Searching for housing in the digital age: Neighborhood representation on internet rental housing platforms across space, platform, and metropolitan segregation

Understanding residential mobility, housing affordability, and the geography of neighborhood advantage and disadvantage relies on robust information about housing search processes and housing markets. Existing data about housing markets, especially rental markets, suffer from accuracy issues and a lack of temporal and geographic flexibility. Data collected from online rental platforms that are commonly used can help address these issues and hold considerable promise for better understanding the full distribution of available rental homes. However, realizing this promise requires a careful assessment of potential sources of bias as online rental listing platforms may perpetuate inequalities similar to those found in physical spaces. This paper approaches the production of rental advertisements as a social process driven by both contextual and property level factors. We compare data from two online platforms for the 100 most populated metropolitan areas in the United States to explore inequality in digital rental listing spaces and understand what characteristics are associated with over and underrepresentation of advertisements in certain areas. We find similar associations for socioeconomic measures between platforms and across urban and suburban parts of these metropolitan areas. In contrast, the importance of racial and ethnic composition, as well as broader patterns of segregation, for online representation differs substantially across space and platform. This analysis informs our understanding of how online platforms affect housing search dynamics through their biases and segmentation, and highlights the potential and limits in using the data available on these platforms to produce small area rental estimates.

The Unequal Availability of Rental Housing Information Across Neighborhoods

As more urban residents find their housing through online search tools, recent research has theorized the potential for online information to transform and equalize the housing search process. Yet, very little is known about what rental housing information is available online. Using a corpus of millions of geocoded Craigslist advertisements for rental housing across the 50 largest metropolitan statistical areas in the United States merged with census tract–level data from the American Community Survey, we identify and describe the types of information commonly included in listings across different types of neighborhoods. We find that in the online housing market, renters are exposed to fundamentally different types of information depending on the ethnoracial and socioeconomic makeup of the neighborhoods where they are searching.

The Costs of Seeking Shelter for Renters With Discrediting Background Records

This study illuminates an understudied pathway through which disadvantage is reproduced in the rental housing market: the housing search, application, and tenant screening process. Using in-depth interviews with 25 housing-seekers with criminal conviction records, past evictions, and damaged credit histories, this article examines the direct role of the rental housing search and application process in reproducing economic precarity and social disadvantage among renters with discrediting background records, beyond delimiting their housing options. Its findings suggest that navigating the housing search from a position of acute market disadvantage comes with significant costs for this population, including the financial burden of repeated application fees and the psychological strains associated with the specter of indefinite housing insecurity. The findings also demonstrate how the housing search process may undermine the willingness of stigmatized renters to contest exploitative or unlawful rental practices by reinforcing awareness of their degraded status in the rental market.

Online housing search: A gravity model approach

In this paper we apply a gravity framework to user-generated data of a large online housing market platform. We show that gravity describes the patterns of inflow and outflow of hits (mouse clicks, etc.) from one municipality to another, where the municipality of the user defines the origin and the municipality of the property that is viewed defines the destination. By distinguishing serious searchers from recreational searchers we demonstrate that the gravity framework describes geographic search patterns of both types of users. The results indicate that recreational search is centered more around the user’s location than serious search. However, this finding is driven entirely by differences in border effects as there is no difference in the distance effect. By demonstrating that geographic search patterns of both serious and recreational searchers are explained by their physical locations, we present clear evidence that physical location is an important determinant of economic behavior in the virtual realm too.