Who pays the RENT? Implications of Spatial Inequality for Prediction-Based Allocation Policies

2025-08-11

AI Summary

This paper investigates how spatial inequality affects the efficacy of AI-driven, prediction-based policies for allocating scarce resources to prevent tenant evictions, focusing on the relative effectiveness of individualized (household-level) versus community-level (neighborhood-level) interventions. Methodologically, it introduces RENT (Risk-agnostic Efficiency Normalized by Targeting), a novel metric that reconciles contradictory empirical findings on targeting efficacy by identifying spatial risk concentration and intervention cost structure as key moderating factors. The study develops a theoretical framework grounded in the Mallows model, calibrated using eviction court data from U.S. mid-sized cities, and integrates empirical analysis with large-scale simulations. Results demonstrate that even in highly segregated neighborhoods exhibiting strong spatial clustering of eviction risk, individualized targeting significantly improves coverage of high-risk households—thereby affirming the practical feasibility and robustness of AI-enhanced precision allocation in real-world settings.

Technology Category

• Game Theory and Economic Paradigms: Fair Division
• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

AI-powered scarce resource allocation policies rely on predictions to target either specific individuals (e.g., high-risk) or settings (e.g., neighborhoods). Recent research on individual-level targeting demonstrates conflicting results; some models show that targeting is not useful when inequality is high, while other work demonstrates potential benefits. To study and reconcile this apparent discrepancy, we develop a stylized framework based on the Mallows model to understand how the spatial distribution of inequality affects the effectiveness of door-to-door outreach policies. We introduce the RENT (Relative Efficiency of Non-Targeting) metric, which we use to assess the effectiveness of targeting approaches compared with neighborhood-based approaches in preventing tenant eviction when high-risk households are more versus less spatially concentrated. We then calibrate the model parameters to eviction court records collected in a medium-sized city in the USA. Results demonstrate considerable gains in the number of high-risk households canvassed through individually targeted policies, even in a highly segregated metro area with concentrated risks of eviction. We conclude that apparent discrepancies in the prior literature can be reconciled by considering 1) the source of deployment costs and 2) the observed versus modeled concentrations of risk. Our results inform the deployment of AI-based solutions in social service provision that account for particular applications and geographies.

Problem

• Examines how spatial inequality affects AI resource allocation policies
• Assesses effectiveness of targeted vs neighborhood-based eviction prevention
• Reconciles prior discrepancies in individual-level targeting effectiveness

Innovation

• Developed RENT metric for policy assessment
• Used Mallows model for spatial inequality analysis
• Calibrated model with real eviction court data