For credit unions with residential mortgage and home equity loan and line-of-credit portfolios, stress testing – looking at the projected behavior of borrowers under various economic scenarios – is necessary to establish loss reserves. In addition, it is also an important risk management tool that can give portfolio managers deep insight into the possible behavior of individual loans helping to drive customer retention and loss mitigation initiatives.
In fact stress testing is now being phased in as part of the regulatory requirements under the Dodd-Frank Act for banks. The past two Novembers, the Federal Reserve has produced a series of three economic scenarios including a baseline, an adverse scenario and a severely adverse scenario that banks must use to stress the performance of balance sheets. These scenarios are made up of a broad series of variables including interest rates and home prices that can be used by sophisticated models to calculate the probabilities of prepayment, default (PD) and loss (LGD).
These models combine the economic scenarios defined by interest rates and home prices (optimally a highly granular, zip code based index) with loan-level information about the mortgage loan, the borrower and the property to establish a view into risk probabilities. As a result, mortgage portfolio managers can better understand the probability that a specific loan will transition from whatever its payment status is at a point in time to another particular status – and when – allowing them to establish loss reserve estimates at the loan-level and other metrics such as timeframe, loan types, geographies or loan vintage. A strong mortgage analytics model is incredibly flexible in terms of the type of insights that can be generated and acted upon.
These results can be used internally by management and to meet the expectations of regulators, who want to see that financial institutions can produce balance sheets and income statements for the entire institution across multiple asset classes and scenarios. Whether the mortgage portfolio has a large potential impact on the overall performance of the institution, or a less significant potential impact, it is important, as part of good risk management practices, that credit unions can generate a variety of alternative scenarios around how their mortgage portfolio may perform.
While a sophisticated analytical model can be very complex in terms of the underlying algorithms and logic, the best will also be very transparent and user-friendly. Credit unions with larger mortgage portfolios (usually 5,000 loans or more) typically decide to license a model to run on their own servers. Others may wish to have the model run for them on a service bureau basis, with the results provided to them by the analytics firm. In either case, the provider of the model should have expert consultants on staff who have significant mortgage experience and can support a credit union in working through issues such as data limitations and analytic capabilities and recommend best-fit solutions.
Managing its mortgage and home equity/line-of-credit portfolio risk is vital to the ability of a credit union to hit its performance targets and help ensure the health of the institution. By tapping an analytics model built on a deep reservoir of historic mortgage loan data and a granular zip code based home price index credit unions will be well-positioned to more effectively manage their risk and prudently grow their mortgage lending operations.