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.

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

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

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.

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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.

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.

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

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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...

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.

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

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 !!!

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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...

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.

There are numerous car datasets available that provide information on various aspects of vehicles. Here is a general description of the common types of information you may find in car datasets:

Make and Model: The brand and model name of the car. Year: The manufacturing year of the vehicle. Price: The price at which the car was listed or sold. Mileage: The number of miles the car has been driven. Fuel Efficiency: The car's average fuel consumption or MPG (Miles Per Gallon) rating. Horsepower: The power output of the car's engine. Number of Cylinders: The number of cylinders in the car's engine. Transmission: The type of transmission system in the car (e.g., automatic, manual). Drivetrain: The configuration of the car's drivetrain (e.g., front-wheel drive, rear-wheel drive, all-wheel drive). Body Type: The category or style of the car (e.g., sedan, SUV, truck, coupe). Engine Displacement: The capacity or size of the car's engine. Dimensions: Information about the car's length, width, height, and weight. Safety Ratings: Data on the car's safety features and crash test ratings. Features: Additional features and specifications such as navigation system, infotainment system, sunroof, etc

Fuel economy data are the result of vehicle testing done at the Environmental Protection Agency's National Vehicle and Fuel Emissions Laboratory in Ann Arbor, Michigan, and by vehicle manufacturers with oversight by EPA.In 2016, the Department of Justice alleged violations of the Clean Air Act by Volkswagen (including Audi and Porsche) covering all of Volkswagen's 2.0L and 3.0L diesel vehicles sold in the United States since model year 2009. All relevant data from the affected vehicles have been removed from this website until there is an EPA-approved emissions.EPA has issued a Notice of Violation to Fiat Chrysler Automobiles N.V. and FCA US LLC for Model Year 2014-2016 light-duty diesel vehicles (Ram 1500 and Jeep Grand Cherokee). All relevant data from the affected vehicles has been removed from this website until further information is available.

Electric Vehicle Sales: ytd: Lucid data was reported at 5,766.000 Unit in Sep 2024. This records an increase from the previous number of 3,822.000 Unit for Jun 2024. Electric Vehicle Sales: ytd: Lucid data is updated quarterly, averaging 2,318.000 Unit from Dec 2021 (Median) to Sep 2024, with 12 observations. The data reached an all-time high of 5,940.000 Unit in Dec 2023 and a record low of 460.000 Unit in Mar 2022. Electric Vehicle Sales: ytd: Lucid data remains active status in CEIC and is reported by Cox Automotive. The data is categorized under Global Database’s United States – Table US.RA008: Electric Vehicle Sales: by Brand and Model: ytd.

Auto Exports in the United States increased to 130.60 Thousand in July from 126.70 Thousand in June of 2025. This dataset includes a chart with historical data for the United States Auto Exports.

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...

2013-2023 Virginia Tenure by Vehicles Available by Census Block Group (ACS 5-Year). Contains estimates and margins of error.

U.S. Census Bureau; American Community Survey, American Community Survey 5-Year Estimates, Table B25044 Data accessed from: Census Bureau's API for American Community Survey (https://www.census.gov/data/developers/data-sets.html)

The United States Census Bureau's American Community Survey (ACS): -What is the American Community Survey? (https://www.census.gov/programs-surveys/acs/about.html) -Geography & ACS (https://www.census.gov/programs-surveys/acs/geography-acs.html) -Technical Documentation (https://www.census.gov/programs-surveys/acs/technical-documentation.html)

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section. (https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.html)

Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section. (https://www.census.gov/acs/www/methodology/sample_size_and_data_quality/)

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties.

Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation https://www.census.gov/programs-surveys/acs/technical-documentation.html). The effect of nonsampling error is not represented in these tables.