Part II: Where are Models Most Needed Now in Mortgages?
(Click here if you missed Part I of this post.)
By: John Straka
A first important question should always be are all of your models, model uses, and model testing strategies, and your non-model processes, sound and optimal for your business? But in today’s environment, two areas in mortgage stand out where better models and decision systems are most needed now: mortgage servicing and loan-quality assurance. I will discuss loan-quality assurance in a future installment.
Mortgage servicing and loss mitigation are clearly one area where better models and new decision analytics continue to have a seemingly great potential today to add significant new value. At the risk of oversimplifying, it is possible that a number of the difficulties and frustrations of mortgage servicers (and regulators) and borrowers in recent years may have been lessened through more efficient automated decision tools and optimization strategies. And because these problems will continue to persist for quite some time, it is certainly not too late to envision and move now towards an improved future state of mortgage servicing, or to continue to advance your existing new strategic direction by adding to enhancements already underway.
Much has been written about the difficulties faced by many mortgage servicers who have been overwhelmed by the demands of many more delinquent and defaulted borrowers and very extensive, evolving government involvements in new programs, performance incentives and standards. A strategic question on the minds of many executives and others in the industry today seems to be, where is all of this going? Is there a generally viable strategic direction for mortgage servicers that can help them to emerge from their current issues—perhaps similar to the improved data, standards, modeling, and technologies that allowed the mortgage industry in the 1990s to emerge overall quite successfully from the problems of the late 1980s and early 90s?
To review briefly, mortgage industry problems of the early 1990s were less severe, of course—but really not dissimilar to the current environment. There had been a major home-price correction in California, in New England, and in a number of large metro areas elsewhere. A “low doc” mortgage era (and other issues) had left Citicorp nearly insolvent, for example, and caused other significant losses on top of the losses generated by the home prices. A major source of most mortgage funding, the Savings & Loan industry, had largely collapsed, with losses having to be resolved by a special government agency.
Statistical mortgage credit scoring and automated underwriting resulted from the improved data, standards, modeling, and technologies that allowed the mortgage industry to recover in the 1990s, allowing mortgages to catch up with the previously established use of this decision technology in cards, autos, etc., thus benefiting the mortgage industry with reduced costs and significant gains in efficiency and risk management. An important question today is, is there a similar “renaissance,” so to speak, now in the offing or at hand for mortgage servicers? Despite all of the still ongoing problems?
Let me offer here a very simple analogy—with a disclaimer that this is only a basic starting viewpoint, an oversimplification, recognizing that mortgage servicing and loss mitigation is extraordinarily complex in its details, and often seems only to grow more complex by the day (with added constraints and uncertainties piling on).
The simple analogy is this: consider your loan-level Net Present Value (NPV) or other key objective of loan-level decisions in servicing and loss mitigation to be analogous to the statistically based mortgage default “Score” of automated underwriting for originations in the 1990s. Viewed in this way, a simple question stemming from the figure below is: can you reduce costs and satisfy borrowers and performance standards better by automating and focusing your servicing representatives more, or primarily, on the “Refer” group of borrowers? A corollary question is can more automated model-based decision engines confidently reduce the costs and achieve added insights and efficiencies in servicing the lowest and highest NPV delinquent borrowers and the Refer range? Another corollary question is, are new government-driven performance standards helpful or hindering (or even preventing) particular moves toward this type of objective.
Is this a generally viable strategic direction for the future (or even the present) of mortgage servicing? Is it your direction today? What is your vision for the future of your quality mortgage servicing?
