Facebook
TwitterThis 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...
Facebook
TwitterWhen 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.
Facebook
TwitterOn 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.
Facebook
TwitterThis statistic illustrates the share of people owning a Ford in the United States. As of **************, ** percent of 18 - 29 year old consumers do so in the U.S. This is according to exclusive results from the Consumer Insights Global survey which shows that ** percent of 30 - 49 year old customers also fall into this category.Statista Consumer Insights offer you all results of our exclusive Statista surveys, based on more than ********* interviews.
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Facebook
TwitterThis 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...