Active Geospatial Search for Efficient Tenant Eviction Outreach
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866929646093729792 |
|---|---|
| author | Sarkar, Anindya DiChristofano, Alex Das, Sanmay Fowler, Patrick J. Jacobs, Nathan Vorobeychik, Yevgeniy |
| author_facet | Sarkar, Anindya DiChristofano, Alex Das, Sanmay Fowler, Patrick J. Jacobs, Nathan Vorobeychik, Yevgeniy |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_17854 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Active Geospatial Search for Efficient Tenant Eviction Outreach Sarkar, Anindya DiChristofano, Alex Das, Sanmay Fowler, Patrick J. Jacobs, Nathan Vorobeychik, Yevgeniy Machine Learning Artificial Intelligence Computers and Society 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. |
| title | Active Geospatial Search for Efficient Tenant Eviction Outreach |
| topic | Machine Learning Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2412.17854 |