Zillow Search Scraper (All-in-one) 🏡 - Pay Per Results
Pay $1.99 for 1,000 results
Zillow Search Scraper (All-in-one) 🏡 - Pay Per Results
Pay $1.99 for 1,000 results
Gather property data from Zillow 🏡, including listings for sale (with filtering options for home type), sold & rentals, all through the Zillow API. No daily call limits 🚫. Scrape millions of listings & export scraped data, run the scraper via API 🔧, schedule tasks ⏰, or integrate with other tools
You can access the Zillow Search Scraper (All-in-one) 🏡 - Pay Per Results programmatically from your own Python applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in Integrations settings in Apify Console.
1from apify_client import ApifyClient
2
3# Initialize the ApifyClient with your Apify API token
4# Replace '<YOUR_API_TOKEN>' with your token.
5client = ApifyClient("<YOUR_API_TOKEN>")
6
7# Prepare the Actor input
8run_input = {
9 "city": "Baltimore MD",
10 "types": "For Sale (All Home Types) 🏠",
11 "maxitems": 300,
12}
13
14# Run the Actor and wait for it to finish
15run = client.actor("scrapestorm/zillow-search-scraper-all-in-one---pay-per-results").call(run_input=run_input)
16
17# Fetch and print Actor results from the run's dataset (if there are any)
18print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
19for item in client.dataset(run["defaultDatasetId"]).iterate_items():
20 print(item)
21
22# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-startZillow Search Scraper (All-in-one) 🏡 - Pay Per Results API in Python
The Apify API client for Python is the official library that allows you to use Zillow Search Scraper (All-in-one) 🏡 - Pay Per Results API in Python, providing convenience functions and automatic retries on errors.
Install the apify-client
pip install apify-clientOther API clients include:
Storm_ScraperActor Metrics
1 monthly user
-
1 star
>99% runs succeeded
Created in Dec 2024
Modified 8 days ago
