英文标题：
《Online Rental Housing Market Representation and the Digital Reproduction
  of Urban Inequality》
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作者：
Geoff Boeing
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最新提交年份：
2019
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英文摘要：
  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, 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 like Craigslist construct new institutions with the power to shape spatial economies.
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中文摘要：
随着租赁住房市场的在线化，互联网提供了各种可能的未来：要么承诺让以前被边缘化的购房者更平等地获得信息，要么再现长期存在的信息不平等。在线房源代表性方面的偏见可能会影响不同社区获取住房搜索信息的机会，通过数字鸿沟强化传统的信息隔离模式。他们还可以限制住房从业者和研究人员从房源中获得广泛市场洞察力的能力，以了解租金供应和可承受性。这项研究调查了全美数百万Craigslist租赁列表，发现它们在空间上集中并过度代表了白人、富人和受过良好教育的社区。在年龄、语言、大学入学率、租金、贫困率和家庭规模方面存在其他显著的人口统计学差异。大多数城市的在线住房市场是按种族和阶级进行数字隔离的，我们讨论了智能城市范式中对住宅流动性、社区易读性、中产阶级化、住房券使用以及自动监控和分析的各种影响。虽然Craigslist包含有价值的众包数据，可以更好地实时了解可承受性和可用租赁供应，但它并不能均匀地代表所有细分市场。互联网承诺信息民主化，在线房源可以降低住房搜索成本，增加选择集。然而，技术获取/偏好和信息渠道隔离可以将此类信息广播的好处集中在已经处于优势的社区，再现传统的不平等现象，并加强住宅分类和隔离动态。像Craigslist这样的技术平台构建了新的机构，有能力塑造空间经济。
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分类信息：

一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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