Active Geospatial Search for Efficient Tenant Eviction Outreach

TL;DR

This paper introduces a hierarchical reinforcement learning framework for active geospatial search to efficiently identify at-risk tenants for eviction outreach, improving over baseline methods in large urban areas.

Contribution

It presents a novel AGS modeling framework combined with hierarchical reinforcement learning to optimize eviction risk detection in large urban environments.

Findings

The proposed method outperforms baseline approaches in identifying eviction cases.

The framework adapts online to new eviction information, enhancing search efficiency.

Evaluation shows significant improvement in eviction case detection in a large urban area.

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.