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.

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

According to a March 2025 analysis, Florence and Bologna 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.

Use this dataset to analyze Airbnb listings in Paris to determine the impact of recent regulations. Find out the Average listings price, find the average accommodation numbers, find the neighbourhood with expensive and least listings. Explore and find more Insights.

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.

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.

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.

In 2020, the average price of an Airbnb or HomeAway accommodation in the Brussels-Capital Region was highest in October, amounting to approximately *** U.S. dollars. The lowest figure was reported in May 2020, when tourists had to pay around ** U.S. dollars to stay in Airbnb or Home Away accommodation.

These are the Airbnb statistics on gross revenue by country.

📊 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.

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

Property managers or investors can maximize rental property revenue with timely data on the region’s pricing and inventory. Equity portfolio managers can track Airbnb's key operating metrics on a timely basis and gain early insights of the company’s revenue trend.

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.

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.

As of July 3, 2020, properties with **** or more bedrooms in Paris had the highest average daily rate (ADR) on Airbnb, at *** U.S. dollars. Shared rooms in Paris had the lowest ADR, with around ** U.S. dollars. Average daily rates for Airbnb are calculated based on the median (**%) average rental price of that type of property. For example the Airbnb supply shows there are ***** listings for a studio in London - the data therefore indicates the average rate of the ***** cheapest property.

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.

Context

Since 2008, guests and hosts have used Airbnb to travel in a more unique, personalized way. As part of the Airbnb Inside initiative, this dataset describes the listing activity of homestays in Seattle, WA.

Content

The following Airbnb activity is included in this Seattle dataset: * Listings, including full descriptions and average review score * Reviews, including unique id for each reviewer and detailed comments * Calendar, including listing id and the price and availability for that day

Inspiration

• Can you describe the vibe of each Seattle neighborhood using listing descriptions?
• What are the busiest times of the year to visit Seattle? By how much do prices spike?
• Is there a general upward trend of both new Airbnb listings and total Airbnb visitors to Seattle?

For more ideas, visualizations of all Seattle datasets can be found here.

Acknowledgement

This dataset is part of Airbnb Inside, and the original source can be found here.