If we in the vehicle remarketing industry were asked to evaluate our own performance, the response from the majority of remarketing managers would be “Great.” Remarketing managers are very diligent in expending significant effort to maximize sale results. We all believe we closely manage our portfolios to achieve strong results, irrespective of market nuances. While we typically measure our effectiveness against a guidebook benchmark, such as Black Book, Kelley, or NADA, do we truly know if we are missing opportunities to generate incremental dollars over our existing results? Typically, we do not. Most of us know how we are performing benchmarked against our historical performance, but how do we measure up against a national market benchmark?
The challenge arises in the data against which we benchmark percentages. Vehicles sell for prices that are “above book,” “back of book,” or reflective of book. The market is dynamic. Prices change, based on supply-and-demand economics, and are influenced by fluctuations in transaction prices on new cars that move daily with either dollar or interest rate incentives. Moreover, we only know the results of the sale venues we utilize. Can we ever be certain how our inventory might have performed at another site, live or cyber, on that day?
Part of our challenge results from the fact that, unlike the stock market, in which a given stock sells for the same value at an instant in time across the marketplace, cars sell for very different price at a particular point in time. Remarketing is not an exact science. Arbitrage opportunities do exist. Our mission is to determine which venues will assist us in maximizing portfolio results. Auctions, in some cases, draw bidders who possess specific buying characteristics. New-car franchise dealers have very distinct buying preferences than do independents or buy-here-pay-here auction attendees.
The challenges of creating a benchmark matrix were dramatically highlighted in the events that followed 9/11. We experience false positives when measuring our results against guidebooks. We tend to take solace in our effectiveness based on percentage of book. “We got 95 percent of book.” “We got 98 percent of book.” Following 9/11 we experienced results that fluctuated dramatically. A given vehicle that generated 85 percent of book immediately following 9/11 generated 105 percent in the following book, and settled at 97 percent in the book after that. Yet the price realized at auction for that specific vehicle was substantively identical during all three periods. Note the green line in Chart 1 that represents benchmarking against actual sales data.
Now that we understand the challenge, what is the solution? We need to create a valid benchmark for every sale. Rather than stating an average result against book across the entire portfolio, we need to benchmark each vehicle in the sale. If we moved the lowest performers at a given site to another sale, we would lift the results for the entire portfolio. In short, we need to manage on a vehicle-by-vehicle basis, and not simply benchmark results on a macro basis by the entire sale result. Note that this strategy applies to heterogeneous portfolios and probably does not have the same applicability to manufacturers’ homogeneous portfolios sold in closed sales to their proprietary franchise dealers.
Proof of the opportunity that exists is graphically demonstrated by the fact that many dealers identify arbitrage opportunities for the vehicles that we sell. For specific vehicles that we track by VIN in RSA’s Center for Advanced Automotive Research, as much as 18 percent is resold by purchasing dealers for an incremental $1,200, on average, within a four week period. The average is comprised of vehicles that result in more than $2,000 gains for some units. It is obvious that those resold within a week are simply being moved to another site. Little or no reconditioning is performed. For those resold in the fourth week after the initial sale, we can assume that some reconditioning work has been performed. Even so, on a net basis, we, as consignors and remarketers, are leaving significant dollars on the table. We will never eliminate all arbitrage since we can never have perfect knowledge of the marketplace, but for those remarketers who benchmark gross sale proceeds by vehicle rather than by entire sale, the percentage of vehicles sold within four weeks drops to less than 2 percent. For our own managed portfolios, utilizing our Performance Intelligence modeling, we reduced the number of vehicles resold in a four-week period from 3 percent to 1 percent for an average gain of less than $500.
An ancillary benefit has been more effectively-set floor prices. If a dealer recognizes that a consignor sets floor prices inappropriately, he will, if there is insufficient competitive bidding, buy those vehicles with floors set too low. Conversely, he will become disenchanted by no-sales of vehicles with floor prices set too high and exit the lane. The same principle applies to Internet sales. Dealers soon identify remarketers who set floor prices too low, creating a feeding frenzy.
We also track the lift that we generate on no-sales. While there will always be no-sales that do not reach the previous high no-sale bid, the true test is measured on average lift over the high no-sale bid on a portfolio basis.
Another benchmark against actual sales data, measured to trim level and mileage, is the number of units sold from your portfolio that exceed that benchmark. Identifying arbitrage opportunities will allow us to exceed the benchmark for more than 50 percent of the sales. However, we must initially utilize a valid benchmark to ensure those results. There is one caveat. The market is dynamic and ever-changing. As more consignors identify a strong market, and more vehicles flow to that specific market, the sale prices may deteriorate and new markets must be identified. A simple example is seasonality and its impact on prices realized. Recognize that these strategies assume that you have a critical quantity of units for each auction sale since lane placement, time of day, and dealers present in the lane at time of sale are key to achieving best-in-class sale results.
We must then measure the incremental sale results for those vehicles moved to an alternate sale to ensure that we are not performing an exercise in futility. What was the net lift over previous vehicle-specific sale results? Was net lift generated? If not, alternate strategies must be explored. Again, we all recognize that remarketing is not an exact science, but to the extent that we are able to migrate from art to science, we will achieve stronger results.
Let’s explore one specific auction (Auction G, Chart 7). Measured against all potential makes, models, and price ranges, Auction G appears to produce weak results, generating auction prices that fall $500 below benchmark 40 percent of the time.
However, when benchmark metrics are utilized to ensure a match between dealer demand and product consignment, Auction G becomes a stronger performer, beating benchmark approximately 60 percent of the time for vehicle values that fall in the $7,500-10,000 range. (Chart 8) Note, however, that Auction G performs poorly for vehicles in the $5,000-7,500 range.
In the next example, Model #11 in Chart 9 performs poorly when overlaid against all auctions. However, for Auction F, Model #11 performs well against benchmark. Model #12 however, performs poorly against benchmark at Auction F. (Chart 10) In short, we need to cross matrix every make, model, trim level, mileage, and price range to create a dynamic auction selection model on a net of expense basis.
While guidebooks serve an important role in remarketing strategy, accessing actual auction results to make valued decisions for sale venue selection is critical to effective remarketing. Knowing our own specific