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

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.

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.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10780924%2F89f04d3acffef3714d6cd9399dfc08a3%2FScreenshot%20from%202023-08-28%2012-43-59.png?generation=1693206863146063&alt=media" alt="">

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!

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.

Used Cars

Analyzing Seller Type, Vehicle Type and Year of Registration

By Data Society [source]

About this dataset

The autos.csv dataset is a comprehensive collection of valuable data about used cars, and provides insight into how the cars are being sold, what price they are being sold for, and all the details about their condition. Each ad contains information such as dateCrawled (the date the ad was first seen), name of the car, seller type (private or dealer), offer type, price, A/B testing information , vehicle type, year of registration (at which year was the car first registered) , gearbox type, power output in PS (horsepower) , model of car , howmany kilometers has it driven so far , monthof registration(when it was first registered)(essentially giving us an idea about its age), fueltype utilized by it( petrol/diesel /electricity/lpg etc.), brand name to which it belongs to notRepairedDamage - if there is any damage on the vehicle that has not been repaired yet. DateCreated gives us information when this particular advertisement was created in ebay or other place where these cars can be posted. The nrofpictures field will give you an estimate regarding how many images have been included with this ad and postalcode contain info regarding area code where car have been posted.. Lastly lastseen give us time estimation when a crawler last scan this particular post online .All these factors are instrumental in determining a suitable price for used vehicles . Meanwhile regression analysis based on average prices related to years can be done from this dataset .So grab your laptop get ready !!!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset is a great resource to begin exploring the factors that affect used car prices. With features such as dateCrawled, name, seller, offerType, price, abtest among other data points it can be used to uncover how different aspects of a vehicle determine the pricing of second hand cars.

The first step would be to explore and understand what each of these fields represent and have an idea about their importance when pricing a used car. One might then proceed by plotting distribution plots for numerical variables such as yearOfRegistration with price or bar graphs for categorical fields like fuelType to observe if there is any correlation with price in these variables. Knowing certain key trends can assist in predicting future market prices more accurately than relying on yearly averages of all car values combined - which might give shapes too broad general trends instead precise predictions when working with this dataset alone.

In addition understanding how long a listing lasts before being sold would give valuable insight into discover how competitive offers should stay when customers come across relevant listings on say ebay or other trading sites that list used cars; this could achieved by utilizing two columns - lastSeen and dateCrawled - to figure out their average lifespan before they were sold out. It's likely that its higher priced counterparts tend to remain listed longer than cheaper listings which quickly disappear after being seen often enough by members in related markets searching those platforms for new vehicles up for sale at any given time within certain parameters established such as location or age amongst others .

Finally one might use supervised learning algorithms such as Linear Regression or Random Forest coupled with feature engineering methods like PCA (Principal Component Analysis) aiming at reducing high dimensionality issues on datasets composed mostly of categorical variables so we can perform actual machine learning operations over extracted numerical feature columns from processes along those lines previously mentioned

Research Ideas

• Analyze the relationship between car prices and age (year of registration) using a linear regression model to suggest which cars provide the best value for money.
• Use classification models to predict vehicle types based on features like powerPS, price, brand, etc.
• Compare and contrast seller types (private vs dealer) by analyzing prices, seller locations and other geographic information in order to give advice on which type of seller provides the best deals for customers

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistri...

The Household Size by Vehicles Available dataset was compiled using information from December 31, 2023 and updated December 12, 2024 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The Household Size by Vehicles Available table from the 2023 American Community Survey (ACS) 5-year estimates was joined to 2023 tract-level geographies for all 50 States, District of Columbia and Puerto Rico provided by the Census Bureau. A new file was created that combines the demographic variables from the former with the cartographic boundaries of the latter. The national level census tract layer contains data on the number and percentage of households by household size by number of vehicles available. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529030

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).

Worldwide car sales grew to around ** million automobiles in 2024, up from around **** million units in 2023. Throughout 2020 and 2021, the sector experienced a downward trend on the back of a slowing global economy, while COVID-19 and the Russian war on Ukraine contributed to shortages in the automotive semiconductor industry and further supply chain disruptions in 2022. Despite these challenges, 2023 and 2024 sales surpassed pre-pandemic levels and are forecast to keep rising through 2025 and 2026. Covid-19 hits car demand It had been estimated pre-pandemic that international car sales were on track to reach ** million. While 2023 sales are still far away from that goal, this was the first year were car sales exceeded pre-pandemic values. The automotive market faced various challenges in 2023, including supply shortages, automotive layoffs, and strikes in North America. However, despite these hurdles, the North American market was among the fastest-growing regions in 2024, along with Eastern Europe and Asia, as auto sales in these regions increased year-on-year. Chinese market recovers After years of double-digit growth, China's economy began to lose steam in 2022, and recovery has been slow through 2023. China was the largest automobile market based on sales with around **** million units in 2023. However, monthly car sales in China were in free-fall in April 2022 partly due to shortages, fears over a looming recession, and the country grappling with the COVID-19 pandemic. By June of that same year, monthly sales in China were closer to those recorded in 2021.

Cash-and-Equivalents Time Series for Cars.com Inc. Cars.com Inc., an audience-driven technology company, provides solutions for the automotive industry in the United States. The company offers cars commerce platforms, such as Marketplace, allowing OEMs and dealers to merchandise their inventory and allowing consumers to add and save vehicles to virtual garage and track the Cars.com market value of current vehicle, as well as provides reputation management technology and digital financing tools and access to in-market consumers who are ready to trade-in its vehicle for a new one. It also provides website creation and platform hosting; digital retailing solutions; and trade and appraisal product, including AccuTrade, uses real-time market data and diagnostic scans to determine the right trade-in offer for every VIN in minutes. In addition, the company offers Cars Commerce Media Network products comprising Cars Social, allows dealers to target and serve native advertisements displaying real-time inventory to in-market car shoppers on Facebook and Instagram; VIN Performance Media, a machine-learning for media campaign, including audience targeting, real-time inventory, and ad placement across search, social, and display; In-Market Video, provides OEMs and dealers to pinpoint serious, ready-to-buy shoppers geographically; and In-Market Display, enable dealers and OEMs to extend reach and access audience of in-market car shoppers. The company serves local dealers, OEMs, dealer groups, and auto-adjacent companies. Cars.com Inc. was founded in 1998 and is headquartered in Chicago, Illinois.

Net-Receivables Time Series for Cars.com Inc. Cars.com Inc., an audience-driven technology company, provides solutions for the automotive industry in the United States. The company offers cars commerce platforms, such as Marketplace, allowing OEMs and dealers to merchandise their inventory and allowing consumers to add and save vehicles to virtual garage and track the Cars.com market value of current vehicle, as well as provides reputation management technology and digital financing tools and access to in-market consumers who are ready to trade-in its vehicle for a new one. It also provides website creation and platform hosting; digital retailing solutions; and trade and appraisal product, including AccuTrade, uses real-time market data and diagnostic scans to determine the right trade-in offer for every VIN in minutes. In addition, the company offers Cars Commerce Media Network products comprising Cars Social, allows dealers to target and serve native advertisements displaying real-time inventory to in-market car shoppers on Facebook and Instagram; VIN Performance Media, a machine-learning for media campaign, including audience targeting, real-time inventory, and ad placement across search, social, and display; In-Market Video, provides OEMs and dealers to pinpoint serious, ready-to-buy shoppers geographically; and In-Market Display, enable dealers and OEMs to extend reach and access audience of in-market car shoppers. The company serves local dealers, OEMs, dealer groups, and auto-adjacent companies. Cars.com Inc. was founded in 1998 and is headquartered in Chicago, Illinois.

Operating-Income Time Series for Cars.com Inc. Cars.com Inc., an audience-driven technology company, provides solutions for the automotive industry in the United States. The company offers cars commerce platforms, such as Marketplace, allowing OEMs and dealers to merchandise their inventory and allowing consumers to add and save vehicles to virtual garage and track the Cars.com market value of current vehicle, as well as provides reputation management technology and digital financing tools and access to in-market consumers who are ready to trade-in its vehicle for a new one. It also provides website creation and platform hosting; digital retailing solutions; and trade and appraisal product, including AccuTrade, uses real-time market data and diagnostic scans to determine the right trade-in offer for every VIN in minutes. In addition, the company offers Cars Commerce Media Network products comprising Cars Social, allows dealers to target and serve native advertisements displaying real-time inventory to in-market car shoppers on Facebook and Instagram; VIN Performance Media, a machine-learning for media campaign, including audience targeting, real-time inventory, and ad placement across search, social, and display; In-Market Video, provides OEMs and dealers to pinpoint serious, ready-to-buy shoppers geographically; and In-Market Display, enable dealers and OEMs to extend reach and access audience of in-market car shoppers. The company serves local dealers, OEMs, dealer groups, and auto-adjacent companies. Cars.com Inc. was founded in 1998 and is headquartered in Chicago, Illinois.

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.

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.

Alesco Data's Automotive records are updated monthly from millions of proprietary sourced vehicle transactions. These incoming transactions are processed through compilation rules and are either added as new, incremental records to our file, or contribute to validating existing records.

Our recent focus is on compiling new vehicle ownership, and the file includes over 14.2 million late model vehicle owners (2020-2025).

In addition, we append our Persistent ID, telephone numbers, and demographics for a complete file that can support your direct mail and email marketing, lead validation, and identity verification needs. A Persistent ID is assigned to each vehicle record and tracks consumers as they change addresses or phone numbers, and vehicles as they change owners.

The database is not derived from state motor vehicle databases and therefore not subject to the Shelby Act also known as the Driver's Privacy Protection Act (DPPA) of 2000. The data is deterministic and sources include sales and service data, warranty data and notifications, aftermarket repair and maintenance facilities, and scheduled maintenance records.

Fields Included: -Make -Model -Year -VIN -Vehicle Class Code (crossover, SUV, full-size, mid-size, small) -Vehicle Fuel Code (gas, flex, hybrid) -Vehicle Style Code (sport, pickup, utility, sedan) -Mileage -Number of Vehicles per Household -First seen date -Last seen date -Email

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.

Change-In-Cash Time Series for CarGurus. CarGurus, Inc. operates an online automotive platform for buying and selling vehicles in the United States and internationally. It operates through two segments, U.S. Marketplace and Digital Wholesale. The company provides an online automotive marketplace that allows customers to search for new and used car listings from its dealers; and connects dealers to a large audience of informed and engaged consumers while providing dealers with actionable data-based insights. It also offers Digital Deal which allows shoppers to start purchase from a VDP on eligible listings that provides them with purchase options; Finance in Advance, where eligible consumers can pre-qualify for financing on cars from dealerships that offer financing from partners; Sell My Car " Top Dealer Offers which allows dealers to make tailored trade-in offers; and Sell My Car " Instant Max Cash Offer which allows consumers to sell vehicles to dealers online. In addition, the company provides dealer listings and data insights products; auto manufacturers and others advertiser products, such as brand reinforcement, category sponsorship, automobile segment exclusivity, and consumer segment exposure; Autolist, an online automotive marketplace through mobile applications and a website; and PistonHeads which is an automotive marketplace, auction platform, and editorial site for automotive enthusiasts. The company was formerly known as CarGurus LLC and changed its name to CarGurus, Inc. in June 2015. CarGurus, Inc. was founded in 2005 and is headquartered in Boston, Massachusetts.

Depreciation Time Series for CarGurus. CarGurus, Inc. operates an online automotive platform for buying and selling vehicles in the United States and internationally. It operates through two segments, U.S. Marketplace and Digital Wholesale. The company provides an online automotive marketplace that allows customers to search for new and used car listings from its dealers; and connects dealers to a large audience of informed and engaged consumers while providing dealers with actionable data-based insights. It also offers Digital Deal which allows shoppers to start purchase from a VDP on eligible listings that provides them with purchase options; Finance in Advance, where eligible consumers can pre-qualify for financing on cars from dealerships that offer financing from partners; Sell My Car " Top Dealer Offers which allows dealers to make tailored trade-in offers; and Sell My Car " Instant Max Cash Offer which allows consumers to sell vehicles to dealers online. In addition, the company provides dealer listings and data insights products; auto manufacturers and others advertiser products, such as brand reinforcement, category sponsorship, automobile segment exclusivity, and consumer segment exposure; Autolist, an online automotive marketplace through mobile applications and a website; and PistonHeads which is an automotive marketplace, auction platform, and editorial site for automotive enthusiasts. The company was formerly known as CarGurus LLC and changed its name to CarGurus, Inc. in June 2015. CarGurus, Inc. was founded in 2005 and is headquartered in Boston, Massachusetts.

The National Automotive Sampling System (NASS) Crashworthiness Data System (CDS) is a nationwide crash data collection program sponsored by the U.S. Department of Transportation. It is operated by the National Center for Statistics and Analysis (NCSA) of the National Highway Traffic Safety Administration (NHTSA). The NASS CDS provides an automated, comprehensive national traffic crash database, and collects detailed information on a sample of all police-reported light ]motor vehicle traffic crashes. Data collection is accomplished at 24 geographic sites, called Primary Sampling Units (PSUs). These data are weighted to represent all police reported motor vehicle crashes occurring in the USA during the year involving passenger cars, light trucks and vans that were towed due to damage.