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 elusive, making them and their interactions with policy interventions difficult to quantify. In this paper, we propose formulating processes of neighborhood change as instances of no-regret dynamics; a collective learning process in which a set of strategic agents rapidly reach a state of approximate equilibrium. We mathematize concepts of neighborhood change to model the incentive structures impacting individual dwelling-site decision-making. Our model accounts for affordability, access to relevant amenities, community ties, and 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 urban amenities.

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