Granted, most of the housing and mortgage industry news out there is deflating, perhaps even deflationary.  For example, see the recent announcement by Zillow that as many as one in five markets may be in store for a ‘double-dip’ in housing prices.  This just adds to the gloomy news about foreclosure volumes, unemployment and an anemic economic recovery.

Yet there are bright spots.  As Exhibit A, we cite the remarkable results that Mortgagebot achieved in the 2009 calendar year.  As reported in HousingWire:

“Wisconsin-based Mortgagebot, which develops Web-based software as a service (SAAS) for mortgage lenders, said it added 200 new clients in 2009, bringing its total client base to nearly 950 organizations. With the new clients came a 25% increase in revenue in 2009 compared to 2008; further boosted by a 25% increase in overall contract value for new sales.”

The movement to “eMortgages” (totally digital, paperless origination of loans) is a long term trend that will need to overcome many obstacles.  But the pieces are being built out today.  Mortgagebot provides a solution that automates and streamlines the online mortgage application process.  Other technology innovators are developing solutions for other aspects of the mortgage origination process.  In time, as the technologies are proven to be reliable, secure and compliant with regulations, we will have an end-to-end eMortgage process.

In the meantime, as mortgage technology companies offer solutions that perform cumbersome tasks more efficiently and reliably, lenders who wish to reduce costs and stay competitive will adopt those solutions.  And that will provide the impetus that mortgage technologists need to grow and thrive, in spite of the general pace of economic recovery.

 

 

Image by jan.deheus 

Until the subprime crisis, the quality control department had always been something of a Rodney Dangerfield in the world of mortgage lending.  No respect.  After all, who wanted to see cold water thrown on a red-hot origination market?  Not the production department, surely.  In fact, many lenders didn’t think twice about setting the Production foxes to guard the Quality Control hens.

Things have changed, though.  As Jan Wetzel of Wetzel Trott sums it up in her recent interview in MortgageOrb:

“It used to be that lenders just did the quality control to fulfill the agency requirements so they could have the results available in case of an audit. Now, they are actually reading the reports at a senior management level and taking corrective actions in their procedures.”

Part of the reason, of course, is that the agencies are paying more attention, following up on audits, and demanding resolution of infractions.  But there is also an awakening in the upper management of lenders that perhaps the quality control process, including fraud detection, could be more than just a hindrance to maximizing profit.  Could it be that mortgage quality control could actually serve to enhance profitability?

As it happens, this is the very value proposition that Cogent has been offering for almost two decades.  We hope this sea change is permanent.

 

Statistics is baffling enough without being footloose with terminology.  So let’s clear up what we mean by a statistical sample.

The term “Statistical Sample” has a very specific meaning in the Cogent system.  It refers to a sample that is randomly selected from the entire population of loans eligible for a particular sample type (aka “audit shell”).  The suggested sample size is calculated every period by the system and is designed to yield a 95% confidence and 2% precision over 12 months.  This is the standard originally established by FNMA, FHLMC, and HUD for lenders who qualify to substitute ‘statistical sampling’ for the traditional 10% random sample.

The generic term ‘statistical sample’ is not very meaningful, in and of itself.  It simply refers to a sample in which some statistical principle has been employed, without defining which principle.  For example, it could refer merely to a randomly drawn sample, without specifying what population is being drawn from or how much precision will be achieved across what period.

 

Image by johnbullas
Rick Astley reference 

To illustrate: most Cogent ProductionQC clients have at minimum a “Production” sample type, for which all loans originated in a particular period (typically a month) are eligible.  When a Statistical Sample (in the Cogent definition) is randomly drawn from this population, all loans have the same probability of being selected.  No distinction is made between loan type, loan source or any other loan characteristic.  It is intended  to establish a baseline of overall loan quality across the organization.

In order to achieve a 95% confidence and 2% precision for a particular category of loan, it is necessary to go beyond the “Statistical Sample”.  For example, in the Cogent system, to achieve this standard for all FHA loans originated, define a Targeted Query (Loan Type = FHA) and run the query.  The resulting screen displays all qualifying loans, including qualifying loans  that were randomly drawn previously in the “Statistical Sample” or any other samples in this period.  These count towards the total required.  Use the embedded Cogent Statistical Calculator to calculate the required (“suggested”) sample size for the period.  From the suggested sample size, subtract the number of qualifying loans that have previously been sampled and enter the result in the Sample Size box. The Cogent system will then randomly select the entered number of loans from the qualifying loans.

The Cogent “Stratified Sample” is in effect a pre-defined Targeted Sample.  Most typically, the Cogent system stratifies originations by Source or Channel and automatically tracks and calculates the sample size required for each stratum (Source or Channel), net of qualifying loans randomly drawn in the “Statistical Sample.”  Over 12 months, the Stratified Sample achieves 95% confidence and 2% precision for each stratum.  In Targeted Samples, this automated operation is performed by the user, using Cogent’s embedded tools.

Thus, in order to leverage the Cogent system’s sampling optimization, clients should begin sampling from the broadest category (all loans eligible) to the most narrow category (e.g., individual underwriters).  In this way, all loans selected in previous broad categories are counted towards ever narrower categories, minimizing the number of loans to be sampled and audited.

Image by Rutty 

Berkery Noyes, “the only middle-market investment bank with research and M&A transaction teams dedicated to the mortgage technology, regulatory and compliance market,” has just published its 2009 Recap and 2010 Predictions For the Mortgage Technology Market.

Providing a view from 30,000 feet, the report lists a number of high-profile M&A transactions from 2009 and offers up a few trends in the mortgage technology space, including:

• accelerating vendor movement towards becoming a complete end-to-end solution provider
• more stringent lending guidelines and regulations increasing the need for compliance, fraud prevention and risk-mitigating technology solutions
• burden of ensuring proper compliance moves to the point of sale rather than merely at closing, and falls on all mortgage industry participants

In the context of these trends, the report states that market participants no longer see compliance and auditing solutions as “nice to have” – they have become “must have” solutions.

We couldn’t agree more.

 

 

One of the ways that servicers or investors can excise nonperforming assets from their portfolios is to try to put them back to originators by claiming fraud and/or breach of representations and warrants.   Potential malfeasance like this is uncovered via forensic audits, which have become increasingly popular since the mortgage crisis hit.  While forensic auditing for this purpose has typically been limited to institutions – investors, servicers, originators and mortgage insurers – the practice has now spread to the retail borrower, as described in today’s MortgageOrb article “When Forensic Loan Audits Are Used Against Lenders.”

This phenomenon has even touched Cogent.  Recently, we’ve been approached by a handful of clients to help them extract loan audit data from their Cogent systems for the purpose of forensic loan auditing.  Typically, this is in support of litigation in which opposing counsel requires every scrap of data that could be relevant.  Without a knowledge of both the mortgage quality control workflow and the application supporting it, this can be difficult for clients to accomplish on their own, especially after layoffs have reduced knowledgeable staff. 

Although projects like this are outside Cogent’s normal scope of work, we have the expertise to help clients.  More than anything, though, this is yet another reminder of why robust quality control is imperative in today’s world.