rl1987 / airbnb-scraper

Airbnb Scraper: Advanced Airbnb Search using Scrapy

Airbnb Scraper: Advanced Airbnb Search using Scrapy

Use Airbnb's unofficial API to efficiently search for rental properties. Regex matching, ranged search, open matched properties in a browser, save to CSV, xlsx, or ElasticSearch (alpha).

Notes

• Airbnb's API is subject to change at any moment, which would break this scraper. They've already changed it several times in the past. Also, using this probably violates their TOS. Please only use for educational or research purposes.
• The scraper was recently updated to work with Airbnb's new v3 GraphQL API. Some features are still being updated.

Installation (nix)

# Create venv
python3 -m venv env

# Enable venv
. env/bin/activate

# Install required packages
pip install -Ur requirements.txt

# Create settings.py
cp deepbnb/settings.py.dist deepbnb/settings.py

# @NOTE: Don't forget to set AIRBNB_API_KEY in settings.py. To find your API key, 
# search Airbnb using Chrome, open dev tools, and look for to the url parameter  
# named "key" in async requests to /api/v2/explore_tabs under the Network tab.

Configuration

Edit deepbnb/settings.py for settings. I've created some custom settings which are documented below. The rest are documented in https://docs.scrapy.org/en/latest/topics/settings.html.

Example Usage

Minimal scraper usage:

scrapy crawl airbnb -a query="Colorado Springs, CO" -o colorado_springs.csv

Advanced examples:

Madrid, fixed dates

scrapy crawl airbnb \
    -a query="Madrid, Spain" \
    -a checkin=2020-10-01 \
    -a checkout=2020-11-31 \
    -a max_price=1900 \
    -a min_price=1800 \
    -a neighborhoods="Acacias,Almagro,Arganzuela,Argüelles,Centro,Cortes,Embajadores,Imperial,Jerónimos,La Latina,Malasaña,Moncloa,Palacio,Recoletos,Retiro,Salamanca,Sol" \
    -s MUST_HAVE="(atico|attic|balcon|terra|patio|outdoor|roof|view)" \
    -s CANNOT_HAVE="studio" \
    -s MINIMUM_WEEKLY_DISCOUNT=20 \
    -s WEB_BROWSER="/usr/bin/chromium" \
    -o madrid.xlsx

New York ranged date search

scrapy crawl airbnb \
    -a query="New York, NY" \
    -a checkin="2020-01-22+7-0" \
    -a checkout="2020-02-22+14-3" \
    -a max_price=1800 \
    -s CANNOT_HAVE="guest suite" \
    -s MUST_HAVE="(walking distance|short walk|no car needed|walk everywhere|metro close|public transport)" \
    -o newyork.csv

Ranged date queries

If you have flexible checkin / checkout dates, use the ranged search feature to search a range of checkin / checkout dates.

Search checkin date range +5 days -2 days

scrapy crawl airbnb \
    -a query="Minneapolis, MN" \
    -a checkin="2020-10-15+5-2" \
    -a checkout="2020-11-15" \
    -o minneapolis.csv

This search would look for rentals in Minneapolis using Oct 15 2020 as base check-in date, and also searching for rentals available for check-in 2 days before, up to 5 days after. In other words, check-ins from Oct 13 to Oct 20. This is specified by the string +5-2 appended to the checkin date 2020-10-15+5-2. The string must always follow the pattern +[days_after]-[days_before] unless [days_after] and [days_before] are equal, in which case you can use +-[days]. The numbers may be any integer 0 or greater (large numbers untested).

Search checkin date +5 days -2 days, checkout date + or - 3 days

scrapy crawl airbnb \
    -a query="Florence, Italy" \
    -a checkin="2020-10-15+5-2" \
    -a checkout="2020-11-15+-3" \
    -o firenze.csv

Scraping Description

After running the crawl command, the scraper will start. It will first run the search query, then determine the quantity of result pages, and finally iterate through each of those, scraping each of the property listings on each page.

Scraped items (listings) will be passed to the default item pipeline, where, optionally, the description, name, and reviews.description fields will be filtered using either or both of the CANNOT_HAVE and MUST_HAVE regexes. Filtered items will be dropped. Accepted items can be optionally opened in a given web browser, so that you can easily view your search results.

Finally, the output can be saved to an xlsx format file for additional filtering, sorting, and inspection.

Parameters

You can find the values for these by first doing a search manually on the Airbnb site.

• query: City and State to search. (required)
• checkin, checkout: Check-in and Check-out dates.
• min_price, max_price: Minimum and maximum price for the period. The Airbnb search algorithm calculates this based upon search length. It will be either the daily or monthly price, depending on the length of the stay.
• neighborhoods: Comma-separated list of neighborhoods within the city to filter for.
• output: Name of output file. Only xlsx output is tested.

Settings

These settings can be edited in the settings.py file, or appended to the command line using the -s flag as in the example above.

• CANNOT_HAVE="<cannot-have-regex>"
  Don't accept listings that match the given regex pattern. (optional)

• FIELDS_TO_EXPORT="['field1', 'field2', ...]"
  Can be found in settings.py. Contains a list of all possible fields to export, i.e. all fields of AirbnbScraperItem. Comment items to remove undesired fields from output. Applies only to xlsx output.

• MINIMUM_MONTHLY_DISCOUNT=30
  Minimum monthly discount. (optional)

• MINIMUM_WEEKLY_DISCOUNT=25
  Minimum weekly discount. (optional)

• MUST_HAVE="(<must-have-regex>)"
  Only accept listings that match the given regex pattern. (optional)

• ROOM_TYPES="['Camper/RV', 'Campsite', 'Entire guest suite']"
  Room Types to filter. (optional)

• SKIP_LIST="['12345678', '12345679', '12345680']"
  Property IDs to filter. (optional)

• WEB_BROWSER="/path/to/browser %s"
  Web browser executable command. (optional)

  Examples:

  • MacOS
    WEB_BROWSER="open -a /Applications/Google\ Chrome.app"

  • Windows
    WEB_BROWSER="C:\Program Files (x86)\Google\Chrome\Application\chrome.exe"

  • Linux
    WEB_BROWSER="/usr/bin/google-chrome"

Requirements

• Python 3.9+
• Scrapy
• openpyxl
• ElasticSearch 7+ if using elasticsearch pipeline
• see requirements.txt for details

Credits

• This project was originally inspired by this excellent blog post by Luca Verginer.
• In converting this to use the unofficial API, https://stevesie.com/apps/airbnb-api was very helpful.
• This analysis of Bali Airbnbs provided inspiration for more eloquent code.