Lending institutions clearly need analytics of all sorts when evaluating their own loan book: static analyses of concentration and associated risk, backward-looking performance analytics, and forward-looking performance forecasts.
Why then are similar analyses of participated loans missing or ignored? Why do the parties involved in a participation usually follow a “transact and forget” approach?
There is every reason to bring the same analytic rigor and focus to participated loans as one would to an “organic” loan book. That sellers and purchasers own only a fraction of the loans does not change the fundamental mantra: what gets measured, gets managed.
“Transact, Track, Learn, Refine”
Almost every analytical tool applicable to organic loans applies equally well to participated loans.
From a portfolio management perspective, your participations represent an integral part of your concentration and risk profile and need to be analyzed as such. Considering your participations as a whole, how diversified are they across different credit scores, loan products, geographies, and industries? Has this risk changed since the transaction date, as prepayments and charge-offs have altered the composition of your participation? And how does the risk associated with your participations look in the context of your overall loan book or balance sheet?
Every portfolio manager needs an analytics product that can handle this multi-level analysis and provide insight into how further transactions — such as further participation sales or purchases — can even out their overall concentration and risk profile.
The value of historical performance measures requires little explanation. Without knowing a participation’s observed prepayment, charge-off, and delinquency rates, one cannot compare the participation’s actual performance to the assumptions made at the time of sale. And without the ability to make that comparison, one cannot judge whether one got the benefit of one’s bargain.
But historical performance measures also provide crucial information for evaluating future participation decisions — for buyers and sellers. Purchasing high credit-score auto loans might seem a wise investment, but are high prepayment rates reducing your interest income, and would you be better served by purchasing lower credit-score auto loans? Similarly, sellers may see that their participations are exhibiting lower prepayment and default rates than anticipated; this could prompt them to raise the price in their next offering. With participation analytics, your decision-making process can respond to real data, not rough rules-of-thumb.
Forecasted Loan Performance
The same goes for forecasts of participation loan performance. Placing to the side the noise and imprecision that partially cloud all forecasts, forecasts of a participation’s IRR (Internal Rate of Return, or yield-to-maturity) provide a concrete measure of performance that can be easily compared against the returns offered by other assets, such as CDs or bonds. And forecasts of prepayment and amortization permit credit unions to better anticipate how quickly their participations will pay down and understand how much interest-rate risk they have and when reinvestment would be appropriate.
Technology Paves the Way
Currently, there is no technical or logistical reason why all parties should not enjoy the same analytics for their participated loans as for their own “organic” loans. Advances in distributed, parallel, and cloud computing and storage have eliminated the technological barriers to generating analytics, even for the largest participations. A robust set of participation analytics only requires leveraging sufficient financial engineering expertise or purchasing an outsourced solution.
Securing the Participation Analytics You Deserve
Participations deserve the same rigorous and thorough analysis as any other asset on your institution’s balance sheet. Given current technology, it is both easy and prudent to accomplish.
This article was written by Ian Lampl, CEO of LoanStreet, Douglas Callahan, Head of Data & Analytics and Michael Lanzarone, Head of Corporate Development, for CUinsight.com. The original article was published on May 27, 2021