Collaborative Research: The Geography of Information: Testing the Effects of Unequal Information in the Market for Rental Housing

在美国，按种族、民族和社会阶层进行的城市居住隔离现象相当严重，这对社区的形成和不平等以及学校隔离都有影响。我们知道，美国大多数城市寻求住房的人现在将互联网作为他们寻找新住所的主要来源。尽管有了这样的发展，但在有关可用住房的信息越来越多地转移到网上的时候，我们对租户如何选择住房知之甚少。虽然这种技术变革在很大程度上没有得到审查，但之前的一些研究表明，出租房屋的广告并不都是一样的；相反，它们会根据广告所在社区的人口统计数据而有系统地不同。该项目分析网上发布的租赁房屋广告，以调查这些差异是否会对人们在找房过程中产生影响。理解个人如何解读他们在在线住房市场上看到的信息，是解释为什么人们会搬到某些地方而不是其他地方的关键，这对居住不平等和种族/民族隔离的未来有影响。这些发现将增进对住房选择过程的理解，并帮助寻求扩大和平等获得信息的政策制定者，这将对改善城市地区的社会和经济福祉产生影响。考虑到在线租赁广告在促进社区构成方面所起的作用，值得注意的是，我们对个人如何解读这些信息知之甚少。使用自然语言处理技术分析了Craigslist上发布的美国50个最大城市的数百万个租赁住房广告，之前的工作发现了不同类型信息在社区中的分布模式，这些社区因种族/民族和贫困率而异。这项提案将使用这些信息来测试在五个大城市地区：洛杉矶、SF-Bay区、纽约市、芝加哥和休斯顿，单位在个人住房和社区偏好上的广告方式存在的这些现实世界差异的因果关系。该项目将在每个地区实施三项调查实验，以测试在线广告如何影响住房决策和居民对当地社区的看法。该项目将比较住房广告中的信息和对社区的看法与包括社区人口统计数据在内的其他类型信息对个人对住房单元兴趣的影响。通过选择这五大地区，该项目可以对黑人、拉丁裔和亚裔少数族裔受访者进行过多抽样。每个调查实验将首先被构建为代表城市地区(n=1,000个/地区)，但随后将从每个地区的特定少数族裔人口(S)中收集额外的受访者：在SF Bay地区额外抽取100-300名额外的亚裔受访者；在纽约和芝加哥的黑人受访者；以及在洛杉矶、芝加哥和休斯顿的拉丁裔受访者(包括说西班牙语的受访者)。通过对少数族裔居民过多抽样的特定城市地区的代表性调查，该项目将通过经济状况差异检验和多元回归模型相结合的方式，分析不同种族群体对住房广告的反应如何不同。该项目将有助于社会学理论，关于社区和单元选择过程，住宅分类，地方声誉的形成，以及潜在租户如何形成对其居住环境的印象。该项目还将通过展示大数据来源和计算技术的效用，帮助改变社会科学中调查实验的开发和实施方式。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

There is considerable urban residential segregation in the United States by race, ethnicity, and social class, with implications for community formation and inequality, as well as school segregation. We know that the majority of urban home-seekers in the United States now use the internet as their primary source to find new places to live. Despite this development, we know relatively little regarding how renters select their housing at a time when information about available housing is increasingly moving online. While this technological transformation has largely gone unexamined, some prior research shows that advertisements for rental housing are not all the same; rather, they differ systematically depending on the demographics of the neighborhood where the housing being advertised is located. This project analyzes rental housing advertisements posted online to investigate if these differences matter for people during their housing search. Understanding how individuals interpret the information they see in the online housing market is key to explaining why people move to certain places and not others, which has implications for the future of residential inequality and racial/ethnic segregation. The findings will advance understanding of residential selection processes and aid policy makers looking to expand and equalize access to information for home-seekers, with implications for improved social and economic well-being in urban areas. Given the role that online rental advertisement plays in promoting neighborhood composition, it is notable that we know so little regarding how individuals interpret this information. Using natural language processing to analyze millions of advertisements for rental housing in the 50 largest U.S. cities posted on Craigslist, prior work has identified patterns in the distribution of different types of information in neighborhoods that vary by race/ethnicity and poverty rate. This proposal will use this information to test the causal effects of these real-world differences in the ways units are advertised on individuals’ housing and neighborhood preferences in five large urban areas: Los Angeles, SF-Bay Area, New York City, Chicago, and Houston. The project will implement three survey experiments in each area to test how online advertisements shape housing decisions and residents’ perceptions of local neighborhoods. The project will compare the effects of information in housing ads and perceptions of neighborhoods to the effects of other kinds of information, including neighborhood demographic data, on individuals’ interest in housing units. Selection of these five large areas allows the project to oversample Black, Latino and Asian minority respondents. Each survey experiment will first be constructed to be representative of the urban area (n=1,000/area), but will then collect additional respondents from specific minority population(s) within each area: oversamples of 100-300 additional Asian respondents in the SF Bay Area; Black respondents in New York and Chicago; and Latino respondents (including Spanish-speakers) in Los Angeles, Chicago, and Houston. By using representative surveys of specific urban areas with oversamples of minority residents, the project will analyze—through a combination of difference of means tests and multiple regression models—how reactions to housing advertisements vary across ethno-racial groups. The project will contribute to sociological theory regarding neighborhood and unit selection processes, residential sorting, the formation of place reputations, and how prospective tenants form impressions of their residential contexts. The project also will help to transform how survey experiments are developed and implemented across the social sciences by demonstrating the utility of big data sources and computational techniques.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.