New Dataset Empowers Researchers To Precisely Track The Effects Of Redlining Over Time
A new dataset will give researchers an unprecedented look at how long-term disinvestment and redlining affects public health.
Funded by a grant from the National Institutes of Health (NIH), the National Community Reinvestment Coalition (NCRC) and Institute for Social Research (ISR) have collaborated to create the Home Mortgage Disclosure Act Longitudinal Dataset (HLD). Covering activity since 1981, the HLD combines more than 40 years of data from the only available set of home mortgage information.
The HLD effectively bridges the gap between the different eras of HMDA data collection and clarifies it for researchers, using the same census tract boundaries to organize the information. This accessible format should open new doors for investigating the historical effects of redlining and disinvestment through new, geographic alignment across multiple decades of mortgage reporting.
The finished product is something entirely new. Previously, researchers had been unable to present this data comprehensively. Home Mortgage Disclosure Act (HMDA) data was collected in four distinct phases, which affected the type and organization of the data and also created a degree of unresolved errors and omissions from the reporting institutions and federal regulators. The resulting datasets involved production and integration of datasets with national coverage of mortgage lending. This will link with other datasets encompassing historic redlining, locations of industrial sites, and public health.
The NCRC research team, utilizing years of experience using HMDA data, was able to cross-reference existing data through the decades to establish common indicators of the number and amounts of mortgage loan originations, the type of lending activity, and the income and race of borrowers. This involved the development of SQL scripts to organize the data accurately and in an understandable way.
Researchers then applied Brown University’s Longitudinal Tract Database (LTDB) in order to establish common geographic boundaries for the data, representing the same neighborhoods consistently year over year. The HLD can be matched with a broad range of US Census data available through the LTDB, facilitating comparisons of the income, race, housing status, and other data available to the research community. This, in turn, should yield new research on crucially important subjects.
The HMDA Longitudinal Dataset will be hosted by the National Neighborhood Dataset (NaNDA), the preeminent source of neighborhood-level data available to researchers. NaNDA is an archive at ICPSR, part of the University of Michigan Institute for Social Research.
Lenders continue to reinforce patterns of structural racism in formerly redlined neighborhoods, regardless of local market dynamics. Fifty-five years after Congress outlawed using discriminatory maps to guide mortgage lending, race-based exclusion from homeownership is still a de facto reality.
Policymakers and analysts can now definitively and precisely connect present-day conditions to past structural discrimination. The new HMDA Longitudinal Dataset (HLD) created to make this report possible clears up data deficiencies that have stymied our understanding for decades.
In light of these findings, it is imperative to implement and firmly enforce better-designed policy measures aimed at mitigating the impact of redlining and addressing residential segregation.Recent enhancements to the Community Reinvestment Act (CRA), and the long-awaited Affirmatively Furthering Fair Housing (AFFH) rules yet to be finalized by the US Department of Housing and Urban Development (HUD), are important steps to mitigate the impact of redlining and diminish residential segregation in communities. But they may not be sufficient on their own, given the stickiness of redlining’s legacy over the half-century since the Fair Housing Act (FHA) became law.
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