Racialized Representations of Neighborhood Quality Across 16 US Metro Areas

Berkeley Computational Social Science Forum

CSS Training Program

March 15, 2022
4:00pm to 5:00pm
Virtual Participation


Berkeley Computational Social Science Forum
Date: Tuesday, March 15, 2022
Time: 4:00-5:00 PM Pacific Time
Location: Virtual Participation – Register to attend via Zoom

Racialized Representations of Neighborhood Quality Across 16 US Metro Areas

Ian Kennedy, PhD Candidate in Sociology, University of Washington
Abstract: Housing dynamics in the United States have historically been racialized through explicitly discriminatory laws like redlining, which created connections between neighborhood quality and racial composition. The perpetuation of racialized housing dynamics, like segregation and gentrification suggests that neighborhood race is still entwined with perceptions of neighborhood quality. This study investigates whether the connection between neighborhood race and perceived quality persists in present day rental advertisements. Using data from the online rental platform Craigslist spanning 16 U.S. metropolitan areas I apply computational text analysis and statistical methods to tease out racialized aspects of neighborhood descriptions appearing in rental advertisements. The results show that contemporary descriptions of neighborhoods online reflect the legacy of more traditional forms of neighborhood racialization. Advertisements tend to describe White neighborhoods more positively than other neighborhoods. For instance, majority Black tracts with a median household income of $150,000 are described about as favorably as majority White tracts with one sixth the median income. These findings have implications for understanding the perceptual nature of the reproduction of racialized housing dynamics.

The Computational Social Science Forum is an informal setting for the interdisciplinary exchange of ideas and scholarship at the intersection of social science and data science. Participants engage in a variety of activities such as presentations of work in progress, discussions and critiques of recent papers, introductions to new tools and methods, discussions around ethics, fairness, inequality, and responsible conduct of research, as well as professional development. This Forum is organized as part of the Computational Social Science Training Program, and weekly meetings are hosted by researchers from BIDS and D-Lab. The group welcomes social scientists and researchers with interests in data science methods and tools, and data scientists with applications or interests in public policy, social, behavioral, and health sciences. Participants include graduate students, postdocs, staff, and faculty, and members are encouraged to attend regularly in order to foster community around improving computational social science research, supporting the development and research of group members, and fostering new collaborations. Interested UC Berkeley community members are invited to use this registration form to receive the schedule and access links. Please contact css-t32@berkeley.edu for more information or if you are interested in presenting current research for an upcoming session.


Ian Kennedy

PhD Candidate in Sociology, University of Washington

Ian Kennedy is a PhD Candidate in Sociology at the University of Washington. They hold MA degrees from New York University and from the University of Washington. They are a computational social scientist trained working at the intersection of race, digital platforms, and text analysis, aiming to contribute to understandings of how contemporary racism works, in both visible and less visible ways. This means looking for data in new places, like in Craigslist rental ad texts, developing new uses for large-scale administrative data, and exploring innovative ways to yield scholarly insights from existing data. They are also committed to producing useful work beyond scholarly publications, working with groups like the Northwest Justice Project to identify illegal Craigslist ads or with the Election Integrity Partnership to monitor misinformation during the 2020 election.