Download Airbnb Data and Discover the Best Markets for Vacation Rentals
It is legal to scrape data that is publicly visible on the web. But there still are some regulations you need to adhere to, especially when it comes to scraping information that might be protected by copyright or could contain personal data. These include the European GDPR or American CCPA for personal data and the DSM Directive in the EU or fair use doctrine in the US.
Now that you know how to scrape Airbnb data, you can surely play around with the input parameters and see just how much data you can get in so little time. Feel free to share your results with us or check out other travel industry scrapers ??
download airbnb data
This article provides some general information on how to contact Airbnb in order to exercise your data subject rights under applicable law. If you have any general questions about privacy or data protection at Airbnb, you can send us an email.
You have the right to ensure certain personal data is accurate and up-to-date. You can edit profile information within the personal information tab of your account. To request the correction or amendment of other personal data, contact us. Note that some personal data may not be eligible for correction, for example changes to reviews are governed by our review policy.
How to download airbnb data for market analysis
Download airbnb data by city, country, or region
Airbnb data download: what you need to know
Best tools to download and analyze airbnb data
Download airbnb data for short-term rental investing
Airbnb data download: how to get historical and future data
Download airbnb data for competitive intelligence
Airbnb data download: how to access and use the API
Download airbnb data for pricing optimization
Airbnb data download: how to get reviews and ratings data
Download airbnb data for occupancy and demand forecasting
Airbnb data download: how to get insights on guest behavior and preferences
Download airbnb data for property management and marketing
Airbnb data download: how to get data on amenities and features
Download airbnb data for benchmarking and performance tracking
Airbnb data download: how to get data on hosts and listings
Download airbnb data for regulatory compliance and reporting
Airbnb data download: how to get data on cancellations and refunds
Download airbnb data for revenue management and profitability
Airbnb data download: how to get data on seasonality and trends
Download airbnb data for machine learning and AI applications
Airbnb data download: how to get data on COVID-19 impact and recovery
Download airbnb data for niche markets and segments
Airbnb data download: how to get data on loyalty and retention
Download airbnb data for customer service and satisfaction
The easiest Airbnb data API to scrape is the search results endpoint. Simply begin recording your web traffic, then run a search in the official Airbnb search field and scroll through the results while recording your traffic. You can then export the HAR file to our tool and download search results.
The following libraries are based off of the unofficial Airbnb mentioned above. In addition to being able to log in via your own Airbnb account (generating an Airbnb API key), you can use the Airbnb scraper Python module for example to collect listing data. You may also want to check the docs first for Airbnb API examples before going too deep into these.
If you would like to do further analysis or produce alternate visualisations of the data, it is available below under a Creative Commons CC0 1.0 Universal (CC0 1.0) "Public Domain Dedication" license.
Here is the data provided for each listing. Please note that while other data can be collected from the site, and while other sites (especially the excellent Inside Airbnb) collect richer data about the host and the details of each listing, I have no plans to expand the scope at the moment:
On December 1 2015, Airbnb made data available about its business in New York City, with much fanfare. A new report by Murray Cox and me shows that the Airbnb data release misled the media and the public.
This document is for people who want to use any of the Airbnb city data that I have collected since November 2013. It describes how the data is collected, and looks at the completeness and accuracy of the data, and notes some areas for improvement. The source code is available at Github.
The source code is in python 3. It scrapes data from the Airbnb web site for a city (labelled a search area) , and stores the result in a database. Each collection of a single city is called a survey. A single database holds many separate surveys, including some of the same city.
The availability of latitude and longitude values means that, if geo-spatial data is available from an individual city, more precise numbers can be reported (SQL Anywhere includes the ability to import ESRI shape files, commonly available on government open data web sites, and to carry out geo-spatial queries). For a few purposes, and for a few cities, such queries have been carried out. It is, however, a labour intensive process as each city makes neighborhood available in its own way.
All listings found in any survey do exist on the Airbnb site. There is evidence from Airbnb statements that the listings are fairly complete. Here are some statements made by Airbnb or by others who have access to internal Airbnb data, and some notes.
A survey I carried out in May 2014 showed 6,609 listings in the San Francisco search area. Some of these listings were in areas such as Oakland that are not part of San Francisco proper and which were excluded from their study. Limiting the listings to the city proper (by using GIS data), I find 4,776 listings: almost exactly the same as the Chronicle.
Figure 2 takes the estimation one step further. If the average length of a visit is the same between groups, then the product of the number of reviews and the per-night price should give a good relative picture of the incomes within groups. In this case, the data is grouped by neighborhood within Berlin.
The list here is not a complete set of the data sets I have. Contact me (tom at tomslee dot net) with requests for more cities or repeated surveys for a single city. Suggestions for improved data/presentation are always welcome.
Analyzing Airbnb data is important to help property investors understand the critical numbers and ratios. Primarily, if you want to start your own Airbnb business, it also answers some of the major questions:
Not all short-term rental analytics tools have complete and accurate Airbnb data. An Airbnb analytics platform should provide sufficient data to make an informed decision. Airbtics provides Worldwide STR Data Coverage for the following:
Still unsure which area is best for Airbnb investment? You can close the door to fruitless properties and filter ONLY the best ones! Profitless markets can be easily identified by looking at historical performance data.
Airbnb data analysis is made easier and well-ordered. With the help of STR analytics tools like Airbtics, you can get Airbnb data! If you already have a specific market in mind, here are the 3 easy steps that you can follow to get Airbnb data:
In summary, Airbnb investment can be risky if market research is not done as the first step. This is where a smart & reliable tool like Airbtics is truly needed! This STR tool offers free Airbnb data that you can take advantage of.
How can I download all my reservation history? Currently, I am only able to view them and print them directly. It would be great if I could export all my reservations in Excell or PDF. Is that possible?
AirBnB data is like short-term rental data, but is specifically information about properties available to rent on AirBnB. It's mostly used by the hospitality industry to better understand the rental market and to make their properties more successful.Learn more
AirBnB data is a sub-category of hospitality, travel & tourism data. It provides insights into the world of AirBnB rentals, such as pricing, availability and competition. It is useful for real estate investors and property owners. Both these users want to maximize the potential of their property. This applies to both long-term, established owners and people looking to get started with an AirBnB business.
Host-side dataThis includes property availabilities, property size and features as well as nightly rates. AirBnB data can also be refined to show data for a specific country or area to ensure that your property is actively competing against.
AirBnB data is used by property owners and businesses to maximize their profitability. You can see which areas and property types are the most successful to ensure that your AirBnBs keep up in the busy market. This data can be useful for established renters or people looking to get started in the market.
In practice, AirBnB data has a range of applications. One of the most frequent use cases is investment and market research. AirBnB investors use external datasets for real estate market analysis. With AirBnB data analysis, they can invest in a property which gives them the best ROI as a short-term rental home.
The second stage is property analysis. Here, an AirBnB database comes into its own. It shows the user details about individual properties. For example, recent sale price, energy efficiency, and occupancy rate.
AirBnB Monthly Rental IncomeAny AirBnB business needs to know how much potential income a property can generate. An AirBnB dataset ensures users balance cost vs revenue by showing investors and property owners monthly income.
Data providers and vendors listed on Datarade sell Airbnb Data products and samples. Popular Airbnb Data products and datasets available on our platform are Airbnb Hotel Listing Data Scraper Extract Airbnb Calendar and Review Actowiz Solutions by Actowiz Appliance of Data & Insights, Airbnb data for research purpose by Airbtics, and Airbnb data 2021 Occupancy, Daily rate, active listings Per country, city, zipcode by Airbtics.
You can get Airbnb Data via a range of delivery methods - the right one for you depends on your use case. For example, historical Airbnb Data is usually available to download in bulk and delivered using an S3 bucket. On the other hand, if your use case is time-critical, you can buy real-time Airbnb Data APIs, feeds and streams to download the most up-to-date intelligence.
To import data into R, click on Import dataset and then on From text (readr). A new window will pop up. Click on Browse and find your data file. Make sure that First row as names is selected (this tells R to treat the first row of your data as the titles of the columns) and then click on Import. After clicking on import, RStudio opens a Viewer tab. This shows you your data in a spreadsheet.
The setwd command tells R where your working directory is. Your working directory is a folder on your computer where R will look for data, where plots will be saved, etc. Set your working directory to the folder where you have stored your data. Now, the read_csv file does not require a directory anymore.
Our dataset contains information on rooms in Belgium listed on airbnb.com. We know for each room (identified by room_id): who the host is (host_id), what type of room it is (room_type), where it is located (country, city, neighborhood, and even the exact latitude and longitude), how many reviews it has received (reviews), how satisfied people were (overall_satisfaction), price (price), and room characteristics (accommodates, bedrooms, bathrooms, minstay).