The statistic presents the value of gross premiums written by motor vehicle insurance companies in the United Kingdom from 2009 to 2013 and a forecast thereof until 2025. The value of motor vehicle insurance sector in the United Kingdom amounted to approximately 20.93 billion U.S. dollars in 2013 and it was projected to grow to approximately 42.54 billion U.S. dollars in 2025.

We collect images of Damaged cars from around the world and create a custom annotations on those images for our customers. Annotations can be customized as per requirement, both Polygon annotation and Bounding box annotations are possible. The collection can also be customized with the desired country of origin, color, model, and Damage types.

Customers can order images from a single car from 1 to 8 angles.

The dataset consists of images in JPEG and PNG formats.

A curated dataset of damaged cars having dents, scratches, bends, cracks, and totaled cars.

The dataset has proven to be suitable for training Artificial Intelligence algorithms to detect type damages in a car by analyzing a picture. The model developed with this dataset is highly sought after in the Insurance industry.

This table contains values from Compare.com's proprietary database of car insurance quotes about average DynamicTable.dataset.coverage.monthly_cost_total car insurance costs DynamicTable.dataset.source.stateAvgPrices

The cost of comprehensive motor insurance in the United Kingdom reached an all-time high in the first quarter of 2024. As of the third quarter of 2024, the average cost of comprehensive motor insurance was approximately *** British pounds. Age also plays a role in the price of car insurance with 20- and 75-year-olds paying the most in 2024.

Abstract copyright UK Data Service and data collection copyright owner.To provide information on consumers' experience, knowledge and attitudes in relation to insurance policies of five different types. The types of policy covered are: 1. Life assurance policies 2. Endowment assurance policies 3. Home insurance (buildings) (Householders survey only) 4. Home insurance (contents) (Householders survey only) 5. Motor vehicle insurance Main Topics: Attitudinal/Behavioural Questions a) How people have taken out their present policy(ies) b) Who advised them and what advice they were given c) Policy holders' awareness of specific clauses in their policies d) Problems that have arisen when making a claim on their policy e) Other difficulties members of the public have had to face in connection with insurance f) Attitudes of non-policy holders to each type of policy and the way in which they would obtain such a policy. Survey consists of 4 separate questionnaires. Variables common to all include: whether informant holds a policy or whether he/she has ever held a policy - if applicable; reason for no longer having one is given. If respondent has never considered taking out a policy, reason' is stated and a record is made of how he/she would go about obtaining one should he/she so decide. Particulars of policy held: company with which policy is held; annual yearly premium on policy; method of payment (e.g. by Giro); whether paid by instalments; if so, frequency of payment is recorded; total sum assured on policy; date policy was taken out. Procedures followed when taking out policy: whether and by whom prompted to take out policy; objectives in taking out policy; decision stages involved in first taking out policy; from whom received advice (8 categories). There is a separate section on insurance brokers, where applicable. Knowledge of policy: particularly, cover of policy; facilities linked with policy (e.g. life insurance policy and mortgage facility); amount which would be received if respondent stopped paying premiums before policy maturation date. Also, familiarity with policy document is tested (e.g. the last time that holder read or even looked at the document, and where it is normally kept). Claims (except in life insurance questionnaire): number of claims made on policy; value of claim; whether handled by self; circumstances leading to claim; difficulties experienced; eventual outcome (e.g. claim met in full) and knowledge of the termaveraging' on claims. Attitudes: circumstances in which respondent might decide to increase the value of his/her policy; whether he/she has ever contacted the insurance company with a view to modifying policy (if yes, who contacted and number of times is recorded); whether respondent thinks that the company should contact policy holders from time to time or whether it should be left to the holder to contact the company. Satisfaction with people who may have been contacted at some stage in connection with policy is gauged on a 7-point scale (people listed are: insurance broker; insurance agent; bank manager; solicitor; other professional adviser). Background Variables Sex, age cohort, marital status, occupational details (including industry, job description and status, qualifications obtained), social grade, household status (i.e. sex, ages, number, occupational status, marital status, relationship to informant), home tenure and area of residence.

This table contains values from Compare.com's proprietary database of car insurance quotes about average full coverage car insurance costs by carrier

United Kingdom Weekly Household Exp: Avg: MG: Insurance: Vehicle Insurance (VI) data was reported at 9.200 GBP in 2016. This records a decrease from the previous number of 9.500 GBP for 2015. United Kingdom Weekly Household Exp: Avg: MG: Insurance: Vehicle Insurance (VI) data is updated yearly, averaging 9.200 GBP from Dec 2006 (Median) to 2016, with 11 observations. The data reached an all-time high of 9.900 GBP in 2012 and a record low of 7.600 GBP in 2007. United Kingdom Weekly Household Exp: Avg: MG: Insurance: Vehicle Insurance (VI) data remains active status in CEIC and is reported by Office for National Statistics. The data is categorized under Global Database’s UK – Table UK.H023: Average Weekly Household Expenditure.

This dataset features over 1,000,000 high-quality images of cars, sourced globally from photographers, enthusiasts, and automotive content creators. Optimized for AI and machine learning applications, it provides richly annotated and visually diverse automotive imagery suitable for a wide array of use cases in mobility, computer vision, and retail.

Key Features: 1. Comprehensive Metadata: each image includes full EXIF data and detailed annotations such as car make, model, year, body type, view angle (front, rear, side, interior), and condition (e.g., showroom, on-road, vintage, damaged). Ideal for training in classification, detection, OCR for license plates, and damage assessment.

1. Unique Sourcing Capabilities: the dataset is built from images submitted through a proprietary gamified photography platform with auto-themed competitions. Custom datasets can be delivered within 72 hours targeting specific brands, regions, lighting conditions, or functional contexts (e.g., race cars, commercial vehicles, taxis).

2. Global Diversity: contributors from over 100 countries ensure broad coverage of car types, manufacturing regions, driving orientations, and environmental settings—from luxury sedans in urban Europe to pickups in rural America and tuk-tuks in Southeast Asia.

3. High-Quality Imagery: images range from standard to ultra-HD and include professional-grade automotive photography, dealership shots, roadside captures, and street-level scenes. A mix of static and dynamic compositions supports diverse model training.

4. Popularity Scores: each image includes a popularity score derived from GuruShots competition performance, offering valuable signals for consumer appeal, aesthetic evaluation, and trend modeling.

5. AI-Ready Design: this dataset is structured for use in applications like vehicle detection, make/model recognition, automated insurance assessment, smart parking systems, and visual search. It’s compatible with all major ML frameworks and edge-device deployments.

6. Licensing & Compliance: fully compliant with privacy and automotive content use standards, offering transparent and flexible licensing for commercial and academic use.

Use Cases: 1. Training AI for vehicle recognition in smart city, surveillance, and autonomous driving systems. 2. Powering car search engines, automotive e-commerce platforms, and dealership inventory tools. 3. Supporting damage detection, condition grading, and automated insurance workflows. 4. Enhancing mobility research, traffic analytics, and vision-based safety systems.

This dataset delivers a large-scale, high-fidelity foundation for AI innovation in transportation, automotive tech, and intelligent infrastructure. Custom dataset curation and region-specific filters are available. Contact us to learn more!

1. Overview This dataset is a collection of 2,000 Licensed and 8,000 HD damaged car images that are ready to use for optimizing the accuracy of computer vision models. All of the contents is sourced from PIXTA's stock library of 100M+ Asian-featured images and videos. PIXTA is the largest platform of visual materials in the Asia Pacific region offering fully-managed services, high quality contents and data, and powerful tools for businesses & organisations to enable their creative and machine learning projects.

2. Use cases for damaged car images (object detection data) The 2,000 Licensed and 8,000 HD Images of damaged car could be used for various AI & Computer Vision models: Damage Inspection, Insurance Value Evaluation, Residual Value Forecast,... Each data set is supported by both AI and human review process to ensure labelling consistency and accuracy. Contact us for more custom datasets.

3. Annotation Annotation is available for this dataset on demand, including:

4. Bounding box

5. Polygon

6. Segmentation ...

7. About PIXTA PIXTASTOCK is the largest Asian-featured stock platform providing data, contents, tools and services since 2005. PIXTA experiences 15 years of integrating advanced AI technology in managing, curating, processing over 100M visual materials and serving global leading brands for their creative and data demands. Visit us at https://www.pixta.ai/ or contact via our email contact@pixta.ai.

United Kingdom Retail Price Index: Weights: Motoring Exp: Vehicle Tax & Insurance data was reported at 24.000 Per 1000 in 2018. This stayed constant from the previous number of 24.000 Per 1000 for 2017. United Kingdom Retail Price Index: Weights: Motoring Exp: Vehicle Tax & Insurance data is updated yearly, averaging 21.000 Per 1000 from Dec 1974 (Median) to 2018, with 45 observations. The data reached an all-time high of 27.000 Per 1000 in 2004 and a record low of 14.000 Per 1000 in 1977. United Kingdom Retail Price Index: Weights: Motoring Exp: Vehicle Tax & Insurance data remains active status in CEIC and is reported by Office for National Statistics. The data is categorized under Global Database’s UK – Table UK.I012: Retail Price Index: Weights.