用智能搜索策略高效识别高风险租户，助力城市防断租行动
Active Geospatial Search for Efficient Tenant Eviction Outreach
- 基于层级强化学习构建动态搜索策略，结合房产信息与实地走访成本
- 在真实城市数据上，识别效率比基线方法提升显著，且可实时更新信息
- 适合城市社会服务部门、非营利组织用于精准干预高危租户
租户被驱逐威胁住房稳定，是众多城市的核心问题。当前关键问题是：数据驱动方法能否提升面向高风险租户的干预项目？本文提出一种新型主动地理空间搜索（AGS）建模框架。该框架整合物业级信息，设计搜索策略以有序巡查租赁单元，既评估其被驱逐风险，也提供必要支持。我们采用分层强化学习方法，训练可在包含数千个地块的大都市区域中运行的搜索策略，平衡探索与利用，同时考虑出行成本与预算限制。重要的是，该策略能在线适应新发现的驱逐信息。使用大规模城市驱逐数据进行评估表明，所提框架与算法在序列化识别驱逐案例方面远优于基线方法。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。