See this in-depth article to learn how to easily scrape Airbnb listings with Python while using Oxylabs’ Airbnb Scraper API to bypass blocks and interruptions.
You can find more information on this topic in our blog post.
- 1. Install prerequisites
- 2. Get a free API trial and test your connection
- 3. Import the libraries
- 4. Store API credentials and Airbnb listing URLs
- 5. Create the payload
- 6. Create parsing instructions
- 7. Create coroutines to process API jobs asynchronously
- 8. Save results to JSON
- 9. Bring everything together
- Scrape Airbnb search page
Start by installing Python from the official website if you don’t have it already. Additionally, we recommend using an Integrated Development Environment (IDE) like PyCharm or VS Code for easier development and debugging processes. Then, install the required Python libraries for this project:
pip install asyncio, aiohttp, requestsThe asyncio and aiohttp modules will be used to make asynchronous requests and thus speed up the scraping process. Additionally, the requests library will be used to send a simple test request.
Head to the Oxylabs dashboard and claim your 7-day free trial for Web Scraper API which includes Airbnb Scraper API. See the steps here on how to get your free trial when logged in to the dashboard.
Web Scraper API comes with a worldwide proxy pool, a Headless Browser, a Custom Parser, batch queries, and other smart features for block-free web scraping. As a result, you won’t need any additional Python libraries like Beautiful Soup, Selenium, or Puppeteer since dynamic page rendering, human-like requests, and data parsing will be done via Web Scraper API.
import requests
from pprint import pprint
payload = {
"source": "universal",
"url": "https://www.airbnb.com/rooms/639705836870039306?adults=1&category_tag=Tag%3A8536&children=0&enable_m3_private_room=true&infants=0&pets=0&photo_id=1437197361&search_mode=flex_destinations_search&check_in=2024-06-28&check_out=2024-07-03&source_impression_id=p3_1708944446_F%2FuHvpf5A7Gvt8Pi&previous_page_section_name=1000",
"geo_location": "United States",
"render": "html"
}
response = requests.post(
"https://realtime.oxylabs.io/v1/queries",
auth=("USERNAME", "PASSWORD"), # Replace with your API credentials
json=payload
)
pprint(response.json())If you see a status_code of 200 within the response, your scraping job has been executed successfully.
The next step to scrape Airbnb data is to use and import the following libraries:
import json, asyncio, aiohttp
from aiohttp import BasicAuthUSERNAME, PASSWORD = "USERNAME", "PASSWORD"
urls = [
"https://www.airbnb.com/rooms/639705836870039306?adults=1&category_tag=Tag%3A8536&children=0&enable_m3_private_room=true&infants=0&pets=0&photo_id=1437197361&search_mode=flex_destinations_search&check_in=2024-06-28&check_out=2024-07-03&source_impression_id=p3_1708944446_F%2FuHvpf5A7Gvt8Pi&previous_page_section_name=1000",
"https://www.airbnb.com/rooms/685374557739707093?adults=1&category_tag=Tag%3A8536&children=0&enable_m3_private_room=true&infants=0&pets=0&photo_id=1514770868&search_mode=flex_destinations_search&check_in=2024-03-17&check_out=2024-03-22&source_impression_id=p3_1708944446_iBXKC59AR9NTQc4y&previous_page_section_name=1000",
"https://www.airbnb.com/rooms/51241506?adults=1&category_tag=Tag%3A8536&children=0&enable_m3_private_room=true&infants=0&pets=0&photo_id=1264515417&search_mode=flex_destinations_search&check_in=2024-04-07&check_out=2024-04-12&source_impression_id=p3_1708944446_zo%2FqBnbRPhn7zqAr&previous_page_section_name=1000",
]payload = {
"source": "universal",
"url": None,
"geo_location": "United States",
"user_agent_type": "desktop",
"render": "html",
"browser_instructons": [],
"parse": True,
"parsing_instructions": {}
}Note that the url parameter is set to None since the Python scraper you’ll build will assign the target URLs dynamically. Moreover, if you want to make sure that elements load before the scraper initiates, you can use browser instructions to wait_for_element:
"browser_instructons": [
{
"type": "wait_for_element",
"selector": {
"type": "xpath",
"value": "//div[@data-section-id='HOST_PROFILE_DEFAULT']"
},
"timeout_s": 30
}
],See the documentation to learn more about the available Web Scraper API parameters and their purpose.
Copy this code into a new Python file and then save the payload into a separate listing_payload.json file to keep the scraper code shorter:
import json
payload = {
"source": "universal",
"url": None,
"geo_location": "United States",
"user_agent_type": "desktop",
"render": "html",
"browser_instructons": [
{
"type": "wait_for_element",
"selector": {
"type": "xpath",
"value": "//div[@data-section-id='HOST_PROFILE_DEFAULT']"
},
"timeout_s": 30
}
],
"parse": True,
"parsing_instructions": {
"titles": {
"title_one": {
"_fns": [
{
"_fn": "xpath_one",
"_args": ["//span/h1/text()"]
}
]
},
"title_two": {
"_fns": [
{
"_fn": "xpath_one",
"_args": ["//div[contains(@data-section-id, 'OVERVIEW')]//h2/text()"]
}
]
}
},
"pricing": {
"price_per_night": {
"_fns": [
{
"_fn": "xpath_one",
"_args": ["//span[@class='_tyxjp1']/text()", "//span[@class='_1y74zjx']/text()"]
}
]
},
"price_total": {
"_fns": [
{
"_fn": "xpath_one",
"_args": ["//span/span[@class='_j1kt73']/text()"]
}
]
}
},
"host_url": {
"_fns": [
{
"_fn": "xpath_one",
"_args": ["//div[contains(@data-section-id, 'HOST')]//a/@href"]
}
]
},
"overall_rating": {
"_fns": [
{
"_fn": "xpath_one",
"_args": ["//*[contains(text(), 'Rated')]/following::div[1]/text()"]
}
]
},
"reviews": {
"_fns": [
{
"_fn": "xpath",
"_args": ["//div[contains(@data-section-id, 'REVIEWS')]//div[@role='listitem']"]
}
],
"_items": {
"rating": {
"_fns": [
{
"_fn": "xpath_one",
"_args": [".//div[contains(@class, 'c5dn5hn')]/span/text()"]
},
{"_fn": "amount_from_string"}
]
},
"date": {
"_fns": [
{
"_fn": "xpath_one",
"_args": [".//div[contains(@class, 's78n3tv')]/text()"]
}
]
},
"review": {
"_fns": [
{
"_fn": "xpath_one",
"_args": [".//span[contains(@class, 'lrl13de')]/text()"]
}
]
}
}
},
"images": {
"_fns": [
{
"_fn": "xpath",
"_args": ["//picture//*[@data-original-uri]/@data-original-uri"]
}
]
}
}
}
with open("listing_payload.json", "w") as f:
json.dump(payload, f, indent=4)Then replace the payload in your main scraper file with the following lines:
payload = {}
with open("listing_payload.json", "r") as f:
payload = json.load(f)If you want to scrape more data points from Airbnb listing pages, you can find a payload file that achieves this in the code files folder.
Oxylabs’ APIs support batch processing of query or url parameter values. Therefore, you can send a single request to process up to 1000 Airbnb URLs with the same scraping and parsing instructions. So, let’s define an asynchronous coroutine which will return job IDs for each submitted Airbnb URL:
async def submit_job(payload):
async with aiohttp.ClientSession(auth=BasicAuth(USERNAME, PASSWORD)) as session:
async with session.post("https://data.oxylabs.io/v1/queries/batch", json=payload) as response:
try:
r = await response.json()
ids = [query['id'] for query in r["queries"]]
print(ids)
return ids
except Exception as e:
print(f"Error has occurred in {e}: {r}")Important
Make sure not to exceed the free trial’s rate limit. Use up to 10 URLs; otherwise, the API will return the “Too many requests” response code. The try-except block will print this error in your terminal. If you want to bypass this rate limit, head to the pricing page and pick a plan that suits you.
async def check_job_status(job_id):
async with aiohttp.ClientSession(auth=BasicAuth(USERNAME, PASSWORD)) as session:
async with session.get(f"https://data.oxylabs.io/v1/queries/{job_id}") as response:
return (await response.json())["status"]async def get_job_results(job_id):
async with aiohttp.ClientSession(auth=BasicAuth(USERNAME, PASSWORD)) as session:
async with session.get(f"https://data.oxylabs.io/v1/queries/{job_id}/results") as response:
return (await response.json())["results"][0]["content"]async def process_job(job_id, url, results_list):
await asyncio.sleep(5)
while True:
status = await check_job_status(job_id)
if status == "done":
print(f"Job {job_id} done.")
results = await get_job_results(job_id)
results["listing_url"] = url
results_list.append(results)
break
elif status == "failed":
print(f"Job {job_id} failed.")
break
await asyncio.sleep(5)async def save_to_json(results_list):
with open("parsed_listings.json", "a") as f:
json.dump(results_list, f, indent=4)async def parse(urls):
payload["url"] = urls
job_ids = await submit_job(payload)
results_list = []
await asyncio.gather(*(process_job(job_id, url, results_list) for job_id, url in zip(job_ids, urls)))
await save_to_json(results_list)
print("Airbnb URLs parsed.")Finally, add the main check to run the Python file when it’s called directly. Here’s the full code snippet that scrapes and parses public Airbnb listing data:
### parse_urls.py
import json, asyncio, aiohttp
from aiohttp import BasicAuth
USERNAME, PASSWORD = "username", "password" # Replace with your API credentials
urls = [
"https://www.airbnb.com/rooms/639705836870039306?adults=1&category_tag=Tag%3A8536&children=0&enable_m3_private_room=true&infants=0&pets=0&photo_id=1437197361&search_mode=flex_destinations_search&check_in=2024-06-28&check_out=2024-07-03&source_impression_id=p3_1708944446_F%2FuHvpf5A7Gvt8Pi&previous_page_section_name=1000",
"https://www.airbnb.com/rooms/685374557739707093?adults=1&category_tag=Tag%3A8536&children=0&enable_m3_private_room=true&infants=0&pets=0&photo_id=1514770868&search_mode=flex_destinations_search&check_in=2024-03-17&check_out=2024-03-22&source_impression_id=p3_1708944446_iBXKC59AR9NTQc4y&previous_page_section_name=1000",
"https://www.airbnb.com/rooms/51241506?adults=1&category_tag=Tag%3A8536&children=0&enable_m3_private_room=true&infants=0&pets=0&photo_id=1264515417&search_mode=flex_destinations_search&check_in=2024-04-07&check_out=2024-04-12&source_impression_id=p3_1708944446_zo%2FqBnbRPhn7zqAr&previous_page_section_name=1000",
]
payload = {}
with open("listing_payload.json", "r") as f:
payload = json.load(f)
async def submit_job(payload):
async with aiohttp.ClientSession(auth=BasicAuth(USERNAME, PASSWORD)) as session:
async with session.post("https://data.oxylabs.io/v1/queries/batch", json=payload) as response:
try:
r = await response.json()
ids = [query['id'] for query in r["queries"]]
print(ids)
return ids
except Exception as e:
print(f"Error has occurred in {e}: {r}")
async def check_job_status(job_id):
async with aiohttp.ClientSession(auth=BasicAuth(USERNAME, PASSWORD)) as session:
async with session.get(f"https://data.oxylabs.io/v1/queries/{job_id}") as response:
return (await response.json())["status"]
async def get_job_results(job_id):
async with aiohttp.ClientSession(auth=BasicAuth(USERNAME, PASSWORD)) as session:
async with session.get(f"https://data.oxylabs.io/v1/queries/{job_id}/results") as response:
return (await response.json())["results"][0]["content"]
async def process_job(job_id, url, results_list):
await asyncio.sleep(5)
while True:
status = await check_job_status(job_id)
if status == "done":
print(f"Job {job_id} done.")
results = await get_job_results(job_id)
results["listing_url"] = url
results_list.append(results)
break
elif status == "failed":
print(f"Job {job_id} failed.")
break
await asyncio.sleep(5)
async def save_to_json(results_list):
with open("parsed_listings.json", "a") as f:
json.dump(results_list, f, indent=4)
async def parse(urls):
payload["url"] = urls
job_ids = await submit_job(payload)
results_list = []
await asyncio.gather(*(process_job(job_id, url, results_list) for job_id, url in zip(job_ids, urls)))
await save_to_json(results_list)
print("Airbnb URLs parsed.")
if __name__ == "__main__":
asyncio.run(parse(urls))Below you can see the JSON output of one of the scraped and parsed Airbnb listings:
[
{
"images": [
"https://a0.muscache.com/pictures/miso/Hosting-639705836870039306/original/a6e9e75e-8fe3-44d9-bd1b-9c20e111f09b.jpeg",
"https://a0.muscache.com/pictures/miso/Hosting-639705836870039306/original/78a13367-2edd-4ad6-83f3-09c468cd2389.jpeg",
"https://a0.muscache.com/pictures/miso/Hosting-639705836870039306/original/cca081b9-b868-4dbe-bdd2-585b3dd3b96a.jpeg",
"https://a0.muscache.com/pictures/miso/Hosting-639705836870039306/original/7c553d75-b316-4961-920a-92c6327b1287.jpeg",
"https://a0.muscache.com/pictures/miso/Hosting-639705836870039306/original/36a1d733-cea9-4802-bd96-f4a81a93c941.jpeg"
],
"titles": {
"title_one": "Private Rooftop Hidden Gem Studio",
"title_two": "Entire rental unit in New York, United States"
},
"pricing": {
"price_total": "$2,840",
"price_per_night": "$478\u00a0"
},
"reviews": [
{
"date": "2 weeks ago",
"rating": 5,
"review": "Located in the heart of New York City, these apartments offer a prime location with easy access to everything you need. The highlight is definitely the amazing terrace, perfect for enjoying the city skyline. Henry, the host, is exceptionally proactive, providing excellent communication and clear instructions throughout the stay. A top choice for anyone looking for a comfortable and convenient stay in NYC."
},
{
"date": "2 weeks ago",
"rating": 5,
"review": "Great location, easy to find the apartment"
},
{
"date": "2 weeks ago",
"rating": 1,
"review": "Although the rooftop was very special, there was significant clanging noise from the heating pipes that made sleeping impossible. We left after the first night, as Henry was not able to solve the problem. Walls were also paper thin and could hear the conversations of neighbors. Can't recommend."
},
{
"date": "3 weeks ago",
"rating": 5,
"review": "Great midtown location near everything!"
},
{
"date": "3 weeks ago",
"rating": 5,
"review": "Henry\u2019s home was absolutely gorgeous. Skyview\u2019s of surrounding buildings and the Empire State building right in your backyard was just breathtaking."
},
{
"date": "3 weeks ago",
"rating": 5,
"review": "This place was perfect! It was in a great location and exactly as described. Henry was very communicative and quick to respond."
}
],
"host_url": "/users/show/461252637",
"overall_rating": "4.84",
"parse_status_code": 12000,
"listing_url": "https://www.airbnb.com/rooms/639705836870039306?adults=1&category_tag=Tag%3A8536&children=0&enable_m3_private_room=true&infants=0&pets=0&photo_id=1437197361&search_mode=flex_destinations_search&check_in=2024-06-28&check_out=2024-07-03&source_impression_id=p3_1708944446_F%2FuHvpf5A7Gvt8Pi&previous_page_section_name=1000"
}
]By default, the Airbnb website loads the first 20 listings on the search page. If you want to load more listings, you must scroll the page, which we'll show how to do later. To scrape an Airbnb search page, you need to instruct the Headless Browser to wait until the 20th listing loads, and then collect the URLs of those 20 Airbnb listings:
### scrape_20_urls.py
import asyncio, aiohttp, json
USERNAME, PASSWORD = "username", "password" # Replace with your API credentials
payload = {
"source": "universal",
"url": "https://www.airbnb.com/",
"geo_location": "United States",
"render": "html",
"browser_instructons": [
{
"type": "wait_for_element",
"selector": {
"type": "xpath",
"value": "//div[@data-testid='card-container']/a"
},
"timeout_s": 30
}
],
"parse": True,
"parsing_instructions": {
"links": {
"_fns": [
{
"_fn": "xpath",
"_args": ["//div[@data-testid='card-container']/a/@href"]
}
]
}
}
}
async def scrape_search():
async with aiohttp.ClientSession(auth=aiohttp.BasicAuth(USERNAME, PASSWORD)) as session:
async with session.post("https://realtime.oxylabs.io/v1/queries", json=payload) as response:
hrefs = (await response.json())["results"][0]["content"]["links"]
urls = ["https://www.airbnb.com" + url for url in hrefs]
with open("20_airbnb_urls.json", "w") as f:
json.dump(urls, f, indent=4)
print("Airbnb URLs saved.")
return urls
if __name__ == "__main__":
asyncio.run(scrape_search())As mentioned earlier, if you’re using the free API trial, make sure to provide up to 10 URLs so you don’t exceed the free trial’s rate limit.
To simplify the process, you can create another Python file that’ll import the scrape_20_urls.py and parse_urls.py files and run these scrapers together:
### airbnb_scraper.py
import asyncio
from scrape_20_urls import scrape_search
from parse_urls import parse
async def main():
urls = await scrape_search()
await parse(urls)
if __name__ == "__main__":
asyncio.run(main())Remember to empty the urls list in the original parse_urls.py file so that it could be populated with newly scraped URLs after running the scrape_20_urls.py file.
If you want to increase the number of Airbnb listings from 20 to around 250, you can instruct the browser to scroll the page by 1000 pixels, wait for 1 second, and then, after doing it 6 times – click the button that says “Show more.” Afterward, instruct the browser to scroll the page 20 more times:
### scrape_250_urls.py
payload = {
"source": "universal",
"url": "https://www.airbnb.com/?tab_id=home_tab&refinement_paths%5B%5D=/homes&search_mode=flex_destinations_search&flexible_trip_lengths%5B%5D=one_week&location_search=MIN_MAP_BOUNDS&monthly_start_date=2023-07-01&monthly_length=3&price_filter_input_type=0&price_filter_num_nights=5&channel=EXPLORE&search_type=category_change&category_tag=Tag:8522",
"geo_location": "Canada",
"render": "html",
"browser_instructions": [
{"type": "scroll", "x": 0, "y": 1000},
{"type": "wait", "wait_time_s": 1}
] * 6 + [
{"type": "click", "selector": {"type": "xpath", "value": "//button[text()='Show more']"}},
{"type": "wait", "wait_time_s": 5}
] + [
{"type": "scroll", "x": 0, "y": 1000},
{"type": "wait", "wait_time_s": 1}
] * 20,
"parse": True,
"parsing_instructions": {
"links": {
"_fns": [
{
"_fn": "xpath",
"_args": ["//div[@data-testid='card-container']/a/@href"]
}
]
}
}
}Evidently, Oxylabs’ solution makes the data scraping process significantly straightforward and scalable. In case you want to build a truly custom Airbnb scraper without Oxylabs’ API, you may want to use a headless browser like Puppeteer to bypass anti-scraping systems and consider implementing proxy servers.