The Unequal Availability of Rental Housing Information Across Neighborhoods

Abstract

As more urban residents find their housing through online search tools, recent research has theorized the potential for online information to transform and equalize the housing search process. Yet, very little is known about what rental housing information is available online. Using a corpus of millions of geocoded Craigslist advertisements for rental housing across the 50 largest metropolitan statistical areas in the United States merged with census tract–level data from the American Community Survey, we identify and describe the types of information commonly included in listings across different types of neighborhoods. We find that in the online housing market, renters are exposed to fundamentally different types of information depending on the ethnoracial and socioeconomic makeup of the neighborhoods where they are searching.

Predatory Inclusion in the Market for Rental Housing: A Multicity Empirical Test

“…As a result, they prioritize unit features over less known neighborhood qualities (DeLuca et al 2013;DeLuca and Rosenblatt 2017;Rosenblatt and DeLuca 2012). Under these circumstances, housing advertisements that mention the acceptance or denial of vouchers may be especially strong signals about the appropriateness of a particular unit (see Besbris, Schachter, and Kuk 2021;Boeing et al 2021;Kennedy et al 2021), and past research has shown that restricted search conditions and fear of discrimination lead voucher holding households to scan rental advertisements specifically for positive or welcoming language (Rosenblatt and Cossyleon 2018).…”

Understanding heterogeneity of investor sentiment on social media: A structural topic modeling approach

“…It is effective in capturing the predictive information about the response but fails to capture the underlying semantic themes within the corpus (Li et al, 2017 ). In comparison to MNIR, Roberts et al ( 2020 ) show that STM has the form of MNIR conditional on the latent variable and both approaches may yield qualitatively similar findings (Besbris et al, 2021 ).…”

Analysis of the impact of investor sentiment on stock price using the latent dirichlet allocation topic model

“…Mai and Pukthuanthong (2021) adopt a semi-supervised LDA approach to extract economic narratives from New York Times articles by providing seed words for each topic to guide the formation of issues towards the predefined themes. Roberts et al (2020) show that Structure Topic Model (STM) has the form of Multinomial Inverse Regression (MNIR) conditional on the latent variable, and both approaches may yield qualitatively similar findings (Besbris et al, 2021). Ji and Han, 2022 employ a structural topic modelling approach to extract topical contents of investor sentiment across multiple financespecific factors.…”