The blurb reads as follows: “With the continuing scrutiny on the mortgage industry and the ever-changing regulatory landscape, it is more important than ever for mortgage servicers to stay on top of the latest regulatory developments and industry trends affecting this volatile area. Join our interactive audio conference, Mortgage Servicing from A to Z, as our expert speaker gives you an insiders view into federal and state regulatory requirements, hot topics and best practices.”
The ‘expert speaker’ in question is Dena M. Roudybush, senior counsel for Compliance Counsel, PC a Virginia based law firm that is dedicated to serving the mortgage banking and financial services industries.
Cogent, for one, has seen tremendous activity in the mortgage servicing arena, with particular interest in servicing quality control. This session sounds like a good way to get up to speed on the major changes taking shape.
At the launch of HAMP (Housing Affordable Modification Program), the Treasury Department and the Department of Housing and Urban Development (HUD) set as their goal the modification of 3m to 4m mortgages. Through December 2009, approximately 850,000 borrowers had been placed into trial modification programs and 66,000 had been placed into permanent modifications. Hardly a blistering pace.
We outlined some of the issues with the structure and deployment of the program in our HAMP Best Practices article published in the December 2009 issue of Servicing Management magazine. Mentioned in that article were the documentation requirements of the program, which slowed the process down. In recognition of this, Treasury and HUD have released new requirements for upfront documents and guidance on permanent modification conversion.
Effective for all trial period plans with effective dates on or after June 1, 2010, a servicer can only evaluate a borrower for HAMP after receiving an initial package that includes a request for modification and affidavit (RMA) form; the Internal Revenue Service (IRS) 4506T-EZ form, which gives servicers the ability to pull the borrower’s tax return; and two pay stubs from the borrower for proof of income.
Said Herb Allison, the assistant secretary for the Treasury: ““We’re making it as easy as possible for homeowners to provide the documents. A lot of the problem of converting the trial mods to permanent mods has been time delay between the verbal communication about their eligibility to actually getting the documents and sending them in.”
As a consequence of the financial crisis, we are undergoing wholesale change in the regulatory environment of real estate finance. The changes are coming hard and fast and continuously, making it difficult to establish and manage new business processes. How do you keep up?
There’s no single answer to this question. We have clients who take it on themselves to keep abreast of regulatory changes because they want full control over their regulatory compliance. Other lenders want to outsource the management of change and turn to third parties to keep them up to date.
Wherever you fall in the spectrum, you will probably welcome any tools that ease the burden of staying current. And such tools are cropping up. For example, Wolters Kluwer has just launched ”The Reg Z Center“, a free online resource which provides an overview of the changes affecting loans covered by Regulation Z, the effective dates for changes and suggested solutions for implementing changes.
Being a for-profit entity, the company does promote its compliance solutions on the site. But this is in keeping with the ‘Web 2.0′ business model, whereby useful information is provided for free, interaction among interested parties is encouraged (via a forum, blog, or discussion group), and a long-term relationship is cultivated while simultaneously establishing credibility.
It’s official. Version 2.0 of CogentQC.NET has now been released. You may have seen the news in Housingwire or MortgageOrb but if not, you can find the Cogent news release here.
2009 was an unusual year for client IT departments (unless it’s actually “the new normal”, God forbid.) After the mortgage crisis and economic meltdown, it seemed like IT staffing and budgets were reduced and lenders were stuck in neutral, reluctant to take any steps other than cost-cutting. IT staff had too many projects to handle and consequently Cogent saw only a handful of clients upgrading to CogentQC.NET.
However, the end of 2009 saw an acceleration of activity. We now have enough upgrade projects to take us through the first quarter, at least. And as business gets back to normal, we have even begun to talk with clients about enhancements to their systems that will automate and optimize more of their business processes.
If you’re interested in the world of mortgage servicing, MortgageOrb has just published an interesting overview of the major milestones of 2009 and what to expect in 2010. With input from several industry veterans, the article surveys the changing role of the servicing function, the performance of various government programs designed to ease the mortgage crisis, the conflicts of interest between servicers and investors, the potential impact of millions more ARM resets due in 2010 and 2011, and more.Well worth a read.
One of our favorite websites is www.informationisbeautiful.net, where David McCandless, an independent London-based “visual & data journalist” (his words) practices the art of data visualization and information design.
We like the way he conveys information, often multi-layered, with the minimum of words. As he puts it, “I’m interested in how designed information can help us understand the world, cut through BS and reveal hidden connections, patterns and stories underneath. Or, failing that, it can just look cool!”
This particular visualization is more wordy that most of his work. For that reason, one is inclined to think that a lot of research went into it. And that is the case. Every familiar argument made by the “skeptics” seems to have been researched and refuted. If you’re a believer in global warming, you might think that this is close to the final word on the debate.
Yet one look at the comments section at the bottom of this page suggests otherwise. It’s worth spending a few minutes examining the visualization and then the comments. That should be enough to convince you that a beautifully presented argument is not proof. It has to be backed by solid evidence. And if there’s any debate that lacks conclusive data, it has to be the global warming issue, where complex meteorological phenomena meet millennial time spans in a cauldron of scant measurement.
In addition to a concise synopsis of the events leading up to the current regulatory overhaul of the mortgage industry, the article outlines some of the changes we can expect in the way compliance reviews are performed. In short: earlier in the origination process, more electronically and with greater automation.
Mortgage compliance and quality control reviews can and should be performed on many levels, and at different points in the loan life cycle. Tools such as automated compliance engines, automated fraud engines and automated valuation models can flag loans that merit further review. The next step is to apply a rigorous methodology to digging deeper in order to:
1) determine whether a complete file review confirms the automated findings;2) fix the individual loans if possible;3) identify the source(s) of the issue(s);4) select additional loans from these source(s) and conduct complete file reviews to see if there is a pattern of issues;5) generate feedback to the field and document corrective actions to fix the flaw(s) in the origination or servicing processes and/or stop doing business with the identified sources.
We are seeing more tools for automating more aspects of the mortgage life cycle. Don’t forget, though, that it was partially the over-reliance on automated underwriting tools that got us into our current mess. Maybe tomorrow will be different, but today we still need human beings checking to see if it all makes sense.
Statistics can serve as a good sleep aid, particularly when they’re presented as a simple row or table of numbers. That’s the way most of us have encountered statistics, with the figures often morphing into zzzzzzzz’s. But it is possible to make statistics come alive. The secret is in helping the audience visualize the data in context so that they can quickly derive meaning. And today’s media tools make this easier than ever. When we find interesting visualizations of data or statistics, we will share them with you. Here’s one to start us off:
If you can’t see the video above, you can find it here:
One of our favorite books is “How to Lie with Statistics”, a tongue-in-cheek primer on using statistics to make just about any argument you like. It illustrates how easy it is to mislead people who are unaware of basic statistical principles.
For instance, what do you make of the headline “Median home price in Jefferson County falls by 27%”? Sounds pretty dire when you read it in bold headline on a newspaper as you’re walking by. You might think that home prices in Jefferson County have all fallen by 27%. But it’s worth digging deeper. What period are we talking about? What are we comparing to? What does ‘median’ mean (compared to ‘mean’, for instance)? How many homes were sold and does that make a difference in the statistic? Are we including detached homes and condos? None of this is clear from the headline, and there are many other questions to ask.
Among other things, basic statistical understanding gives you a sense of context, scale and precision. In the knowledge economy, where information is coming at you from a variety of sources, that’s pretty important. Whenever you see a statement involving statistics - or indeed, any measurement - it’s worth asking whether there is any ambiguity in the statement. And if there is, dig deeper.
To help you get comfortable with some basics, we’ve compiled a short list of statistical concepts for mortgage quality control. If there’s anything else you’d curious about, let us know.
Key Statistical Concepts for Mortgage Quality Control
CONTENTS:
Sample inferences & statistical precision
Random Selection
Sample size estimation
Qualitative analysis & defect rates
Random variation & statistical control
Sampling error & non-sampling error
Correlation vs. Causation
Sample inferences & statistical precision
The fundamental purpose of sampling for Quality Control is to render judgments regarding quality of the overall loan portfolio, i.e., to infer general conclusions from the sample’s findings. The degree to which those conclusions can be reliably inferred is measured by statistical precision. Keep in mind that the goal of Quality Control is to focus on the forest, not the trees. Accordingly, your objective is not to identify and correct errors or defects in specific loan files, but to use the incidence of such errors to infer conclusions about your loan origination process.
Critical issues:
Statistical inference must be based on random selection; the most common error is to draw conclusions from a non-random sample. To avoid this error, you should eliminate all non-random selections from any group used to make statistical inferences to the population.
Statistical precision (e.g., of two percent) must be demonstrated on the actual sample defect rate (i.e., the number of loans with defects divided by the number of loans reviewed). If you were unable to review some of your randomly sampled loan files, then the precision achieved by your process will be degraded.
It’s encouraging to see the adoption of statistical methods in the world of mortgage quality control. Done right, it can lead to enormous returns on your investment in qualiy control - what we call ’return on quality’. But you have to do it right. There is plenty of misinformation about statistics on the Internet and a non-expert may have difficulty sifting through what’s right or wrong, especially as applied to mortgage quality control.
So we’d like to present the principles and methods that Cogent’s statisticians and QC experts have honed over the past 15 years. We invite your comments. We begin with an overview of statistical sampling in mortgage quality control, which is available as a white paper (PDF) here.
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Statistical Sampling in Mortgage Quality Control
By Hakki Etem, CEO, Cogent Economics
Introduction
Statistical methods are well-established tools for efficiently measuring and improving product quality in a variety of industries. Unfortunately, statistical analysis has been slow to gain acceptance in the mortgage industry, although the ability to originate the best quality product at the lowest possible cost is just as valuable to mortgage originators as it is to automobile manufacturers. There are many reasons why the mortgage industry has avoided statistical methods, but surely one reason is the subject itself: few disciplines can be as mind-numbing as statistical theory.
Nevertheless the most effective way for QC managers to measure and improve loan quality — at the lowest possible cost — is to employ statistical methods. Although this means that QC managers must necessarily become familiar with basic statistical concepts, with the right tools and professional support the process can be greatly simplified.
