Holding 17 different
factors steady in a complex statistical analysis of more than 2 million
conventional mortgage applications for home purchases, we found that lenders
were 40% more likely to turn down Latino applicants for loans, 50% more likely
to deny Asian/Pacific Islander applicants, and 70% more likely to deny Native
American applicants than similar white applicants. Lenders were 80% more likely
to reject Black applicants than similar white applicants. These are national
In every case, the
prospective borrowers of color looked almost exactly the same on paper as the
white applicants, except for their race.
The industry had criticized previous
similar analyses for not including financial factors they said would explain
disparities in lending rates but were not public at the time: debts as a
percentage of income, how much of the property’s assessed worth the person is
asking to borrow, and the applicant’s credit score.
The first two are now
public in the Home Mortgage Disclosure Act data. Including these financial data
points in our analysis not only failed to eliminate racial disparities in loan
denials, it highlighted new, devastating ones.
We found that lenders
gave fewer loans to Black applicants than white applicants even when their
incomes were high — $100,000 a year or more — and had the same debt ratios. In
fact, high-earning Black applicants with less debt were rejected more often
than high-earning white applicants who have more debt.
“Lenders used to tell us,
‘It’s because you don’t have the lending profiles; the ethno-racial differences
would go away if you had them,’” said José Loya, assistant professor of urban
planning at UCLA who has studied public mortgage data extensively and reviewed
our methodology. “Your work shows that’s not true.”
We sent our complete
analysis to industry representatives: The American Bankers Association, The
Mortgage Bankers Association, The Community Home Lenders Association, and The
Credit Union National Association. They all criticized it generally, saying the
public data is not complete enough to draw conclusions, but did not point to
any flaws in our computations.
Blair Bernstein, director
of public relations for the ABA, acknowledged that our analysis showed
disparities but that “given the limitations” in the public data we used, “the
numbers are not sufficient on their own to explain why those disparities
In written statements,
the ABA and MBA criticized The
Markup’s analysis for not including credit scores and for focusing on
conventional loans only and not including government loans, such as those
guaranteed by the Federal Housing Administration and Department of Veterans
loans from government loans is common in mortgage research because they are
different products, with different thresholds for approval and loan terms.
Government loans bring people who wouldn’t otherwise qualify into the market
but tend to be more expensive for the borrower.
Even the Federal Reserve
and Consumer Financial Protection Bureau, the agency that releases mortgage
data, separate conventional and FHA loans in their research on lending
disparities. Authors of one academic study out of Northeastern and George
Washington universities said they focus on conventional loans only because FHA
loans have “long been implemented in a manner
that promotes segregation.”
As for credit scores, it
was impossible for us to include them in our analysis because the CFPB strips
them from public view from HMDA data — in part due to the mortgage
industry’s lobbying to remove
them, citing borrower privacy.
When the CFPB first
proposed expanding mortgage data collection to include the very data that
industry trade groups have told us is vital for doing this type of analysis —
credit scores, debt-to-income ratio, and loan-to-value ratio — those same
groups objected. They didn’t want the government to even collect the data, let
alone make it public. They cited the risk of a cyberattack, which could reveal
borrowers’ private information.
“These new (data) fields
include confidential financial data,” several large trade groups wrote in
a letter to the CFPB, including the ABA and MBA. “Consequently, if this (sic)
data are inadvertently or knowingly released to the public, the harm associated
with re-identification would be even greater.”
Government regulators do
have access to credit scores. The CFPB analyzed 2019 HMDA data and found that
accounting for credit scores does not eliminate lending
disparities for people of color.
In addition to finding
disparities in loan denials nationally, we examined cities and towns across the
country individually and found disparities in 89 metropolitan areas spanning
every region of the country. In Charlotte, where Crystal Marie and her family
searched for a home, lenders were 50% more likely to deny loans to Black
applicants than white ones with similar financial profiles. In other places,
the gap was even larger.
Black applicants in
Chicago were 150% more likely to be denied by financial institutions than
similar white applicants there. Lenders were more than 200% more likely to
reject Latino applicants than white applicants in Waco, Texas, and to reject
Asian and Pacific Islander applicants than white ones in Port St. Lucie,
Florida. And Native American applicants in Minneapolis were 100% more likely to
be denied by financial institutions than similar white applicants there.
“It’s something that we
have a very painful history with,” said Alderman Matt Martin, who represents
Chicago’s 47th Ward.
now-outlawed practice of branding certain Black and immigrant neighborhoods too
risky for financial investments that began in the 1930s, can be traced back to
Chicago. Chicago activists exposed that banks were
still redlining in the 1970s, leading to the establishment of the Home Mortgage
Disclosure Act, the law mandating the collection of data used for this story.
“When you see that maybe
the tactics are different now, but the outcomes are substantially similar,”
Martin added, “it’s just not something we can continue to tolerate.”
Who makes these loan
decisions? Officially, lending officers at each institution. In reality,
software, most of it mandated by a pair of quasi-governmental agencies.
Mac and Fannie Mae were
founded by the federal government to spur homeownership and now buy about half
of all mortgages in America. If they don’t approve a loan, the lenders are on
their own if the borrower skips out.
And that power means
Fannie and Freddie essentially set the rules for the industry, starting from
the very beginning of the mortgage-approval process.
Fannie and Freddie
require lenders to use a particular credit scoring algorithm, “Classic FICO,”
to determine whether an applicant meets the minimum threshold necessary to even
be considered for a conventional mortgage, currently a score of 620.
This algorithm was
developed from data from the 1990s and is more than 15 years old. It’s widely
considered detrimental to people of color because it rewards traditional credit,
to which white Americans have more access. It does not consider, among other
things, on-time payments for rent, utilities, and cellphone bills — but will
lower people’s scores if they get behind on them and are sent to debt
collectors. Unlike more recent models, it penalizes people for past medical
debt even if it’s since been paid.
“This is how structural
racism works,” said Chi Chi Wu, a staff attorney at the National Consumer Law
Center. “This is how racism gets embedded into institutions and policies and
practices with absolutely no animus at all.”
Potentially fairer credit
models have existed for years. A recent study by
Vantage Score — a credit model developed by the “Big Three” credit bureaus to
compete with FICO — estimated that its model would provide credit to 37 million
Americans who have no scores under FICO models. Almost a third of them would be
Black or Latino.
Yet Fannie and Freddie
have resisted a steady stream of plaintive requests since 2014 from advocates,
the mortgage and housing industries, and Congress to update to a newer model.
Even the company that created Classic FICO has lobbied for the agencies to
adopt a newer version, which it said expands credit to more people.
“A lot of things that
minorities and underserved borrowers are doing, responsible financial
behaviors, are going under the radar,” said Scott Olson, executive director of
CHLA, a trade group representing small and midsized independent mortgage
Fannie’s and Freddie’s
regulator and conservator, the Federal Housing Finance Agency, continues to
allow the companies to stick with Classic FICO, more than five years after
ordering them to study the effects of switching to something newer. The FHFA
has also expressed concern about the “cost and operational implications” if
they would have to continually test new credit scoring models.
Neither of the companies
would answer questions from The Markup about why they still require Classic
“They’ve been testing
alternate scores for years, and I don’t know why the process is taking so
long,” said Lisa Rice, president and CEO of the National Fair Housing Alliance,
a consortium of hundreds of fair housing organizations. “Well-deserving
consumers are being left behind.”
Fannie’s and Freddie’s
approval process also involves other mysterious algorithms: automated
underwriting software programs that they first launched in 1995 to much fanfare
about their speed, ease and, most important, fairness.
“Using a data base as
opposed to human judgment can avoid influences by other forces, such as
discrimination against minority individuals and red-lining,” Peter Maselli,
then a vice president of Freddie Mac, told The New York Times when it launched
its software, now called Loan Product Advisor. A bank executive told Congress
that year the new systems were “explicitly and implicitly ‘color blind,’” since
they did not consider a person’s race at all in their evaluations.
But, like similar
promises that algorithms would make colorblind decisions in criminal risk
assessment and health care, research shows that some of the factors Fannie and
Freddie say their software programs consider affect people differently
depending on their race or ethnicity. These include, in addition to credit
histories, the prospective borrowers’ assets, employment status, debts, and the
size of the loan relative to the value of the property they’re hoping to buy.
“The quality of the data
that you’re putting into the underwriting algorithm is crucial,” said Aracely
Panameño, director of Latino affairs for the Center for Responsible Lending.
“If the data that you’re putting in is based on historical discrimination, then
you’re basically cementing the discrimination at the other end.”
Research has shown
that payday loan sellers usually place branches in neighborhoods populated
mainly by people of color, where bank branches are less common. As a result,
residents are more likely to use these predatory services to borrow money. This
creates lopsided, incomplete credit histories because banks report both good
and bad financial behavior to credit bureaus, while payday loan services only
report missed payments.
Gig workers who are
people of color are more likely to report that those jobs are their primary
source of income — rather than a side hustle they’re using for extra cash —
than white gig workers. Having multiple sources of income or unconventional
employment can complicate the verification process for a mortgage, as Crystal
Marie and Eskias McDaniels learned.
applicant’s assets beyond the down payment, which lenders call “reserves,” can
cause particular problems for people of color. People with fatter bank accounts
present a lower risk because they can more easily weather a setback that would
leave others unable to pay the mortgage. But, largely due to intergenerational
wealth and past racist policies, the typical white family in America today
has eight times the wealth of
a typical Black family and five times the wealth of a Latino family. People of
color are more likely to have smaller savings accounts and smaller (or
nonexistent) stock portfolios than white people.
“This is a relatively new
world of automated underwriting engines that by intent may not discriminate but
by effect likely do,” said David Stevens, a former president and CEO of the
Mortgage Bankers Association, now an independent financial consultant.
Not even home valuations
are free from controversy. The president of the trade group representing real
estate appraisers, who determine property values for loans, recently acknowledged that
racial bias is prevalent in the industry and launched new programs to combat
“Any type of data that
you look at from the financial services space has a high tendency to be highly
correlated to race,” said Rice, of the National Fair Housing Alliance.
In written statements,
Fannie said its software analyzes applications “without regard to race,” and
both Fannie and Freddie said their algorithms are routinely evaluated for
compliance with fair lending laws, internally and by the FHFA and the
Department of Housing and Urban Development. HUD said in an email to The Markup
that it has asked the pair to make changes in underwriting criteria as a result
of those reviews but would not disclose the details.
“This analysis includes a
review to ensure that model inputs are not serving as proxies for race or other
protected classes,” Chad Wandler, Freddie’s director of public relations, said
in a written statement. He declined to elaborate on what the review entails or
how often it’s done.
No one outside Fannie and
Freddie knows exactly how the factors in their underwriting software are used
or weighted; the formulas are closely held secrets. Not even the companies’
regulator, the FHFA, appears to know, beyond broad strokes, exactly how the
software scores applicants, according to Stevens, who served as Federal Housing
commissioner and assistant secretary for housing at HUD during the Obama
The Markup’s analysis
does not include decisions made by Fannie’s and Freddie’s underwriting algorithms
because, while lenders are required to report those decisions to the
government, the CFPB scrubs them from public mortgage data, arguing that
including them “would likely disclose information
about the applicant or borrower that is not otherwise public
and may be harmful or sensitive.” Lenders’ ultimate mortgage decisions are
public, however. Borrowers’ names are not reported to the government and
addresses are not in the public data.
Fannie and Freddie
declined to answer our questions about why their algorithms’ decisions are
excluded from the public data but said in a 2014 letter to the CFPB that the
revelation could allow their decision-making algorithms to be reverse-engineered.
Loan officers say the
software’s decisions are mysterious even to them.
“When you run so many
deals through the automated system, you’ll look at one deal that didn’t get an
approval, and you just know that that’s a better client than someone else that
might’ve gotten approved,” said Ashley Thomas III, a broker and owner of LA Top
Broker, Inc., a minority-owned real estate agency and brokerage in South Los
Angeles. “That lack of transparency in the technology is very concerning.”
The Community Home
Lenders Association sent a letter to
Fannie and Freddie in April complaining about unannounced changes to both of
their underwriting software programs that members discovered when applicants
who had previously been approved suddenly were denied.
Scott Olson, executive
director of CHLA, said there’s no good reason to keep lenders in the dark: “The
more transparent, the more clear the guidance is, the easier it is for
borrowers to know what they need to do to be in a position to qualify.”
Earlier this month — and
weeks after we began asking about its algorithms — Fannie announced in a news
release that it would start incorporating on-time rent payments in its loan
approval software starting in mid-September. When we asked about the timing of
that change, spokesperson Katie Penote emailed The Markup a statement saying
the company wanted prospective borrowers “to have this option as soon as
possible” but was silent about what prompted it.
In addition to using
Fannie’s or Freddie’s software, many large lenders also run applicants through
their institutions’ own underwriting software, which may be more stringent. How
those programs work is even more of a mystery; they are also proprietary.
When we examined the
reasons lenders listed for denying mortgages in 2019, the most common reason
across races and ethnicities, with the exception of Native Americans, was that
applicants had too much debt relative to their incomes. When lenders did list
“credit history” as the reason for denial, it was cited more often for Black
applicants than white ones in 2019: 33% versus 21%.
When we examined the
decisions by individual lenders, many denied people of color more than white
applicants. An additional statistical analysis showed that several were at
least 100% more likely to deny people of color than similar white borrowers.
Among them: the mortgage companies owned by nation’s three largest home
The two principal laws
forbidding housing and lending discrimination are the 1968 Fair Housing Act and
the 1974 Equal Credit Opportunity Act. An alphabet soup of federal agencies can
refer evidence of violations of these laws to HUD or the Justice Department for
investigation, but referrals have dropped precipitously over the past
Marcia Fudge, who took
over HUD leadership earlier this year, told Axios in June
that part of the reason Black ownership rates are so low in America is that “we
have never totally enforced the Fair Housing Act.” In an email, HUD press
secretary Meaghan Lynch told The Markup that Fudge intends to tackle “systemic
discrimination in the housing and credit markets that is at the heart of the
racial homeownership gap.”
“We do have laws that
explicitly protect against discrimination, and yet you still see these
disparities that you’re finding, so that suggests that we need better
enforcement of existing laws, and more investigations,” said Kevin Stein,
deputy director of the California Reinvestment Coalition. “Agencies need to do
a better job of ferreting out discrimination and taking serious action once
they find it.”
Another key housing law,
the federal Community Reinvestment Act (CRA) of 1977, allows the federal
government to penalize lenders who fail to invest in low-income or blighted
neighborhoods but makes no requirements regarding borrowers’ race. Stein’s
group has lobbied for the law to be reformed.
Lenders who violate fair
lending rules can be punished with fines in the millions of dollars. Rep. Al
Green, a Texas Democrat, has sponsored legislation wending its way through
Congress that would make it a crime to engage in lending discrimination.
“Banks already have laws
that punish people who commit fraud,” he said. “You can be imprisoned for — I
hope you have your seatbelt on — 30 years. Why not have some similar law that
deals with banks who are invidiously discriminating against people who are
trying to borrow money?”
And some fair lending
advocates have begun to ask whether the value system in mortgage lending should
“As an industry, we need
to think about, what are the less discriminatory alternatives, even if they are
a valid predictor of risk,” said David Sanchez, a former Federal Housing
Finance Agency policy analyst who currently directs research and development at
the nonprofit National Community Stabilization Trust. “Because if we let risk
alone govern all of our decisions, we are going to end up in the exact same
place we are now when it comes to racial equity in this country.”
Crystal Marie McDaniels
said whatever effect race may have had on her denial, it wasn’t overt.
“I’m not sure you ever
really know, because there’s no klansmen in our yard or anything — but it’s
definitely something we always think about,” she said. “It’s just something
that we always understand might be a possibility.”
The lender, loanDepot,
denied race had anything to do with the decision. The company’s vice president
of communications, Lori Wildrick, said in an email that the company follows the
law and expects “fair and equitable treatment” for every applicant. “We take
the issues raised by Ms. (McDaniels) very seriously and are conducting a
thorough review of her concerns.”
Crystal Marie said buying
a house was crucial for her because she wants to pass on wealth to her son
someday, giving him an advantage she never had. So when the loan officer told
her the deal wasn’t going to happen, she refused to give up.
With the help of their
real estate agent, and multiple emails from her employer on her behalf, she and
her husband Eskias pushed back against the denial.
Around 8 p.m. on the
night before the original closing date, Crystal Marie got an email from the
lender: “You’re cleared to close.”
She still doesn’t
understand how the lender went from a no to a yes, but she was relieved and
“It means so much to me,
as a Black person, to own property in a place where not that many generations
ago you were property,” said Crystal Marie, who said she is descended from
slaves in neighboring South Carolina.
She said her family has
always had a fraught relationship with money. Some relatives were so
mistrustful of banks that they’d insisted on dealing only in cash, she said,
making it impossible to build up credit or wealth for future generations.
“It’s meant so much,” she
said, “that we were able to go through this process and finally, eventually, be