Gentrification is a process of neighborhood change in which the primary beneficiaries tend to be homeowners and newcomers, as opposed to incumbent renters. However, operational definitions of gentrification and other concepts of neighborhood change are more elusive, making them and their interactions with policy interventions extremely difficult to quantify. We propose formulating processes of neighborhood change as instances of no-regret dynamics; a natural collective learning process by which a set of strategic agents rapidly reach a state of approximate equilibrium. We mathematize concepts of neighborhood change to develop a model for the collective incentive structures impacting dwelling site decision-making. Our model accounts for access to relevant urban amenities, community ties, and dwelling site upkeep. We showcase our model with computational experiments that provide semi-quantitative insights on the spatial economics of neighborhood change, particularly on the influence of residential zoning policy and the placement of transit-related amenities.
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