REQUIRED READING: A Risk Management Primer

In the good old days, mortgage lending operations were largely local and, while not entirely without risk, fairly safe. Most lenders were closely regulated by state or federal entities. They loaned on a relatively small scale and primarily only within their local markets.

By sticking to their home territories, lenders reduced their risk of default because they knew their markets. Underwriters were familiar with the local neighborhoods and their property values, knew the local employers and wage scales, and had personal knowledge of the skills and reputation of the local appraisers, real estate agents and closing/escrow agents.

Improvements in technology allowed lenders to increase efficiency and reduce origination costs. When the general public began to see the Internet as a safe and convenient portal to businesses, lenders quickly tapped into e-commerce as a way to tap into a national residential mortgage market.

In the meantime, the government took the position that homeownership was the most accessible way for average citizens to increase their personal assets. Changes in program guidelines, including down payment assistance and 100% financing, allowed more borrowers to reach for the American Dream. Falling interest rates made it even more affordable. Housing demand increased and the national economy grew.

Use of the Internet as a channel for lenders and brokers to reach remote borrowers introduced another layer of risk as transactions became increasingly remote and anonymous. How could a lender evaluate the credit-worthiness of a borrower whom the lender had never met? The answer was initially found in automated underwriting systems.

Automated underwriting systems (AUSs) were originally developed by the governement-sponsored enterprises (GSEs) as a way to increase underwriting consistency, improve efficiency and assist in the pre-funding analysis of loan applications. But increased volumes and the pressure to do more in less time, AUS use evolved into a final decisioning tool. This unintended use of AUSs created a new risk: decision atrophy.

AUSs do not verify the data submitted by the borrowers, nor do they identify significant inconsistencies or misrepresentations. The use of AUSs as a decisioning tool inadvertently caused underwriters to lose their ability to recognize inconsistencies in loan applications.

Some of the unintended consequences of this included:

  • Loans being made to borrowers who overstated the collateral value in transactions originated by mortgage professionals who were related to the borrowers,
  • Loans being made to renters who were buying multiple investment properties,
  • Loans to borrowers who were buying second homes within three miles of their primary residences,
  • Loans of a half-million dollars or more being made to waiters and hair stylists, and
  • Investors and speculators being able to obtain loans on owner-occupied terms and pricing.

At first, escalating real estate values masked these errors. So long as values kept climbing, the defaults caused by AUS-driven decisioning were covered by the borrowers' ability to refinance or sell the home at par or better. Even when a property was foreclosed, the rising markets allowed lenders to avoid most financial losses. This allowed lenders to develop a false sense of security and provided the underpinning for the development and wide use of loan programs that layered risk in previously unimaginable ways.

But then the music stopped. Borrowers of all stripes began to hesitate in the face of astronomically high prices and unsustainable rates of appreciation. Volumes and values began to level off. The press began to speculate about whether the U.S. was experiencing a housing bubble, or whether it was about to deflate or worse, go bust.

A significant increase in defaults in the subprime loan sector in late 2006 started a chain reaction. Investors who had previously allowed the substitution of nonperforming loans began to demand the repurchase of entire pools. Mortgage lenders began to fail at an alarming rate. Securities were downgraded, resulting in huge losses. Borrowers who could afford to pay their mortgages – and especially those who obtained 100% financing – started to walk away when declining property values resulted in negative equity.

Wall Street completely lost confidence in its ability to judge either the risk or value of mortgage-backed securities (MBSs). The money that had been pouring into the market has slowed to a trickle, leaving the GSEs and the Federal Housing Administration as major players in what remains of the market.

The fallout reverberates to this day. Most commentators agree that the bottom has not yet been reached and that it is unlikely that the market will normalize until that happens. It is hard to imagine that confidence will be restored until investors start to see improvement in loan performance, and we still have a long way to go.

Restoring confidence
The bad news is that there is no ‘easy button’ when it comes to managing risk. Risk is a multi-dimensional problem that requires a multi-pronged approach incorporating multi-dimensional solutions. A good risk management program should include the following considerations.

Focus on prevention.
It's a lot cheaper to prevent problems before a loan is funded than it is to clean up afterwards.

Empower underwriters to make decisions.
Training and frequent refreshers, coupled with a top-down commitment to quality, are the first line of defense. Underwriters need to be aware of emerging schemes and techniques, including the alteration and forgery of documents.

Minimize risk of data integrity issues. Use technology to separate ‘clean’ loans that can be expedited from loans with discrepancies that will require additional verification and/or exceptions.

Make smart use of borrower data.
Use advances in data gathering and analytics to validate borrower information, identify discrepancies and score the application in the pre-funding stage. Lenders should also closely examine the data submitted by their loan origination systems to make sure that enough loan level detail is captured for analysis and review. When critical participants are not included in the analysis, connections between the parties, indicative of fraud, will escape notice.

Recognize the traps in collateral value.
Property values can be easily manipulated in a chaotic market through the collusion of appraisers and real estate agents, especially in a short sale or a sale following foreclosure. While automated valuation models (AVMs) and other technologies provide a good estimate of value, it is critical to monitor each appraiser's performance by monitoring appraised values and AVM values over time.

Obtain on-site field reviews in any areas when such discrepancies are found. These present the highest risk of loss. Leverage field-review data to flag problem areas for additional scrutiny going forward and to minimize the risk of loss. If a pattern of inaccurate or overstated values is discovered, make sure that the parties involved are put on your watch or exclusionary list.

Identify the source of problem loans. Do they involve common participants? Are they clustered in a certain area? How many more loans involving these participants are in your inventory? Advanced technology can help to find these patterns pre-funding. When participants are associated with an unacceptable level of data inconsistencies, put them on your watch or exclusionary lists. As a best practice, the sales department should not be involved in the development or maintenance of these watch lists.

Identify organized ‘for profit’ fraud schemes. Identifying problem participants whose activities affect multiple lenders is a key element in identifying schemes. Best of breed technology can connect the dots between lenders, without sacrificing consumer privacy.

Obtain and use feedback. Dashboard programs should be used to monitor and analyze employee and manager performance. Sophisticated loan-scoring systems can help to identify problems with service providers and mortgage professionals. Track loan program performance to determine weaknesses and vulnerabilities to help forge improvements in your products.

Ann Fulmer is vice president at Interthinx, Agoura Hills, Calif. She can be reached at


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