RESUMO
Racial discrimination has been a central driver of residential segregation for many decades, in the Seattle area as well as in the United States as a whole. In addition to redlining and restrictive housing covenants, housing advertisements included explicit racial language until 1968. Since then, housing patterns have remained racialized, despite overt forms of racial language and discrimination becoming less prevalent. In this paper, we use Structural Topic Models (STM) and qualitative analysis to investigate how contemporary rental listings from the Seattle-Tacoma Craigslist page differ in their description based on neighborhood racial composition. Results show that listings from White neighborhoods emphasize trust and connections to neighborhood history and culture, while listings from non-White neighborhoods offer more incentives and focus on transportation and development features, sundering these units from their surroundings. Without explicitly mentioning race, these listings display racialized neighborhood discourse that might impact neighborhood decision-making in ways that contribute to the perpetuation of housing segregation.
RESUMO
Research on rental housing markets in the United States has traditionally relied on national or local housing surveys. Those sources lack temporal and spatial specificity, limiting their use for tracking short-term changes in local markets. As rental housing ads have transitioned to digital spaces, a growing body of literature has utilized web scraping to analyze listing practices and variations in rental market dynamics. Those studies have primarily relied on one platform, Craigslist, as a source of data. Despite Craigslist's popularity, the authors contend that rental listings from various websites, rather than from individual ones, provide a more comprehensive picture. Using a mixed-methods approach to study listings across various platforms in five metropolitan areas, this article demonstrates considerable variation in both the types of rental units advertised and the features provided across those platforms. The article begins with an account of the birth and consolidation of online rental platforms and emergent characteristics of several selected websites, including the criteria for posting, search parameters, search results priority, and first-page search results. Visualizations are used to compare features such as the 40th percentile of rent, rent distribution, and bedroom size based on scraped data from six online platforms (Padmapper, Forrent.com, Trulia, Zillow, Craigslist, and GoSection8), 2020 Fair Market Rents, and 2019 American Community Survey data. The analyses indicate that online listing platforms target different audiences and offer distinct information on units within those market segments, resulting in markedly different estimates of local rental costs and unit size distribution depending on the platform.
RESUMO
Small Area Fair Market Rents (SAFMRs) are calculated at the 40th percentile of the U.S. postal ZIP Code instead of the metropolitan area in an effort to capture localized rents to expand choice for voucher holders to access housing in higher-opportunity neighborhoods. Existing studies on the potential and actual outcomes of SAFMRs demonstrate that findings vary for different types of housing markets. Furthermore, the decisions public housing authorities (PHAs) make in the implementation process affect PHAs' program budget and the rent burden and locational outcomes for voucher households. This study aims to address how these implementation factors are affected by local rental market conditions for three PHAs-Housing Authority of the City of Fort Lauderdale, San Antonio Housing Authority, and Seattle Housing Authority-in diverse housing markets. By comparing different sources of market rent estimates with SAFMRs in each location, we contribute new information about how this rule is likely to produce different residential outcomes in terms of increased access to low-poverty neighborhoods and adjustments to payment standards in low-rent neighborhoods. The findings reveal differences across rent measures in terms of estimated levels and relative differences across ZIP Codes. These findings suggest that housing authorities may face challenges in meeting the objectives of the SAFMR final rule without some form of local adjustments.