Home sharing as affordable housing for all? Revealing the exclusionary language of shared rental listings through AI

Shared renting offers affordability opportunities in unaffordable neighborhoods, but uniquely impels existing and prospective tenants to match on both unit and personal characteristics—creating new opportunities for discrimination and segregation. This study investigates how this matching unfolds. Do existing tenants construct “idealized co-tenants” to signal their selection criteria and signal who is and is not welcome to apply? We analyze online rental listings in Los Angeles, California through a mixed-methods research design, leveraging both quantitative deep learning models of listing language and qualitative content analysis of how listers present selection criteria. We find that, relative to whole unit listings, shared unit listings uniquely emphasize personal characteristics, rental rules, and privacy concerns. Although selection criteria describing behaviors—rather than personal traits—dominate, references to several protected classes appear. Listers often operationalize compatibility as similarity, relying on in-group communication strategies and covert insider signaling. This suggests how shared housing may perpetuate socio-spatial segregation by restricting precious affordability opportunities to narrow subpopulations. Policymakers should craft tenant protections addressing the unique relational nature of shared renting to enable more diverse shared households and counteract trends that reinforce inequitable status quos.