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.

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.

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.

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).

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US Used Car Market Size 2025-2029

The US used car market size is forecast to increase by USD 40.2 billion, at a CAGR of 4.3% between 2024 and 2029.

The used car market in the US is witnessing significant growth, driven by the excellent value proposition that used cars offer to consumers. The increasing popularity of websites dedicated to selling used cars has expanded market reach and convenience, allowing consumers to browse and purchase vehicles online. Stringent emission regulations are restricting the sales of non-compliant used cars, necessitating investments in upgrading and maintaining commercial vehicle fleets to meet regulatory requirements. These regulations necessitate investments in emission testing and certification processes, increasing operational costs for dealers. To capitalize on opportunities, dealers can focus on offering certified pre-owned vehicles and implementing robust emission testing procedures.
Additionally, leveraging digital marketing strategies and offering flexible financing options can help attract and retain customers. Overall, the used car market presents both challenges and opportunities for players, requiring strategic planning and innovation to succeed.

What will be the size of the US 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 US continues to evolve, with various sectors adapting to emerging trends and technologies. Vehicle data analysis plays a pivotal role in understanding vehicle depreciation curves and return on investment for dealers. Payment processing systems streamline sales transactions, while sales performance metrics and customer lifetime value inform strategic decision-making. Fraud detection systems ensure compliance with legal standards, and insurance cost factors influence acquisition channel efficiency. Inventory turnover rate, a key performance indicator, varies across dealerships. Compliance audits and dealer training programs maintain legal compliance and improve customer satisfaction. Market penetration rate and resale value prediction help dealers optimize pricing models.
Consumer protection laws and financing product offerings shape customer trust and loyalty. Operating costs analysis, customer service feedback, and sales conversion rates contribute to profit margin calculation. Risk assessment models, employee performance metrics, marketing spend efficiency, and pricing model validation are essential for long-term success. A recent study reveals a 5% increase in sales for dealerships implementing advanced data analytics. Industry growth is expected to reach 3% annually, driven by these evolving market dynamics.

How is this market segmented?

The US used car 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.

Distribution Channel

  3P channel sales
  OEM channel sales


Product

  Mid size
  Full size
  Compact size


Vendor Type

  Organized
  Unorganized


Fuel Type

  Diesel
  Petrol


Geography

  North America

    US

By Distribution Channel Insights

The 3P channel sales segment is estimated to witness significant growth during the forecast period.

The used car market in the US is an active and dynamic sector, driven by various factors. With the constant launch of new vehicle models, the supply of used cars increases, resulting in lower prices compared to new cars. This trend encourages car owners to sell their vehicles and upgrade to newer models, shortening the average ownership cycle. Online advertising platforms play a significant role in connecting buyers and sellers. Pre-purchase inspections and vehicle history reports ensure transparency and build trust. Repairs cost estimation and parts sourcing networks help in managing the expenses of used car ownership. Market segmentation strategies cater to different customer needs, while customer relationship management tools foster loyalty.

Emissions testing standards ensure the environmental sustainability of used vehicles. Auto appraisal value tools help in determining fair prices, and loan term comparison aids in financing decisions. Marketing campaign effectiveness is measured through customer acquisition cost and interest rate calculation. Mobile apps offer functionalities like mechanical inspection checklists, paint depth measurement, and damage assessment tools. Dealer inventory management, detailing services, and vehicle photography techniques enhance the sales process. Industry growth is expected to continue, with the used car market projected to expand by 3% annually. For instance, a dealership successfully increased its sales by 15% thr

View monthly updates and historical trends for US Total Vehicle Sales. from United States. Source: Bureau of Economic Analysis. Track economic data with Y…

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Description:

Welcome to the "Used Car Listings Dataset with Geographic Information" on Kaggle! This comprehensive dataset provides detailed information about a diverse collection of used cars available for sale in various regions across the United States. With a total of approximately 27,500 entries, this dataset offers a rich resource for analyzing and modeling the factors that influence used car prices and market trends.

Dataset Overview:

The dataset comprises three main files: train.csv, test.csv, and lat_long.csv. The primary data files, train.csv and test.csv, contain the following columns:

• id: A unique identifier for each car listing.
• region: The location where the car is listed for sale.
• year: The year the car was manufactured.
• manufacturer: The manufacturer of the car.
• condition: The condition of the car (e.g., excellent, good, fair).
• cylinders: The number of cylinders in the car's engine.
• fuel: The type of fuel the car uses (e.g., gas, diesel).
• odometer: The mileage on the car.
• title_status: The status of the car's title (e.g., clean, rebuilt).
• transmission: The type of transmission in the car (e.g., manual, automatic).
• drive: The type of drive in the car (e.g., fwd, rwd, 4wd).
• size: The size category of the car (e.g., compact, mid-size, full-size).
• type: The body type of the car (e.g., sedan, SUV, convertible).
• paint_color: The color of the car.
• state: The state where the car is listed for sale.
• **********price**: The listing price of the car.

These attributes collectively provide a comprehensive overview of each used car listing, making it an ideal dataset for exploratory analysis, feature engineering, and predictive modeling. While this dataset provides a comprehensive overview of each used car listing, it's important to be aware that some data cleaning, preprocessing, and advanced feature engineering might be necessary to ensure the most accurate and reliable analysis and modeling.

Geographic Information:

In addition to the main data files, the dataset includes lat_long.csv, which contains latitude and longitude information for the states mentioned in the primary dataset. This supplemental file facilitates geographical analysis and enables users to associate geographic coordinates with each car listing's location.

Potential Applications:

The "Used Car Listings Dataset with Geographic Information" is suitable for a variety of data science tasks, including but not limited to:

• Price Prediction: Build predictive models to estimate used car prices based on various attributes.
• Market Insights: Analyze market trends and consumer preferences in different regions.
• Geospatial Analysis: Combine geographic data to visualize and study spatial patterns.
• Feature Engineering: Create new features by combining or transforming existing attributes.

Acknowledgments:

We extend our gratitude to the data contributors for compiling this diverse and valuable dataset, which offers insights into the used car market across different regions in the United States.

Citation:

If you use this dataset for research or any other purpose, please provide appropriate credit and citation to the dataset and its contributors.

Explore, analyze, and innovate with the "Used Car Listings Dataset with Geographic Information" to uncover hidden insights and drive meaningful conclusions. Please note that this dataset's raw state might necessitate advanced preprocessing and feature engineering for optimal results. Happy analyzing!

Context

This is a data set for used car sales in the US. In total ~160k sales records over a period of 20 months in 2019 and 2020. There won't be any more updates to this dataset as eBay stopped providing full ZIP codes.

Content

Each sample contains information about a used car sale, like selling price, location, details about the car (Make, model, year, mileage, etc).

The data was scraped from eBay. I tried to filter the data when updating, meaning that if the same seller sells the same car again I'll remove the previous sale as it was most likely not a successful sale (Do you know that eBay bids on cars are non binding?)

Acknowledgements

Header Image Credit: Laitr Keiows [CC BY 3.0 (https://creativecommons.org/licenses/by/3.0)]

Inspiration

I'm curious what the community can do with this data to identify trends or even predict a fair price for a used car.

In 2024, the auto industry in the United States sold approximately 15.9 million light vehicle units. This figure includes retail sales of about three million passenger cars and just under 12.9 million light trucks. Lower fuel consumption There are many kinds of light vehicles available in the United States. Light-duty vehicles are popular for their utility and improved fuel economy, making them an ideal choice for savvy consumers. As of Model Year 2023, the light vehicle manufacturer with the best overall miles per gallon was Kia, with one gallon of gas allowing for 30.4 miles on the road. Higher brand satisfaction When asked about light vehicle satisfaction, consumers in the United States were most satisfied with Toyota, Subaru, Tesla, and Mercedes-Benz models. Another survey conducted in 2018 and quizzing respondents on their stance regarding the leading car brands indicated that Lexus was among the most dependable brands based on the number of problems reported per 100 vehicles.

Key information about United States Motor Vehicle Sales: Commercial Cars

• United States Motor Vehicle Sales: Commercial Cars was reported at 13,356,433.000 Unit in Dec 2024.
• This records an increase from the previous number of 12,892,621.000 Unit for Dec 2023.
• US Motor Vehicle Sales: Commercial Cars data is updated yearly, averaging 9,154,354.000 Unit from Dec 2005 to 2024, with 20 observations.
• The data reached an all-time high of 13,356,433.000 Unit in 2024 and a record low of 5,200,478.000 Unit in 2009.
• US Motor Vehicle Sales: Commercial 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: Commercial Vehicle (CV).

Analysing Second Hand Car Sales Data with Supervised and Unsupervised Learning Models

The second-hand cars market is a dynamic and very complex sphere which is impacted by different criteria among them - manufacturer, model, engine specification, and fuel consumption, year of production, mileage, and price. In this exercise, we will look through mock data that contains facts on sale of second-or-used cars in UK. The data is made up of 50,000 different records that describe a transaction of a car sale singularly. Through the utilization of supervised learning and unsupervised learning, we plan to perform an analysis of the dataset. This analysis will facilitate car price prediction via a regression model, as well as cluster pattern identification.

Single Numerical Input Feature Regression Models We started our work by using the regression model predicting the car price for each numerical input factor like the mileage, a size of the vehicle etc. This is followed by analyzing the associations over variables such as the car's price and numerical factors like the engine size, the vehicle model year, and mileage. The engine size was found to be the variable having the strongest relation to the auto price, which provided evidence that it is the most powerful driver. While a linear model was appropriate for the year of manufacture, other features that were more complicated like engine size needed a non-linear model in order for their interactions and price fluctuations to be accurately detected.

Multiple Numerical Input Feature Regression Models The analysis was further expanded by incorporating several numeric input parameters while estimating the accuracy of the price predictions. What we reasonably benefited from the usage of extra usages like year of making a car and a number of its kilometers achievement was an improvement of predictive performance in comparison with single-input features models. This holistic approach of studying the many variables that influence car's prices has brought the importance to a limelight of using predictive models by considering many factors simultaneously.

** Regression Model with Categorical Variables** To expand our prediction models, we took categorical variables into account and added attributes of manufacturer and model into the regression. This increased the effectiveness of the algorithm theories more roads less traffic intersections construction of roads should take road traffic distribution between roads as well as traffic intersections into account busier streets less traffic less intersections

** Artificial Neural Network (ANN) Model**

To achieve that, we have implemented the Artificial Neural Network (ANN) model. The ANN showed competitive performance in respect to other supervised learning models which can be attributed to its ability to learn even very complex relationships from the dataset. The architecture and hyper parameters of ANN were thoroughly tweaked for the best results in order to demonstrate its flexibility and effectiveness in dealing with complex datasets.

Model Comparison and Conclusion After comprehensive assessment the Random Forest Regress or model was found to be the most efficient model for forecasting car prices. It’s incorporating both numerical and categorical variables and showing a strong predicting power made it a preferred one. Evaluation metrics and visualizations were given which gave the full picture of the model performance and helped us to arrive at our conclusion that the Random Forests regress or was better.

k-Means Clustering Algorithm Coming to unsupervised learning, we employed the k-Means clustering algorithm to detect clusters in the car sales dataset. Changing input feature variables space in batches, we determined the number of clusters (k) using evaluation metrics by silhouette score. The variables like engine size, year of manufacture and mileage appeared to be critical in getting the most ideal clusters which emphasized their significance in segmenting the data set. Comparison with Other Clustering Algorithms Lastly, we observed the outcomes of the k-Means clustering technique adding the success of the other clustering techniques, for example, DBSCAN, and hierarchical clustering. Evaluation with metrics of rigorous title of the each method worked we assessed the performance to the dataset effective approach in cluster was identified. Just like k-Means achieved promising results, DBSCAN provided us with a base to be further extended by comparing with other algorithms like DBSCAN and emphasizing that several algorithms should be considered for clustering. Conclusion Finally, our extensive discussion on the sales data for used cars has demonstrated favorable results of supervised as well as unsupervised learning techniques towards understanding the information through regression models and so...

Graph and download economic data for Motor Vehicle Retail Sales: Domestic Autos (DAUTOSA) from Jan 1967 to Aug 2025 about headline figure, retail trade, vehicles, domestic, new, sales, retail, and USA.

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...

Key information about United States Motor Vehicles Sales Growth

• United States Motor Vehicles Sales grew 2.4 % in Aug 2025, compared with a growth of 6.7 % in the previous month.
• US Motor Vehicles Sales Growth rate is updated monthly, available from Jan 1977 to Aug 2025, with an average growth rate of -10.5 %.
• The data reached an all-time high of 109.5 % in Apr 2021 and a record low of -45.9 % in Apr 2020.
• Car Sales of US recorded 1,491,440.0 units in Aug 2025.

This driver tracks the number of new vehicles, including cars and light trucks, purchased in the United States in a given year. Data is sourced from the US Bureau of Economic Analysis and is forecast using data from the US Energy Information Administration (EIA).

Graph and download economic data for Retail Sales: Used Car Dealers (MRTSSM44112USN) from Jan 1992 to Jul 2025 about used, dealers, retail trade, vehicles, sales, retail, and USA.

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...

Graph and download economic data for Consumer Price Index for All Urban Consumers: New Vehicles in U.S. City Average (CUUR0000SETA01) from Mar 1947 to Sep 2025 about vehicles, urban, new, consumer, CPI, inflation, price index, indexes, price, and USA.