This dataset provides comprehensive information about used cars available for sale in the United States. It includes detailed data on various aspects of each vehicle, making it a valuable resource for car buyers, sellers, and data enthusiasts. The dataset contains the following key attributes:

• Brand: The brand or manufacturer of the car.
• Model: The specific model of the car.
• Mileage: The number of miles the car has been driven.
• Year: The manufacturing year of the car.
• Status: Indicates whether the car is new, used, or certified pre-owned.
• Dealer: Information about the dealer or seller offering the car.
• Price: The listed price of the car in USD.

This dataset is ideal for data analysis, machine learning projects, and market research related to the used car industry in the United States. Whether you are interested in predicting car prices, understanding market trends, or simply searching for your next vehicle, this dataset provides a wealth of information to explore.

Data Source: More info on my GitHub repository

Data Format: CSV

Total Vehicle Sales in the United States decreased to 15.30 Million in October from 16.40 Million in September of 2025. This dataset provides the latest reported value for - United States Total Vehicle Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The U.S. auto industry sold nearly ************* cars in 2024. That year, total car and light truck sales were approximately ************ in the United States. U.S. vehicle sales peaked in 2016 at roughly ************ units. Pandemic impact The COVID-19 pandemic deeply impacted the U.S. automotive market, accelerating the global automotive semiconductor shortage and leading to a drop in demand during the first months of 2020. However, as demand rebounded, new vehicle supply could not keep up with the market. U.S. inventory-to-sales ratio dropped to its lowest point in February 2022, as Russia's war on Ukraine lead to gasoline price hikes. During that same period, inflation also impacted new and used car prices, pricing many U.S. consumers out of a market with increasingly lower car stocks. Focus on fuel economy The U.S. auto industry had one of its worst years in 1982 when customers were beginning to feel the effects of the 1973 oil crisis and the energy crisis of 1979. Since light trucks would often be considered less fuel-efficient, cars accounted for about ** percent of light vehicle sales back then. Thanks to improved fuel economy for light trucks and cheaper gas prices, this picture had completely changed in 2020. That year, prices for Brent oil dropped to just over ** U.S. dollars per barrel. The decline occurred in tandem with lower gasoline prices, which came to about **** U.S. dollars per gallon in 2020 - and cars only accounted for less than one-fourth of light vehicle sales that year. Four years on, prices are dropping again, after being the highest on record since 1990 in 2022.

Detailed Dataset Description: Car Sales Transactional Data This dataset provides a rich, multi-dimensional view of individual car sales transactions, capturing valuable information across customer demographics, car specifications, pricing metrics, payment details, sales performance, and seasonal or regional context. Each row in the dataset represents a single car sale transaction, allowing for granular-level analysis of how various factors influence profitability, sales trends, and customer behavior.

📅 Date & Temporal Context Date: The exact date of the transaction, allowing daily trend analysis.

Sale Year, Month, Quarter, Day of Week, and Season: These columns offer temporal segmentation that helps identify seasonal patterns, monthly performance, and weekday vs weekend trends.

🧑‍💼 Salesperson and Customer Information Salesperson: Identifies the individual responsible for the sale, useful for tracking salesperson performance, commission analysis, and productivity metrics.

Customer Name, Age, and Gender: Offers demographic insights, enabling segmentation by age group and gender, and understanding customer preferences in vehicle types and pricing.

🚗 Vehicle Details Car Make and Model: Specifies the brand and specific vehicle model sold.

Car Year: Indicates the model year of the vehicle, helpful in analyzing the popularity of newer vs older models.

💵 Financial and Sales Metrics Quantity: Number of cars sold in the transaction (typically 1, but can vary in business fleet cases).

Sale Price and Cost: Gross sale price and internal cost incurred by the dealership.

Profit: Computed as the difference between sale price and cost, giving direct insight into transaction-level profitability.

Discount: Shows the discount offered as a decimal (e.g., 0.05 = 5%), aiding in understanding the impact of promotions on sales.

💳 Payment & Incentive Structure Payment Method: Indicates how the customer paid (e.g., Cash, Loan, Credit), helping identify payment preferences over time or across customer types.

Commission Rate & Commission Earned: Details the salesperson's incentive structure and earnings from the sale, supporting analysis of commission efficiency, reward optimization, and employee motivation.

🌎 Geographic Coverage Sales Region: Highlights the physical region where the sale occurred (e.g., Alaska), allowing for regional performance comparison, mapping in BI tools, and assessing market penetration across different areas.

Use Cases and Applications This dataset can be effectively used for:

Business Intelligence Dashboards (e.g., Tableau, Power BI)

Sales & Profitability Analysis

Customer Demographics and Segmentation

Payment Method Trends

Salesperson Performance Monitoring

Seasonal Demand Forecasting

Regional Sales Comparisons

Commission Strategy Optimization

Its wide coverage across multiple dimensions makes it ideal for predictive modeling, trend visualization, and data storytelling in sales, marketing, and operations

Car Production in the United States increased to 11.04 Million Units in August from 10.42 Million Units in July of 2025. This dataset provides - United States Car Production- actual values, historical data, forecast, chart, statistics, economic calendar and news.

SpaceKnow uses satellite (SAR) data to capture activity in electric vehicles and automotive factories.

Data is updated daily, has an average lag of 4-6 days, and history back to 2017.

The insights provide you with level and change data that monitors the area which is covered with assembled light vehicles in square meters.

We offer 3 delivery options: CSV, API, and Insights Dashboard

Available companies Rivian (NASDAQ: RIVN) for employee parking, logistics, logistic centers, product distribution & product in the US. (See use-case write up on page 4) TESLA (NASDAQ: TSLA) indices for product, logistics & employee parking for Fremont, Nevada, Shanghai, Texas, Berlin, and Global level Lucid Motors (NASDAQ: LCID) for employee parking, logistics & product in US

Why get SpaceKnow's EV datasets?

Monitor the company’s business activity: Near-real-time insights into the business activities of Rivian allow users to better understand and anticipate the company’s performance.

Assess Risk: Use satellite activity data to assess the risks associated with investing in the company.

Types of Indices Available Continuous Feed Index (CFI) is a daily aggregation of the area of metallic objects in square meters. There are two types of CFI indices. The first one is CFI-R which gives you level data, so it shows how many square meters are covered by metallic objects (for example assembled cars). The second one is CFI-S which gives you change data, so it shows you how many square meters have changed within the locations between two consecutive satellite images.

How to interpret the data SpaceKnow indices can be compared with the related economic indicators or KPIs. If the economic indicator is in monthly terms, perform a 30-day rolling sum and pick the last day of the month to compare with the economic indicator. Each data point will reflect approximately the sum of the month. If the economic indicator is in quarterly terms, perform a 90-day rolling sum and pick the last day of the 90-day to compare with the economic indicator. Each data point will reflect approximately the sum of the quarter.

Product index This index monitors the area covered by manufactured cars. The larger the area covered by the assembled cars, the larger and faster the production of a particular facility. The index rises as production increases.

Product distribution index This index monitors the area covered by assembled cars that are ready for distribution. The index covers locations in the Rivian factory. The distribution is done via trucks and trains.

Employee parking index Like the previous index, this one indicates the area covered by cars, but those that belong to factory employees. This index is a good indicator of factory construction, closures, and capacity utilization. The index rises as more employees work in the factory.

Logistics index The index monitors the movement of materials supply trucks in particular car factories.

Logistics Centers index The index monitors the movement of supply trucks in warehouses.

Where the data comes from: SpaceKnow brings you information advantages by applying machine learning and AI algorithms to synthetic aperture radar and optical satellite imagery. The company’s infrastructure searches and downloads new imagery every day, and the computations of the data take place within less than 24 hours.

In contrast to traditional economic data, which are released in monthly and quarterly terms, SpaceKnow data is high-frequency and available daily. It is possible to observe the latest movements in the EV industry with just a 4-6 day lag, on average.

The EV data help you to estimate the performance of the EV sector and the business activity of the selected companies.

The backbone of SpaceKnow’s high-quality data is the locations from which data is extracted. All locations are thoroughly researched and validated by an in-house team of annotators and data analysts.

Each individual location is precisely defined so that the resulting data does not contain noise such as surrounding traffic or changing vegetation with the season.

We use radar imagery and our own algorithms, so the final indices are not devalued by weather conditions such as rain or heavy clouds.

→ Reach out to get a free trial

Use Case - Rivian:

SpaceKnow uses the quarterly production and delivery data of Rivian as a benchmark. Rivian targeted to produce 25,000 cars in 2022. To achieve this target, the company had to increase production by 45% by producing 10,683 cars in Q4. However the production was 10,020 and the target was slightly missed by reaching total production of 24,337 cars for FY22.

SpaceKnow indices help us to observe the company’s operations, and we are able to monitor if the company is set to meet its forecasts or not. We deliver five different indices for Rivian, and these indices observe logistic centers, employee parking lot, logistics, product, and prod...

This dataset included Information about 43 brands, and 445 models of vehicles for sale in the US. The period is from 2013 to 2022 Data source: www.goodcarbadcar.net, www.marklines.com/en/vehicle_sales/index

Autos include all passenger cars, including station wagons. The U.S. Bureau of Economic Analysis releases auto and truck sales data, which are used in the preparation of estimates of personal consumption expenditures.

🚗 2025 Used Car Market Dataset 🚗 This dataset is carefully prepared for data scientists, analysts, and researchers who want to analyze the 2025 used car market. With approximately 2,500 rows and 13 different features, this dataset serves as a powerful resource for exploring pricing trends, brand-model preferences, and vehicle history.

📊 Dataset Contents:

price → Vehicle price brand → Brand model → Model year → Manufacturing year mileage → Mileage information title_status → Vehicle title status (Clean, Salvage, etc.) color → Color information vin, lot → Vehicle identification details 🎯 Use Cases: ✔️ Machine learning projects – Price prediction, regression models ✔️ Data analysis & visualization – Analyzing market trends ✔️ Used car market research

🔹 This dataset is clean, well-structured, and ready for use—start your analysis right away! We’d love to hear feedback from the Kaggle community. 🚀

👉 Let’s explore this data and uncover valuable insights together! 💡

Datasys Automotive Owners dataset includes 80M+ verified U.S. car owners tied to 120M+ registered vehicles. Each record is enriched with make, model, year, and ownership details, enabling precise targeting for automotive brands, insurance providers, and aftermarket services. Updated quarterly, this dataset helps marketers identify in-market car owners, segment by vehicle type, and align offers with real-world ownership data.

Car Registrations in the United States increased to 241.10 Thousand in August from 221.50 Thousand in July of 2025. This dataset provides - United States Car Registrations - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Exports of Automotive Vehicles in the United States decreased to 12390 USD Million in February from 13547 USD Million in January of 2024. This dataset includes a chart with historical data for the United States Exports of Automotive Vehicles.

Key information about United States Motor Vehicle Sales: Passenger Cars

• United States Motor Vehicle Sales: Passenger Cars was reported at 2,984,039.000 Unit in Dec 2024.
• This records a decrease from the previous number of 3,116,647.000 Unit for Dec 2023.
• US Motor Vehicle Sales: Passenger Cars data is updated yearly, averaging 7,689,110.000 Unit from Dec 2005 to 2024, with 20 observations.
• The data reached an all-time high of 7,761,592.000 Unit in 2006 and a record low of 2,858,575.000 Unit in 2022.
• US Motor Vehicle Sales: Passenger Cars data remains active status in CEIC and is reported by International Organization of Motor Vehicle Manufacturers.
• The data is categorized under World Trend Plus’s Association: Automobile Sector – Table RA.OICA.MVS: Motor Vehicle Sales: by Country and Type: Passenger Car (PC).

Introduction

Welcome to the US English Language In-car Speech Dataset, a comprehensive collection of audio recordings designed to facilitate the development of speech recognition models specifically tailored for in-car environments. This dataset aims to support research and innovation in automotive speech technology, enabling seamless and robust voice interactions within vehicles for drivers and co-passengers.

Speech Data

This dataset comprises over 5,000 high-quality audio recordings collected from various in-car environments. These recordings include scripted wake words and command-type prompts.

Participant Diversity:

- Speakers: 50+ native English speakers from the FutureBeeAI Community.

- Regions: Ensures a balanced representation of United States of America1 accents, dialects, and demographics.

- Participant Profile: Participants range from 18 to 70 years old, representing both males and females in a 60:40 ratio, respectively.

Recording Nature: Scripted wake word and command type of audio recordings.

- Duration: Average duration of 5 to 20 seconds per audio recording.

- Formats: WAV format with mono channels, a bit depth of 16 bits. The dataset contains different data at 16kHz and 48kHz.

Dataset Diversity

Apart from participant diversity, the dataset is diverse in terms of different wake words, voice commands, and recording environments.

Different Automobile Related Wake Words: Hey Mercedes, Hey BMW, Hey Porsche, Hey Volvo, Hey Audi, Hi Genesis, Hey Mini, Hey Toyota, Ok Ford, Hey Hyundai, Ok Honda, Hello Kia, Hey Dodge.

Different Cars: Data collection was carried out in different types and models of cars.

Different Types of Voice Commands:

- Navigational Voice Commands

- Mobile Control Voice Commands

- Car Control Voice Commands

- Multimedia & Entertainment Commands

- General, Question Answer, Search Commands

Recording Time: Participants recorded the given prompts at various times to make the dataset more diverse.

- Morning

- Afternoon

- Evening

Recording Environment: Various recording environments were captured to acquire more realistic data and to make the dataset inclusive of various types of noises. Some of the environment variables are as follows:

- Noise Level: Silent, Low Noise, Moderate Noise, High Noise

- Parking Location: Indoor, Outdoor

- Car Windows: Open, Closed

- Car AC: On, Off

- Car Engine: On, Off

- Car Movement: Stationary, Moving

Metadata

The dataset provides comprehensive metadata for each audio recording and participant:

Participant Metadata: Unique identifier, age, gender, country, state, district, accent, and dialect.

Other Metadata: Recording transcript, recording environment, device details, sample rate, bit depth, file format, recording time.

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of English voice assistant speech recognition models.

License

This US English In-car audio dataset is created by FutureBeeAI and is available for commercial use.

US Motor Vehicle Registrations

Historical US Vehicle Registrations

By Throwback Thursday [source]

About this dataset

Each entry in this dataset includes various attributes that contribute to its richness. Key variables include state-level data, which allows for analysis on a regional basis, as well as more granular details such as vehicle type (e.g., passenger cars, trucks) and weight class (e.g., light-duty vehicles). Moreover, additional information on annual changes in registrations is provided, enabling users to observe fluctuations within specific years or compare registration numbers across different time periods.

The value of this dataset lies not only in its extensive coverage but also in its potential for conducting research across different fields such as transportation studies, urban planning, environmental impact analysis, and automotive industry analysis. The inclusion of historical data enables researchers to explore long-term trends that may have influenced societal behavior or policy decisions related to transportation infrastructure.

How to use the dataset

• Understand the Data:

  • The dataset provides a comprehensive record of motor vehicle registrations in the United States from 1900 to 1995.

  • The columns in the dataset include:

    a. Vehicle Type: Represents different types of vehicles (e.g., cars, motorcycles, trucks).

    b. Registration Count: Indicates the number of registered vehicles for each vehicle type and year.

• Analyze Vehicle Type Distribution:

  • To understand the distribution of registered vehicles by type over time, group the data by Vehicle Type and analyze registration counts.

• Identify Trends and Patterns:

  • By analyzing trends in registration counts over time, you can gain insights into changes in vehicle ownership patterns or preferences throughout history.

• Compare Different Vehicle Types:

  • Compare registration counts between different vehicle types to determine which types are more popular during various periods.

• Visualize Data:

  • Use various visualization techniques like line charts, bar graphs, or stacked area plots to represent registration counts with respect to time or compare different vehicle types side by side.

• Explore Historical Events:

  • Analyze how historical events (e.g., economic recessions, oil crises) affected motor vehicle registrations at specific points in time.

• Study Specific Time Periods:

  a. Early 20th Century:

  i) Investigate registrations from 1900-1920: Understand early trends and adoption rates of motor vehicles after their introduction

  ii) Explore changes during World War I: Analyze how war impacts influenced registrations

  b) Post-World War II Boom:

  i) Focus on growth patterns during post-WWII years (1945-1960): Identify if there was an acceleration in car registrations after wartime restrictions were lifted

• Conduct Further Research:

  • Supplement this dataset with additional sources to gain comprehensive insights into motor vehicle registrations in the U.S.

• Share Visualizations and Insights:

  • Compile interesting visualizations or insights gained from this dataset to inform others about motor vehicle registration history in the United States

Research Ideas

• Analyzing the growth and trends of motor vehicle registrations over time: This dataset allows for a detailed analysis of how motor vehicle registrations have evolved and expanded in the United States from 1900 to 1995. It can be used to identify patterns, changes in adoption rates, and shifts in popularity between different types of vehicles.
• Studying the impact of historical events on motor vehicle registrations: With this dataset, it is possible to explore the impact that major historical events and periods had on motor vehicle registrations. For example, one could analyze how registrations were affected by World War II or economic recessions during this time period.
• Comparing registration rates between different states and regions: This dataset provides information at a national level as well as broken down by state or region. It can be used to compare registration rates between different states or regions within specific years or over an extended time frame. This can provide insights into socioeconomic factors, population changes, and varying transportation needs across different areas of the country

Acknowledgements

If you use this dataset in your research, please credit the or...

Imports of Automotive Vehicles in the United States increased to 38524 USD Million in February from 37559 USD Million in January of 2024. This dataset includes a chart with historical data for the United States Imports of Automotive Vehicles.

Annual state-reported motor vehicle registration data for the 50 states and DC reported in Highway Statistics table MV-1.

This dataset is a national, VIN-resolved automotive file containing detailed vehicle attributes, ownership signals, and linked consumer demographics. Every row is anchored by a full 17-character VIN, allowing precise matching, decoding, and enrichment across insurance, lending, automotive analytics, marketing, and identity-resolution workflows. The file covers 387M+ U.S. vehicles across all major OEMs, model types, and price tiers.

The dataset includes vehicles from domestic manufacturers (e.g., Ford, GM, Stellantis) as well as foreign/import brands (e.g., Toyota, Honda, BMW, Mercedes, Hyundai, Kia). The manufacturerbased field clearly identifies where the OEM is headquartered, supporting segmentation such as domestic vs foreign, mainstream vs luxury, SUV vs sedan, gas vs hybrid vs electric, and new vs used ownership patterns.

Vehicle & VIN Attribute Coverage

Each record contains core vehicle details:

vin – Full 17-character Vehicle Identification Number

year – Model year

make / model – OEM brand and specific model name

manufacturer / manufacturerbased – Company name and domestic/foreign origin

fuel – Fuel type (gas, diesel, hybrid, EV, flex-fuel)

style – Marketing style (SUV, crossover, coupe, convertible, etc.)

bodytype / bodysubtype – Body classification such as SUV, sedan, pickup, hatchback

class – Market class (mainstream, luxury, premium, truck, etc.)

size – Compact, mid-size, full-size, etc.

doors – Number of doors

vechicletype – Passenger car, light truck, SUV, etc.

enginecylinders – Cylinder count

transmissiontype / transmissiongears – Automatic, manual, CVT, and gear count

gvwrange – Gross Vehicle Weight Rating (light duty vs heavy duty)

weight / maxpayload – Weight/payload estimates

trim – Detailed trim level

msrp – Original MSRP for pricing tiers and value modeling

validated / rankorder – Internal quality indicators

These fields support risk modeling, valuation, depreciation curves, fleet analysis, replacement cycles, and comparisons across domestic and foreign OEMs.

Ownership Signals & Lifecycle Indicators

The dataset includes rich ownership timing and household-level automotive information:

purchasedate – Date the vehicle was obtained, enabling:

Tenure modeling

Trade-in prediction

Lease/loan lifecycle analysis

Service interval modeling

purchasenew – Purchased new vs used

number_of_vehicles_in_hh – Total vehicles linked to the household

validated – Confirmed record flag

These attributes power auto replacement models, refinance targeting, multi-vehicle household insights, and OEM loyalty analytics.

Consumer Identity & Address Standardization

Each VIN record is linked to standardized consumer and household metadata:

consumer_first / consumer_last / consumer_suffix – Owner name fields

consumer_std_address – USPS-style standardized address

consumer_std_city / consumer_std_state / consumer_std_zip – Clean geographic identifiers

consumer_county_name – County for underwriting and geo-risk segmentation

consumer_std_status – Address quality/verification status

consumer_latitude / consumer_longitude – Geocoded coordinates for mapping, heatmaps, and risk scoring

This enables identity resolution, entity matching, household-level modeling, and geographic segmentation.

Consumer Demographics & Economic Indicators

The auto file connects vehicles to extensive demographic and lifestyle fields, including:

consumer_income_range – Household income band

consumer_home_owner – Homeowner vs renter

consumer_home_value – Home value range

consumer_networth – Net worth category

consumer_credit_range – Modeled credit tier

consumer_gender / consumer_age / consumer_age_range – Demographic segment fields

consumer_birth_year – Year-of-birth

consumer_marital_status – Single/married

consumer_presence_of_children / consumer_number_of_children – Household composition

consumer_dwelling_type – Housing type

consumer_length_of_residence / range – Stability indicator

consumer_language, religion, ethnicity – Cultural/language segments

consumer_pool_owner – Lifestyle attribute

consumer_occupation / consumer_education_level – Socioeconomic indicators

consumer_donor / consumer_veteran – Contribution and service attributes

These fields enable hyper-granular segmentation, lifestyle-based modeling, wealth indexing, market analysis, and insurance/lending underwriting.

Phone, Email & Contact Intel

Each record may include up to three phones and three emails:

consumer_phone1/2/3 – Contact numbers

consumer_linetype1/2/3 – Wireless, landline, VOIP

consumer_dnc1/2/3 – Do-Not-Call indicators

consumer_email1/2/3 – Email addresses

This supports compliant outreach, multi-channel activation, CRM enrichment, and identity graph expansion.

Primary Use Cases Insurance & Risk Modeling

VIN decoding, ownership tenure, household economics, and geo data support auto underwriting, pricing, rating territory analysis, and fraud screening.

Auto Finance, Lending & Refinance

Model trade-in window...

mammoth666/cars-make-model-year-chunk-30 dataset hosted on Hugging Face and contributed by the HF Datasets community