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      Online Rental Housing Market Representation and the Digital Reproduction of Urban Inequality

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          Abstract

          As the rental housing market moves online, the Internet offers divergent possible futures: either the promise of more-equal access to information for previously marginalized homeseekers, or a reproduction of longstanding information inequalities. Biases in online listings' representativeness could impact different communities' access to housing search information, reinforcing traditional information segregation patterns through a digital divide. They could also circumscribe housing practitioners' and researchers' ability to draw broad market insights from listings to understand rental supply and affordability. This study examines millions of Craigslist rental listings across the US and finds that they spatially concentrate and over-represent whiter, wealthier, and better-educated communities. Other significant demographic differences exist in age, language, college enrollment, rent, poverty rate, and household size. Most cities' online housing markets are digitally segregated by race and class, and we discuss various implications for residential mobility, community legibility, gentrification, displacement, housing voucher utilization, and automated monitoring and analytics in the smart cities paradigm. While Craigslist contains valuable crowdsourced data to better understand affordability and available rental supply in real-time, it does not evenly represent all market segments. The Internet promises information democratization, and online listings can reduce housing search costs and increase choice sets. However, technology access/preferences and information channel segregation can concentrate such information-broadcasting benefits in already-advantaged communities, reproducing traditional inequalities and reinforcing residential sorting and segregation dynamics. Technology platforms construct new institutions with the power to shape spatial economies and human interactions.

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          Patterns and causes of uncertainty in the American Community Survey.

          In 2010 the American Community Survey (ACS) replaced the long form of the United States decennial census. The ACS is now the principal source of high-resolution geographic information about the U.S. population. The margins of error on ACS census tract-level data are on average 75 percent larger than those of the corresponding 2000 long-form estimate. The practical implications of this increase is that data are sometimes so imprecise that they are difficult to use. This paper explains why the ACS tract and block group estimates have large margins of error. Statistical concepts are explained in plain English. ACS margins of error are attributed to specific methodological decisions made by the Census Bureau. These decisions are best seen as compromises that attempt to balance financial constraints against concerns about data quality, timeliness, and geographic precision. In addition, demographic and geographic patterns in ACS data quality are identified. These patterns are associated with demographic composition of census tracts. Understanding the fundamental causes of uncertainty in the survey suggests a number of geographic strategies for improving the usability and quality ACS.
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            Cycle of Segregation

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              Gender and the GeoWeb: divisions in the production of user-generated cartographic information

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                Author and article information

                Journal
                13 July 2019
                Article
                1907.06118
                4a91b164-613d-476e-a12e-cabfc077e570

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                Environment and Planning A: Economy and Space, 2019
                econ.GN q-fin.EC stat.AP

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