stl-scraper - Short-Term Listings Scraper

Scrape short-term listings providers (Airbnb).

Given a search query, e.g. "San Diego, CA" or "Rome, Italy", search Airbnb inventory and collect data on listings. Save results to a CSV file or Elasticsearch.

Notice

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. I have not personally used this scraper in several months, and therefore it may need to be updated to work with the latest version of the Airbnb website.

Usage

Usage (basic installation)

# activate the virtual env
. .venv/bin/activate

# run the script
./stl.py search "Madrid, Spain"

Usage (advanced installation)

# spin up the containers
docker compose up -d

# ensure dependencies installed
docker compose exec jupyter-scipy-notebook conda install --yes --file work/requirements.txt

# run the script
docker compose exec jupyter-scipy-notebook /opt/conda/bin/python work/stl.py search -v "Madrid, Spain"

Options

Short-Term Listings (STL) Scraper

Usage:
    stl.py search <query> [--checkin=<checkin> --checkout=<checkout> 
                  [--priceMin=<priceMin>] [--priceMax=<priceMax>]] 
                  [--roomTypes=<roomTypes>] [--storage=<storage>] [-v|--verbose]
    stl.py calendar (<listingId> | --all)
    stl.py pricing <listingId> --checkin=<checkin> --checkout=<checkout>
    stl.py data <listingId>

Arguments:
    <query>          The query string to search (e.g. "San Diego, CA")
    <listingId>      The listing id

Options:
    --checkin=<checkin>    Check-in date, e.g. "2023-06-01"
    --checkout=<checkout>  Check-out date, e.g. "2023-06-30"
    --priceMin=<priceMin>  Minimum nightly or monthly price
    --priceMax=<priceMax>  Maximum nightly or monthly price
    --all                  Update calendar for all listings (requires Elasticsearch backend)

Global Options:
    --currency=<currency>  "USD", "EUR", etc. [default: USD]
    --source=<source>      Only allows "airbnb" for now. [default: airbnb]

Requirements

• Python >= 3.10, or Docker Compose

Installation

Installation via pip (Option 1 - Basic)

This option assumes you have Python >= 3.10 installed, and that you will manage dependencies using the python venv module with pip. You can connect to your own instance of Elasticsearch. However, Elasticsearch is not required.

# create the config file
cp .env.dist .env

# create the virtual env
python3 -m venv .venv

# activate the virtual env
. .venv/bin/activate

# install dependencies in virtual env
pip install -r requirements.txt

Installation via docker compose (Option 2 - Advanced)

This option uses docker compose to build:

• jupyter-scipy-notebook: jupyter scipy notebook, python, conda
• setup temporary container that configures elasticsearch & kibana security
• es01: elasticsearch container
• kibana: kibana container

# create the config file
cp .env.dist .env

# Create the containers
docker compose up -d

# Install project requirements
docker compose exec jupyter-scipy-notebook conda install --yes --file work/requirements.txt

Using kibana

You can directly view records in Elasticsearch by using Kibana.

1. Scrape some listings using above commands
2. Browse to http://localhost:5601/app/management/kibana/dataViews (u: elastic / p: abc123)
3. Click "Create new data view" on top right
4. Use short-term-listings as name and index pattern
5. Click "Save data view to Kibana"
6. Click "Analytics > Discover" on the main menu, selecting the short-term-listings data view, and see JSON records

Troubleshooting

• If using Elasticsearch, you may need to run sudo sysctl -w vm.max_map_count=262144 on your host machine (see https://www.elastic.co/guide/en/elasticsearch/reference/current/vm-max-map-count.html)