Title: Using data science as a community advocacy tool to promote equity in urban renewal programs: An analysis of Atlanta's Anti-Displacement Tax Fund,
Abstract: Cities across the United States are undergoing great transformation and urban
growth. Data and data analysis has become an essential element of urban
planning as cities use data to plan land use and development. One great
challenge is to use the tools of data science to promote equity along with
growth. The city of Atlanta is an example site of large-scale urban renewal
that aims to engage in development without displacement. On the Westside of
downtown Atlanta, the construction of the new Mercedes-Benz Stadium and the
conversion of an underutilized rail-line into a multi-use trail may result in
increased property values. In response to community residents' concerns and a
commitment to development without displacement, the city and philanthropic
partners announced an Anti-Displacement Tax Fund to subsidize future property
tax increases of owner occupants for the next twenty years. To achieve greater
transparency, accountability, and impact, residents expressed a desire for a
tool that would help them determine eligibility and quantify this commitment.
In support of this goal, we use machine learning techniques to analyze
historical tax assessment and predict future tax assessments. We then apply
eligibility estimates to our predictions to estimate the total cost for the
first seven years of the program. These forecasts are also incorporated into an
interactive tool for community residents to determine their eligibility for the
fund and the expected increase in their home value over the next seven years.