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

In 2021, Baby Boomers were the main new car buyers in the United States, representing around ** percent of new car sales. By contrast, Gen X made up the majority of the used car buyers, at close to ** percent of the sales.

This statistic displays the gender of car-sharing apps users in China from 2018 to 2020. In 2020, male car-sharing app users accounted for approximately **** percent of all car-sharing app users in China.

In the fourth quarter of 2024, there were around ***** million vehicles operating on roads throughout the United States. Almost **** million used vehicles changed owners in the U.S. between the fourth quarter of 2023 and the fourth quarter of 2024, while new registrations of vehicles came to about **** million units during that period. Automotive market disparities The number of licensed drivers had been steadily increasing up to just under ******* in 2023, but the automotive market has been impacted by economic developments over the past few years. The U.S. vehicle fleet is aging, reflected by the slow increase in the average vehicle age from **** years in 2018 to over ** years in 2024. This is in part due to market disparities. The average selling price of new vehicles has been increasing to nearly ****** U.S. dollars in 2024, up from under ****** in 2016. Used car prices have been declining after the chip shortages linked to the COVID-19 pandemic, reaching around ****** U.S. dollars in 2024. The majority of U.S. car owners earned more than ****** U.S. dollars per years, with the ****** to ****** income group owning over ** percent of the vehicles in use. The boom of the used vehicle market Close to ************* of new car buyers were born between 1946 and 1981, with Gen X being the leading consumers by age group for both the new and used vehicle market. Used light vehicle sales have been steadily increasing since 2010, representing well over double the size of the new light vehicle market in 2024. With a product range priced below new vehicle prices, used vehicles are gaining momentum in the United States. The average American household spends some ***** U.S. dollars on vehicle purchases annually, with consumers in income groups earning above 100,000 U.S. dollars per year spending above ***** dollars annually on car buying. Used vehicle financing options are naturally more affordable than new vehicle financing options, with an average monthly payment over *** dollars for loan payments for new vehicles.

This statistic shows the gender and city tier distribution of automobile 4S shop customers in China as of the fourth quarter of 2018. In that period, around **** percent of the visitors at the automobile 4S stores in China's first tier cities were male.

Graph and download economic data for Consumer Price Index for All Urban Consumers: New Vehicles in U.S. City Average (CUUR0000SETA01) from Mar 1947 to Sep 2025 about vehicles, urban, new, consumer, CPI, inflation, price index, indexes, price, and USA.

Mexico Consumer Price Index (CPI): OAT: Vehicle Use: Car Services (CS) data was reported at 103.461 16Jul2018-31Jul2018=100 in Mar 2019. This records an increase from the previous number of 102.885 16Jul2018-31Jul2018=100 for Feb 2019. Mexico Consumer Price Index (CPI): OAT: Vehicle Use: Car Services (CS) data is updated monthly, averaging 66.020 16Jul2018-31Jul2018=100 from Jan 1980 (Median) to Mar 2019, with 471 observations. The data reached an all-time high of 103.461 16Jul2018-31Jul2018=100 in Mar 2019 and a record low of 0.037 16Jul2018-31Jul2018=100 in Jan 1980. Mexico Consumer Price Index (CPI): OAT: Vehicle Use: Car Services (CS) data remains active status in CEIC and is reported by National Institute of Statistics and Geography. The data is categorized under Global Database’s Mexico – Table MX.I002: Consumer Price Index: Second Half July 2018=100.

Graph and download economic data for Consumer Price Index for All Urban Consumers: New Vehicles in U.S. City Average (CUSR0000SETA01) from Jan 1953 to Sep 2025 about vehicles, urban, new, consumer, CPI, inflation, price index, indexes, price, and USA.

This statistic illustrates the results of a survey on the different types of videos watched by car buyers when looking to buy a vehicle in Italy in 2018. According to the results, the most popular types of video content were feature highlight videos and vehicle walk-arounds, watched by ** and ** percent of the respondents, respectively.

This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission using data from the U.S. Census Bureau.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2014-2018). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

s

Significance flag for change: 1 = statistically significant with a 90% Confidence Interval, 0 = not statistically significant, blank = cannot be computed

Suffixes:

_e18

Estimate from 2014-18 ACS

_m18

Margin of Error from 2014-18 ACS

_00_v18

Decennial 2000 in 2018 geography boundary

_00_18

Change, 2000-18

_e10_v18

Estimate from 2006-10 ACS in 2018 geography boundary

_m10_v18

Margin of Error from 2006-10 ACS in 2018 geography boundary

_e10_18

Change, 2010-18

Avg Consumer Price: Passenger Car: New: Foreign Brand: Far East Federal District (FE) data was reported at 2,626,958.900 RUB in Feb 2025. This records an increase from the previous number of 2,621,096.970 RUB for Jan 2025. Avg Consumer Price: Passenger Car: New: Foreign Brand: Far East Federal District (FE) data is updated monthly, averaging 1,447,651.910 RUB from Jan 2017 (Median) to Feb 2025, with 98 observations. The data reached an all-time high of 2,626,958.900 RUB in Feb 2025 and a record low of 966,036.600 RUB in Apr 2018. Avg Consumer Price: Passenger Car: New: Foreign Brand: Far East Federal District (FE) data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Automobile Sector – Table RU.RAF001: Average Consumer Price: Passenger Car: New. In November 2018 Republic of Buryatia and Zabaikalsk Territory have been reassigned from the Siberian Federal District to the Far East Federal District (Presidential decree from 03.11.2018 N632.)

Avg Consumer Price: Passenger Car: New Domestic: Far East Federal District (FE) data was reported at 1,338,423.090 RUB in Feb 2025. This stayed constant from the previous number of 1,338,423.090 RUB for Jan 2025. Avg Consumer Price: Passenger Car: New Domestic: Far East Federal District (FE) data is updated monthly, averaging 378,796.670 RUB from Jan 2003 (Median) to Feb 2025, with 266 observations. The data reached an all-time high of 1,338,423.090 RUB in Feb 2025 and a record low of 136,872.340 RUB in Jan 2004. Avg Consumer Price: Passenger Car: New Domestic: Far East Federal District (FE) data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Automobile Sector – Table RU.RAF001: Average Consumer Price: Passenger Car: New. In November 2018 Republic of Buryatia and Zabaikalsk Territory have been reassigned from the Siberian Federal District to the Far East Federal District (Presidential decree from 03.11.2018 N632.)

Number of units and total sales of new motor vehicles by vehicle type and origin of manufacture, monthly.

In China, in-car audio saw a surge in paying users between 2018 and 2020, then remained around one percent in 2023. By 2028, the paying rate among in-car audio users was estimated to slightly increase to *** percent.

Mexico Consumer Price Index (CPI): Own Acct Transport: Vehicle Use: Car Services data was reported at 106.541 16Jul2018-31Jul2018=100 in Mar 2019. This records an increase from the previous number of 106.005 16Jul2018-31Jul2018=100 for Feb 2019. Mexico Consumer Price Index (CPI): Own Acct Transport: Vehicle Use: Car Services data is updated monthly, averaging 70.490 16Jul2018-31Jul2018=100 from Jan 1995 (Median) to Mar 2019, with 291 observations. The data reached an all-time high of 106.541 16Jul2018-31Jul2018=100 in Mar 2019 and a record low of 20.142 16Jul2018-31Jul2018=100 in Jan 1995. Mexico Consumer Price Index (CPI): Own Acct Transport: Vehicle Use: Car Services data remains active status in CEIC and is reported by National Institute of Statistics and Geography. The data is categorized under Global Database’s Mexico – Table MX.I002: Consumer Price Index: Second Half July 2018=100.

Russia Avg Consumer Price: Passenger Car: New: Foreign Brand data was reported at 975,727.230 RUB in Jan 2019. This records an increase from the previous number of 950,020.290 RUB for Dec 2018. Russia Avg Consumer Price: Passenger Car: New: Foreign Brand data is updated monthly, averaging 915,987.380 RUB from Jan 2018 (Median) to Jan 2019, with 13 observations. The data reached an all-time high of 975,727.230 RUB in Jan 2019 and a record low of 888,792.070 RUB in Jan 2018. Russia Avg Consumer Price: Passenger Car: New: Foreign Brand data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Prices – Table RU.PA014: Average Consumer Price: Vehicles.