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Community Investments Vol. 15, Issue 1
Working Wheels: A Seattle Success Story
Author(s): Susan Crane, Executive Director, Port Jobs; and Judith Olsen, VP
and CRA Officer, Pacific Northwest Bank
April 2003
"I am a responsible working mother who wants only to provide the best
means of life for my daughter. She deserves the opportunities that I, myself,
did not have…" wrote Keisha, a single mom of a one-year old daughter, on
her Working Wheels application.
Keisha, a former welfare recipient, landed a job as an office coordinator. Like
many Working Wheels clients, she lives in a section of King County where
the rents are lower, but where good jobs are scarce. Keisha had a two-hour
commute to work by bus. She was often late to work in the morning and had
trouble getting to her daycare center on time to pick up her daughter. As
other working parents know, tardiness in picking up a child from daycare
results in additional fees, which Keisha could ill afford. She also had an
opportunity for a promotion--training staff in other offices on the use of a
new database--but needed her own car to take advantage of that
opportunity. Keisha, like many low-income working parents, was not eligible
for a used car loan from a bank or a credit union because her credit score
was too low. Through Working Wheels, Keisha was able to improve her
credit and purchase a car.
Poor credit or lack of a credit history drives many low-income people to the
only resource available to them if they want to purchase a car-predatory
lenders. By selling reliable and affordable used cars to low-income workers
and helping them qualify for the loans they need to purchase them, car
ownership programs like Working Wheels offer new options (www.workingwheels.org). Since opening for business in May 2002, Working Wheels has
sold 75 cars to qualified purchasers.
The Sketch of an Idea
Port Jobs, a Seattle area nonprofit organization that is closely associated
with the Port of Seattle, created Working Wheels. Focused primarily on
creating employment opportunities for economically disadvantaged
residents, Port Jobs takes on complex problems and works with community
organizations, employers and other stakeholders to craft effective solutions
(www.portjobs.org). The impetus for Working Wheels grew out of a
discussion with the coordinator of Port Jobs' Apprenticeship Opportunities
Project, who reported that she had a number of clients that were otherwise
qualified to enter union apprenticeships, but couldn't because they lacked
one critical tool to getting and keeping those jobs--their own automobiles. In
a field where the jobsite changes every few months, and where the worker
has to carry his own tools, the bus just does not work.
In 1999, Port Jobs began the research that ultimately led to the creation of
Working Wheels. The research consisted of a nationwide study of best
practices in low-income car ownership programs, a literature search, and
several community forums that Port Jobs convened with the help of the
University of Washington and the city of Seattle. The director of WorkFirst,
Washington's welfare-to-work program, attended these forums and
concluded that a car ownership program could fill a critical gap for welfareto-work clients. WorkFirst has contributed financial support for both the
creation and operation of Working Wheels.
The Blueprint for the Model
When Port Jobs conducted its initial research on low-income car ownership
programs in 1999, only a handful of such programs existed. However, new
programs have emerged every year since then. At last count, more than 50
such programs were operating across the country. Some programs sell or
give away reconditioned used cars donated by the public, others are solely
loan programs, and still others offer free or cheap car repair.1 From this
research, Port Jobs was able to distill a set of best practices that have been
combined with program elements unique to Working Wheels. Following are
some of the key features that make the Working Wheels program a success:
Program Operator
Port Jobs contracted with Fremont Public Association (FPA), King
County's largest community development corporation, to operate
Working Wheels. FPA was an ideal choice for a partner because it
operates both a financial literacy program and a garage. FPA was also
reputed to be an honest broker. This was an important selection factor
to Port Jobs because a large network of organizations refers clients to
Working Wheels (www.fremontpublic.org).
Vehicles Inventory
Most of the cars used in the Working Wheels program are retired fleet
vehicles. These cars are newer, have lower mileage and have received
more consistent maintenance than the donated cars upon which most
other car programs rely. The city of Seattle has agreed to donate 50
retired fleet vehicles to Working Wheels each year. Working Wheels
has also purchased additional vehicles from the city at cost.
Training and Support Services
Working Wheels clients receive basic auto maintenance instruction,
financial literacy training and if needed, credit repair assistance.
Working Wheels also helps pay for auto insurance during the early part
of the loan term, when needed.
Financing
Car loans for Working Wheels' clients are made through a mainstream
financial institution, Credit Union of the Pacific (CUP). Working Wheels
sells its vehicles for about $1,500, which is equal to half or more of the
retail value of the vehicles. In other words, LTV is less than 50%. The
loan term is three years and no down payment is required. At an APR
of 7%, loan payments are around $50 per month. Borrowers can
choose to have their loan payments made through automatic payment
from their checking accounts, but it is not required. Due to the already
low interest rate that is charged to Working Wheels borrowers, they do
not receive a further reduction if they choose automatic payment.
Loan Guarantee Fund
The Federal Home Loan Bank of Seattle provided a recoverable grant
to CUP through the Bank's Access grant program. Loans to Working
Wheels customers are guaranteed through this grant. At the end of the
six-year grant period, the bank can either recover the unused funds or
extend the grant period.
What Does the Research Say About Car Ownership?
People who own cars are more likely to be employed and to work more
hours than those who do not own cars
Access to a car shortens periods of unemployment
Car ownership = increased earnings, especially for African Americans
and low-skilled workers
Welfare recipients who received cars through one car ownership
program increased their earnings and reduced their dependence on
public support payments
Setting Working Wheels in Motion
Port Jobs designed Working Wheels to be a gateway to asset building for
low-income working families. First, Working Wheels gives them access to
reliable, affordable used cars. Second, Working Wheels connects these
families to mainstream financial services and helps them build credit and
make wise financial choices. Third, the program makes credit and banking
services available to an underserved segment of the community that is
targeted by predatory and payday lenders. Several Seattle-area financial
institutions that share this vision helped make Working Wheels a reality.
Some, like Cascadia Revolving Loan Fund and Pacific Northwest Bank
(PNWB), continue to furnish vital technical assistance to the program. PNWB
continues to play an invaluable role in refining the Working Wheels model
and taking it to scale, and is currently helping Port Jobs raise additional loan
guarantee funds so that Working Wheels can serve more people.
"Working Wheels is an important first step to help people working in lowwage jobs move up so that they can become homeowners some day,"
explained Judy Dailey, vice president for community research and
development at the Federal Home Loan Bank of Seattle, when asked about
the FHLB's role in funding loan guarantees for Working Wheels. Beyond the
grant, the FHLB, through Ms. Dailey, was also instrumental in identifying a
lender willing to operate the loan program and in arranging an introduction
with that lender, Credit Union of the Pacific.
CUP, which has significant experience making car loans and other consumer
loans to its members, assigned an experienced loan officer to the Working
Wheels program. Together, staff from both organizations developed a
screening and referral protocol for loans coming to the credit union from
Working Wheels. Based on this protocol, Working Wheels pre-packages the
loan applications and presents them to the loan officer for consideration.
After a quick review, CUP underwrites, processes and closes the loan.
Working Wheels reaches people who do not qualify for conventional bank
financing. CUP has set a minimum credit score of 550 for Working Wheels
borrowers who have credit histories, but makes occasional exceptions after
conferring with the Working Wheels program manager. Working Wheels also
serves customers who do not have credit histories. Nearly half of Working
Wheels customers, many of them immigrants, fall into this category. Once
they get their loans, Working Wheels clients join the credit union and open
savings accounts. As they develop a history of timely payments, CUP offers
them access to additional credit union services such as low-cost checking
accounts and bankcards. When asked about CUP's experience with the
Working Wheels program, CUP president Laurie Stewart replied: "Working
Wheels has given us an opportunity to provide banking services to folks who
had been unbanked. So far, we've had zero delinquencies. Every account is
performing and we're now providing additional banking services to Working
Wheels customers. We believe that these customers have been doing so well
because Working Wheels helps create responsible car owners."
1
Office of Port Jobs, Working Wheels Update: Car Ownsership Program
Practices Nationwide: October 2001
[http://www.portjobs.org/resources/research
/working_wheels_update.htm].
Biographies
Judith Olsen is the vice president and CRA officer for
Pacific Northwest Bank, head quartered in Seattle, Washington. Pacific
Northwest Bank operates 58 financial centers in the Pacific Northwest,
including the metropolitan areas of Seattle and Bellevue, Washington and
Portland, Oregon.
Judith brought 25 years of banking and investment experience with her
when she joined Pacific Northwest Bank five years ago. She is active on
many boards, including the Washington Community Reinvestment
Association, where she chairs membership development; Impact Capital,
where she serves as secretary; and the Opportunity Council where she
serves as treasurer. Judith is a graduate of the Pacific Coast Banking School-the premier graduate school of banking--at the University of Washington.
Susan Crane is the executive director of Port Jobs, a
Seattle area nonprofit focused on connecting low-income community
members to career opportunities. Port Jobs conducts research on
employment issues and develops innovative programs that respond to
community-wide needs in this area. Current Port Jobs programs include the
Apprenticeship Opportunities Project, Airport Jobs- an airport wide
employment center at SeaTac International Airport, and Working Wheels,
Port Jobs newest program.
Susan has 26 years of experience in public service. She came to Port Jobs in
1997 after 10 years of service to the Seattle City Council as a policy analyst.
Before that, she worked for several area nonprofits that aid victims of sexual
and domestic violence. Susan is the public member of the Washington State
Apprenticeship and Training Council. She has a masters degree in public
administration from Seattle University and is a graduate of Harvard
University’s Kennedy School program for senior executives in state and local
government.
Community Investments Vol. 15, Issue 1
Restoring Lives to Rebuild Communities
Author(s): Mike Tandy, President and CEO, Restoration Enterprises, Inc.
March 2003
Conventional wisdom sees criminals as a destructive element representing a
financial burden to society through loss and the cost of administering justice.
A bold new model based on Restorative Justice (www.restorativejustice.org)
challenges this wisdom and instead represents an opportunity to rebuild
communities. "Restorative justice is a systematic response to wrongdoing
that emphasizes healing the wounds of victims, offenders and communities
caused or revealed by the criminal behaviour." It involves the collaboration
of the public and private sectors to improve the safety and well being of the
community.
For Shasta County, California, a picturesque locale situated almost on the
Oregon border, the concept of restorative justice was pursued in response to
the serious overcrowding in the county's main jail facility and as a means of
lowering the rate of recidivism among offenders. With a primary focus on
reducing recidivism, community partnerships were formed to apply the best
services and resources available to address the "barriers" in the lives of
minimum level offenders as a practical alternative to jail time. Instead of
handing out incarceration, this approach offered a "hand-up", allowing
offenders to make a personal investment in their lives. A seamless
integrated plan with accountability and consequences provides a person who
is serious about changing his life an opportunity to get substantial assistance
while making restitution to the community. Restoration Enterprises
(www.restoringshasta.org), created in 1998 as a nonprofit public benefit
corporation to assist the county in realizing its new objective, is the private
sector partner in this arrangement.
The "barriers" that Restoration Enterprises must address are not just
associated with the minimum level offenders, but apply to "at risk"
individuals (low-income, underemployed, welfare-to-work, etc.) as well.
These barriers or missing elements include housing, transportation, jobs,
training, substance abuse, literacy, education, life skill development and
others. Restoration Enterprises' role is to act as a bridge in forming
community partnerships between law enforcement, public agencies,
community and faith-based service providers to help create a safer and
more involved community. Our mission is to provide people and
organizations the opportunity and services they need to realize their Godgiven potential as contributing members of the community.
One of the unique programs developed in pursuit of this mission is our auto
loan fund. According to the California Department of Social Services,
CalWORKS, the number one barrier to employment for the welfare-to-work
client is transportation. With bus systems that do not cover distant locations
or odd work hours, many CalWORKS clients face a growing risk of losing
their employment. In some cases, accepting a new position of employment
or a promotion to another branch operation is impossible because of
transportation barriers. As most rural communities do not enjoy the mass
transit systems common to urban cities, the transportation issues
surrounding child and day care for the working parent are substantial.
Restoration Enterprises created a demonstration pilot project that uniquely
addresses the problem of transportation and other client dynamics. Two
separate grants from CalWORKS in Shasta county allowed us to establish a
revolving loan fund and to support administration of the project. Our
analysis revealed that none of our clients would not meet conventional
financing because of blemished credit histories. Home budgeting and
management skill training were non-existent. Personal and family financial
resources were exhausted and not otherwise available. Working closely with
CalWORKS, a client eligibility screening and referral mechanism was
developed and the automobile loan project was launched. However, the
program is less about cars than it is about jobs.
Borrowers must be CalWORKS clients who have exhausted every other
option to acquire reliable transportation. If they demonstrate a need to
acquire reliable transportation in order to accept a job, stay employed or
improve employment, they are referred to Restoration Enterprises by
CalWORKS to complete a loan application. We look at their credit history,
including bill payment track record, verify their sources of income, and
review other pertinent information--loan clients are not declined because of
poor credit history alone. Applicants that meet approval standards must
complete our home budgeting course, which requires that they create their
own personal budget and in some cases affirm certain creditor obligations.
Approved loan applicants are offered a free pre-purchase vehicle inspection
at our auto repair facilities.1
Now two years old, the loan fund has had positive results and the loan
performance has been very successful. Close communication and frequent
follow up to reaffirm good performance is a regular part of our staff's daily
schedule and one of the key factors in the program's success. Compensation
for risk is offset against the loan pool, using accrued interest for any vehicle
collection expenses or chargeoffs. CalWORKS of Shasta County has notified
us that they are going to more than double our loan fund in the next two
months. We would also like to establish a parallel fund to serve nonCalWORKS clients in Shasta County. And there is room to do more.
"When U.S. Bank became aware of Restoration Enterprises' auto loan
program, we were very impressed with how comprehensive it is and
how successful it is in meeting the needs of its target audience. So
many rural residents do not have access to affordable and reliable
transportation, nor do they have public transportation as an
alternative form of transportation for traveling to work. The Bank
made a contribution to Restoration Enterprise for operating support to
assist them in expanding the program in Redding as well as other rural
areas. We believe that the program could benefit the residents of
many communities, as it provides not only an affordable auto loan, but
other components that help the customer succeed, such as financial
education, assistance in selecting a reliable auto and ongoing support."
(Joyce Keane, VP & Community Development Manager)
With ever-tightening state and local budgets, low-income families with these
same employment barriers will fall through the cracks in our cities and
counties. We are dedicated to expanding this proven transportation template
to meet that challenge and have developed an expansion plan to grow this
program into other rural counties with even greater transportation barriers
to employment: eastern Shasta, Tehama, Trinity, Siskiyou, Modoc, Lassen
and Humboldt counties. With enough resources, expansion into larger
metropolitan areas, such as Sacramento and beyond, is also quite feasible.
Creating productive tax paying citizens is smart. It enhances community
safety, promotes well being and eventually creates bankable clients and
homeowners. CalWORKS has made a significant investment in partnering
with us to develop this program, but this is only a beginning. The investment
to create a multi-bank loan pool would greatly enable us to expand our
expertise in restoring lives to rebuild communities. We encourage inquiries
from banks that want to learn more about our program and are interested in
a simple but unique opportunity to invest in low-income families.
To inquire, contact Mike Tandy at 530/245-0500 or via email.
1
Restoration Enterprises auto repair facility opened in May of 1999. The
shop hires ex-drug offenders, giving them a chance to start life over. A lot of
the repair work comes from CalWORKs, Dept. of Rehabiliation, Northern
Valley Catholic Social Services, PIC/SMART clients and the surrounding
community.
Biography
Michael R. Tandy has spent over 30 years in banking
and economic development, culminating with the co-founding of Country
National Bank in Redding, California, in 1982, where he served as the
executive VP and senior loan administrator. During his banking career, Mike
became a specialist in SBA lending, covering ten northern counties of
California. He was eventually recruited as a bank VP responsible for SBA
lending for the state of Nevada.
His background includes serving as general manager of a NASCAR Winston
Cup racing team, which attained NASCAR's Rookie of the year. Currently, he
serves as president and CEO of Restoration Enterprises, Inc., a nonprofit
community organization. Mike is also an ordained minister and pastor. He is
a volunteer chaplain at California's High Desert State Prison and chaplain at
Shasta Speedway racetrack.
Community Investments Vol. 15, Issue 1
Ways to Work
Author(s): Elaine L. Hogue, Community Development Officer/CRA, American
Savings Bank
March 2003
With many of Hawaii's working poor teetering on the edge of being able to
make ends meet, an unpredictable challenge such as a car breakdown, can
plunge a family into crisis. In Hawaii's busiest and most popular tourist
destination--the island of Oahu, many workers commute long distances by
bus because they cannot afford to live near their jobs.
A Ways to Work loan has given Lynn Resurreccion the means to turn her life
around. Resurreccion is pictured in front of her new, used car with American
Savings Bank community development officer Elaine Hogue and loan officer Jerry
Felipe. (Photo courtesy of Hawaiian Electric Industries.)
As of the 2000 census, 64.9% of Oahu's population is very low- to
moderate-income. This statistic represents a considerable number of people
that may at sometime have a need for a short-term loan. Consuelo
Foundation (www.consuelo.org), whose mission is to improve the quality of
life for disadvantaged children and families in Hawaii and the Philippines,
recognized this need and began to look for a way to deal with an issue that
presented a significant problem for many of the community's most
financially vulnerable. Consuelo Foundation also wanted to find a solution
that would embrace their philosophy of providing a "hand-up" rather than "a
hand-out." The national Ways to Work family loan program appeared to be
the right solution. Because of our strong track record of responsiveness on
other projects, Consuelo approached American Savings Bank to take part in
this innovative opportunity, and we responded with our resources to help
bring the Ways to Work family loan program to Hawaii.
The Ways to Work loan program was started by the McKnight Foundation in
1985 in the Minneapolis-St. Paul area. Ways to Work loans are used to help
low-income parents, who cannot get traditional loans elsewhere, pay for
expenses that could interfere with their ability to keep a job or stay in
school. Loans ranging from $500 to $4,000 help families purchase a used
car, pay for car repairs, childcare, certain housing costs and other qualified
purposes. In Hawaii, borrowers repay the loan at an affordable eight
percent, which is also the program's national limit and well below the
interest rate these families with poor credit would be charged by alternative
lenders such as payday and pawnbrokers. Borrowers must meet certain
requirements, such as being a custodial parent and Oahu resident. They
must also be employed for at least 19 hours per week for six months prior to
applying for the loan or be a post-high-school student with verifiable income.
Beginning in 1996 and with the support of the McKnight Foundation, Ways to
Work, Inc. launched the national expansion of this effective, outcome-driven
program. As of 2002, 42 member organizations in 22 states made over
4,000 Ways to Work loans exceeding $8 million. The national program
default rate of 13.2 percent is deemed exemplary, considering that the loans
are to high -risk borrowers. 88 percent of borrowers utilized these loans to
buy used cars. Many of the borrowers have also experienced dramatic
decreases in use of public assistance.
In 2002, through Consuelo Foundation's Ways to Work program on Oahu,
American Savings Bank funded $82,375 to 33 families in need. With a
default rate of 10.1 percent the Hawaii model compares favorably to the
national experience and reflects many of the same positive outcomes. Of
these customers, 73 percent are single mothers and 20 percent are selfreported victims of domestic violence. Preliminary outcomes show that 80
percent of borrowers improved their credit score since receiving their loans,
50 percent of borrowers increased their income and well over 100 families
acquired money management skills through program training.
Consuelo Foundation manages the program and originates the loans through
a loan program manager, a role that combines the skills of a banker and the
attitude of a social worker. The loan program manager helps borrowers
navigate the lending process from start to finish, offering necessary
guidance along the way. Loan decisions are rendered by a committee of local
volunteers from the social service, public administration, auto sales and
banking professions with American Savings Bank providing two such
volunteers.
Ways to Work, Inc. was established as a subsidiary of the nonprofit Alliance
for Children and Families to help member organizations create loan
programs in their communities. Ways to Work, Inc. provides initial and
ongoing technical assistance with fund-raising, program development,
program operations and customized software. As a federally certified
Community Development Financial Institution, Ways to Work, Inc. also offers
low-interest capital to start-up and existing programs. Member organizations
wishing to replicate the program are typically required to raise a minimum of
$340,000 to fund a loan pool and loan loss reserve to be held at a financial
institution, plus funds to operate the program. However, Hawaii's program
(one full year in the making) is unique.
Ways to Work provides ongoing technical assistance. Consuelo Foundation
pledged up to $90,000 of its own funds annually for five years for program
operations and $40,000 to fund a loan loss reserve. American Savings Bank
committed a grant of $100,000 to be paid out over five years to help start
up and operate the program. Additionally, to provide Consuelo Foundation
the funds for a back-up loan loss reserve, American provided a $310,000
equity-like loan at three percent. The loan utilizes a uniquely crafted
investment agreement that consists of a 10-year term with interest-only
payments, a subordinate position on existing or future debt obligations, no
prepayment penalty, and the option of extending the maturity date for an
additional five-year term at the foundation's discretion. Now paid in full, this
complex transaction resulted in American Savings Bank obtaining investment
test credit during the relevant review period under the CRA.
The combination of continual hands-on guidance from Consuelo Foundation's
loan program manager and Consuelo's commitment to purchase all loans
over 60 days delinquent has enabled American Savings Bank to lend money
directly to these high-risk borrowers. On our end, we had communityminded legal and loan servicing staff that were willing to set up special
reporting and monitoring procedures to accommodate this special needs
program. As a result, borrowers who maintain a good payment record can
improve their credit score, and because the bank closes, funds, and services
the loans, we are eligible to receive lending and service test credit under the
CRA.1
This program is a win-win-win! Hawaii's credit-needy working poor families
get life-sustaining loans, Consuelo Foundation achieves its aim of improving
the lives of women and families, and we at American Savings Bank meet our
goal of being a full-service bank that is responsive to Hawaii's community
development needs both in policy and in practice.
For more information or advice on internal tools American developed to
establish the Consuelo Foundation's Ways to Work program, contact Elaine
L. Hogue. To learn more about the national Ways to Work program, contact
Kevin P. Stewart, National Program Director, at Ways to Work, Inc., (800)
221-3726, ext. 3656; web site: www.alliance1.org.
1
Service test credit was received for American Savings Bank senior
management participation on Consuelo Foundation's board of directors.
Biography
Since 2000, Elaine Hogue has been in charge of
American Savings Bank's community development and CRA program;
helping the bank build strong relationships in needy communities throughout
Hawaii. Currently, Hogue is the president of Hawaii's CRA Association.
Previously, Hogue served as the grants administrator at the Harold K.L.
Castle Foundation, as the annual giving executive at Castle Medical Center
and as a paralegal with a large real estate law firm in downtown Honolulu.
She resides in Kailua with her husband, state Senator Bob Hogue, and their
four teenage children.
Community Investments Vol. 15, Issue 1
Credit Scoring Overview
March 2003
Credit scoring is an underwriting tool used to evaluate the creditworthiness
of prospective borrowers. Utilized for several decades to underwrite certain
forms of consumer credit, scoring has come into common use in the
mortgage lending industry only within the last ten years. Scoring brings a
high level of efficiency to the underwriting process, but it also has raised
concerns about fair lending with regard to historically underserved
populations.
In order to explore the potential impact of credit scoring on mortgage
applicants, the Federal Reserve System's Mortgage Credit Partnership Credit
Scoring Committee has produced a five-installment series. This first
installment provides a context for the subsequent installments. An important
goal of this series is to provide the industry and concerned groups and
individuals the opportunity to comment on issues surrounding credit scoring.
This installment incorporates statements requested from the following
organizations, selected because of their interest in and differing perspectives
on credit scoring and fair lending:
Freddie Mac
A stockholder-owned corporation chartered by Congress to create a
continuous flow of funds to mortgage lenders in support of homeownership
and rental housing. It serves as a secondary market for mortgage loans by
purchasing mortgages from lenders across the country and packing them
into securities that can be sold to investors.
Fair, Isaac and Company, Inc.
Originally an operations research consulting firm, Fair, Isaac and Company,
Inc. introduced the use of credit scoring for risk management in the financial
services industry. They apply statistical decision theory to business decisions
through the development of predictive and decision models.
American Bankers Association
Based in Washington, D.C., the American Bankers Association (ABA)
represents banks of all sizes on issues of national importance for financial
institutions. The ABA's mission is to serve its member banks and enhance
their role as pre-eminent providers of financial services.
Calvin Bradford and Associates
Calvin Bradford has been a fair lending, fair housing and community
reinvestment consultant for over 25 years. His firm engages in research,
training, program development and evaluation, and expert witness work for
government, private industry, public interest and community-based clients.
Representatives from each of these organizations received a request to
comment on the following statement:
A variety of research studies, emanating from the Federal Reserve System,
other regulatory and government institutions, and private research
organizations, have suggested unexplained variances in mortgage
acceptance rates and pricing between majority and minority mortgage
applicants. Though not uniformly the focus of these studies, credit scoring is
now a commonly used tool in the mortgage underwriting process. Creditscoring advocates maintain that as an underwriting tool, credit scoring has
allowed the underwriting function to be streamlined for highly creditworthy
applicants, allowing human underwriters to allot more time to applications
where credit issues are present, and has reduced overall costs of
underwriting. Detractors claim that factors considered within statistical
credit-scoring models, even if not intended, favor majority applicants and
create a new barrier to homeownership for minority mortgage applicants.
Please describe, from your perspective, fair lending issues that might arise
as a result of the use of credit-scoring technology in the mortgage
underwriting process and what your organization does to address these
issues.
Statement of Ellen P. Roche
Director of Corporate Relations
Freddie Mac
An increasing number of consumers have benefited from the speed,
accuracy, and fair treatment provided by the use of credit scoring and
automated underwriting over the last several years. In addition to
summarizing these benefits, we describe how automated underwriting and
credit scoring benefit the consumer during the mortgage application process.
American families now enjoy more choice and opportunity in the mortgage
market than ever. Home-buying families can choose a mortgage product
that meets their specific financing needs and they can do so by telephone,
on the Internet, or in a face-to-face transaction. Loan approval procedures,
which once took many weeks, now take days. The once time-consuming
credit review process now takes place in minutes, thanks to technologies
that have automated the underwriting process.
Manual underwriting characterized the mortgage market before the 1990s.
This slow process provided only a limited ability to analyze multiple risk
factors and sift through layered risks. Without the ability to precisely
measure distinctions in risk with speed and accuracy, lenders and investors
developed guidelines that broadly defined creditworthiness. For decades
these guidelines served well the vast majority of mortgage borrowers in
what came to be known as the prime market.
Over the years, easier access to credit and a rising bankruptcy rate meant
that an increasing number of borrowers with blemished credit histories fell
outside the mainstream that the industry's typical guidelines were able to
address. Some did not get mortgages. Some resorted to the subprime
market. In either case, potential borrowers could not take advantage of the
efficiencies available in the prime sector.
Now, powerful tools are fundamentally changing the market's ability to
assess and manage credit risk. Automated underwriting now makes it
possible to extend the efficiency of the prime market to those who have until
now been beyond its reach.
Instantaneous and Accurate Risk Assessment
Automated underwriting is one of the keys to opening new doors of
opportunity, because it allows for the instantaneous and accurate
assessment of a multitude of risk factors. Freddie Mac has led the
development of this critical tool, by introducing the state-of-the-art
automated underwriting service, Loan Prospectorâ (LP), in 1995.
The predictive power of automated underwriting helps lenders and borrowers
alike. It gives lenders the tools they need to make more mortgages and
reach out to new borrowers. It gives consumers confidence that mortgages
are evaluated the same way, every time, for every borrower, encouraging
more borrowers to enter the housing finance system.
Automated Underwriting Revealed
Automated underwriting is necessary to provide a full picture of mortgage
eligibility. Automated underwriting is faster and fairer than manual
underwriting and provides a more precise evaluation of risk. Credit is a very
important part-but just a part-of the evaluation process. Credit scoring is the
fastest and fairest way to evaluate credit. It has been proved predictive for
all population groups. Credit scores evaluate previous credit performance,
the current level of indebtedness, the length of credit history, the types of
credit in use, and the pursuit of new credit.
Automated underwriting benefits consumers when applying for a mortgage
in several different ways.
Access to the System: Consumers should not be rejected during a quick
preapplication screening. Lenders should conduct a full analysis of their
homeownership potential. Freddie Mac discourages lenders from using credit
scores as a screening device because it does not provide a full picture of the
borrower's ability to pay a mortgage. LP considers credit, collateral, and
capacity but does not consider race, age, or marital status, and thus, it can
provide a fair and thorough evaluation of the mortgage in a few minutes.
The proof of any underwriting system lies in its ability to assess risk-and LP
has proved to be highly predictive of default for borrowers from all racial and
ethnic groups and all types of neighborhoods. Whether a borrower is AfricanAmerican, Hispanic or white, loans in the lowest-risk groups performed
significantly better over time than those in higher-risk groups. Because it is
blind to an applicant's race and ethnicity, LP promotes fair and consistent
mortgage lending decisions. Moreover, LP predicts well across income groups
and neighborhoods as well. Automated underwriting reduces the need to
prescreen mortgage applicants.
Objective Sources of Information: Consumers should have access to credit
counseling to help them understand the risks and rewards of homeownership
and to assist them in getting their mortgage application approved. Freddie
Mac supports AHECI, NAACP, and the national Urban League as well as other
organizations that provide homeownership and financial literacy counseling.
Consumers can request their credit reports before applying for a mortgage
to check the accuracy of their credit information. Consumers have the right
to correct the credit information LP uses in evaluating credit history.
Full and Fair Information: Interest rate, payment amount, adjustable rates,
late fees, and prepayment penalties need to be explained and understood.
Freddie Mac requires lenders to follow fair-credit and fair-lending laws and
also requires lenders to report when borrowers do pay their bills on time, so
borrowers can get credit for a job well done.
Fair Lending Practices: If borrowers are eligible for "A" mortgages, lenders
should charge "A" mortgage rates. Freddie Mac's LP provides the lender with
the lowest-risk mortgage rate regardless of the lender' classification of the
mortgage.
Explanation for Mortgage Denial: Lenders should provide borrowers with
information that can guide them to improve their chances for acceptance. LP
does not deny a mortgage application. On higher-risk loans, LP requests
additional support documentation and requires the lender to share some of
the higher risk. Alternatively, LP offers to purchase the loan with additional
fees to compensate for the additional risk. In any case, LP provides the
lenders with feedback to guide them in improving their application. For
example:
If tax returns are used to document source of income or to verify
income, obtain signed IRS form from borrower;
or
Use stated income for qualification and obtain most recent year-todate paystub to verify employment for borrower.
In addition Fair, Isaac scoring products also provide up to four reason codes,
in order of importance, that indicate why a score is not higher. For example,
"derogatory public record or collection filed," or "amount owed on accounts
is too high."
While the techniques for evaluating risk have advanced, the general rules for
improving your credit and your ability to obtain a mortgage remain the
same:
Pay your bills on time;
Keep your credit card balances low; and
Make sure your credit records are accurate.
Using credit scoring as part of automated underwriting helps more borrowers
get mortgages because of the speed, accuracy, and fair treatment inherent
in these tools. If the alternative is manual underwriting, there is no
comparison.
Statement of Paul Smith
Senior Counsel
The American Bankers Association
Actually, our bankers tell us that credit scoring, in fact, gives greater access
to mortgage credit rather than creating new barriers for minority mortgage
applicants. The use of credit-scoring models to better predict whether an
applicant might default allows the lender more flexibility in making
traditional home loans. During the last 10 years, the banking industry has
greatly expanded its efforts to make credit available to less qualified
applicants. For example, the housing mortgage secondary market agencies,
Fannie Mae and Freddie Mac, have broadened their underwriting criteria to
accept alternatives to the traditional qualifications. Banks have started lower
interest-rate or no-fee affordable housing programs, created first-time
homebuyer programs in which borrower training replaces some of the
missing qualifications of the borrower, and expanded the list of qualifications
for potential borrowers.
Many bankers also have said that credit-scoring models have been crucial in
permitting banks to approve more borrowers' applications than traditional
underwriting criteria would have. All of them said that today they make
home loans with the use of credit-scoring systems that they could not have
made or sold to the secondary mortgage market in the past. None of the
bankers consulted for this comment reported that they used a credit-scoring
system exclusively, but rather, as part of the overall mortgage underwriting
process. In a home mortgage loan, the property's appraised value, the loanto-value ratio, the available resources for closing costs and down payment,
the applicant's disposable income, and other underwriting standards all must
be factored into the credit decision. Nonetheless, use of a credit scoring
system in the mortgage process is increasing-not only because of the
customers' demand for faster underwriting decisions but also because of
bankers' interest in expanding credit availability. For example, a higherthan-required credit score might allow the bank to accept a higher loan-tovalue ratio than its general lending policy permits. This would permit the
applicant to make a lower down payment, and thus, make up for having
fewer financial resources than the traditional applicant. This kind of
increased flexibility in underwriting by bankers and the secondary market
agencies has led to a significant expansion in the access to mortgage credit
during the 1990s.
Bank compliance officers also have said that the use of a validated creditscoring system by the bank reduces the subjectivity of the final credit
decision and allows compliance officers to better monitor fair-lending
compliance. One example of that is described in the 1999 settlement
between the Department of Justice and Deposit Guaranty Bank
(www.usdoj.gov/crt/housing/caselist.htm#lending). Although the bank was
said to be using credit scoring, the crux of the case was that lending officers
were allowed to freely override the credit score, that is, either granting a
loan that should not have been granted according to the score (a low-side
override) or not granting a loan that should have been granted according to
the score (a high-side override). Thus, the fair-lending violations were not in
the credit-scoring model but in the ignoring of the credit scoring as a factor
in the lending decision. The settlement also describes in detail how the
successor bank to Deposit Guaranty ensures fair-lending compliance through
several mechanisms, including using a credit-scoring system. Key to that
bank's program (and many other banks' programs) is the use of credit
scoring to ensure standard treatment of applicants, the limitation of
authority to override credit scores, and reviews of any such overrides as well
as reviews of many of the denied applications-to determine if the bank has
an alternative loan product or program for which the applicant could be
qualified.
Besides these and many other steps by banks to ensure fair lending and fair
use of credit scores, the bank regulatory agencies have detailed fair lending
examination procedures that require bankers and examiners to review
credit-scoring models for validity and fairness. These examination
procedures are available for review by the public at
www.ffiec.gov/fairlend.pdf with the Appendix on Credit Scoring Analysis at
www.ffiec.gov/fairappx.pdf. All of these steps and others have been taken to
address issues of the fairness of credit scoring and to enlarge the access to
mortgage credit for low- and moderate-income individuals. And, we believe
that these steps have succeeded.
Statement of Calvin Bradford
President
Calvin Bradford and Associates, Ltd.
The wide-scale use of credit scoring represents a significant efficiency in the
competitive world of mortgage finance. Both the Federal Reserve, by its
regulations, and lenders who use credit scoring refer to it as an objective
process as opposed to judgmental systems. The largest purveyor of credit
scores, Fair, Isaac and Company, has continually maintained that its scores
could not be discriminatory because they do not contain race as an explicit
variable. All of these statements appear to support a confidence in the
fairness and equality in the use of credit scoring that is, in fact,
unwarranted.
Credit scoring has not been intentionally discriminatory in its typical uses.
Nonetheless, regulators, researchers, and the developers of credit-scoring
systems have all recognized that, on average, minorities have lower credit
scores than majority populations. Therefore, the use of credit-scoring
systems will frequently have an overall discriminatory effect. Such an effect,
however, is not illegal if it is based on an overriding business necessity and if
there is no less discriminatory way to achieve the underwriting goal.
With the understanding that all credit-scoring systems need to be calibrated
to the particular population of each individual lender and re-evaluated
periodically, I offer several representative examples of fair-lending issues.
Most Rejected Applicants Are Not Expected to Default
Consider the example, which I have made extreme for the sake of clarity, of
a lender who finds that 100 percent of the loans predicted to go into default
under its scoring system fall below the score of 620. This lender would
assume that using this scoring model is a great business benefit because he
could be reasonably confident that the system would exclude all borrowers
who might default. Therefore, let us assume that the lender rejects, or "cuts
off," all applicants with scores under 620.
A scoring system is able to predict, for any cutoff score, the percentage of
applicants at or below that score who are likely to go into default (the odds
of defaulting), but it is not able to precisely identify which specific individuals
will default. While 100 percent of those predicted to default may have scores
under 620, there also are many other applicants with scores under 620 as
well. Indeed, in our example and in reality, whenever a lender chooses a
particular cutoff score, most of the applicants with scores below the cutoff
are, in fact, not predicted to default. In fact, in our example, it is fair to
assume that the odds of any particular applicant with a score below 620
defaulting might be only 10 percent. That is, 90 percent of those with scores
below 620 would not be predicted to default.
Credit-Scoring Systems Disproportionately Reject Minority
Applicants
Most lenders and secondary investors, as well as those who develop and
market scoring systems, agree that, overall, minorities do have lower credit
scores than whites. Suppose that all minority applicants in a given market,
but only some whites, have scores that fall below 620. Obviously, all
minority applicants would be excluded by a 620 cutoff. The lender, however,
would argue that this clearly disproportionate impact on minorities is not
unlawfully discriminatory because it is a justifiable business necessity.
To clarify further, let us suppose that 3 percent of all people with any score
will default. Out of 100,000 applicants, this would be 3,000 applicants. Now
suppose that, of those 100,000 applicants, 30,000 had scores under 620. If
our system predicts that 10 percent of all applicants under 620 will default,
then these 30,000 applicants would include the 3,000 who will default, as
well as 27,000 others who will not.
In our example, if the entire population of applicants included 10,000
minorities, all 10,000 would have scores under 620. There also would be
90,000 whites in the population. Of these, 20,000 would have scores under
620, making up the total of 30,000 applicants with these scores that we
have specified in our example. There also would be 70,000 whites with
scores at or above 620. If the 3,000 borrowers who will default were spread
proportionately between whites and minorities in the group with scores
under 620, then 2,000 whites (10 percent) and 1,000 minorities (10
percent) would be predicted to default. There would also be 18,000 whites
and 9,000 minorities with scores under 620 who would not be predicted to
default.
In this case, 90 percent of all minorities would be rejected even though the
scoring system predicted that they would not default. But, of the total of
90,000 whites, only 18,000 with scores under 620 will be rejected, even
though the model predicts that they will not default. The disparate impact is
clear. If all applicants under 620 are rejected, 90 percent of the minority
population, but only 20 percent of the white population, will be rejected
when the model predicts that they will not default on their loans.
Obviously this is an extreme example, but in reality, the difference is only
one of degree. If the Equal Credit Opportunity Act regulations permit using a
credit-scoring system-if it is statistically reliable, but prohibit a
discriminatory impact, absent a clear business necessity-then where should
the "necessity" threshold be set? In other words, what level of differential
impact of rejected good minority applicants to rejected good white applicants
is acceptable and what level crosses over into discrimination? Would it be
acceptable in our example to reject all applicants with a score below 620
because of the ability to weed out all applicants expected to default, even if
90 percent of the rejected minorities would not be expected to default? Or,
on the other hand, do we decide that unless a credit score can achieve a less
discriminatory impact, it has not achieved enough validity to be accepted?
Should we, for example, disallow systems having a discriminatory impact
unless they at least predicted that more than 50 percent of those with scores
below the cutoff would be likely to default? At present, in the real world of
credit scoring, the cutoffs used in prime lending are nowhere near that level
of separation; they are much closer to the 90 percent rejection of
predictably good loans used in our example.
Current Systems Measure Default in Discriminatory Ways
Credit systems actually are based on the prediction of early default, not
lifetime default. While early default is important, it generally does not
explain most of the loans that go into default over the life of the loan
because most defaults and foreclosures take place several years into the
loan, not during the first 6 to 18 months. Therefore, not only do the present
scoring systems have a discriminatory effect, but they are based on a
default of only a few months against loans that typically last for several
years-and that last even longer for minorities who buy, sell, and refinance
less often than whites.
As a measure of early default, credit scores do not incorporate many of the
factors that research suggests cause most defaults: job loss, temporary or
long-term unemployment, divorce, and so on. Because these factors are
rarely part of credit bureau databases used in scoring models, such factors
are not part of the scoring process. Of course, these events and factors often
are not items that could be used in a score at the time of application
because they are events and activities that have not yet happened. The
result is that the scoring models actually are not predicting default
altogether, but only that part of default that can be related to data stored in
credit bureaus, and then only inasmuch as the defaults show up very early in
the life of the loan.
Many "Predictive" Factors Used in Systems May Have No Causal
Connection with Default
In social science research, the critical issue of the explanatory power of
statistical models relates to the linkage between correlation and causation.
Credit-score developers try to squeeze all the correlation they can out of the
limited set of factors stored at credit bureaus. In a general sense, they may
seem to match correlation with causation, such as in the apparent logic
between linking future credit performance to past performance. Still, many
correlations raise serious questions of causal relationships. For example,
where there is a correlation between the number of inquiries and later
default-for some applicants-this may reflect attempts by a person with poor
credit habits searching for an acceptance. For others, numerous inquiries
may represent the impact of discrimination that forces borrowers to contact
more lenders in search of a fair loan.
In one historical file, I saw an applicant with a low score where the main
factor was listed as too many open lines of credit. After the person had
consolidated his debts, credit bureaus continued to generate low scores on
the basis that he now had too few credit lines. Although debt consolidation
often is recommended by credit counselors, the result in this case was lower
scores, even though this applicant had never had a delinquent account.
Credit-scoring companies, lenders, and investors often respond to such
examples by insisting that their models are complex and not subject to
simple understanding. We need to ask, however, as a matter of policy,
whether-if we accept a scoring system because of its claimed statistical
reliability-are we really accepting correlation without requiring a sound basis
for causation? Why should we accept a process with a clearly discriminatory
effect when it fails to meet the social science test of having a demonstrable
linkage to causation?
Scoring Models Based on Non-Mortgage Credit Are Not Likely to
Predict Mortgagor Behavior as Well
Most credit-scoring models are not geared to mortgage loans but to all
credit. Minorities stay in their homes longer than whites. Many lenders,
counselors, and other players in the home sales market have perceived that
a home is treated differently by many moderate-income and lower?income
buyers-who also are disproportionately minority-than by higher-income
buyers. The home is more than a commodity that can be replaced, for these
buyers. More sacrifice may be made to keep the home than to protect other
forms of credit from default. This is an example of just one aspect of lending
that may separate the treatment of home-loan credit from other forms of
credit that minorities use. Credit scoring used in mortgage loans needs to be
based on mortgage loans, and perhaps even loans for the same type of
mortgage product, in order to develop patterns that truly reflect mortgage
risk.
Credit Scoring Ignores Change in Borrower Behavior
Scoring systems do not account for the ability of interventions to change
behavior. For example, many lenders and special loan programs have
discovered that pre-purchase counseling (when done well) and post?default
counseling or interventions (when done rapidly at the point of first
delinquency) can substantially reduce the likelihood of default or the
likelihood that a default will result in foreclosure. Since these types of
programs have been targeted disproportionately to minorities (usually either
by the effect of geographic area or income targets), the failure to account
for this ability to change predicted behavior results in credit scores imposing
a discriminatory effect even though less discriminatory alternatives exist.
This undermines the business necessity argument for the use of credit
scores in an environment where they have a discriminatory effect.
Industry Claims That Scoring Frees Time to Spend on Applicants with
Problems Are Unrealistic
The speed and economy of using credit scores allegedly frees up lenders to
spend more time with those whose credit histories need more work. But, in a
market of extreme competition and with a growing range of products for all
credit scores, lenders are less likely to use the system to devote real time to
problem scores than they are to simply divert those with low scores to
higher-cost loan programs. They are, for example, not as likely as in the
past to review the accuracy and basis of credit issues or even to ask
borrowers to verify that derogatory information in their accounts are,
indeed, the applicant's accounts and that they are correct. Lenders also are
not as likely-as with non?scoring underwriting-to ask for explanations of
credit issues. Therefore, credit blemishes that previously were considered
acceptable because they were not the fault of the borrower or were
considered temporary-such as a death in the family, medical bills, or
temporary unemployment-may now simply be counted against the borrower
just as a voluntary disregard for credit would tarnish the borrower's credit
history. We know from socioeconomic studies and health studies, for
example, that minorities suffer loss of job and serious medical bills more
often than the majority population.
Correcting bad information can be hard and time-consuming. The lender also
may be concerned that the investor purchasing the loan will not have access
to the corrected information or may secure a score from another credit
bureau that does not contain the corrected information. Therefore, in a
random quality control audit or in a review if the loan goes into default, the
lender may face negative ratings or even the requirement to repurchase the
loan. Because derogatory credit ratings happen most often with minority
loan applications, the lender may want to find ways to respond to the
application that avoid having to verify and correct bad credit. This may lead
to rejecting the loan or to encouraging the applicant to withdraw the loan at
the earliest time during the application process. Alternatively, when faced
with low credit scores, a lender may introduce a judgmental system of
overrides, which can introduce discrimination into the system.
Rather than reject a loan with credit issues, a lender may steer the borrower
away from prime conventional products toward FHA or subprime products,
rather than try to deal with investigating a low credit score or correcting bad
information. This would have the effect of imposing higher rates or more
onerous terms on the borrower, or it could contribute to concentrations of
FHA loans in minority areas-which have historically been shown to have an
adverse effect on both the borrowers and the community. Recent studies
indicate a similar concentration of subprime lending in minority communities,
with similar adverse impacts.
These are some examples of how credit scores, both directly and indirectly,
may have a discriminatory impact or may lead to differential treatment. The
potential for discrimination and liability should not be ignored, either as an
internal part of the scoring system or in the manner in which it is applied.
Ellen Roche
Response to Statement of Calvin Bradford
In his essay, Calvin Bradford poses an important question when he asks
where the line should be drawn between approval and rejection. However,
we must be careful not to oversimplify our consideration of this important
issue.
Credit scores represent a leap forward in efficiency and access to the
mortgage market compared to manual or judgmental underwriting. We
should not be satisfied with our current achievements and should continue to
work toward increasing the speed and fairness. However, in our efforts to
critique the current arrangements, we should consider the alternatives. If we
set an arbitrary standard for scoring systems, lenders might be forced to
return to manual underwriting-a slower and more subjective approach to
underwriting. We want to move forward and improve the current systems.
Fortunately, scoring systems will improve over time, because competition
will drive lenders and investors to develop more accurate risk assessments.
Statement of Peter L. McCorkell
Executive Vice President & General Counsel
Fair, Isaac and Company, Inc.
During the 1970s and 1980s, credit scoring and automated underwriting
became widely accepted for most forms of consumer lending, other than
mortgages. Mortgage lenders began using credit scoring much later, starting
around 1995. Lenders have widely accepted scoring technology because it
allows for expanded lending while maintaining or even reducing loss rates.
During the years that credit-scoring technology was being developed, there
were few, if any, serious concerns on the part of regulators or consumer
activists that scoring might somehow restrict access to credit for any
significant subset of the population. However, during the past four or five
years, such concerns have been raised more and more frequently.
Consumer and Regulatory Concerns
Most regulators and consumer activists accept the claims of lenders and
scoring-system developers that credit scoring provides an effective and costefficient decision tool for the general population of borrowers. But, when it
comes to traditionally underserved segments of the population, they may
become very skeptical. Most of these concerns can be grouped into a few
broad categories:
How can a statistically based system deal with segments of the population
that are unrepresented or underrepresented in the historical data?
This is a reasonable question, but it is premised on a hidden assumption.
The assumption is that when underrepresented groups seek mainstream
credit, the factors that predict good and bad performance will be different for
them than what has proved predictive for past borrowers. Clearly, there are
some differences in what is predictive for various subpopulations. However,
more than 40 years of experience in developing credit-scoring systems for
lenders in 60 countries have demonstrated that the similarities in what is
predictive of credit performance outweigh the differences. The same
question can be applied to individual applicants: "If an applicant has little or
no mainstream credit history, how can a scoring system evaluate such an
applicant?" Again, the question has a hidden premise that satisfactory
performance with nontraditional obligations will predict satisfactory
performance with traditional credit obligations. Since there is little, if any,
systematic collection of nontraditional credit histories, no one really knows
whether that premise is correct.
Credit-bureau-based scoring systems require a minimum amount of reported
credit history in order to produce a score. An "unable to score" code should
trigger a judgmental evaluation, but that may not always happen. Bureau
scoring systems also may employ separate scorecards for "thin file"
populations, and special application scorecards have been developed for "no
hit" populations¾those with no credit bureau history.
Don't inaccuracies in credit bureau data result in inaccurate scores?
Of course inaccurate data will cause inaccurate scores, but inaccurate data
also affect judgmental credit decisions. However, the current use of scoring
in mortgage lending does produce some real differences. For example, prior
to the use of credit scores in mortgage origination, when an applicant
disputed information in the credit report the underwriter could choose to
disregard that information. Alternatively, the provider of the merged credit
report usually used in mortgage lending might have been willing to change
the data in that report, even though the credit repositories had not made a
corresponding change.
Now that the credit-bureau-based score is the primary tool for evaluating
the credit history of mortgage applicants, the score will not change unless
and until the data in the underlying repository report are changed. The
major secondary market lenders¾principally Fannie Mae and Freddie
Mac¾as well as scoring developers have advised originators that they can
and should ignore scores based on inaccurate data. However, some
underwriters may not make the effort needed to document such cases to
satisfy a potential investor.
Aren't there inequities in overrides, quality of assistance, and so on?
Even in a situation where a scoring system encompasses substantially all of
the available information and can account for most of the final decisions,
there is still room for human intervention. An override occurs when the final
decision is contrary to that indicated by the scoring system. Scoring
developers would argue that overrides are not a scoring problem but rather
a problem caused by ignoring the scoring system. The September 1999
complaint and consent decree by the U.S. Department of Justice against
Deposit Guaranty National Bank supports the argument of scoring
developers that overrides¾that is judgmental decisions¾may be more
vulnerable to discrimination claims than decisions that follow the scoring
system.
Similarly, there have been many claims that the "quality of assistance"
offered to minority borrowers is systematically inferior to the assistance
offered to white borrowers. While substantively that issue is no different in a
scored environment than in a judgmental environment, the scoring system
nevertheless may be perceived as the culprit by rejected minority borrowers.
Don't scoring systems reject many applicants who would have performed
well and accept many who go delinquent?
The short answer to the question is, "Yes." But the question should be
whether credit scoring or human judgment does a better job of accepting
"good" borrowers and turning away those who would, if accepted, eventually
perform badly. Here the evidence is clear: The use of scoring consistently
produces 20 to 30 percent improvements¾either in reduced delinquency
rates or increased acceptance rates¾compared with judgmental evaluation.
In addition, the available data suggest that similar or even greater
improvements can be obtained by applying scoring to traditionally
underserved segments of the population.
Doesn't scoring result in higher reject rates for certain minorities than for
whites?
Again, the short answer is, "Yes," but it is the wrong question. The question
ought to be: "Does credit scoring produce an accurate assessment of credit
risk regardless of race, national origin, etc.?" Studies conducted by Fair,
Isaac, and Company, Inc. (discussed in more detail below) strongly suggest
that scoring is both fair and effective in assessing the credit risk of lowerincome and/or minority applicants.
Unfortunately, income, property, education and employment are not
distributed equally by race/national origin in the United States. Since all of
these factors influence a borrower's ability to meet financial obligations, it is
unreasonable to expect an objective assessment of credit risk to result in
equal acceptance and rejection rates across socioeconomic or race/national
origin lines. By definition, low-income borrowers are economically
disadvantaged, so one would not expect their score distributions to mirror
those of higher-income borrowers.
Is Scoring "Fair" to Minority and Low-Income Borrowers?
Since scoring systems are designed to provide the most accurate possible
assessment of credit risk¾regardless of race, national origin and so on¾they
will never satisfy critics who believe "fair" means the elimination of all
discrepancies in both acceptance and rejection rates. If, however, fair is
defined as "assesses credit risk consistently regardless of race, national
origin, or income" then the available data strongly suggest that creditscoring systems are fair when applied to these borrowers. Two research
studies conducted by Fair, Isaac and Company, Inc. early in 1996 support
this finding.
The first study used data from more than 20 credit portfolios to look at score
distributions and differences in characteristics between low- and moderateincome ("LMI") applicants and the general population. This study
(hereinafter, the "LMI study") also compared the acceptance rates and
default rates for LMI segments resulting from actual judgmental
underwriting on eight of these portfolios with the results that could have
been obtained using scoring.
Not surprisingly, the score distribution of the LMI segment was lower than
that of the general population. Thus, at any given cut-off score, the LMI
population would have a lower acceptance rate. However, the score-to-odds
relationships of the LMI and general populations were virtually identical
(especially in the range where most cutoff scores would be set). To the
extent there were any differences in the score-to-odds relationships, those
discrepancies consistently favored the LMI applicants. That is, at any given
score, the risk for LMI applicants is the same as or slightly greater than the
risk for other applicants.
The second half of the LMI study produced some very interesting results. For
the eight different portfolios, we compared acceptance and delinquency rates
for LMI borrowers that had resulted from judgmental underwriting with the
results that would have been obtained if credit scoring had been used to
evaluate the same applicants. In every case, scoring could have produced a
significant increase in the acceptance rate for LMI applicants if the bad rate
were held constant, or a significant decrease in the bad rate if the
acceptance rate were held constant.
The second study (hereinafter, the "HMA study") compared credit bureau
scores and characteristics of consumers living in zip codes with high
concentrations of blacks and Hispanics (the "HMA zip codes") against those
of consumers living in other zip codes. Zip code was used as a surrogate for
race/national origin simply because direct race/national origin information
was not available. The average household income (as indicated by census
data) in HMA zip codes was only about two-thirds that for the non-HMA zip
codes. Once again, while the score distribution for the HMA zip codes was
lower than for the non-HMA zip codes, the score-to-odds relationships were
very similar across populations. As in the LMI study, what discrepancies did
exist in the score-to-odds relationships consistently favored the HMA
population: At any given score, HMA borrowers present the same or greater
risk as non-HMA borrowers receiving the same score.
Conclusion
In short, these studies indicate that scoring is both fair and effective when
applied to LMI and minority populations. These findings are consistent with
results reported by others, including Fannie Mae and Freddie Mac (where
direct race/national origin information is available from HMDA data).
Moreover, the LMI study indicates that scoring can produce substantial
improvements in the quality of decisions when compared with judgmental
underwriting.
Despite guidance from secondary market investors and scoring developers,
at least some mortgage lenders are overly reliant on credit scores. The
scores most often used in mortgage lending are generic bureau-based scores
that consider only credit history information, and were not designed
specifically to assess mortgage risk. Ignoring other relevant information in
the mortgage decision process is not in the best interests of either borrowers
or lenders. And in cases where the lender is satisfied that inaccuracies exist
in the underlying credit information on which the score is based, it is
irrational to continue to rely on the score. But, there is evidence that many
lenders do not make the effort to manually review and document these
cases.
These problems may be exacerbated if overrides and assistance also are not
dispensed evenly; higher-income white borrowers may be approved despite
marginal credit scores, while low-income and minority borrowers with similar
scores are turned away. Such practices would better be described as the
misuse of scoring, but the rejected applicant is still left with the perception
that the credit scoring system is unfair.
Calvin Bradford
Response to Statement of Peter L. McCorkell
The response from Fair, Isaac and Company, Inc. made reference to specific
studies that supported its claim that minorities were not unfairly
disadvantaged by credit scoring systems. Since Fair, Isaac is asserting that
their research is sound in a statistical and social science context, one needs
to assess whether their studies measure up by these standards.
For example, in the above-referenced LMI study, we are told only that the
data are from several unnamed lenders for some unnamed type of
installment loans from 1992 to 1994. Are these mortgage loans, auto loans,
personal loans, home equity loans, student loans? Different loan types
attract different types of applicants. The study reviews characteristics taken
from credit applications and credit bureau information, but it provides no
definitions of any of these characteristics. We are not told if all the lenders
used compatible application forms with common definitions for each
characteristic. We are provided with tables (in the referenced LMI study)
that indicate which applicant and credit bureau characteristics made "large
differences," "moderate differences," and "negligible differences." We are
given numbers, but we do not know if these numbers are from tests of
significance, differences in raw percentages, or some other collection of
measures.
The comparison of the outcomes for the judgmental and credit scoring
system was actually done in a separate study based on data from lenders
seeking to replace their judgmental system. This is a clearly biased sample.
Were these judgmental systems among the most subjective and least
structured in the industry? The indication is that the lenders already saw
them as failures.
The above-referenced HMA study of minority differences was based on ZIP
codes, where all residents of the ZIP code were treated as either minority or
not. Yet the minority composition of the ZIP codes ranged from 40 percent
to 90 percent, with the report data based on ZIP codes that were more than
70 percent black and Hispanic. We are not told what percent of all minorities
live in such ZIP codes. Such a grouping is not specific with respect to the
race of individuals. Only large segregated minority populations would be
included in such definitions. This is likely to exclude the majority of Hispanics
and most higher-income minorities. We are not told the time period for the
data in this study. The markets are constantly changing. Subprime lending,
which was seen in these studies as related to personal finance companies,
now relates to a large and rapidly growing industry of subprime lenders
providing everything from home purchase loans to auto title loans.
Therefore, one historical study is not adequate, even if it was sound at the
time.
Fair, Isaac's response emphasizes the need for a broad range of studies by
researchers from different perspectives and disciplines. Until this happens,
the Fair, Isaac claims of a neutral, or even favorable, treatment of minorities
should be treated with skepticism. Fair, Isaac, like Freddie Mac, needs to
seek out a broader range of perspectives for its own reviews. The true test
for credit scoring, however, will lie in the continuing review of many different
systems by many different researchers.
This concludes the introductory installment of Perspectives on Credit Scoring
and Fair Lending: A Five-Installment Series. The Federal Reserve System's
Mortgage Credit Partnership Credit Scoring Committee would like to thank
the respondents for their participation. The next article will explore the
interrelated issues of lending policy, credit-scoring model development and
model maintenance.
Editor's Note: The term score-to-odds relationship refers to the relationship
between any given credit score and the degree to which applicants with that
score are likely to exhibit the risk that the scoring system is designed to
predict. For example, in a system designed to predict the likelihood-or
"odds"-that an applicant will default in a loan within two years, a score of
700 might relate to or predict a 1 percent likelihood of default, while a score
of 660 might relate to a 3 percent likelihood of default. In such an example,
the default risk "odds" would be 1 in 100 for a score of 700 and 3 in 100 for
a score of 660.
Community Investments Vol. 15, Issue 1
Credit Scoring and Model Development and
Maintenance
March 2003
Credit scoring is an underwriting tool used to evaluate the creditworthiness
of prospective borrowers. Utilized for several decades in granting certain
forms of consumer credit, scoring has come into common use in the
mortgage lending industry only within the last 10 years. Scoring brings a
high level of efficiency to the underwriting process, but it has also raised
concerns about fair lending with regard to historically underserved
populations.
To explore the potential impact of credit scoring on mortgage applicants, the
Federal Reserve System's Mortgage Credit Partnership Credit Scoring
Committee is producing a five-part series of articles. This is the second. An
important goal of the series is to provide the industry and concerned groups
and individuals with the opportunity to comment on issues surrounding
credit scoring.
The first article provided a context for the issues to be discussed in the
series and gives further background information on the Mortgage Credit
Projects.
Each representative for this article received a request to comment on the
following text:
Lending institutions face various pressures in the course of their credit
operations. They must consistently achieve and increase profitability, comply
with a complex regulatory framework, and contend with new sources of
competition. An institution's loan underwriting policy, and, in particular, its
credit-scoring model, reflect the institution's appetite for risk, targets for
profitability, and role in serving the credit needs of its market.
Credit-scoring models have predictive power; they give lenders the ability to
expeditiously assess the likelihood of borrower default. There is general
agreement that to retain their predictive power, models must be maintained
and adjusted to reflect changes in loan performance and in market demands
and demographics. In addition, observers argue that absent proper
maintenance, a lender risks using a model with diminished predictive
capability, which may produce an unjustifiable disparate impact on
prohibited basis groups.
From your perspective and experience, what can lenders do to ensure that
the credit-scoring models they develop or purchase will accurately predict
the performance of their applicant base? What steps might lenders take to
effectively update and maintain their models? Finally, what methods should
lenders employ to monitor the performance of their credit-scored loans,
particularly with respect to the fairness and accuracy of their models?
This article incorporates statements requested from representatives of three
organizations, selected because of their interest in and differing perspectives
on credit scoring and fair lending.
James Wheaton
Neighborhood Housing Services of Chicago
Mr. Wheaton has worked for and with nonprofit community development
organizations since the mid 1970s. He now serves as the associate director
of Neighborhood Housing Services of Chicago, Inc. (NHS), a position he has
held since 1993. Mr. Wheaton's responsibilities include administration of
NHS's home-improvement and purchase/rehab lending programs, as well as
new program and product development. NHS of Chicago was established in
1975 as a nonprofit corporation that partners with financial institutions,
community residents, city government, and Chicago businesses. NHS of
Chicago has citywide lending programs as well as targeted neighborhood
programs operating in 11 of Chicago's neighborhoods. NHS also recently
created a program for victims of predatory lending. NHS of Chicago
originates 500 loans annually, totaling $15 million.
Thomas P. Fitzgibbon, Jr.
Manufacturers Bank
Mr. Fitzgibbon is a senior vice president and chief retail banking officer for
Manufacturers Bank, and is the president of Manufacturers Community
Development Corporation. Mr. Fitzgibbon is a 30-year veteran of the banking
industry, having served as a principal banking officer in lending and retail
banking operations for institutions in Washington, DC and Minnesota prior to
moving to Chicago in 1990. He has served on the Steering Committee of the
Mortgage Credit Access Partnership and the Small Enterprise Capital Access
Partnership for the Federal Reserve Bank of Chicago since 1995, and
currently he is on the boards of directors for Bethany Hospital, DevCorp
North, NHS of Chicago, the Northwest Housing Partnership and Regional
Redevelopment Corp., and the Woodstock Institute. Manufacturers Bank, a
$1.4 billion community bank with 13 offices, is ranked as the one-hundredth
leading small-business lender in the nation (American Banker) and the third
leading small-business lender in low- and moderate-income markets in Cook
County, IL. Manufacturers Community Development Corporation is a sixyear-old subsidiary of the bank, managing more than $40 million in directequity investments and loans in real-estate and small-business ventures.
Alex Stricker
Fannie Mae
Dr. Stricker is an economist for credit policy at Fannie Mae. He has worked
on development of Fannie Mae's automated underwriting models for the past
two years, with emphasis on fair-lending implications. Prior to joining Fannie
Mae, he pursued doctoral studies at Syracuse University specializing in urban
economics and housing discrimination. Fannie Mae is a stockholder-owned
corporation chartered by the Congress to create a continuous flow of funds
to mortgage lenders in support of homeownership and rental housing. It
serves as a secondary market for mortgage loans by purchasing mortgages
from lenders across the country, aggregating groups of loans into mortgagebacked securities, and selling the securities to investors.
Response of James Wheaton
Neighborhood Housing Services (NHS) of Chicago
Along with the pressures to increase profitability, comply with complex
regulatory requirements, and contend with new and ever more aggressive
sources of competition, mortgage lenders, like other businesspeople, must
also manage rapid change in technology. In the lending arena, this change is
evident in the approval of loans through automated underwriting, made
possible in part by the use of credit scoring. The past few years have seen a
dramatic increase in the use of credit scoring in mortgage lending, yet there
is substantial anecdotal evidence that credit scoring may not be a
particularly responsive tool for the low- to moderate-income borrower.
Credit-scoring proponents point to the speed, accuracy, and fair treatment it
brings to the lending process, but credit-scoring models require regular
maintenance, testing, and updating to reflect changing market conditions,
without which both lender and borrower will suffer. Nonetheless, it appears
that some lending institutions rely on scoring models with limited predictive
power, and they miss significant business opportunities as a result.
NHS of Chicago's direct lending is targeted to low- to moderate-income
(LMI) neighborhoods and borrowers. Many of these communities did not,
until fairly recently, have a neighborhood banking or lending branch. The
primary providers of credit to many residents were financial entities that
were aggressive in pursuing LMI borrowers; today, many of them would be
characterized as subprime lenders. Because credit-scoring models factor in
the types of credit used by a borrower in the past (and subprime credit has a
negative impact on the score), many borrowers from these neighborhoods
may be adversely affected when dealing with a conventional lender who
relies on credit scores. Further, my own observation of credit scores of firsttime buyers and LMI homeowners is that negative factors have an
immediate effect on scores, while positive factors influence the score much
more gradually.
Supporters of credit scoring also maintain that its use frees the lender to
more closely examine the marginal borrower and spend the time and effort
necessary to close the loan. At NHS, though, we have seen too many
situations where credit scoring has actually been used to limit access to firsttier credit. In the Spring 2000 issue of the Federal Reserve Bank of Boston's
Communities & Banking, Calvin Bradford argues that the use of credit
scoring does not always result in more underwriting time being spent on
applicants with marginal credit but may actually serve as a tool to identify
candidates for higher-cost loans. Absent proper maintenance of a scoring
model and its underlying assumptions, and without diligence to ensure its
fair application across all applicants, credit scoring could further widen the
gap between low- and high-income borrowers.
I believe that scoring models' predictive power is worse for low-income
borrowers than it is for the average mortgage applicant. NHS understands
and appreciates that the acquisition of a home and the opportunity to
thereby build both financial and social wealth is a powerful incentive. I do
not believe that any credit-scoring model factors in the emotional impact of
potential homebuyers when they are the first members of their families for
generations to own a home or buy a home in the newly revitalized
neighborhood in which they grew up. Human judgment is still essential in
weighing these factors. And as Peter McCorkell of Fair, Isaac & Company,
Inc. states in the article mentioned above, the scoring models most often
used in mortgage lending were not specifically designed to assess mortgage
risk.
Lending institutions that use credit scoring to identify customers who would
benefit from a second look, prepurchase, or credit counseling are to be
applauded. With government-sponsored enterprises such as Fannie Mae and
Freddie Mac currently offering products with more flexible terms for the
credit-challenged borrower (such as Fannie Mae's Timely Payments Rewards
product), lenders can offer conventional pricing more readily than before.
Credit scoring proponents further maintain that a primary benefit of scoring
is that it increases people's access to credit. I take this to mean that its
primary goal is to provide credit that is reasonably priced and without
excessive fees or burdensome loan terms. To reach this goal, all parties with
a vested interest in the activities of lenders using credit-scoring technology
need to ensure that the credit-scoring tool is working as effectively and fairly
as possible. While a scoring system may be developed on the basis of
statistics, the developers' role cannot be ignored. Just as lending institutions
and secondary-market investors are held to a standard of fairness, scoringsystem developers should share in the obligation to ensure that their models
do not unfairly exclude borrowers.
It has been our recent experience that lending institutions most sensitive to
the needs of LMI borrowers are increasingly those institutions that rely less
on credit scoring and more on individual assessment of the borrower.
Community lenders (such as NHS) that are focused on LMI neighborhoods
have an understanding of the local environment and neighborhood
dynamics, and they provide competitively priced mortgages to LMI
borrowers in considerable volume. For national lenders, this kind of handson approach is not feasible. An underwriter in St. Louis cannot be expected
to know and understand the characteristics of a buyer and a property on the
West Side of Chicago; there needs to be some adjustment to the automated
system that might wrongfully deny that buyer access to credit.
If credit scoring is going to be a factor in credit decisions for the foreseeable
future, models that more adequately assess mortgage risk need to be
developed and put into general use. Scoring system developers need to
develop methodologies that are more responsive to a borrower's positive
credit behavior and that incorporate some of the more subjective, but very
relevant, data that often factor into a human being's decision about
someone's creditworthiness.
Underwriting and Training Policies with Respect to Credit Scoring
Lending institutions clearly need to do a better job of training their personnel
about the purpose and limitations of credit scores. I do not suggest that
underwriters be divested of the capacity to override a credit-scored decision.
However, excessive overrides raise serious concerns about disparate
treatment of borrowers. Access to credit for a borrower who is qualified by a
credit score (even marginally) should not be denied because of the
underwriter's or loan officer's personal assessment of the borrower's gender,
ethnicity, lifestyle, personality, temperament, family connections, and the
like. Human nature being what it is, a lending policy allowing for "high-side"
overrides-in which an applicant's score suggests they deserve a loan yet
they are denied it-opens the door to potential misuse, and I do not believe a
responsible lending institution would either tolerate such decisions or accept
such liability.
Second review of all adverse actions should be standard operating procedure
for lending institutions, both to ensure fair and equal access to credit and to
ensure that acceptable business opportunities are not missed. For lenders
that offer subprime products, I would suggest that their second review be
conducted in the context of trying to qualify their customers for a
conventional product. Lending staff involved in second reviews should have
special training in the use of credit scores, including some education about
how scores are developed, what a score is designed to predict, and what
factors in a borrower's credit history will affect the score (either positively or
negatively). The scoring-system developers are key in this process, and an
acceptable middle ground must be struck between protecting their
proprietary systems and educating lenders on the use and limitations of
credit scoring.
In summary, access to credit continues to be a critical need in many LMI
communities. The recent increase in the homeownership rate in this country
indicates that there is a large population striving to be homeowners and
making some progress to achieve that goal. To the extent that credit-scoring
technology has made this possible, that is very positive. However, lenders,
especially those who have developed their own credit-scoring model on the
basis of their own experience and portfolios, must maintain and upgrade the
credit-scoring model in the same way that they maintain other systems.
Maintenance and regular upgrades of credit-scoring models to reflect market
conditions should be part of the business plan and evaluated on a regular
basis. Such evaluation should include an analysis of the performance of
credit-scored loans versus those that were overridden, and especially an
analysis of the performance of those credit-scored loans that were identified
as marginal. Just as no institution would attempt to run its business with
outdated hardware, it should not be using an outdated scoring model to
direct credit decisions.
Response of Thomas P. Fitzgibbon, Jr.
Manufacturers' Bank
What can lenders do to ensure that the credit-scoring models they develop
or purchase will accurately predict the performance of their applicant base?
For the successful use of predictive scoring models in the credit decisionmaking process, the models must be based on similar products,
environments, and populations. In addition, the attributes and application of
the criteria parameters in the models must be refreshed routinely to ensure
that the applications produce results consistent with the expectations when
the models were developed or purchased.
Model use is a two-step process. First, the lender must select the right
model for the loan product. Second, the lender must consistently refine the
model, which requires dedicating resources long after original development.
This refinement requirement can be easy to ignore, especially in the early
stages of a product rollout when there is little product performance to point
to as indicators of performance shortfalls. However, this initial stage is the
time when even more due diligence needs to be devoted to fine tune the
model and avoid unintended results. Higher than anticipated pull-through
rates or adverse action rates are early indicators that the model has serious
flaws requiring immediate attention.
Most purchased credit-scoring models have solid data to support their
predictability. In addition, the best model vendors require lenders to supply
the results of their experience so the vendor can improve and enhance its
own data for future models. This feedback improves the quality of the
predictive factors and model fairness. Consistent feedback is part of the
model-refreshing process; however, modification of the model criteria by the
lender can degrade the model's results.
Lenders who develop their own models often need to compensate for their
small population performance base by comparing experience for an extended
time, and even more care should be given to reviewing results during the
initial product rollout. Comparing customer performance results, as well as
application approval and pull-through rates, will yield richer data. These data
will help the user identify fairness issues (adverse impact), adverse selection
(capturing undesired applications), and low pull-through (closing) rates that
could indicate a competitive disadvantage of the product.
Senior management and boards of directors should be wary of "proxy-like"
models, either in-house or purchased from a vendor, that were developed
for a loan product or population somewhat similar to another lender's
product or population. Because such similarities can be hard to define, this
practice can have disastrous results in both fairness to applicants and the
bottom line. Management should perform adequate due diligence on the
criteria and, if not convinced, employ outside resources to provide evaluation
and recommendations related to the model.
What steps might lenders take to effectively update and maintain their
models?
As I stated previously, most model vendors insist that lenders provide
specific information related to model performance, including applications
received, approval rates, pull-through rates, and servicing results. These
data will also provide the lender with information that can be employed to
change the criteria of the lender's model, product price, collateral value (if
included in the model), population attributes, brokers or mortgage bankers
who bring applications to the lender, and other levers, in order to achieve
the desired results.
Most lenders employ models to develop results based on return on assets
(ROA) objectives, understanding there will be losses in any model that is
employed. Loan pricing should reflect performance expectations and results.
Therefore, consistent review of pricing (rate, fees, and so on) will be
necessary to achieve the ROA and to ensure that the pricing reflects the
risks associated with the population and security characteristics, thus
ensuring fairness to all populations.
Lenders who develop their own models need to take steps to consistently
review adverse actions: comparing protected-class applicants to the
applicant pool, reviewing approval and pull-through rates related to the
expectations, and comparing the servicing results to the ROA projections.
Deviations from model projections should guide the lender to change the
model, including credit score (FICO, Delphi, and the like), loan-to-value
categories, applicant attributes, and vendors (if used).
In the initial stages of the product rollout, the lender needs to review early
performance indicators that do not meet the expectations of the design
phase. Even small indicators of performance shortfalls, such as low
application rates from prohibited basis groups, higher-than-expected
adverse action rates (especially where protected-class populations are
concerned), or lower-than-expected pull-through rates, are indications that
the model may have flaws that need to be addressed.
What methods should lenders employ to monitor the performance of their
credit-scored loans, particularly with respect to the fairness and accuracy of
their models?
The methods lenders should employ include the following:
Due diligence review of all adverse actions to ensure that the model is
applied correctly,
Comparative analysis of adverse actions to evaluate model results on
protected-class applicants,
Comparison of computer records (data input) with application sampling
to ensure quality control,
Review of any subjective decision-making performed on scored
applications that changes the model decision or modifies the pricing or
product parameters, and
Review of closed-loan packages (quality control) to ensure that the
loan parameters approved are the same as the parameters in the
closed loan.
Consistency and diligence are imperative in developing and using creditscoring models. Early indications of performance that are different than
predicted allow action to be taken early in the process to change the model
parameters and modify elements that caused the deviations. Vendors and
lenders need to stay alert to changes and intervene quickly.
Response of Alex Stricker
Fannie Mae
Automated technologies in credit-granting institutions have expanded
dramatically in the past 10 years and credit-scoring applications are now
common. These applications aid significantly in the effort to streamline
origination processes and cut costs while delivering consistent and objective
decisions about an applicant's creditworthiness. Scoring models relate an
applicant's past credit performance and current financial characteristics to
future debt repayment. They are often characterized as generic or custom.
Generic scores are created to be predictive of delinquency for generic
consumer debt, using large amounts of credit data. Custom scores are
designed to be predictive of repayment performance for specific types of
credit or perhaps for a specific lender's customer base. With custom scores,
additional non-credit-report information may be used in the modeling effort.
Regardless of who builds a scoring model, there are common considerations
in the development process and maintenance of the model.
Follow a Clear and Explainable Development Process
Scoring-model development occurs with the coordination of market analysts,
credit-risk managers, statisticians, database administrators, and computer
programmers. Each part of the process must be carefully planned to ensure
development and implementation of a successful model.
Objective
The first step in the technical development of a scoring model is to
determine what measure of performance to model. Models may predict the
probability of default (nonperforming loans that terminate and do not prepay
in full), the probability of becoming delinquent, the financial losses an
institution expects for each loan, or some combination of delinquency,
default, and losses. A lender that uses another company's underwriting
system to make loans to hold in its portfolio should be aware of the
implications of the scoring model objectives for lending patterns. For
example, models designed to predict serious mortgage delinquency tend to
place more importance on past-credit-history variables than models
designed to predict default. By contrast, mortgage default models give more
weight to loan-to-value ratios.
Data Collection and Sample Design
The data available for use in statistical modeling are the single most
important technical element of model development. Lender data retention is
crucial for model construction and testing. Typically, the more information
available, the more precise the results can be. Lenders developing their own
system are best served by data that come not only from their existing
customer base but also from other segments of the market that represent
potential applicants. The selection of risk factors included in a scoring model
is determined in part by their availability to the modeler. Therefore, it is vital
to capture and retain as much origination and subsequent performance
information as possible.
After a sample has been constructed, the scoring limitations created by the
available data sample need to be identified. For example, at this time,
Fannie Mae's Desktop Underwriter does not process 95 percent loan-to-value
ratio refinance loans with a cash-out component on non-owner-occupied,
three- to four-unit housing. Our experience with this product is currently too
limited to model, but as we learn more and acquire more data, the risk of
this product may become better understood and be modeled appropriately.
Statistical Tools
Most scoring applications predict the likelihood of an event. Many statistical
tools are available. For example, default probabilities can be estimated by
means of logistic regression. The logistic procedure, well known and
understood by economists, is fast and straightforward to implement. The
specific tool chosen depends on the goal of the scoring model and any
deficiencies in the development sample. In the case of sample deficiency,
data-augmentation methods are available to improve estimation on thin
samples, as are procedures to account for potential biases stemming from
missing information. The result of a scoring model is the generation of a
scorecard. Thus, the scorecard's combination of points may be influenced by
the statistical tools and methods employed in the model.
Validation and Testing
A variety of statistical tests are available to aid in the validation of a model.
No single test provides a complete answer. Fannie Mae has estimated
hundreds of models, with all potential variables, divided and clustered, to
yield the statistically strongest model. The typical measures of qualitativedependent-variable modeling are used, such as gini coefficients, K-S
statistics, and concordance. The overall idea is that the model must do the
very best job of separating high-risk and low-risk loans. Since many model
variations may be tested using several criteria, it is important to have rules
for what constitutes a more predictive model. Equally important is how well
the model predicts for subgroups of the intended population. For example,
does a model designed to predict delinquency for borrowers of all income
levels produce an appropriate ordering of risk when it is applied only to lowincome borrowers? The answer depends in part on how diverse the
development data are with respect to income. Testing a model's differential
validity is necessary before implementing it in production.
Cutoffs and Overrides
During model development, attention should be given to determining how
much risk to tolerate. The model itself may predict how likely default is for a
particular loan. However, consideration must be given to how much
collective credit risk the company is willing to take. This is determined by
market analysis of likely application volumes, the length of time loans are
expected to stay in the book of business, capital requirements, and pricing
and revenue targets. A periodic review of these targets is necessary to
ensure that the approved mix of business continues to meet revenue
objectives.
Limits within the scoring engine can be reached if the scoring model tries to
evaluate values for certain risk factors that are improbable in the scorecard
application. At Fannie Mae, our system filters out for manual review all
applicants with total debt-to-income ratios greater than 65 percent. The
Desktop Underwriter program refers the application to the underwriter to
determine whether the data were entered incorrectly or if the relatively high
debt-to-income ratio is manageable for the applicant.
Monitor Application Decisions
Is the production-decision process working in a way similar to the process
tested? Generic creditworthiness scores might be used only in part to make
a decision, so it is important to keep track of how these scores relate to the
final decision. Custom systems may be used to support a comprehensive
evaluation of applications and to monitor who is being approved or denied at
the recommendation of the automated-scoring system. At Fannie Mae, we
have monthly reports on applications through our Desktop Underwriter
system. We examine the system's recommendations across various financial
and demographic characteristics. When changes or irregularities are
observed, more detailed examination follows. Such monitoring is vital to
remedy problems or irregularities.
Monitor Performance
Regardless of what the system is designed to predict, performance can be
tracked from one month after origination. The most important report will
show how loan performance varies by the scoring system's recommendation.
Are the approved loans performing differently than the loans made with an
automated recommendation for further review? If generic scores were used
in the decision to make the loan, are higher-scored loans performing better
than lower-scored loans? Other analysis should focus more narrowly on
loans scoring near the cutoff to be sure that those marginal loans are
performing as expected. A complete examination will involve tracking
performance for numerous loan subsets across product, financial,
demographic, and geographic segments of the market. The particular array
of reports depends on the financial institution's lending goals and regulatory
requirements. Simple reporting, done regularly and completely, will alert
management, marketing personnel, and model developers to potential
problems and areas to investigate further.
Model Evolution
Expect to update your model. Experience will improve the effectiveness of a
scoring system. As such, the development process must be flexible to allow
for changes suggested through the learning. At Fannie Mae we are
continuously investigating and developing new models. Every new model we
generate is an evolution of the model it replaces. Approximately annually,
the Desktop Underwriter scorecard is re-estimated to utilize additional
performance data that come with the passage of time and variation in the
economy. There is no secret formula for success. Able statistical analysis is
necessary to generate a system. Its success requires the coordination of
market analysis, data retention and reporting, and skilled risk managers.
Community Investments Vol. 15, Issue 1
Third Party Brokers
March 2003
The purpose of the Credit Scoring Committee is to collect and publish
perspectives on credit scoring in the mortgage underwriting process,
specifically with respect to potential disparities between majority and
minority homebuyers in the home search or credit application process. The
introductory article provided the context for the issues addressed by the
series. The second article dealt with lending policy development, credit
scoring model selection and model maintenance.
The topic of the third article in the series is how lenders oversee the
practices of their third-party brokers, especially for compliance with fairlending laws, pricing policies, and the use of credit scoring models. We
solicited feedback from industry, consumer and regulatory representatives to
ensure a variety of perspectives. The following individuals provided their
perspectives for the third installment in the series.
Edward Kramer
The Housing Advocates, Inc.
Mr. Kramer is a civil rights attorney, director and cofounder of The Housing
Advocates, Inc. (HAI), a fair housing agency and public interest law firm.
The organization, founded in June of 1975, receives monies from the U.S.
Department of Housing and Urban Development, private foundations, and
various local governments. One of the programs operated by HAI is the
Predatory Lending Project. The project provides legal assistance to low- and
moderate-income residents to prevent predatory lending activities and other
consumer fraud problems, especially in Wards 5 and 15 of the City of
Cleveland. When violations of the law are identified, they are referred to
private attorneys or to the Fair Housing Law Clinic. The clinic is a joint
venture between HAI and Cleveland State University, Cleveland Marshall
College of Law where second- and third-year law students have an
opportunity to do real life cases and to get experience outside the
classroom.
Christopher A. Lombardo
Office of Thrift Supervision
Mr. Lombardo is the Assistant Director for Compliance in the Office of Thrift
Supervision's Central Region. Based in Chicago, he manages the compliance
examination, community affairs, and consumer affairs programs impacting
savings institutions in a seven-state area that stretches from Tennessee to
Wisconsin. Mr. Lombardo has 18 years of regulatory experience that includes
examination and examination management work with the Office of Thrift
Supervision (OTS) and its predecessors; regional office policy and
enforcement work with OTS and the Federal Deposit Insurance Corporation;
and compliance policy work in Washington, D.C. Mr. Lombardo has
participated in and led interagency policy initiatives. He has been active in
examiner and industry education. The OTS, an office within the U.S.
Department of the Treasury, is the primary federal supervisory agency for
savings associations. There are approximately 1,050 thrift institutions, and
they have assets of approximately $950 billion. OTS is headquartered in
Washington, D.C., and it operates through five regional offices. The agency's
mission is to effectively and efficiently supervise thrift institutions to
maintain their safety and soundness in a manner that encourages a
competitive industry to meet America's housing, community credit and
financial service needs and to provide access to financial services for all
Americans.
Kathleen Muller
HOPE HomeOwnership Center
Ms. Muller is the executive director of the HOPE Home Ownership Center in
Evansville, Ind. She has been with HOPE for about 12 years. HOPE provides
housing counseling services to residents throughout the entire Evansville
MSA. For 35 years, HOPE has been helping families assess their need for
housing and their ability to buy through credit and budget analysis and has
been certifying their eligibility for special innovative loan packages. During
the last year, HOPE served 450 individuals and families.
Sandy Ross
Retired, Department of Justice
Mr. Ross recently retired from the Civil Rights Division of the Department of
Justice. For more than 35 years, he worked on lawsuits brought by the
United States to enforce civil rights statutes forbidding discrimination in
voting, employment, education, public accommodations, housing and
lending. His position for many years prior to retirement was special litigation
counsel for the division's Housing and Civil Enforcement Section, where he
investigated and prosecuted matters involving a pattern or practice of
discrimination in home mortgage and consumer lending. Mr. Ross was the
division's lead lawyer in several landmark fair-lending cases.
The contributors to this installment in the article series were asked
to respond to the following statement:
While lending institutions may actively review and assess their own credit
scoring models for potential unlawful disparities, it is also important for
lenders to monitor their relationships with third-party brokers. Mortgage
brokers make credit available in communities that do not have traditional
lending institutions. Lenders establish relationships with third-party brokers
to reach these markets.
Lenders need to consider how their third-party brokers comply with fairlending laws and use credit scoring models. Lenders who knowingly work
with noncompliant brokers and take no action may be liable as co-creditors.
The following situations may lead to increased regulatory risk exposure for
the lending institution:
The lender may build in a high broker overage tied to the credit score.
The broker may obtain a credit report or credit score and use it to
underwrite and price a proposed deal prior to submitting it to a lender.
A broker may screen applicants or steer them to higher-priced
products even if the applicant's overall risk profile (credit score) does
not necessarily warrant it.
Considering the credit scoring issues outlined above, what strategies can
lenders adopt to better manage their third-party broker relationships? What
can third-party brokers do to ensure compliance with fair- lending
regulations?
Response of Sandy Ross
The answers may be different, depending on whether scores are to be used
in the accept/deny context or for placing borrowers in different price tiers. In
either case, however, it is essential that the broker be fully informed as to
the lender's underwriting criteria. Further, whenever the scores themselves
are affected by the information gathered by the broker, the broker must do
as good a job as the lender in documenting the borrower's qualifications.
When credit scores are used to accept or deny, the broker's obligation is the
same as it would be with manual underwriting. If the broker (a) fails to
obtain documentation or (b) screens out applicants without adherence to the
same processes the lender does with its direct applicants, both the broker
and the lender are headed for trouble.
When credit scores affect pricing, the broker must depend on its full and
accurate use of the lender's pricing criteria to avoid surprises and legal
problems. For example, if the broker thinks it is presenting a "B" quality loan
and has priced it with the borrower accordingly, the deal may not work if the
lender prices it at "B-." On the other hand, if a broker knows the borrower
has "A" credit but places the loan with a subprime lender at an unnecessarily
high price to increase the broker's profit (when that lender would accept
higher broker fees), the broker risks involving itself and the lender in
deceptive practices, violations of the Real Estate Settlement Procedures Act
(RESPA) and, if members of protected groups are adversely affected,
possible violations of the fair-lending laws.
Response of Edward Kramer
The Housing Advocates, Inc.
Financial institutions can have a great deal of control over the practices of
their third-party mortgage brokers, especially for compliance with fairlending laws, pricing policies, and the use of credit scoring models.
There is a very close relationship between the traditional financial
institutions, mortgage brokers, and real estate agents. Brokers know where
to get their clients financed, and lenders have a history of doing business
with certain mortgage brokers and real estate agents. It is a symbiotic
relationship. Lenders know who is breaking the law and who is skirting the
law. They know who the "bad guys" are. In fact, those were the words used
by a mortgage broker who recently confided, "We know in our industry, and
certainly the financial institutions know, which mortgage brokers are really
doing a disservice to clients."
The reason mortgage brokers know the "good guys" from the "bad guys" is
that they have dealt with them over a number of years. In a situation where
there have been excessive defaults on loans from the same mortgage
broker, or if defaults often occur within several months after the loans, it is
not difficult for a financial institution to gather evidence of what happened,
and of potential wrongdoing. There may have been problems with these
loans: The applications are being falsified, the income levels are being
falsified, the credit report has inconsistencies on it, or credit scoring doesn't
really match. The credit score is not sufficient to justify the loan.
On the opposite end of the spectrum, it would be relatively easy for financial
institutions to identify mortgage brokers who try to maximize their
commissions by charging some borrowers more than what is usual and fair
in points, rates and fees. These are situations where borrowers should be
able to qualify for traditional "A" loans but are being offered subprime "C"
loans.
One strategy for the financial institution to avoid third-party liability is to
test loan application files. In this fair-lending review, the Truth in Lending
Act (TILA) statement and the U.S. Department of Housing and Urban
Development's Good Faith Estimate documents regarding the costs of the
loan should be examined. Look at the cost of the appraisal and other fees to
determine if they may be excessive or unusual. Look for credit life insurance
packages built into the loan and see whether the consumer is being required
to pay up front for this credit life insurance or for the life of the loan. If the
financial institution begins to see inconsistencies from broker to broker, that
should send up a red flag. Such a pattern would result in a closer scrutiny of
all new loans being submitted by this particular mortgage broker.
Unfortunately, these predatory lending practices are often being funded by
financial institutions. This practice may be driven by the need to comply with
their Community Reinvestment Act (CRA) obligations. The Act was meant to
help meet the credit needs of all communities in a bank's assessment area,
including low- and moderate-income (LMI) neighborhoods. However, in a
perverse way, the CRA has in some cases had the opposite effect. Banks,
rather than trying to find and use their own branch system of loan offices,
instead closed down their own branches and limited access and services to
these customers. These banks have relied upon third parties, such as
mortgage brokers and real estate agents, to generate CRA loans.
Lending to LMI borrowers can be profitable for financial institutions, but it
causes severe hardships for the consumer, who is often a minority and/or
female head of household. A third-party arrangement allows unscrupulous
mortgage brokers or real estate agents to misuse or abuse the system. The
banks are really looking at, "Will this help me meet my CRA needs and will it
meet our profit motive?" So when some argue that this third-party system is
more efficient, what they really mean is that it is more profitable. However,
this is not necessarily what financial institutions should do if they are going
to be good neighbors and good businesses for our community. They need to
make a commitment to the community, which was the original purpose of
the CRA. It was to require banks to commit themselves to the community, to
those areas in their credit service areas that have not been served by them
in the past.
What are the risks if financial institutions don't respond to predatory lending
issues being raised today? They face new and costly legislative and
regulatory initiatives. More importantly, they will face substantial risk of
litigation. Unlike TILA or other consumer laws, the federal and Ohio fair
housing laws place special obligations on the entire housing industry,
including financial institutions. One of these obligations is that the duty of
fair housing and fair lending is nondelegable. Almost a quarter century ago,
in one of the first cases involving a racially discriminatory refusal to make a
home loan, our federal court found in favor of the victim of discrimination in
Harrison v. Otto G. Heinzeroth Mortgage Co., 430 F. Supp. 893, 896?97
(N.D. Ohio 1977) and held that:
Thus the Court has no difficulty in finding the defendant Haugh liable to the
plaintiff. Under the law, such a finding impels the same judgment against
the defendant Company and the defendant Heinzeroth, its president, for it is
clear that their duty not to discriminate is a non?delegable one, and that in
this area a corporation and its officers are responsible for the acts of a
subordinate employee, even though these acts were neither directed nor
authorized. This ruling troubles the Court to some extent, for it seems harsh
to punish innocent and well?intentioned employers for the disobedient
wrongful acts of their employees. However, great evils require strong
remedies, and the old rules of the law require that when one of two innocent
people must suffer, the one whose acts permitted the wrong to occur is the
one to bear the burden of it. [citations omitted]
This decision is not unique in the law. The courts have rejected arguments
from real estate brokers that they should not be held liable for the
discriminatory acts of their independent agents. (Marr v. Rife, 503 F.2d 735
[6th Cir. 1974]; Green v. Century 21, 740 F.2d 460, 465 [6th Cir. 1984]
["Under federal housing law a principal cannot free himself of liability by
delegating a duty not to discriminate to an agent."]). Furthermore, using the
analogy to the Fair Housing Act, the courts have found that finance
companies have a non?delegable duty not to discriminate under the Equal
Credit Opportunity Act, which cannot be avoided by delegating aspects of
the financing transaction to third parties. (Emigrant Sav. Bank v. Elan
Management Corp., 668 F.2d 671, 673 [2d Cir. 1982]; United States v.
Beneficial Corp., 492 F. Supp. 682, 686 [D.N.J. 1980], aff'd, 673 F.2d 1302
[3d Cir. 1981]; Shuman v. Standard Oil Co., 453 F. Supp. 1150, 1153?54
[N.D. Cal. 1978]).
Now apply this case law to financial institutions that refuse to monitor their
relationship with mortgage and real estate brokers. These lenders can be
subjected to substantial damage awards. Playing ostrich will not insulate
them from any illegal actions of mortgage brokers and real estate agents
with which they deal. If there can be shown a pattern and practice, then
financial institutions are assumed to have control. They have the ability to
say "yes" or "no." They have a right to monitor and determine whether or
not these "independent actors" are breaking the law. If they knew or should
have known, they can be held liable.
Financial institutions and mortgage brokers should also follow another
example of the real estate industry. The larger real estate firms have their
own in-house fair housing program to train their staff. Large companies have
their own programs because they want to make sure that their real estate
agents are aware of the law and of company policies. They want these
policies implemented. All employees and independent contractors must know
the law, the company's policies, and that everyone will uphold fair housing
and fair-lending laws.
Response of Christopher A. Lombardo
Office of Thrift Supervision
Before addressing a financial institution's relationships with mortgage
brokers, we ought to identify three undeniable facts that represent changes
in the mortgage business landscape over the past decade.
First, financial institutions increasingly rely on fee income. Interest rate
spreads are, and are likely to remain, razor thin. Second, automation
(including credit scoring), securitization, and specialization have
revolutionized who does what and how they do it. Third, financial institutions
rely on independent mortgage brokers to maintain a steady supply of loan
originations. Employees in financial institution branches typically no longer
generate the business. Call this progress-in-action in a free enterprise
system or call this a recipe for disaster. In reality, the system is far from
free: It is heavily regulated. With the scourge of predatory lending, personal
and individual disasters have become more common, or at least more widely
recognized. Systemic disasters remain rare.
We also ought to clarify our terminology. As is most common, I will consider
the financial institution (insured depository institution) to be the funding,
originating lender, and the independent broker to be the point of contact
with the applicant/borrower and the processor of the loan. The lender-broker
relationship is covered by a mutual agreement that the other party is
suitable and reliable. The lender provides the broker with their underwriting
guidelines, highlighting any deviations from market standards. The lender
provides the broker with rates, fees and term information–weekly, daily, or
as needed. Operating under a lender-broker arrangement, the broker
registers a rate lock-in and processes the paperwork. The loan passes down
one of two main paths: The lender table-funds the loan and reviews it
afterward, or the lender reviews and approves each loan package prior to
closing.
Numerous custom and hybrid lending arrangements exist. However, one
ought to consider what a financial institution examiner sees: performing
loans; the occasional rejected deal, if the lender documented it; and the
occasional defaulted loan. The examiner does not know what transpired
between the broker and the borrower. The examiner does not know who
ordered, paid for, or prepared the application. Lenders should know this
information and ought to be highly selective about the brokers who bring
them business, and lenders ought to be expert in spotting a loan that yells:
"Run, don't walk, from this deal!" The general standard to which the lender
should be held responsible for the broker's act, error, or omission is a
"knew-or-should-have-known standard."
The compliance examiner assesses how well a financial institution manages
its compliance risks and responsibilities. Regarding relationships with
mortgage brokers, this most notably includes compliance with laws such as
the Fair Housing Act, Equal Credit Opportunity Act, Home Mortgage
Disclosure Act, Fair Credit Reporting Act, Real Estate Settlement Procedures
Act, and Truth in Lending Act. These laws are relatively new; in addition,
there are rules governing the privacy of consumer financial information,
consumer protection rules for insurance sales, and the Flood Disaster
Protection Act. This demonstrates that we're not describing free enterprise
as envisioned in the 18th century by Adam Smith.
Beyond the U.S. Department of Housing and Urban Development's
advertising rules implementing the Fair Housing Act and the Federal Reserve
Board's advertising rules implementing the Equal Credit Opportunity and
Truth in Lending Acts, thrift institutions are prohibited from any inaccuracy
or misrepresentation regarding contracts or services, including any and all
aspects of their mortgage lending. The examiner gets a glimpse of lender
activities and an even briefer look at what the broker has done. Wellmanaged financial institutions make it a point to take a good look at what
the broker has done, but it is very difficult for the lender to police the
broker's activities. With the growing awareness of predatory lending, most
lenders now have systems in place to detect transactions that involve fee
packing, equity stripping, and flipping. Lenders have shifted from presuming
that the refinancing deal presented for funding is what the borrower
originally needed or wanted, and many are applying some sort of benefit-tothe-borrower standard.
As a general observation, mortgage market automation (including the
general use and acceptance of credit scoring), standardization, and
specialization have not posed great hazards for most financial institutions.
They have internally motivated systems for identifying and correcting
problems outside the supervisory and enforcement process. The fee-driven
nature of the business and reliance on broker business does pose hazards,
however. Every financial institution has stories of mortgage brokers who
proposed compensation arrangements that would violate the Real Estate
Settlement Procedures Act. Most lenders have stories of broker efforts to
push unsophisticated individuals (with or without marginal credit scores) into
higher-priced deals that offer greater compensation to the broker. The
former issue of unearned fees and kickbacks is fairly easy to spot. The latter
defies detection, often until much damage has been done.
The uniform interagency examination procedures adopted by the federal
banking supervisory agencies for fair lending focus on activity at the margin.
In general terms, it is in transactions involving marginal applicants that
underwriting discrimination may be identified. The same holds for pricing
and the use of credit scoring. A financial institution needs to have a vigorous
review system in place for the actions of brokers in this regard. This review
system should reinforce the lender's message about the kinds of deals it is
seeking and the kind of treatment that will be extended to individuals who
are prospective customers of the institution.
Aside from individual credit transactions, it is lenders straying far from the
mainstream market who are most exposed to allegations of credit
discrimination. Regulators are more sensitive to issues involving innovation,
automation, cost control, and stability of income. It is in this testing of new
ideas that we try to draw a line between acceptable and unacceptable risk
taking. Financial institutions whose stated or unstated goal is to skate on the
edge of the law should expect and be prepared to deal with problems--some
of them potentially huge.
Lenders need to seek assurance that scoring representations accurately
reflect their applicant's score, particularly when the score drives the
approve/deny decision but also when it results in a loan pricing or productsteering decision, and ultimately, when it impacts broker or lender
compensation, even indirectly. Aside from scrutiny of documents, lenders
should require that the broker provides copies of all credit reports and
scoring information generated in connection with a mortgage application.
The lenders should also require copies of all loan applications generated. The
final application that the borrower sees, but may not read, at closing may
bear little resemblance to the representations of the broker and borrower
from start to end of the transaction.
The lender may be restricted under his correspondent agreement from
making direct contact with a mortgage applicant. However, the broker
should be willing to encourage lender contact to learn the applicant's
understanding of the lending process, rather than lose all of that lender's
business and see the borrower damaged along the way.
A short post-closing lender survey completed by the borrower can be a very
useful evaluation tool for lenders. The purpose is to identify and isolate to
particular brokers deals closed under some duress or involving fees and
terms to which the borrower did not understand or agree. These issues are
best dealt with before the borrower is in default or sitting in the office of his
congressional representative.
In closing, the vast majority of financial institutions manage their mortgage
broker relationships in an acceptable manner, as we have found from years
of regular compliance examinations. Our more recent and detailed inquiry
into the ability of financial institutions to steer clear of predatory lending
practices while working through independent brokers and seeking fee
income has both reinforced the observation that the industry is doing a good
job and highlighted some new concerns. That credit scoring and improved
access to individual credit information has added speed and reduced cost is
generally accepted. What has been done with that new information remains
an open question for both lenders and regulators.
Response of Kathleen Muller
HOPE HomeOwnership Center
The use of credit scores alone does not ensure that credit remains available
to persons who would qualify for a low-interest loan. Lenders should always
have multi-criteria that help to balance or offset shortfalls in a person's
credit score, which could be reduced by the use of subprime lenders or by a
hesitancy to utilize credit at all. For example, if a customer scores 10 to 25
points less than the minimum score determined to be necessary for loan
qualification, but he has three or more years on the job, that strength of
character could offset the low score. In addition, third-party mortgage
brokers who do not try to look at credit scoring in a flexible way-such as
looking at work history-and rely on poor scores without honest subjective
analysis may benefit from higher-cost loans.
During a recent training session in Evansville, Ind., on "Predatory Lending: A
Professional Alert," for brokers, appraisers, inspectors, title agents-all those
who deal with the consumer along the path to getting a mortgage-Nick
Tilima of Education Resources suggested that "most consumers who contact
a mortgage broker expect the broker to arrange a loan with the best terms
and at the lowest possible rate. Most mortgage brokers do just that, and
charge a reasonable fee for their services. However, in the subprime market,
there are mortgage brokers who do just the opposite. That is, the broker will
attempt to sell the borrower on a loan with the most fees and highest rate
possible so that the broker will get more compensation. Some of these
brokers may charge fees of 8 to 10 points. In addition, the broker may get
additional compensation from arranging a higher-than-necessary interest
rate for the consumer. For example, the consumer may qualify for an 8
percent interest rate, but if the broker can sell the consumer a 9 percent
rate, he can keep the differential." To address this issue, standardized fee
schedules would go a long way to provide fair lending to individuals with
lower credit scores.
Brokers and lenders also should be aware that high credit scores do not
necessarily mean a loan is guaranteed. What may have generated the score
to begin with-the ability to handle many credit lines on a timely basisenhances most credit scores. However, the lender is ignoring the fact that
multiple obligations also burden the person's ability to repay a new debt.
Since lenders and brokers may take advantage of a consumer's lack of
knowledge or poor credit rating to charge high interest rates and hidden
fees, disclosure and pre-loan education is a must. At a minimum, everyone
should be required to have some sort of education before buying or
refinancing a house. Consumers would be well-advised to address the credit
problems that keep them from being considered for a prime loan; but if they
cannot correct these problems, they should be aware of the availability of
subprime loans that are not predatory.
Code of Ethics for Lenders
As part of its efforts to fight predatory lending in Evansville, the Tri-State
Best Practices Committee, of which I am a member, developed a Code of
Ethics for Lenders. Lenders should require their third-party brokers to adopt
this code to help ensure compliance with fair-lending laws:
Protect all they deal with against fraud, misrepresentation or unethical
practices of any nature.
Adopt a policy that will enable them to avoid errors, exaggeration,
misrepresentation or the concealment of any pertinent facts.
Steer clear of engaging in the practice of law and refrain from
providing legal advice.
Follow the spirit and letter of the law of Truth in Advertising.
Provide written disclosure of all financial terms of the transaction.
Charge for their services only such fees as are fair and reasonable and
which are in accordance with ethical practice in similar transactions.
Never condone, engage in or be a party to questionable appraisal
values, falsified selling prices, concealment of pertinent information
and/or misrepresentation of facts, including the cash equity of the
mortgagor in the subject property.
Not knowingly put customers in jeopardy of losing their home nor
consciously impair the equity in their property through fraudulent or
unsound lending practices.
Avoid derogatory comments about their competitors but answer all
questions in a professional manner.
Protect the consumer's right to confidentiality.
Disclose any equity or financial interest they may have in the collateral
being offered to secure the loan.
Affirm commitment to the Fair Housing Act and the Equal Credit
Opportunity Act.
This concludes the third installment in our series. The Federal Reserve
System's Mortgage Credit Partnership Credit Scoring Committee thanks the
respondents for their participation. The fourth installment will deal with
training of staff, the level and consistency of assistance provided to
prospective borrowers in the loan application process, and the degree to
which applicants are informed about the ramifications of credit scoring in the
mortgage application and underwriting process.
Addendum
The topic of the third installment of the Perspectives on Credit Scoring and
Fair Mortgage Lending discussed how lenders oversee the practices of their
third-party brokers, especially for compliance with fair-lending laws, pricing
policies, and the use of credit scoring models. Following publication of that
article, the Federal Reserve System's Mortgage Credit Partnership Credit
Scoring Committee received a letter from the Mortgage Bankers of America
(MBA) offering comments on the issues identified in the third article. The
Committee thanks the MBA for sharing its insights on the third-party broker
issues. The letter from the MBA follows.
April 24, 2002
Dear Credit Scoring Committee:
The Mortgage Bankers Association appreciates the opportunity to comment
on issues being considered by the Federal Reserve's Mortgage Credit
Partnership/Credit Scoring Committee. The Mortgage Bankers Association of
America ("MBA") is a trade association representing approximately 3000
members involved in all aspects of real estate finance. Our members include
national and regional lenders, mortgage brokers, mortgage conduits, and
service providers. MBA encompasses residential mortgage lenders, both
single-family and multifamily, and commercial mortgage lenders.
In order to adequately assess the fair lending responsibilities of mortgage
bankers in brokered transactions with regard to the underwriting or pricing
of mortgage loans, it is imperative to fully understand the structure of the
mortgage banking transaction and distinguish among the roles of the
different players involved.
Banker vs. Broker
Although there are wide variations in the roles performed by the numerous
entities involved in mortgage lending transactions, there are several
fundamental distinctions that can be drawn between the functions of the
mortgage banker and the mortgage broker. Although entities vary greatly in
terms of amounts and types of services they perform, it is possible to
provide generalized descriptions of their functions in the mortgage loan
transaction.
The core function of the mortgage banker is to supply the funds necessary
for the making of a mortgage loan. As the "lender" of the moneys in the
transaction, the central role of the mortgage banker entails the performance
of all the necessary underwriting analysis on a loan transaction and the
actual funding to close a loan, using either its own funds or funds acquired
from warehouse lines of credit. Generally, mortgage lenders do not make
loans in order to retain the asset as an investment. Rather, a mortgage
lender will usually sell its residential mortgage loans immediately in the
"secondary market."
Mortgage lenders can, and do, engage in "retail loan origination," which is
the part of the process that entails everything from advertising and
solicitation of the loan product to the taking of the loan application and
performing some or all of the processing of the application information.
When mortgage lenders engage in the "retail" portion of the loan business,
they deal directly with the potential borrowers, and thus perform such
"origination" functions as interviewing and counseling borrowers, gathering
personal information and taking the necessary steps to process, underwrite,
close and fund the loan. The "retailing" of loans requires not only the time of
lender personnel, but also the bearing of the cost of real estate ownership or
rental, i.e., the "bricks and mortar," as well as the expense of payroll and
benefits, business machines, supplies, insurance and other costs necessary
to maintain a retail branch.
The mortgage broker, in turn, specializes only in the loan "origination"
portion of the transaction. By doing business with a mortgage broker, the
lender will save on all these operating costs. In addition to sparing the
lender the "brick and mortar" and other retail office expenses, brokers will
perform many of the services required to originate loans that Lender would
otherwise have to perform. Mortgage brokers also allow a lender to broaden
its market and reach customers who, because of geography, or a lack of
contact or knowledge, might otherwise have never accessed the lender's
products, thereby increasing competition.
As such, the broker will take a consumer's application, will counsel the
applicant and process the application, and will then ship the loan package to
the lender for proper underwriting, and eventually, closing of the loan. In
some instances, the lender may actually close the loan in the broker's name
with the lender's funds ("table funding").
It is also worth noting that the role of the mortgage broker vis-à-vis the
consumer and vis-à-vis the mortgage lender can vary greatly. In the vast
majority of cases, however, the broker will have developed relationships
with various lenders, and will serve as the "retailer" of the lenders' loan
products to consumers. In that role, the broker serves as an independent
contractor with respect to both the consumer and the lender. In such
instances, the broker/lender relationship is non-exclusive, and the broker is
under no obligation whatsoever to submit any borrower's loan application to
any particular lender for approval and funding. On the contrary, brokers are
free to choose any one of several wholesale lenders' products for a particular
borrower.
Automated Underwriting
Over the past several years, the process of mortgage loan underwriting has
gone through considerable evolution. In today's world, the mortgage
industry is increasingly relying on automated underwriting systems to assess
the risk of applicants in a more efficient and fair manner. These automated
systems function by permitting lenders to input pertinent borrower
information into the computer and allowing the program to assess the
applicant's risk profile under pre-set lending guidelines. The guidelines used
under these systems vary greatly. Most automated systems incorporate
guidelines created either by secondary market investors, including the
Fannie Mae and Freddie Mac, or mortgage insurers. In some instances, they
may be proprietary systems created by the mortgage lender itself based
solely on its own lending and risk experience. The common factor under
these automated systems is that they perform the underwriting process
efficiently and in very quick timeframes, providing fair and non-biased loan
decisions based only on the data entered into the system.
It must be noted, however, that even the most advanced automated
underwriting systems allow for significant discretion by lenders. These
systems are designed to complete a standard underwriting analysis leaving
more complicated loan decisions to human underwriters. In fact, automated
systems are generally designed so that no applicant is ever denied a loan on
the basis of artificial intelligence alone. When an applicant's loan file
information does not meet the standards established under the lender's
system, the computer will "refer" the loan to "manual" underwriting to allow
a human analyst to reconsider the loan file and approve it, determine if it fits
into a special or alternative loan program, or deny it altogether. The
important item to note is that although automated underwriting systems
increase efficiency and lower cost by quickly approving applicants with
clearly satisfactory loan risk profiles, they leave the decision-making in
borderline or more complicated cases to lenders, who must still make the
hard calls.
The Nature of the Credit Decision and the Role of Credit Scoring
The ultimate decision of whether to lend to any specific applicant, is not a
"science" involving strict mathematical formulas. Rather, it is an "art" that
relies heavily on various underwriting factors that are assigned differing
weights depending on the experience or risk preference of the lender or
investor. There are a myriad of factors that come into play in mortgage
lending determinations. Some of the more common factors analyzed by
underwriters are loan-to-value ratios, debt-to-income ratios, bank reserves,
down-payment size, down-payment source, loan type, loan duration, among
many others. Credit scoring is just one factor in the analysis. The "art" of
underwriting does not lie in assigning numerical values to any of these
factors, along with "pass" or "fail" ratings. Underwriting requires that each
factor be accounted for and interpreted in light of the other factors and in
the context of each applicant and property. In the end, the final decision is
based on a judgment call regarding the full set of circumstances that are
unique to each borrower and each transaction.
Pricing of the Loan
In wholesale broker transactions, lenders will generally offer a variety of
loan products to the broker, along with prices at which it will purchase each
product. Using complex and proprietary computerized models, lenders will
generate prices for their wholesale mortgage products, and these prices will
typically change daily. This pricing information is then transmitted to the
approved mortgage brokers in what are known as "rate sheets."
In general terms, the "price" that a lender is willing to offer for a particular
loan product is a function of the predicted value of that loan when it is resold
in the secondary market. The pricing may also differ based on the credit
quality of the loan. Furthermore, numerous other price adjusters may be
imposed by the lender to reflect risk characteristics, such as loan amount,
two to four family dwellings, high rises, loan-to-value ratios, etc.
Furthermore, the wholesale price lenders make available to mortgage
brokers differs from the "retail" price in that it excludes many of the costs
that are necessary to advertise and originate the loan to the consumer such
as the cost of the broker's services.
In wholesale loan transactions, it is the mortgage broker who ultimately sets
the "retail" price that the consumer eventually pays for the loan. The fact
that brokers have the ultimate role in establishing final "retail" prices is vital.
As described above, the broker has a crucial role in the transaction. The
broker serves as the "retailer" of the loan in providing the "bricks and
mortar" that would otherwise be provided by the lender. The broker markets
and advertises the lenders' loan products. The broker also provides an array
of originating and processing services to the borrower and lender. The
broker then executes the loan documents in favor of the lender or closes the
loan in its own name ("table funding"). In all instances, the broker is
performing real services, providing real goods, and interfacing with
consumers. As the provider of such services, mortgage brokers require
compensation. It is the broker-not the lender-who in negotiation with the
consumer must appropriately make the final determination of how the
broker will price its own services.
It is essential that brokers retain the independence to price their own
services in order to assure that they meet the individual needs of their
customers, as well as their cost structures and operating expenses. In
today's mortgage market, mortgage brokers will retail the products of
various lenders to consumers and recover their own costs (plus profits). The
flexibility in pricing allows them to receive their payment in a way that
accommodates the borrower's available cash for closing. For instance, the
borrower can pay all of the broker's costs directly, or alternatively they can
have the lender pay some or even all of these costs (a payment commonly
called a yield spread premium) in exchange for a slightly higher interest
rate. When the process works right, brokers and borrowers select the best
loan options to meet the consumers' needs and negotiate the terms of the
loan within the constraints imposed by the lender's rate sheet. Lenders are
"once-removed" from this negotiation process, and are generally indifferent
as to the pricing option combination of interest rate and upfront closing costs
selected by the borrower and broker pursuant to the lender's rate sheet
except insofar as the lender ultimately receives the same return after it sells
the loan on the secondary market.
Lenders recognize that some brokers may attempt to gouge consumers. For
this reason, many lenders cap the fee that the broker can receive in order to
protect customers. However, such caps are designed only to limit
discretionary pricing not eliminate the negotiation process between the
broker and borrower. Caps therefore are not intended to and do not ensure
that all borrowers pay a uniform price. In fact, the unavoidably individualized
nature of each loan transaction would dictate otherwise.
Comments on Specific Questions
As demonstrated by the description of the lending process set forth above,
the framing of certain questions posed by the Committee reflect certain
misconceptions about the lender-broker relationship.
The lender may build a maximum broker overage tied to the credit
score.
It is generally true that lenders may impose "caps" or maximum limits on
the compensation that brokers can collect on any given transaction. These
"caps" are generally imposed in order to assure that loans originated by
mortgage brokers are fully compliant with applicable RESPA and Fair Lending
requirements. It is important to note, however, that these "caps" are
generally not structured on the basis of maximum limits on the points
charged over the 'par' rate. Rather, lenders generally set maximums based
on fees that they will pay to the broker for origination services performed.
The broker, on the other hand, determines what dollar amount it must
collect on any given transaction (limited, of course, by the "cap" that may be
specified by the lender), and then builds this fee into the yield spread pricing
that is ultimately offered to, and negotiated with, the consumer.
Although the credit score is an important tool in the underwriting of the loan,
many lenders do not use credit score to set the maximum broker's
compensation. Nevertheless, mortgage brokers may access the applicant's
credit score directly prior to submission to a lender in order to assess the
applicant's creditworthiness and the lenders and products that may be best
for the applicant. Mortgage brokers may also price differently based on
credit score as a proxy for how difficult the loan approval process likely will
be. As per federal law requirements, the broker's compensation is calculated
on the basis of services performed or goods provided by the mortgage
broker so the mortgage broker can charge more for loans that will require
more work on the mortgage broker's part. Other than by perhaps setting
outside numerical caps, and requiring adherence to applicable state and
federal laws, lenders are not involved in the setting of broker compensation
on individual loans.
It is not possible for a lender to stop mortgage broker price discrimination
without fixing loan price which it cannot do. Furthermore, a lender is unlikely
to have all loans originated by a broker and thus does not know the broker
price on all of the broker's loans in order to perform a fair lending analysis.
Even if the lender had the data and could engage in such an expensive and
onerous review, the only recourse would be to stop doing business with the
broker thus reducing the access of credit to borrowers in that marketplace.
The lender may provide brokers with access to the lender's scoring
programs.
This statement is generally inaccurate, and to the extent such access occurs,
it is of negligible impact in the market. As set forth above, lenders use
scoring programs that are developed by large industry players such as
Fannie Mae or Freddie Mac, as well as programs developed in-house, on the
basis of the lender's own lending experience. In the latter case, the
programs are proprietary and are therefore not shared with third party
originators. Even in cases of lenders that employ programs used by large
industry participants, such programs may be "tweaked" and altered to
reflect the lender's experience, regional variations and/or risk preferences of
the particular lender.
The broker may obtain a credit report or credit score and use it to
underwrite and price a proposed deal prior to submitting it to a lender.
The "pulling" of credit scores or credit reports by mortgage brokers prior to
the submission of the loan package to the lender is a longstanding and noncontroversial practice in the mortgage industry. In fact, mortgage brokers
must be able to ascertain an applicant's credit background in order to
perform the critically important duties of properly advising and counseling
borrowers. The fact that this practice is generally accepted is demonstrated
by HUD pronouncements under existing RESPA rules and regulations. In a
statement of policy dated issued in 1999 (64 FR 10080), HUD identified
various services that are normally performed by brokers in the origination of
a loan. Among those items, HUD describes various counseling-type activities
that specifically include "prequalifying prospective borrowers" and "assisting
the borrower in understanding and clearing credit problems." Under each of
these functions, brokers must have access to credit reports and credit scores
in order to properly guide and counsel prospective borrowers.
Although brokers may do a preliminary underwriting review in order to assist
the consumer in choosing a lender and product, typically, the broker does
not perform the final underwriting nor make the credit decision. Many broker
agreements with lenders do not have a repurchase obligation because the
broker does not have the capital or access to capital required to fund a loan.
As a result, only correspondent lenders would have the ability to make a
credit decision since they would also have a repurchase obligation if the loan
did not meet the lender's underwriting requirements. In the rare instance
that a broker is engaged in underwriting, it performs this function under
some type of outsourcing agreement, following the lender's strict guidelines,
and acting as the lender's agent. In this capacity, and pursuant to federal
law, it is clear that the lender would remain liable for all fair lending
consequences that flow from the actions and decisions of its "broker-agent."
Stephen A. O'Connor
Vice President, Government Affairs
Mortgage Bankers Association of America
1919 Pennsylvania Avenue, NW
Washington, D.C. 20006-3438
www.mbaa.org
Community Investments Vol. 15, Issue 1
Staff Training, Loan Pricing and Data
Accuracy
March 2003
Credit scoring is an underwriting tool used to evaluate the creditworthiness
of prospective borrowers. Used for several decades to underwrite certain
forms of consumer credit, scoring has become common in the mortgage
lending industry only in the past 10 years. Scoring brings a high level of
efficiency to the underwriting process, but it also has raised concerns about
fair lending among historically underserved populations.
The mission of the Federal Reserve System's Credit Scoring Committee is to
publish a variety of perspectives on credit scoring in the mortgage
underwriting process, specifically with respect to potential disparities
between white and minority homebuyers. To this end, the committee is
producing a five-installment series of articles. The introductory article
provided the context for the issues addressed by the series. The second
article dealt with lending policy development, credit-scoring model selection
and model maintenance. The third article explored how lenders monitor the
practices of their third-party brokers, especially for compliance with fairlending laws, pricing policies and the use of credit-scoring models.
The fourth article focuses on staff training, the level and consistency of
assistance provided to prospective borrowers and the degree to which
applicants are informed about the ramifications of credit scoring and data
accuracy in the mortgage application and underwriting process.
Representatives of three organizations were asked to comment. They were
selected because of their different perspectives on credit scoring and fair
lending.
William N. Lund
Maine Office of Consumer Credit Regulation
Mr. Lund is director of Maine's Office of Consumer Credit Regulation. A
graduate of Bowdoin College and the University of Maine School of Law, he
worked in private practice and with the Maine Attorney General's Office prior
to assuming his current position in 1987. Mr. Lund has served as chair of the
Federal Reserve Board's Consumer Advisory Council. He writes and speaks
frequently on consumer law issues.
John M. Robinson III and Ken Dunlap
Midwest BankCentre
Mr. Robinson is the audit director/compliance officer and Community
Reinvestment Act officer for Midwest BankCentre in St. Louis. Robinson has
16 years of banking experience with the last 10 in internal audit and
compliance management. He is a graduate of Westminster College, of
Cambridge University's master's program and of the American Bankers
Association's National Compliance School. He is chairman of the Missouri
Bankers Association Compliance Committee and a board member and
speaker on compliance topics for the Gateway Region Center for Financial
Training.
Mr. Dunlap is the loan processing manager/chief underwriter for Midwest
BankCentre. Dunlap has 11 years of experience in mortgage underwriting,
compliance, Home Mortgage Disclosure Act and Community Reinvestment
Act reporting, and loan platform maintenance. He is a graduate of Southeast
Missouri State University and has been with Midwest BankCentre for five
years.
Midwest BankCentre, a $720 million community bank with nine offices, was
named "Outstanding Small Lender" by the Small Business Association in
2000. The bank originated 569 mortgage loans in 2000 and had a higher
percentage of mortgage home improvement loans in compliance-sensitive
segments than did its community peer banks. Midwest BankCentre was
instrumental in helping to establish the Lemay Housing Partnership.
Josh Silver
National Community Reinvestment Coalition
Josh Silver has been the vice president of research and policy at the National
Community Reinvestment Coalition (NCRC) since 1995. He has a major role
in developing NCRC's policy positions on the Community Reinvestment Act
(CRA) and other fair-lending laws and regulations. He has also written
congressional testimony and conducted numerous research studies on
lending trends to minority and working class communities. These studies
include NCRC's Best and Worst Lenders, a comprehensive analysis of home
lending in 20 metropolitan areas, and a report sponsored by HUD on the
performance of Fannie Mae and Freddie Mac in financing home loans for
minority and low- and moderate-income borrowers. Prior to joining NCRC,
Mr. Silver was a research analyst with the Urban Institute. Mr. Silver holds a
masters' degree in public affairs from the Lyndon B. Johnson School of Public
Affairs at the University of Texas in Austin and a bachelor's degree in
economics from Columbia University. NCRC is the nation's CRA trade
association of more than 800 community groups and local public agency
member organizations. For more information about NCRC, call (202) 6288866 or visit the coalition's web page at http://www.ncrc.org.
The contributors to this article were asked to respond to the
following statement:
In the past, the terms "thick file syndrome" and "thin file syndrome" were
used to describe the allegation that white and minority mortgage applicants
received differing levels or quality of assistance in preparing mortgage
applications. These terms were used primarily before the advent of credit
scoring in mortgage lending. In the current mortgage market environment,
credit and mortgage scoring have taken a front seat to judgmental systems.
With greater reliance on these automated systems and less human judgment
in the decision process, the quality of assistance provided applicants is even
more important.
Given the increased reliance on automated underwriting, what should
lenders do to ensure that:
Lending policy is strictly observed and that any assistance offered to
loan applicants or prospective applicants to improve their credit score
is offered equitably.
Applicants have a clear understanding of the importance of their credit
score to the approval and pricing processes.
Staff training and oversight regarding credit policy and fair lending
guidelines are adequate to ensure consistent and fair treatment of loan
applicants.
Response of William N. Lund
Maine Office of Consumer Credit Regulation
As a regulator enforcing Maine's credit reporting laws, I have tried to learn
as much as I can about credit scoring. The ingenuity of the scoring models
and the complexity of the applied mathematics are very impressive, and I
have no doubt that use of such scores permits creditors to make fast
decisions on consumers' applications. However, from the consumer's
perspective, I harbor great concerns about the exponential growth in the use
of such scores. I can summarize my concerns as follows:
Concern #1: Credit scoring has led to a "re-mystification" of the
credit reporting system.
In 1969, during the debate on the original Fair Credit Reporting Act (FCRA),
Wisconsin Senator William Proxmire spoke of the congressional intent behind
the law:
"The aim of the Fair Credit Reporting Act is to see that the credit reporting
system serves the consumer as well as the industry. The consumer has a
right to information which is accurate; he has a right to correct inaccurate or
misleading information, [and] he has a right to know when inaccurate
information is entered into his file…. The Fair Credit Reporting Act seeks to
secure these rights."
In other words, passage of the FCRA represented an effort to "de-mystify"
the credit decision-making process. In the years since passage of the act,
consumers and creditors have become relatively comfortable with the use of
traditional credit reports.
However, I fear that the creation and use of credit scoring systems
constitutes a step backward from the goals of the Fair Credit Reporting Act
to make credit reporting data accessible, understandable and correctable,
and to make credit reporting agencies responsive to consumers. In other
words, just as the FCRA "de-mystified" the storage and use of credit
information, credit scoring is now serving to "re-mystify" that process.
Concern #2: A double impact results when an error in the underlying
data impacts a credit score.
The fact that a large percentage of credit report data is accurate is of little
comfort to a consumer whose report contains harmful errors. If errors in the
underlying data result in a low credit score, in effect the original error is
compounded.
In addition, the consumer now finds himself twice removed from the actual
problems. A credit-scoring system creates a new layer of data, and that new
layer separates the consumer from the raw data. The system as a whole
becomes less accountable to consumers. When the Federal Trade
Commission decided not to treat credit scores the same as traditional
reports, not only did this decision remove the legal responsibility to disclose
the score but also to correct an inaccurate score and notify previous
recipients at the consumer's request.
Concern #3: Because there are so many different products, and
because these products are ever-changing, consumers cannot be
educated about common rules or standards.
Let's look at the current range of products: Trans Union has Emperica,
Experian uses the name Experian/Fair Isaac, and Equifax offers Beacon. In
addition, Fannie Mae has developed Desktop Underwriter (DU), while Freddie
Mac uses its Loan Prospector. Other lenders use Axion or Pinnacle.
Over the years, those of us who assist consumers with credit report issues
have managed to get our arms around the "big three," but it is much more
difficult to make sense of the myriad variations on the credit-scoring theme.
Even something as simple as score values is very confusing: My files contain
the statements of four different experts who describe the range of scores in
the basic Fair, Isaac (FICO) model as 300 to 900, 400 to 900, 336 to 843,
and 395 to 848. If products offerings are such that the "experts" can't agree
on basic information, how can consumers be expected to gain a meaningful
understanding of the scoring process and its impact?
Concern #4. Reason codes: Everyone gets four, regardless of how
good or bad their scores.
For those with great scores, four may be too many. For those with low
scores, four may be too few.
Why can't reason codes be specific, as in, "The fact that your 1972 Pinto was
repossessed in January results in a reduction of about 40 points from your
score." Don't we have the technology to do that?
In addition, some of the factors used to determine scores seem illogical on
their face, the most obvious being the effect of closing existing, older,
unused credit accounts. From most real-life perspectives, closing such
accounts should be a good thing. From a scoring perspective, however, that
action harms a score in two ways: First, it increases the ratio of used credit
to available credit, by reducing the denominator of that fraction. Second, it
decreases the average age of a consumer's credit lines, resulting in further
score reduction.
As another example, industry sources have told me that a consumer gains
points for doing business with established banks, but loses points for doing
business with small loan companies or check-cashers, even if payment
histories are identical. In other words, there is good credit and bad credit,
which may have more to do with a consumer's neighborhood and lifestyle
than with an accurate prediction of the chances of future repayment.
And consider the advice that consumer advocates have given for years:
Compare APRs and shop around for credit to get the best deal. Shopping
around these days means piling up inquiries on one's credit report. Despite
recent efforts within Fair, Isaac-based models to discount groups of
inquiries, the fact remains that inquiries form a component of a credit score.
The use of credit scores for non-credit decisions adds to the illogic. For
example, should paying cash for purchases result in an increase in a
consumer's auto insurance rates? That is the outcome when a "thin" file
results in a low credit score which is used by an insurer to set premiums.
Concern #5: Creditors will probably begin to rely too heavily and
exclusively on credit scores, despite "instructions" to the contrary.
What was introduced as a tool to be used in conjunction with other criteria is
quickly becoming a litmus test. Creditors are busy, and underwriters are
often not rewarded for taking risks. The logical result will be a dependency
on credit scores and a reluctance to look to a broader picture. To quote Chris
Larsen, CEO of online lender E-Loan: "Lenders are increasingly relying on
these scores. Many loan products, including some home equity loans and
auto loans, are based almost entirely on your FICO score."
Conclusion
Many aspects of the credit scoring process have now gotten ahead of the
ability of consumers to make sense of the system and of regulators to
meaningfully assist those consumers. Providers of credit scores should be
required to share responsibility for ensuring the accuracy of the underlying
data, of correcting that data and of disseminating the correct information if
requested by the consumer. Despite repeated assertions by the industry that
credit scoring is not a mysterious black box, the lack of any uniformity,
oversight or accountability make that analogy too close to the truth.
Response of John M. Robinson III and Ken Dunlap
Midwest BankCentre
Given the increased reliance on automated underwriting, what should
lenders do to ensure that their lending policy is strictly observed and that
any assistance offered to loan applicants or prospective applicants to
improve their credit score is offered equitably?
Lending policies must be observed to ensure sound financial business
decisions and to avoid any potential disparate treatment of applicants. At the
same time, policies must allow lenders to evaluate individual credit needs
and varying applicant scenarios. Lenders must be conscious of nontraditional
applicants for whom relaxed underwriting may be key in obtaining a loan.
For example, Midwest BankCentre offers the FreddieMac Affordable Gold
"97" mortgage product for first time home-buyers. This program, in contrast
to many others, allows for a 3% down payment from any source (e.g.,
gifts).
How a mortgage credit decision is made is one of the two keys of potential
discrimination. Prescreening is the other. Underwriting standards and policy
adherence are very important. Allowing excessive overrides creates an
atmosphere for potential discrimination-when a lender decides to override an
established and proven underwriting decision, the reason is personal more
times than not. Banks should have workable, clearly written policies and
underwriting guidelines. Every lending decision should be fully and clearly
documented, especially if a lender overrides a prescribed credit score and
makes the loan. Lending institutions must give equal assistance to all
applicants.
To avoid problems with loan policy standards, the following steps should be
taken:
Review bank policies and procedures. Compare them with actual file
reviews.
Review all underwriting and credit score overrides. Look for patterns.
Review loan files and denials for adequate documentation. Look at all
forms, documents and disclosures in the files.
Given the increased reliance on automated underwriting, what should
lenders do to ensure that their lending policy is strictly observed and that
any assistance offered to loan applicants or prospective applicants to
improve their credit score is offered equitably?
Generally speaking, the average mortgage applicant - especially the firsttime home buyer - does not understand clearly how a credit score affects
the mortgage outcome. Applicants who have never had a loan or a problem
with a loan decision probably have never heard of a credit score. Knowing
how to use a credit score involves knowing what is in the score and what it
does and does not tell about the prospective applicant. Because the score is
based on data provided by a credit bureau, applicants should be instructed
on how to rectify any error or problem that appears on their credit bureau
reports.
If a bank or creditor does not use a credit bureau service, then the
applicant's credit history is not recorded. These scores do not reflect
information such as the amount of down payment, income, cash flow, or
other mitigating assets. The score is only part of the applicant's credit
picture. Therefore, one may conclude that too much reliance on credit scores
or on automated decisions could raise flags of disparate impact issues. In
actuality, there may be many reasons why a low score would not be a
negative in the bank's decision. For example, a large down payment or
significant cash flow could justify overriding a low score. We do make loans
to applicants who may not have stellar credit-Freddie Mac guidelines allow
for A- offerings-but the interest rates are usually higher.
Given the increased reliance on automated underwriting, what should
lenders do to ensure that staff training and oversight regarding the credit
policy and fair-lending guidelines are adequate to ensure consistent and fair
treatment of loan applicants?
First, all lenders in the bank should know the products offered and always
explain to prospective applicants the loan product choices and their
associated potential costs. We need to take our responsibility to customers
seriously. We earn the trust of customers by how we treat them.
Lenders using their own instincts instead of a score have a different
perspective on customer relationships. When looking at the overrides in
credit scores, management should look at the decisions made and where
and by whom (which branch/lender). Management should look at patterns
and at loans that have gone bad and compare them with any initial credit
score. Self-testing and self-analysis with an eye on patterns and trends
related to any disparity are vital to the organization.
Lenders should follow these basic steps:
Disclose and explain any conditions for a product or service as well as
the benefits of each one.
Offer the same product to everyone who has comparable
qualifications.
To ensure fair and equal treatment of all customers in the application of our
credit policies, Midwest BankCentre's compliance department holds annual
mandatory fair- lending and diversity awareness training seminars for staff.
The sessions are intended to generate discussion about how well employees
understand fair-lending laws and issues of cultural diversity in the
workplace. We use a video titled True Colors, the ABC Prime Time Live
telecast filmed on location in St. Louis, and each attendee receives the
booklet Closing the Gap - A Guide to Equal Opportunity Lending, published
by the Federal Reserve Bank of Boston. We have also used other videos
from corVISION Media Inc.-in particular, Valuing Diversity at the
Interpersonal Level. Participants complete and discuss a self-assessment
checklist that underscores their own perceptions of understanding
differences and adopting changes.
Being a community bank, we do not rely heavily on credit scoring; we still
consider the individual borrower's overall credit reputation. Because we
continue to have direct interaction with our applicants throughout the credit
process, it is important that our mortgage lenders receive ongoing training
in what constitutes fair and consistent treatment.
Response of Josh Silver
National Community Reinvestment Coalition
Toward meaningful disclosure and discussion of credit scores
All of us have credit scores, but most of us don't know what they mean. If
we knew what they meant, would we be more likely to get approved for a
low-cost loan? The answer is probably, but the disclosures of credit scores
have to be meaningful if they are to be helpful to the borrower.
Credit scores are numbers ranging from 300 to 800 that are supposed to
reflect the risk that we, as borrowers, pose to banks. The higher the score,
the less risky we are and the less likely we will be late on loan payments or
default on the loan altogether. Credit scores are calculated on the basis of a
credit history that is collected and stored in three major credit reporting
agencies or private sector credit bureaus. The record of paying on time or
paying late, the amount of debt compared with the amount of available
credit on credit cards, and the length of time using credit are major factors
that contribute to the score.
If a borrower has a score above 660, he most likely will qualify for a prime
rate loan at interest rates advertised in newspapers. If a borrower has a
score significantly below 660, he is likely to receive a subprime loan at
interest rates ranging from 2 to 4 percentage points above widely advertised
rates. The rationale behind the higher rate on subprime loans is that the
bank is compensated for accepting the higher risk of delinquency and default
associated with lending to a consumer with blemished credit.
Credit scores have been used for decades for consumer and credit card
lending. In the mid-1990s, credit scores became a widely used tool in
mortgage lending as well. It is not the only criterion banks and mortgage
companies use, but it is an important criterion, ranking up there with loanto-value ratios and total debt-to-income ratios. Proponents of credit scoring
assert that its use has increased lending to minority and low- and moderateincome borrowers because it is an objective assessment of a borrower's
creditworthiness: Subjectivity is removed from the loan process, and the
chances of discrimination are decreased. It is further claimed that credit
scoring makes the loan process much more efficient and saves resources
that can be devoted to carefully analyzing marginal cases.
The National Community Reinvestment Coalition (NCRC) does not believe
that credit scoring has revolutionized access to credit, and neither has the
advent of subprime lending, for that matter. Instead, the strengthening of
the Community Reinvestment Act (CRA) and the stepped up enforcement of
fair-lending laws have been the major forces behind the explosion of credit
for minority and low- and moderate-income borrowers during the 1990s.
Lenders made only 18 percent of their home mortgage loans to low- and
moderate-income borrowers in 1990. The low- and moderate-income loan
share surged 8 percentage points to 26 percent by 1995, but by 1999 it had
climbed only 3 more percentage points, to 29 percent.
Let's review the major events coinciding with the big jump in lending during
the first part of the 1990s and the major events during the lending
slowdown in the second half. Congress mandated the public dissemination of
CRA ratings in 1990 and the improvement of Home Mortgage Disclosure Act
(HMDA) data to include the race, income and gender of the borrower. In
1995, after a highly visible and lengthy review process during previous
years, federal banking agencies strengthened CRA regulations to emphasize
lending performance as opposed to process on CRA examinations. During
the same time period, the Justice Department settled several fair-lending
lawsuits with major lending institutions. After 1995, the mortgage industry
widely adopted credit scoring, and subprime lending took off. Home
mortgage lending increased in the first part of the decade as policy-makers
strengthened and applied CRA and fair-lending laws. Lending slowed down in
the second half of the decade; during this period, credit scoring and
subprime lending were on the rise. Economic conditions played less of a role
in the different trends in lending because we were blessed with a
tremendous economic recovery during the entire 1990s.
The reason credit scoring was not responsible for the explosion of home
mortgage lending to low- and moderate-income borrowers is that credit
scoring is not designed to serve those who have the least experience with
the financial industry. Credit scoring depends on an established credit
history, so that econometric equations can judge the odds of a borrower
paying late or defaulting. Officials at one large bank NCRC interviewed for
this article stated that they do not use credit scores in their approval
decisions regarding special affordable loan programs. They indicated that
those people among the low- and moderate-income population who are
targeted by special affordable loan programs have low credit scores because
they do not have much of a credit history. Instead, the bank uses
nontraditional credit history, such as evaluating the timeliness of rent and
utility payments. It is likely that CRA encouraged this bank to establish the
special affordable loan programs. For this large bank, and probably for many
other banks, CRA has more to do with increasing lending to low- and
moderate-income borrowers than credit scoring.
Why disclosure would help
While credit scoring has not had a noticeable impact on increasing credit to
traditionally underserved borrowers, meaningful disclosures of credit scores
would nevertheless help increase access to affordable credit. The optimal
time for disclosure is before a customer applies for a loan. If a customer
obtains a credit score and the major factors for that score before reaching
the loan application stage, he would have a good idea of his
creditworthiness. The customer would be in a better position to know if he
was getting a good deal on the loan or whether to bargain with the lender.
The caveat is that a consumer must have a clear understanding of what the
credit score is and what factors affected his score. The disclosure of the
number itself has little meaning. If the credit score is low, for example, the
consumer needs to know which factors in his credit history had the most
impact on lowering the score. He could then decide whether to delay
applying for the loan and how best to clean up his credit. For this reason,
HomeFree-USA, a counseling agency in Washington, D.C., and a member
organization of NCRC, always includes credit score counseling in its
homebuyer preparation courses. Similarly, NCRC educates consumers about
their credit scores in its financial literacy curriculum.
Although credit scores are imperfect estimators of creditworthiness,
disclosure of credit scores can help reduce the incidence of discrimination in
prices, particularly in the area of subprime lending. Fannie Mae's chief
executive officer has been quoted as saying that 50 percent of subprime
borrowers could have qualified for lower rates. Freddie Mac issued a
statement on its web page a few years ago saying that up to 30 percent of
subprime borrowers could have qualified for lower-priced credit. A paper
commissioned by the Research Institute for Housing America concluded that
after controlling for credit risk, minorities were more likely to receive
subprime loans.
An unanswered question is how many borrowers who were inappropriately
placed into the subprime loan category could have avoided this if they had
simply known about their credit scores. Also, how many of them could have
obtained lower interest rate loans, even if the loans remained subprime? For
example, if an educated borrower knew that his score was 620, which is
generally considered A- credit, and was quoted an interest rate 4 percentage
points higher than the widely advertised rate, he would know that he was
being overcharged. While other underwriting factors, such as loan-to-value
and debt-to-income ratios, also contribute to the pricing decision,
meaningful credit score disclosures alert borrowers when quotes are or at
least seem far higher than they should be.
As California was passing a law requiring credit bureaus to disclose credit
scores, Fair, Isaac and Co. Inc., one of the major firms producing scores,
took a constructive step and made credit scores available for a small fee
through its web site, myfico.com. The company also has a description on its
web page of the major factors influencing the score and the weight of each
factor.
How banks should disclose and use credit scores
The new California law also requires banks to disclose credit scores to
consumers applying for loans. California is the only state to require this
disclosure. Several bills working their way through Congress would also
require credit bureaus and banks to disclose credit scores.
For the consumer, it is advantageous to be armed with credit score
information and to take action to improve the score, if needed, before
applying to a bank. However, if a consumer does not have a credit score
prior to application, disclosure by the lending institution is still valuable. In a
loan approval decision, for example, disclosure of the credit score will help
the borrower understand why his loan had a certain interest rate. If the
interest rate is in the subprime range, the borrower may want to take steps
to improve his credit before closing on the loan. In the cases of loan denial,
a lender is required under the Equal Credit Opportunity Act to send a
borrower an "adverse action notice." If the reason for the rejection involved
one of the factors in a credit score, that factor must be discussed in the
adverse notice.
Lending institutions can run afoul of fair-lending laws quickly if they are not
careful about using credit scores when helping borrowers apply for loans. For
example, in 1999, the Department of Justice settled a fair-lending lawsuit
with Deposit Guaranty National Bank over Deposit Guaranty's alleged
arbitrary and discriminatory use (or disregard) of credit scores. The lawsuit
came about after an examination by the Office of the Comptroller of the
Currency concluded that Deposit Guaranty disregarded credit scores when
approving loans for whites but rejected blacks with similar credit scores. As
a result, the black rejection rate was three times the declination rate for
whites.
It is important and valuable for a bank to institute a review process for
declined applicants, especially those on the margins of approval. Such a
review process may help banks make more loans to minority and low- and
moderate-income applicants with little traditional credit history. A
judgmental review process must establish consistent criteria by which to
overrule credit scores. Such criteria can include consideration of
nontraditional credit, including rental and utility payment histories.
Disclosure with a twist
The NCRC believes that information in the HMDA data about credit scores
could be instrumental in resuming steady increases in access to credit for
minority and low- and moderate-income borrowers. Several months ago, the
Federal Reserve Board asked for public comment on its proposal to include
the annual percentage rate (APR) in HMDA data.
In response to the Federal Reserve's proposal, NCRC pointed out that the
APR, along with credit score information, could vastly improve our
knowledge of how credit scores impact pricing and approval decisions.
Because many kinds of credit scores exist, it would be difficult to interpret
what actual numerical scores mean if they were added to HMDA data. At the
very least, the loan-by-loan data could indicate if a credit-scoring system
was used and the type of credit-scoring system, such as a bureau or custom
score. Policymakers would then have important insights as to whether most
loans to minority and low- and moderate-income borrowers are credit-scored
and whether banks using credit-scoring systems are more or less successful
in approving loans to traditionally underserved borrowers. Community
groups and counseling agencies could then use this additional information in
HMDA data in their advice to borrowers about which banks are most likely to
use credit-scoring systems in a fair manner to provide loans at reasonable
rates.
Conclusion
In announcing a Bush administration proposal to provide the public with data
on the quality of nursing homes and Medicare health plans, Thomas Scully, a
senior official at the Department of Health and Human Services, stated:
"Collecting data and publishing it changes behavior faster than anything
else." The motivational force of data disclosure under CRA and HMDA has
helped activists and the public at large work with banks to increase lending
to minority and working class borrowers. Meaningful disclosures of credit
scores to consumers and incorporating credit score information in HMDA
data would be two more valuable tools for building wealth in traditionally
underserved communities.
Community Investments Vol. 15, Issue 1
Overrides and Second-Review Process
March 2003
The purpose of the Federal Reserve System's Credit Scoring Committee is to
publish a variety of perspectives on the credit-scoring process and to identify
areas where the use of credit scores may create disparities in the home
mortgage process. The first four installments in this series addressed
aspects of the use of credit scores and fair lending concerns, including the
maintenance of scoring models, the use of third-party brokers, and the
provision of assistance in the credit-application process.
The topic of the fifth and final installment addresses the use of
counteroffers, overrides, and second reviews of credit-scored decisions. We
have solicited feedback from industry, consumer, and regulatory
representatives to ensure a variety of perspectives on these topics.
Contributors to this collection were asked to respond to the following
statement:
The emergence of credit scoring in the home buying process has been a
significant contributor to the increase in mortgage lending activity around
the country. Proponents of scoring systems argue that their purely objective
nature constitutes a significant fair lending benefit by virtually assuring
against disparate treatment on a prohibited basis. Others point out that
when inaccurate information is contained in the credit report, the consumer
may not have the opportunity to rectify the report, and the lending decision
will be made with inaccurate data. Another concern that has been raised is
that the objectivity of the credit score is lost when a lender supplements the
scoring process with overrides, counteroffers, or second review programs
that are subjective in nature or in use.
Credit-scoring overrides and counteroffers can serve important functions in
maximizing access to credit. However, their nature and usage could result in
unlawful discrimination. A frequent use of overrides would suggest a
mismatch between the scoring system and the lenders' credit policies or
objectives. In addition, inconsistency in the use of either "high-side" or "lowside" overrides to reach a credit decision, or inconsistent counteroffers made
to similarly situated applicants, may result in disparate treatment on a
prohibited basis.
Furthermore, if a lender engages in a subjective second review process,
unlawful disparities may result from the absence of well-established,
consistently applied second review guidelines that include clear explanations
of judgmental factors and cut-off scores.
Considering the credit-scoring issues outlined above, please comment on the
following questions:
1. What methods should lenders adopt to optimize the usefulness of
overrides, minimize their frequency, and ensure their use is in
compliance with the fair lending laws?
2. What actions could lenders take to ensure counteroffers are extended
fairly?
3. What measures and systems should be instituted to ensure that the
second review process is operating in a manner that is consistent and
fair?
4. Describe steps the lenders could take to ascertain the level of staff's
compliance with its policies and procedures.
CONTRIBUTORS
Chris Aldridge is a vice president and director of community affairs for Fifth
Third Bank, where he administers and oversees community affairs for the
bank's Cincinnati and affiliate markets. He is also responsible for BLITZ, a $9
billion community development initiative to fund building, lending,
investments, and technology zones over the next three years.
Mr. Aldridge is experienced in developing and implementing alternative
business strategies to help financial institutions realize their return on
investments. He has been instrumental in establishing relationships with
minority brokers that generate CRA loans, and he has launched programs to
increase product sales and support business development.
Prior to joining Fifth Third, Mr. Aldridge was the managing principal for
NuCapital Management in Southfield, Michigan. He holds a juris doctor
degree from Wayne State University and a bachelor's degree in economics
from Harvard College.
Dan Immergluck is a faculty member at the School of Public and Nonprofit
Administration at Grand Valley State University in Grand Rapids, Michigan.
He recently joined the university after having served as senior vice president
of the Woodstock Institute for many years. He has written extensively about
access to credit, community reinvestment, and community and economic
development, and he has worked with community organizations and
government agencies on a wide array of community reinvestment and
development projects. Mr. Immergluck holds a doctorate in urban planning
and policy from the University of Illinois-Chicago.
Michael LaCour-Little joined Wells Fargo Home Mortgage in 2000 as a vice
president in the Risk Management Group. Previously, he was the director of
financial research at CitiMortgage. He is an adjunct professor of real estate
finance at the John M. Olin School of Business at Washington University in
St. Louis, where he teaches MBA courses in real estate finance and
mortgage-backed securities. He also has taught at the University of
Wisconsin-Madison, Southern Illinois University-Edwardsville, and the
University of Texas-Arlington.
Mr. LaCour-Little holds a doctorate from the University of WisconsinMadison. His papers have appeared in Real Estate Economics, Journal of Real
Estate Finance and Economics, Journal of Real Estate Research, Journal of
Real Estate Literature, Journal of Housing Research, Journal of Housing
Economics, Journal of Fixed Income, and Mortgage Banking.
Stanley D. Longhofer holds the Stephen L. Clark Chair of Real Estate and
Finance in the Barton School of Business at Wichita State University, where
he founded the Center for Real Estate in 2000. He has been actively involved
in local urban redevelopment issues, co-authoring several reports on the
viability of proposed redevelopment projects and serving as chairman of a
special committee that addressed regional land-use concerns.
Prior to coming to Wichita State, Mr. Longhofer was a financial economist at
the Federal Reserve Bank of Cleveland, where he was a founding member of
the Federal Reserve System's Fair Lending Advisory Group.
Mr. Longhofer's research on mortgage discrimination, financial contracting,
and bankruptcy has been published in leading academic journals, including
the Journal of Real Estate Finance and Economics, the Journal of Money,
Credit, and Banking, the Journal of Financial Intermediation, and the
European Economic Review. In addition, he has written several popular
articles on the mortgage market and other topics. He holds a doctoral
degree in economics from the University of Illinois.
Kevin Stein is the associate director of the California Reinvestment
Committee, a statewide CRA coalition of more than 200 nonprofit
organizations and public agencies. CRC works with community-based
organizations to promote access to credit and economic revitalization of
California's low-income and minority communities. Mr. Stein works primarily
on housing issues, including efforts to fight predatory mortgage lending. He
was the primary author of CRC's recent report, Stolen Wealth: Inequities in
California's Subprime Mortgage Market, which investigated subprime lending
practices in the state.
Before joining CRC, Mr. Stein worked for the Community Economic
Development Attorney at the East Palo Alto Community Law Project and for
HomeBase, a law and social policy center on homelessness. He is a graduate
of the Georgetown University Law Center and Stanford University.
Statement of Dan Immergluck
Grand Valley State University
As a researcher and an advocate for fair lending and community
reinvestment, I have shared the concerns of many over the now-ubiquitous
use of credit scoring in the mortgage lending process. Many of my concerns
have been articulated by others in earlier articles in this series. For example,
in Part I, Cal Bradford points to the disparate impact of credit-scoring
systems and questions where the threshold be set in determining whether a
scoring system meets the "business necessity" test under the Equal Credit
Opportunity Act and Regulation B. If lowering the threshold for approving
loans reduces disparate impact but increases loan losses, what standard is to
be used to determine whether such losses have increased too much?
Lenders may argue that pressures for ever-increasing earnings force them to
push loan losses lower and lower, therefore raising approval thresholds. Who
determines how low losses need to be-the market's invisible hand? Even
conceding such a market-based approach, who determines where the
invisible hand has set that threshold-the lender or the regulator?
Previous commenters have pointed to other important issues, such as the
lack of transparency in scoring models and the focus on correlation over
causation. Before exploring particular issues with overrides and
counteroffers, however, I feel obliged to spend a little time on a couple of
issues that I feel did not receive enough attention in earlier parts of this
series. First, alluded to in other essays but perhaps not addressed directly,
are the problems that increasingly sophisticated lending tools pose for lesssophisticated loan applicants. As lending processes become more difficult to
understand (even if there is greater disclosure, credit-scoring systems often
remain more complex and mystifying than pervious systems), those who
have less understanding of how credit works or less-developed mathematical
skills will be more confused about why they are denied credit or charged
higher rates. Without such an understanding, it is unlikely that people will be
able to improve their credit prospects very much. While some counseling
programs do a good job of dealing with this problem, the proliferation of
credit scoring has not been matched by an equivalent investment in home
buyer and home owner counseling resources.
Another larger issue posed by credit scoring is often referred to as the
problem of "paradigm shift" and has been brought up more often in the
context of safety and soundness concerns. Credit-scoring systems are
relatively new, only having grown into common use in the mortgage market
since the mid-1990s. Most have not been tested extensively during a
substantial change in the business cycle (although that is likely occurring
now to some degree). When a major business cycle or technological change
occurs, scoring models may not do a good job at predicting behavior. While
these concerns typically have focused on the possibility of scoring systems
yielding approval rates that are too high (thus causing safety and soundness
problems), it is also possible that paradigm shifts cause changes in the
importance of different variables in predicting loan performance-which, if not
corrected, could unfairly disadvantage minority applicants. For example,
some systems disproportionately penalize some minority applicants for
having more credit activity with finance companies. If the regulation of
finance companies were to improve significantly, we might expect the
negative effect of such interactions would diminish, thus becoming a less
important determinant of repayment.
An often-overlooked issue with credit scoring is its use in data-mining and
marketing efforts by lenders and mortgage brokers. It is now possible to
obtain data on the credit scores of residents of specific neighborhoods,
enabling lenders to target specific areas with different types of productswhich, in turn, can lead to increasingly segregated lending markets.
Turning now to the more specific problems of overrides and counteroffers,
there are a number of issues that lenders, regulators, and advocates should
be particularly concerned about. First, to be clear, overrides and
counteroffers are not problems in and of themselves, and they can be an
important part of mortgage lending operations. The growth in credit scoring
means that such practices have become more prevalent, however, and so
can create greater fair lending risks.
As shown in the Deposit Guaranty case, where the lender was found to favor
non-minority applicants in the override process, lenders must monitor such
practices closely. They should look especially at aspects of the scoring
system where minority borrowers may be disadvantaged (for instance,
failure to consider a history of rental payments in the evaluation of credit
history).
In terms of counteroffers, if above-standard pricing is used, lenders should
be careful to use real risk-based pricing and should be required to document
and justify this to regulators. Arbitrary risk premiums should not be
tolerated. Regulators should compare the pricing and approval systems to
those of other lenders.
Clearly, retail lenders must be concerned with both the fairness of overrides
and the fairness of pricing in overrides. However, regulators need to clarify
and enforce the fact that wholesale lenders-or lenders with correspondent
relationships-are liable for any discriminatory behavior on the part of their
brokers or correspondents. Because brokers are disproportionately active in
minority communities, this is an important point. Effectively, lenders may
attempt to "outsource" discriminatory overrides by having brokers perform
the override function so that the lender itself ends up with few overrides, if
any at all.
Related to this problem is the common scenario of one holding company
owning several affiliates (bank and nonblank) that engage in mortgage
lending. If, for example, the bank affiliate tends to make retail loans to white
borrowers, and the non-bank affiliate tends to make wholesale loans through
brokers to nonwhite borrowers, then an override system that applies only to
the bank may disproportionately benefit white applicants when considering
all applications to the holding company and its brokers. This problem, in
turn, is related to the larger need for fair lending examinations to be
conducted on a holding company basis, not just on a bank basis.
Second reviews, overrides, and counteroffers can be an important part of a
lender's program to adequately serve all segments of a market. Guarding
against fair lending problems requires a comprehensive system of oversight
and controls and a regulatory framework that includes close and
comprehensive scrutiny of the override process.
Statement of Chris Aldridge
Fifth Third Bank
Within predominately minority neighborhoods, subprime financing accounts
for over 50 percent of the mortgage lending activity. Separate HUD and
Fannie Mae studies have found that many of these borrowers (up to 50
percent) would have qualified for prime or near-prime financing. This
situation has generated a flurry of local lending regulations, and it has
refocused attention on the impact of credit scoring on the availability of
prime-rate products in certain markets.
The perceived negative impact of credit scoring is counterintuitive if the tool
is used properly. The reduction in time and resources spent underwriting
high-score applicants should expand resources to manually underwrite cases
in which the borrower is a good risk but has no credit history or inaccurate
information in the mortgage application. More important, it could also free
resources to offer more labor-intensive complementary products that use a
combination of credit training, rehabilitation, and recent payment history to
offer prime- or near-prime-rate products. Thus, the proper use of credit
scoring should increase properly priced credit in all market segments.
This series of articles on the use and monitoring of credit-scoring-based
origination programs reflects concern over the proper use of credit scores
and of policies and processes to ensure this increasingly prevalent tool is
used fairly. However, this focus on tactical compliance ignores the more
important, proactive impact that a bank's strategic focus can have on fair
lending and credit-policy adherence.
Specifically, an organization's overall strategy establishes the vigor with
which each market segment is pursued. A business strategy that requires
"fair-share" penetration across all segments within the company's footprint
aligns business line and compliance objectives and provides top-down
pressure to ensure adherence to credit policy and aggressive outreach
efforts. It also signals an institutional intolerance for fair lending and creditpolicy violations.
The illustration below provides a framework for discussing how strategic
orientation and fair lending compliance combine to generate more equitable
results.
The most important phase of the origination process is the establishment of
a market focus and business goals. Business goals that include penetration
targets and objectives for all market segments drive the marketing,
advertising and outreach programs that bring prospects into the system. In
the absence of such a program, a perfect fair lending and credit policy still
would generate an inequitable result.
In addition, inclusive business goals authored by senior management signal
to originators and underwriters that failure to observe policy equitably has
consequences for performance reviews.
This business line pressure to perform reinforces the compliance program
and ultimately produces more equitable lending results and a stronger
compliance program.
Fifth Third Bank's senior executives sponsor an aggressive Senior Diversity
Strategy Initiative (SDSI), which seeks to identify opportunities to increase
share in each market segment within our footprint. In the context of fair
lending and credit access, its most important function is to signal executive
management's interest in serving every segment of our markets to line
employees who are responsible for lending and assistance programs. SDSI
establishes benchmarks and business objectives, creating top-down pressure
to aggressively capture all "good credit risks" and prospects requiring
additional help.
The SDSI complements our ongoing business process, which establishes
aggressive business goals for each tract within our market area and holds
management accountable for meeting these objectives. These goals include
both volume and loan-default performance targets. As a result, our
marketing program and outreach efforts are structured to reach areas of
underperformance. This effort results in more than fair-share allocation of
underwriting resources to underserved markets. The goals must be
aggressive enough to make inequitable behavior expensive at the personal
level.
Banks should invest in strong training and education programs to ensure
that each individual involved in the lending process is proficient in their
understanding of lending policy and the critical importance of equitable
treatment. Each person should be aware of the tools available to our
customers to improve credit scores. The program should include classroom
instruction as well as follow-up training programs that include some selfstudy component. Participation in such training regimens should be
mandatory, with a tracking mechanism to verify progress.
A secondary review process that compares similarly situated applicants
provides the most effective and timely method to ensure that policy is
followed and assistance is offered on a consistent basis. The secondary
review process allows the bank to compare performance to policy, to spot
patterns that may indicate a breakdown in the training regime, or to identify
opportunities to assist prospects in obtaining credit.
Banks should offer portfolio products that do not rely completely on the
automated underwriting process. These products have proven profitable for
bank and non-bank lenders. The more flexible process generally leads to a
more complete discussion of credit factors. It often allows banks to capture
business from individuals who are good risks but, for one reason or another,
are not identified in a purely automated process. A flexible product with
stretch goals creates an environment in which all credit issues are
thoroughly discussed.
Banks should, through their training programs, make certain that originators
are well trained in credit and its impact on the approval and pricing process,
as well as the applicability of alternative products in the case of credit
problems. The availability of products with different credit-score thresholds,
in combination with strong training and aggressive goals, will invariably lead
to a full discussion of credit issues.
An executive management commitment to each market segment and stretch
goals for production and credit performance create an environment in which
disparate treatment becomes personally expensive. The resulting
performance pressures ensure that all applicants become critical to business
line success and, thus, the recipient of all reasonable efforts.
Good intentions mean nothing without the right tools. An aggressive internal
training program that includes diversity as well as credit and product
components is critical to ensuring that our staffs have the requisite
knowledge to deliver consistent service to all of our loan applicants. We
track training participation and send reminders to personnel who fall behind
in their training.
To police actual performance, we conduct a second review of all denied
mortgages for minority mortgage applicants. These second reviews are
conducted weekly, and committee members include the mortgage business
line manager and staff members from compliance and community affairs.
In addition, a formal fair lending audit is conducted at least twice each year.
Fair Lending Wiz includes a number of tools that allow us to spot patterns for
further review.
Summary
A combination of senior management involvement, strategic focus, and a
sound compliance program are critical to generating equitable fair lending
results on a consistent basis. Unless business goals include volume from
underserved markets, the most perfect compliance system will generate
meaningless results.
The combination of strategic focus through our BLITZ program, an
aggressive training program, and compliance audits have allowed Fifth Third
Bank to produce a number of impressive results. First, we boast a denial
rate for African American applicants in our home market that is 25 percent
lower than the HMDA aggregate. Second, we have continued to meet our
aggressive business growth targets in each of the past two years. Finally, we
continue to boast superior credit performance within our peer group.
Statement of Kevin Stein
California Reinvestment Committee
Introduction
The use of credit-scoring models to evaluate creditworthiness has become
widespread, even finding its way into the insurance arena, despite concerns
about the fairness and utility of these models. Credit-scoring models were
developed and adopted primarily as a means of helping financial institutions
manage credit risk. The California Reinvestment Committee (CRC) believes
financial institutions should be working instead to develop and adopt
innovative methods of safely extending low-cost credit to underserved
borrowers and communities. Most observers accept that the use of creditscoring models has had a disparate impact on people of color. Below are
various reasons to question whether heavy reliance on credit scores furthers
the nation's interest in fair lending and equal access to credit, as well as the
safety and soundness of financial institutions.
The Larry Rule. In early 1996, an unlikely report came out that then-Federal
Reserve Board Governor Larry Lindsey, now President Bush's chief domestic
economic adviser, was denied a Toys "R" Us credit card because he did not
have an adequate credit score. This incident raised questions about which
and whose values underlie credit-scoring models and how financial
institutions react to these models. American Banker reported that "the result
of all this flap will be what we call the Larry Rule," whereby financial
institutions look harder at credit scores to ensure the factor that apparently
tripped up Mr. Lindsey-too many credit inquiries-didn't result in denials to
creditworthy borrowers. All of this leads us to wonder if the credit denials of
any low-income, immigrant, of color, or elderly credit applicants resulted in
similar introspective industry discussions.
The underlying data may be inaccurate. Credit scores are based on reports
from the main credit bureaus, even though these reports often contain
errors. The Home Buyer Assistance and Information Center, located in
Oakland and serving consumers in the San Francisco Bay Area, estimates
that at least half of all credit reports reviewed by trained counselors contain
errors. What may be an inconvenience for many becomes a significant
barrier to credit for people who lack the resources to discover the mistake,
appreciate its significance, and correct the error. Further, we now know that
unscrupulous creditors, such as predatory mortgage lenders, often do not
report their borrowers' good payment history to credit-reporting agencies in
order to keep them in the subprime market.
People who understand the game can improve their score. With some
knowledge about how credit scores are derived, credit applicants can
improve their credit scores. Prospective borrowers can even pay a fee to find
out how to improve their score. Apparently, such programs are being offered
by none other than the companies that devise the credit-scoring model
themselves. But which consumers will find out about these services, and
who will pay for them? Is the person who opened a new account or closed an
old one in order to manipulate her score really a better credit risk than she
was before she was advised to make these changes? Is she really more
likely to pay off her mortgage than the applicant who did not know how to
manipulate her score?
Disparate levels of assistance. Much can happen in the handling of a home
loan application. Often, a lender or broker wants to see additional
documentation to support the application of a nontraditional borrower.
Problems can arise when applicants are not given equal assistance in
securing the necessary documentation. Testing conducted by fair housing
councils in California revealed that customers of color are treated differently
than white customers upon entering a bank or thrift, less often given a home
loan application, less often encouraged to speak to bank staff, and less often
given key information that could strengthen their application.
The two-tiered banking system is perpetuated and punishes the victim.
Disturbingly, credit-scoring models may downgrade borrowers who have
accounts with finance companies or subprime and payday lenders. These
borrowers are in the subprime market because they and their neighborhoods
have been abandoned by mainstream banks and thrifts. A recent CRC study
of subprime borrowers in California revealed that a shocking 72 percent of
respondents did not even approach a bank or thrift for their mortgage loan,
even though most reported they had seen their credit score or credit report
and that it was "good" or "excellent." These figures are consistent with
estimates by Fannie Mae that up to 50 percent of borrowers in the subprime
market could have qualified for prime loans. Using the subprime market may
lower one's credit score, essentially punishing those with few real or
perceived mainstream credit alternatives, many of whom have good credit.
Not all borrower behavior is based on the values that likely underlie creditscoring models. Credit-scoring models are based, by and large, on how the
majority of "mainstream" consumers use credit. Such models are designed
to match credit applicants with the manifest behavior of middle-class
consumers. It is unclear how such models account for our legacy of
discrimination in access to credit. Credit-scoring models that penalize people
with no established credit are not a good indicator of whether a borrower will
repay the mortgage. Instead, lenders should accept alternate forms of
credit, such as utility and rent payments, as evidence of a borrower's
creditworthiness.
The Need for Secondary Review
Given the disparities that may result from credit decisions based solely on
credit scores, there is a role for secondary review of loan applications.
Unfortunately, existing secondary-review programs can appear more
theoretical than real, merely affirming the initial decision to deny low-cost
credit to low-income borrowers and borrowers of color. In designing and
implementing a process for secondary review, the following principles should
be observed:
Clear guidelines must be established. The danger of disparate treatment of
applications based on impermissible considerations, such as race, gender,
and age, are heightened when underwriters are allowed to override creditscore determinations. Thus, clear rules regarding overrides must be
developed and applied consistently. When exceptions or overrides are made,
the file should clearly reflect the reasons for doing so.
Focus on compensating factors for low-side overrides. Override guidelines
should be geared toward ensuring that applicants whose credit scores fall
below a given cut-off will be evaluated in a comprehensive fashion.
Underwriters should review the whole file, considering character issues. For
applicants with little or no credit history or those with spotty credit,
underwriters should consider the existence of alternate credit, such as utility
payments and history of making housing payments in a timely fashion. This
is especially important for applications for prime credit, because denial could
mean the unnecessary and costly relegation of a creditworthy borrower to
the subprime, higher-cost, loan market.
High-level review. Secondary reviewers who consider overriding a decision
based on credit score should be senior-level staff. The more people at an
institution who may override a credit decision, the more opportunity for
applications to be treated differently, the more risk of fair lending violations.
Override authority should rest with a small number of key staff.
Fair lending training at all levels. Staff at all levels of the institution should
be trained in fair lending and its implications for the institution's use of
credit-scoring models. The same should hold true for mortgage brokers who
account for the majority of home loans today. Institutions should have clear
nondiscrimination policies that are adhered to at all stages of the loan
process.
Periodic loan file review. Implementation of a company's credit-scoring
policies must be monitored periodically for consistency in acceptance and
denials of home loan applications, as well as the terms of loans originated.
All loans that have gone through secondary review must be examined and
analyzed to determine whether the secondary review and override process is
having a disparate impact on any group. Similarly, lenders should review
whether the company's general use of credit-scoring models is having a
disparate impact on protected classes and should revise the model or its
usage appropriately.
Equal assistance to loan applicants. Lenders and brokers should always and
consistently explain to credit applicants the meaning and significance of their
credit scores, and they should assist all borrowers equally in improving their
credit scores to qualify for a loan. Lenders should develop a policy on how to
assist applicants who disagree with an initial determination of the lender.
Heavy Reliance on Credit Scoring Means More Must Be Done to Ensure Equal
Access to Credit
Prime lenders must develop better marketing, outreach, and products for
underserved communities. Prime lenders need to better serve qualified lowincome, elderly, and immigrant borrowers and borrowers of color. The fact
that half of all subprime borrowers might qualify for prime loans means that
thousands of borrowers are losing thousands of dollars in home equity and
wealth because they are not being well served by the prime lending banks,
thrifts, and mortgage companies. The other side of this equation is that
these borrowers also represent lost business opportunities for financial
institutions. Los Angeles Neighborhood Housing Services recently reported
having difficulty finding prime lenders to originate home loans to hundreds
of high-credit-score borrowers who presented linguistic and other
underwriting challenges.
Refer qualified borrowers up for prime products. Several banks and thrifts
own subprime lending subsidiaries and affiliates that do not refer qualified
loan applicants with appropriately high credit scores to the prime lending
bank or thrift. Given that subprime applicants are more likely to be people of
color and the elderly, failure to have an effective referral up program raises
serious fair lending questions.
Improve HMDA. The Federal Reserve Board must help root out
discrimination in home lending more aggressively by enhancing Home
Mortgage Disclosure Act (HMDA) data to include credit scores and the annual
percentage rate on all HMDA-reportable loans. Without such price and credit
data, HMDA is very limited. Each year, community groups analyzing HMDA
data note disparities in lending. Each year, industry groups respond by
pointing out the limitations of HMDA. At the same time, industry groups
continue to oppose efforts to include credit-score data in HMDA, and they
have successfully lobbied the bank regulators to postpone implementation of
changes to HMDA that will include the reporting of APR data on home loans
for the first time.
Investigate these issues further. The Federal Reserve should conduct a study
that includes a review of existing loan files to examine the impact of credit
scoring on borrowers, especially protected classes. As with credit-scoring
models, the public is in the dark when it comes to the validity of credit
decisions. The Fed, which has access to bank loan files, can illuminate these
issues for the public, thereby enhancing the public's faith in the lending
industry. The Boston Fed went a long way in this direction when it developed
its study on mortgage lending and race in the early 1990s.
Conclusion
Credit is not available to all consumers equally, and the public knows it. The
National Community Reinvestment Coalition commissioned a national poll,
which found that three-quarters of Americans believe steering minorities and
women to more costly loan products than they actually qualify for is a
serious problem. Eighty-six percent feel that laws are needed to ensure
banks do not deny loans to creditworthy borrowers based on race, religion,
ethnicity, or marital status. Prime lenders are missing out on significant
business opportunities, and the public continues to view banks, thrifts, and
mortgage and finance companies with distrust.
Response of Michael LaCour-Little
Wells Fargo Home Mortgage
Wells Fargo Home Mortgage strongly believes that credit scoring has
provided significant net benefits to both the mortgage industry and the
public. Credit scoring has helped to make mortgage credit more widely
available to all households, including traditionally underserved market
segments, and it has helped to fuel the growth in homeownership that has
occurred over the past decade. We welcome open public dialogue about
credit scoring and second reviews and, thus, we are pleased to address the
following questions.
1. What methods should lenders adopt to optimize the usefulness of
overrides, minimize their frequency, and ensure their use is in
compliance with fair lending laws?
Credit scores can incorporate only a limited set of factors. Overrides
tend to occur most frequently when certain important risk factors are
omitted from the credit score. Consequently, a high rate of overrides
may indicate that it is time to redevelop the credit score. In addition,
lenders should, as part of a comprehensive fair lending program,
institute procedures to monitor the incidence of overrides to ensure
they do not favor or disfavor any class of loan applicant
disproportionately.
2. What actions could lenders take to ensure that counteroffers are
extended fairly?
Monitoring counteroffers is just as important as monitoring the
incidence of overrides. Lenders may wish to establish a centralized
monitoring function within a staff department, such as the compliance
function, to ensure adherence to corporate policies and procedures
regarding credit scoring, overrides, and second reviews.
3. What measures and systems should be instituted to ensure that the
second review process is operating in a consistent and fair manner?
In connection with credit scoring, a second review process typically
reviews loan applications that do not meet credit-score guidelines-that
is, those that are turned down under strict reliance on the score.
Second reviews seek to determine whether compensating factors that
are not captured in the score are present and whether, on balance,
those factors outweigh the negative outcome of the scoring process.
Monitoring the use and outcomes of the second reviews is key.
Understanding the decisions made as a result of second reviews can
provide important information, ensure adherence to corporate policies
and procedures, and help to ensure there is no disproportionate effect
on any class of loan applicant.
4. What steps lenders can take to ascertain the level of staff's compliance
with its policies and procedures?
Often, effective monitoring processes are based on the principles of
quality assurance, testing samples of actual transactions to determine
defect rates, reporting results to management, and then initiating
corrective action as required. Corrective action might include broad
training, individualized coaching, and a range of more punitive
sanctions for repeated violations.
Response of Stanley D. Longhofer
Wichita State University
One of the most significant developments in the mortgage market over the
last decade has been the formation and growing acceptance of computerized
credit-scoring models as a supplement to-or a replacement for-traditional
manual underwriting techniques. Programs such as Fannie Mae's Desktop
Underwriter and Freddie Mac's Loan Prospector incorporate performance
information from literally hundreds of thousands of mortgage loans to
provide a fast, objective, and statistically reliable method for comparing the
complex trade-offs inherent in mortgage underwriting.
In addition to assisting lenders in risk assessment, these objective scoring
models can be a powerful tool for increasing consumers' access to mortgage
credit. Not only does their increased efficiency translate into reduced closing
costs for consumers-in and of themselves, a significant barrier for many
lower-income households-if used exclusively, these models could effectively
eliminate overt bigotry and disparate treatment from the underwriting
process, as protected class status is explicitly excluded from these models.
Thus, scoring models hold out great promise to make the mortgage market
more fair and accessible.
Ultimately, however, mortgage underwriting can never be fully relegated to
a scoring model, nor indeed should it be; subjective human evaluation will
always be essential for some portion of all mortgage applications. Why?
Despite the power of scoring models, there are often factors an underwriter
would like to consider for which there is insufficient historical data for
computers to analyze, or for which a subjective interpretation is required.
For example, a lender may wish to discount a period of past delinquencies
that can be traced to a documented medical problem from which the
applicant has recovered. Such "idiosyncratic" factors cannot be incorporated
into an objective scoring model, even though they may provide information
that is vital to underwriting credit risk.
This subjective analysis may, in fact, have further benefits in improving
access to mortgage credit, particularly for lower-income and minority
households. Research over the last two decades-including the notorious
Boston Fed study-has provided evidence that these households are more
prone to the very "application idiosyncrasies" that scoring models may be
unable to process. Thus, subjective analysis is a crucial step in ensuring that
creditworthy minority and lower-income households receive the credit for
which they are qualified.
At the same time, however, many perceive a dark side to the use of
"overrides" in the underwriting process. In particular, this subjective analysis
may allow lenders to inject (intentional or inadvertent) prejudicial bias back
into the underwriting process. On the flip side, lenders may be too unwilling
to reverse the conclusions of the scoring model, either because the
subjective analysis itself is too much effort or because secondary-market
purchasers may be unwilling to purchase loans that were originally
"rejected" by the scoring model. As a result, many consumer advocates are
skeptical that the benefits promised by mortgage-scoring programs will
actually be realized.
Thus, we are faced with the question of how to extract the benefits inherent
in scoring models while ensuring that any follow-up subjective analysis is
applied fairly and consistently. In other words, the challenge is to make sure
that any overrides to the objective analysis promote rather than hinder
credit-access objectives.
The main point we wish to make in this essay is that this problem is
fundamentally no different from what must already be done in the context of
a manual mortgage underwriting process. In fact, we argue that the term
"override" is a misnomer in the context of mortgage underwriting, as the
scoring model is not designed to provide a definitive underwriting decision.
To understand how subjectivity and "overrides" fit into the mortgage-scoring
process, it is important to understand how scoring models are used and how
they are not used.
The process of mortgage underwriting is essentially the same, whether it is
done manually or with scoring models. An applicant's characteristics are
compared to an explicit set of "ideal" standards (for instance, maximum
expense and loan-to-value ratios, maximum number of delinquencies,
sufficient verified liquid assets). Although these standards are stated as the
lender's "requirements," as a matter of practice all applicants who exceed
this ideal are approved, as well as many who fall short. This implies that the
lender's true minimum underwriting standard is lower than that required by
the objective guidelines.
Instead, these objective standards are used to sort the applications into
three groups that we characterizes as Yes, No, and Maybe. Applications that
possess all of the ideal characteristics (the Yes group) are almost universally
approved. When they are rejected, it is usually because of a material change
in the information that put them into the Yes group to begin with (for
example, the applicant suffered a sudden layoff).
Similarly, the No group consists of applications for which no further analysis
is necessary because they clearly represent too great a credit risk.
Applicants in this group may have severe blemishes on their credit reports,
very unstable income, or high proposed loan-to-value ratios. As a practical
matter, the No group is generally quite small, as such individuals will rarely
even complete the application process.
The remaining applications represent the vast group of Maybes, which must
be reevaluated using more subjective analysis. At this stage, the underwriter
attempts to ascertain whether the applicant's favorable characteristics are
sufficient to outweigh any factors that fail to meet the ideal standard, or if
there are mitigating circumstances that offset the fact that the application
does not meet the ideal standards.
Whether a scoring model or a manual underwriting model is employed, the
purpose of the objective analysis is not to determine which applications
should be approved and which should be denied, but rather to isolate those
applications that require further subjective evaluation. There are several
ways in which scoring models can improve the integrity and efficiency of the
subjective process. First, automated systems can process many more
applications much more quickly than a manual analysis. This not only
shortens the time lapse between application and loan closing, it also reduces
the cost of processing relatively standard applications, freeing up an
underwriter's time to focus on the Maybe group.
Second, scoring models are developed using objectively verified performance
information, and therefore they can do a more effective job of assessing risk
layering or considering the trade-offs among different factors. For example,
is a 20 percent front-end ratio enough to offset a 45 percent back-end ratio?
Is a spotless credit record over the last year enough to offset three 60-day
mortgage delinquencies that occurred two years ago? While underwriters can
make subjective assessments of such trade-offs, scoring models can do this
quickly, objectively, and consistently across applications. The upshot is that
scoring models effectively reduce the number of Maybes (generally moving
many into the Yes group), once again allowing underwriters to focus their
efforts on applications that really require human judgment.
Third, the purpose of the subjective analysis itself is different when used in
conjunction with a scoring model. Subjective analysis is used only if the
application contains factors that occur too infrequently in the general
population for the scoring model to accurately assess, or if the application is
missing some crucial information required by the scoring model. These same
judgments must be made with a manual underwriting process as well.
However, manual underwriting must also evaluate subjectively the impact of
risk layering. In other words, manual underwriting involves the subjective
consideration of both "irregular" applications and "marginal" applications, the
latter of which can be sorted objectively by a scoring model. Thus, using a
scoring model actually reduces a lender's reliance on subjectivity in making
underwriting decisions.
As described above, the intent of a subjective review is to collect and weigh
all of the relevant information in order to come to a Yes or No decision for
each application that a scoring model identifies as a Maybe. Clearly, a
subjective review does not "override" an underwriting decision made by the
scoring model, as no such decision is actually made. Instead, the subjective
review comes to a Yes or No underwriting decision that the scoring model
explicitly recognized it could not make.
This is in contrast to what typically occurs with the use of credit scores in
making consumer credit decisions. With credit cards and other personal
loans, an applicant's score, as reported by a credit bureau, is often the only
factor a lender considers, and deviations from a predetermined cut-off are
relatively infrequent. In this context, the term "override" is perfectly
appropriate to describe, for example, a decision to lend to an applicant
whose score does not meet the cut-off.
Mortgage lending decisions involve much more complex trade-offs than
consumer credit, however, so lenders never rely solely on a credit bureau
score the way they may for unsecured consumer credit. In addition, the
opportunity to subjectively review the Maybe group is essential if lenders are
to use scoring models to create greater access to credit. If the subjective
process were eliminated or curtailed in a meaningful way out of concerns
about fairness or bias, the efficiency of a scoring model would be
compromised.
For example, if subjectivity were eliminated, lenders would be forced to
either deny loans sorted into the Maybe group or lower the bar defining what
constitutes a Yes. If the first path is taken, minority and lower-income
applicants would bear the brunt of this policy, because of their greater
likelihood of falling into this group. On the other hand, if the Yes bar is
lowered, then the cost of mortgage credit would have to increase to offset
the poor underwriting decisions the scoring model would be forced to make.
Once again, this would disproportionately affect lower-income applicants
because their ability to afford home ownership is affected more directly by
mortgage pricing.
The real question, therefore, is how to make sure that any subjective
analysis is conducted both fairly and accurately. Consistency across
applications is the key. Yet this is inherently difficult, given that these
applications require subjective analysis precisely because they are unique
and not completely comparable with others. As a result, a subjective process
can mask illegal discrimination, both intentional and inadvertent.
It is important to acknowledge, however, that this problem is fundamentally
no different from a fair lending perspective than it always has been with
manual underwriting. Thus, the techniques that lenders should apply to
monitor subjective analysis for compliance with fair lending laws are the
same with scoring models as they are with manual underwriting.
While there are differences in the supporting role played by subjectivity with
scoring models versus manual underwriting, we believe these differences
give scoring models a unique and important role in expanding access to
mortgage credit. Their superior ability to assess the layering of risks
(especially in the case of marginal applications) significantly reduces the
number of applications to which subjectivity is applied. Scoring models also
greatly improve underwriting efficiency, in part by allowing lenders to focus
their underwriting efforts on applications that are too unique for computers
to analyze. Furthermore, these models provide a benchmark for lenders in
conducting their subjective assessments, giving them better information with
which to make their evaluations. In the end, lenders' ability to combine
scoring models and subjective analysis will bring the full power of scoring
models to promote fair lending and broader credit-market access.
This installment concludes the five-part series of articles on credit scoring
and fair mortgage lending. Many thanks go to the respondents that
contributed to the articles-they brought a diversity of perspectives on this
complex and often controversial subject that was enlightening and
challenging.
The Mortgage Credit Partnership Credit Scoring Committee's goal has been
to raise awareness about the fair lending implications of credit scoring. We
hope the dialogue we have started will keep these issues at the forefront as
the use of credit scoring increases.
Mortgage Credit Partnership
Credit Scoring Committee
The Committee comprised Community Affairs representatives from the
Federal Reserve Banks of Boston, Chicago, Cleveland, San Francisco, and St.
Louis and the Board of Governors of the Federal Reserve System. The
Committee was chaired by Michael Berry, Federal Reserve Bank of Chicago.
@FedFRASER