Losses happen. No matter how stringent an originator’s underwriting, loans will take losses. Prime 15-year first-lien mortgages may see rare defaults with low severity; unsecured consumer loans should expect more common defaults with higher severity. For the latter, understanding what losses mean for returns is critical to managing expectations, especially for boards, lending committees, regulators and auditors less familiar with each loan type.
A worked example
Take new 60-month unsecured loans with an 8% coupon, bought at par, with a 9% cumulative loss expectation. Late in the loans’ life, small losses turn into large constant default rates (CDR) because so little principal remains. (Loss-timing curves come from static pool analysis of loan-level performance.)
At a 20% Conditional Prepayment Rate (CPR), the weighted-average life is about 1.8 years. A 9% cumulative loss then works out to roughly 5% a year, for a net after-loss yield of about 3%.
Returns aren’t evenly distributed
That 3% lifetime return is not earned evenly. With few losses early on, holders receive the full interest and see high monthly and life-to-date returns. Once losses begin, net income declines, monthly returns can turn negative, and lifetime return gradually settles at the expected 3% by maturity. Early outperformance offsets the later period when losses peak.
It’s easy to forget this while losses are peaking, especially since real losses rarely follow a smooth curve. Tracking performance over time with the right tools lets you show stakeholders the complete picture.
The takeaway
Short-term returns don’t paint the full picture. Calibrating your expectations to the loss-timing curve builds confidence in your decisions, especially in the face of seemingly negative short-term results.