Over at City Observatory, Daniel Hertz, has a really interesting three–part series on housing affordability . Essentially, what it boils down to is how we commonly talk about housing affordability is incomplete, at best. Our most common rule of thumb is 30% of income as the affordability measure. I use it all the time and it’s a common figure people pull from Census data. The problem here — as with any ratio or percent, like debt to GDP, or the unemployment rate — is that it has both a numerator and a denominator. Movements in either, or both, can be driving the result. Furthermore, when it comes to household budgets, it’s not always shares of income or spending that matter but their overall level as well. In particular, do you have enough to afford the basics (housing, clothing, transportation, food, etc)? That’s really what we care about, and the 30% of income measure is a rough gauge to get us there, or at least we think so.
However, to really answer that question, Daniel highlights research about residual income. This work analyzes how much money a household needs to afford the basics after paying their mortgage or rent. Take a household’s total amount of income, subtract out their housing costs and what remains is the household’s residual income (hence the name). Is that remainder enough to pay for transit, food and the like?
Given the complexity of the actual research work — where it gets in-depth and nuanced as it adjusts for household composition (single, married, single parent, etc) and some cost of living type work — I set out to try and create a more simple, easy-to-use and replicable measure of residual income. The graphs below show residual income for renters by zip code in Oregon* by taking median household income for renters and subtracting median rents. I think that’s a pretty good estimate, although it does not adjust for those nuances of household composition.
Just as the 30% of income rule of thumb exists, I wanted to have a rough equivalent for residual income. As a starter — and I would love to hear your ideas — that measure is the non-shelter expenditures from the BLS consumer expenditure survey, for the second quintile of households. That’s a mouthful, I know. Non-shelter (or non-housing) expenditures are readily available from the BLS data and I chose the second quintile as most in the first quintile are below the poverty line. Clearly that is not sufficient to measure affordability. The second quintile represents households that are above poverty and how they spend their money. In this case, it works out to about $25,500 annually on non-shelter items.
Across Oregon, renters in 54% of the state’s zip codes are classically cost burdened, using the 30% rule. However, 70% of zip codes are facing residual income issues, meaning they do not have enough money left over after housing costs to afford what the typical low to moderate income household does. Some of the zip codes change from being affordable to not or vice versus depending upon the measure. That was Daniel’s whole point. A hypothetical example is a Doctor. She may spend 50 percent of her income on a really nice house in an expensive neighborhood, however her residual income would still be plenty. She is not truly cost burdened even if she would be counted as so by the traditional measure. Same here at the zip code level.
Zip codes are more granular and provide a more detailed look at the state then just county level data. Daniel even turned this same data into a nice interactive version of the graph (check it out!) although it includes both renters and owners, I believe. However, I think analysis along these lines would be great to incorporate at the neighborhood or census tract level when discussing housing affordability. Highlighting the amount of residual income vs the 30% measure can be illuminating in terms of identifying areas of need or concern.
*2013 ACS data, 5 year estimates. I will update in December when 2014 zip code data is released.