  An urban planner might design the spatial layout of transportation amenities
so as to improve accessibility for underserved communities -- a fairness
objective. However, implementing such a design might trigger processes of
neighborhood change that change who benefits from these amenities in the long
term. If so, has the planner really achieved their fairness objective? Can
algorithmic decision-making anticipate second order effects? In this paper, we
take a step in this direction by 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 transit 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
transit amenities.
