The operating profit of Airbnb with headquarters in the United States amounted to *** 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 *** billion U.S. dollars. The trend from 2020 to 2024 shows, however, that this increase did not happen continuously.

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,...

This statistic shows the growth in revenue of Airbnb rooms in New York City over the previous year from 2011 to 2015, with forecasts from 2016 to 2018. In 2015, the revenue of Airbnb rooms in New York City grew ** percent over the previous year.

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.

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

These are the Airbnb statistics on gross revenue by country.

Airbnb net profit margin for the quarter ending March 31, 2025 was 22.6%. Airbnb average net profit margin for 2024 was 33.79%, a 12.12% decline from 2023. Airbnb average net profit margin for 2023 was 38.45%, a 113.97% decline from 2022. Airbnb average net profit margin for 2022 was 17.97%, a 120.2% increase from 2021. Net profit margin can be defined as net Income as a portion of total sales revenue.

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

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.

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.

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

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

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

In 2024, Airbnb reported an operating profit of over *** billion U.S. dollars, showing growth over the previous year. The home sharing economy platform, gives users an alternative to traditional hotel accommodation by allowing them to rent accommodation from people who are willing to share their homes.

The global homestay booking software market is experiencing robust growth, driven by the increasing popularity of alternative accommodations and the surge in online travel bookings. The market, estimated at $2.5 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by several key factors. Firstly, the rising preference for unique and personalized travel experiences is boosting demand for homestays over traditional hotels. Secondly, the proliferation of smartphones and readily available high-speed internet access has made online booking incredibly convenient for both hosts and guests. Technological advancements in software, enabling seamless integration with payment gateways, customer relationship management (CRM) systems, and sophisticated revenue management tools, further enhance the market's appeal. The segmentation of the market, encompassing boutique and ordinary homestays catering to personal, family, and team travel needs, offers diverse revenue streams and growth opportunities. Competition is fierce, with established players like Airbnb, Booking.com, and Expedia vying for market share alongside specialized homestay platforms. However, opportunities exist for smaller, innovative companies focusing on niche markets or offering unique features like personalized concierge services or sustainable travel options. Geographic expansion, particularly in rapidly developing economies in Asia and South America, presents significant growth potential. While the market shows immense promise, certain restraints exist. These include the challenges of maintaining consistent service quality across a decentralized network of hosts, ensuring data security and privacy, and effectively managing regulations related to tourism and short-term rentals. Addressing these challenges effectively will be crucial for sustained market growth. The evolving regulatory landscape in various regions also poses a challenge, demanding continuous adaptation and compliance from software providers. Furthermore, increasing competition, particularly from established players with deep pockets, will require software providers to innovate continuously, offering competitive pricing, advanced features, and targeted marketing strategies to capture and retain market share. The future of the homestay booking software market hinges on its ability to navigate these challenges while capitalizing on its tremendous growth potential.

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