WARNER: Weakly-Supervised Neural Network to Identify Eviction Filing Hotspots in the Absence of Court Records

The widespread eviction of tenants across the United States has metamorphosed into a challenging public-policy problem. In particular, eviction exacerbates several income-based, educational, and health inequities in society, e.g., eviction disproportionately affects low-income renting families, many of whom belong to underrepresented minority groups. Despite growing interest in understanding and mitigating the eviction crisis, there are several legal and infrastructural obstacles to data acquisition at scale that limit our understanding of the distribution of eviction across the United States. To circumvent existing challenges in data acquisition, we propose WARNER, a novel Machine Learning (ML) framework that predicts eviction filing hotspots in US counties from unlabeled satellite imagery dataset. We account for the lack of labeled training data in this domain by leveraging sociological insights to propose a novel approach to generate probabilistic labels for a subset of an unlabeled dataset of satellite imagery, which is then used to train a neural network model to identify eviction filing hotspots. Our experimental results show that WARNER acheives a higher predictive performance than several strong baselines. Further, the superiority of WARNER can be generalized to different counties across the United States. Our proposed framework has the potential to assist NGOs and policymakers in designing well-informed (data-driven) resource allocation plans to improve the nationwide housing stability. This work is conducted in collaboration with The Child Poverty Action Lab (a leading non-profit leveraging data-driven approaches to inform actions for relieving poverty and relevant problems in Dallas County, TX). The code can be accessed via https://github.com/maryam-tabar/WARNER.