Redlining risk: slaying the three-headed dragon.
Looking for the beast: Where to focus your redlining review
In the realm of financial services, redlining practices were outlawed in the 1970s by the Equal Credit Opportunity Act (ECOA), Community Reinvestment Act and other fair lending pronouncements. Consequently, regulatory scrutiny of redlining risk evolved into a "two-headed beast" that often required formal surveillance as part of a bank's CRA and fair lending monitoring programs. For years, redlining reviews have been focused on mortgage lending activities (i.e., those reportable under the Home Mortgage Disclosure Act [HMDA]) to prevent disproportionate residential real estate lending from occurring based on a prohibited factor within a particular geographic area (such as a county, census tract or neighborhood).
Today regulators have renewed interest in preventing redlining activities from harming minority neighborhoods and other areas predominantly occupied by protected classes or low-to-moderate-income (LMI) individuals.
To further complicate matters, there is now a third head on the "redlining dragon," namely non-HMDA lending activities (i.e., other consumer loan types not reportable under HMDA, such as credit cards and auto loans).
Because of the broadened scope of redlining risk, it is more important than ever for lenders to ensure their fair lending and CRA teams collaborate on redlining reviews to increase efficiencies via information leveraging, correlate risk across lending products and better position their institution for regulatory examinations.
As a basic concept, a redlining review provides a geographic evaluation of lending distribution patterns in majority-minority versus non-minority geographies, or LMI versus non-LMI geographies, to identify potential areas of fair lending risk. Ultimately the objective is to identify conspicuous gaps in lending activities in majority-minority or LMI areas within key markets being served, then document a case to explain those gaps or implement swift corrective action to resolve them. Today's regulators perform redlining reviews in three key areas:
* Mortgage /ending--HMDA-reportable data is examined for compliance with the ECOA and Fair Housing Act;
* Non-HMDA lending--Lending activities outside of mortgage lending (e.g., credit cards, indirect auto, home equity) are examined for compliance with ECOA; and
* CRA lending test--Small business and small farm loans as well as HMDA-reportable data are examined as part of CRA performance evaluations.
Tips for Success: Consistent methodologies should be documented and employed for redlining reviews across the various loan types. Additionally, it is important that you understand the methodology and results, and can readily articulate both during examinations. This helps to demonstrate the strength of your compliance management system (CMS) to regulators and helps to minimize any negative conclusions they may formulate.
Building your arsenal: How to meet review objectives
Redlining reviews are not "one-size-fits-all" analyses. Additionally, there is no central guidance for how such reviews are performed by the various regulatory agencies, and they do not necessarily perform redlining reviews the same way.
You can obtain snippets of recommendations and verbal guidance related to redlining examinations, but it's like putting together a thousand-piece puzzle.
Your institution's reviews should be tailored to the scope, breadth, complexity, geographic footprint, channels, lending activities and strategic objectives of your institution. But this is easier said than done.
Leveraging automated compliance tools can help expand redlining reviews across multiple portfolios and proactively address potential areas of risk. Additionally, the following tactics can add significant strength to your redlining reviews:
Manage data integrity
The simple metaphor commonly used is "garbage in, garbage out." Redlining analytics are only as good as the underlying data, so step 1 has to be ensuring that the data is accurate and complete. Lenders must strive to affirmatively answer the following questions:
* Are the physical addresses accurate?
* Is the geocode data accurate?
* Are applications coded properly for the action taken (e.g., are some withdrawn applications actually denials)?
Most lenders rely on a data-management program to find issues and correct data in preparation for annual regulatory submissions and advanced analytics. An automated process can significantly increase data quality because more tests can be performed on the data with less potential for human errors.
Know your story
It is important to establish performance contexts around key markets where your institution lends. Specifically, explore:
* How much of the geographies in each market consist of majority-minority and LMI census tracts?
* Are there intrinsic barriers to lending (e.g., a prison occupies a particular LMI tract)?
* What are the key demographic characteristics of the market, such as unemployment rate, poverty level and percentage of owner-occupied versus rental units?
* Where are branches in relationship to minority and LMI neighborhoods?
* How much competition is there for each specific lending activity?
* What are the lending trends for the entire market?
* What are your institution's lending strategies in each market?
* How does your institution rank in the market for both deposits and loans?
It takes significant effort to fully grasp--and document--what is going on in each market, but the effort is well worth the time and focus. Such information goes a long way to prepare for examinations, provide defensible arguments for areas of scrutiny, and identify opportunities to remediate when warranted by the analytical results.
Run key analytics
A common approach to redlining reviews consists of three key components: 1) peer and market comparisons, 2) lending distribution maps and 3) tract penetration rates. Figure 1 summarizes these metrics and provides common challenges in performing such reviews.
Ending the battle: Best practices for concluding redlining reviews
Once the analyses are completed, the following steps should be taken to conclude a redlining review:
* Evaluate composite results for each in-scope market (i.e., analyze individual review components collectively by market);
* Document methodologies and approach;
* Summarize results in an executive report and, if necessary, obtain guidance from legal counsel on sharing with regulators (especially if executed under attorney-client privilege);
* Work with business lines to formulate action plans (if warranted for potential areas of risk); and
* Provide management reporting on outcomes and action plans (if any).
Thoughtful consideration of these factors as you design and execute redlining reviews will help your institution meet regulatory expectations pertaining to fair lending and CRA risk-monitoring practices. Your three-headed dragon will be relegated back to mythology--where it belongs.
Brenda Baylor is the client solutions executive at Treliant Solutions LLC in Washington, D.C. She has 20 years of experience working in the financial services industry and advises lenders on the implementation of regulatory compliance programs, risk assessments, monitoring and statistical analysis programs, examination readiness and risk management strategies. Treliant Solutions, an affiliate of Treliant Risk Advisors, is a provider of compliance and risk management technologies and services for the financial services industry. Baylor can be reached at email@example.com.
REDLINING REVIEW KEY COMPONENTS Analysis Type Description Peer/Market Lender-to- Comparing lending Comparisons lender and performance in lender-to- majority-minority or aggregate low-to-moderate- analysis income (LMI) tracts to that of a target peer group and the market aggregate Lending Intra- Visually assessing Distribution institution lending patterns Maps analysis in majority-minority or LMI geographies Tract Intra- Comparing tract Penetration institution penetration rates Rates analysis in majority-minority or LMI tracts to thosein non-majority- minority or non-LMI tracts Analysis Common Pitfalls Peer/Market * Inconsistent peer selection Comparisons methodology can create the appearance of "cherry picking" peers to put the institution in the best light. * Comparisons to lenders dissimilar in lending activities or business model can lead to faulty conclusions. Lending * Subjective interpretations can Distribution cause variations in conclusions. Maps * Map views that are too broad or congested can be difficult to analyze. Tract * This analysis does not provide Penetration perspective on the volume of Rates lending occurring (i.e., one application or loan = census tract was penetrated). * Analyzing a small number of tracts can lead to unreliable results. SOURCE: Treliant Solutions
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|Title Annotation:||THE BRIEFING|
|Comment:||Redlining risk: slaying the three-headed dragon.(THE BRIEFING)|
|Date:||Oct 1, 2016|
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