Evaluating tenant-landlord tensions using generative AI on online tenant forums

Xin Chen, Cheng Ren () and Timothy A. Thomas
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Xin Chen: Stanford University
Cheng Ren: University at Albany, State University of New York
Timothy A. Thomas: University of California

Journal of Computational Social Science, 2025, vol. 8, issue 2, No 24, 21 pages

Abstract: Abstract Tenant-landlord relationships exhibit a power asymmetry where landlords’ power to evict the tenants at a low-cost results in their dominating status in such relationships. Tenant concerns are thus often unspoken, unresolved, or ignored and this could lead to blatant conflicts as suppressed tenant concerns accumulate. Modern machine learning methods and Large Language Models (LLM) have demonstrated immense abilities to perform language tasks. In this study, we incorporate Latent Dirichlet Allocation with GPT-4 to classify Reddit post data scraped from the subreddit r/Tenant, aiming to unveil trends in tenant concerns while exploring the adoption of LLMs and machine learning methods in social science research. We find that tenant concerns in topics like fee dispute and utility issues are consistently dominant in all four states analyzed while each state has other common tenant concerns special to itself. Moreover, we discover temporal trends in tenant concerns that provide important implications regarding the impact of the pandemic and the Eviction Moratorium.

Keywords: Large language models; Natural language processing; Tenant-landlord relationships; Tenant concerns; GPT-4 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s42001-025-00378-8

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