How to scrape real estate listings from Zillow?

Looking for a Zillow scraper in Python? Zillow is the #1 real estate platform in North America, with millions of listings for rent and for sale. This Zillow scraper Python guide shows you how to extract structured property data at scale — both rental and purchase listings — using ScrapingBot's API.

1. Why scrape Zillow?

The US real estate market is one of the most dynamic in the world. Zillow alone lists over 135 million homes across the country. Scraping it gives you access to:

• Price trends by city, neighborhood, or ZIP code
• Rental vs. purchase market comparisons
• Time-on-market data to spot negotiation opportunities
• Agency and agent activity tracking
• Surface area in square feet and price per sqft analysis

We've already covered scraping Funda in the Netherlands and Rightmove in the UK — Zillow follows the same pattern but with US-specific data formats (USD, square feet, ZIP codes).

2. Technical challenges

Zillow is one of the most aggressively protected real estate sites to scrape. Here's what you'll run into:

• Advanced bot detection — Zillow uses fingerprinting and behavioral analysis to block automated requests.
• JavaScript rendering — Listing data is injected dynamically via React components.
• IP rate limiting — Repeated requests from the same IP trigger CAPTCHAs or bans.
• Geo-restrictions — Some listing data varies by region and requires US-based IPs.

3. How ScrapingBot handles them

ScrapingBot's Real Estate API handles all of this for you: it rotates residential US IPs, renders JavaScript fully, and returns clean structured JSON — whether you're scraping rental or purchase listings.

4. Step-by-step: build your Zillow scraper in Python

Install the library

pip install requests

Basic setup

import requests

# Your ScrapingBot credentials
USERNAME = "your_username"
API_KEY  = "your_api_key"

def scrape_zillow(url):
    api_url = "https://api.scraping-bot.io/scrape/real-estate"
    payload = {"url": url}

    response = requests.post(
        api_url,
        json=payload,
        auth=(USERNAME, API_KEY)
    )

    if response.status_code == 200:
        return response.json()
    else:
        raise Exception(f"Error {response.status_code}: {response.text}")

5. Scraping rental listings

Let's start with a rental listing in Los Angeles. Zillow rental pages include the monthly rent, surface area in sqft, number of bedrooms, and the managing agency.

import requests, time

# Scrape a rental listing in Los Angeles
RENTAL_URL = "https://www.zillow.com/homes/for_rent/Los-Angeles-CA/"

def scrape_rentals(n_pages=3):
    results = []
    for page in range(1, n_pages + 1):
        url = f"{RENTAL_URL}{page}_p/"
        data = scrape_zillow(url)
        results.extend(data.get("listings", []))
        time.sleep(1)  # polite delay
    return results

rentals = scrape_rentals()
print(f"Collected {len(rentals)} rental listings")

Here's what a typical rental listing returns:

6. Scraping purchase listings

For purchase listings, the data structure is slightly different — you get the sale price, the ZIP code, and the publishing date, which is particularly useful for spotting older listings where the price can be negotiated.

# Scrape purchase listings in New York
BUY_URL = "https://www.zillow.com/homes/for_sale/New-York-NY/"

def scrape_purchases(n_pages=3):
    results = []
    for page in range(1, n_pages + 1):
        url = f"{BUY_URL}{page}_p/"
        data = scrape_zillow(url)
        results.extend(data.get("listings", []))
        time.sleep(1)
    return results

purchases = scrape_purchases()
print(f"Collected {len(purchases)} purchase listings")

Here's what a typical purchase listing returns:

7. Sample output data

Both rental and purchase responses follow the same JSON structure from ScrapingBot's API. You can normalize them into a single dataframe for cross-market analysis:

import pandas as pd

# Combine rental and purchase data
all_listings = rentals + purchases
df = pd.DataFrame(all_listings)

# Save to CSV
df.to_csv("zillow_listings.csv", index=False)
print(df.head())

8. Going further

Once your Zillow scraper Python script is running, you can schedule it with a cron job to monitor price changes daily, or plug the data into a visualization tool like Plotly to build interactive price-trend dashboards. ScrapingBot also supports Funda, Rightmove, and Airbnb with the same API interface.