In 2007, a cash-strapped Brian Chesky came up with a shrewd way to pay his $1,200 San Francisco apartment rent. He would offer “Air bed and breakfast”, which consisted of three airbeds,...

The total revenue of Airbnb reached **** billion U.S. dollars in 2024. This was an increase over the previous year's total of **** billion. The decrease in revenue in 2020 can be attributed to the coronavirus (COVID-19) pandemic, which caused travel disruption across the globe. When breaking down Airbnb revenue by region, ***************************************, brought in the most revenue in 2024. Where are Airbnb’s biggest markets? Airbnb is a home sharing economy platform that operates in many countries around the world. The company’s biggest market is in ************* where Airbnb’s gross booking value amounted to **** billion U.S. dollars. Meanwhile, Latin American travelers stayed more nights with Airbnb on average than those in the Asia Pacific region. How did COVID-19 impact Airbnb? The COVID-19 pandemic impacted the travel and tourism industry worldwide, with many countries initiating stay at home orders or travel bans to prevent the spread of the virus. In addition to a decrease in revenue in 2020, the company also experienced a reduction in the number of nights and experiences booked with Airbnb. Bookings fell to under *** million in 2020 due to these travel restrictions. In 2024, Airbnb reported over *** million booked nights and experiences, a significant increase over the previous year.

************* was the region that brought in the highest amount of Airbnb’s worldwide revenue in 2024, at ************ U.S. dollars. As the company is based in the United States, this is not surprising. However, the Europe, Middle East, and Africa (EMEA) region was not too far behind with *********** U.S. dollars in revenue.************** also reported the highest average number of nights booked by region with Airbnb in 2024.

These are the Airbnb statistics on gross revenue by country.

This is the complete breakdown of how much revenue Airbnb makes in commission from listings in each region.

The revenue of Airbnb with headquarters in the United States amounted to 11.1 billion U.S. dollars in 2024. The reported fiscal year ends on December 31.Compared to the earliest depicted value from 2020 this is a total increase by approximately 7.72 billion U.S. dollars. The trend from 2020 to 2024 shows, furthermore, that this increase happened continuously.

In 2024, Airbnbs in Noosa Heads in Queensland, Australia, had the highest average annual revenue across the cities and regions represented, with a revenue of over 103,500 Australian dollars. Byron Bay was also a highly profitable Airbnb location for hosts that year.

Airbnb net income/loss for the twelve months ending March 31, 2025 was $5.808B, a 47.03% decline year-over-year. Airbnb annual net income/loss for 2024 was $2.648B, a 44.74% decline from 2023. Airbnb annual net income/loss for 2023 was $4.792B, a 153.14% increase from 2022. Airbnb annual net income/loss for 2022 was $1.893B, a 637.78% decline from 2021.

The current average price per night globally on Airbnb is $137 per night.

In financial year 2020, the revenue of accommodation platform Airbnb India stood at *** million Indian rupees. This was a significant increase compared to the previous two years. The financial year 2020 was already impacted by the coronavirus (COVID-19) pandemic. In late March 2020, the Indian government imposed a countrywide lockdown as well as travel restrictions.

Airbnb has a total of 6,132 employees that work for the company. 52.5% of Airbnb workers are male and 47.5% are female.

Listings per region on Airbnb declined from 2020 to 2021. Globally in 2021, there were a total of 12.7 million listings.

The majority of guests on Airbnb are women. Most Airbnb guests are aged 25 to 34.

Airbnb Inc report offers Analysis review with SWOT PESTLE Value Chain Financial Insight and ESG strategies covering trends and risks for strong business growth.: “ Read More

According to a March 2025 analysis, Rome reported the highest number of Airbnb listings among the selected Italian cities. As of that month, there were over 34,000 establishments listed on Airbnb in the Italian capital. Milan and Florence followed behind, with over 16,000 and 12,000 listings on Airbnb. What are the leading brands for accommodation bookings in Italy? According to the Statista Consumer Insights Global survey, Airbnb was the second most popular brand for hotel and private accommodation online bookings in Italy in 2024, with over a quarter of respondents having booked accommodation via that website. That year, Booking.com topped the ranking, with almost three-quarters of the sample reporting using that provider. Booking Holdings vs. Airbnb Booking Holdings, which operates the Booking.com brand, and Airbnb are among the biggest companies in the online travel market. In 2025, Booking Holdings had the highest market cap of the leading online travel companies worldwide, while Airbnb ranked second. Both companies experienced an annual increase in earnings in 2024. That year, Booking Holdings' revenue peaked at almost 24 billion U.S. dollars. Meanwhile, Airbnb's revenue also reached an all-time high for the company in 2024.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Airbnb (ABNB) Stock: A Travel Revolution in the Making

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The operating profit of Airbnb with headquarters in the United States amounted to 2.6 billion U.S. dollars in 2024. The reported fiscal year ends on December 31.Compared to the earliest depicted value from 2020 this is a total increase by approximately 6.1 billion U.S. dollars. The trend from 2020 to 2024 shows, however, that this increase did not happen continuously.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Airbnb Stock: Is It a Buy, Sell, or Hold for the Next 3 Months?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Airbnb Stock: A Hold for the Next 6 Months

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The vacation rental market, valued at $86.12 billion in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) of 25.79% from 2025 to 2033. This surge is driven by several factors. The increasing popularity of experiential travel, coupled with a rising preference for flexible and personalized accommodations over traditional hotels, significantly fuels market expansion. Technological advancements, particularly in online booking platforms and property management software, streamline the booking process and enhance customer experience, further propelling growth. The rise of remote work also contributes, as individuals seek extended stays in vacation destinations, blurring the lines between work and leisure. Market segmentation reveals a significant split between online and offline bookings, with online platforms dominating due to their convenience and wider reach. Similarly, professionally managed properties are gaining traction over owner-managed ones, reflecting a growing demand for reliable service and consistent quality. Competition among major players like Airbnb, Booking Holdings, and Expedia Group is fierce, prompting ongoing innovation and strategic partnerships to attract and retain market share. However, certain restraints impact market growth. Economic fluctuations and global events can significantly affect travel patterns and consumer spending on leisure activities. Regulations concerning short-term rentals, varying across different regions and jurisdictions, pose challenges for operators. Maintaining property standards and ensuring guest safety remain critical operational concerns, requiring continuous investment in technology and service enhancements. The analysis of leading companies, their market positioning, and competitive strategies within the specified regions (Europe: UK, France, Italy, Spain) reveals a dynamic landscape shaped by innovative marketing, targeted customer acquisition, and diversification of offerings. Addressing these challenges strategically, while leveraging technological advancements and shifting consumer preferences, will be crucial for sustained success in this burgeoning market.