In the wake of the 2007–08 housing crash, the Black–white wealth gap reached a staggering 20 to 1. Since then, a growing chorus of influential voices has proposed measures to increase the Black homeownership rate as a means to narrow the gap. Others, however, have argued that the uneven racial geography of home price appreciation necessarily restricts the degree to which Black households, in the aggregate, can build wealth via homeownership. We aim to contribute to these debates by theorizing a racial appreciation gap as a central feature of urban housing markets under racial capitalism, and analyzing how neighborhood racial and income characteristics have structured home price appreciation from before the height of the housing boom (2000–03) to its post‐crisis recovery (2014–16). Focusing on the two counties that encompass Atlanta, Georgia, USA—an area famous for Black prosperity—we find that a neighborhood's racial composition has a more salient impact on home price change than its income. Results indicate that when a place is marked as Black, this may itself inhibit home price appreciation, suggesting that an enduring racial appreciation gap may limit the potential for Black homeownership to substantively narrow the racial wealth gap.

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In the late 1930s, an agency of the United States government called the “Home Owners’ Loan Corporation” (HOLC) graded thousands of urban neighborhoods on the perceived risk they posed to property owners. To make these determinations, HOLC field agents collected vast amounts of socioeconomic, demographic, and housing data about these places and presented their findings in an impressive set of maps. While these “redlining” maps have received considerable academic and media attention, the neighborhood-level race, housing, and socioeconomic data used to assign risk grades—available for most cities in their “area description” sheets—remain virtually unusable. Correcting this issue, I convert eight of the most consequential variables from 129 cities into an accessible and analyzable tabular format. These include the Black population percentage, “foreign-born” population percentage and group, family income, occupation class, average building age, home repair status, and mortgage availability. This data product will allow researchers to gain a more complete picture of the HOLC’s City Survey program, and it will provide a valuable new source of historical socio-demographic data at the neighborhood level.

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Subcounty housing unit counts are important for studying geo-historical patterns of (sub)urbanization, land-use change, and residential loss and gain. The most commonly used subcounty geographical unit for social research in the United States is the census tract. However, the changing geometries and historically incomplete coverage of tracts present significant obstacles for longitudinal analysis that existing datasets do not sufficiently address. Overcoming these barriers, we provide housing unit estimates in consistent 2010 tract boundaries for every census year from 1940 to 2010 plus 2019 for the entire continental US. Moreover, we develop an “urbanization year” indicator that denotes if and when tracts became “urbanized” during this timeframe. We produce these data by blending existing interpolation techniques with a novel procedure we call “maximum reabsorption.” Conducting out-of-sample validation, we find that our hybrid approach generally produces more reliable estimates than existing alternatives. The final dataset, Historical Housing Unit and Urbanization Database 2010 (HHUUD10), has myriad potential uses for research involving housing, population, and land-use change, as well as (sub)urbanization.