■ ■ ■ Cross-channel differences in pricing;
■ ■ ■ Pricing and fee differences by and across loan originators;
■ ■ ■ Broker pricing analysis based on aggregating a broker’s loans
across HMDA reporters;
■ ■ ■ Redlining and reverse redlining analysis of reverse mortgages
and home equity lines; and
■ ■ ■ Product steering between fixed- and adjustable-rate mortgages,
or between closed-end loans and home equity lines.
Regulators and others will also be able to search for indicators
of potential predatory lending patterns based on loan product
and borrower characteristics that some consider to be “higher
risk,” such as teaser rates, non-amortizing products, high-fee loans
and high-debt-to-income borrowers. In the context of redlining
analysis, it will be possible for lenders to refine peer comparisons
based on the new channel information, by comparing a lender
to other lenders that have the same types of distribution channels (retail, wholesale, or correspondent). Beyond that, regulators
could use credit score, LTV, DTI and loan product information
(ARM versus fixed in addition to conventional versus government)
to evaluate whether differences among lenders in minority area
lending penetration may be attributable to differences in their
product focus and credit policies.
Of course, members of the public will also have greater access
to some of the key determinants of underwriting and pricing
decisions, as well as much more complete data regarding loan
characteristics, which can be expected to provide fertile ground
for class action plaintiff attorneys. The new data-rich environment
may allow litigators to present a more credit prima facie case to a
court when disparities are found than is the case with the current
HMDA data, because the disparities would be estimated after
controlling for a broader range of pricing and underwriting factors. That, in turn, could make it easier for class action lawyers to
survive a motion to dismiss and to get a class certified. The data
may also provide opportunities for more narrowly targeted claims
based on such things as disparities in points and fees or origination
charges; product-specific claims relating to equity loans, reverse
mortgages, ARM products, or “non-traditional” products; and age
discrimination claims. Similarly, there will be increased reputation
risk based on advocacy group studies of the data.
What’s a Banker to Do?
The foregoing considerations should shape how financial institutions
analyze their own data. Mortgage lenders will need to be more diligent
than ever in evaluating themselves for fair lending and reputation
risk. Continuing to perform regular monitoring of underwriting,
pricing and redlining risk will remain as important as ever, but will
not be enough. Lenders will need to get creative and think about
the potential new risk indicators that could be mined from the new
HMDA data fields and monitor for those additional risks. If there
are prohibited basis disparities in terms of the new HMDA data
fields, it will be important to evaluate the reasons for those dispari-
ties and whether they are explainable in non-discriminatory terms.
This means performing more extensive analysis, understanding the
processes and policies that underlie the data, and evaluating whether
sufficient controls are in place to limit fair lending risk.
Here are some Key Risk Indicators to consider:
■ ■ ■ Are there pricing or fee differences among loan originators or
branches that create fair lending risk?
■ ■ ■ Are there prohibited basis disparities in subcomponents of
pricing, particularly those that may be subject to discretionary adjustments?
■ ■ ■ Are discretionary pricing concessions, lender credits, and fees
sufficiently controlled, justified, and documented to avoid fair
■ ■ ■ Are there disparities in broker compensation, at either the
portfolio level or for individual brokers?
■ ■ ■ Are there unexplained disparities based on age or for specific
race and ethnicity subcategories in any of the risk indicators?
■ ■ ■ Does the distribution of home equity or reverse mortgage lending based on neighborhood racial or ethnic demographics
differ from the distribution of your forward mortgage lending
or that of peer lenders?
■ ■ ■ Are there sufficient controls regarding which products are offered to a borrower, and are there differences in incentive compensation across product classes that may create steering risk?
Time is of the Essence
Finally, the velocity of fair lending monitoring may need to increase. The Bureau has indicated that it plans to start releasing the
data to the public earlier than has traditionally been the case—as
soon as a month or less after the annual March 1 data submission
deadline, instead of late September of each year. In addition, large
institutions will be required to report their data on a quarterly
basis starting in 2020, and the quarterly data submissions can be
expected to be released publicly on a similar timely basis. These
regulatory schedule changes will put a greater emphasis on timely
internal monitoring to stay a step ahead of the regulatory examiners and others who might use the data. ■
ABOUT THE AUTHOR
DAVID SKANDERSON is a vice president in the Washington, D.C.
office of Charles River Associates, an economic consulting firm. He
spends his workdays pouring through data and estimating complex
statistical models to help lenders assess, monitor and manage their
fair lending risk, including the evaluation of credit scoring systems. He
also serves as an expert in mortgage litigation matters. Dr. Skanderson
previously led departments responsible for fair lending analysis,
HMDA compliance, and compliance loan review at Washington
Mutual Bank. He can be reached at email@example.com.
The views and opinions expressed herein are those of the author and do
not reflect or represent the views of Charles River Associates or any of
the organizations with which the author is affiliated.
In addition, large institutions will be required
to report their data on a quarterly basis
starting in 2020, and the quarterly data submissions
can be expected to be released publicly
on a similar timely basis.