Datasys Automotive Demographics provides a detailed view of 60M+ U.S. vehicle owners enriched with age, income, household, lifestyle, and geographic attributes. This dataset enables marketers to understand the people behind the vehicles, build more relevant campaigns, and target based on ownership plus demographic context. It is valuable for insurers, auto dealers, and service providers aiming to reach specific consumer groups with tailored offers.

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 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.

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.

Light truck retail sales in the United States increased to 12.9 million units in 2024. This was a year-over-year increase in sales of some 3.94 percent compared to 2023. In contrast, 2022 was the second drop in sales in a decade, after the drop reported in 2020, at the onset of the COVID-19 pandemic. Sales had been increasing since 2010, when the auto industry began recovering from low vehicle purchases after the 2008-2009 financial crisis. In 2024, sales of light trucks accounted for about 81.2 percent of the approximately 15.9 million light vehicles sold in the United States. Ford, with its signature truck, the Ford F-150, was one of the leading North American car brands in the United States. Why are consumers buying trucks now? Before the coronavirus pandemic hit in 2020, the U.S. economy had largely recovered from the woes of the financial crisis and unemployment in the United States fell to 3.7 percent in 2019. This meant that consumers were better able to purchase new vehicles. Similarly, due to lower gasoline and diesel fuel prices, motorists were more willing to buy trucks over smaller, more fuel-efficient sedans. 2022 presented a challenge for this automotive market, with Russia's war on Ukraine leading to motor fuel price inflation and to higher new and used car prices.

The Florida Department of Transportation (FDOT or Department) has identified processed, authoritative datasets to support the preliminary spatial analysis of equity considerations. These processed datasets are available at larger geographies, such as the United States Census Bureau tract or county-level; however, additional raw datasets from other sources can be used to identify equity considerations. Most of this raw data is available at the Census block group, parcel, or point-level—but additional processing is required to make suitable for spatial analysis. For more information, contact Dana Reiding with the FDOT Forecasting and Trends Office (FTO).The American Community Survey (ACS) Vehicle Availability Variables – Boundaries layer is identified to support the equity community indicator of transportation accessibility. The layer contains the most current release of data from the ACS about household size by number of vehicles available. These are 5-year estimates shown by tract, county, and state boundaries. The layer is owned and managed by the ESRI Demographics Team. Data Link: https://www.arcgis.com/home/item.html?id=9a9e43ec1603446880c50d4ed1df2207 Available Geography Levels: State, County, Tract Owner/Managed By: ESRI Demographics FDOT Point of Contact:Dana Reiding, ManagerForecasting and Trends OfficeFlorida Department of TransportationDana.Reiding@dot.state.fl.us605 Suwannee Street, Tallahassee, Florida 32399850-414-4719

This dataset provides detailed information on automotive owners, including demographics such as age and homeownership status. It also features vehicle details like year, make, and model. Ideal for analyzing consumer trends, understanding ownership patterns, and targeting marketing strategies within the automotive industry.

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...

Pickup Truck Market Outlook

According to our latest research, the global Pickup Truck market size reached USD 230.7 billion in 2024, reflecting robust demand across multiple sectors. The market is anticipated to register a CAGR of 4.9% from 2025 to 2033, projecting the market value to reach USD 352.8 billion by 2033. This growth trajectory is driven by increasing infrastructure projects, rising e-commerce logistics, and a surge in both commercial and personal vehicle ownership. The steady advancements in vehicle technology, coupled with evolving consumer preferences and government incentives for electric and hybrid vehicles, are pivotal factors propelling the market expansion.

One of the primary growth drivers for the Pickup Truck market is the expanding construction and infrastructure development activities worldwide. Governments and private entities are investing heavily in new construction projects, urbanization, and rural development, which necessitate reliable and versatile transportation solutions. Pickup trucks, known for their durability and payload capacity, are increasingly favored for transporting materials, equipment, and personnel. The adoption of advanced safety features, telematics, and connectivity solutions further enhances the operational efficiency and safety of pickup trucks, making them indispensable assets for construction and related industries. As a result, the demand for both light-duty and heavy-duty pickup trucks is witnessing a substantial upswing, especially in rapidly developing economies.

Another significant factor fueling the pickup truck market is the diversification of applications beyond traditional uses. While pickup trucks have historically been associated with agriculture and construction, their role in logistics, e-commerce delivery, and even personal mobility has grown remarkably. The rise of small and medium-sized enterprises (SMEs) and the proliferation of last-mile delivery services have spurred the need for versatile vehicles that can navigate urban and rural terrains efficiently. Moreover, the integration of comfort and luxury features in modern pickup trucks has attracted a new demographic of individual buyers seeking vehicles that combine utility with lifestyle appeal. This trend is particularly pronounced in North America and Asia Pacific, where consumer expectations are evolving rapidly.

Technological advancements are also reshaping the pickup truck market landscape. The development of electric and hybrid pickup trucks, driven by stringent emission regulations and growing environmental awareness, is transforming the competitive dynamics of the industry. Leading manufacturers are investing heavily in research and development to introduce vehicles that offer superior fuel efficiency, lower emissions, and enhanced performance. Innovations in battery technology, lightweight materials, and autonomous driving capabilities are further elevating the market’s value proposition. As governments worldwide implement stricter emission standards and offer incentives for green vehicles, the adoption of electric and hybrid pickup trucks is expected to accelerate, contributing significantly to overall market growth.

From a regional perspective, North America continues to dominate the global pickup truck market, accounting for the largest share in 2024. This leadership is attributed to the region’s strong automotive culture, high disposable incomes, and a well-established infrastructure for commercial and personal vehicle usage. However, Asia Pacific is emerging as the fastest-growing market, driven by rapid urbanization, expanding industrial activities, and increasing investments in transportation infrastructure. Europe and Latin America are also witnessing steady growth, supported by rising demand for fuel-efficient and technologically advanced pickup trucks. The Middle East & Africa region, while currently holding a smaller market share, is expected to register significant growth due to expanding construction and logistics sectors.

Vehicle Type Analysis

The data available for individual trucks include make, model, weight, and type of ownership. The following data are also available: leasing information (type, length, and options), major use and principal products carried (including percent of time carrying hazardous material), maintenance information, and equipment data (engine, transmission type, brake type, power steering, fuel conservation equipment, body and vehicle type and size class, and cab type).

The State in which the truck i s registered and the State and county of the truck's base of operation.

Data contained in the file refer to the year of the survey, 1977. First taken in 1963, the census is taken every 5 years.

The increasing diversity of vehicle type holdings and growing demand for BEVs and PHEVs have serious policy implications for travel demand and air pollution. Consequently, it is important to accurately predict or estimate the preference for vehicle holdings of households as well as the vehicle miles traveled by vehicle body and fuel type to project future VMT changes and mobile source emission levels. The current report presents the application of a utility-based model for multiple discreteness that combines multiple vehicle types with usage in an integrated model, specifically the MDCEV model. We use the 2019 California Vehicle Survey data here that allows us to analyze the driving behavior associated with more recent EV models (with potentially longer ranges). Important findings from the model include:

Household characteristics like size or having children have an expected impact on vehicle preference: larger vehicles are preferred. College education, rooftop solar ownership, and the number of employed workers in a household affect the preference for BEVs and PHEVs in the small car segment dominated by the Leaf, Bolt, Prius-Plug-in and the Volt often used as a commuter car. Among built environment factors, population density and the walkability index of a neighborhood have a statistically significant impact on the type of vehicle choice and VMT. It is observed that a 10% increase in population density reduces the preference for ICEV pickup trucks by 0.34% and VMT by 0.4%. However, if the increase in population density is 25%, the reduction in preference for pickup trucks is 8.4% and VMT is 8.6%. The other built environment factor we consider is the walkability index. If the walkability index of a neighborhood increases by 25%, it reduces the preference for ICEV pickup trucks by 15% and their VMT by 16%. Overall, these results suggest that if policies encourage mixed development of neighborhoods and increase density, it can have an important impact on ownership and usage of gas guzzlers like pickup trucks and help in the process of electrification of the transportation sector.

Methods The dataset used in this report was created using the following public data sources:

2019 California Vehicle Survey: "Transportation Secure Data Center." ([2019]). National Renewable Energy Laboratory. Accessed [04/26/2023]: www.nrel.gov/tsdc. The Smart Mapping Tool by EPA: https://www.epa.gov/smartgrowth/smart-location-mapping

American Community Survey: https://www.census.gov/programs-surveys/acs

Buyers between 35 and 44 years old acquired the most light-duty pickup trucks in 2021, followed closely by buyers within the ** to ** age group. By contrast, younger car buyers between ** and ** did not opt for light pickup trucks. Light trucks were the best-selling type of vehicle in the United States.

US Census Bureau American Community Survey 2013-2017 Estimates in the Keys the Valley Region for Race/Ethnicity, Educational Attainment, Unemployment, Health Insurance, Disability and Vehicle Access.

The American Community Survey (ACS) is a nationwide survey designed to provide communities with reliable and timely social, economic, housing, and demographic data every year. Because the ACS is based on a sample, rather than all housing units and people, ACS estimates have a degree of uncertainty associated with them, called sampling error. In general, the larger the sample, the smaller the level of sampling error. Data associated with a small town will have a greater degree of error than data associated with an entire county. To help users understand the impact of sampling error on data reliability, the Census Bureau provides a “margin of error” for each published ACS estimate. The margin of error, combined with the ACS estimate, give users a range of values within which the actual “real-world” value is likely to fall.

Single-year and multiyear estimates from the ACS are all “period” estimates derived from a sample collected over a period of time, as opposed to “point-in-time” estimates such as those from past decennial censuses. For example, the 2000 Census “long form” sampled the resident U.S. population as of April 1, 2000. The estimates here were derived from a sample collected over time from 2013-2017.

Race/Ethnicity

·
WPop: Total population of those who identify as white alone (B01001A).

·
PWPop: Percentage of total population that identifies as white alone (B01001A).

·
BPop: Total population of those who identify as black or African American alone (B01001B).

·
PWPop: Percentage of total population that identifies as black or African American alone (B01001B).

·
AmIPop: Total population of those who identify as American Indian and Alaska Native alone (B01001C).

·
PAmIPop: Percentage of total population that identifies as American Indian and Alaska Native alone (B01001C).

·
APop: Total population of those who identify as Asian alone (B01001D).

·
PAPop: Percentage of total population that identifies as Asian alone (B01001D).

·
PacIPop: Total population of those who identify as Native Hawaiian and Other Pacific Islander alone (B01001E).

·
PPacIPop: Percentage of total population that identifies as Native Hawaiian and Other Pacific Islander alone (B01001E).

·
OPop: Total population of those who identify as Some Other Race alone (B01001F).

·
POPop: Percentage of total population that identifies as Some Other Race alone (B01001F).

·
MPop: Total population of those who identify as Two or More Races (B01001G).

·
PMPop: Percentage of total population that identifies as Two or More Races (B01001G).

·
WnHPop: Total population of those who identify as White alone, not Hispanic or Latino (B01001H).

·
PWnHPop: Percentage of total population that identifies as White alone, not Hispanic or Latino (B01001H).

·
LPop: Total population of those who identify as Hispanic or Latino (B01001I).

·
PLPop: Percentage of total population that identifies as Hispanic or Latino (B01001I).

Educational Attainment

·
EdLHS1824: Total population between the ages of 18 and 24 that has not received a High School degree (S1501).

·
PEdLHS1824: Percentage of population between the ages of 18 and 24 that has not received a High School degree (S1501).

·
EdLHS1824: Total population between the ages of 18 and 24 that has received a High School degree or equivalent (S1501).

·
PEdLHS1824: Percentage of population between the ages of 18 and 24 that has received a High School degree or equivalent (S1501).

·
EdSC1824: Total population between the ages of 18 and 24 that has received some amount of college education or an associate’s degree (S1501).

·
PEdSC1824: Percentage of population between the ages of 18 and 24 that has received some amount of college education or an associate’s degree (S1501).

·
EdB1824: Total population between the ages of 18 and 24 that has received bachelor’s degree or higher (S1501).

·
PEdB1824: Percentage of the population between the ages of 18 and 24 that has received bachelor’s degree or higher (S1501).

·
EdL9: Total population ages 25 and over that has received less than a ninth grade education (S1501).

·
PEdL9: Percentage of population ages 25 and over that has received less than a ninth grade education (S1501).

·
Ed912nD: Total population ages 25 and over that has received some degree of education between grades 9 and 12 but has not received a high school degree (S1501).

·
PEd912nD: Percentage of population ages 25 and over that has received some degree of education between grades 9 and 12 but has not received a high school degree (S1501).

·
EdHS: Total population ages 25 and over that has received a high school degree or equivalent (S1501).

·
PEdHS: Percentage of population ages 25 and over that has received a high school degree or equivalent (S1501).

·
EdSC: Total population ages 25 and over with some college education but no degree (S1501).

·
PEdSC: Percentage of population ages 25 and over with some college education but no degree (S1501).

·
EdAssoc: Total population ages 25 and over with an associate’s degree (S1501).

·
PEdAssoc: Percentage of population population ages 25 and over with an associate’s degree (S1501).

·
EdB: Total population ages 25 and over with bachelor’s degree (S1501).

·
PEdB: Percentage of population ages 25 and over with bachelor’s degree (S1501).

·
EdG: Total population ages 25 and over with a graduate or professional degree (S1501).

·
PEdG: Percentage of population ages 25 and over with a graduate or professional degree (S1501).

Unemployment, Health Insurance, Disability

·
UnempR: Unemployment rate among the population ages 16 and over (S2301).

·
UnIn: Total non-institutionalized population without health insurance (B27001).

·
PUnIn: Percentage of non-institutionalized populations without health insurance (B27001).

·
Disab: Total non-institutionalized population with a disability (S1810).

·
PDisab: Percentage of non-institutionalized populations with a disability (B27001).

Vehicle Access

·
OwnNV: Total number of owner-occupied households without a vehicle (B25044).

·
POwnNV: Percentage of owner-occupied households without a vehicle (B25044).

·
OwnnV: Total number of owner-occupied households with n numbers of vehicles (B25044).

·
POwnnV: Percentage of owner-occupied households with n numbers of vehicles (B25044).

·
RentNV: Total number of renter-occupied households without a vehicle (B25044).

·
PRentNV: Percentage of renter-occupied households without a vehicle (B25044).

·
RentnV: Total number of renter-occupied households with n numbers of vehicles (B25044).

·
POwnnV: Percentage of renter-occupied households with n numbers of vehicles (B25044).

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.

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...

Compact Pickup Truck Market Outlook

According to our latest research, the global compact pickup truck market size reached USD 109.6 billion in 2024, reflecting robust consumer demand and ongoing innovation in the automotive sector. The market is projected to expand at a CAGR of 5.8% from 2025 to 2033, reaching approximately USD 182.1 billion by the end of the forecast period. This strong growth trajectory is underpinned by increasing urbanization, the rising need for versatile transportation solutions, and the rapid adoption of electric and hybrid technologies within the compact pickup segment.

The growth of the compact pickup truck market is primarily driven by shifting consumer preferences toward smaller, more fuel-efficient vehicles that do not compromise on utility. Urban dwellers, in particular, are seeking vehicles that combine maneuverability with sufficient payload capacity for personal and business needs. The compact size allows for easier navigation in congested city environments, while still providing the ruggedness and cargo space associated with traditional pickups. Furthermore, the integration of advanced safety features, infotainment systems, and connectivity options is making compact pickups increasingly attractive to younger buyers and first-time truck owners.

Another significant growth factor for the compact pickup truck market is the emergence of electric and hybrid models, which are gaining traction as governments worldwide implement stricter emission standards and offer incentives for green vehicles. Manufacturers are responding by investing heavily in research and development to produce compact pickups with improved fuel efficiency, lower emissions, and enhanced performance. The availability of electric and hybrid variants is expanding the market's appeal, particularly among environmentally conscious consumers and fleet operators looking to reduce their carbon footprint while maintaining operational flexibility.

Commercial and industrial applications are also fueling the expansion of the compact pickup truck market. Small and medium-sized enterprises, as well as large corporations, are increasingly opting for compact pickups to fulfill a variety of logistical, delivery, and maintenance tasks. The lower operating costs, ease of customization, and adaptability to diverse business requirements make compact pickups a preferred choice across multiple industries. Additionally, the rise of e-commerce and last-mile delivery services is propelling demand for compact, agile vehicles that can efficiently transport goods within urban and suburban areas.

From a regional perspective, the Asia Pacific region is leading the compact pickup truck market, accounting for the largest share in 2024, followed by North America and Europe. Asia Pacific's dominance is attributed to its large population, rapid urbanization, and growing middle class, which are collectively driving vehicle sales. North America remains a strong market due to the popularity of pickup trucks in both personal and commercial segments, while Europe is witnessing steady growth thanks to increasing adoption of electric and hybrid trucks. The market in Latin America and the Middle East & Africa is also expanding, albeit at a slower pace, as infrastructure development and economic growth in these regions continue to support automotive sales.

Fuel Type Analysis

The fuel type segment in the compact pickup truck market is witnessing a dynamic transformation, primarily influenced by evolving regulatory frameworks and consumer preferences. Traditionally, gasoline-powered compact pickups have dominated the market due to their affordability, widespread availability, and established fueling infrastructure. However, as environmental concerns intensify and governments introduce stricter emission standards, there has been a marked shift toward diesel, electric, and hybrid options. Diesel variants continue to appeal to buyers seeking superior torque and fuel efficiency, particularly in commercial and industrial applications, although their market share is gradually declining due to emission-related challenges.

The advent of electric compact pickup trucks marks a significant milestone in the industry, with several leading manufacturers launching or announcing new models tailored t

Daily vehicle miles traveled (VMT) is a distance- and volume-based measure of driving on roadways for all motorized vehicle types—car, bus, motorcycle, and truck—on an average day. Per capita VMT is the same measure divided by the same area's population for the same year. Per vehicle VMT divides VMT by the number of household vehicles available by residents of that geography in the same year. These three value types can be selected in the dropdown in the first chart below. Use the legend items to explore various geographies. The second chart below shows per capita and total personal vehicles available to the region’s households from the American Community Survey.

Normalizing VMT by a county or region's population, or household vehicles, is helpful for context, but does not have complete parity with what is measured in VMT estimates. People and vehicles come into the region from other places, just as people and vehicles leave the region to visit other places. VMT per capita compares all miles traveled on the region's roads to the region's population (for all ages) from the U.S. Census Bureau's latest population estimates. Vehicle counts for VMT are classified by vehicle types, but not by vehicle ownership. In 2017, statewide estimates for VMT by motorcycles, passenger cars, and two-axle single-unit trucks with four wheels made up 88% of Pennsylvania's VMT, and 95% of New Jersey's. These vehicle types are highly likely to be personal vehicles, owned by households, but a small percent could be fleet vehicles of companies or governments. The remaining VMT is made up of vehicle types like school and commercial buses and trucks with more than two axles so they are highly likely to be commercial vehicles.

Graph and download economic data for Expenditures: Vehicle Purchases: Cars and Trucks, New by Housing Tenure: Home Owner (CXUNEWCARSLB1702M) from 1984 to 2023 about homeownership, purchase, trucks, vehicles, expenditures, new, housing, and USA.

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