What Lies Behind the Answers: Applying Machine Learning to Understand Tenants’ Reaction to Eviction

Cheng Ren, Timothy A. Thomas, Xin Chen and Leyi Zhou

No uscxh_v1, OSF Preprints from Center for Open Science

Abstract: Eviction is one of the most pressing challenges facing millions of households today, particularly in urban areas, with filings expected to rebound or even surpass pre-pandemic levels. While non-payment of rent is frequently cited as the primary reason for eviction, the underlying causes of non-payment have not been thoroughly examined. This study analyzes eviction court files from 2014 to 2017, obtained from Pierce County, Washington, and applies advanced data science techniques, including layout analysis and natural language processing, to investigate renters’ responses to summonses and their stated reasons for non-payment. The findings reveal that the most frequently mentioned responses include “going to court,” “moving out,” and “payment plan.” Furthermore, among cases that provide explanations for eviction, the most commonly cited causes are “job loss,” “family emergencies,” and “medical issues.” Based on insights from the answer files, we propose interventions such as mediation and tenant rights education. Finally, we discuss the study’s limitations and suggest directions for future research.

Date: 2025-01-31
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:uscxh_v1

DOI: 10.31219/osf.io/uscxh_v1

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