With thousands of abandoned homes located in neighborhoods of varying shape and character, Chicago has a massive challenge in returning its vacant properties to active use. Can algorithms help leaders decide on the right solutions?
"Last month, fellows with the University of Chicago’s Data Science for Social Good began working with the Chicago area’s newly born Cook County Land Bank Authority (CCLBA)," reports Brady Dale. "The aim is to create a tool that will make it easier to process data on foreclosures, real estate trends and the like to determine which properties are the best candidates for redevelopment. Think of it as a data-backed triage unit for vacant land."
“'I don’t think we’ll be able to create a model that makes those choices easy,' Thomas Plagge, a post-doc in astrophysics and mentor for the land use fellows said. 'What I think that we’ll be able to prototype out is a selection of algorithms that the land bank staff can apply to different neighborhoods in different situations.'”
“A lot of times when the government intervenes in the real estate market, they have this kind of one-size-fits-all solution,” Plagge continued. “We want to be more flexible, and we also want to be able to evaluate our interventions on the fly and bake that into the cake.”
FULL STORY: Applying Big Data to Vacants, and Other Steps Taken By Chicago’s Land Bank
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