Active Geospatial Search for Efficient Tenant Eviction Outreach
This addresses tenant eviction outreach for cities, but it is incremental as it applies a novel method to a known bottleneck in data-driven social programs.
The paper tackles the problem of identifying at-risk tenants for eviction by proposing an active geospatial search framework that uses hierarchical reinforcement learning to sequentially canvas rental units, demonstrating it is more effective than baseline methods in a large urban area.
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.