Eviction Prediction

Eviction prediction research focuses on developing accurate and efficient methods to identify individuals or properties at high risk of eviction, leveraging diverse data sources like legal case records, public records, and electronic health records. Current approaches utilize natural language processing (NLP) techniques for text analysis and machine learning models, including transformer-based architectures, to analyze large datasets and predict eviction likelihood. This research is significant because it can improve the targeting of preventative interventions and resource allocation, ultimately aiming to reduce housing insecurity and its associated negative health and social consequences.