Originally published by CityLab.
Almost 17,000 houses sit boarded-up and vacant throughout Baltimore. These are the ones deemed officially unlivable by the city, some with rooftops or walls missing. But those structures represent just a fraction of a larger problem. Estimates from the Census and other community surveys suggest anywhere between 30,000 and 54,000 other homes are currently unoccupied. The question is: Which ones?
It’s a similar story in other cities that have experienced severe population drops, such as Detroit and Cleveland. Keeping track of the exact locations of vacancies can prove difficult as the only occupancy data available is often out of date or incomplete. This information gap represents a challenge for housing authorities trying to stabilize shaky neighborhoods.
So what’s a city to do? Baltimore is taking an unorthodox approach to the problem by enlisting some heavenly assistance. Roughly two years ago, through Johns Hopkins University’s 21st Century Cities Initiative, the Baltimore Housing and then-deputy commissioner Michael Braverman reached out to Tamas Budavari, a Hopkins astrophysicist who researches the statistical challenges of mapping the universe. His task, among many others, is to use big data to help the city find unoccupied buildings before they reach a state of terminal disrepair. To accomplish that, he does what astronomers do when they study distant stars: Look into the past to predict the present.
Budavari and Phil Garboden, a doctoral student in sociology and applied math, are working on a statistical tool to predict abandonment. They’re combining publicly available data with GIS technology to create a database of the city’s housing stock. This will serve as a base to do high-level statistical analyses that can help officials make better, data-driven evaluations of current and future interventions. It could help Baltimore study, among other things, when and why homes are abandoned, and at what point a vacant home starts affecting nearby properties.
Once abandoned, a home is more likely to attract crime and lower the property value of surrounding houses, in turn driving more neighbors away. If cities can predict where clusters of vacant homes are likely to form, they can intervene before the entire neighborhood empties. They can, for example, consider lower-cost alternatives to demolition. Getting rid of all 17,000 homes in Baltimore would take $500 million and half a century—money and time the city doesn’t have on hand.
