Auto Finance – Virag Consulting

Lenders at Top of Funnel

Chase Auto recently rolled out a digital platform for car shopping … and financing. I like it. The link is here. It seems that everyone today has a vehicle search page. The original cast, Autotrader and Cars.com, with about a dozen TPC competitors, are now joined by OEM sites, public dealer groups, and marketplaces from Roadster and Carvana.

“More vehicle shoppers than ever have started to look for vehicle financing before ever setting foot in a dealership.”

Competition hinges on which information the customer will seek first. In an era of reduced purchasing power, many customers will want to “secure financing before going to the dealer.” That’s the prompt on the Chase website. There’s a prequal button right there between the Lariat and the XLT.

Don’t take my word for it, though. This J.D. Power study found that nearly half of all customers shop for financing before visiting a dealer – 62% among Gen Z – and they start more than 30 days out.

This is probably a negative development for captives, and indirect finance in general. Banks have a lower cost of capital and better rates. Chase, as you know, is also popular as an indirect lender. They say there’s no conflict with their dealer channel, but what if they had to choose?

The reach hierarchy, by customer base, is:

Banks – eight digits (ongoing)
Car Makers – millions of cars per year
Dealer Groups – hundreds of thousands

Banks have more customers, by an order of magnitude, than even the largest car makers. Ten years’ worth of loyal Toyota drivers doesn’t approach Bank of America’s 66 million customers. The same ranking goes for website reach, with the banks getting 120 to 190 million visits per month, while Carvana, Ford, and Autotrader each get twenty something.

Capital One, by the way, also has a shopping platform. Ally has a dealer locator. Bank of America has a redirect to Dealertrack. Capital One is pretty shrewd about encouraging buyers to bring the app with them into the dealership, so they can update the deal as needed. Mobile-first responsive is good, but an app is better. Bank customers will carry their bank’s app.

Captives have the home field advantage once the customer is in the dealership and, likewise, their position online is downstream from the OEM brand. Captives are advised to be front and center on their manufacturer’s website.

Dealer groups, like AutoNation, must rely on their own brand to draw customer attention. In terms of unit sales, even the largest dealer groups fall below tenth-ranked Subaru. Note that Lithia chose to develop a new brand, Driveway, for their online business.

Of course, none of these is a direct measure of financing intent. Only a fraction of online banking traffic is looking for an auto loan. The point is that they’re looking for the loan first, and then the car.

Seasonal Adjustment Factors

I felt like doing something quantitative, so this week we look at seasonal adjustment factors. Everybody always talks about SAAR, and you probably know that it stands for “seasonally adjusted annual rate,” but what does this really mean?

Well, suppose it’s March 2017, and you are wondering what total sales will be for the year. The industry sold 1.55 million vehicles that month so, if you multiply by twelve months, you might estimate 18.6 million for the year.

You would be wrong, though, because March is always a strong month. Here are the estimates produced by the simple “times twelve” method, relative to the actual total for 2017, which was 17.2 million.

Using data from Fred for the five years 2013 through 2017, and converting everything to a percentage, you can see how March always overestimates the year’s results. Each year’s dots are a different color, though it doesn’t really matter which is which.

Some months are highly variable, like September. Not a good gauge of anything. Remember to distrust any SAAR figures published in September. April, oddly, is a tight group and bang on the annual rate. April 2018 sales were 1.4 million, so a good guess for the year is 16.8 million.

Taking an average across the five years, we find that March, May, August, and December each overshoot the annual rate by roughly 10%. Finally, we convert these percentages into monthly adjustment factors.

Instead of multiplying last month’s sales by 12, multiply by the monthly factor to predict the year’s total. Of course, we have more data than just a single month. We can also look at cumulative sales since January. For example, do a quarter of the year’s sales occur in the first quarter?

No. It takes a while to make up for the weak January and February, and then the actual historical cumulative pace slowly comes into alignment with the idealized linear cumulative pace. I made that chart, too, but it’s not pretty. That’s enough quantitative stuff for this week.

Life (Credit Life) without Recursion

I was chatting with Tim Gill the other day about auto finance math, and the topic of recursion came up. Tim is one of the few vendors in this space with his own “calculations engine.” Otherwise, there are not many people who will talk to me about esoteric math problems. That’s why I write a blog.

People commonly describe Credit Life as a recursive calculation or, more properly, an iterative one. This is because the premium must cover the amount financed, and the premium is itself financed. So, if we write the premium as CLP, a function of the amount financed, A, then:

This is generally how people solve it. They run a few iterations, and CLP converges quickly. This is a preference, however, not a requirement. Assuming that the premium calculation is distributive over addition, which it is, we can just as easily set the problem up as:

… which can be solved analytically. This approach will work for most of your popular recursive calculations, like GAP insurance. For an example, let’s take a typical “cost per thousand” insurance calculation, where f works out to ten percent. You could go the infinite series route, which looks like this:

Or, you could simply work the algebra problem:

Now, I know what you’re thinking. You’re thinking that credit life calculations are far too complicated for this approach. You may also be thinking that the premium is based on the monthly payment, M, not A. In fact, these complaints are related. The payment is directly related to the amount financed, through the PV annuity factor, which combines the term and the APR into this handy relation:

So, when you see a payment formula like this one:

The insurance carrier is actually helping you, by combining the calculations for premium and monthly payment. By the way, the last time I checked, C# did not have the payment and related methods from VB and Excel. You are much better off coding your own PV annuity factor, and using it as described here.

Now, if you are designing a calculations engine, you may still prefer to use iteration, for the same reason that you may not want to algebraically reverse all your tax and fee calculations. It is better, though, to use your algebra and know your options, than to rely blindly on iteration.

Automotive News Corrigendum

Regular readers know that, from FMCC to Spartefinanz Abteilung, I am a captive finance booster. See here, for example. So, I was disappointed to see this omission from the BMW Centenary coverage in Automotive News.

I was employee number six behind, if memory serves, Kevin Westfall, David Paul, Mark Mundahl, Bob Devine, and John Dick. David is quoted in this ancient interview. Take a bow, gentlemen. Kudos also to the professional staff supplied by Bank One.

It’s worth noting the structure of this partnership. BMW had hired PWC to administer an RFP. Of many strong entries, Bank One was the only partner willing to use our computer systems. They were aware of our intention ultimately to bring the enterprise in-house, and control of the systems was key. This planned migration from a service provider to insourcing is the same structure Kevin employed for AutoNation Financial Services, and one I would still recommend today.