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Housing scholars stress the importance of the information environment in shaping housing search behavior and outcomes. Rental listings have increasingly moved online over the past two decades and, in turn, online platforms like Craigslist are now central to the search process. Do these technology platforms serve as information equalizers or do they reflect traditional information inequalities that correlate with neighborhood sociodemographics? We synthesize and extend analyses of millions of US Craigslist rental listings and find they supply significantly different volumes, quality, and types of information in different communities. Technology platforms have the potential to broaden, diversify, and equalize housing search information, but they rely on landlord behavior and, in turn, likely will not reach this potential without a significant redesign or policy intervention. Smart cities advocates hoping to build better cities through technology must critically interrogate technology platforms and big data for systematic biases.

In this article we examine how the online rental housing market reflects the desirability of different neighbourhoods in St. Louis, MO, a metropolitan area with long-standing high levels of Black–White residential segregation. Using a large digital corpus of advertisements for rental housing, we first show that adverts in neighbourhoods with more Black residents are less likely to list a neighbourhood name than adverts for available housing units in neighbourhoods with more White residents. Advertisements for housing in neighbourhoods with more Black residents are also more likely to list a different, higher-income neighbourhood name than the one in which they are located. Next, using a survey of St. Louis residents, we find that neighbourhoods with more Black residents are perceived as less desirable by both White and Black St. Louisans. We then employ a pair of survey experiments and find that interest in renting a particular housing unit changes if the advert does not list a neighbourhood name or uses a different neighbourhood name than one commonly associated with its location. Altogether, our findings reveal that postings in online housing markets reflect and reproduce existing racial-spatial patterns and may contribute to the avoidance/stigmatisation of certain neighbourhoods. [ABSTRACT FROM AUTHOR]

Past research has demonstrated the racially and spatially uneven impacts of economic shocks and environmental disasters on various markets. In this article, we examine if and how the first few months of the COVID-19 pandemic affected the market for rental housing in the 49 largest metropolitan areas in the United States. Using a unique data set of new rental listings gathered from Craigslist and localized measures of the pandemic's severity we find that, from mid-March to early June, local spread of COVID-19 is followed by reduced median and mean rent. However, this trend is driven by dropping rents for listings in Black, Latino, and diverse neighborhoods. Listings in majority White neighborhoods experience rent increases during this time. Our analyses make multiple contributions. First, we add to the burgeoning literature examining the rental market as a key site of perpetuating sociospatial inequality. Second, we demonstrate the utility of data gathered online for analyzing housing. And third, by reflecting on research that shows how past crises have increased sociospatial inequality and up-to-date work showing the racially and spatially unequal effects of the COVID-19 pandemic, we discuss some possible mechanisms by which the pandemic may be affecting the market for rental housing as well as implications for long-term trends. [ABSTRACT FROM AUTHOR]