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.

Recently, the Urban Institute called for the creation of "neighborhood-level early warning and response systems that can help city leaders and community advocates get ahead of (neighborhood) changes." Open data and open-source analytics allows community stakeholders to mine data for actionable intelligence like never before. The objective of this research is to take a first step in exploring the feasibility of forecasting neighborhood change using longitudinal census data in 29 Legacy Cities (Figure 2). The first section provides some motivation for the analysis. Section 3 provides results and the final section concludes with a discussion of community-oriented neighborhood change forecasting systems. Neighborhoods change because people and capital are mobile and when new neighborhood demand emerges, incumbent residents rightfully worry about displacement.