as well. Examples of the specific
exchanges named as indicative of
■■ ■ A loan officer cited the bank’s
minimum credit score inconsistently, saying to the African-American tester that the minimum score
was approximately 20 points higher
than bank policy stated.
■ ■ ■ A loan officer provided written estimates
of closing costs and monthly payments
to the white tester, but not to the African-American tester.
■ ■ ■ When communicating the home purchase
price for which the testers would qualify, a loan
officer encouraged the white tester to call her if
she found a home up to $35,000 more expensive
than the tester qualified for, while telling the African-American tester the same for a home only $10,000 above
the price for which the tester qualified.
■ ■ ■ A loan officer estimated higher closing costs for the African-American tester.
■ ■ ■ A loan officer cited different allowable debt-to-income ratios
for the two testers, indicating a lower, more stringent ratio to
the African-American tester.
■ ■ ■ A loan officer provided a greater level of assistance to the white
tester by providing recommendations for realtors in the area
and describing desirable neighborhoods in town in which to
look for a home, while providing no such advice to the African-American tester.
A strong fair lending program will address this risk through
policy documents and robust training for mortgage loan officers,
inclusive of guidance for equal treatment of customers throughout the loan process. To incorporate customer interaction into a
compliance monitoring initiative, your institution may consider
employing mystery shoppers pre-emptively as both a preventive
(as loan officers may be informed they could be shopped) and
detective control. While many other aspects of a redlining review
can be performed by analyzing data, customer interactions are
best monitored through qualitative means.
Receipt of Applications
After determining the implications of your branch locations and
the manner in which loan officers are interacting with customers,
the analysis turns to mortgage applications actually received by
the bank. Examiners will compare the percentage of mortgage
applications received by the bank to corresponding percentages
of minority populations in nearby areas. They will then compare
the number of applications received from minority-concentrated
areas by your institution to numbers of applications received by
peer institutions in the same areas. Ideally, of course, you will
already have performed a similar analysis and will not be surprised
by the conclusions drawn by examiners.
In the event your analysis shows that the applications received
by your bank do not come close to reflecting the demographic
makeup of the areas surrounding your branches, the next step is
to determine (and document!) why that is the case. For instance,
is the area with higher minority populations not yielding mortgage applications because the housing there is principally rental
property? The same is true when your bank receives fewer applications from majority-minority and high-minority populated
areas than peer institutions; being able to attribute trends to a
reasonable explanation can prevent examiners from concluding
that the discrepancy is the result of discrimination by the institution. In concert with providing a documented explanation for any
disparity, examination risk in this space can be further mitigated by
planning and executing reasonable efforts to increase applications
from underserved areas. This could include deploying loan officers
to hold mortgage education seminars in the region, increasing
advertising efforts or diversifying advertising content, or assigning
a greater loan officer presence to branches closest to the area. Of
course, this could also indicate that some applicants are somehow
discouraged from applying and the causes for that will need to be
evaluated and resolved. It could be as simple as word-of-mouth
that occurs entirely outside of the bank’s influence or indicative of
an unwritten “policy” or perhaps an internal misunderstanding.
Alternatively, it may be that your institution is receiving applications from all areas within its REMA, but the HMDA data the
bank reports does not accurately reflect the activity occurring at
branches. By ensuring that branch staff understands the thresholds
that constitute a HMDA-reportable application and enhancing
controls for data integrity at the branch level (such as through
training, job aids, and automation where possible), an institution
may be able to explain to regulators that questions around receipt
of applications resulted from incomplete data submissions as opposed to redlining. Of course, if the HMDA data is inaccurate,
the bank will need to take corrective action on that issue too. I S T O