Financial institutions that sell loans to Freddie Mac and Fannie Mae collect data that can help them efficiently target loans to cure defects before they become problems using predictive analytics. In this article, Edem Togbey and Jonathan Glowacki provide an example showing how lenders can employ predictive analytics to reduce their quality control expense.
Assume lender “XYZ Mortgage Company” developed a scoring algorithm that segments its production into three levels of defect risk: low, medium, and high. The table in Figure 1 demonstrates how the process described above can reduce XYZ’s repurchase risk on 1,000 loans delivered to the GSEs. We assume 40% of potential defects are cured through a pre-funding quality control review.
In the above hypothetical example, XYZ would be able to significantly reduce its repurchase exposure by targeting high-risk loans pre-funding. Specifically, a random pre-funding review would correct 12 defects while a targeted approach would correct 25 defects while reviewing the same level of 10% of the loans. Assuming an average loan balance of $200,000 and a severity of 30% for a repurchase, this would result in a reduced repurchase exposure of $780,000 for 1,000 loans originated by XYZ for a savings around $780 per loan (see Figure 2 below).