The average motorist in the United Kingdom drove up to 10,000 miles per year. In 2018, 35 percent of respondents stated that they drove between one to 5,000 miles annually, which was an increase compared to the year before. Mileage for private travel was highest when compared to other purposes, according to a governmental survey among British car drivers. In 2017, an average of roughly 4,500 miles were driven for private reasons.

Company cars have highest mileage

The above mentioned governmental survey also found that those driving company cars tended to travel more than private car users. This was mainly due to the extra distances travelled for business and commuting.

Most UK residents use a car every day

A 2018 Statista survey found that more than half of the UK residential population used a car every day. Only one percent of respondents reported using a car less often than several times a year, with not one never using such passenger vehicles.

This statistic represents the distance travelled by driving, either as a driver or as a passenger in England in 2018, by age and gender. Male drivers travelled the longest distance by car or van, especially those aged between 40 and 59. The female public present a similar trend, the females who travelled the longest distances were drivers aged between 40 and 59. In general, male drivers presented a trend of longer travelled distances than female drivers. However, female passengers travelled longer distances than male passengers.

This statistic compares mileage for different travel purposes according to the car ownership type in Great Britain in 2017. People driving company cars travelled a lot more than those with private cars, due to the extra distances travelled for business and for commuting.

Cars and vans were the most popular mode of transport for commuting purposes in England. In 2019, car and van drivers travelled an average of 782 miles per person per year, roughly three times the distance travelled by those using surface rail as a means of commuting. In Great Britain overall, the car was used by more than two thirds of commuters, by far the majority of those travelling to work. In the past three years, the popularity of the car was unchallenged.

Train commutes the longest

The average commuting time for those travelling via rail was an hour, which suggested that those using rail tended to do so when needing to traverse greater distances. By comparison, the average car commute was 26 minutes long in 2017.

Company cars have greatest annual mileage

The greatest commuting mileage of car drivers was covered by those driving company vehicles. In 2018, a company car user drove nearly four times the distance someone using a private car travelled.

This statistic compares the mileage of cars travelling to commute, for business or for other private reasons in Great Britain between 2002 and 2017. Mileage was estimated by the survey respondents. Among all three travel purposes, mileage fell over this period. Business travel had the lowest mileage among the three and by 2017 the number had reached 500 miles according to respondents.

A dataset of vehicle MPG ratings and fuel cost calculations based on manufacturer, model, and fuel type.

The statistics "Traffic in kilometres (VK)" represent the annual driving performance by means of the characteristics vehicle type, vehicle age as well as fuel type and energy source. The data refer to motor vehicles registered in Germany with registration plates and have been published annually since the reporting year 2013. The data required for the compilation of the mileage statistics are collected as part of the carrying out of general inspections (HU). Based on the HU tests submitted by the Central Body (ZS) to the Federal Motor Transport Authority (KBA) for one year, the mileage for the average vehicle fleet of the Central Vehicle Register (ZFZR) is estimated using a model calculation. Please note the methodological explanations.

A dataset of van MPG ratings and fuel cost calculations based on manufacturer, model, and fuel type.

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UK Used Car Market Size 2025-2029

The uk used car market size is forecast to increase by USD 39.5 billion, at a CAGR of 6.2% between 2024 and 2029.

The Used Car Market in the UK is driven by the excellent value for money proposition that pre-owned vehicles offer, making them an attractive alternative to new cars for many consumers. Another significant trend shaping the market is the increasing preference for car subscription services, which provide flexibility and convenience for customers. However, the market also faces challenges, including the growing importance of digital touchpoints in the car buying process and the need for dealers to adapt and improve their online presence. Additionally, the rise of car subscription services poses a threat to traditional dealership models, requiring dealers to explore new business models and revenue streams to remain competitive. Companies seeking to capitalize on market opportunities and navigate challenges effectively should focus on enhancing their digital presence, offering flexible and convenient purchasing options, and exploring partnerships with car subscription services.

What will be the size of the UK Used Car Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The used car market in the UK is influenced by various factors, including the exterior and interior condition of the vehicles, financial history, economic trends, and consumer demand. Financially sound buyers prefer cars with well-maintained exteriors and interiors, ensuring lower car ownership costs in the long run. Economic trends, such as inflation and interest rates, impact car financing options and vehicle affordability. Maintaining a vehicle's fuel consumption within acceptable limits and adhering to the vehicle maintenance schedule is crucial for reliable performance and resale value. Financial institutions consider a vehicle's title, accident history, and service records when assessing car financing options. Emerging technologies, such as electric vehicles and autonomous driving, are transforming the industry, while insurance coverage, safety ratings, and vehicle age & mileage remain essential factors in consumer decision-making. Previous owners, engine size & type, transmission options, and vehicle features & equipment also influence consumer preferences. Car repair costs, loan terms, car financing options, and industry innovations contribute to market volatility. Registration documents, vehicle history records, and insurance coverage are essential for transparency and trust. Understanding the impact of these factors on car ownership costs is crucial for businesses operating in the UK used car market.

How is this market segmented?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ChannelOrganizedUnorganizedVehicle TypeCompact carSUVMid sizeSales ChannelDealershipsOnline PlatformsPrivate SalesFuel TypePetrolDieselHybridElectricGeographyEuropeUK

By Channel Insights

The organized segment is estimated to witness significant growth during the forecast period.

The used car market in the UK is characterized by various entities that influence its dynamics and trends. Depreciation and car insurance premiums are significant factors that impact the affordability of used cars. Safety features, a priority for consumers, are increasingly being incorporated into used vehicles through refinishing and upgrades. Car rental companies offer flexible mobility solutions, while automotive technology advances drive the adoption of vehicle diagnostics and digital car retailing. Used car dealerships and online marketplaces facilitate transactions with vehicle inspections, mileage verification, and consumer reviews. Sustainable transportation initiatives and online payment systems are shaping the market, as are car leasing agreements, price elasticity, and inflation rates. Fuel efficiency, car finance options, and driving assistance systems are key considerations for buyers. Government incentives and emissions standards influence consumer spending patterns, with a growing interest in alternative fuel vehicles and hybrid car technology. Fleet management services and car maintenance costs are essential services for businesses and individuals alike. Industry regulations and consumer protection laws ensure transparency and trust in the market. Used car warranty, customer satisfaction ratings, and brand reputation are crucial factors for buyers. The market share dynamics of organized companies, including dealership chains, online marketplaces, and OEM-affiliated dealerships, are shaped by their ability to provide guara

This statistic compares the average distance travelled per person annually for all purposes in England in 2018, by mode of transport. Car drivers covered the greatest distance, at 3,244 miles per person per year.

This statistic illustrates the average distance travelled per person annually for leisure purposes, such as visiting friends at home and elsewhere, entertainment, sport, holiday or day trip, in England in 2018, by mode of transport. Travel by car or van as a passenger was in first place, with an average of 1,053 miles travelled per person per year for leisure purposes.

Revenue for the Motor Vehicle Maintenance and Repair industry is forecast to rise at a compound annual rate of 0.3% over the five years through 2024-25 to £34.9 billion. The number of registered cars on UK roads is rising, pushed by a boom in used car sales, which is strengthening the need for repairs. Membership schemes have been a game changer for the industry employed by large companies like Halfords and Kwik Fit, with the main purpose of ensuring customers do not put off car repairs because of high costs. The schemes provide a steady stream of revenue. However, technological advances have lengthened the life and safety of vehicles, reducing collisions. Modern cars are more durable, prolonging their replacement cycles and reducing the volume of work for garages. Soaring fuel prices have also dented demand; higher running costs have put people off from driving as often, reducing wear and tear and limiting sales for garages. A fall in fuel prices in 2024-25 is getting more people back on the road, leading to more wear and tear – elevating the frequency of repairs. Revenue is forecast to rise by 2.2% in 2024-25. Revenue is forecast to expand at a compound annual rate of 2.7% over the five years through 2029-30 to £39.8 billion. The total number of vehicles on UK roads is expected to climb in the coming years, and most will need annual servicing, repairs and MOT tests, creating a stream of work for garages. Surging used vehicle sales have raised the average age of UK cars, boosting the need for future repairs. Fuel prices are anticipated to remain high, meaning fewer miles will be driven in the coming years as motorists try to cut running costs. Government policies to boost the uptake of electric cars will mean mechanics need to update their knowledge and equipment to make sure they know how to carry out repairs properly. Work on electric vehicles brings with it higher repair prices, enhancing profitability.

There are four interrelated datasets in this collection: In Study 1 we investigated the effects of musical characteristics (i.e., presence of lyrics and loudness) in the context of simulated urban driving. We studied the potentially distracting effects of processing lyrics through exposing young drivers to the same piece of music with/without lyrics and at different sound intensities (60 dBA [soft] and 75 dBA [loud]) using a counterbalanced, within-subjects design. Six simulator conditions were included that comprised low-intensity music with/without lyrics, high-intensity music with/without lyrics, plus two controls – ambient in-car noise and spoken lyrics. Between-subjects variables of driving style (defensive vs. assertive) and sex (women vs. men) were explored. The SPSS data file contains demographic data (i.e. sex, age, ethnicity), anthropometric data (i.e. height, weight, body mass index [BMI]), years of driving experience, estimated annual mileage, International Personality Item Pool (IPEP) items, Multidimensional Driving Style Inventory items, Simulator Sickness Questionnaire items, NASA Task Load Index (NASA-TLX) items, Affect Grid responses (affective valence and arousal), Rating Scale Mental Effort (RSME) responses, word-search scores ('filler' task), HRV indices, simulator data (e.g. total time, mean speed), simulator trigger ratings (for five on-road triggers), standardised scores for variables and studentised residuals. In Study 2, we investigated the interactive effects of driving task load and music tempo on cognition, affect, cardiac response and safety-relevant behaviour during simulated driving. A counterbalanced, within-subjects design was used. The SPSS data file contains the demographic data (i.e. sex, age, age group [1 = young adult, 2 = middle-aged adult], personality [1 = introvert, 2 = extrovert]) for each of the 46 participants (presented with one participant per row). Behavioural measures relating to the driving simulation are included. These include the elapsed time (mins) for each trial. Also, mean speed (mph), brake pedal use (i.e. 0 = no pressure applied, 1 = maximal braking), accelerator pedal use (i.e., 0 = no pressure applied, 1 = maximal acceleration) and risk ratings (on a scale from 1 [safe driving] to 4 [reckless driving]). Note that these performance-related measures appear 12 times in total; that is for each simulator trigger (i.e. a pedestrian who walked at 5 km/h across a zebra crossing, a garbage truck that moved slowly in the left-hand lane and prompted an overtaking manoeuvre, traffic lights that changed to red, a slow vehicle on a stretch of road on which overtaking was prohibited and a vehicle that cut across unexpectedly at a four-way intersection) across all three high-load (urban) conditions. Additionally, the scores across all conditions for the measures of the NASA Task Load Index (NASA-TLX), Affect Grid (affective valence and affective arousal), Rating Scale Mental Effort (RSME) and word-search ('filler' task) are included. The psychophysiological measures of heart rate variability (HRV) and mean heart rate (HR) are also included. For HRV and HR, specifically, we present mean HR, minimum HR, maximum HR, standard deviation of normal RR intervals (SDNN), HR standard deviation and root mean square of successive differences (RMSSD). z-scores (i.e. standardised scores) for each variable are also included. Note that each participant was exposed to six experimental conditions (high load/fast tempo, high load/slow tempo, high load/no music, low load/fast music, low load/slow music and low load/no music). Accordingly, the measures that pertain to each trial (i.e. NASA-TLX, RSME, Affect Grid, word-search task, HRV indices, risk ratings, mean speed, brake pedal use and accelerator pedal use) appear six times in the data file. In Study 3, we investigated the effect of participant-selected (PSel) and researcher-selected (RSel) music on urban driving behaviour in young men. A counterbalanced, within-subjects design was used with four simulated driving conditions: PSel fast-tempo music, PSel slow-tempo music, RSel music and an urban traffic-noise control. The between-subjects variable of personality (introverts vs. extroverts) was explored. As evident in the SPSS data file, a range of psychological measures was administered to assess subjective mental workload, core affect and personality traits. To measure perceived mental workload, we employed the Rating Scale Mental Effort (RSME) and a multidimensional, six-item instrument, the NASA Task Load Index (NASA-TLX). To measure core affect after each trial, we administered the Affect Grid; a single-item instrument used to assess affect along the dimensions of pleasure–displeasure (relating to affective valence) and arousal–sleepiness (relating to affective arousal). To measure personality, we used the Big Five Inventory, which is a relatively brief, self-report inventory designed to measure the Big Five personality dimensions (i.e., extroversion, agreeableness, conscientiousness, neuroticism and openness). Three types of behavioural data were acquired from the urban driving simulation: (a) a risk-rating (on the scale from 1 [safe driving] to 4 [reckless driving]) derived from video data (without any audible sound) and pertaining to driving performance in the entire trial. Three members of the research team conducted the ratings and inter-rater reliabilities were computed; (b) course completion time (min); (c) mean speed (mph), and (d) accelerator and brake pedal positions (i.e., 0 = no pressure applied, 1 = maximum braking). In Study 4, we conducted an inductive content analysis to assess driving experiences under a variety of music conditions. This Excel data file includes each participant’s responses to the 11 qualitative questions that were asked in the survey after a set of experimental trials. The 11 questions were: (1) Are you able to describe and differences among the six simulator trials that you completed over the last 90 minutes?; (2) How did each of the trials make you feel emotionally, in general, while you drove in the simulator (try to be specific)?; (3) Prior to this study, had you ever used music to influence your emotional state while driving in an urban environment and, if so, how exactly?; (4) Has listening to music during an urban driving simulation changed your perception of the experience in any way and, if so, how?; (5) Would listening to music during real urban driving make you likely to drive more safely in the future?; (6) What aspects of your emotions of behaviour during real urban driving is music likely to change?; (7) Would listening to a talk radio station or podcast during real urban driving make you likely to drive more safely in the future?; (8) What aspects of your emotions or behaviour during real urban driving would a talk radio station or podcase be likely to change?; (9) What sort of music would help you to drive more safely in a real urban environment (try to also give specific artists/albums/tracks)?; (10) Which trial did you think was most conducive to safe urban driving in the simulator and why?; and (11) Are there any other comments you would like to make in relation to the experimental protocol you have just completed?