Leveling the Field: Combining Proprietary Micro-Level Data with Machine Learning Methods for Housing Justice Research

The rapid population growth in U.S. cities has intensified urban development pressures. There have always been several equity concerns arising from these pressures that have resulted in displacement, gentrification, and low-income housing demolitions just to name a few. The increasing availability of data in the last years provide new avenues to track and document the extent of these urban issues that were previously hard to quantify. In this talk I would show how property-level gentrification index and code-enforcement bias assessment are two examples of leveraging these new data sources with machine learning and artificial intelligence techniques to level the plain field in data-driven equity advocacy in urban issues.

Dr. Esteban Lopez Ochoa is an Assistant Professor of Urban and Regional Planning and the Associate Director of the Sustainable Pervasive Urban Resilience (SPUR) Center at the University of Texas at San Antonio (UTSA). His research seeks to take advantage of the wider availability of “bigger” data to examine housing, economic development, and environmental issues that contribute to the unjust burden of spatial inequities in our communities, both in the U.S. and Latin American contexts. Esteban has actively collaborated with several academic and community-based institutions concerned with understanding how to foster community development without the negative side effects of displacement and neighborhood change, such as the National Association of Latino Community Asset Builders (NALCAB), the Lincoln Institute of Land Policy, and the Historic Westside Neighborhood Association in San Antonio (TX).

Prior to UTSA, Dr. Lopez Ochoa was an Assistant Professor of Economics at the Business School of the Universidad Adolfo Ibáñez (UAI) in Viña del Mar, Chile. He obtained his PhD in Regional Planning as a Fulbright Fellow in 2016 from the University of Illinois at Urbana-Champaign. He has two master of science degrees, one in Applied Economics and another in Regional Science.

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