Learn From Your Year End Losses So You Don’t Repeat Them!

Posted 2 years ago

By Kenneth Shilson

President, Subprime Analytics


                In preparation for 2022, it is hoped that you attended the November NIADA/NABD conference in Orlando so that you understand the challenges ahead and are already working on the solutions.  As discussed in Orlando, two of the biggest challenges will be collecting from subprime customers in the post-pandemic environment and getting the capital needed to regrow your portfolio.

                At this time each year, buy here, pay here operators scurry to close out their books for the year and aggregate all their bad debt losses in order to claim a tax deduction. Unfortunately, after these losses are compiled and deducted, they are quickly forgotten!  This approach results in a very expensive tax deduction!

                In the challenging economic times ahead, with no more economic stimulus payments, and in the absence of additional unemployment benefits, subprime customers will face serious liquidity challenges.  Therefore, subprime auto finance operators will be faced with tougher underwriting decisions in order to identify subprime customers who will be able to pay without government economic assistance.  In these circumstances, it is time to “look under the hood” of your own portfolio and learn from your past.

In these challenging economic times, with lower consumer liquidity, tighter credit availability, and a highly competitive industry environment, operators must learn from their losses instead of repeating them, if they expect to prosper.  Deducting bad debt losses for tax purposes is important; understanding what caused them so they are not repeated is essential to future growth and profitability

                Operators are encouraged to perform a careful analysis of their subprime portfolio performance (in essence, a financial MRI) which should include the following:

  1. Static pool and loss/liquidation (the inverse of CRR) calculations which compute performance statistics correlated to their periods of loan origination. This allows operators to determine whether losses are increasing or decreasing and to identify important trends. In addition, it is important to compare the pace of losses between comparable periods of each year.  If you are not familiar with these metrics, visit the videos on my website at www.subanalytics.com.
  2. Loss rate analysis should include gross loss (before recoveries), net loss (after recoveries) and default rate calculations.  In this type of analysis, the entire portfolio is segregated by origination (usually months) intervals so that loss trends can be identified easily.

3.      Recovery rates should be calculated to ascertain whether yields on repos and other recovery techniques are working successfully.  Improving recovery rates mitigates net bad debt losses even when defaults occur. Operators should know what their average recovery proceeds are and compare them with the industry benchmarks I issue annually.

4. A comparison should be made of bad debt losses with deals that performed to identify underwriting differences and areas where future changes are needed. This comparison should focus on your business model (its’ elements such as the vehicle cost, sales price, markup, down payment, payment amount, loan term and other attributes of each deal).

                There are only three (3) key elements to every buy here, pay here deal: 1.) the customer, 2.) the vehicle, and 3.) the deal structure. However, all three of these elements are vitally important to repayment success or failure. In order to maximize profits and cash flow, operators should avoid learning by trial and error.  This type of approach can cost millions of dollars in losses!  Instead, use portfolio analysis to identify a successful underwriting strategy before additional bad-debt losses are incurred.

                Some operators believe that internal credit scoring systems are the key to successful underwriting. However, credit scoring assures only consistency and not that the underlying credit decisions will be successful.  Only when credit scores are correlated with portfolio loss metrics can the results be accurately evaluated.  As individual portfolios perform differently, custom credit scoring models are needed for each individual portfolio to produce predictable results.

                In order to analyze your portfolio efficiently, you should mine the data electronically from your own DMS system.  At Subprime Analytics, we can help you do this cost effectively and compare your results with industry peers.

                If your focus is on rapid portfolio growth, you will need access to additional capital to fund it.  Unfortunately, capital availability will be limited and very competitive.  Therefore, operators need to balance how quickly they grow “with how much they will owe”.  This will require more prudent financial management of debt, cash flow, expenses and avoiding “selling yourself out of business”.  The aforementioned metrics analysis will help you make better financial and underwriting decisions and differentiate your portfolio performance from your industry competition.

                In summary, compiling bad debt losses to compute year-end tax deductions should be the start of portfolio analysis, not the end!  Without careful analysis, underwriting mistakes will likely be repeated and the future may be worse than the past.  Good luck in the New Year!


Kenneth B. Shilson, CPA, is President of Subprime Analytics, www.subanalytics.com, a consulting company, which provides subprime portfolio analysis services. Subprime Analytics utilizes state-of-the-art data mining and extraction technology in order to identify loss trends and areas for underwriting improvement. Questions can be directed to him at ken@kenshilson.com or call 281-723-9508.