The ECONOMIC PERFORMANCE SCORE assesses total cost of vehicle ownership. The assessment is based on initial vehicle purchase price and forecasted fuel costs according to fuel type and grade per U.S. Department of Energy and U.S. Energy Information Administration appreciation models.
The rating shows the abridged life-cycle carrying and operating costs of vehicle ownership to assess the economic benefits of owning and operating conventional and alternatively powered vehicles. Consumers can now assess if in fact the cost premium paid upfront for fuel-efficient, hybrid, plug-in hybrid electric, and battery electric vehicles will ultimately lead to net savings over time when compared to conventional counterparts.
The distinguished BEST ECONOMIC PERFORMANCE AWARD is given to the vehicle with the lowest cost of ownership in its class.
We developed an Economic Model to assess the financial costs associated with vehicle purchase and operation. The Economic Model was developed to utilize data derived from the Fuel Model to ensure economic indicators are accurately accounted for in the vehicle operation and fuel input cycles.
The Economic Model allows for the assessment of economic benefits or detriments of owning and operating conventional versus alternatively powered vehicles. The model identifies if and when in fact the cost premium paid upfront for fuel-efficient vehicles, hybrid vehicles, PHEVs, and EVs ultimately lead to net savings over time when compared to conventional counterparts.
ASG removed several cost variables that could not be predicted with any level of certainty or consistency, and only incorporated economic performance indicators that could be applied consistently across a class of vehicles. The outcome is an economic performance assessment that is relative, and therefore of greatest comparable value in each given class for new car buyers.
The Vehicle Model looks directly to the biggest economic factor – MSRP – to assess the upfront investment relative to other comparable vehicles in class. While MSRP is a good economic indicator, it does not reflect any dealer markdowns, as these discounts throughout the sales cycle cannot be accurately reflected across all models. MSRP however is an accurate starting point for all vehicles sales, and as such, was the prime indicator used in the Vehicle Model. For alternatively powered vehicles that qualify for a Federal tax credit, the applicable credit for the specific model was applied in the Vehicle Model.
Operating costs such as state license, taxes and insurance premiums were assessed in the Economic Model, however they were removed from the model given the limited scope of using such indicators to differentiate automobiles of the same class. For example, existing research suggests that car buyers focus their car search among vehicles that are comparable in size, class and price. Given this common understanding, and the fact that cars of comparable size, class and price often incur similar licensing, state taxes and insurance premiums based on the owners personal driving record, these operating expenses are not useful indicators in differentiating market offerings from a cost perspective.
Further, vehicle depreciation was assessed as an operating cost in our initial model, however we determined that depreciation is highly speculative and cannot be forecasted with any level of consistency over time given the many unforeseen devaluations specific to each vehicle – the result of poor maintenance, type of vehicle miles traveled, condition of roads frequently travelled, personal driving behavior, body work, minor and major damage, etc. These variables dictate each vehicles residual value and are in fact more important to residual value than brand and model reputation. Residual values between the two vehicles of the same model but in a different condition (excellent versus poor condition, or good versus a damaged title) can differ by many thousands of dollars, often a larger difference than the difference between competing models of the same mileage and operating condition.
While there are indeed merits in assessing residual value for the used car market on a vehicle by vehicle basis, the forecasted residual value for new car buyers was determined a poor economic indicator for comparative assertions and was therefore omitted from consideration in this Study.
We first utilized the Vehicle Miles Traveled (VMT) Model, which forecasts the average miles each vehicle will likely travel over the course of its lifetime. A new passenger vehicle will be driven an average 227,200 miles over its lifetime. We also assume new passenger vehicles are driven an average of 13,476 miles per year (FHWA, 2016) over the first 6.5 years. With new car buyers continuing a trend of driving their vehicles longer, the average length of ownership is now at 79 months (IHS Markit). By combing the FHWA average annual VMT, and the IHS Markit average length of vehicle ownership, we assumed the new car buyer would travel an average 87,594 VMT in the first 6.5 years of vehicle ownership. Focusing our assessment on the first 6.5 years of vehicle ownership fully aligns with our Fuel Model assumptions, and provides an improved comparison between conventional and alternatively powered vehicles. These revised assumptions provide clarity for a more direct and consistent vehicle comparison of the new car marketplace. It also reduces the margin of error in forecasted assumptions on electricity grid improvements (CO2 per kWh produced) and fuel costs.
The Fuel Model then combines the VMT modeled data with each vehicle’s fuel economy specifications to calculate fuel requirements in volume. Fuel volume is then assessed in terms of cost per unit at the fuel pump station, using the national average prices per fuel type and grade. By reducing the lifetime VMT to an annual forecast, fuel costs can be modeled over future years according to fuel supply and demand expectations.
Fuel type and grade is critical to the comparison of different fuel types that have different energy conversion efficiencies – such as comparing a gasoline powered vehicle, with diesel, PHEVs and EVs. Our Fuel Model incorporated data and forecasts derive from the U.S. Department of Energy and U.S. Energy Information Administration Energy Outlook models.
The PHEV is by far the most complicated model to develop given the dual mix of fuel sources and mileage allocation. This model adopted the Gamma Distribution Methodology (Lin et al., 2011, 2012) to estimate the daily VMT and its distribution of gasoline and electric fuel costs, as previously discussed in the Environmental Performance Fuel Model section. We applied the average U.S. electricity mix and cost to electric power shares of VMT, but also incorporated annual forecasted improvements in energy mix and costs across future years as modeled in our Grid Improvement Model discussed previously.