As of December 2024, San Diego recorded the highest average price per night of Airbnb listings among the selected cities in the United States. In this city, accommodation listed on the Airbnb website cost on average *** U.S. dollars per night. Meanwhile, prices in New York City amounted to an average of *** U.S. dollars per night.

As of December 2024, the average price per night of Airbnb listings in Edinburgh was *** British pounds. Meanwhile, the average price per night of Airbnb listings in London stood at *** British pounds, which was around ** British pounds less than in Greater Manchester.

According to a June 2025 analysis, Florence reported the highest average price per night of Airbnb listings among the selected Italian cities, at *** euros. Meanwhile, Airbnb listings in Venice and Rome cost an average of *** and *** euros per night, respectively.

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

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

Tabular performance metrics for short-term rental markets in 15 U.S. cities, including ADR, occupancy rate, annual revenue, median property price, and cash-on-cash return.

In 2025, Airbnbs in the Mornington Peninsula area of Victoria, Australia, had the highest average daily rates across the Australian cities and regions represented, with an average daily rate of around *** Australian dollars. Airbnbs in Noosa Heads and Shoalhaven had the next highest daily rates that year.

The New York City Airbnb 2019 Open Data is a dataset containing varius details about a listed unit, when the goal is to predict the rental price of a unit.

This dataset contains the details for units listed in NYC during 2019, was adapted from the following open kaggle dataset: https://www.kaggle.com/datasets/dgomonov/new-york-city-airbnb-open-data. This, in turn was downloaded from the Airbnb data repository http://insideairbnb.com/get-the-data.

This dataset is licensed under the CC0 1.0 Universal License (https://creativecommons.org/publicdomain/zero/1.0/).

The typical ML task in this dataset is to build a model that predicts the average rental price of a unit.

📊 The Dataset

The data used for this project is sourced from a publicly available Airbnb Listings dataset. The dataset contains over 560,000 records across 10 major cities, including Paris. For this project, the data is filtered to focus solely on Paris listings.

Key Fields:

host_since: Date when the host started listing on Airbnb

neighbourhood: The neighborhood where the listing is located

price: The price per night for the listing

accommodates: Number of people the listing can accommodate

The Dataset Column Descriptions

host_since - Date when the host joined the Airbnb platform. neighbourhood - Name of the neighborhood in Paris where the listing is located. city - City name. This dataset is filtered for Paris listings only. accommodates - The maximum number of guests the listing can accommodate. price - Price per night for the listing in USD. room_type - Type of room offered in the listing (e.g., Entire home/apt, Private room, Shared room). availability_365 - Number of days the listing is available for booking throughout the year. number_of_reviews - Total number of reviews the listing has received. review_scores_rating - Average rating score given by guests for the listing (out of 100). minimum_nights - Minimum number of nights required for booking the listing. host_listings_count - Number of listings managed by the host. latitude - Latitude coordinate of the listing. longitude - Longitude coordinate of the listing.

In 2025, Airbnbs in Perth, Western Australia, had the highest average occupancy rates across the Australian cities and regions represented, with an average occupancy of around ** percent. Airbnbs in the Surfers Paradise, Brisbane, and Gold Coast areas had the next highest occupancy rates that year.

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,...

In Marseille, as of 2017, renting a one-bedroom apartment via Airbnb cost on average 63 euros.

Data Set Information

This data set describes the listing activity of Airbnb homestays in New Orleans, Louisiana, as part of the Inside Airbnb initiative. The data set was compiled on November 7, 2021. See the New Orleans Airbnb data visually here.

Some personally identifying information has been removed from the data uploaded here.

Contents

The following Airbnb activity is included in this New Orleans data set:

Listings, including full descriptions and average review score (new_orleans_airbnb_listings.csv) Reviews, including unique id for each reviewer and detailed comments (reviews.csv)

Acknowledgements

Data credit goes to Murray Cox and Inside Airbnb. The original source for this particular New Orleans data can be found here--where you can also find information on the different listing ids and their price and availability for different calendar dates (if you're interested in looking at how Airbnb rental listing price fluctuates over time).

Context

The data set can be used to answer some interesting questions, such as: - Can you predict how much a short-term rental in New Orleans should charge per night based on it's location and amenities? - Can you describe the vibe of each neighborhood in using listing descriptions? - What are the most common amenities to have among short-term rental listings in New Orleans? - What elements contribute to a popular or highly-rated listing? - Is there any noticeable difference in favorability among different NOLA neighborhood/areas and what could be the reason for it?

Furthermore, it's also important to note that Inside Airbnb (provider of dataset) is a mission driven activist project with the objective to provide data that quantifies the impact of short-term rentals on housing and residential communities; and also provides a platform to support advocacy for policies to protect cities from the impacts of short-term rentals.

According to travel guides, New Orleans is one of the top ten most-visited cities in the United States. It was severely affected by Hurricane Katrina in August 2005, which flooded more than 80% of the city, killed more than 1,800 people, and displaced thousands of residents, causing a population decline of over 50%. Since Katrina, major redevelopment efforts have led to a rebound in the city's population. Concerns about gentrification, new residents buying property in formerly closely knit communities, and displacement of longtime residents have all been a major discussion topic.

Bearing the given context in mind, this data set shared by Inside Airbnb also allows you to ask fundamental questions about Airbnb in any neighbourhood, or across the city as a whole, such as: - How many listings are in my neighbourhood and where are they? - How many houses and apartments are being rented out frequently to tourists and not to long-term residents? - How much are hosts making from renting to tourists (compare that to long-term rentals)? - Which hosts are running a business with multiple listings and where they?

The questions (and their answers) get to the core of the debate for many cities around the world, with Airbnb claiming that their hosts only occasionally rent the homes in which they live. In addition, many city or state legislation or ordinances that address residential housing, short term or vacation rentals, and zoning usually make reference to allowed use, including: - how many nights a dwelling is rented per year - minimum nights stay - whether the host is present - how many rooms are being rented in a building - the number of occupants allowed in a rental - whether the listing is licensed

(Visit their site for more details.)

This study estimates the impact of Airbnb on housing prices and residential mobility in Madrid from 2010 to 2018. Using a comprehensive dataset that includes Airbnb activity, housing prices, and residential moves at the neighborhood-year level, I employ a shift-share instrumental variable approach that leverages variations in neighborhoods' attractiveness to tourists and Airbnb's rapid growth. My findings indicate that, on average, an increase of 100 Airbnb listings in a neighborhood leads to a 2\% rise in housing prices and a corresponding decrease in the number of new residents moving into the neighborhood. Furthermore, results from a causal mediation analysis using the same instrumental variable reveal that the negative effect of Airbnb on residential inflows is primarily driven by its impact on house prices. Consistent with this result, I find that the reduction in residential inflows caused by Airbnb's impact on house prices is predominantly driven by residents without a college degree. This paper contributes to the literature on the impacts of the digital economy by providing evidence that short-term rental platforms may trigger or intensify gentrification processes.

In New York City, one of the United States’ most iconic destinations, Airbnb has established itself as a key player in the accommodation market. In 2025, Airbnb customers booked an average of ** nights per stay, with an average price of *** U.S. dollars per night. Meanwhile, the average income per property was ***** U.S. dollars that year. Are Airbnb rentals expensive in New York City? As of early 2024, the most expensive Airbnb properties per night in the United States were in *************. This was followed by *************************. In comparison, the average cost of a night’s stay at an Airbnb property in New York City is less than half of the cost of a night in *************. How many Airbnb properties are there in New York City? In early 2024, the Airbnb market in New York City offered more than **** thousand properties accommodating to the different needs of visitors to the city. There are various types of Airbnb properties in New York City, the most common of which were entire homes and apartments, followed by private rooms. The majority of Airbnb listings also catered for longer-term stays, in light of city regulations on housing.

As of 2018, the average price for an Airbnb in the center of Amsterdam amounted to approximately *** euros for a whole house, ****** euros for a private room and *** euros for a shared room per rent. The cheapest district of Amsterdam for a whole house and private room was Southeast costing *** and ***** euros, respectively. When observing the total average price of Airbnb accommodations in Amsterdam, it increased steadily between 2016 and 2018. In 2016, people paid on average *** euros, whereas by 2018 this amounted to roughly *** euros.

Annual increase of price Airbnb

Although the average price of Airbnb’s listings grew annually in Amsterdam, the number of overnight stays decreased from 2017 to 2018. In total, *** million nights were spent at Airbnb accommodations in the capital city of the Netherlands, whereas in 2018 this figure decreased slightly, reaching approximately **** million registered overnight stays. Other major cities in the Netherlands, such as Rotterdam, The Hague and Utrecht, had an increase in overnight stays of Airbnb accommodations, even though the number of nights spent is significantly lower compared to Amsterdam.

Number of hotel nights increased annually in Amsterdam

Looking at Airbnb’s competitors, the volume of hotel nights in Amsterdam increased annually between 2008 and 2018. In 2008, hotels registered **** million overnight stays whereas by 2018 this figure more than doubled with approximately ***** million nights that were spent in hotels in Amsterdam.

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

As of April 2022, one bedroom Airbnbs in Sydney, Australia had an average daily rate of *** Australian dollars. In comparison, one bedroom Airbnbs in Perth, Australia recorded an average daily rate of *** Australian dollars that same month.

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 statistic shows the most affordable cities for Airbnb accommodation worldwide as of December 2014. According to the GoEuro Accommodation Price Index, Tirana in Albania was the cheapest city with an average cost of 30 U.S. dollars per night.