How to scrape real estate listings from property websites?

Elen Honorata · 2024-12-18T07:21:30+00:00

Scraping real estate listings involves extracting structured data like property titles, prices, locations, and descriptions from property websites. Most real estate websites organize listings in a consistent layout, making it easier to identify the required HTML elements. However, challenges arise when these sites use JavaScript to load data dynamically or have pagination. Tools like BeautifulSoup work well for static pages, but for JavaScript-heavy sites, Selenium or Puppeteer is more suitable. Additionally, many real estate websites provide filters for location or property type, which can be leveraged to scrape targeted data efficiently.Here’s an example using BeautifulSoup to scrape property listings:import requestsfrom bs4 import BeautifulSoupurl "https://example.com/properties"headers {"User-Agent": "Mozilla/5.0"}response requests.get(url, headersheaders)if response.status_code 200: soup BeautifulSoup(response.content, "html.parser") listings soup.find_all("div", class_"property-item") for listing in listings: title listing.find("h2", class_"property-title").text.strip() price listing.find("span", class_"property-price").text.strip() location listing.find("span", class_"property-location").text.strip() print(f"Title: {title}, Price: {price}, Location: {location}")else: print("Failed to fetch property listings.")Dynamic pages often require interaction with filters or pagination, which can be achieved using Selenium. To avoid IP bans, rotating proxies and rate limiting should be implemented. How do you manage handling large datasets when scraping property listings?

General Web Scraping

How to scrape real estate listings from property websites?

Posted by Elen Honorata on 12/18/2024 at 7:21 am
Scraping real estate listings involves extracting structured data like property titles, prices, locations, and descriptions from property websites. Most real estate websites organize listings in a consistent layout, making it easier to identify the required HTML elements. However, challenges arise when these sites use JavaScript to load data dynamically or have pagination. Tools like BeautifulSoup work well for static pages, but for JavaScript-heavy sites, Selenium or Puppeteer is more suitable. Additionally, many real estate websites provide filters for location or property type, which can be leveraged to scrape targeted data efficiently.
Here’s an example using BeautifulSoup to scrape property listings:
```
import requests
```
```
from bs4 import BeautifulSoup
```
url = "https://example.com/properties"
headers = {"User-Agent": "Mozilla/5.0"}
response = requests.get(url, headers=headers)
if response.status_code == 200:
soup = BeautifulSoup(response.content, "html.parser")
listings = soup.find_all("div", class_="property-item")
for listing in listings:
title = listing.find("h2", class_="property-title").text.strip()
price = listing.find("span", class_="property-price").text.strip()
location = listing.find("span", class_="property-location").text.strip()
print(f"Title: {title}, Price: {price}, Location: {location}")
else:
print("Failed to fetch property listings.")
Dynamic pages often require interaction with filters or pagination, which can be achieved using Selenium. To avoid IP bans, rotating proxies and rate limiting should be implemented. How do you manage handling large datasets when scraping property listings?
Elen Honorata replied 4 days, 17 hours ago 1 Member · 0 Replies
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