Kristin Moyer

Contrasted to credit card issuers and consumer lenders, mortgage servicers are being less aggressive with the use of predictive analytics, optimization and behavior modeling.  For one thing, mortgage servicers have fewer levers to use and less data to leverage in reducing loss compared to issuers of revolving credit.  The cost of servicing has also gone up due to high levels of distress, making it difficult for servicers to invest in new technologies.  And consumer focus on credit obligations have changed as a result of negative home equity, making mortgage a lower priority payment for consumers (source:  Effectively Managing Risk in the New Economy,” Equifax, April 2009).

Servicers are overwhelmed by volumes and often do not have the resources they need, both in terms of personnel and technology (due to the increasing costs of servicing).  For example, they lack technologies (such as loan portfolio management) to determine the best option for each loan in distress, whether that be loss mitigation, a short sale, a third party sale or foreclosure.  They have yet to adjust to their new role as “life coaches” (not just loan counselors focused on completing a task) in working with so many distressed borrowers.   Regulations, such as the Home Affordable Modification Plan (HAMP) in the US, have been challenging to implement and execute.

So why should the residential mortgage industry use loan portfolio management – now?

Loan portfolio management has been shown to reduce re-default and significantly improve average unpaid-principal-balance increase in net present value (NPV) from modifications using loan portfolio management (relative to nonoptimized loan modifications using general risk scores) from vendors such as Response Analytics (Distressed Portfolio Management and others).  Segmentation and clustering analysis supports optimal treatment strategies as well.  For example, First American CoreLogic (WillCap) provides cluster-driven treatment strategies that segments borrowers into more than 20 segments in order to reduce default rates, decrease losses and accomplish socio-political goals (for example, keeping borrowers in their homes and reducing foreclosures).

And preventing a customer from falling behind on payments may be the best course of all.  I spoke with someone in the industry today that said something like 20%-30% of borrowers who are current on their mortgage at the time of modification later end up defaulting.  If a borrower has fallen behind on payments, the rate is more like 70%-80%.  Pre-delinquency management is also a core capability of loan portfolio management.

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Category: operations     Tags: analytics, loan portfolio management

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