Run a Google search for “gentrification” and you’ll get thousands of news items and scholarly articles on how urban revitalization risks pushing low-income communities out of cities. Despite all that research, Ken Steif, director of the Urban Spatial Analytics grad program at the University of Pennsylvania, says it’s remarkable how cities are still struggling to stop displacement when investment starts to rise.
On Jan. 31, he debuted a model that may help. It provides policymakers with a rough glimpse into the future by relying heavily on U.S. Census data to predict gentrification. The initial framework, which was rolled out through his consulting firm Urban Spatial, was applied to 29 legacy cities mostly clustered around the Northeast, but also included urban areas like Chicago, Birmingham, New Orleans and St. Louis.
Along with researchers Alan Mallach, Michael Fichman and Simon Kassel, Steif blended datasets of median housing prices for 3,991 census tracts in these cities during 1990, 2000 and 2010. They also recorded the average housing prices for groups of census tracts surrounding each census tract, and looked at other variables like changes in resident income, to measure the incline of housing values in a given region.
If they could plug data from 1990 and 2000 into this model and get a prediction for 2010 that lined up with what housing prices actually looked like in 2010, the experiment would’ve proven successful. And that’s sort of what happened. The margins of error in Cincinnati, Baltimore and Pittsburgh, for example, rested between 14 percent and 12 percent. At the lowest end of the spectrum, South Bend, Indiana, and Erie, Pennsylvania, were both under 5 percent.
What researchers did find notable was that there weren’t any distinct patterns of error — for example, larger cities on average didn’t show different proportions of error than smaller cities. “The model is not biased toward smaller cities or larger ones or those with booming economies,” note the authors, concluding that “this is evidence that our final model is generalizable to a variety of urban contexts.”
But it’s not the first time researchers have attempted to peer into future growth patterns. In a 2016 article published at Cityscape, the U.S. Department of Housing and Urban Development’s research journal, authors Karen Chapple and Miriam Zuk from the University of California, Berkeley point out that policymakers have been consulting “early warning systems” to scope out neighborhood shifts since at least the 1980s.