The durability of a vehicle is its expected lifetime.
With data analytics we calculate and rank the factual historical lifetimes of the all models and brands in our car samples or populations, from first registration to scrapping or de-registration. Older cars are ranked by median lifetimes, while newer cars are ranked based on cars still on the road as a surrogate for life-expectancy.
Our analytics is being developed to include predictions on expected lifetimes based on early performance combined with historical lifetimes of the specific model or brand.
The carbon footprint of a vehicle is its impact on the environment over the entire lifetime.
When calculation the carbon footprint of a vehicle we use published research to estimate and include the production and material phase emissions and combine this with historical data on the life-expectancy of the specific model, type or brand of cars. We compare and rank this information with similar cars or bands and models.
The energy production mix for the country in which the vehicle is driven as well as expected personal usage is factored in to enable our user to choose the most environmentally friendly option for them.
COST OF OWNERSHIP
The cost of ownership represents all the cost that an owner will incure chile owning a vehicle.
When calculation the cost of owning a car, we include fuel, insurance, tolls and taxes, tyres, services, repairs and maintenance. The value decrease or depreciation during the time of the ownership is calculated based on actual historical life expectancies for the specific model or brand.
We compare and rank this information with similar cars brands and models, and factor in other weighed variables such as upcoming legislation that may have an impact on the overall cost of ownership.
The residual value of a vehicle is its expected sale price after a certain period of usage.
To calculate the residual value, B2L use list prices and depreciation from the same database as dealers of second-hand cars use, but we supplement the estimates with our own data analytics on life expectancy (durability) based on actual model-specific historical performance.
Our model also factor-in distance (kilometre) as well as averages of current listings, and use machine learning to constantly improve our estimates for deviations versus actual prices obtained in the market.