> ## Documentation Index > Fetch the complete documentation index at: https://docs.zenrows.com/llms.txt > Use this file to discover all available pages before exploring further. > ## Agent Instructions > The Zenrows API (Fetch, Extract via extract=auto, and Batch) is described in OpenAPI 3.1 at https://www.zenrows.com/openapi.json and listed in the RFC 9727 API catalog at https://www.zenrows.com/.well-known/api-catalog. Use the spec for exact parameter names and types. # Extract real estate listings and property data > Get property details from Zillow, Redfin, Idealista and other real estate sites as clean JSON with Zenrows Extract, from one listing to thousands. 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](/extract/setup) with [`extract=standard`](/extract/setup#extraction-modes) and [`extract_fields`](/extract/setup#selecting-the-fields-you-need), [Batch](/batch/introduction) and [Adaptive Stealth Mode](/fetch/features/adaptive-stealth-mode) (`mode=auto`) ## Ask your AI agent With the [Zenrows MCP server](/mcp/overview) connected to Claude, Cursor or any MCP client, paste this prompt. Your agent picks the right Zenrows tools and settings on its own. ```text theme={"dark"} 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. ```python Python (Requests) theme={"dark"} 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)) ``` ```javascript Node.js theme={"dark"} 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}: ${await response.text()}`); const { parsed: listing } = await response.json(); console.log(listing.property.title, listing.price.display, listing.property.bedrooms); ``` ```bash cURL theme={"dark"} curl "https://api.zenrows.com/v1/?apikey=YOUR_ZENROWS_API_KEY&url=https%3A%2F%2Fwww.redfin.com%2FTX%2FAustin%2F5804-Ginre-Cv-78759%2Fhome%2F31311780&mode=auto&extract=standard&extract_fields=property.title%2Cprice.display" ``` Listings sell and expire. Swap in a current listing URL from the site you're working with; any Zillow, Redfin or Idealista property page works the same way. ## Example output ```json theme={"dark"} { "location": { "street_address": "5804 Ginre Cv", "city": "Austin", "district": "Jollyville", "region": "TX", "postal_code": "78759", "country": "US", "latitude": 30.3963431, "longitude": -97.7686819 }, "price": { "value": 1039000, "currency": "$", "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 Python (Requests) theme={"dark"} 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]) ``` ```text Output theme={"dark"} 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 Python (SDK) theme={"dark"} # 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") ``` ```text Output (trimmed) theme={"dark"} 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](/academy/scrape-from-a-list-of-urls). ## Customize it | Change | How | | - | - | | Every detail a site publishes | `extract=auto` instead of `standard`, see [Extraction modes](/extract/setup#extraction-modes) | | Listings from a specific country | Add `proxy_country`, for example `es` for Idealista, see [Proxy country](/fetch/features/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](/academy/extract/auto-extraction#prepare-a-new-domain) | | Fresh listings every morning | Turn the Batch job into a [scheduled job](/academy/batch/scheduled-jobs) | | A webhook when the job finishes | Add `webhook` to the job, see [Get notified when a Batch job finishes](/academy/batch/webhooks) | ## 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](/academy/extract/auto-extraction#prepare-a-new-domain) 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`. ## Related recipes How Extract works on any prepared domain, and how to add a new one. Batch from Python, Node.js, cURL, the CLI and MCP. Re-extract listings daily or weekly with no server to run. Discover every listing URL across a site's pages. This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.