Redlining Maps from the Home Owners Loan Corporation: Pennsylvania, 1937
Western Pennsylvania Regional Data Center Open Data · 1937 Full Details
Full Details
- Title
- Redlining Maps from the Home Owners Loan Corporation: Pennsylvania, 1937
- Description
- Most of the text in this description originally appeared on the [Mapping Inequality](https://dsl.richmond.edu/panorama/redlining/#loc=11/40.442/-80.153&city=pittsburgh-pa&text=intro) Website. Robert K. Nelson, LaDale Winling, Richard Marciano, Nathan Connolly, et al., "Mapping Inequality," American Panorama, ed. Robert K. Nelson and Edward L. Ayers, "HOLC staff members, using data and evaluations organized by local real estate professionals--lenders, developers, and real estate appraisers--in each city, assigned grades to residential neighborhoods that reflected their "mortgage security" that would then be visualized on color-coded maps. Neighborhoods receiving the highest grade of "A"--colored green on the maps--were deemed minimal risks for banks and other mortgage lenders when they were determining who should received loans and which areas in the city were safe investments. Those receiving the lowest grade of "D," colored red, were considered "hazardous." Conservative, responsible lenders, in HOLC judgment, would "refuse to make loans in these areas [or] only on a conservative basis." HOLC created area descriptions to help to organize the data they used to assign the grades. Among that information was the neighborhood's quality of housing, the recent history of sale and rent values, and, crucially, the racial and ethnic identity and class of residents that served as the basis of the neighborhood's grade. These maps and their accompanying documentation helped set the rules for nearly a century of real estate practice. " HOLC agents grading cities through this program largely "adopted a consistently white, elite standpoint or perspective. HOLC assumed and insisted that the residency of African Americans and immigrants, as well as working-class whites, compromised the values of homes and the security of mortgages. In this they followed the guidelines set forth by Frederick Babcock, the central figure in early twentieth-century real estate appraisal standards, in his Underwriting Manual: "The infiltration of inharmonious racial groups ... tend to lower the levels of land values and to lessen the desirability of residential areas." These grades were a tool for redlining: making it difficult or impossible for people in certain areas to access mortgage financing and thus become homeowners. Redlining directed both public and private capital to native-born white families and away from African American and immigrant families. As homeownership was arguably the most significant means of intergenerational wealth building in the United States in the twentieth century, these redlining practices from eight decades ago had long-term effects in creating wealth inequalities that we still see today. Mapping Inequality, we hope, will allow and encourage you to grapple with this history of government policies contributing to inequality." Data was copied from the Mapping Inequality Website for communities in Western Pennsylvania where data was available. These communities include Altoona, Erie, Johnstown, Pittsburgh, and New Castle. Data included original and georectified images, scans of the neighborhood descriptions, and digital map layers. Data here was downloaded on June 9, 2020.
- Creator
- Western Pennsylvania Regional Data Center
- Publisher
- Western Pennsylvania Regional Data Center Open Data
- Temporal Coverage
- 1937
- Date Issued
- 2020-06-09 21:53:48
- Access Rights
- Public
- Format
- Shapefile
- Language
- English
- Date Added
- April 27, 2021
Location
Links
Cite and Reference
-
Citation
Western Pennsylvania Regional Data Center (2020). Redlining Maps from the Home Owners Loan Corporation: Pennsylvania, 1937. Western Pennsylvania Regional Data Center Open Data. https://data.wprdc.org/dataset/redlining-maps-from-the-home-owners-loan-corporation (dataset) -
BTAA Geoportal Link
