Real Estate Scraper in Python — Zillow, Craigslist & Listing Sites to JSON

Property data drives comps, investment screens and market dashboards. These examples extract listings as structured JSON — address, price, beds/baths, days-on-market — without per-site HTML parsing.

Listings with one schema

import requests

API = "https://app.quantumproxies.io/api/v1/scraper/extract"
KEY = "qp_live_YOUR_KEY"

SCHEMA = {"listings": [{"address": "string", "price": "string", "beds": "string",
                        "baths": "string", "sqft": "string", "url": "string"}]}

r = requests.post(API, headers={"Authorization": f"Bearer {KEY}"},
                  json={"url": "https://www.zillow.com/austin-tx/",
                        "render": True, "country": "us", "extract": SCHEMA},
                  timeout=180)
for l in r.json()["data"]["listings"][:10]:
    print(l["price"], l["beds"] + "bd", l["address"])

Runnable multi-source version with CSV: listings.py.

Site guides

• Scraping Zillow real estate data — Zillow is one of the most defended sites on the web; this covers what actually works
• Scraping Craigslist listings — the IP-ban patterns and the RSS trick
• Car listings from Autotrader & Cars.com — same mechanics, different vertical
• The umbrella playbook: real estate data scraping with proxies

From listings to a product

Cross-source listing data feeds comparison products — the architecture (normalize, dedup, refresh cadence) is the same as building a price comparison website.

Get access

Powered by the QuantumProxies Extract API over residential exits — Zillow-grade targets need them. Keys: app.quantumproxies.io/api-keys.