How to Scrape Airbnb Using Python

1. Prerequisites & project setup

Set up a fresh virtual environment and install the libraries below. I recommend testing on 1–2 pages before scaling.

python -m venv venv
# macOS / Linux
source venv/bin/activate
# Windows (PowerShell)
venv\Scripts\Activate.ps1

pip install requests beautifulsoup4 lxml selenium webdriver-manager pandas python-dotenv playwright
# if using Playwright:
playwright installCopy

2. Methods — pick the right one

Here’s the rule of thumb: try Requests + BeautifulSoup first. If listings are missing because JS populates them, use a browser automation tool.

Requests + BeautifulSoup (fast & cheap)

Ideal if the HTML contains the listing info or an embedded JSON blob. Inspect the page (DevTools → Elements) to confirm.

# requests_bs4_listing.py
import requests
from bs4 import BeautifulSoup

url = "https://www.airbnb.com/rooms/ROOM_ID"
headers = {"User-Agent":"Mozilla/5.0"}
r = requests.get(url, headers=headers, timeout=15)
r.raise_for_status()
soup = BeautifulSoup(r.text, "lxml")
title = soup.find("meta", property="og:title")
print("Title:", title and title.get("content"))Copy

Look for <script type="application/ld+json"> or other script blocks that include JSON. Those are gold — parse instead of scraping text.

Selenium / Playwright (reliable for dynamic pages)

Use a headless browser to load the page, wait for elements, then extract them. Playwright has auto-wait features; Selenium is widely supported.

# selenium_search_results.py
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.chrome.service import Service
from webdriver_manager.chrome import ChromeDriverManager
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC

opts = Options()
opts.add_argument("--headless=new")
driver = webdriver.Chrome(service=Service(ChromeDriverManager().install()), options=opts)
driver.get("https://www.airbnb.com/s/Paris--France/homes")
wait = WebDriverWait(driver, 15)
items = wait.until(EC.presence_of_all_elements_located((By.CSS_SELECTOR,"div[role='group'] a[href*='/rooms/']")))
for el in items[:10]:
    print(el.get_attribute('href'))
driver.quit()Copy

Prefer WebDriverWait and explicit conditions over time.sleep for robustness.

3. Pagination & infinite scroll (practical)

Airbnb uses lazy-loading extensively. Two proven patterns are: scroll-to-load and offset/pagination parameters.

Scroll to load more (Selenium)

# scroll_load.py
import time

last_height = driver.execute_script("return document.body.scrollHeight")
while True:
    driver.execute_script("window.scrollTo(0, document.body.scrollHeight);")
    time.sleep(2)  # let JS load new items
    new_height = driver.execute_script("return document.body.scrollHeight")
    if new_height == last_height:
        break
    last_height = new_heightCopy

Offset pagination (Requests)

# requests_pagination.py
import requests, time, random
from bs4 import BeautifulSoup

base = "https://www.airbnb.com/s/Paris--France/homes"
headers = {"User-Agent":"Mozilla/5.0"}
for offset in range(0, 200, 20):
    params = {"items_offset": offset}
    r = requests.get(base, headers=headers, params=params, timeout=15)
    soup = BeautifulSoup(r.text, "lxml")
    # parse listings...
    time.sleep(random.uniform(2,4))Copy

4. Reverse-engineering network/API calls

Often the browser talks to JSON endpoints. Open DevTools → Network → XHR and look for requests returning JSON with listing data. Hitting those endpoints is cleaner than parsing HTML.

Example approach:

1. Open the search page in Chrome, filter Network by XHR.
2. Apply your search (city, dates) and watch requests that look like /search_results or contain listings.
3. Inspect the response JSON; often it contains structured fields such as price, id, coords, and amenities.

# example_api_request.py (hypothetical)
import requests

api_url = "https://www.airbnb.com/api/v2/search_results"
params = {"location":"Paris","_limit":20}
headers = {"User-Agent":"Mozilla/5.0","Accept":"application/json"}
r = requests.get(api_url, headers=headers, params=params)
data = r.json()
for item in data.get('search_results', []):
    print(item['listing']['id'], item['pricing_quote']['rate']['amount'])Copy

Caveat: undocumented APIs may change and may have usage limits or legal restrictions. If you discover one, document the responses and rate limits, and be respectful.

5. Images, ALT text & SEO

Include screenshots and diagrams on your published guide to improve trust and SEO. Use descriptive filenames and ALT text (e.g., paris-airbnb-search-results.jpg).

6. Data fields, cleaning & export

Fields to extract: title, listing id, nightly price (normalize currency), location (city, neighborhood), rating, reviews_count, amenities array, host name (if public), and URL.

Save to CSV (pandas)

import pandas as pd

rows = [
  {"id":123,"title":"Cozy Studio","price":45,"url":"https://airbnb.com/rooms/123"},
  {"id":456,"title":"Sunny Apt","price":70,"url":"https://airbnb.com/rooms/456"}
]
df = pd.DataFrame(rows)
df.to_csv('airbnb_listings.csv', index=False, encoding='utf-8-sig')Copy

Store raw JSON

Also save raw JSON responses alongside parsed rows — they help when selectors change and you need to re-parse historical data.

Tip: add a scrape_date column to track when a snapshot was taken.

Contact & downloadable package

If you’d like a ready-to-run ZIP (this page + separate Python scripts for Requests, Selenium and Playwright + sample CSV and a .env.example), email me — tell me which city and method you prefer.

Email me (request package)    Contact form