Extract real estate listings and property data

Turn property pages into clean JSON: price, address, bedrooms, bathrooms, living area, agent and photos. Use extract=standard and Zillow, Redfin and Idealista return the same field names, so their listings land in one table without mapping. Then go from a search results page to every listing on it with Batch. Uses: Extract with extract=standard and extract_fields, Batch and Adaptive Stealth Mode (mode=auto)

Ask your AI agent

With the Zenrows MCP server connected to Claude, Cursor or any MCP client, paste this prompt. Your agent picks the right Zenrows tools and settings on its own.

Use Zenrows to get the first 10 listings from https://www.redfin.com/city/30818/TX/Austin.
Then scrape all 10 listing pages in one batch and give me a table with address, price,
bedrooms, bathrooms and living area for each. The same prompt works for hundreds of listings.

Extract one property page

Send the listing URL with extract=standard. Add extract_fields to keep the groups and fields you need: price and location keep whole groups, property.bedrooms keeps one field.

import json
import requests

params = {
    "apikey": "YOUR_ZENROWS_API_KEY",
    "url": "https://www.redfin.com/TX/Austin/5804-Ginre-Cv-78759/home/31311780",
    "mode": "auto",
    "extract": "standard",
    "extract_fields": "property.title,property.property_type,property.bedrooms,property.bathrooms,property.living_area,property.living_area_unit,price,location",
}

response = requests.get("https://api.zenrows.com/v1/", params=params, timeout=180)
response.raise_for_status()

listing = response.json()["parsed"]
print(json.dumps(listing, indent=2))

const params = new URLSearchParams({
  apikey: "YOUR_ZENROWS_API_KEY",
  url: "https://www.redfin.com/TX/Austin/5804-Ginre-Cv-78759/home/31311780",
  mode: "auto",
  extract: "standard",
  extract_fields: "property.title,property.property_type,property.bedrooms,property.bathrooms,property.living_area,property.living_area_unit,price,location",
});

const response = await fetch(`https://api.zenrows.com/v1/?${params}`, {
  signal: AbortSignal.timeout(180_000),
});
if (!response.ok) throw new Error(`${response.status}: ${await response.text()}`);

const { parsed: listing } = await response.json();
console.log(listing.property.title, listing.price.display, listing.property.bedrooms);

curl "https://api.zenrows.com/v1/?apikey=YOUR_ZENROWS_API_KEY&url=https%3A%2F%2Fwww.redfin.com%2FTX%2FAustin%2F5804-Ginre-Cv-78759%2Fhome%2F31311780&mode=auto&extract=standard&extract_fields=property.title%2Cprice.display"

Listings sell and expire. Swap in a current listing URL from the site you’re working with; any Zillow, Redfin or Idealista property page works the same way.

Example output

{
  "location": {
    "street_address": "5804 Ginre Cv",
    "city": "Austin",
    "district": "Jollyville",
    "region": "TX",
    "postal_code": "78759",
    "country": "US",
    "latitude": 30.3963431,
    "longitude": -97.7686819
  },
  "price": {
    "value": 1039000,
    "currency": "$",
    "display": "$1,039,000",
    "price_per_area": 305,
    "period": null,
    "deposit": null,
    "qualifier": null,
    "recurring_fee": null,
    "value_max": null
  },
  "property": {
    "title": "5804 Ginre Cv, Austin, TX 78759",
    "property_type": "Single-family",
    "bedrooms": 3,
    "bathrooms": 3.5,
    "living_area": 3404,
    "living_area_unit": "sq ft"
  }
}

Pick standard or native fields

extract=standard returns the canonical real estate schema. extract=auto returns the site’s own extraction, which carries everything that site shows, such as Redfin’s climate risks, schools, tax history and payment estimate.

Group in `standard`	Fields
`property`	`title`, `property_type`, `listing_type`, `listing_status`, `bedrooms`, `bathrooms`, `rooms`, `living_area`, `living_area_unit`, `land_area`, `year_built`, `parking_spaces`, `features`, `description`, `listing_id`, `listing_url`, `published_date`
`price`	`value`, `value_max`, `currency`, `display`, `qualifier`, `price_per_area`, `period`, `deposit`, `recurring_fee`
`location`	`street_address`, `city`, `district`, `region`, `postal_code`, `country`, `latitude`, `longitude`
`agent`	`name`, `phone`, `agency_name`, `agency_url`, `agency_logo`
`media`	`primary_image`, `images`, `image_count`, `floor_plans`, `virtual_tour_url`

Use standard when you merge several sites into one dataset. Use auto when you work with one site and want every detail it publishes.

Collect listing URLs from a search page

Search and city pages list many properties. For search pages, use extract=auto, and keep the listing links with extract_fields:

Python (Requests)

from urllib.parse import urljoin
import requests

search_url = "https://www.redfin.com/city/30818/TX/Austin"
params = {
    "apikey": "YOUR_ZENROWS_API_KEY",
    "url": search_url,
    "mode": "auto",
    "extract": "auto",
    "extract_fields": "listings.listing_url,listings.list_price_usd",
}

response = requests.get("https://api.zenrows.com/v1/", params=params, timeout=180)
response.raise_for_status()

listings = response.json()["parsed"]["listings"]
listing_urls = [urljoin(search_url, item["listing_url"]) for item in listings]
print(len(listing_urls), listing_urls[:2])

Output

41 ['https://www.redfin.com/TX/Austin/5804-Ginre-Cv-78759/home/31311780', 'https://www.redfin.com/TX/Austin/2841-San-Gabriel-St-78705/home/31409609']

Redfin returns relative links, so urljoin turns them into full URLs. Field names on search pages follow each site: send one request without extract_fields to read them.

Extract every listing with Batch

Send the listing URLs to Batch as one job, with extract=standard in zenrows_params. Batch runs them in parallel and retries transient failures, and each result is the same JSON a single request returns.

Python (SDK)

# pip install zenrows requests
import json
from urllib.parse import urljoin
import requests
from zenrows import ZenRowsBatchClient

API_KEY = "YOUR_ZENROWS_API_KEY"
search_url = "https://www.redfin.com/city/30818/TX/Austin"

# 1. Read the search page and collect listing URLs
params = {
    "apikey": API_KEY,
    "url": search_url,
    "mode": "auto",
    "extract": "auto",
    "extract_fields": "listings.listing_url",
}
response = requests.get("https://api.zenrows.com/v1/", params=params, timeout=180)
response.raise_for_status()
listing_urls = [urljoin(search_url, item["listing_url"]) for item in response.json()["parsed"]["listings"]]
print(f"Found {len(listing_urls)} listings")

# 2. Extract every listing in one Batch job, with the canonical schema
client = ZenRowsBatchClient(api_key=API_KEY)
job = client.submit_regular(
    listing_urls,
    zenrows_params={"mode": "auto", "extract": "standard"},
)
run = job.run.wait(timeout=900)
print(f"{run.stats.successful}/{run.stats.total} listings extracted")

# 3. Read each result
for task in run.results(status="successful"):
    listing = json.loads(run.download_task_to_memory(task))["parsed"]
    print(listing["property"]["title"], "|", listing["price"]["display"], "|",
          listing["property"]["bedrooms"], "bd")

Output (trimmed)

Found 41 listings
41/41 listings extracted
1411 W 10th St, Austin, TX 78703 | $5,999,500 | 7 bd
901 St Elmo Cir, Austin, TX 78745 | $699,000 | 3 bd
810 W Johanna St, Austin, TX 78704 | $2,495,000 | 5 bd

To save every listing as a row in a spreadsheet, flatten each parsed object with pandas.json_normalize and call to_csv. For Node.js, the REST API and the CLI, see Scrape a list of URLs.

Customize it

Change	How
Every detail a site publishes	`extract=auto` instead of `standard`, see Extraction modes
Listings from a specific country	Add `proxy_country`, for example `es` for Idealista, see Proxy country
Rentals, other cities or filters	Change the search URL; the code stays the same
A site that isn’t prepared yet	Ask for it with `POST /v1/extract/prepared-domains`, see Prepare a new domain
Fresh listings every morning	Turn the Batch job into a scheduled job
A webhook when the job finishes	Add `webhook` to the job, see Get notified when a Batch job finishes

Troubleshooting

400 with REQS004 naming extract_fields: a field name doesn’t exist on this page. The error lists the available fields; copy names from it, or omit extract_fields.
403 with REQS007: this kind of page on the domain isn’t prepared yet. Prepare it with the URL in example_urls.
A listing returns 404 or 410: the property was sold or removed. Collect fresh URLs from the search page before each run.
422 with no_data_extracted: the page loaded without its data. Retry with js_render=true&premium_proxy=true&wait=3000 instead of mode=auto.

Get structured JSON from any page

How Extract works on any prepared domain, and how to add a new one.

Scrape a list of URLs

Batch from Python, Node.js, cURL, the CLI and MCP.

Run a Batch job on a schedule

Re-extract listings daily or weekly with no server to run.

Crawl a website from a start URL

Discover every listing URL across a site’s pages.

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Production

Extract real estate listings and property data

Ask your AI agent

Extract one property page

Example output

Pick standard or native fields

Collect listing URLs from a search page

Extract every listing with Batch

Customize it

Troubleshooting

Get structured JSON from any page

Scrape a list of URLs

Run a Batch job on a schedule

Crawl a website from a start URL

​Ask your AI agent

​Extract one property page

​Example output

​Pick standard or native fields

​Collect listing URLs from a search page

​Extract every listing with Batch

​Customize it

​Troubleshooting

​Related recipes

Get structured JSON from any page

Scrape a list of URLs

Run a Batch job on a schedule

Crawl a website from a start URL

Ask your AI agent

Extract one property page

Example output

Pick standard or native fields

Collect listing URLs from a search page

Extract every listing with Batch

Customize it

Troubleshooting

Related recipes