By Kenneth Shilson
President / Founder
Subprime Analytics www.subanalytics.com
With 2019 in the rearview mirror BHPH operators should be identifying ways to improve their portfolio performance in 2020! Improvement can’t be found by comparing financial statement results or reviewing the number of units you sold in 2019. Both of these measurements measure sales originations, but success in Buy Here, Pay Here results from “keeping them sold”. Financial statements and unit sales are merely snapshots of a point in time and not a predictor of the future.
In this article I will focus on ways to improve your future portfolio performance and discuss whether credit scoring tools need to be implemented into your underwriting procedures.
Over the past 15 years I have analyzed more than 2 million subprime auto installment contracts aggregating $24 billion. In addition, I also perform portfolio analysis services for capital providers and subprime operators around the nation. After studying my aforementioned database, I have the following conclusions:
1. Each installment portfolio represents the operator’s most important financial asset because these contracts generate the cash flow which is the “mother’s milk” of their operation. BHPH really is the “finance business”, not the car business.
2. Asset quality is more important to success than size. The best portfolios are built over time, not overnight! Bigger is not necessarily better!
3. The best portfolios are built using a consistent underwriting approach which is best developed by correlating the underwriting characteristics of the customers, the vehicles, and the deal structures with historical loss rates. This is most efficiently done by using data mining technology and correlation analysis on your own data.
4. Centralized underwriting does not assure positive performance – it only assures consistency! Successful underwriting matches the vehicle being sold with each customer’s ability to pay.
5. The local economic environment for operators impacts performance, either positively or negatively, more than the overall national economic conditions. For instance, a significant plant closure in an economic area can have a devastating adverse impact on portfolio repayment performance even when national employment data is improving. Therefore, it is imperative to monitor portfolio performance periodically and adjust to changes occurring in your local economy.
6. The differences in local economic conditions like income, employment data, and customer stipulations necessitate the need for different business models nationally. I believe that is the reason why many of the nation’s most successful operators utilize different business models, and why regional performance can vary when the same business models are used in different regions. There are many ways to succeed in BHPH and not just one way! This is the major reason why a custom credit scoring system should be built around your own underwriting experience using your own customer base.
7. Bad debt losses are a reality in BHPH (because customers lack adequate financial capacity to withstand normal life events like medical problems, job changes, divorce, etc.) It is imperative that operators mitigate their losses by maximizing recoveries when they occur. When vehicle costs rise more rapidly than down payments and repayments, maximizing recoveries becomes critical to profitability. Therefore, you should identify and respond quickly to changes and trends.
8. The most successful BHPH operators are able to maintain consistency in their performance regardless of how the national automotive industry performs. They adjust to local economic conditions quickly and avoid “trial and error” mistakes which result in significant charge-offs. In addition, they monitor recoveries closely and seek to maximize them as part of their overall collection strategy. This is the case, even in states where remedial recoveries (garnishments and property liens) are not permitted.
9. A custom credit scoring system should integrate your data with your underwriting experience. This is accomplished by correlating your credit score bands with actual default rates. The best score bands should have the lowest defaults and vice versa. However, the real test is all the scores in between! The default rates on these should correlate in a lineal progression. When score bands don’t correlate you should determine the reasons why. That requires analyzing the underlying data. If you are using a scoring system, do you have direct access to the underlying data? If so, have you identified the most important customer attributes?
My subprime data contains metrics and data mining evaluations of installment portfolios for many of the nation’s most successful BHPH operators. These metrics include static pool (measures the severity and frequency of losses) and loss / liquidation (measures the pace of loss occurrences), and “drill downs” covering virtually every aspect of each installment deal. This analysis looks at customer attributes, vehicle types, and all the various components of each deal structure, including the amount financed, contract term, repayment intervals, payment amount, interest rate, markup, etc.
Using historical metrics, I am able to forecast future loss rates and predict collection performance by plotting loss curves. I then utilize cash recovery projections for each portfolio to compare the cash returns with each operator’s investment in the portfolio. This enables me to determine their return on investment (ROI). In addition, I have created ROI performance gradients for below average, average and above average returns. Such gradients allow me to rate each portfolio’s ROI and collective performance against an overall master database of comparable peers. In addition, it allows me to assess the cash efficiency and profitability for each operator’s business model.
A detailed study of the above has caused me to conclude that the best performance results are achieved by studying the losses in each portfolio to identify what is working and what isn’t!
When studies are performed utilizing data from multiple periods (months and years) of originations, the results can be used to make the underwriting adjustments needed to maintain and improve portfolio performance. Significant underwriting changes should be avoided; “trial and error” mistakes can cost millions of dollars! A custom credit scoring system can be developed from your data to make future results consistent and predictive.
In addition, the capital markets today are extremely competitive for subprime operators. Metrics and consistent underwriting are needed to attract the necessary capital. If you don’t have metrics or a scoring system, you should get both!
Now is the perfect time to “look under the hood” of your own portfolio. Maybe it needs a tune-up? Good luck!
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 firstname.lastname@example.org or by calling 281-723-9508.