As we discussed in the Digital Journal article, How climate change is fueling ‘gentrification’ in many cities, gentrification is the process of renewal and rebuilding accompanying the influx of middle-class or affluent people into deteriorating areas that often displaces poorer residents.
Gentrification has become a buzz-word in urban planning today as low-income neighborhoods are increasingly getting makeovers. The investment and redevelopment opportunities, along with the transition to smart-city status by many municipalities have left some social justice advocates and many neighborhoods worried over displacement because of prohibitive prices.
However, as a result of the concerns being expressed, policymakers and urban planners are now working on strategies to mitigate the effects of gentrification in recently developed or soon-to-be-developed neighborhoods, such as providing “low-cost amenities and rent controlled or low-income housing,” according to Data-Smart City Solutions.
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Mapping tools for predicting gentrification
In order to understand gentrification, where it has occurred in the past, where it is occurring now and where it will occur in the future, cities have used mapping to see developing trends. They have used historical data, usually from public sources to map gentrification and displacement, allowing them to forecast possible new areas of displacement.
But even with all those maps, it is still difficult to stop displacement once it has set it. So what if we had an early warning system? Something that would detect or predict where price appreciation or decline is about to occur? There are some predictive tools already in use across the country, like the one used in San Francisco, California.
The California predictive tool allows city leaders and nonprofits to pinpoint where to keep affordable housing, where to build more and where to attract future business investments, but like many of these tools, it may be too academic or obscure to be used by everyone, and it’s really not clear how it is being used by city planners.
Dr. Ken Steif, Director of the Master of Urban Spatial Analytics at the University of Pennsylvania, in partnership with Alan Mallach, a senior fellow with the Center for Community Progress in Washington DC. has developed an analytical tool that will help city planners to predict where gentrification will occur before it starts.
Mallach’s non-profit group has been working on developing a predictive tool that is, let’s call it, “user-friendly.” The group focuses on revitalizing distressed neighborhoods in what they call “legacy cities,” like Baltimore, Flint, Michigan or St. Louis.
Legacy cities are places that have experienced population loss, economic distress due to lost jobs, and suffer from property vacancies, neighborhood blight, and unemployment. Mallach wants to better understand what has led these neighborhoods down that particular path and what and which ones will do an about-face and recover, including what it will take for that to happen.
This is a far better way of looking at gentrification and displacement than sitting at a desk pointing to areas on a map, red-circling the next neighborhood where urban blight is beginning to grow. And while Mallach can make intuitive predictions, based on real estate prices, median income, and race, an objective assessment can help greatly in getting a more accurate picture.
This is where Ken Steif’s expertise comes into play. Steif has consulted with a number of cities and non-profits on place-based data analytics. Urban Spatial consults at the intersection of data science and public policy. Founded in 2014 by Dr. Steif, Urban Spatial combines spatial analysis, econometrics, and predictive analytics to help government, business, and the non-profit sector more efficiently allocate their limited resources across space.
Steif has developed a number of algorithms to predict the movement of housing markets, using costly private data from places like Zillow. Mallach asked him to try and develop an algorithm using census data which is free and standardized.
The phenomenon of ‘endogenous gentrification’
The phenomenon of endogenous gentrification is likened to an increase in wealthy home prices causing people to move into less costly ones in the vicinity, like a wave that spreads, according to Steif. He explains this in his Blog Post:
“Typically, urban residents trade off proximity to amenities with their willingness to pay for housing. Because areas in close proximity to the highest quality amenities are the least affordable, the theory suggests that gentrifiers will choose to live in an adjacent neighborhood within a reasonable distance of an amenity center but with lower housing costs.”
Steif used census data from 1990 and 2000 to predict housing price change in 2010 in 29 big and small legacy cities. His algorithm took into account several key factors, including the values of median priced homes in a census tract to the ones around it, how close those homes were to high-cost areas, and the spatial patterns in home price distribution. He also factored in race, income, availability of housing and other things.
After checking his 2010 predictions with actual home prices in 2010, he was able to project neighborhood change all the way up to 2020. Steif’s algorithm was able to “compute the speed and breadth of the wave of gentrification over time reasonably well, overall,” reports CityLab.
But in reading Steif’s Blog Post, one thing does stand out – He says that “no matter how well the model performs, if insights cannot be converted into equity and real policy, then predictive accuracy is meaningless.” Algorithms and technological innovations such as the information system Speif has developed can be useful tools in how cities allocate their limited resources.
