Year: 2025
Type: conference-paper
Abstract: Tenant evictions threaten housing stability and are a major concern for many cities. An open question concerns whether data-driven methods enhance outreach programs that target at-risk tenants to mitigate their risk of eviction. We propose a novel active geospatial search (AGS) modeling framework for this problem. AGS integrates property-level information in a search policy that identifies a sequence of rental units to canvas to both determine their eviction risk and provide support if needed. We propose a hierarchical reinforcement learning approach to learn a search policy for AGS that scales to large urban areas containing thousands of parcels, balancing exploration and exploitation and accounting for travel costs and a budget constraint. Crucially, the search policy adapts online to newly discovered information about evictions. Evaluation using eviction data for a large urban area demonstrates that the proposed framework and algorithmic approach are considerably more effective at sequentially identifying eviction cases than baseline methods.
Institutions: Washington University in St. Louis, George Mason University
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
Awards: FAI: FairGame: An Audit-Driven Game Theoretic Framework for Development and Certification of Fair AI, RI: HCC: Medium: Equity, Justice, and Incentives in Societal Resource Allocation, SaTC: CORE: Small: Socio-Technical Approaches for Securing Cyber-Physical Systems from False Claim Attacks, CAREER: Adversarial Artificial Intelligence for Social Good, RI: Small: Large-Scale Game-Theoretic Reasoning with Incomplete Information
OpenAlex ID: W4409361324