Background

Housing costs have risen rapidly over the last year as a result of inflation, especially in New York, with median monthly rents for one-bedroom apartments in Manhattan reaching nearly $4,000. While rents initially decreased when the pandemic began in 2020, the cost of housing in New York is now surging and has surpassed pre-pandemic levels. 

I learned that a general rule of thumb for the percentage of income that should go to rent is 30%, and decided to use this as a base for my map visualizations. I wanted to pinpoint areas throughout New York where people are paying 30% or more for rent using data from this year, and I also wanted to compare this data to the median household income to gain a better understanding of housing affordability in 2022.

Materials

Dataset:

The two datasets I chose to look at were the ACS Housing Costs Variables feature layer and the ACS Median Household Income feature layer that are included in ArcGIS’ Living Atlas. For the housing costs dataset, I really liked how it included information for renter households, households with mortgages, and households without mortgages. While my primary focus is on rent, it would be interesting to compare the percentage of households who are spending 30% or more of their income on rent with households with and without mortgages. For the median household income dataset, I like that the household income includes data on household income by age range (under 25, 25-44, 45-64, and 65+ years old) as well as race for each area.

Software:

I used ArcGIS to find my data and to create my visualizations.

Methods

The first layer I worked on was the housing costs feature layer. For this layer, I thought that “percent of renter households for whom gross rent is 30.0 percent or more of household income” works great as an attribute for my map, as generally, households are recommended to spend 30% of their total income on housing. For the color scheme, I decided on a red and yellow palette, with the deepest red symbolizing areas where the greatest percentage of renter households are paying 30% or more on rent, and the palest yellow symbolizing areas with the lowest percentage of renter households paying 30% or more on rent.

I added popups for the housing costs layer with the percentages of renter households, households with mortgages, and households without mortgages spending more than 30% of total income on housing for that area. To aid with comparing the values, I also added a bar chart depicting that information in the popup.

Next, I worked on the household income feature layer. The “median household income in past 12 months” attribute worked well for presenting my map, and I chose a diverging color scheme of green and gray to represent the values. The data includes a national median income of $65,000 and this value is represented by the white/light green color between the darker green, which symbolizes incomes higher than $65,000, and a dark gray, which symbolizes incomes lower than $65,000. Using this diverging color scheme gives users the opportunity to pinpoint areas that make more or less than the median household income, as well as how much higher or lower those incomes are.

Each popup on the household income layer includes the median household income for that area, along with bar charts portraying the median household income by age and race for that area to give users better insights as to the demographics for people living in those areas.

I used the Sketch layer to add vector points to plot counties where both the household income is significantly higher than the national average and the percentage of renter households spending more than 30% of their income on housing is also high.

Results and Interpretation

After taking a look at my map, I learned that the counties where both the household income is high and the percentage of households spending more than 30% of their income on housing tend to be within the southeastern areas of New York state. This makes a lot of sense as New York City is in that area, and has a large concentration of high earners and high cost-of-living. One of the few exceptions is Bronx County, where the median household income is much lower than average but about 57% of renters spend more than 30% of their income on housing.

I also found that for most counties in New York, at least 40% of renter households are spending more than 30% of their total income on housing. Across New York City, only about 43% of renter households in Manhattan spend more than 30% of their total income on housing, but approximately 50% of renter households in the Bronx, Brooklyn, Queens, and Staten Island spend more than 30% of their total income on housing.

I also observed that while for most of New York state, the percentage of households with mortgages spending more than 30% of total income on housing is about half that of renter households, in New York City, the percentage of households with mortgages spending more than 30% of total income on housing is comparable to that of renter households.

Reflection

My general impression from working on this assignment is that housing is definitely not in an affordable state right now, especially with so many people spending more than 30% of their total income on rent, and I hope that housing costs will eventually stabilize.

If I were to continue working on this project, I would have liked to use more recent data in my visualization. While I liked that my two datasets were both last updated on the same date in April 2022 for consistency’s sake, there is about seven months’ worth of data that had not been accounted for in this assignment. It would be enlightening to see what has changed throughout the rest of 2022, and if housing may become more affordable in the coming years.