Protect Your Vehicle Residual Value Using the Power of Data

According to various sources, a new car loses 9–11% of its value the moment you drive off the dealer’s lot. Over 2-3 years, its price may diminish by more than half! Although it might seem that the situation is helpless, data science can provide a cure. Learn how applying the right algorithms on telematics data can help you protect and boost the value of your vehicles.

What you'll learn

In a world where the car value starts dropping from the moment of its purchase, every saved penny of residual value counts. Whether we are talking about individual car owners, public transportation, or private fleets, vehicle depreciation is drivers’ most horrifying nightmare, right next to soaring fuel prices. 

Even though cars will continue to wear and tear, decreasing in value, mobility data science solutions can help slow down this process, by providing invaluable insights into how the vehicle’s components are used, challenged, and torn, and what actions to take to reduce their wear.

55%-65% is considered a ‘good’ residual value for a 36-month car lease (Source). This means that you should think yourself lucky if you get €18K for a car that cost €30K three years ago. Still, most cars are sold at 40%-50% of their primary worth.

What is residual value?

The definition of residual value is quite straightforward; it’s the value of a car at the end of the lease period/time of sale compared to its original worth. However, the factors that dictate its amount are not as simple. 

Traditionally, to estimate the expected car depreciation (so how much value the vehicle lost over its lifetime), dealers, lease companies, and fleet managers would rely on several key metrics: the car’s age, mileage, and maintenance. However, as the choice of brands, engine and fuel options, and in-car technologies are expanding, accurate calculation of a vehicle’s residual worth becomes more complex. 

On top of that, another question arises when we only consider basic components when trying to foresee the residual value of a car. Namely, how to slow down the depreciation and preserve as high residual value as possible. To answer it, we need to dig deep into data.


What causes cars to depreciate?

To find ways to optimize a car’s residual value, let’s first establish what impacts its worth. Sometimes, these factors aren’t intuitive. For example, you might be surprised to learn that some car makes hold value better than others. This is down to elements such as perceived (or confirmed) durability, reliability, or popularity on the market, making them easier for owners to sell.

Or think about the skyrocketing fuel prices. They are pushing record numbers of drivers to switch to electric cars. In the USA, 53% of shoppers are considering transitioning to a more fuel-efficient vehicle following the surge in gas prices. Europe mimics that trend, with the number of battery-powered cars doubling in 2022 compared to the previous year. 

What does it have to do with residual value? Well, think how much your combustion engine vehicle will be worth in three years when the demand for gas-powered cars diminishes significantly (especially among the recent news that the EU authorities have banned the sales of new petrol and diesel cars from 2035).

Sophisticated AI-driven predictive analytics solutions can help anticipate demand trends for specific vehicle brands, body types, or power sources. But these predictions still tend to be volatile and require more data to deliver accurate, actionable insights. Moreover, not every organization can afford to implement and sustain enterprise-level data hubs, nor feed them enough data for that investment to pay off.

However, there’s a way out. Already today, you can accurately predict and optimize your car’s or fleet’s residual value based on smart but simpler solutions that analyze vehicle maintenance and performance data in a broader context of the road, weather, and the driver’s style.

Biggest factors for deprecation

Improving vehicle resale value with data—how does DSaaS work?

How one drives a car and under what conditions directly influence the components’ longevity, performance, and—ultimately—residual value. Modern mobility analytics solutions combine road and weather metrics with the driver’s style and abilities to show how you or your drivers wear and tear their vehicles. These metrics include road quality, slope, curvature, weather conditions, traffic density, participants, landmarks, etc., and braking, acceleration, rest habits, and so on, on the driver’s end.

Using this information, the DSaaS mobility analytics system can assess and demonstrate how various external and driver-related factors impact the wear of particular components. With these insights, it then may predict with high accuracy when maintenance works are needed or when a given component needs to be replaced. Fleet-wise, a mobility platform will point you to the specific vehicles that may require more investment in repairs, leading to higher depreciation. And now, it’s your turn to act on this data. 

First, by using it to schedule maintenance based on each car’s condition and needs, not the calendar (you can learn more about this in our previous article). Secondly, by coaching your drivers or improving your own skills to lessen the impact on the vehicle performance, and—by extension—protecting residual value.


Which data do you need to decrease car depreciation?

‘All right,’ you may ask. ‘But how do I know what data you need to help me protect my residual value?’. Data analysts have got you covered. For example, imagine that your drivers often park in confined, crowded spaces, resulting in bumps and car scratches that directly affect car resale value in your fleet. Or that you struggle to preserve the battery in good condition, fearing that by the time it comes to selling your car, you will need to invest in replacement or just face massive depreciation on your vehicle. 

Our data scientist team can address these and other concerns with mobility data analytics. By scrutinizing contextual and telematics car data and applying machine learning models, they will infer every component’s wear and how the driver’s behavior affects that.

Based on the data sets selected for your use case, they will assign health scores to the historical trips of each car and assess how each car’s health is expected to deteriorate based on use. Analyzing driving performance throughout these trips, they will then measure the health status of several vehicle components and, consequently, the vehicle’s health status. The result demonstrates how various road, weather, and driving conditions impact the vehicle’s component performance and lifestyle, allowing you to understand how the car’s residual value is affected. 


Maximizing vehicle lifetime value: core components to measure

The type and scope of data points used to calculate the impact of various factors on car depreciation may vary from case to case. However, typically, the following components are analyzed to come up with insights that help retain residual value of a car:


The next step to protect your vehicle’s residual value

The issue of optimizing residual value is a complex and multi-faceted one, but developments in data analytics already allow for taking significant steps in lessening car depreciation. It doesn’t take massive volumes of data or thousands of car trips to pull insights that can help noticeably preserve your car’s worth over time. Given the right, easily deployable solution is selected, you can gain precious insights with a minimal dataset.

Since at Motion-S we specialize exclusively in mobility solutions, empowering fleets, drivers, public transport companies, and other organizations to optimize their mobility-related costs, we know exactly what data to choose to help you ensure timely maintenance, improve driving skills, and effectively reduce tear and wear to keep your cars in optimum running condition. Take the first step to protect your vehicle’s resale value and contact our data experts today!

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