There is considerable urban residential segregation in the United States by race, ethnicity, and social class, with implications for community formation and inequality, as well as school segregation. We know that the majority of urban home-seekers in the United States now use the internet as their primary source to find new places to live. Despite this development, we know relatively little regarding how renters select their housing at a time when information about available housing is increasingly moving online. While this technological transformation has largely gone unexamined, some prior research shows that advertisements for rental housing are not all the same; rather, they differ systematically depending on the demographics of the neighborhood where the housing being advertised is located. This project analyzes rental housing advertisements posted online to investigate if these differences matter for people during their housing search. Understanding how individuals interpret the information they see in the online housing market is key to explaining why people move to certain places and not others, which has implications for the future of residential inequality and racial/ethnic segregation. The findings will advance understanding of residential selection processes and aid policy makers looking to expand and equalize access to information for home-seekers, with implications for improved social and economic well-being in urban areas.
Given the role that online rental advertisement plays in promoting neighborhood composition, it is notable that we know so little regarding how individuals interpret this information. Using natural language processing to analyze millions of advertisements for rental housing in the 50 largest U.S. cities posted on Craigslist, prior work has identified patterns in the distribution of different types of information in neighborhoods that vary by race/ethnicity and poverty rate. This proposal will use this information to test the causal effects of these real-world differences in the ways units are advertised on individuals’ housing and neighborhood preferences in five large urban areas: Los Angeles, SF-Bay Area, New York City, Chicago, and Houston. The project will implement three survey experiments in each area to test how online advertisements shape housing decisions and residents’ perceptions of local neighborhoods. The project will compare the effects of information in housing ads and perceptions of neighborhoods to the effects of other kinds of information, including neighborhood demographic data, on individuals’ interest in housing units. Selection of these five large areas allows the project to oversample Black, Latino and Asian minority respondents. Each survey experiment will first be constructed to be representative of the urban area (n=1,000/area), but will then collect additional respondents from specific minority population(s) within each area: oversamples of 100-300 additional Asian respondents in the SF Bay Area; Black respondents in New York and Chicago; and Latino respondents (including Spanish-speakers) in Los Angeles, Chicago, and Houston. By using representative surveys of specific urban areas with oversamples of minority residents, the project will analyze—through a combination of difference of means tests and multiple regression models—how reactions to housing advertisements vary across ethno-racial groups. The project will contribute to sociological theory regarding neighborhood and unit selection processes, residential sorting, the formation of place reputations, and how prospective tenants form impressions of their residential contexts. The project also will help to transform how survey experiments are developed and implemented across the social sciences by demonstrating the utility of big data sources and computational techniques.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
