Graph and download economic data for Consumer Price Index for All Urban Consumers: Used Cars and Trucks in U.S. City Average (CUSR0000SETA02) from Jan 1953 to Sep 2025 about used, trucks, vehicles, urban, consumer, CPI, inflation, price index, indexes, price, and USA.

In the United States, the average selling price for a new light vehicle came to around ****** U.S. dollars in 2024. New light vehicles were about *** percent more expensive in 2024 than in 2023. What is the price of a new car in the U.S.?     New automobiles and light trucks were on around ****** U.S. dollars more expensive than used light vehicles: the average selling price for used vehicles amounted to about ****** U.S. dollars in 2024. This increasing price gap between new and used vehicles is partly linked to the industry's recovery from the semiconductor shortage, which led to higher new vehicle inventory and reduced pressure on the used vehicle market. The semiconductor shortage  The hike in prices across the board recorded in 2022 can be in part attributed to the global automotive chip shortage, which started in 2021 as demand rebounded from the COVID-19 pandemic. The average price rise could be observed across segments, with new full-size SUV/crossover vehicles averaging ****** U.S. dollars as of December 2022.

Used Car Price Prediction Dataset is a comprehensive collection of automotive information extracted from the popular automotive marketplace website, https://www.cars.com. This dataset comprises 4,009 data points, each representing a unique vehicle listing, and includes nine distinct features providing valuable insights into the world of automobiles.

• Brand & Model: Identify the brand or company name along with the specific model of each vehicle.
• Model Year: Discover the manufacturing year of the vehicles, crucial for assessing depreciation and technology advancements.
• Mileage: Obtain the mileage of each vehicle, a key indicator of wear and tear and potential maintenance requirements.
• Fuel Type: Learn about the type of fuel the vehicles run on, whether it's gasoline, diesel, electric, or hybrid.
• Engine Type: Understand the engine specifications, shedding light on performance and efficiency.
• Transmission: Determine the transmission type, whether automatic, manual, or another variant.
• Exterior & Interior Colors: Explore the aesthetic aspects of the vehicles, including exterior and interior color options.
• Accident History: Discover whether a vehicle has a prior history of accidents or damage, crucial for informed decision-making.
• Clean Title: Evaluate the availability of a clean title, which can impact the vehicle's resale value and legal status.
• Price: Access the listed prices for each vehicle, aiding in price comparison and budgeting.

This dataset is a valuable resource for automotive enthusiasts, buyers, and researchers interested in analyzing trends, making informed purchasing decisions or conducting studies related to the automotive industry and consumer preferences. Whether you are a data analyst, car buyer, or researcher, this dataset offers a wealth of information to explore and analyze.

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.

In 2024, the average selling price of used vehicles came to around ****** U.S. dollars. In 2024, new automobiles and light trucks were on average almost ****** U.S. dollars more expensive than used light vehicles. The used car boom As the price for new vehicles has been increasing, more Americans are turning towards buying second-hand cars and light trucks. Buying new cars and light trucks is typically seen as a privilege that few may do often. Generally, used vehicle sales are on the rise in most countries. Thanks to ever-improving technology, automobiles may keep running for many years and remain in good condition. This allows the average age of light vehicles in operation in the United States to increase slowly. Although new cars are often seen as status symbols, vintage cars are also very popular in the United States, the main reason for this trend being aesthetic and cultural values.

Gain valuable insights into the automotive market with our comprehensive Car Prices Dataset. Designed for businesses, analysts, and researchers, this dataset provides real-time and historical car pricing data to support market analysis, pricing strategies, and trend forecasting.

Dataset Features

Vehicle Listings: Access detailed car listings, including make, model, year, trim, and specifications. Ideal for tracking market trends and pricing fluctuations. Pricing Data: Get real-time and historical car prices from multiple sources, including dealerships, marketplaces, and private sellers. Market Trends & Valuations: Analyze price changes over time, compare vehicle depreciation rates, and identify emerging pricing trends. Dealer & Seller Information: Extract seller details, including dealership names, locations, and contact information for lead generation and competitive analysis.

Customizable Subsets for Specific Needs Our Car Prices Dataset is fully customizable, allowing you to filter data based on vehicle type, location, price range, and other key attributes. Whether you need a broad dataset for market research or a focused subset for competitive analysis, we tailor the dataset to your needs.

Popular Use Cases

Market Analysis & Pricing Strategy: Track vehicle price trends, compare competitor pricing, and optimize pricing strategies for dealerships and resellers. Automotive Valuation & Depreciation Studies: Analyze historical pricing data to assess vehicle depreciation rates and predict future values. Competitive Intelligence: Monitor competitor pricing, dealership inventory, and promotional offers to stay ahead in the market. Lead Generation & Sales Optimization: Identify potential buyers and sellers, track demand for specific vehicle models, and enhance sales strategies. AI & Predictive Analytics: Leverage structured car pricing data for AI-driven forecasting, automated pricing models, and trend prediction.

Whether you're tracking car prices, analyzing market trends, or optimizing sales strategies, our Car Prices Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

Used Car Prices YoY in the United States decreased to 0 percent in October from 2 percent in September of 2025. This dataset includes a chart with historical data for the United States Used Car Prices YoY.

View monthly updates and historical trends for US Consumer Price Index: Used Cars and Trucks. Source: Bureau of Labor Statistics. Track economic data with…

Used Car Prices MoM in the United States decreased to -2 percent in October from -0.20 percent in September of 2025. This dataset includes a chart with historical data for the United States Used Car Prices MoM.

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.

The Car Prices dataset contains detailed information about various car models, including their manufacturing year, make, model, trim, body type, transmission, and state of condition. With over 550,000 entries, this dataset is an excellent resource for exploring trends in car prices, analyzing market value fluctuations, and developing predictive models for the automotive industry.

Features

1. Year: Manufacturing year of the vehicle (Range: 1982-2015).
2. Make: Brand of the car (Top makes: Ford - 17%, Chevrolet - 11%).
3. Model: Specific car models, including Altima (3%), F-150 (3%), and others.
4. Trim: Vehicle trim level or variant (Base - 10%, SE - 8%).
5. Body: Body type, such as Sedan (36%), SUV (21%), etc.
6. Transmission: Type of transmission (85% Automatic, 12% unknown).
7. VIN: Vehicle Identification Number, unique to each car.
8. State: Location where the vehicle is registered (e.g., CA - 13%, FL - 15%).
9. Condition: Condition score of the car (1–5 scale).
10. Odometer: Mileage covered by the car (Range: 1 to 1,000,000+ miles).

Use Cases

• Price Prediction: Build machine learning models to predict car prices based on features like make, model, year, and mileage.
• Market Analysis: Identify trends in car preferences and sales across different regions.
• Depreciation Study: Study how different brands and models lose value over time.

Statistics Overview

• Years Distribution: The dataset spans from 1982 to 2015, with most entries concentrated in the 2000s.
• Popular Brands: Ford and Chevrolet dominate the entries.
• Body Type Distribution: Sedans and SUVs make up over 50% of the dataset.
• Condition Scores: Vehicles with a condition score of 4-5 are most common.

Example Records

License

This dataset is available under the MIT License, making it suitable for both commercial and non-commercial use.

Tags

• Car Pricing
• Automotive Market
• Machine Learning
• Price Prediction
• Data Analysis

Download Now and explore the intricacies of car prices with this rich and diverse dataset!

In 2024, the average electric vehicles price in the electric vehicles market worldwide was modeled to stand at ****** U.S. dollars. Between 2016 and 2024, the figure dropped by *** U.S. dollars, though the decline followed an uneven course rather than a steady trajectory. The average electric vehicles price is forecast to decline by *** U.S. dollars from 2024 to 2029, fluctuating as it trends downward.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Electric Vehicles.

The automobile industry has seen a dramatic shift in pricing over the past decade. With the introduction of new technologies, increased competition, and changing consumer preferences, car prices have been on the rise. This dataset will explore the changes in car prices over time, looking at factors such as inflation, technological advances, and consumer demand.

Inflation is one of the main drivers of car price increases. As the cost of living rises, so do the costs associated with producing cars. This means that manufacturers must raise their prices in order to remain profitable. In addition to inflationary pressures, technological advances have also had an impact on car prices. Automakers are constantly introducing new features and technologies into their vehicles which can add to their cost. For example, cars with advanced safety features such as lane-departure warning systems or automatic emergency braking can be more expensive than those without these features.

Consumer demand is another factor that affects car prices. As more people become interested in buying cars, manufacturers must raise their prices in order to meet this increased demand. Additionally, certain types of cars may be more popular than others at any given time which can also affect pricing trends. For example, SUVs and crossovers have become increasingly popular over the past few years and as a result their prices have risen significantly compared to other types of vehicles.

Overall, car prices have been steadily increasing over time due to a combination of factors such as inflationary pressures, technological advances, and consumer demand. It is important for consumers to be aware of these changes so they can make informed decisions when purchasing a vehicle. By understanding how these factors affect pricing trends they can ensure they get the best value for their money when buying a car.

The average for 2021 based on 165 countries was 108.07 index points. The highest value was in Singapore: 355.77 index points and the lowest value was in Syria: 58.27 index points. The indicator is available from 2017 to 2021. Below is a chart for all countries where data are available.

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.

China Automobile Price: DM: Passenger Car (PC) data was reported at 127,000.000 RMB/Unit in Aug 2023. This records a decrease from the previous number of 127,500.000 RMB/Unit for Jul 2023. China Automobile Price: DM: Passenger Car (PC) data is updated monthly, averaging 106,652.000 RMB/Unit from Jan 2007 (Median) to Aug 2023, with 196 observations. The data reached an all-time high of 128,800.000 RMB/Unit in Mar 2023 and a record low of 102,200.000 RMB/Unit in Nov 2012. China Automobile Price: DM: Passenger Car (PC) data remains active status in CEIC and is reported by Price Monitoring Center, NDRC. The data is categorized under China Premium Database’s Price – Table CN.PB: Price Monitoring Center, NDRC: Automobile Price.

Average car price in Brazil

Abou the data

Data extracted from FIPE.

Every observation (row) corresponds to a average car price, calculated to a month of a year of reference. In other words, in a year (year_of_reference) there are 12 observations related to the same car, however avg_price_brl might differ.

The variables fuel, gear and engine_size were extracted from values of column model, as in original there is no column dedicated to those values. Since some values for model don't contain the information of engine size, this dataset doesn't contain the whole data from FIPE's original. Additionally, if 'Aut.' is not present in model, the car is assumed to be manual.

The prices are calculated by FIPE and they're here as original (in BRL).

FIPE updates the information on a monthly basis. Here, the referred month is given as month_of_reference, as the corresponding year has variable named as year_of_reference

The FIPE codes (fipe_codes) are the model identifier used in FIPE webpage.

Variables

• year_of_reference: year of reference of the observation, i.e., the year the data corresponds to.
• moth_of_reference: month of reference of the observation, i.e., the month the data corresponds to. The average price is calculated by FIPE each month.
• fipe_code: unique id corresponding to a model for easy search on FIPE webpage.
• authentication: unique code that authenticates the consult in FIPE's site.
• brand: car's make.
• model: a description of the car containing the name and other descriptive information, as provided in FIPE table.
• fuel: fuel used by the car. Some of gas cars are actually alcohol and gas (totalflex), which is common in Brazil.
• gear: the way gears are shifted.
• engine_size: Engine size measured in cubic centimeters.
• year_model: those values corresponds to the year of reference, and may not be the same of the year of manufacture, which in case will corresponds to a year before year_model. Observations with year_model = year_of_reference mean the car is brand new for that year of reference, i.e., a 2021 car with year_of_reference = 2021 and moth_of_reference = July mean that the observation (mainly the average price) corresponds to a brand new car in the year of 2021, of the month of July. The same model may have a different average price for different month.
• avg_price_brl: average car's price, as measured by FIPE, in BRL.

- (variable deleted in Version 2).

Questions

Here are some questions that may be used as starting point of analysis.

• How average prices depend on car's age?
• How the engine size contribute to the average price?
• Can FIPE's prices be used to predict car prices?
• Gather some data from real stores. Can you compute you own average? How's it different from FIPE's?
• Build a time series and make explore them accordingly to engine size and gear type.
• Scale prices with Brazil's minimum wage in order make analysis period wise.`

The average price in the 'Luxury Cars' segment of the passenger cars market in the United States was modeled to be ****** U.S. dollars in 2024. Between 2013 and 2024, the average price rose by ****** U.S. dollars, though the increase followed an uneven trajectory rather than a consistent upward trend. The average price is forecast to decline by *** U.S. dollars from 2024 to 2029, fluctuating as it trends downward.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Luxury Cars.

1. Understanding the Features

Brand and Model: Analyze how different brands and models influence car prices. Are luxury brands significantly more expensive than economy brands? Year of Manufacture: Discuss the depreciation of car prices over time. How does the year affect pricing, and are there notable trends for specific brands? Engine Size: Explore the relationship between engine size and price. Does a larger engine correlate with a higher price, and how does this vary across different fuel types? Fuel Type: Evaluate how fuel types (Petrol, Diesel, Electric, Hybrid) impact pricing. Are electric vehicles priced higher due to their technology, or do they vary based on other factors? Transmission: Discuss if manual or automatic transmissions affect car pricing, especially in different markets or demographics.

2. Price Predictions and Modeling

Machine Learning Models: Explore which models (e.g., linear regression, decision trees, or ensemble methods) are best suited for predicting car prices using this dataset. Feature Importance: Discuss the importance of different features in predicting price. Which features contribute most to the price prediction accuracy, and how can feature selection improve the model?

3. Market Trends and Insights

Price Distribution: Analyze the distribution of car prices. Are there a lot of high-priced luxury cars, or is the dataset skewed towards more affordable options? Mileage vs. Price: Investigate the correlation between mileage and price. How does higher mileage affect pricing, and is there a threshold where price reduction becomes significant? Condition Impact: Discuss how the condition of the car (New, Used, Like New) influences the price. Are there significant price drops for used cars compared to new ones?

4. Regional Analysis

Location Impact: If geographic location is included, discuss how prices vary by region. Are there markets where certain brands/models are more popular and thus command higher prices? Economic Factors: Consider how broader economic factors (like inflation, fuel prices, and consumer preferences) might influence car prices in different regions.

5. Future Developments and Trends

Electric Vehicle Market: With the rise of electric vehicles, discuss how this dataset reflects the growing demand and pricing trends for EVs compared to traditional fuel cars. Impact of Technology: Consider how advancements in technology, safety features, and autonomous driving capabilities might influence future pricing.

6. Limitations of the Dataset

Data Completeness: Discuss any potential limitations in the dataset, such as missing values or biases in the data collection process. Generalization: Reflect on the ability to generalize the findings from this dataset to broader car markets or regions. Are there potential confounding factors that should be considered?

7. Potential Applications

Pricing Strategies: How can dealerships or private sellers utilize insights from this dataset to set competitive pricing? Consumer Decision-Making: Discuss how consumers can leverage this dataset to make informed purchasing decisions based on price predictions and feature evaluations.

These discussion points can help guide deeper analysis and insights into the Car Price Prediction dataset, making it a valuable resource for both academic and practical applications. If you have specific areas you want to focus on, let me know!

Graph and download economic data for Consumer Price Index for All Urban Consumers: New and Used Motor Vehicles in U.S. City Average (CUSR0000SETA) from Jan 1993 to Sep 2025 about used, vehicles, urban, new, consumer, CPI, inflation, price index, indexes, price, and USA.