Where I Should Live, According to Math

I don't live in Washington, DC. I live near Washington, DC. I would like to live in it, but it's an expensive city, and my income, while above the national average, is well below the regional average, and finding a two bedroom in our price range is difficult.

This got me thinking about affordable housing more broadly. For instance, where could I find a good, walkable neighborhood, anywhere in the country, that is within my price range? That got me started on my current project.

Using census data, I decided to map the variables of affordability and walkability. Affordability wasn't hard; I mapped all the census tracts in the country that had a median income within $10,000 of mine, both above and below.

This shows me where I can afford to live, but a lot of the areas are rural places that I would never want to live in. My next task was to map walkability.

Walkability was harder to map. Even though Walkscore covers everywhere in America, it only offers it's data in downloadable form for Washington, DC. So I downloaded the data, calculated the average Walkscore for census tracts in DC, downloaded virtually the entire American Community Survey, and compared the data therein to the average Walkscore to look for correlation. I found nineteen variables that had some significant correlation with Walkscore.

Where I Should Live, According to Math

I took each of these variables and gave them a score of one or zero, one if Walkscore would be above 70 at the value, or a zero if it would be below. Then I multiplied that score by each variables' R-Square value, and added all the variables together to get a weighted Walkability score. I eliminated the bottom 50% of these values, and added the remainder to the map.

Where I Should Live, According to Math

I was pretty happy with the result. With the exception of a few large tracts in western states, walkable places are where you would expect them to be; densely concentrated around major metropolitan areas.

I intersected the two layers to get tracts that were both affordable and walkable.

Where I Should Live, According to Math

This led to an interesting pattern: a few small, walkable town centers on the edge of metropolitan areas, but mostly urban neighborhoods outside of the downtown or in inner-ring suburbs.

However, it was still too many places to look at as a group, so I assigned a score to each tract based on how walkable and how affordable they are. I added these two together to get a combined score for what neighborhood would be best for us, based on these two criteria. In case you wanted the full equation for this score, it is

Combined score = (a – |a – b|) / a + ((if(c ≥ 373.6958, 1, 0) * 0.3153) + (if(d ≥ 21.2983, 1, 0) * 0.2725) + (if(e ≤ 38.8903, 1, 0) * 0.2803) + (if(f ≥ 68.0899, 1, 0) * 0.2971) + (if(g ≥ 67.4557, 1, 0) * 0.3350) + (if(h ≥ 59.9592, 1, 0) * 0.4048) + (if(i ≤31.4668, 1, 0) * 0.2529) + (if(j ≥ 65.5846, 1, 0) * 0.2734) + (if(k ≥ 65.3918, 1, 0) * 0.2839) + (if(l ≥ 58.6467, 1, 0) * 0.3533) + (if(m ≤35.7247, 1, 0) * 0.2576) + (if(n ≥226.8280, 1, 0) * 0.2763) + (if(o ≥78.1848,1, 0) * 0.2779) + (if(p ≥3.8273, 1, 0) * 0.2943) + (if(q ≥ 602.4307, 1, 0) * 0.2795) + (if(r ≤ 4.1293, 1, 0) * 0.2698) + (if(s ≥ 732.9079, 1, 0) * 0.2573) + (if(t ≥ 21.1155, 1, 0) *0.3974) + (if(u ≥82.4877, 1, 0) * 0.2810)) / 5.6596

Where:

a = Your Personal Income

Data for Each Tract from the American Community Survey:

b = Median Income

c = Nonrelatives in Household

d = % with at Least a Bachelor's Degree

e = % Born in State of Residence

f = % 16 and Older in Labor Force

g = % 16 and Older in Civilian Labor Force

h = % 16 and Older Employed in Civilian Labor Force

i = % 16 and Older Not in Labor Force

j = % Females 16 and Older in Labor Force

k = % Females 16 and Older in Civilian Labor Force

l = % Females 16 and Older Employed in Civilian Labor Force

m = % 16 and Older Driving to Work Alone

n = Workers 16 and Older Walking to Work

o = Workers 16 and Older Commuting to Work by Other Means

p = % 16 and Older Commuting to Work by Other Means

q = Houses Built 1939 or Earlier

r = % 10-14 Years Old

s = Population 25-34 Years Old

t = % 25-34 Years Old

u = % 18 Years and Older

So, what got the highest score?

Capitol Hill, Seattle led the pack. To be honest, I was expecting something a smaller, affordable Midwest town or something, but it the highest scoring areas were usually just outside of major downtowns. Other top areas included Cambridge and Somerville outside of Boston, and the South End in Boston; Columbia Heights, Washington, DC; The Mission District, Lower Haight, and Russian Hill, San Francisco; Midtown, Atlanta; Greenwood, Dyker Heights, Kensington, and Sheepshead Bay, Brooklyn; Graduate Hospital in Philadelphia, where we used to live; Lake View, Chicago; and Five Points, Denver.

Where I Should Live, According to Math

Holly and I won't be moving out of the region any time soon, but it's good to have some idea of where to look if we decide to. And good to know that Columbia Heights is probably the neighborhood in DC for us, when the time comes. The formula isn't perfect; it's hard to control for things like how much of people's income goes toward housing, and there is still a lot of wiggle room in these walkability measures. But it is a reasonable guideline that has provided interesting results.


This article has been reposted with permission from Munson's City. To read in its entirety, head here. For more from Dave Munson, you can check out his blog here or follow him on Twitter here.

Dave is an urban planner and designer working in the Washington, DC area.