The short-term rental market has its own specifics and dynamics. To track changes in the industry and respond to them quickly, you need to analyze the largest platforms in the niche. One of the leading services for renting accommodation to travelers is Airbnb. In this article, we’ll explain how to scrape data from Airbnb without breaking the law or getting blocked, what approaches and pitfalls exist, and we’ll walk through an example of a working Python script.
When You Might Need to Scrape Airbnb and What Data Is Usually Collected
Let’s look at this in terms of typical tasks and the data you’d want to gather.
- General analysis of the short-term rental market. For example, to understand price levels in different countries, regions, cities, and even specific neighborhoods within a city. To do this, you’ll most likely need to collect data such as: base price per night (base price), extra fees (cleaning or service fee), listing availability via the calendar, number of reviews, average rating/score, property type, and its affiliation with a particular area on the map.
- Competitor monitoring. Here, you’re more interested in deep analytics over a long period of time, as well as for specific dates (seasonality, holidays, etc.). In this case, you’ll want to collect price dynamics, the set of amenities and options to understand service quality, detailed availability patterns, calendar API data and so on.
- Quality control and ranking of your own listings. In this scenario, scraping listing collections is useful - especially search results pages for specific keywords and filters - plus monitoring the appearance of new reviews and changes in rating (scores).
- Integrating Airbnb data into your own websites and services. There are many possible use cases here, and you might need practically any information, depending on your project’s goals and requirements. The minimal set is often scraping rating/score data, similar to IMDb scraping.
Is It Legal to Collect Data from Airbnb?
Scraping any website is always a bit like walking on thin ice. On one hand, websites may officially prohibit collecting data from their pages and using automation tools such as bots or scrapers. On the other hand, when a regular user browses a site, they are not violating anything: they can save certain information from the pages at any time for their own needs. Moreover, Airbnb users can even export their profile data in HTML, Excel, or JSON formats, and experienced developers are given access to an API.
However… Airbnb officially forbids automated scraping - this is stated in clause 11.1 of the Terms of Use (and there is a similar explicit restriction for working with the API). If you violate these rules, the platform may block your access to the site, cancel all bookings, restrict, suspend, or completely delete your Airbnb account. If your actions disrupt the service or cause any other damage, you may face a formal claim and legal proceedings.
The main issue with any scraping is its purpose. If you collect personal data, use the collected information for hacking or other unlawful purposes, or, for example, copy and publish someone else’s content on your own site, such actions can have serious consequences.
So there is no clear-cut “yes” or “no” answer to the question of whether scraping Airbnb is legal. But if your goals are research-oriented, and you do not access protected sections (for example, those behind login or explicitly excluded in the robots.txt directives), you are unlikely to run into problems. Many companies have been scraping Airbnb for years and even provide ready-made datasets for analytics. Of course, it is important to keep the load on the hosting to a minimum and not send a large burst of requests all at once.
Problems and Limitations You Can Face When Scraping Data from Airbnb
First, the service is heavily dependent on JavaScript. Airbnb is a full-fledged SPA (Single Page Application) with dynamic data loading. This means that:
- The raw HTML code you get from standard HTTP requests contains almost no useful information; the data only appears after JS execution.
- Prices, photos, coordinates, availability, reviews - all of this is loaded via separate XHR/Fetch requests (internal APIs).
- With a simple HTTP client (like requests or curl), it’s practically impossible to get the data you need. You’ll definitely need a headless browser and a web driver to control it (Playwright, Selenium, Puppeteer etc.).
As a result, processing time increases and hardware requirements go up.
Second, the resulting HTML uses random selectors, data-attributes and IDs that differ from page to page. This makes navigating the DOM much more complex and essentially kills the classic parsing approach based on syntactic analyzers (xpath, BeautifulSoup etc.). The most robust strategy in such conditions is to use AI, which in turn makes Airbnb scraping even more expensive or imposes special hardware requirements.
Third, you have to know and account for geographic, language and regional differences. Web scraping Airbnb becomes significantly more complicated due to the large number of regional site versions, each with its own quirks in layout, navigation structure, and so on. A simple example is price formatting, which may look like: 1,234.56, 1.234,56, 1234 etc. There can also be different element names, special blocks, widgets and URL patterns.
Fourth, the platform actively defends itself against bots and automation systems. It uses one of the strongest WAF setups, relies on CSRF tokens, has special session protection mechanisms, deeply analyzes browser fingerprints, tracks high request volumes, and detects repetitive navigation patterns. In short, everything is designed to make life harder for anyone trying to analyze Airbnb data.
As a result, even if you know the real URL patterns or internal API endpoints Airbnb uses, you still won’t be able to simply plug in your own parameters to speed up scraping. Most likely you’ll either get an empty server response or a CAPTCHA challenge.
Fifth, Airbnb’s engineers have thought through many details. For example, coordinates are intentionally distorted, with an error of several hundred meters. To scrape the availability calendar from listing pages, you need to send many requests and follow a complex sequence of actions. When accessing the API versus viewing real listings, you may see different prices due to different pricing algorithms (with or without extra fees). All key data on a single listing page comes from different sources — it’s extremely difficult to consolidate because it is split across different parts of the layout.
Overview of the Main Approaches to Scraping Airbnb Data
So how can you respond to all these challenges? Scraping Airbnb data is almost guaranteed to cause difficulties even for experienced developers.
Here are the main approaches you can use for Airbnb scraping:
- A mix of classic content parsing and headless browsers through syntax analyzers and headless browsers. We’d actually recommend replacing plain “headless” browsers with anti-detect browsers that can manage many browser profiles and imitate believable digital fingerprints
- . The pipeline looks roughly like this: you load the rendered page in a browser, grab the resulting page source and pass it to BeautifulSoup, XPath, etc., extract only the needed URLs, cleaning out everything unnecessary (image links, styles, JS scripts, and so on). This way you can at least get a list of pages that you then scrape for your specific business tasks. Collecting deeper, domain-specific data this way is much harder - it’s difficult to reliably extract it at the code level.
- Using AI and computer vision. Because the DOM is effectively protected/obfuscated by random identifiers, the fastest option is often not to look for patterns in the code at all, but to let AI extract the data you need and return it in a structured format. There are two main ways to do this:
- Sending the resulting HTML. You pass the rendered HTML to the model. But because of the sheer volume of characters, your tokens will disappear very quickly. That’s why it’s best to “lighten” the HTML beforehand by stripping everything unnecessary.
- Sending and recognizing screenshots. In this case, the neural network can more accurately interpret the role of layout elements, and you get back highly reliable data. At the same time, you can ask it to do extra work: describe the room/property, capture how visible it is in lists/filters etc. The big downside of computer vision, however, is that it can’t “see” links/URLs, even though they are critical on the page.
- Using absolute DOM navigation. This is a highly undesirable option. Here, your parser doesn’t look for a specific pattern (like a selector name or a particular element ID), but relies on the full path from the HTML root. The problem is that even the slightest change in the code will “break” your parser. And there is no guarantee that typical pages will all have an identical element structure in the first place.
In any of these scenarios, you’ll need headless or anti-detect browsers and high-quality proxy servers. We recommend using mobile proxies right away, as they tend to be the most trusted.
Example of a Python-Script for Scraping
How to scrape Airbnb data in Python (don’t forget to install Selenium and wait for the headless browsers to finish loading):
Here’s what the script does step by step:
- It takes as input a page for a specific location, for example New York (USA) or Istanbul (Turkey) - whatever you need.
- It loads this page in a headless browser. The browser itself must work through a proxy. To avoid surprises, use rotating proxies, for example our Froxy proxies.
- After waiting for the page to fully load (we set a generous 45-second delay), the resulting HTML is sent to ChatGPT. Using a special prompt, the model returns only a list of links to individual listing pages that are present on the start page. They look like: https://www.airbnb.com/rooms/1004901216747721423. If you want, you can implement scrolling or follow pagination links. However, pagination on Airbnb is made deliberately complex.
- Next, the headless browser goes through the list of listings and takes screenshots.
- Each screenshot is sent again to ChatGPT, which, according to the prompt, returns only structured information about prices, number of reviews, etc. Prices are immediately normalized to a single standard (without writing additional code).
- The data is written to a CSV file.
You can ask the AI to collect any other information as well - you’ll just need to adjust the prompt and the structure of how data is recorded into the table.
# Note: this is a skeleton example. It shows the general architecture of integrating Selenium with OpenAI.
# Where possible, you should adapt it to your own infrastructure, keys and actual Airbnb behavior. .
import csv
import time
import random
import re
import base64
from pathlib import Path
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.common.by import By
from selenium.common.exceptions import (
WebDriverException,
TimeoutException,
NoSuchElementException
)
from openai import OpenAI
# ==============================
# CONFIG
# Replace the parameters with yours
# ==============================
START_URL = "https://www.airbnb.com/s/New-York--NY/homes"
PROXY_HOST = "your-proxy-host"
PROXY_PORT = 12345
PROXY_LOGIN = "proxylogin"
PROXY_PASSWORD = "proxypass"
RETRY_ATTEMPTS = 5 # the number of connection attempts through proxy
OUTPUT_CSV = "airbnb_parsed.csv"
OPENAI_API_KEY = "YOUR-API-KEY" # a working key for API Chat GPT is required here
MODEL_FOR_LINKS = "gpt-4.1" # to analyze the HTML search page
MODEL_FOR_LISTINGS = "gpt-4.1" # to analyze the ad screenshots
client = OpenAI(api_key=OPENAI_API_KEY)
# ==============================
# The delay between connection attempts is random, from 3 to 10 seconds, you can change that if you like.
# ==============================
def random_delay(a=3, b=10):
time.sleep(random.uniform(a, b))
# ------------------------------
# AI: extracting links from the HTML of the start page
# ------------------------------
def ai_extract_room_links(html: str):
prompt = (
"Extract all Airbnb listing page URLs from the HTML. "
"Return only a JSON array of clean URLs, like:\n"
'["https://www.airbnb.ru/rooms/12345", "https://www.airbnb.ru/rooms/67890"]\n'
"Do not include query parameters. Detect all unique listings."
)
response = client.chat.completions.create(
model=MODEL_FOR_LINKS,
messages=[
{"role": "system", "content": prompt},
{
"role": "user",
"content": [
{"type": "input_text", "text": html}
]
}
]
)
text = response.choices[0].message.content
# Note: this is a primitive way to extract JSON; in a real project it's better to use json.loads
urls = re.findall(r'https://www\.airbnb\.ru/rooms/\d+', text)
return list(set(urls))
# ------------------------------
# AI: extracting data from the listing screenshot + HTML
# ------------------------------
def ai_extract_listing_data(image_bytes: bytes, html: str):
prompt = (
"You are an assistant that extracts structured data from Airbnb listing pages. "
"You receive a screenshot (image) and the HTML markup of the listing page. "
"Extract the following fields:\n"
"- title\n"
"- address (if available)\n"
"- price_per_night (integer, normalized)\n"
"- total_price (integer, normalized)\n"
"- rating\n"
"- review_count\n\n"
"Return JSON only with those keys."
)
b64_img = base64.b64encode(image_bytes).decode("utf-8")
response = client.chat.completions.create(
model=MODEL_FOR_LISTINGS,
messages=[
{"role": "system", "content": prompt},
{
"role": "user",
"content": [
{"type": "text", "text": "Here is the screenshot and HTML."},
{"type": "input_text", "text": html},
{
"type": "input_image",
"image": image_bytes
}
]
}
]
)
return response.choices[0].message.content
# ------------------------------
# Initializing Selenium with a proxy
# ------------------------------
def build_browser():
# Note: for HTTP proxies with login/password you need a separate Chrome extension.
# Below is the simplest "built-in" option, but in a real project it's better to use a dynamic plugin.
# This is just a skeleton.
chrome_options = Options()
chrome_options.add_argument("--headless=new")
chrome_options.add_argument("--disable-blink-features=AutomationControlled")
# Proxy with no authorization: chrome_options.add_argument(f"--proxy-server=http://{PROXY_HOST}:{PROXY_PORT}")
# For proxy with authorization - use the extension
plugin_path = Path("proxy_auth_plugin")
plugin_path.mkdir(exist_ok=True)
manifest_json = r"""
{
"version": "1.0.0",
"manifest_version": 2,
"name": "ProxyAuthPlugin",
"permissions": ["proxy", "tabs", "unlimitedStorage", "storage", "<all_urls>", "webRequest", "webRequestBlocking"],
"background": {"scripts": ["background.js"]}
}
"""
background_js = f"""
var config = ,
bypassList: []
}}
}};
chrome.proxy.settings.set(, function() );
function callbackFn(details)
}};
}}
chrome.webRequest.onAuthRequired.addListener(
callbackFn,
,
['blocking']
);
"""
(plugin_path / "manifest.json").write_text(manifest_json, encoding="utf-8")
(plugin_path / "background.js").write_text(background_js, encoding="utf-8")
chrome_options.add_argument(f"--load-extension={plugin_path.resolve()}")
driver = webdriver.Chrome(options=chrome_options)
# We used the simplest option - a long delay of 45 seconds to make sure all data is fully loaded.
driver.set_page_load_timeout(45)
return driver
# ------------------------------
# Loading the page with retries
# ------------------------------
def load_page_with_retries(driver, url: str, attempts: int = RETRY_ATTEMPTS):
for attempt in range(1, attempts + 1):
try:
print(f"[INFO] Attempt {attempt}/{attempts} to load: {url}")
driver.get(url)
time.sleep(5)
return True
except (TimeoutException, WebDriverException):
print("[WARN] Failed to load page, retrying...")
random_delay()
return False
# ------------------------------
# The main process
# ------------------------------
def main():
print("[INFO] Starting browser...")
driver = build_browser()
# ---------------------------------------------------
# 1. Load the start URL and send the HTML to the model
# ---------------------------------------------------
print("[INFO] Loading start URL...")
if not load_page_with_retries(driver, START_URL, RETRY_ATTEMPTS):
print("[ERROR] Cannot load start page. Aborting.")
driver.quit()
return
start_html = driver.page_source
print("[INFO] Extracting listing URLs with AI...")
listing_urls = ai_extract_room_links(start_html)
print(f"[INFO] AI returned {len(listing_urls)} listing URLs")
# ---------------------------------------------------
# 2. Prepare the CSV file; you can change the file name to whatever you like
# ---------------------------------------------------
csv_file = open(OUTPUT_CSV, "w", newline="", encoding="utf-8")
writer = csv.writer(csv_file)
writer.writerow([
"url",
"title",
"address",
"price_per_night",
"total_price",
"rating",
"review_count"
])
# ---------------------------------------------------
# 3. Process the ads
# ---------------------------------------------------
for url in listing_urls:
print(f"[INFO] Parsing listing: {url}")
ok = load_page_with_retries(driver, url, RETRY_ATTEMPTS)
if not ok:
print(f"[ERROR] Cannot load listing page: {url}")
continue
# Screenshot
screenshot_bytes = driver.get_screenshot_as_png()
html = driver.page_source
# AI
print("[INFO] Sending listing to AI...")
json_text = ai_extract_listing_data(screenshot_bytes, html)
# Note: in this example we use a simple way to extract JSON.
# In a real project it's better to use json.loads(json_text).
fields = {
"title": "",
"address": "",
"price_per_night": "",
"total_price": "",
"rating": "",
"review_count": "",
}
# The simplest scraper. If possible, replace with json.loads.
for key in fields.keys():
m = re.search(rf'"{key}"\s*:\s*"?(.*?)"?[,}}]', json_text)
if m:
fields[key] = m.group(1)
writer.writerow([
url,
fields["title"],
fields["address"],
fields["price_per_night"],
fields["total_price"],
fields["rating"],
fields["review_count"]
])
csv_file.flush()
# Another random delay, from 2 to 5 seconds
random_delay(2, 5)
csv_file.close()
driver.quit()
print(f"[INFO] Finished. Saved to {OUTPUT_CSV}")
if __name__ == "__main__":
main()
Conclusion and Recommendations
The Airbnb team has done a lot to make data scraping as difficult as possible. Everything is thought through down to the smallest detail: from passing unique tokens for every server request to an unreadable DOM structure. The user’s browser is analyzed from all angles, so unprotected headless instances are blocked right at the connection stage. You really have to work hard if you want to collect data from the pages. We chose the simpler route and used AI.
But this approach has its own nuances as well: the budget grows sharply, because working with neural networks via API usually means token-based billing. The alternative is to run the model locally, but then you’ll have to invest in suitable hardware and its configuration.
When scraping Airbnb, you need to take into account everything you possibly can: simulating user actions, rotating trusted browser fingerprints, using natural delays, locations, device types, and so on. High-quality proxies are absolutely essential. We recommend starting with mobile proxies right away.