The data fields proposed to be excluded from public reporting
are as follows:
■ ■ ■ Universal loan identifier
■ ■ ■ Date of application
■ ■ ■ Date of action taken
■ ■ ■ Property street address and zip code
■ ■ ■ Credit score
■ ■ ■ Automated underwriting system result
■ ■ ■ Loan originator’s NMLS number
■ ■ ■ Optional free-form text fields that may be used to report race,
ethnicity, name and version of credit score model, reasons for
denial, and automated underwriting system name.
Of those excluded fields, the application number, application date
and action date are already excluded from public reporting, currently.
The data fields to be modified for public reporting are as follows:
■ ■ ■ Loan amount: midpoint of the $10,000 interval into which
the actual value falls, with a separate indicator of whether the
loan amount exceeds the applicable conforming loan limit.
■ ■ ■ Applicant age: discrete ranges (less than 25, 25 to 34; 35 to 44;
45 to 54; 55 to 64; and 65 to 74) plus an indicator of whether
the age is 62 or higher (which effectively splits the 55 to 64 bin
into 55 to 61 and 62 to 64).
■ ■ ■ Debt-to-income ratio: actual value if it is greater than or equal
to 40% but less than 50%, and discrete intervals otherwise (less
than 20%, 20% to less than 30%, 30% to less than 40%, and
50% to less than 60%).
■ ■ ■ Property value: midpoint of the $10,000 interval into which
the actual value falls.
Future disclosure of loan amount will actually be less granular
than the approach of publishing loan amounts rounded to the
nearest $1,000, as reported by the lender. See sidebar for all of
the data fields to be reported in unmodified form.
The proposed Policy Guidance is silent on whether the Bureau would report that ethnicity or race were collected based on
visual observation or surname, as opposed to self-reported by
Fair Lending Monitoring:
Now More than Ever
The annual public release of the HMDA data is typically accompanied by a statement like the following one from the FFIEC’s
press release on the 2016 HMDA data (September 28, 2017):
“The current HMDA data alone cannot be used to determine whether a lender is complying with fair lending laws.
The data do not include many potential determinants of
loan application and pricing decisions, such as the applicant’s credit history and debt-to-income ratio, the loan-to-value ratio, and other considerations. Therefore, when
examiners conduct fair lending examinations, including ones
involving loan pricing, they analyze additional information
before reaching a determination about an institution’s compliance with fair lending laws.”
This statement will remain true, but to a far lesser extent.
Regulatory agencies will have access to a much wider array of
data than in the past and effectively have it at their fingertips.
Regulators will not have to issue a pre-examination data request
or Civil Investigative Demand to start screening financial institutions for risk indicators, or for building regression models to
test for disparities.
As a result, the process of scoping and targeting fair lending
regulatory examinations can be expected to change and accelerate dramatically. Currently, agencies must rely on a limited
set of data for initial scoping, and later obtain “HMDA Plus”
data fields as part of a pre-examination information request.
In the future, they will have the rough equivalent of the current HMDA Plus data for the universe of HMDA reporters in
a highly standardized format, in March of each year. This will
allow them to develop sophisticated data screening, data mining
and statistical modeling routines that can be applied uniformly
across the HMDA reporter universe to sift through and identify
institutions with indications of elevated fair lending risk or other
compliance risk. Their HMDA-based statistical models will still
be subject to significant limitations because they will not include
all underwriting and pricing factors, but they will be much more
comprehensive than they are currently. This is because the most
important determinants of differences in credit outcomes across
consumers will be available for modeling.
Opportunities for Regulators
to Target More Precisely
To understand how the new HMDA data will allow more refined
analysis by regulators, consider how HMDA-based pricing analysis
will change. The old HMDA data provides very limited ability to
perform analysis of pricing disparities:
1. Currently, it is only possible to test whether there are prohibited
basis disparities in the percentage of loans with reportable APR
spreads and in the average sizes of those spreads.
2. There are only a handful of pricing-related factors that could
be used in a regression model that attempts to explain disparities, and they do not include the most important loan-level
• Loan type
• Loan amount
• Property type
• Loan purpose
• Ratio of loan amount to income
• General occupancy status
• Lien status
Regulatory agencies will have
access to a much wider array of data
than in the past and effectively
have it at their fingertips.