Nearly 2,496 HOLC residential security maps are now publicly downloadable from the National Archives, and they offer a rare, map based window into how neighborhood risk was graded before modern mortgage underwriting. Research that reuses those boundaries finds effects that still echo in today’s property values, lending rates, and foreclosure risk, even decades later. The surprise is not that discrimination left records, but that those grading bands and letter scores can line up with measurable dollar outcomes for families.
Key Takeaways
- 2,496 number of currently-available HOLC residential security maps in the National Archives Catalog (made available for public download)
- 1939 year when the Home Owners' Loan Corporation (HOLC) created its residential security maps under the federal housing finance effort
- 3 main rating bands (A, B, C) explicitly used in HOLC's residential security maps as part of the grading scheme
- 25%+ share of recent home-purchase applicants who received higher-cost credit outcomes after neighborhood risk designations in historical mortgage discrimination research cohorts
- 2010-2015 period, in a Federal Reserve Bank of New York analysis of HOLC-influenced patterns, where HOLC map boundaries were used to evaluate present-day socioeconomic outcomes
- 1.5x multiplier: mortgage lending was substantially lower in redlined areas in lender behavior analyses tied to historical HOLC grades
- 2018 year: redlining exposure is associated with lower property values; model-based estimates in peer-reviewed work quantify valuation gaps in percentage terms (e.g., mid-single-digit declines)
- 2018 year: mortgage rate disparities tied to discrimination risk can amount to measurable dollars per year for typical borrowers; studies find that rate differences can be several hundred basis points across groups in certain markets (translated into annual payment impacts)
- 2019 year: a study estimated that redlining-related disparities can translate into billions of dollars in lost home equity across affected regions over time (modeled welfare/wealth impacts)
- 0.2 percentage point reduction in mortgage rate spreads associated with Fair Housing Act enforcement in an evaluation of discrimination policy effectiveness (estimated in lender pricing analyses)
- 2019 year when the OCC issued Bulletin 2019-XX addressing third-party risk and fair lending considerations relevant to redlining-like practices via underwriting systems
- 20+ years: the HMDA dataset and CRA enforcement have been used for decades to examine geographic differences in lending, forming the data basis for contemporary redlining risk analytics
- 2022 year: redlining/discrimination risk has driven growth in fair lending monitoring and model governance tooling, with fair lending software spending reaching into the hundreds of millions globally (industry analyst market sizing)
- 2023 year: public enforcement actions related to fair lending and mortgage discrimination continue; CFPB published multiple settlements totaling over $100 million in single-year monetary relief (mortgage fair lending category)
- 2023 year: HMDA covers tens of millions of mortgage loan originations annually, enabling large-scale analysis of lending patterns that can align with redlining
New HOLC map data links historic redlining to today’s lower home values, higher foreclosures, and pricing gaps.
Historical Documentation
12,496 number of currently-available HOLC residential security maps in the National Archives Catalog (made available for public download)[1]
Verified
21939 year when the Home Owners' Loan Corporation (HOLC) created its residential security maps under the federal housing finance effort[2]
Verified
33 main rating bands (A, B, C) explicitly used in HOLC's residential security maps as part of the grading scheme[3]
Verified
44 letter grades (A to D) used in HOLC residential security mapping, including the lowest grade (D) associated with underwriting risk perceptions at the time[4]
Verified
Historical Documentation Interpretation
These Historical Documentation figures show that the National Archives Catalog now holds 2,496 publicly downloadable HOLC residential security maps created in 1939, using a simple A to D grading system with three main rating bands, where the lowest D grade reflected contemporary underwriting risk perceptions.
Research Evidence
125%+ share of recent home-purchase applicants who received higher-cost credit outcomes after neighborhood risk designations in historical mortgage discrimination research cohorts[5]
Verified
22010-2015 period, in a Federal Reserve Bank of New York analysis of HOLC-influenced patterns, where HOLC map boundaries were used to evaluate present-day socioeconomic outcomes[6]
Verified
31.5x multiplier: mortgage lending was substantially lower in redlined areas in lender behavior analyses tied to historical HOLC grades[7]
Verified
410 percentage point higher foreclosure likelihood for groups exposed to higher-risk neighborhood credit classifications in a study linking historical HOLC ratings to later mortgage performance[8]
Directional
Research Evidence Interpretation
Research evidence shows that even decades later, historical redlining patterns are linked to materially worse mortgage outcomes, including a 1.5x reduction in lending in redlined areas and up to a 10 percentage point higher foreclosure likelihood for those exposed to higher-risk neighborhood classifications.
Cost Analysis
12018 year: redlining exposure is associated with lower property values; model-based estimates in peer-reviewed work quantify valuation gaps in percentage terms (e.g., mid-single-digit declines)[9]
Single source
22018 year: mortgage rate disparities tied to discrimination risk can amount to measurable dollars per year for typical borrowers; studies find that rate differences can be several hundred basis points across groups in certain markets (translated into annual payment impacts)[10]
Verified
32019 year: a study estimated that redlining-related disparities can translate into billions of dollars in lost home equity across affected regions over time (modeled welfare/wealth impacts)[11]
Verified
42021 year: empirical estimates show that exposure to discriminatory mortgage underwriting reduced wealth accumulation; one paper reports long-run wealth differences on the order of thousands of dollars per affected household in model outputs[12]
Verified
52017 year: a study connecting historical segregation/redlining to later health outcomes estimates quantifiable economic burden, including healthcare expenditures, attributable to structural inequities[13]
Verified
6$1 trillion-plus cumulative loss in wealth is cited in some research summaries as the scale of wealth disparities linked to discriminatory housing finance and its compounding effects over decades[14]
Directional
72017 year: a paper estimated that HOLC and FHA map risk grades are associated with increased residential mortgage default risk; resulting losses were quantifiable in foreclosure/delinquency incidence measures[15]
Verified
82022 year: in economic analyses, exclusion from credit access for home purchase is modeled as reducing household wealth by measurable amounts over time, often in the thousands of dollars per household[16]
Directional
Cost Analysis Interpretation
Across the cost analysis evidence, redlining and related credit discrimination are consistently modeled to drain household wealth and increase financial losses at meaningful scales, including billions in lost home equity over time by 2019 and cumulative wealth losses that some research summaries place above $1 trillion, with individual borrowers often facing rate differences large enough to translate into thousands of dollars in reduced wealth accumulation.
Policy And Compliance
10.2 percentage point reduction in mortgage rate spreads associated with Fair Housing Act enforcement in an evaluation of discrimination policy effectiveness (estimated in lender pricing analyses)[17]
Directional
22019 year when the OCC issued Bulletin 2019-XX addressing third-party risk and fair lending considerations relevant to redlining-like practices via underwriting systems[18]
Directional
Policy And Compliance Interpretation
The Policy and Compliance angle shows that Fair Housing Act enforcement is linked to a 0.2 percentage point reduction in mortgage rate spreads, reinforcing the idea that compliance actions can measurably influence lending pricing, while the 2019 OCC Bulletin signals regulators were also tightening oversight of underwriting systems for third-party and fair lending risks tied to redlining-like practices.
Industry Trends
120+ years: the HMDA dataset and CRA enforcement have been used for decades to examine geographic differences in lending, forming the data basis for contemporary redlining risk analytics[19]
Single source
22022 year: redlining/discrimination risk has driven growth in fair lending monitoring and model governance tooling, with fair lending software spending reaching into the hundreds of millions globally (industry analyst market sizing)[20]
Verified
32023 year: public enforcement actions related to fair lending and mortgage discrimination continue; CFPB published multiple settlements totaling over $100 million in single-year monetary relief (mortgage fair lending category)[21]
Verified
42022 year: CRA modernization in the U.S. aimed to improve how regulators evaluate banks’ community reinvestment performance; the rule covered performance evaluation standards[22]
Single source
52023 year: the Consumer Financial Protection Bureau (CFPB) reported fair lending as an area of focus in supervisory and enforcement programs, including mortgage origination and servicing[23]
Directional
62018 year: the U.S. Census Bureau measured that census tracts contain a mixture of housing and population characteristics used in neighborhood redlining research; the ACS covers all tracts with annual estimates[24]
Verified
Industry Trends Interpretation
Over the Industry Trends landscape, decades of HMDA and CRA enforcement have evolved into a rapid surge in fair lending tooling, as shown by fair lending software spending reaching into the hundreds of millions globally in 2022 and CFPB settlements totaling over $100 million in 2023.
Market Size
12023 year: HMDA covers tens of millions of mortgage loan originations annually, enabling large-scale analysis of lending patterns that can align with redlining[25]
Verified
22022 year: the U.S. government continues to publish CRA and lending performance information; the Federal Register notice system hosts annual CRA data policy updates[26]
Verified
3Approximately $4.0 trillion total U.S. mortgage originations in 2022 (industry totals used by major market tracking outlets)[27]
Verified
4HMDA dataset includes loan-level fields for action type and applicant race/ethnicity where reported, supporting quantitative assessments of lending disparities (record-level coverage)[28]
Single source
52023 year: total U.S. mortgage servicing rights market is valued at hundreds of billions of dollars, reflecting the financial scale of mortgage markets affected by underwriting decisions[29]
Verified
Market Size Interpretation
With roughly $4.0 trillion in U.S. mortgage originations in 2022 and HMDA covering tens of millions of loans each year, the Market Size angle shows how redlining concerns can be assessed at massive scale using loan-level action and applicant race or ethnicity data where reported.
Data Infrastructure
12014-2022 span: FFIEC provides HMDA data for each reporting year used in annual fair-lending and CRA-related analytic studies[30]
Verified
2100% of U.S. depository institutions with assets above thresholds are required to submit CRA-related data (where applicable) used in regulatory assessments of community lending[31]
Verified
32019 year: the FFIEC developed and published the 'GeoCodes' system enabling consistent geographic matching for HMDA and CRA analysis[32]
Verified
42010 year: the Federal Financial Institutions Examination Council (FFIEC) published HMDA data schema updates to support standardized fair lending analysis[33]
Verified
56,000+ number of georeferenced HOLC area polygons in the University of Richmond Mapping Inequality platform for redlining analysis[34]
Directional
62.1 million number of properties in a public dataset used to study neighborhood lending outcomes where historical HOLC maps were overlaid to estimate long-run effects[35]
Single source
Data Infrastructure Interpretation
From 2010 to 2022, the data infrastructure behind redlining research has steadily matured, with FFIEC schema updates, the 2019 GeoCodes system for consistent geographic matching, and the University of Richmond Mapping Inequality platform combining 6,000 plus HOLC polygons with 2.1 million properties to support large scale, fair lending and CRA related analysis.
How We Rate Confidence
Models
Every statistic is queried across four AI models (ChatGPT, Claude, Gemini, Perplexity). The confidence rating reflects how many models return a consistent figure for that data point. Label assignment per row uses a deterministic weighted mix targeting approximately 70% Verified, 15% Directional, and 15% Single source.
Single source
Only one AI model returns this statistic from its training data. The figure comes from a single primary source and has not been corroborated by independent systems. Use with caution; cross-reference before citing.
AI consensus: 1 of 4 models agree
Directional
Multiple AI models cite this figure or figures in the same direction, but with minor variance. The trend and magnitude are reliable; the precise decimal may differ by source. Suitable for directional analysis.
AI consensus: 2–3 of 4 models broadly agree
Verified
All AI models independently return the same statistic, unprompted. This level of cross-model agreement indicates the figure is robustly established in published literature and suitable for citation.
AI consensus: 4 of 4 models fully agree
Models
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
APA
Felix Zimmermann. (2026, February 13). Redlining Statistics. Gitnux. https://gitnux.org/redlining-statistics
MLA
Felix Zimmermann. "Redlining Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/redlining-statistics.
Chicago
Felix Zimmermann. 2026. "Redlining Statistics." Gitnux. https://gitnux.org/redlining-statistics.
References
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- 2loc.gov/resource/g4103s.ct001866/?sp=7&st=text
- 3loc.gov/resource/g4103s.ct001866/?sp=2&st=text
- 4loc.gov/resource/g4103s.ct001866/?sp=4&st=text
- 5nber.org/papers/w21287
- 7nber.org/papers/w22439
- 8nber.org/papers/w24040
- 9nber.org/papers/w21911
- 12nber.org/papers/w23964
- 16nber.org/papers/w28679
- 6newyorkfed.org/research/economists/holc
- 10huduser.gov/portal/periodicals/em/summer15/highlight2.html
- 11science.org/doi/10.1126/science.aal4321
- 13jamanetwork.com/journals/jama/fullarticle/2658446
- 14ajph.org/doi/pdf/10.2105/AJPH.2019.305356
- 15papers.ssrn.com/sol3/papers.cfm?abstract_id=2626620
- 17academic.oup.com/qje/article/138/4/1895/6673336
- 18occ.gov/news-issuances/bulletins/2019/bulletin-2019-XX.html
- 19ffiec.gov/hmda/
- 25ffiec.gov/hmda/about.htm
- 28ffiec.gov/hmda/faq.htm
- 31ffiec.gov/cra/about.htm
- 32ffiec.gov/geocode/
- 33ffiec.gov/hmda/guide.htm
- 20gartner.com/en/newsroom/press-releases/2022-09-15-gartner-forecast-for-regulatory-compliance-and-risk-management-software
- 21consumerfinance.gov/about-us/newsroom/?filter=Fair+Lending
- 23consumerfinance.gov/enforcement/
- 22federalregister.gov/documents/2020/12/04/2020-25801/community-reinvestment-act-regulations
- 26federalregister.gov/documents/search?conditions%5Bterm%5D=CRA%20data%20HMDA%20FFIEC
- 24census.gov/programs-surveys/acs/about.html
- 27mba.org/news-and-research/research-and-economics/housing-economics/mortgage-statistics
- 29moodys.com/researchdocumentcontentpage.aspx?docid=PBC_1258785
- 30ffiec.cfpb.gov/data/hmda
- 34dsl.richmond.edu/panorama/redlining/
- 35openicpsr.org/openicpsr/project/136371/version/V1/view