Neighborhood Desirability and Decision-Making in Online, Multiracial, Metropolitan America

In the United States, the housing search process has largely moved online for urban residents, yet little work has examined if and how the information homeseekers are exposed to on housing websites matters for their assessments of potential destinations. We designed a unique, geographically contextual survey experiment that uses real neighborhood names and actual online housing advertisement text to test if residents’ ratings of neighborhoods as desirable are affected by novel information. We find that online housing information—descriptions of housing units, neighborhoods, and how to apply for leases—largely reproduces the existing racial-spatial hierarchy, where non-poor White neighborhoods are rated as the most desirable, and poor Black and Latinx neighborhoods are rated as the least, with Asian neighborhoods in between. Residents’ prior familiarity with neighborhoods in their metro attenuates but does not fully explain away these effects, and the effect varies by race/ethnicity, with White residents the most sensitive to novel information. We offer a sophisticated model of the digital information environment in which an ethnoracially diverse population is exposed to neighborhood options, and we detail how our methods improve survey experiment design more broadly. Our results show that relatively small amounts of seemingly race-neutral information can affect residential preferences.