What drives urban redevelopment activity? Evidence from machine learning and econometric analysis in three American cities

This paper uses the question posed in its title—what drives urban redevelopment activity?—to frame a comparison of machine learning and econometric approaches for modeling parcel change. It starts by arguing that geographical science has an obligation to weigh the tradeoffs of methods as they emerge into the mainstream—especially when they spread like wildfire as, machine learning has. The empirical analysis, which makes up the middle sections of the paper, examines parcel changes in Boston, Chicago, and Seattle between 2010 and 2020. Two machine learning approaches, k nearest neighbors and random forest, are benchmarked against an econometric approach, probit. The models are explained in a way that is intended to be accessible to a broad audience and evaluated using intuitive metrics; throughout, an effort is made to draw a clear link between machine learning and econometric methods. The modes of analysis rest on different knowledge bases, so analysts should take care to ensure to distinguish between the two. The paper closes with a summary and some concluding thoughts. Overall, it suggests that machine learning and econometric approaches extend the reach of other’s capabilities and, therefore, should be viewed as complements, not substitutes.