Can Big Data Predict Housing Abandonment? – CityLab


A pair of surviving rowhomes surrounded by vacant lots at dusk in Baltimore. The city has some 17,000 vacant buildings. (Patrick Semansky/AP)

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.

Source: Can Big Data Predict Housing Abandonment? – CityLab