The mortgage industry’s current state of flux has everyone wondering what is happening and, more importantly, what is going to happen. An in-depth analysis of defect rates and trending patterns provide valuable data that can be used in the manufacturing process to increase the quality of loans and decrease default rates. Tom Showalter, Chief Analytics Office at Digital Risk, provides an analysis of these defect rates over time.
The chart below shows Loan Quality Index (LQI) scores for the previous 12 months. The scores are based on values ranging from 0% to 100%, with 100% being complete loan quality. As you can see, LQI scores have trended slightly downward, indicating a modest decline in loan quality. Consequently, the loan defect rate has evolved from less than 9% to over 10% during the same timeframe.
The presence of a defect rate estimated as 10% is in excess of industry guidelines, where most industry professionals believe their loans to have a 2% defect rate, with the most aggressive assumptions being in the vicinity of 5%. The actual LQI derived defect rate is much greater. While the LQI derived defect rate may be overstated, it is not likely that it is overstated by 200% or more. This suggests that the industry’s ability to detect (and manage) loan quality defects is less than the situation may demand.
The chart below reveals the composition of the defect rate. As shown, the most prevalent form of defect is material defect in valuation; that is a defect in which the estimated value of the subject property is approximately 15%+ overstated, bringing the defect rate to greater than 4%. The next most prevalent defect rate is the statement of the borrower’s assets and liabilities at closing; coming in also high at greater than 3%.
The volatility of these trends is due largely to inconsistencies in the manufacturing process, which allows these types of errors to be volatile. Limitations in the typical loan manufacturing process can be blamed for much of these inconsistencies. The origination process, while it contains many boxes to check, rarely employs any robust defect detection technology to test for the likelihood of a defect, especially across a large population of loans.
The manufacturing process relies almost exclusively upon expert judgment and the data supplied by the loan originator and the defect detection methodology is likewise: relying on an experienced underwriter to manually perform quality assurance on a loan by loan basis, usually on a small population of loans, approximately 2,500 per month. In this manual quality control process each loan review can absorb over $250/loan, making this evaluation methodology highly inefficient for use on a large population of loans.
The need for greater quality control and more efficient means of analyzing and identifying a greater population of loans is evident. However, this requires an investment of time, money, technology and a great deal of industry expertise to successfully bring default rates to an acceptable level.
Chief Analytics Officer, Digital Risk LLC