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Financial Services Regulatory
Managing Fair Lending Risk in Wholesale Mortgage Pricing*
A Joint Article by PricewaterhouseCoopers LLP and K&L Gates LLP
March 2010
This article was first published in March I April 2010 issue of ABA Bank Compliance.
Table of ContentsIntroduction .................................................................................. 1
Wholesale lending – legal theories and risks ....................................... 1
Legal framework ............................................................................ 1
Enforcement cases ......................................................................... 1
Class actions ................................................................................ 2
Monitoring is the safest policy .......................................................... 2
Structuring and implementing a monitoring program .............................. 2
Develop an “external view” of wholesale fair lending risks through statistical analysis of HMDA data .......................... 3
Select appropriate non-HMDA loan price measure(s) for effective fair lending compliance monitoring ........................................ 4
Identify “baseline” wholesale loan pricing differences between protected class and control group borrowers and use these baseline pricing differences to risk-focus the fair lending regression analysis .................................................... 5
Develop regression models to estimate “demographically-neutral” loan prices for each wholesale loan .................................................. 6
Test for statistically significant “unexplained” loan price differences between protected class and control group borrowers at various geographic levels and by originating broker ........... 9
Addressing the results ................................................................... 10
Conclusion ................................................................................. 10
This article was first published in March I April 2010 issue of ABA Bank Compliance.
Managing Fair Lending Risk in Wholesale Mortgage Pricing 1
IntroductionOne of the thorniest fair lending compliance issues currently confronting banks and other mortgage lending institutions is whether to monitor wholesale loans – i.e., loans originated by mortgage brokers – for potential disparities in pricing by race or national origin.1 Historically, many small and even some large institutions opted against monitoring wholesale loans for two key reasons. First, there is no legal authority expressly stating that lenders are legally responsible for pricing differences resulting from the acts of independent, third party mortgage brokers. Further, even if lenders decide to address such differences, it is extremely difficult if not impossible to prevent them from arising.
The landscape is changing, however. Stepped up federal and state enforcement efforts – including a notable increase in investigations and settlements involving smaller players – have captured the attention of many compliance officers and in-house counsel concerned with avoiding fair lending risk. These factors combined with strong signals from the Obama administration that lending discrimination will be a major enforcement priority2 and a wave of fair lending class action lawsuits have prompted many smaller banks and nonbank mortgage lenders to join the larger players and implement wholesale price monitoring programs.
Making the decision to monitor is just the beginning. Lenders that choose to invest the resources to create and implement a monitoring program have to address many practical challenges. The main goal is to develop and implement a program that will identify and effectively alleviate price disparities across different races and ethnicities while creating as little as possible disruption to the lender’s business. Balancing these considerations is difficult, but managing the fair lending risks associated with wholesale lending is now more important than ever.
Wholesale lending – legal theories and risks
Legal framework
It is well established that the two federal antidiscrimination statutes3 that are applicable to mortgage lending – the Fair Housing Act4 (“FHA”) and Equal Credit Opportunity Act5 (“ECOA”) – prohibit lenders from charging minority borrowers more for their loans than similarly situated non-minority borrowers.6 Less clear is whether these laws can be construed to hold a lender liable for price differences that are caused by the pricing practices of third party mortgage brokers.
Although there may be exceptions, it generally is the case that differences in loan price across borrower groups in wholesale mortgage transactions are driven by differences in broker compensation.7 Furthermore, mortgage lenders receive loans from multiple (in many cases hundreds or even thousands of)
independent, third party brokers – yet in some cases receive only a small handful of loans from any given broker in any given year. When loans originated by many different brokers are combined into one portfolio, they can result in differences in average price across borrower groups, even when no individual broker charged its minority customers more than its non-minority customers.8 A critical question from a legal and policy perspective is whether a lender should bear responsibility for these differences.
The legal answer to this question is complicated and unclear, because there is no express statute, regulation or judicial precedent that squarely addresses it. In summary,9 however, there are sound arguments under both the FHA and ECOA that lenders are not legally responsible for differences in broker pricing. In 2003, the United States Supreme Court issued a decision in a housing discrimination case, Meyer v. Holley,10 stating the FHA does not impose a nondelegable duty on persons to ensure that the parties with whom they do business do not discriminate. Rather, whether a person is liable for the discriminatory acts of another should be determined under traditional principles of vicarious liability – i.e., if there is a principal-agency relationship. Thus, unless a mortgage broker legally qualifies as a lender’s agent – which is almost never the case – a lender could argue that under Meyer, it cannot be held responsible for pricing discrimination resulting from broker conduct.
There also are arguments under ECOA that support the view that lenders cannot be held liable for broker pricing differences. Regulation B, the ECOA’s implementing regulation, states that a “person is not a creditor regarding any violation of [ECOA] committed by another creditor unless the person knew or had reasonable notice of the act, policy, or practice that constituted the violation before becoming involved in the credit transaction.”11 (emphasis added). Under this standard, a lender could reasonably argue that it cannot be held responsible for broker price disparities, because there is no way a lender could know “before becoming involved in the credit transaction” whether the broker’s pricing on any given loan was discriminatory as compared to the broker’s pricing on other loans.12
Enforcement cases
Although the federal antidiscrimination statutes do not state that lenders are liable for broker pricing disparities, and there are strong legal arguments supporting the opposite view, it is clear that from a policy perspective, federal and state enforcement authorities believe that lenders should bear such responsibility. In addition to the many wholesale fair lending investigations currently underway,13 there have been numerous federal and state fair lending settlements involving wholesale pricing.
Managing Fair Lending Risk in Wholesale Mortgage Pricing 2
For example, in 1996, the United States Department of Justice (“DOJ”) settled retail and wholesale price discrimination claims against Long Beach Bank, FSB. This was the first major wholesale pricing settlement and included an unprecedented payment of $3 Million in compensation to alleged victims, and an additional $1 Million to fund a consumer education campaign. In 2000, the DOJ settled wholesale price discrimination claims with Delta Funding Corporation.14 The settlement included a $7.25 Million Remediation Fund (the fund also was used to compensate borrowers in an earlier New York state lending practices settlement) and a requirement that Delta implement a wholesale price monitoring program. More recently, in 2006, the New York State Attorney General settled retail and wholesale price discrimination claims with Countrywide Home Loans, Inc. The Countrywide settlement included a $3 Million payment to fund a consumer education program and enhanced fair lending price monitoring for the company’s retail and wholesale lending channels. Further, in 2008, the Federal Trade Commission settled retail and wholesale price discrimination allegations against Gateway Funding, Inc. The Gateway settlement included a $2.9 Million suspended judgment and a comprehensive price monitoring program.
Most lenders settle fair lending investigations in an effort to avoid the expense and disruption of litigation. Unless a court hears a wholesale case and finds that lenders are not liable for broker pricing, or a law or regulation is passed that changes the way brokers earn their compensation, the industry can expect more wholesale pricing investigations in the months to come.
Class actions
In addition to the government enforcement activity against wholesale lenders, beginning in 2007, private plaintiffs began filing fair lending class action lawsuits against most major mortgage lenders.15 The cases involve retail, wholesale and correspondent lending, and the common theme is that the lenders’ discretionary pricing policies allow loan officers, brokers and/or correspondent lenders to deviate from “rate sheet” pricing. According to the plaintiffs, these discretionary pricing policies have an unlawful disparate impact on minority borrowers, because those borrowers wind up being charged more than similarly situated white borrowers. Many of these cases have settled, but a few remain active.16 It remains to be seen whether any of these cases will result in a judicial decision addressing whether lenders are responsible for broker pricing.
Monitoring is the safest policy
Historically, wholesale lenders resisted monitoring for pricing disparities because the law did not require it, and, more significantly, it is extremely difficult to meaningfully respond to price disparities when they arise in the wholesale context. Notwithstanding these considerations, in recent years many or
most of the largest wholesale lenders have adopted some form of wholesale monitoring process. Many factors prompted this development, including a desire to manage the problem, no matter how difficult, encouragement from banking regulators, and concerns about enforcement and reputational risk. More recently, smaller wholesale lenders have begun to monitor, either because they have been targeted in a fair lending investigation or have perceived an increase in fair lending risk.
Although monitoring wholesale pricing can be complicated, costs money and can potentially irritate the brokers on whom a lender relies for business, the benefits will in most cases offset the challenges. If structured and implemented properly, a monitoring program can help to materially reduce the very serious risks associated with fair lending enforcement and litigation. Further, lenders that implement monitoring programs can enhance their reputational standing with regulators, enforcement authorities and other parties by showing that they are committed enough to fair lending to expend the resources needed to monitor. In the current regulatory environment, these benefits can make a real difference.
Structuring and implementing a monitoring programAlthough the federal bank regulatory agencies have published their fair lending examination procedures,17 and many lenders leverage these procedures in the design of their overall fair lending compliance programs, there is currently no formal information available on how these agencies perform their own statistically-based fair lending tests of mortgage loan pricing. Additionally, based on our own experience with the fair lending examination practices of different agencies, we note that there is no uniform testing methodology or approach used by these agencies that a lender can reliably leverage in designing its own monitoring program. As such, there is no “silver bullet” approach – only different alternatives none of which is perfect or beyond potential regulatory critique. What we present below is a discussion of the important design decisions that need to be addressed in the design of a wholesale price monitoring program – as well as certain monitoring program features for consideration.
In general, the development of a wholesale price monitoring program involves the following steps:
Develop an “external view” of wholesale fair lending risks •through statistical analysis of HMDA data.
Select appropriate non-HMDA loan price measure(s) for •effective fair lending compliance monitoring.
Identify “baseline” wholesale loan pricing differences between •protected class and control group borrowers and use these
Managing Fair Lending Risk in Wholesale Mortgage Pricing 3
baseline pricing differences to risk-focus the fair lending regression analysis.
Develop regression models to estimate “demographically-•neutral” loan prices for each wholesale loan.
Test for statistically significant “unexplained” loan price •differences between protected class and control group borrowers at various geographic levels and by originating broker.
As necessary, implement appropriate corrective actions.•
In the following sections, more detail is presented around each of these steps.
Develop an “external view” of wholesale fair lending risks through statistical analysis of HMDA dataIt is widely accepted that analysis of a lender’s Home Mortgage Disclosure Act (“HMDA”) data, by itself, is insufficient to determine whether there is evidence of potential lending discrimination.18 Nevertheless, many federal and state regulatory agencies, state attorneys general, and consumer advocacy groups target fair lending examinations and investigations to lenders that appear, from this analysis, to have elevated levels of fair lending risk in credit decisioning and/or pricing.
From a pricing perspective, these analyses are typically of the following form:
Identification of statistically significant differences in the •frequency by which “higher priced” loans are originated to protected class and control group borrowers; and
For those borrowers who received a “higher priced” loan, •identification of statistically significant differences in the average HMDA-reported price between protected class and control group borrowers.
Hispanic BorrowersWhite-Non Hispanic Borrowers
DifferenceStatistically Significant?
Frequency of APR Rate-Spread Reportable Loans
23.1% 15.6% 7.5% No
Average APR Rate-Spread Amount
3.78% 3.32% 0.46% Yes
“Higher Priced” loans are identified using HMDA’s APR rate-spread measure. It represents the amount by which the APR charged on a loan exceeds a contemporaneous conventional mortgage rate benchmark, and is only reported for loans where this rate spread exceeds a specific threshold (3 percentage points for first liens and 5 percentage points for second liens).19
Table 1 provides an example of this type of analysis for first lien loans.
In this case,
The lender originated “higher priced” loans to its Hispanic customers (a protected class) 7.5% more often than to its White-Non Hispanic customers (the control group). However, this difference is not statistically significant;20 and
Of the “higher priced” loans originated to both groups, Hispanic borrowers paid – on average – 46 basis points (0.46%) more in APR than White-Non Hispanic customers. This difference is statistically significant.21
We note that the results of these pricing comparisons are merely indicators of potential fair lending risk and not considered evidence of actual disparate treatment in loan pricing since the analyses do not control for any transactional differences between the two borrower groups (e.g., the average LTV for the Hispanic borrower group may be higher than the average LTV for the control group – which may explain fully or partially the higher loan prices charged to the former group). Said differently, these comparisons are not based on similarly-situated borrowers and, as such, could be explained by a number of legitimate reasons.
Despite their limitations, these analyses do provide a useful “external view” of your lending results and, as such, you are able to see your data as the federal and state enforcement agencies and potential third-party adversaries see it. Elevated fair lending risk may be
Table 1
Managing Fair Lending Risk in Wholesale Mortgage Pricing 4
present when observed pricing disparities are: (a) adverse to one or more protected class groups, (b) statistically significant,22 and (c) relatively large in magnitude.
One final point to note is that these HMDA-based tests are typically performed by external parties along various geographic and transaction dimensions. For example,
State regulators and enforcement agencies typically focus on •mortgage originations secured by properties located within their states. As such, performing these analyses at both the national and individual state levels would be appropriate.
Many federal regulators and enforcement agencies, as well as •local consumer advocacy groups, typically focus on potential pricing disparities to borrowers within localized Metropolitan Statistical Areas (“MSAs”).23 As such, these more granular geographic analyses are now increasingly common in fair lending compliance monitoring programs.
Certain transaction types may be analyzed separately from •others to either make the HMDA price comparisons more comparable, or to identify elevated fair lending risks in certain sub-segments that may be masked when aggregated to higher levels – for example,
First vs. second liens-
Owner occupied properties vs. Non-owner -occupied properties
Purchase vs. refinance loans-
Conventional vs. Government-insured (FHA/VA) loans-
Lenders may wish to consider structuring their HMDA analyses in a similar fashion.
Select appropriate non-HMDA loan price measure(s) for ef fective fair lending compliance monitoringIn light of the previous discussion, one might ask why we do not recommend the exclusive use of the HMDA APR Rate-Spread metric for fair lending price monitoring. The simple answer is that there are certain limitations to this loan price metric:
1. It only captures loan pricing data for a relatively small segment of a lender’s loan originations (i.e., its “higher priced” loans). Loan pricing data for the majority of originations is not available.
2. For those loans where it is reported, it is expressed as a relative price – that is, a deviation from a contemporaneous market benchmark rate – rather than as an absolute price metric such as APR.
3. For wholesale originations, it is not a direct measure of the discretionary fees paid by borrowers that create most, if not all,
of the fair lending risk in loan pricing – i.e., total broker fees.Because of these limitations, most lenders perform their internal fair lending analyses on one or both of the following loan price metrics – neither of which has these limitations:
Total broker fees
Total Broker Fees (“TBF”) represent the sum of:
Any up-front points• 24 paid directly by the borrower to the broker (customarily at closing), and
Any yield spread premium• 25 paid directly by the lender to the broker at closing based on the loan’s note rate.
We note that Total Broker Fees reflects differences in discretionary loan pricing across borrowers in terms of up-front points – as opposed to APR (discussed below) which reflects differences in discretionary and non-discretionary loan pricing across borrowers in terms of annual interest rate. As a loan price metric, TBF has the desirable property that it represents the purest measure of broker-based discretionary pricing in a wholesale loan transaction which makes it particularly useful for fair lending analysis. Additionally, since it incorporates both up-front broker fees and yield spread premiums, TBF reflects the totality of discretionary broker fees as well as borrower choices on how to pay these fees; for example, some borrowers may prefer paying less fees up-front in exchange for a slightly higher interest rate (i.e., yield spread premium), while other borrowers may prefer a slightly lower note rate (i.e., no yield spread premium) in exchange for paying higher up-front fees.
Regardless of how TBF is split between up-front points and yield spread premium, we note that – as a fair lending price metric – it also has its share of challenges:
Incomplete and/or inaccurate TBF data is not uncommon – •particularly the up-front broker points. To the extent feasible, data quality should be evaluated and appropriate corrections should be made as necessary,
TBF data on the separate first and second lien components of •piggyback transactions may not be individually meaningful; as such, analyzing TBF on the combined transaction may be more appropriate, and
Focusing fair lending analysis solely on TBF would not capture •any lender-based discretionary pricing differences such as price exceptions and lender fee waivers. To address this limitation, lenders should consider analyses of APR (discussed below) as well.
Finally, we note that most lenders impose caps on TBF to mitigate fair lending risk. While such caps represent a prudent preventative fair lending compliance control by weeding out the most aggressive
Managing Fair Lending Risk in Wholesale Mortgage Pricing 5
(and, consequently, the higher risk) brokers from a lender’s portfolio, they do not eliminate the need to monitor for potential TBF disparities between borrower groups since there can still be statistically significant disparities in TBF within the cap range.
Annual percentage rate
A loan’s annual percentage rate (“APR”) represents the total loan price measure disclosed by the lender under Section 226.22 of Regulation Z and is expressed in terms of annual interest rate rather than up-front points. As a loan price metric, the APR has the following desirable properties:
It is a standardized measure of loan price that is readily •available for virtually all lenders,
It reflects the loan’s annual note rate as well as any up-front •finance charges – thereby incorporating both rate and price differences into a single measure,
It captures the effect of TBF (both up-front broker points and •yield spread premiums), and
It captures the effect of discretionary lender-based pricing •differences – such as price exceptions, lender fee waivers, etc.
However, we note that APR analysis also has some challenges:
APR differences across borrowers reflect a number -of discretionary and non-discretionary price and rate differences – for example:
Discretionary pricing charges such as TBF and lender -price exceptions,
Non-discretionary pricing charges such as risk-based price -adjustments (e.g., FICO, CLTV, documentation type) and mortgage insurance premiums,
Variation in the general level of market interest rates over -time, and
Changes to a lender’s rate sheet structure over time. -
Because of this mixture of discretionary and non-discretionary •pricing differences, it is more challenging to isolate and test the purely discretionary pricing differences embedded within APRs relative to the direct measurement of these differences in TBF. This is because, unlike TBF, the discretionary component of APR can only be captured indirectly by first controlling for the non-discretionary loan pricing differences through regression analysis, and then assuming that all remaining APR differences are discretionary in nature. However, modeling the non-discretionary loan pricing differences is necessarily imperfect26 – which implies that the resulting indirect measure of discretionary pricing differences is also imperfect and, therefore, may yield false positive results.
Because it is expressed in terms of rate, small up-front price •
differences become even smaller APR differences since their impact is effectively spread out over the contractual term of the loan (e.g., 30 years). As such, APR analysis is best suited to detect pricing differences driven either by note rate or by relatively large up-front point / fee differences, and
The APR is sensitive to the trade-offs borrowers make between •up-front points and annual interest rate – and it is notoriously difficult to capture these tradeoffs through regression analysis. For example, borrowers who choose to pay up-front discount points to obtain a lower annual interest rate typically have lower APRs than borrowers who pay fewer discount points in exchange for a higher annual interest rate – even though the lender may be indifferent between the two sets of loan terms. Additionally, even if two borrowers have the same TBF, the borrower who finances these fees through a yield spread premium typically has a higher APR than the borrower who pays these fees through up-front points.
Given the considerations discussed above, typical practice is for lenders to focus solely on APR or to perform analyses based on both APR and TBF – the latter of which appears to be the most conservative route.
Identify “baseline” wholesale loan pricing dif ferences between protected class and control group borrowers and use these baseline pricing dif ferences to risk-focus the fair lending regression analysisOnce you have selected your non-HMDA loan price measures for monitoring, the next step is to identify “baseline” loan price differences – where by “baseline” we mean average price differences that do not control for differences in transaction characteristics between protected class and control group borrowers. Despite the limitations of this type of analysis27, the benefits are as follows:
It provides a clearer picture of elevated fair lending risk areas •than the HMDA-based analysis since:
It is based on more traditional measures of loan price (i.e., -APR and TBF),
The prices of all loans are included in the analysis (rather -than just HMDA rate-spread reportable loans), and
It includes a direct measure of broker discretionary -pricing (TBF).
Managing Fair Lending Risk in Wholesale Mortgage Pricing 6
It is based on the same loan price measures that would be •used by state and federal regulatory and enforcement agencies in fair lending examinations and investigations, and
It provides a useful baseline or starting point for subsequent •regression analysis as it captures the types and magnitudes of observed loan pricing differences that exist and, therefore, need to be explained.
Table 2 provides an illustrative example of the results of these “baseline” price disparity tests for a lender’s total first lien loan originations.
In this case,
The average first lien APR for Hispanic borrowers (the protected class group) is 6.89% – which is 47 bps higher than the average first lien APR for White Non-Hispanic borrowers (the control group). Based on a T-test, this difference is statistically significant.
The average first lien TBF for Hispanic borrowers is 2.89% (i.e., 2.89% of the transaction loan amount) – which is 87 bps higher than the average first lien TBF for White Non-Hispanic borrowers. This difference is also statistically significant.
These results indicate an elevated fair lending risk of loan pricing for first lien loans and, as a result, the lender should perform regression analysis to determine whether differences in non-discretionary transaction characteristics between the two groups can fully or partially explain the 47 bps APR difference and/or the 87 bps TBF difference.
In addition to providing a lender with the “baseline” loan pricing differences that need to be explained, these analyses can also be used to risk-focus the subsequent regression analysis to only those lending segments exhibiting elevated fair lending risk in loan pricing – for example, lenders may perform these analyses for different lending segments (such as first vs. second liens, owner occupied properties vs. non-owner occupied properties, conventional vs. FHA/VA loan products, etc.) and perform regression analysis only
Loan Price Metric Hispanic BorrowersWhite-Non Hispanic Borrowers
DifferenceStatistically Significant?
Average APR 6.89% 6.42% 0.47% Yes
Average TBF 2.89% 2.02% 0.87% Yes
on those segments with statistically significant pricing differences. While this practice is broadly consistent with federal bank regulatory examination procedure,28 there is currently a diversity of industry practice in this area – with some institutions performing such risk-focused testing, and others being more conservative by performing regression analysis on all segments regardless of the baseline pricing analysis results. (Table 2)
Develop regression models to estimate “demographically-neutral” loan prices for each wholesale loanOnce a lender has determined the scope of its regression testing (i.e., all loan originations or only those lending segments with elevated fair lending risk), the next step is to build APR and/or TBF regression models to control for transactional differences across borrower groups. This will allow a comparison of pricing between “similarly situated” protected class and control group borrowers and, as such, provides a more effective means to identify potential fair lending issues and to take appropriate corrective actions.
For fair lending purposes, the regression model is a statistical technique that allows a lender to estimate a “demographically-neutral” APR or TBF for each loan—where this “demographically neutral” loan price is based on statistically-derived relationships between certain non-discretionary transaction characteristics and observed loan-level prices across all borrowers. These loan price estimates are considered “demographically neutral” since they will be exactly the same for each “similarly situated” borrower regardless of each borrower’s race, ethnicity, or gender. A lender can then use these demographically-neutral loan price estimates to identify “unexplained” price differences for each loan which represent the amount of each loan’s APR or TBF that cannot be attributed to the legitimate transaction-based pricing factors contained in the regression model. As discussed further in the next section, lenders typically focus their fair lending corrective action program on statistically significant differences in these “unexplained” price differences between protected class and control group borrowers.
Table 2
Managing Fair Lending Risk in Wholesale Mortgage Pricing 7
As a very simplified example of fair lending regression modeling, we supplement the “baseline” TBF analysis from the previous section with data on the average transaction loan amount between the protected class and control group borrowers (Table 3).
Here we see that Hispanic borrowers, on average, have an average loan amount that is $120,000 lower than the average loan amount for the control group borrowers. This suggests that differences in average loan size may explain the observed 87 bps difference in average TBF across the two groups. However, before we make this conclusion, the following questions need to be addressed:
Is there statistical evidence supporting this apparent •relationship between transaction loan amount and TBF?
Is there a justifiable business reason for this relationship?•
How much of the 87 bps average TBF difference can be •attributed to the $120,000 average loan amount difference?
To address the first question, the graph below represents TBF and loan amount for the wholesale loans under analysis.
Hispanic Borrowers White Non-Hispanic Borrowers Difference
Average TBF 2.89% 2.02% 0.87%
Average Loan Amount $270,000 $390,000 ($120,000)
We can see from this graph that there appears to be a relatively strong inverse relationship between transaction loan amount and TBF across all loans in the analysis (each point represents an individual loan). The solid black line through the dots represents the estimated statistical relationship between these two variables that was obtained through ordinary least squares regression analysis. This relationship can be expressed in mathematical form as:
TBF = 4.53 – 0.0000627 x Loan Amount
We note that the full regression analysis results (not shown) confirm that the relationship between TBF and transaction loan amount is statistically significant (i.e., there is less than a 5% probability that the observed inverse relationship is due to random chance). We also note that the estimated slope of the regression line (-0.0000627) indicates that, on average for these loans, a $10,000 increase (decrease) in average transaction loan amount leads to a 6.27 bps decrease (increase) in TBF.
Now that we have demonstrated through regression analysis that there is a statistically significant relationship between TBF and transaction loan amount, the next question to address is whether there is a justifiable business reason underlying this estimated relationship. According to our interviews with originators, to achieve a certain target dollar amount of compensation per loan (e.g., $7,800), they need to charge a greater percentage of the loan amount on smaller loans, and can charge a smaller percentage of the loan amount on larger loans. This pricing behavior therefore drives the observed inverse relationship between TBF and transaction loan amount.
Table 3
Managing Fair Lending Risk in Wholesale Mortgage Pricing 8
Finally, to address the third question, a lender can use the estimated regression equation presented above to determine how much of the 87 bps average TBF difference can be attributed to the $120,000 average loan amount difference. To do this, the estimated regression equation is used to predict a “demographically neutral” TBF for each loan based on each loan’s specific transaction loan amount. An average of these TBFs for each group is then calculated as shown in the second row of Table 4.
From these results, we note the following:
Based on transaction loan amounts, the regression model •estimates a “demographically neutral” average TBF for Hispanic borrowers of 2.84% – which is 5 bps lower than the 2.89% average TBF that was actually charged. Therefore, according to this analysis, there is an unfavorable 5 bps unexplained TBF difference for Hispanic borrowers.
For White Non-Hispanic borrowers, the model estimates a •“demographically neutral” average TBF of 2.08% which is 6 bps higher than the 2.02% that was actually charged. Therefore, there is a favorable -6 bps unexplained TBF difference for White Non-Hispanic borrowers.
In total, the regression model explains 76 bps of the original •87 bps “baseline” average TBF difference between the two borrower groups. An 11 bps difference that is unfavorable to the protected class group remains unexplained.
In the next section, we will discuss how these unexplained price differences can be used to focus fair lending corrective actions. However, before turning to that topic, we conclude this section with some final points on these regression models:
In actual practice, TBF regression models typically contain •more factors than just loan amount. Other transaction characteristics that may help explain TBF differences across borrowers include loan product, prepayment penalty term, occupancy status, property type, property location (e.g., state and/or MSA), and settlement month. It is important to note that factors included in the final model should have statistically significant relationships to TBF and their relationships to TBF
should be intuitive, consistent with known business practices, and reviewed with legal counsel.
One predictive factor that has received significant attention •from certain regulatory agencies is “Originating Broker”. Many lenders control for the originating broker in their wholesale regression models to reflect the fact that different brokers can charge different average TBF to their customers – just as different consumer service providers (such as plumbers) may charge different average fees for similar services. If a wholesale lender does not control for the originating broker, these cross-broker pricing differences will likely show up as unexplained TBF differences for one or more protected class groups29 – even in the absence of discriminatory pricing practices of individual brokers. Based on our recent experience, it appears that certain regulatory agencies are objecting to the use of “Originating Broker” as a regression model factor – thereby raising the legal, compliance, and reputational risks for wholesale lenders.
The process for analyzing APRs using regression analysis is •similar to the TBF example above. The primary differences, however, are:
APR regression models tend to have a significantly larger -number of predictive factors since they must also control for non-discretionary pricing differences driven by the lender’s rate sheets – for example, the following represent typical APR regression model factors: Loan Product, Occupancy Status, Loan Purpose, Property Type, Number of Units, Prepayment Penalty Term, Mortgage Insurance Amount, Rate Lock Date, Rate Lock Length, FICO Score, CLTV, Documentation Type, Loan Amount, and Property Location (e.g., state and/or MSA).
Lenders may develop separate APR regression models for -different product segments – for example, Fixed Rate vs. ARM products, or Prime vs. Subprime loans.
“Originating Broker” can also be included as a factor in APR regression models; however, the same considerations apply as discussed above for TBF regression models.
Hispanic Borrowers White Non-Hispanic Borrowers Difference
Average TBF 2.89% 2.02% 0.87%
Average “Demographically Neutral” TBF
2.84% 2.08% 0.76%
Average Unexplained TBF
0.05% -0.06% 0.11%
Table 4
Managing Fair Lending Risk in Wholesale Mortgage Pricing 9
Test for statistically signif icant “unexplained” loan price dif ferences between protected class and control group borrowers at various geographic levels and by originating brokerThe final step of the wholesale price monitoring process is to use these unexplained price differences to focus fair lending corrective actions. Typically, this final stage of testing is performed as follows:
1. Identify cases where the average unexplained price difference for the protected class group is greater than the average unexplained price difference for the control group,
2. For these cases, perform a T-test to determine whether the difference in these group-level average unexplained price differences are statistically significant, and
3. For those cases where the difference is statistically significant, determine whether the magnitude of the difference exceeds a minimum threshold for corrective action.
As an example, consider our TBF example from the previous section. Using the TBF regression model, the average unexplained TBF for Hispanic borrowers was calculated to be 5 bps and the average unexplained TBF for White Non-Hispanic borrowers was calculated to be -6 bps – thereby creating an 11 bps unexplained TBF disparity that is unfavorable to the protected class group. Table 5 satisfies the first step in our testing process above.
Moving on to the second step of the testing process, a two-tailed T-test is applied to determine whether the 11 bps unexplained TBF difference is statistically significant. As indicated in the above table, the results of our test confirm the statistical significance of this difference – indicating that there is less than a 5% probability that this difference is due to random chance.
With the first two steps satisfied, we now move to the third and final step. We note that the magnitude of the estimated unexplained TBF disparity is 11 bps. While this disparity is statistically significant, it should also be economically meaningful. That is, with large enough
sample sizes, unexplained TBF disparities of even 1 bps may be statistically significant. However, given the imprecision associated with regression modeling, it is entirely possible that the 1 bps difference could be explained if additional data were available or if more resources were devoted to refining the regression model further.30 Because of this imprecision, it is common for lenders to establish a minimum disparity threshold to reflect a reasonable margin of error. It is important to note, however, that there is no formal or informal regulatory guidance associated with these thresholds – in terms of either their general acceptability for fair lending testing, or acceptable threshold values. While many lenders incorporate such thresholds into their monitoring programs, we recommend that they be used cautiously and set conservatively (i.e., at relatively low levels). Additionally, in setting thresholds, lenders should be aware that a 1 bps APR difference has a much greater impact on the borrower than a 1 bps TBF difference since the former is expressed in terms of annual interest rate and the latter is expressed in terms of up-front fees.
Finally, in addition to performing this testing at an aggregate level (i.e., across all wholesale loan originations), it is important to “drill down” into various geographies and brokers to identify statistically significant “unexplained” loan price differences that may be masked when aggregated together. Based on our experience, we recommend testing be performed at the following levels:
National-level•
State-level•
MSA-Level•
Broker-Level•
Once these analyses are performed, a lender will have a good picture of its overall fair lending risk, and next will need to determine how to respond to the results.
Hispanic Borrowers White Non-Hispanic Borrowers DifferenceStatistically Significant?
Average TBF 2.89% 2.02% 0.87% Yes
Average “Demographically Neutral” TBF
2.84% 2.08% 0.76%
Average Unexplained TBF
0.05% -0.06% 0.11% Yes
Table 5
Managing Fair Lending Risk in Wholesale Mortgage Pricing 10
Addressing the resultsIf the statistical analyses reveal no significant disparities, the lender can be reasonably sure that its fair lending risk is low, and that if it receives a fair lending inquiry, it will be well positioned to defend. If the analyses reveal significant price disparities by race or national origin, a lender will typically want to attempt to identify the causes of the disparities and determine what, if any, corrective action to take. This can be difficult, because the loan application and other loan file documents typically do not contain explanations of all the factors that influenced the final loan price or why the borrower accepted one set of loan terms over another. The ideal goal is to determine whether there are nonstatistical explanations for the differences across borrower groups, and, if this is not possible, take action to prevent a recurrence.
Identifying after the fact the factors that cause statistical disparities and responding to them effectively is an important, but often difficult, task that is highly dependent on the lender’s operations, appetite for risk and willingness to expend resources. Investigating the cause of disparities can include reviewing files for documentation showing reasons for loan pricing31 and interviewing individual brokers in an effort to understand what motivated their pricing decisions. If a lender determines that one or more brokers is driving the overall disparities in a particular region, it should seek to prevent a recurrence by, for example, counseling the brokers, requiring them to take fair lending training, capping the amount of broker compensation they may charge or ceasing doing business with them. Where the disparities are particularly material and cannot be explained, and in some cases at the insistence of their regulators, certain lenders choose to issue refunds to borrowers.
Ultimately, how a lender responds to monitoring disparities depends on each lender’s unique situation. Lenders should work with their statistical consultants and lawyers to develop a responsive action plan that fits their overall circumstances.
ConclusionNotwithstanding the legal arguments against third-party pricing responsibility, it may be prudent for wholesale lenders as a matter of policy to mitigate their legal, compliance, and reputational risks through development and implementation of a wholesale price monitoring program. Since there is no single method or approach either endorsed or required by the federal bank regulatory agencies, we have focused our discussion on the important design decisions that need to be addressed and on certain monitoring program features for consideration.
Managing Fair Lending Risk in Wholesale Mortgage Pricing 11
Endnotes1 Although federal antidiscrimination laws also prohibit
discrimination on other bases, including age, gender
and marital status, most recent fair lending enforcement
and litigation has focused on race and ethnicity.
Although this article also focuses on race and ethnicity,
the concepts discussed herein apply to the other
protected groups as well.
2 In fact, as part of an expansion of the Civil Rights
Division announced in January 2010, the U.S.
Department of Justice has set up and staffed a new unit
to pursue home lending discrimination cases.
3 Note that most states also have antidiscrimination
statutes that apply to mortgage lending.
4 42 U.S.C. § 3601 et seq.
5 15 U.S.C. § 1691 et seq.
6 See, e.g., 15 U.S.C. 1691(a) (prohibiting
discrimination with respect to “any aspect” of a credit
transaction), and 42 U.S.C. § 3605 (prohibiting
discrimination in the “terms and conditions” of dwelling-
secured loans.
7 Assuming a wholesale lender does not have large
discretionary fees and that it applies its risk based price
adjusters objectively, it is virtually always the case that
differences in APR across borrower groups are caused
by differences in broker compensation.
8 The following is an overly simplified illustration of this
concept. Suppose that, for a given loan transaction,
Broker A tends to charge its customers 2.0% in broker
compensation while Broker B tends to charge its
customers 2.5%. If Broker A’s customer base is 60%
White Non-Hispanic and 40% Hispanic, and if Broker
B’s customer base is 60% Hispanic and 40% White
Non-Hispanic, then – all else equal – the average
broker compensation for Hispanics will be 2.3% and
the average broker compensation for White Non-
Hispanic borrowers will be 2.2%. The resulting 10 bps
unfavorable difference for Hispanic borrowers is not
driven by the discriminatory pricing of either Broker A
or Broker B – since each charges approximately the
same broker compensation to all its customers. Rather,
this difference emerges simply from the aggregation of
different average prices across brokers.
9 A comprehensive discussion of the legal analysis of this
issue is beyond the scope of this article.
10 530 U.S. 280 (2003).
11 12 C.F.R. § 202.2(l). Note that under ECOA, the
term “creditor” includes persons who regularly arrange
for the extension of credit (15 U.S.C. 1691a(e)), and
thus includes mortgage brokers.
12 Furthermore, most lenders will never be able to know
whether their brokers have pricing disparities because
they only receive a tiny fraction of each broker’s
overall originations. The best a lender can do is
analyze whether there appears to be disparities in
those loans it receives from the broker.
13 We are aware of wholesale pricing investigations
currently ongoing at DOJ, the Department of Housing
and Urban Development, FTC, several federal
banking regulators and multiple states attorneys
general.
14 DOJ also has settled fair lending pricing cases with
indirect auto finance companies. These cases are
similar to wholesale mortgage lending cases in that
they often seek to hold auto finance companies
responsible for the pricing activities of third party
auto dealers. See, e.g., United States v. First United
Security Bank, No. 09-0644 (S.D. Ala. filed Sept.
30, 2009).
15 See, e.g., Jeffries v. Wells Fargo Bank, NA, et al.,
No. 3:07-cv-03880-MJJ (N.D. Cal. 2007); Miller,
et al. v. Countrywide Bank, N.A., et al., No. 1:07-
11275 (W.D. Ky. 2007); Payares v. J.P. Morgan
Chase & Co., et al., No. 2:07-cv-05440-ABC-SH
(C.D. Cal. 2007); Zamora v. Wachovia Corporation,
No. 3:07-cv-04603-JSW (N.D. Cal. 2007); Rivas v.
Lehman Brothers Bank, FSB and BNC Mortgage, No.
08 CV 3685 (S.D.N.Y. 2008).
16 See, e.g., Barrett v. H&R Block, et al., No.08-
10157 (D. Mass. 2008); Miller, No. 1:07-11275;
Payares, No. 2:07-cv-05440-ABC-SH.
17 See the FFIEC’s Interagency Fair Lending Examination
Procedures (August 2009).
18 See 1994 Interagency Policy Statement on
Discrimination in Lending, “A pattern or practice of
disparate treatment on a prohibited basis may also
be established through a valid statistical analysis
of detailed loan file information, provided that the
analysis controls for possible legitimate explanations
for differences in treatment. …Data reported by
lenders under the HMDA do not, standing alone,
provide sufficient information for such an analysis
because they omit important variables, such as credit
histories and debt ratios.”
19 See Sec 203.4(12) for further details on HMDA APR
Rate Spreads.
20 A Chi-Square test of independence is typically
performed when testing for statistically significant
differences in APR Rate-Spread frequency.
21 A two-tailed T-test is typically performed when testing
for statistically significant differences in APR Rate-
Spread amounts.
22 The industry standard is to identify a difference as
“statistically significant” if there is less than a 5%
probability that the observed difference is due to
random chance.
23 According to the Interagency Fair Lending
Examination Procedures, “The scope of an
examination encompasses the loan product(s),
market(s), decision center(s), time frame, and
prohibited basis and control group(s) to be analyzed
during the examination;” (p. 1) and “If the institution
is large and geographically diverse, examiners
should select only as many markets or underwriting
centers as can be reviewed readily in depth, rather
than selecting proportionally to cover every market.
As needed, examiners should narrow the focus to
the MSA or underwriting center that is determined to
present the highest discrimination risk.” (p. 4)
24 Loan points represent up-front fees typically paid by
the borrower that are expressed as a percentage
of the loan amount. For example, 1 point on a
$100,000 loan would be equal to $1,000.
25 A yield spread premium represents fees paid by the
lender to the broker based on the amount by which
the note rate on a loan exceeds the lender’s base
or minimum note rate. Yield spread premiums are
expressed in terms of up-front points – for example,
the lender may pay a yield spread premium of 1
point to the broker in exchange for a note rate that
is 30 bps higher than the minimum note rate for the
transaction.
26 These imperfections are due primarily to data
limitations on non-discretionary pricing factors and an
inability to model precisely the manner in which these
factors impact APR.
27 See Footnote 18.
28 Risk-focusing fair lending analysis is consistent with
the Interagency Fair Lending Examination approach
which states: “No single examination can reasonably
be expected to evaluate compliance performance
as to every prohibited basis, in every product, or
in every underwriting center or subsidiary of an
institution.” (p. 4) We note, however, that some
institutions do perform regression analysis on all their
loan originations in order to obtain full coverage of
their risks.
29 See Footnote 8.
30 As a point of reference, a 1 bps TBF difference on a
$250,000 loan amount is equivalent to $25.
31 Some lenders are requiring loan originators to
specifically document their reasons when they charge
a borrower more or less than a particular amount.
This creates somewhat more work for the originator,
but could be very useful in seeking to defend a
discrimination claim.
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