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In this article, we study how the marketing of single-family homes explains the racial and income makeup of mortgage applicants in a neighborhood. We use a case study of the robust housing market of Charlotte, North Carolina, and annual, longitudinal real estate listing advertisements alongside mortgage lending data, to demonstrate how the share of properties advertised a certain way in a neighborhood in 1 year explains shares of mortgage applicants by race and income the following year. We classify property advertisement text using a semi-supervised learning algorithm into five categories following a housing investment and disinvestment to renewal continuum. We find stark racial disparities in mortgage applicants by housing type, even after controlling for income. We find that Black applicants nearly exclusively apply for mortgages in neighborhoods with a high share of properties advertised as disinvested with little profit-making promise. High-income White applicants rise as the share of advertised properties becomes more homogenous. 

This article examines the characteristics of residents moving into new rail transit neighborhoods using longitudinal, individual‐level data from the Housing Mortgage Disclosure Act. To disentangle the role of transit from other neighborhood amenities that may give rise to shifts in the socioeconomic or demographic profile of homebuyers, an exploratory text analysis is first performed on property advertisements in transit‐adjacent neighborhoods. This informs the creation of variables for our models that estimate the probability of an applicant applying for a loan by race and income, and highlights where light rail is most prominently advertised as an amenity. We do not find that the announcement of a new light rail line significantly alters the income profile of loan applicants. Rather, proximity to the center city is a more important determinant in attracting higher income applicants. We do find that the announcement of the transit line is significant in explaining changes in the racial profile of applicants. Postannouncement, White applicants are significantly more likely to apply for loans in transit‐adjacent neighborhoods, while Blacks are significantly less likely to. As for other amenities, the walkability of a neighborhood is significant in predicting where White applicants are more likely to apply for home purchase loans. 

In this commentary we reflect on the potential and power of geographical analysis, as a set of methods, theoretical approaches, and perspectives, to increase our understanding of how space and place matter forall. We emphasize key aspects of the field, including accessibility, urban change, and spatial interaction and behavior, providing a high‐level research agenda that indicates a variety of gaps and routes for future research that will not only lead to more equitable and aware solutions to local and global challenges, but also innovative and novel research methods, concepts, and data. We close with a set of representation and inclusion challenges to our discipline, researchers, and publication outlets.