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Neighborhood income is useful context, but it is not a measure of an individual applicant’s income or a complete measure of mortgage access. It can conceal differences in borrower finances, lender availability, application outcomes, and loan costs—so a neighborhood’s income category cannot, by itself, tell you who can obtain a mortgage or on what terms.
What neighborhood income data measures—and what it does not
Credit records do not contain income, so the Consumer Financial Protection Bureau (CFPB) uses neighborhood income as a proxy when examining lending patterns. Its September 2026 visualization groups census tracts by the tract’s median family income relative to the median family income of the surrounding metropolitan statistical area or micropolitan area, or the county for consumers outside those areas. The CFPB describes its approach this way: “Since credit records do not contain income, we focus on neighborhood income levels.” CFPB, “Lending by Neighborhood Relative Income Level”.
| CFPB tract category | Tract median family income as a share of the applicable area median |
|---|---|
| Low | Below 50% |
| Moderate | 50% to below 80% |
| Middle | 80% to below 120% |
| Upper | 120% or higher |
These are geographic groupings, not applicant-income bands. A tract classified as low-income can include households with very different earnings, debts, credit histories, and down payments. Conversely, a higher-income tract does not establish that a particular applicant has the income or credit profile to qualify. Neighborhood median family income and an applicant’s income answer different questions.
Why neighborhood income alone misses access differences
Mortgage access is shaped not only by who lives in a tract but also by how many lenders serve it and what happens to applications. The CFPB’s December 2023 staff report examines 2018–2020 Home Mortgage Disclosure Act (HMDA) data and measures lender presence using mortgage originators per capita. It finds wide variation across neighborhoods, associated with income, poverty, internet access, and racial and ethnic composition. The report does not establish that these neighborhood characteristics cause lender presence or lending outcomes. CFPB staff report on neighborhood geography and mortgage credit access.
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The report also compares transactions within groups characterized as posing similar credit risk. In those comparisons, applications in neighborhoods with more originators per capita were less likely to be rejected; borrowers who received mortgages paid lower origination charges and lower total loan costs. These are study associations, not proof that increasing the number of lenders would cause rejection rates or costs to fall. They do show why a tract’s income category cannot stand in for lender availability, approval outcomes, or the price of credit.
Borrower finances and market conditions matter too
Underwriting evaluates the applicant, not just the neighborhood. Debt-to-income (DTI) ratio—the relationship between a borrower’s debt payments and income—is one important factor, alongside other aspects of an application and loan. A 2026 Federal Reserve Bank of St. Louis Review article by Manu García and Carlos Garriga uses expanded public HMDA data from 2018–2024, including DTI, combined loan-to-value ratios, denial reasons, and rates on originated loans, to examine home-purchase applications.
The authors report that mortgage-market tightening in 2022–2023 raised the aggregate home-purchase denial rate from 12.2% to 15.7% through the DTI channel. At the 50% DTI mark, denial rates rose by 15–17 percentage points. They also report racial disparities that remain after the controls described in the article. These figures concern the article’s analysis of home-purchase applications; they should not be assumed to describe refinance or home-improvement applications. They demonstrate that changing borrower and market conditions can shift access even when neighborhood-income categories do not change. García and Garriga, Federal Reserve Bank of St. Louis Review, 2026.
Use the right measure for the question
Different measures describe different parts of the mortgage market. For a useful comparison, keep the unit, geography, year, and data source visible rather than treating them as interchangeable.
Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problems| Measure | What it describes | What it cannot establish by itself |
|---|---|---|
| Applicant income and DTI | Borrower-level finances considered in underwriting; the St. Louis Fed’s 2018–2024 HMDA analysis uses DTI for home-purchase applications. | Whether lenders are available in the applicant’s neighborhood or how neighborhood-level access differs. |
| Neighborhood relative median family income and poverty | Tract context, classified against an MSA, micropolitan-area, or county income benchmark in the CFPB’s 2026 display. | An individual applicant’s income, ability to qualify, or access to a lender. |
| Originators per capita | Local presence of mortgage originators, as measured in the CFPB’s analysis of 2018–2020 HMDA data. | Whether a particular applicant will be approved or receive a particular rate or fee. |
| Rejections, loan costs, and originations | Observed outcomes, which can be compared across groups and locations when the relevant data and definitions are specified. | On their own, why the difference occurred or whether it reflects unlawful discrimination. |
HMDA is valuable for studying applications and outcomes, but the available fields and coverage vary across years. A neighborhood lending display is not automatically an approval-rate measure, and counts of originations do not show the full set of people who applied, were rejected, or did not apply.
Why comparisons across years can mislead
Geographic definitions and data vintages can change the apparent pattern. The Federal Financial Institutions Examination Council (FFIEC) says its census and income files support HMDA and Community Reinvestment Act analysis; those files may differ from Census Bureau data and generally remain static between five-year refreshes. A tract’s classification can therefore depend on which geography file and income benchmark an analysis uses. FFIEC census and income data.
Historical Federal Reserve analysis illustrates the scale of measurement issues. In 2005–2006, reported HMDA borrower income was 30% or more above American Community Survey borrower income in several states; in 2009–2010, the two measures were within 10% in almost every state. These are historical comparisons, not current estimates. The same 2012 report estimated that applying newer census-based income categories would have added about 150,000 loans—about 22% more than the then-current count of loans to low- and moderate-income neighborhoods in 2011. That, too, is a historical classification effect, not a current lending figure. Federal Reserve Board, “The Mortgage Market in 2011”.
Tract boundaries and reference incomes can change without any change in the lending on a specific property. When comparing places or years, check whether the analysis uses consistent tract definitions, the same income benchmark, and comparable HMDA fields.
Best Value
What the available evidence can—and cannot—say
Neighborhood-income data can identify broad geographic patterns, but it cannot tell the whole story of mortgage access. The CFPB’s 2018–2020 analysis links lender presence to neighborhood characteristics and reports differences in rejection and loan costs among similar-credit-risk groups; its public summary does not provide effect sizes for every result. The CFPB’s September 2026 display is more recent and shows lending activity through February 2026, but its latest six months are preliminary and it is not itself a direct approval-rate measure.
Differences in HMDA outcomes are important to examine, but they do not by themselves prove illegal discrimination. The Federal Reserve Board’s 2012 report states: “The HMDA data do not include sufficient information to determine the extent to which these differences reflect illegal discrimination.” Federal Reserve Board, 2012 HMDA report. That limitation does not make outcome gaps irrelevant; it means they need careful analysis alongside borrower, loan, lender, and market factors.
Quick Recap
A checklist for reading a mortgage-access claim
- Identify the unit: Is the number about an applicant, a loan, a lender, or a census tract?
- Check the year and geography: Which lending years, tract boundaries, and income benchmark does the analysis use?
- Separate the income measures: Is it applicant income, tract median family income, poverty, or an area-relative category?
- Look for lender presence: Does the analysis measure originators per capita or another indicator of local availability?
- Define the outcome: Is it applications, denials, originations, fees, total loan costs, or something else?
- Distinguish association from cause: Does the evidence show a relationship, or does it establish that one factor caused another?
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