On average, there are 1.88 vehicles per U.S. household. According to the U.S. Department of Transportation, the percentage of households without a car or light truck came to around nine percent in 2017, meaning that about 90 percent of households had at least one light vehicle at their disposal in that same year.

Most Americans drive daily

In a recent Gallup poll among U.S. adults, about 64 percent of respondents claimed to drive daily, while another 19 percent of respondents stated that they would use a motor vehicle multiple times in an average week. These figures are in line with the U.S. motorization rate, which stood at 821 vehicles per 1,000 inhabitants in 2015.

 These streets were made for driving  

The United States has the most extensive road network, compared to any other country in the world: its road network encompasses almost 6.6 million kilometers or about four million miles. In 2018, there were about 270 million vehicles roaming the streets of the country.

When asked about "Car ownership", * percent of U.S. respondents answer ********************. This online survey was conducted in 2025, among 13,687 consumers. Looking to gain valuable insights about car owners across the globe? Check out our reports about consumers of car brands worldwide. These reports provide readers with a detailed understanding of car owners: their identities, preferences, opinions, and how to effectively engage with them.

Around **** of all car owners in the U.S. are over the age of 60 years old. High upfront and running costs can be expensive, and many Americans must either save up or wait until they have the income to afford vehicle ownership.

The graph displays the number of registered motor vehicles in the United States by type in 2023. The x-axis represents vehicle types—cars, buses, trucks, motorcycles, and all motor vehicles—while the y-axis shows the total number of registrations for each type. Registrations range from 967,525 buses to 177,228,271 trucks, with cars totaling 96,901,563 and motorcycles at 9,516,910. The total number of all registered motor vehicles stands at 284,614,269, highlighting the dominance of trucks compared to other vehicle types.

This statistic shows the average length of vehicle ownerships in the United States in 2006 and 2016, by vehicle type. In 2016, new-car buyers kept their vehicles for about 79 months, up from around 52 months in 2006.

Chevrolet was the brand which was most frequently the primarily used car of Millennials, Gen Xers, and Baby Boomers in the United States, based on a June 2025 survey. Comparatively, ** percent of Gen Zers reported Honda as the make of their main household car.

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...

The car owner's statistics by vehicle type and gender

Graph and download economic data for Expenditures: Vehicle Purchases: Cars and Trucks, New by Age: from Age 65 to 74 (CXUNEWCARSLB0408M) from 1984 to 2023 about 65-years +, age, purchase, trucks, vehicles, expenditures, new, and USA.

Over the course of the 20th century, the number of operational motor vehicles in the United States grew significantly, from just 8,000 automobiles in the year 1900 to more than 183 million private and commercial vehicles in the late 1980s. Generally, the number of vehicles increased in each year, with the most notable exceptions during the Great Depression and Second World War.

In 2019, Ownership of Passenger Cars in the US was 405 Units Per Thousand Persons. Discover more data with NationMaster!

We asked U.S. consumers about "Car ownership by make / brand" and found that *********** takes the top spot, while ************ is at the other end of the ranking.These results are based on a representative online survey conducted in 2025 among 13,254 consumers in the United States. Looking to gain valuable insights about car owners across the globe? Check out our reports about consumers of car brands worldwide. These reports provide readers with a detailed understanding of car owners: their identities, preferences, opinions, and how to effectively engage with them.

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).

We also append our Persistent ID, telephone numbers, and demographics for a complete file that can support your direct mail and email marketing campaigns, 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 Data 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

Graph and download economic data for Expenditures: Vehicle Purchases: Cars and Trucks, New by Race: White, Asian, and All Other Races, Not Including Black or African American (CXUNEWCARSLB0902M) from 1984 to 2023 about asian, white, purchase, trucks, vehicles, expenditures, new, and USA.

This layer shows household size by number of vehicles available. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the count and percentage of households with no vehicle available. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B08201 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census: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 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This is a data set of individuals in the United States tha own a vehicle made by Cadillac. The data set includes First name, Last name, email, phone number, address, make, model, Year, and VIN. Please feel free to reach out if you have any questions about this data set. If interested there is also data on 20 other manufacturers.

Motor Vehicle Registrations by State and Type

US Motor Vehicle Registrations by Year, State, Type

By Throwback Thursday [source]

About this dataset

The dataset titled US Motor Vehicle Registrations 1900 - 1995 offers a comprehensive overview of motor vehicle registrations in the United States spanning from the early 20th century to the mid-1990s. This valuable dataset presents detailed information on the number of registered vehicles based on various factors, including year, state, vehicle type, and total registrations. It consists of five columns that represent essential data: Year (numeric), State (text), Vehicle Type (text), and Number of Registrations (numeric). With this dataset at your disposal, you can explore and analyze historical trends in motor vehicle registrations across different states and examine the preferences for various types of vehicles over time

How to use the dataset

Introduction:

Step 1: Familiarize yourself with the columns: Take a moment to understand each column present in this dataset:

• Year: Represents the specific year when motor vehicle registrations were recorded.
• State: Indicates the state in which these registrations were documented.
• Vehicle Type: Identifies the type of vehicles for which registrations were recorded (e.g., passenger cars, trucks, motorcycles).
• Number of Registrations: Indicates the total count of motor vehicle registrations for a particular year, state, and vehicle type.

Step 2: Set your research objectives: Determine your research or analysis goals before diving into data exploration. Clearly define what aspects you want to examine using this dataset. Possible research questions may include:

a) How have motor vehicle registration numbers changed over time? b) Which states have had consistently high or low registration numbers? c) What are some trends regarding different types of vehicles registered across states?

Step 3: Filter data based on specific criteria: To answer your research questions effectively, you might need to filter relevant data points. Here's how you can do it:

a) Year-wise Analysis - Filter data for specific years if you want a focused view. b) State-wise Analysis - Choose records related only to certain states if regional comparisons are required. c) Vehicle Type Analysis - Isolate data related to particular types of vehicles (passenger cars/trucks/motorcycles), if necessary.

Step 4: Analyze and visualize data: Once you have filtered the dataset based on your research objectives, it's time to analyze and visualize the information to gain insights. You can use statistical measures, charts, or graphs for a better understanding of the data distribution and trends.

a) Total Vehicle Registrations over Time - Visualize how motor vehicle registrations have changed from 1900 to 1995 using line charts or bar graphs. b) State-wise Comparisons - Utilize charts or maps to compare registration numbers between different states. c) Vehicle Type Breakdown - Explore pie charts or stacked bar graphs to understand the proportion of different vehicle types registered within specific states

Research Ideas

• Analyzing trends in motor vehicle registrations: By examining the number of registrations over a span of 95 years, researchers can identify trends and patterns in vehicle ownership and usage across different states and vehicle types. This information can be valuable for urban planning, transportation system design, and policy-making.
• Comparing vehicle preferences across states: The dataset allows for a comparison of vehicle types preferred by residents of different states. Researchers can analyze whether certain states have a higher proportion of trucks or motorcycles compared to passenger cars, which can provide insights into regional cultural preferences or economic factors.
• Studying the impact of technological advancements on vehicle registrations: Since the dataset covers a period spanning from 1900 to 1995, it provides an opportunity to study how technological advancements such as the introduction of automobiles or changes in engine efficiency impacted motor vehicle registrations over time. This information can contribute to understanding historical shifts in transportation patterns and inform predictions about future trends as well

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: Week 21 - US Motor Vehicle Registrations 1900 - 1995.csv | Column name | ...

Average weekly household expenditure on goods and services in the UK. Data are shown by region, age, income (including equivalised) group (deciles and quintiles), economic status, socio-economic class, housing tenure, output area classification, urban and rural areas (Great Britain only), place of purchase and household composition.