Seattle Climbs But Austin Sprawls: Data, Methods, and Results

Today in The Upshot, I explain that the suburbanization of America continues, not only for the country overall but in four-fifths of the largest metros. The few that buck the trend and are in fact becoming more urban are generally those that were denser to begin with. This supplemental post describes the data, methods, and results behind these findings.

Data and methods

The main measure is the change in metro-level density between 2010 and 2016. The two data sources used to create this measure are the 2016 Census Bureau population estimates for counties and U.S. Postal Service estimates of occupied housing units (i.e. residential addresses receiving mail) for Census tracts.

Most large metros comprise a handful of counties; some metros, like San Diego, Las Vegas, and New Haven, consist of a single county. County trends alone, therefore, show little or nothing about population shifts within metros. To get a more granular view, I augmented the 2016 Census county population estimates with Census-tract-level counts of occupied housing units from the U.S. Postal Service, which are also available through 2016. (The most recent Census-tract data from the Census are from the 2015 five-year American Community Survey, which averages data over the years 2011-2015, in effect lagging the USPS counts by three years.)

I use Census tract density, rather than political city boundaries, as an indicator of urban and suburban. Cities, as defined by political boundaries, vary considerably in how urban they are. Furthermore, in many metros there are places within the main city’s border that are less dense – i.e. more suburban – than places outside the main city: Hoboken is more urban than Staten Island, and the western portions of the San Fernando Valley within the City of Los Angeles are more suburban than West Hollywood and Santa Monica. See this post for more on density as an indicator of urbanness and suburbanness.

For each year, I allocated each county’s Census population estimate to Census tracts in proportion to the tract’s share of USPS occupied addresses in the county. I then calculated the change in tract-weighted average density from 2010 to 2016, using the estimated population of each tract in 2010 and 2016 and tract household density from the 2010 Census. By definition, average neighborhood density increased in metros where higher-density (that is, more urban) tracts grew faster than lower-density (more suburban) tracts; average density decreased in metros where lower-density tracts grew faster than higher-density ones. Note that this “average neighborhood density” measure reflects only the change in density due specifically to the changing spatial distribution of the population within a metro, and is unaffected by population growth that is spatially uniform within a metro.

This method uses Census data to the degree possible and USPS counts where necessary, since the Census is more definitive while the USPS data are more recent and granular. An alternative is to rely solely on the USPS occupied-address counts for tracts. The results are essentially the same: the metro-level correlation between the change in density measured using the USPS-only alternative and the change in density using my preferred hybrid Census-USPS approach is 0.97.

I also looked at home-price changes within metros using two different ZIP-code-level data sources: FHFA and Zillow. My measure of whether home prices are rising faster in urban or suburban neighborhoods within a metro is the coefficient from a tract-level regression of the 2010-2016 change in home prices on the log of household density, weighted by the number of households in the tract.

Density for metros as a whole is tract-weighted households per square mile in 2010.

Data on the local prevalence of urban planners come from the Bureau of Labor Statistics’ Occupational Employment Statistics. I used the location quotient, which reflects the share of a metro’s workforce that is urban planners, relative to the share of the national workforce that is urban planners. The metros with the highest location quotients for urban planners are Sacramento (3.1, which means that the share of urban planners there is more than three times the national average), Seattle (2.6), and San Francisco (2.2). Note that the BLSs published tables report metropolitan divisions, whereas I calculated the data for metropolitan areas (CBSAs).


All of the results are for the 51 metropolitan areas (Core Based Statistical Areas) with at least one million people in 2010, using the latest (2015) definitions.

The suburbanization of America is the result of two distinct shifts: between metros and within metros. First is that the densest metros – places like New York and San Francisco – are growing somewhat more slowly that less-tightly-packed metros like Austin, Raleigh, and Orlando. (The correlation among the 51 largest metros between (1) the log of tract-weighted metro density in 2010 and (2) population growth from 2010 to 2016 is -0.17, which is not statistically significant at the 5% level.) Second is that in 41 of the 51 largest metros, lower-density Census tracts grew faster than higher-density Census tracts from 2010 to 2016 – i.e. they become more suburban.

Among the 51 largest metros, the change in metro-level density from 2010 to 2016 – my key measure of trending urbanization or suburbanization – is correlated with several relevant variables. The correlation with the change in metro-level density is:

  • -0.49 for metro population change, 2010-2016. That is, faster-growing metros became more suburban.
  • 35 for the log of tract-weighted metro density in 2010. That is, denser metros became more urban.
  • 28 for the urban-planner location quotient. That is, metros with a higher share of urban planners became more urban. Notably, Austin is an exception, with a high share of urban planners (LQ=1.9) yet faster growth in lower-density neighborhoods.

All of the above correlations are statistically significant at the 5% level.

There were also patterns in which metros saw faster home-price growth in urban than in suburban neighborhoods. Within metros, home prices rose faster in higher-density neighborhoods than in lower-density neighborhoods in 44 of the 51 largest metros, according to the FHFA home price index (and in 37 of 51, according to the Zillow index). That is: in most metros prices rose faster in urban than suburban neighborhoods. The correlation with the extent to which home prices grew faster in the more urban neighborhoods of a metro is:

  • 45 for metro population change, 2010-2016. That is, in faster-growing metros, home price increases were higher in urban relative to suburban neighborhoods.
  • 32 for the log of tract-weighted metro density in 2010. That is, in denser metros, home price increases were higher in urban relative to suburban neighborhoods.

Both of the above correlations are for the FHFA home price index and are statistically significant at the 5% level. The correlations are very similar when calculated with the Zillow index instead of the FHFA home price index.