Two years ago, Automotive Lease Guide (ALG) developed a residual projection model for vehicles serviced in commercial and daily rental car fleets. ALG Fleet Residuals are based on the company’s econometric model, enhanced and refined over the past 30 years, and used to set retail residuals. Fleet residuals are comprised of a set of assumptions that focus on auction performance of 0 to 2-year-old vehicles. ALG’s fleet modeling analyzes the relationship between 0 to 2-year-old auction prices and variables such as used supply, new car incentives, and changes to fleet penetrations. ALG Fleet Residuals provide fleet professionals with accurate means to predict vehicle depreciation. An understanding of vehicle depreciation enables fleet managers to learn lifecycle and seasonality trends allowing fleet managers to make more educated purchasing decisions.
ALG Fleet Model
ALG’s Fleet Residuals model is based on the following factors:
ALG’s new-vehicle fleet residuals are provided on a bimonthly or monthly basis in dollar or percentage format. Fleet managers can select terms between three and 60 months. Below average, average, or clean vehicle condition reports can be provided. Residuals are based on an annual mileage of 21,000; however, the basis can be modified from 18,000 to 30,000 miles/year depending on the client’s needs.
“For the commercial product, we’re targeting vehicles with higher mileage. Our daily rental product focuses on vehicles under 24 months,” said Jonathan Banks, ALG’s director of consulting services. Equipment can also be adjusted in the residual projections. Residuals are available to advanced users 40 days prior to the effective date. However, standard subscribers are provided data on the effective date. Available formats include hard-copy guides, Web interface via ALG’s Web site: www.alg.com/, or electronic data formats at a cost of $200 per year. Information is updated every two months. The electronic formats are available in Word or database-compatible formats such as Excel.
Depreciation Cost Analyzer
ALG’s depreciation cost analyzer provides fleet managers with a report enabling the user to select different inception and return months. This service is provided in addition to residual values and is not included in the $200 subscription rate. The report provides depreciation costs for each scenario (by model trim level) that can be analyzed to minimize depreciation costs by maximizing the effects of seasonality on auction prices.
This model enables fleet managers to analyze depreciation curves and identify changes in seasonality depreciation. The model also considers fluctuations in used supply for each specific model and segment, and incorporates the short-term impact on used prices by changes in manufacturer incentive spending and overall fleet penetration. The purpose of the tool is to provide fleet managers with information to minimize the cost of ownership for models included in the fleet by adjusting the mix of models or terms such as inception month, termination month, and length of service. This model is developed by incorporating accurate estimates of the following factors:
ALG calculates a seasonality curve based on the industry as a whole and for more specific groups by modeling a large sample of auction prices. The depreciation cost analyzer incorporates the typical seasonality for each specific model.
The depreciation cost analyzer incorporates ALG’s updated depreciation rates calculated from auction records.
ALG’s proprietary cross-sectional model estimates the impact of different incentive levels on a model’s resale values. Historical and expected incentive levels are combined with the impact estimates to fine-tune the forecasts.
Fleet Penetration Levels:
As in the case of incentives, fleet penetration levels have an impact on resale values that must be factored into ALG’s forecasts. ALG’s modeling estimates the impact of fleet penetration on residual values. These impact estimates are combined with historical and expected fleet penetration levels to fine-tune residual forecasts. The depreciation cost analyzer allows fleets to determine the appropriate inception month, months in service, and termination month by incorporating the factors that drive resale values and depreciation costs.
ALG provides quarterly market reports focusing on microeconomic trends such as segment trends and brand performance. These reports provide fleet managers with background information on residual changes, auction price performance, and future trends and market conditions. ALG also provides a biannual report on macroeconomic conditions and the impact of changes in key variables on the overall used market. This report offers ALG’s economic variable assessment of the used-vehicle consumer price index. ALG provides forecasts for estimated changes and the expected impact on the used market.
Fleet Residual Accuracy Reports
The online reporter tracks auction performance on a monthly basis to evaluate portfolio performance and reassess portfolio risk. ALG provides monthly updates on each model’s auction data and a report showing the original residual value, actual retention value, and difference between the two. Data is available every month and expressed in percentage or dollar values. This tool enables a user to evaluate risk at each vehicle trim level to establish reserves and provide early indicators on vehicle price trends to minimize losses or maximize gains. ALG’s Auction Price Report enables fleet managers to upload auction data files to ALG’s database and run various comparison reports showing residual accuracy or price differences between channels. Comparisons use ALG’s auction prices as a benchmark giving the user a method of comparing current remarketing efforts to the overall industry. “In the future, we’ll create online tools to allow fleet managers to upload their remarketing results and evaluate the value of the residuals they have on their books and compare their remarketing efforts to national average auction prices,” said Banks. “We’re creating different kinds of tools. We are constantly updating and improving our models to improve the accuracy of our numbers.”