Talk:Mortgage discrimination

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POV

Hi gang - worried that the 'contemporary' section is bit of a hatchet job on Democrats. I actually have sympathy with the point (and think they deserve it!) but clearly looks unbalanced in context of otherwise good, neutral wikipedia article. Thoughts? — Preceding unsigned comment added by 67.164.96.8 (talk) 03:18, 25 November 2012 (UTC)

Misleading

Lending practices - "Leading minority applicants to subprime loans"

"While the situation has somewhat improved, mortgage discrimination still occurs in another way. African Americans and other minorities are being disproportionately led to sub-prime mortgages with higher interest rates than their white counterparts."

This is misleading and highly biased.

1. Loan applicants with good credit will not be offered subprime interest rates. Competition in the marketplace will ensure that any loan applicant has options when it comes to interest rates. I don't see any statistics supporting the section quoted. If such statistics do exist, are they adjusted for the credit rating of the observed applicants?
2. Bank underwriters typically have no way of knowing whether a loan applicant is black or white. Although anti-discrimination legislation (ironically) requires that race be identified during the loan process, underwriters specifically need to ask for this information in order to get it.
3. Many underwriters, loan officers, and other bank employees are black, particularly in poorer areas where discrimination is most often alleged to happen. It is highly doubtful that they would deny loans to black applicants or offer them subprime rates.

On the contrary, in case you are not aware as to comment #1, numerous reports were reported about not only black applicants, but also white applicants receiving subprime loans when their financial situation qualified them for prime loans (I'm assuming that, by "good," you're referring to FICO scores starting at 680 to 700). This phenomenon was covered by The Washington Post within the last 2-3 years. So, the first statement is not entirely true. This statement requires references (these references do exist, and will take a little time to find them) to support the statement.

As for comment #2, bank underwriters have more information at their disposal that the loan applicant realizes. Just for starters, a ZIP Code will provide a lot of information that is readily available about the demographics of the property location (the ZIP Code is included on the loan application). Anyone can find extensive information about the demographics of a ZIP Code (e.g., housing type, income, number of children, racial/gender statistics on ownership and community make-up, level of poverty, among other characteristics of the ZIP Code) simply by visiting the Census Bureau's web site. I'm sure that this same information is also available to commercial entities who use it for marketing purposes.

Back to comment #1 -- From my own personal experience, a mortgage broker tried to get away with steering our case (spouse and me) to subprime status two years ago for a 3-1 ARM at 9 percent, in addition to asking for 3 points (my own FICO score was beyond what I defined here as "good"). Also, a credit union where I "bank" (I bank at two of them) wanted to charge 7 percent with 2 points and PMI, and a mortgage company had the nerve to say that no bank would give us a prime loan, so we had few choices in the market (I wasn't buying it, and the mortgage was calling my husband to "work on me" to change my mind (i.e., be reasonable"). We walked away from those ridiculous propositions and financed somewhere else (my primary credit union, a direct lender that is a well-known, large credit union that also services its own loans) at 5-1/2 fixed for 30 years with no points, no PMI and with no problems. Just to let you know that many greedy, unscrupulous players had pervaded the entire financial industry and sucked in those who either didn't know any better or who were pressed for time during the height of the real estate boom.

Comment #3 -- Bank employees, regardless of their job duties and employment, merely do the loan processing work so that they can get paid. Therefore, the comment #3 does not make much sense since these employees have little input or influence on the bank's policies as to granting or denying mortgage loans. If anything, the financial interests of loan officers would be in conflict with the loan applicant, since loan officers in many cases receive bonuses or commissions based on the interest rate contracted for by the applicant. Ditto for mortgage brokers, but that's another situation altogether when one factors in yield spread premiums when the loan applicant agrees to take the higher rate -- that's how mortgage brokers typically earn money from the wholesale lender. '→Lwalt ^♦ talk 11:42, 11 February 2008 (UTC)

Denial of loans

"African Americans and other minorities found it nearly impossible to secure mortgages for property located in redlined zones.[2] The systematic denial of loans was a major contributor to the urban decay that plagued many American cities during this time period. Minorities who tried to buy homes continued to face direct discrimination from lending institutions into the late 1990s. The disparities are not simply due to differences in creditworthiness.[3] With other factors held constant, rejection rates for Black and Hispanic applicants was about 1.6 times that for Whites in 1995[4]."

Banks lend money based on the appraised value of the collateral. When a property is thought to be declining in value or not worth the sale price (as is often the case in poorer areas), the bank may consider the risk too high and not lend to the applicant, regardless of the applicant's credit rating. Where is the acknowledgment of this fact?

• A bank would never lend money on an asset where it is not worth the sale price, to me thats common sense, and that is the point in having a valuation, cos even if someone has a good credit rating it doesnt mean to say they will ALWAYS pay their debts off. Peoples circumstances change and all the credit rating does is give a history of that persons behaviour of paying off credit, and only gives a prediction for the future.

• A bank has to protect their money and in the instance where a proprety is not worth the sale price then the buyer should look towards having the vendor reduce the price accordingly. To me race is not the issue there, the issue is the asset and even in a deprived area if a property is valued by the bank at $40,000 and its for sale for $25,000 then the bank would more likely lend (provided the person passes the necessary credit checks)as they have a chance of getting their money back if the person defaults paying the mortgage. --PrincessBrat (talk) 20:31, 12 June 2008 (UTC)

alleging

In regard to this diff. Lawsuits are filed alleging that crimes or torts were committed. This is a technical term and "on the grounds that" is not an accurate substitute. Thoughts? — alf laylah wa laylah (talk) 04:04, 19 May 2013 (UTC)

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Proposed expansion: Structural barriers, credit scoring, appraisal bias, and recent enforcement

Disclosure: I work at the National Community Reinvestment Coalition (NCRC), which conducts research on fair lending issues. The sources I am proposing are peer-reviewed academic publications and government data from independent institutions.

Extended content

I noticed this article's substantive content largely ends around 2016, with most coverage focused on the subprime crisis era. There has been significant peer-reviewed research and regulatory activity since then that I think would strengthen the article. I have drafted proposed content in my sandbox at User:GeographerJay/sandbox/Mortgage_Discrimination and wanted to get community input before making changes.

Proposed additions

1. Structural barriers and the legacy of redlining

Recent research has documented how historical redlining continues to shape current lending patterns. Key peer-reviewed sources include:

• Lynch et al. (2021) in SSM - Population Health: Found neighborhoods with greater historic redlining scores were significantly more likely to experience current lending discrimination (OR = 1.73) using 2018 HMDA data
• Swope et al. (2022) scoping review in Journal of Urban Health: Examined 33 studies and developed a conceptual framework linking redlining to present-day outcomes through segregation, disinvestment, and reduced wealth accumulation
• Urban Institute (2023): Research on the broader web of discriminatory housing policies beyond redlining alone

2. Credit scoring limitations

Research has identified significant limitations in how credit scores predict mortgage default, with disproportionate impacts on minority borrowers:

• FHFA 2021 working paper: Analysis of 200 million mortgages found subprime borrowers had stressed default rates similar to higher-score borrowers when controlling for loan terms
• Albanesi & Vamossy (NBER Working Paper): Machine learning models significantly outperformed credit scores; 17% of subprime borrowers displayed default behavior consistent with near-prime borrowers
• Urban Institute research on alternative data: Found past mortgage payment history is a stronger predictor than credit scores; 45% of Black consumers have subprime scores vs 18% of white consumers

3. Appraisal bias

This is a significant topic with substantial peer-reviewed research that the article does not currently address:

• Brookings Institution (2018, 2024): Homes in majority-Black neighborhoods valued 21-23% below comparable non-Black neighborhoods, representing approximately $162 billion in cumulative losses
• FHFA data (2022): Properties in high-minority tracts undervalued at 1.74 times the rate of white tracts
• Freddie Mac (2021): 12.5% of appraisals in Black neighborhoods below contract price vs 7.4% in white neighborhoods
• PAVE Task Force (2022): Biden administration's interagency initiative on appraisal equity

4. Recent enforcement actions

The DOJ's Combating Redlining Initiative (launched October 2021) represents a major development not reflected in the article:

• Over $107 million in settlements through 2023
• 15 redlining settlements under Biden administration
• Major cases include Trident ($22.4M), City National ($31M), Park National ($9M), Lakeland ($12M)
• Recent developments under Trump administration (2025) seeking to terminate consent orders early

5. Persistent disparities in lending outcomes

Recent HMDA analysis documenting ongoing disparities:

• Urban Institute denial rate analysis showing 63% of Black home improvement applicants denied
• Research showing 17% of Black-white homeownership gap remains unexplained after controlling for all measurable variables

Sources

All proposed sources are peer-reviewed academic publications or government/institutional research:

• Lynch, E.E. et al. (2021). "The legacy of structural racism." SSM - Population Health, 14, 100793.
• Swope, C.B. et al. (2022). "The Relationship of Historical Redlining with Present-Day Neighborhood Environmental and Health Outcomes." Journal of Urban Health, 99, 959-983.
• Perry, A.M. et al. (2018, 2024). Brookings Institution research on home devaluation
• Federal Housing Finance Agency UAD Aggregate Statistics
• Freddie Mac (2021). "Racial and Ethnic Valuation Gaps in Home Purchase Appraisals"
• U.S. Department of Justice press releases on Combating Redlining Initiative
• Urban Institute Housing Finance Policy Center research

Discussion

I think these additions would bring the article up to date with current research and regulatory developments. The existing content on historical redlining and subprime crisis provides good context, but the article would benefit from coverage of the substantial body of research published in the last decade.

Happy to discuss any concerns or suggestions. GeographerJay (talk) GeographerJay (talk) 17:35, 28 November 2025 (UTC)

Reply 2-JUL-2026

  Unable to review  

• Your edit request could not be reviewed because it is unclear which references are connected to which claim statements in the text of your proposal. When proposing edit requests it is important to highlight in the text, through the use of ref tags, which specific sources are doing the referencing for each claim. The point of these inline ref tags is to allow the reviewer and readers to check that the material is sourced; that point will be lost if the ref tags are not clearly placed. Note the examples below:

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The Sun's diameter is 864,337 miles, while the Moon's diameter is 2,159 miles. The Sun's temperature is 5,778 Kelvin.

References

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      1. Sjöblad, Tristan. The Sun. Academic Press, 2019, p. 1.
      2. Harinath, Paramjit. (2019) "Size of the Moon", Science, 51(78):46.
      3. Uemura, Shū. The Sun's Heat. Academic Press, 2019, p. 2.

In the first example above there are three references provided, but the claim statements do not contain ref tags indicating which reference applies where. The references instead have been placed outside of the text, bundled together towards the bottom. Your edit request similarly bundles references together outside of the area of the requested text. These links between material and their source references must be more clearly made, as shown in the next example below:

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The Sun's diameter is 864,337.3 miles,<ref>{{cite book|last1=Sjöblad|first1=Tristan|title=The Sun|url=http://www.booksource.com|publisher=Academic Press|date=2020|page=1}}</ref> while the Moon's diameter is 2,159 miles.<ref>{{cite journal|last1=Harinath|first1=Prisha|title=Size of the Moon|journal=Science|issue=78|volume=51|url=http://www.journalsource.com|date=2020|page=46}}</ref> The Sun's temperature is 5,778 Kelvin.<ref>{{cite book|last1=Uemura|first1=Shu|title=The Sun's Heat|url=http://www.websource.com|publisher=Academic Press|date=2020|page=2}}</ref>

Which displays as:

The Sun's diameter is 864,337.3 miles, while the Moon's diameter is 2,159 miles. The Sun's temperature is 5,778 Kelvin.

References

---

• In the second example above, the links between the provided references and their claim statement ref tags are perfectly clear. Kindly reformulate your edit request so that it aligns more with the second example above, and feel free to re-submit that edit request at your earliest convenience.

Regards,  Spintendo  06:09, 3 July 2026 (UTC)

Reply GeographerJay 3-JUL-2026

@Spintendo: Thank you for the feedback. I am still very new to wikipedia editing, thank you for being patient. I am resubmitting below with the full proposed text and inline tags attached to each claim, as requested.

The proposed text to be added to the article with tags is as follows:

Structural barriers and the legacy of redlining

Research has documented how historical discriminatory lending practices continue to shape contemporary mortgage markets. A 2021 peer-reviewed study in SSM - Population Health examining Milwaukee, Wisconsin found that neighborhoods with greater historic redlining scores were significantly more likely to experience current lending discrimination, with an odds ratio of 1.73. The study combined two lending indicators from 2018 Home Mortgage Disclosure Act data to measure current discrimination: low lending occurrence and high-cost loans.

A 2022 scoping review in the Journal of Urban Health examined 33 studies on health outcomes in historically redlined neighborhoods, all published after 2017, and developed a conceptual framework showing how redlining led to racial segregation, community disinvestment, and reduced wealth accumulation from homeownership, with effects persisting to the present day.

The Urban Institute's 2023 research on housing discrimination noted that while redlining contributed to modern patterns of housing instability, it represents one component in a broader system of discriminatory policies including racially restrictive covenants, racial steering by realtors, and exclusionary zoning that collectively shaped present-day segregation and inequality.

Credit scoring limitations

Research has identified significant limitations in the predictive accuracy of credit scores for mortgage default, with disproportionate impacts on minority borrowers. A 2023 peer-reviewed study in the Review of Finance analyzing more than 200 million mortgages originated from 1990 to 2019 found that while below-660 credit score borrowers carried higher baseline default risk, the stressed default rate for this group rose largely in sync with that of higher-score borrowers during the housing boom, suggesting that the buildup of mortgage risk before the 2008 financial crisis was not concentrated among low-credit-score borrowers.

Research by economists Stefania Albanesi and Domonkos Vamossy published as an NBER working paper found that machine learning models significantly outperformed conventional credit scores in predicting consumer default. The study found that among subprime borrowers, who comprise 21% of the population, 17% displayed default behavior consistent with near-prime borrowers, indicating substantial misclassification by credit scores.

Credit score disparities fall heavily along racial lines. As of October 2020, 45.1% of Black consumers and 31.5% of Hispanic consumers had subprime credit scores, compared to 18.3% of white borrowers. The Consumer Financial Protection Bureau has estimated that millions of Americans have limited or no credit history, with those lacking a scored credit record concentrated among young, low-income, and minority populations.

Urban Institute research has found that past mortgage payment history is a stronger predictor of future mortgage performance than credit scores. Even for households with low FICO scores, those with no missed mortgage payments were significantly less likely to default on mortgages in subsequent years. This research supports incorporating alternative data such as rental payment history and cash-flow information into mortgage underwriting, which could disproportionately benefit Black and Latino borrowers who are more likely to lack traditional credit histories.

Appraisal bias

Research has documented systematic undervaluation of homes in majority-Black neighborhoods. A 2018 Brookings Institution study found that homes in majority-Black neighborhoods are valued approximately 23% below what their valuations would be in comparable non-Black neighborhoods, after controlling for home and neighborhood quality characteristics. Updated 2024 analysis estimated this devaluation results in approximately $162 billion in cumulative losses for Black communities.

Data from the Federal Housing Finance Agency shows that properties in high-minority census tracts (80.1-100% minority population) experienced undervaluation at a rate of 23.3% in 2021, compared to 13.4% in predominantly white tracts (0-50% minority), a ratio of 1.74.

A 2021 Freddie Mac study analyzing more than 12 million appraisals found that 12.5% of appraisals for home purchases in Black neighborhoods and 15.4% in Latino neighborhoods came in below the contract price, compared to 7.4% of appraisals in white neighborhoods. The study found these gaps persisted even after controlling for structural and neighborhood characteristics.

In March 2022, the Biden administration announced the Property Appraisal and Valuation Equity (PAVE) Task Force, the first interagency initiative to address inequity in home appraisals. The task force recommended diversifying the appraisal industry, which according to Bureau of Labor Statistics data is 98% white, and requiring anti-bias and fair housing training.

Recent enforcement actions

In October 2021, Attorney General Merrick Garland announced the Combating Redlining Initiative, the Department of Justice's most aggressive coordinated enforcement effort to address redlining. The initiative partnered with U.S. Attorneys' Offices, the Consumer Financial Protection Bureau, federal financial regulatory agencies, and state attorneys general offices.

By October 2023, the initiative had secured more than $107 million in relief for communities affected by discriminatory lending practices, with over two dozen active investigations spanning neighborhoods across the country. Major settlements included:

• Trident Mortgage Company (July 2022): $22.4 million settlement for redlining in the Philadelphia area
• City National Bank of Los Angeles (January 2023): $31 million, the largest redlining settlement in DOJ history at the time, for avoiding mortgage lending services to majority-Black and Hispanic neighborhoods in Los Angeles County
• Park National Bank (March 2023): $9 million for redlining in the Columbus, Ohio metropolitan area
• Lakeland Bank (October 2022): $12 million for failing to provide mortgage lending services to Black and Hispanic neighborhoods in the Newark, New Jersey area

The DOJ's complaints typically alleged that lenders concentrated branches and loan officers in majority-white neighborhoods, failed to market or advertise in minority neighborhoods, and generated mortgage applications at rates significantly below peer lenders in majority-Black and Hispanic areas.

In 2025, under the Trump administration, the Department of Justice began filing motions to terminate consent orders early, arguing that banks had satisfied settlement terms. Fair housing advocacy groups sought to intervene in several cases, arguing the DOJ had not presented adequate evidence to dismiss the cases.

Persistent disparities in lending outcomes

Analysis of Home Mortgage Disclosure Act data continues to document racial disparities in mortgage lending. Urban Institute research found that in 2020, home improvement loan applications had the highest denial rate at 38.8%, with more than half of Black applicants (63.0%) and Hispanic applicants (56.6%) denied loans to make repairs and renovations.

A 2024 study using expanded HMDA data found that after conditioning on key elements of borrower creditworthiness including credit score, income, and down payment, racial and ethnic disparities in mortgage approval and pricing were smaller but remained economically meaningful. Sensitivity analysis indicated that omitted factors as predictive of approval and race as credit score could not fully explain approval disparities for several racial and ethnic groups.

Urban Institute research has found that in 2017, after controlling for credit profiles, the "real denial rate" showed Black applicants denied at 37% compared to 30% for white applicants, a gap that persisted even after accounting for differences in creditworthiness. The research also found that 17% of the Black-white homeownership gap remains unexplained after controlling for key variables including income, credit, wealth, and demographics, suggesting discrimination or unmeasured structural factors may contribute to the gap.

Following up, apologies for the missing signature above. The resubmission with inline citations is still awaiting review. Happy to make any further adjustments. GeographerJay (talk) 10:49, 8 August 2026 (UTC)