median income (36%) and in rent (30.4%) from 2010 to 2016. Crown heights and Prospect in Brooklyn saw a large decrease in Black population by 17.6 %, but saw 21.8% increase in property values. .The following maps are the percentage changes of Asians, Black, Latin, and White populations from 2010 to 2016 (Figure 5).

Figure 5

Median rent has increased in all districts, the largest percent change being in Greenpoint and Williamsburg by 46.6% increase. Median Property has mostly increased in districts in Manhattan and Brooklyn but in Riverdale, Fieldston, Kingsbridge in the Bronx saw an 11% decrease. Property values in the outer districts of Queens have decreased as well.

The next bar graph shows the top six and bottom six changes of population in poverty by community districts from 2010 to 2016 (Figure 6). The highest levels of population in poverty are experienced in the Bronx, particularly Morris Heights, Fordham South, and Mount Hope saw a 3.5% increase in its population in poverty to 41.6%.

Percentage Changes in Population in Poverty by Community Districts (2010-2016)

Figure 6

The following map shows the percentage of the population in poverty by neighborhood tabulation area as of 2016 (Figure 7). Notice, neighborhood in the South Bronx experience 30% to 50% of their populations living at or below the poverty line, where 40% to 70% of the population are Latin. Districts in the South Bronx were heavily redlined, with parts of Riverdale excluded and properties valued higher/ not risky. Today Riverdale experiences very minimal population in poverty, a stark contrast to the other districts in the Bronx.

Percentage of Population in Poverty by Neighborhood (2016)

Figure 7

Figure 8 on the left shows Stata output of a test for association between 2010 and 2016 variables. Among the Racial subgroups regressors, the white population Figure 8

showed the highest correlation to property value, median rent, mean household income.

Figure 9 below, shows a test for association between the percentage change variables from 2010 to 2016. Percentage change in white and percentage change in rent had the highest correlation of 0.4554, which is mildly correlated. The test also showed increase in income is strongly negatively correlated with increase in population in poverty and that increase in rent was strongly positively correlated with increase in income. This is a good indicator for checking if the statistical tests corresponds to what intuitively makes sense and what is observed in reality.

Figure 9

Figure 10 below shows a regression for a test of significance among percentage change variables. The independent variable, Percentage Change in Rent, is described with Percentage change in: Hispanic Population, White population, Black population, Asian population, population in Poverty, and Income. Percentage change in income was the only statistically significant regressor. Therefor, on average, holding all other variables constant, percentage increase in Mean Household Incomes corresponds to 0.36 percentage increase in Median Rent across all community districts.

Figure 10

The next regressions are on variables from 2010 and 2016, not percentage change but the estimates for each variable. Figure 11 below shows a regression of Median Property Values described by population of : Hispanic, White, Black, and Asian estimates in 2010 and figure 12 below is the same regression but with 2016 variables.

Tests for Significance: Regression of Property Values, 2010 and 2016

Figure 11

Figure 12

Notice the white population regressor is the only statistically significant regressor and it’s beta coefficient increased from 2.35 to 2.95 from 2010 to 2016. Therefor, on average, holding all other variables constant, unit increase in white population corresponds to a 2.34 increase in 2010 Median Property Values across all districts, and 2.95 in 2016.

Discussion and Conclusion Comparing Poverty of districts alongside HOLC redlined areas show how persistent discriminatory housing policy is. Regressions showed increase in white population does impact rent and property values, positively correlated because of the gap in income and wealth between races. Specializing the data and calculating percentage changes give valuable context for conversation of gentrification and displacement in New York City.

Limitations of the study stem from the complicated nature of real estate; property values and rent is volatile in the short run and changes because of other external variables like housing

policy and private investment. Statistical tests are sensitive to how data is organized and represented. Further studies into the impact of race on place require geographically weighted statistical tests and study into the generational impact of certain policies. Including datasets that date back to before certain policies have been implemented and after to understand measure its’ impact To conclude, New York City is a complicated socioeconomic ecosystem. Historic American neoliberal economic and housing policies have orchestrated structural inequality that permeates our cities today. Urban poverty, wealth, income, infrastructural and investment disparities are indicative of the financial and power relations between races and institutions.

We must be intrinsically and thoroughly skeptical of the private and public forces shaping our built environment and aware of the generationally accumulated consequences that policy has produced. If we can engineer poverty and inequality then we most certainly can engineer sustainability, equity, and empathy.

References

Corinnajk. “The Housing Crisis Is a Displacement Crisis.” The Newsletter, 18 Sept. 2018, bostonpewg.org/2018/09/17/the-housing-crisis-is-a-displacement-crisis/.

Badger, Emily. "How Redlining’s Racist Effects Lasted For Decades". Nytimes.Com, 2019, https://www.nytimes.com/2017/08/24/upshot/how-redlinings-racist-effects-lasted-fordecades.html.

ACS NYC Economic, Housing, and Demographic estimates 2010 & 2016 : https:// www1.nyc.gov/site/planning/data-maps/nyc-population/american-communitysurvey.page