Active Geospatial Search for Efficient Tenant Eviction Outreach
Anindya Sarkar, Alex DiChristofano, Sanmay Das, Patrick J. Fowler, Nathan Jacobs, Yevgeniy Vorobeychik
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.
BibTeX
@article{Sarkar_DiChristofano_Das_Fowler_Jacobs_Vorobeychik_2025, title={Active Geospatial Search for Efficient Tenant Eviction Outreach}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35055}, DOI={10.1609/aaai.v39i27.35055}, abstractNote={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.}, number={27}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Sarkar, Anindya and DiChristofano, Alex and Das, Sanmay and Fowler, Patrick J. and Jacobs, Nathan and Vorobeychik, Yevgeniy}, year={2025}, month={Apr.}, pages={28340-28348} }