Graph and download economic data for Consumer Price Index for All Urban Consumers: Airline Fares in U.S. City Average (CUSR0000SETG01) from Jan 1989 to Sep 2025 about air travel, travel, urban, consumer, CPI, price index, indexes, price, and USA.

View monthly updates and historical trends for US Consumer Price Index: Airline Fares. Source: Bureau of Labor Statistics. Track economic data with YChart…

United States - Consumer Price Index for All Urban Consumers: Airline Fares in U.S. City Average was 270.33600 Index 1982-84=100 in September of 2025, according to the United States Federal Reserve. Historically, United States - Consumer Price Index for All Urban Consumers: Airline Fares in U.S. City Average reached a record high of 322.64500 in March of 2013 and a record low of 128.00000 in January of 1989. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Consumer Price Index for All Urban Consumers: Airline Fares in U.S. City Average - last updated from the United States Federal Reserve on December of 2025.

Quarterly domestic (short and long haul) and international air fares, by fare type group (business class, economy, discounted and other).

In 2025, the seasonally adjusted consumer price index for all airline fares in the United States was ******. Over the given period, the CPI-U peaked at ***** in 2013, before decreasing significantly to ***** in 2021.

The dataset provides a comprehensive overview of flight details, focusing on various key attributes related to airline operations. It includes information on:

• Airlines: Names of the airlines operating the flights.
• Source and Destination: Cities where flights originate and land.
• Total Stops: Number of stops made by the flights.
• Price: Ticket prices for the respective flights.
• Date, Month, and Year: Specific dates on which the flights are scheduled.
• Departure and Arrival Times: Detailed hours and minutes for both departure and arrival.
• Duration: Total duration of flights in hours and minutes.

This dataset is valuable for analyzing flight pricing trends, travel times, and patterns in airline operations. It offers insights into how different airlines operate across various routes, how prices vary, and the impact of stops on overall travel duration.

Amongst selected European airlines, Ryanair had by far the lowest average passenger fare in 2021, with approximately ** euros per passenger. The low-cost airline is followed by its rivals, Wizz Air and Norwegian, with an average ticket price of ** euros and ** euros respectively.

About Dataset:

This dataset provides detailed information on airline flight routes, fares, and passenger volumes within the United States from 1993 to 2024.

Data Features:

1. tbl: Table identifier 2. Year: Year of the data record 3. quarter: Quarter of the year (1-4) 4. citymarketid_1: Origin city market ID 5. citymarketid_2: Destination city market ID 6. city1: Origin city name 7. city2: Destination city name 8. airportid_1: Origin airport ID 9. airportid_2: Destination airport ID 10. airport_1: Origin airport code 11. airport_2: Destination airport code 12. nsmiles: Distance between airports in miles 13. passengers: Number of passengers 14. fare: Average fare 15. carrier_lg: Code for the largest carrier by passengers 16. large_ms: Market share of the largest carrier 17. fare_lg: Average fare of the largest carrier 18. carrier_low: Code for the lowest fare carrier 19. lf_ms: Market share of the lowest fare carrier 20. fare_low: Lowest fare 21. Geocoded_City1: Geocoded coordinates for the origin city 22. Geocoded_City2: Geocoded coordinates for the destination city 23. tbl1apk: Unique identifier for the route

Potential Uses: 1. Market Analysis: Assess trends in air travel demand, fare changes, and market share of airlines over time. 2. Price Optimization: Develop models to predict optimal pricing strategies for airlines. 3. Route Planning: Identify profitable routes and underserved markets for new route planning. 4. Economic Studies: Analyze the economic impact of air travel on different cities and regions. 5. Travel Behavior Research: Study changes in passenger preferences and travel behavior over the years. 6. Competitor Analysis: Evaluate the performance of different airlines on various routes.

Airfare Price Drop Protection Market Outlook

According to our latest research, the global airfare price drop protection market size in 2024 stands at USD 1.17 billion, reflecting robust demand for travel cost optimization solutions across the globe. The market is expected to expand at a CAGR of 18.2% from 2025 to 2033, reaching a projected value of USD 5.16 billion by 2033. This remarkable growth is primarily fueled by increasing consumer awareness of dynamic airfare pricing, the proliferation of digital travel platforms, and a heightened focus on user-centric travel experiences.

One of the primary growth drivers for the airfare price drop protection market is the growing volatility and unpredictability of airline ticket prices. As airlines increasingly adopt dynamic pricing algorithms, travelers often face substantial price fluctuations between the time they search for and book tickets. This uncertainty has led to a surge in demand for solutions that can offer financial protection against post-purchase price drops. The integration of advanced analytics and artificial intelligence in travel platforms has further facilitated the development of automated price monitoring and refund mechanisms, making these services more accessible and user-friendly for a broad spectrum of travelers.

The rapid digital transformation of the travel industry has also been a significant catalyst for market expansion. The widespread adoption of online travel agencies (OTAs), mobile travel apps, and meta-search engines has made it easier for consumers to compare prices and access ancillary services, including price drop protection. These platforms have leveraged big data and machine learning to enhance their offerings, providing real-time notifications and seamless refund processes. Additionally, the increasing penetration of smartphones and high-speed internet in emerging economies has expanded the addressable market, enabling even budget-conscious travelers to benefit from airfare price drop protection services.

Another crucial factor propelling the market is the changing expectations of both individual and corporate travelers. With business travel rebounding post-pandemic and leisure travelers seeking greater value for money, there is a heightened emphasis on risk mitigation and cost savings. Corporate travel managers are increasingly integrating airfare price drop protection into their travel policies to optimize budgets and improve employee satisfaction. Simultaneously, leisure travelers, empowered by technology, are demanding more transparent and flexible booking options. This evolving consumer mindset is pushing airlines, OTAs, and travel agencies to differentiate themselves by offering innovative price assurance features.

From a regional perspective, North America remains the largest contributor to the airfare price drop protection market, owing to high digital adoption rates and a mature travel ecosystem. However, Asia Pacific is emerging as the fastest-growing region, driven by a burgeoning middle class, increased international travel, and rapid technological advancements. Europe also holds a significant share, supported by a well-established airline network and a strong culture of travel insurance adoption. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth, bolstered by the expansion of low-cost carriers and increased online travel bookings.

Product Type Analysis

The product type segment of the airfare price drop protection market is broadly categorized into Automatic Refund, Manual Claim, Subscription-Based, and Pay-Per-Use models. Automatic refund solutions have gained significant traction due to their seamless, user-friendly experience. These services automatically monitor ticket prices after purchase and initiate refunds or credits if a lower fare becomes available, eliminating the need for customer intervention. The integration of real-time fare tracking algorithms and secure payment gateways has made automatic refund offerings highly attractive to both individual and corporate travelers, driving their adoption across major online travel agencies and airline platforms.

Manual claim models, while less automated, remain relevant, particularly among traditional travel agencies and consumers who prefer a more hands-on approach. In this model, travelers must

Airline Fares CPI - Historical chart and current data through 2025.

In 2024, the consumer price index (CPI) of airplane fares in Japan reached ***** points, increasing by **** points compared to the base year in 2020. This was a significant increase and the highest index during the surveyed period.

This dataset was used in my dissertation project to find the best time to buy the airline ticket based on number of days before departure date which was inspired by the work of Domínguez-Menchero et al (2014) '*Optimal purchase timing in the airline market*'.

The dataset was scraping using Selenium and BeatifulSoup python package. It contains direct flight data of flights from London to Bangkok, Hong Kong, Tokyo, Seoul, and Singapore. The data were gathered 30 days and 66 days before the departure date consisting of route, airline name, direct flight type, departure date, departure date (format), search date, days before departure, ticket price, price on departure date, saving rate, and day of week.

Amongst low-cost airlines in the United States there is considerable difference in average ticket prices: on one extreme is Spirit, with an average domestic ticket price of ** U.S. dollars, while on the other extreme is JetBlue, whose average domestic ticket price stood at *** U.S. dollars in the 12 months ending December 31, 2020. Ultra-low-cost carriersVariance in ticket prices between low-cost carriers has led some analysts to talk of a new industry segment – ultra-low-cost carriers (ULCC). ULCCs differ in that their business model is aimed at finding untapped locations to create extremely cheap flights. This business model creates new demand through courting consumers who do not normally fly, rather than structuring services according to existing demand. The ULCC model has proved to be successful, with ULCCs such as Allegiant and Frontier recording strong growth in operating revenue over recent years, as has the overall ULCC segment. Low cost carriersMore broadly, the low-cost carrier segment has been consistently expanding its share of the American airline market over the last decade. This trend extends beyond America, with low cost carriers dominating the global list of airlines which launched the newest routes in 2018. The U.S. ULCCs Allegiant, Frontier and Spirit all featured in the top 20 of this list.

Learn more about air fares data from RDC Aviation, providing network wide air fare coverage for the world's leading airlines.

Graph and download economic data for Harmonized Index of Consumer Prices: Passenger Transport by Air for European Union (28 Countries) (CP0733EU28M086NEST) from Dec 2000 to Jan 2020 about passenger, air travel, EU, travel, harmonized, transportation, Europe, CPI, price index, indexes, and price.

Multivariate regression data set from: https://link.springer.com/article/10.1007%2Fs10994-016-5546-z : The Airline Ticket Price dataset concerns the prediction of airline ticket prices. The rows are a sequence of time-ordered observations over several days. Each sample in this dataset represents a set of observations from a specific observation date and departure date pair. The input variables for each sample are values that may be useful for prediction of the airline ticket prices for a specific departure date. The target variables in these datasets are the next day (ATP1D) price or minimum price observed over the next 7 days (ATP7D) for 6 target flight preferences: (1) any airline with any number of stops, (2) any airline non-stop only, (3) Delta Airlines, (4) Continental Airlines, (5) Airtrain Airlines, and (6) United Airlines. The input variables include the following types: the number of days between the observation date and the departure date (1 feature), the boolean variables for day-of-the-week of the observation date (7 features), the complete enumeration of the following 4 values: (1) the minimum price, mean price, and number of quotes from (2) all airlines and from each airline quoting more than 50 % of the observation days (3) for non-stop, one-stop, and two-stop flights, (4) for the current day, previous day, and two days previous. The result is a feature set of 411 variables. For specific details on how these datasets are constructed please consult Groves and Gini (2015). The nature of these datasets is heterogeneous with a mixture of several types of variables including boolean variables, prices, and counts.

This statistic shows the price per route of low cost airlines in Europe in 2013 and 2014. The average cost per route of budget airline Ryanair was 65.67 euros in 2014, up from 58.45 euros the previous year.

Graph and download economic data for Harmonized Index of Consumer Prices: Passenger Transport by Air for Netherlands (CP0733NLM086NEST) from Jan 1996 to Oct 2025 about passenger, Netherlands, air travel, travel, harmonized, transportation, CPI, price index, indexes, and price.

Airline Pricing Optimization Market Outlook

According to our latest research, the global airline pricing optimization market size reached USD 2.45 billion in 2024, with a robust year-over-year growth trajectory. The market is anticipated to expand at a compelling CAGR of 13.7% during the forecast period, reaching an estimated USD 7.23 billion by 2033. This significant growth is primarily driven by the increasing adoption of advanced analytics, artificial intelligence, and machine learning technologies by airlines to maximize revenue, enhance customer experience, and stay competitive in a volatile and dynamic aviation environment.

One of the primary growth factors fueling the airline pricing optimization market is the rising complexity of air travel demand patterns and the need for airlines to respond dynamically to fluctuating market conditions. Airlines are increasingly leveraging sophisticated pricing optimization solutions to analyze massive volumes of real-time data, including historical booking trends, competitor pricing, seasonality, and macroeconomic indicators. By doing so, they can implement dynamic pricing strategies that adjust fares in real-time, maximizing load factors and revenue per available seat mile (RASM). The proliferation of digital booking channels and the demand for personalized travel experiences have further amplified the need for agile, data-driven pricing systems that can deliver optimal price points for both the airline and the customer.

Another critical driver of market expansion is the growing emphasis on ancillary revenue streams. As traditional ticket sales face pressure from intense competition and price-sensitive travelers, airlines are increasingly turning to ancillary services such as baggage fees, seat selection, onboard amenities, and loyalty programs to boost profitability. Pricing optimization platforms enable carriers to analyze customer preferences and willingness to pay, allowing for granular pricing of these services. This not only enhances overall revenue but also helps airlines differentiate their offerings and foster brand loyalty. The integration of these solutions with customer relationship management (CRM) and revenue management systems ensures a holistic approach to revenue optimization across all touchpoints.

The accelerated digital transformation across the aviation sector, particularly in the wake of the COVID-19 pandemic, has also played a pivotal role in market growth. Airlines are investing heavily in cloud-based pricing optimization tools and leveraging artificial intelligence to automate and streamline pricing decisions. This shift is driven by the need to reduce operational costs, improve agility, and respond faster to market disruptions. The adoption of cloud-based solutions also facilitates scalability and seamless integration with other airline IT systems, making it easier for carriers of all sizes to implement advanced pricing strategies. As a result, both legacy full-service carriers and emerging low-cost airlines are increasingly embracing these technologies to maintain profitability and competitiveness in a rapidly evolving landscape.

From a regional perspective, North America currently dominates the airline pricing optimization market, driven by the presence of major airlines, a mature aviation ecosystem, and early adoption of digital technologies. Europe follows closely, with significant investments in revenue management and pricing analytics. The Asia Pacific region is poised for the fastest growth, fueled by the rapid expansion of the aviation sector, increasing passenger traffic, and the emergence of new low-cost carriers. Latin America and the Middle East & Africa are also witnessing steady adoption, albeit at a slower pace, as airlines in these regions modernize their operations and seek to enhance profitability through advanced pricing strategies.

Software Analysis

The software segment represents the backbone of the airline pricing optimization market, encompassing a wide array of platforms and applications designed to automate and enhance the pricing process. These solutions leverage advanced algorithms, artificial intelligence, and machine learning to process vast datasets, identify demand patterns, and recommend optimal fare structures in real-time. The increasing complexity of airline operations, coupled with the need for rapid decision-making, has driven airlines to invest in robust software solutions that can integrate seamlessly with existi

Dynamic Pricing for Airlines Market Outlook

According to our latest research, the global dynamic pricing for airlines market size reached USD 2.37 billion in 2024, reflecting a robust adoption rate across the aviation sector. The market is projected to expand at a CAGR of 17.8% during the forecast period, reaching approximately USD 7.87 billion by 2033. This remarkable growth is primarily fueled by the increasing need for airlines to optimize revenue streams, enhance operational efficiency, and deliver personalized customer experiences in a highly competitive environment. The proliferation of advanced analytics and artificial intelligence (AI) technologies is further accelerating the adoption of dynamic pricing solutions within the airline industry.

A key growth driver for the dynamic pricing for airlines market is the escalating demand for revenue maximization in a volatile and competitive landscape. Airlines are increasingly leveraging dynamic pricing strategies to adjust fares in real-time based on fluctuating demand, competitor pricing, and other market variables. This allows carriers to maximize seat occupancy and optimize ticket revenues, especially during peak travel periods and in response to sudden shifts in demand. The integration of machine learning and predictive analytics has made these pricing models more sophisticated, enabling airlines to analyze vast datasets and make data-driven pricing decisions that directly impact profitability.

Another significant factor contributing to market growth is the surge in digital transformation initiatives within the airline industry. As airlines invest heavily in digital infrastructure, the adoption of cloud-based dynamic pricing platforms has gained momentum. These platforms offer scalability, flexibility, and seamless integration with existing airline reservation and distribution systems. Furthermore, the shift towards personalized pricing models, which consider individual passenger preferences, booking history, and loyalty status, is enhancing customer satisfaction and loyalty. This trend is particularly prevalent among full-service carriers striving to differentiate themselves in a crowded marketplace.

The increasing emphasis on ancillary revenue streams is also propelling the dynamic pricing for airlines market forward. Airlines are now applying dynamic pricing not only to ticket fares but also to ancillary services such as baggage fees, seat selection, in-flight meals, and priority boarding. This holistic approach to revenue management enables carriers to capture additional value from every customer interaction, thereby driving overall profitability. As passenger expectations evolve and competition intensifies, airlines are compelled to adopt innovative pricing strategies that address both primary and ancillary revenue opportunities.

From a regional perspective, North America currently dominates the dynamic pricing for airlines market, owing to the presence of major airlines, advanced technological infrastructure, and a high degree of market maturity. However, the Asia Pacific region is expected to exhibit the fastest growth rate over the forecast period, driven by the rapid expansion of the aviation sector, rising passenger traffic, and increasing adoption of digital technologies by regional carriers. Europe also represents a significant market, characterized by a strong focus on customer experience and regulatory compliance. Latin America and the Middle East & Africa are emerging markets, with potential for substantial growth as airlines in these regions embrace dynamic pricing to enhance competitiveness and profitability.

Component Analysis

The component segment of the dynamic pricing for airlines market is bifurcated into software and services. Software solutions form the backbone of dynamic pricing strategies, providing airlines with the tools and algorithms necessary to analyze market data, forecast demand, and implement real-time price adjustments. These platforms are increasingly incorporating artificial intellige