How to use Zillow Scraper

Step by step: in the Apify Store, over the API, from JavaScript or Python, in n8n, and from AI agents through MCP or LangChain.

$2.10 per 1,000 properties on the Free plan, down to $1.20 on higher plans (live Apify price on 2026-11-02).

1. In the Apify Store (no code)

1. Open arcadium/zillow-properties and sign in to Apify (a free account works).
2. Fill in the input form. The fields below are the ones the example uses.
3. Set a maximum cost per run in the run options if you want a hard cap.
4. Start the run. Rows appear in the Output tab as they arrive.
5. Export the dataset as JSON, CSV or Excel, or connect an integration.

Example input:

{
  "locations": [
    "Austin, TX"
  ],
  "maxResultsPerSearch": 50
}

2. Over the Apify API

The synchronous endpoint runs the actor and returns its rows in one call (for runs up to about five minutes; use the asynchronous run endpoint for longer ones).

curl -X POST "https://api.apify.com/v2/acts/arcadium~zillow-properties/run-sync-get-dataset-items" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"locations":["Austin, TX"],"maxResultsPerSearch":50}'

3. From JavaScript

// npm install apify-client
import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('arcadium/zillow-properties').call({
  "locations": [
    "Austin, TX"
  ],
  "maxResultsPerSearch": 50
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

4. From Python

# pip install apify-client
import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])
run = client.actor("arcadium/zillow-properties").call(run_input={
    "locations": ["Austin, TX"],
    "maxResultsPerSearch": 50,
})
items = client.dataset(run.default_dataset_id).list_items().items  # apify-client 3.x returns a Run object
print(items)

5. In n8n

Paste this node into a workflow (it is a plain HTTP Request node). Create a Header Auth credential with name Authorization and value Bearer <APIFY_TOKEN>; each returned row becomes one item for the next node.

{
  "nodes": [
    {
      "name": "Run arcadium/zillow-properties",
      "type": "n8n-nodes-base.httpRequest",
      "typeVersion": 4,
      "position": [
        0,
        0
      ],
      "parameters": {
        "method": "POST",
        "url": "https://api.apify.com/v2/acts/arcadium~zillow-properties/run-sync-get-dataset-items",
        "authentication": "genericCredentialType",
        "genericAuthType": "httpHeaderAuth",
        "sendBody": true,
        "specifyBody": "json",
        "jsonBody": "{\"locations\":[\"Austin, TX\"],\"maxResultsPerSearch\":50}",
        "options": {
          "timeout": 300000
        }
      }
    }
  ],
  "connections": {}
}

6. From an AI assistant (MCP)

Add Apify’s MCP server to Claude Desktop, Cursor, VS Code or any MCP client, limited to this actor:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?tools=arcadium/zillow-properties",
      "headers": {
        "Authorization": "Bearer <APIFY_TOKEN>"
      }
    }
  }
}

More on the MCP page.

7. From LangChain

# pip install langchain-apify   (reads APIFY_API_TOKEN from the environment)
from langchain_apify import ApifyActorsTool

tool = ApifyActorsTool("arcadium/zillow-properties")
rows = tool.invoke({"run_input": {
    "locations": ["Austin, TX"],
    "maxResultsPerSearch": 50,
}})
# or give `tool` to an agent: create_react_agent(llm, [tool])

Sample output

One row, from the example in the actor’s README. Long text is shortened and lists are cut to two items.

{
  "rowType": "property",
  "platform": "zillow",
  "propertyId": "70345655",
  "url": "https://www.zillow.com/homedetails/3207-Wickham-Ln-Austin-TX-78725/70345655_zpid/",
  "status": "FOR_SALE",
  "price": 324900,
  "currency": "USD",
  "address": "3207 Wickham Ln, Austin, TX 78725",
  "city": "Austin",
  "state": "TX",
  "zipcode": "78725",
  "latitude": 30.232096,
  "longitude": -97.5937,
  "beds": 4,
  "baths": 3,
  "livingArea": 2575,
  "lotArea": 4800.312,
  "lotAreaUnit": "sqft",
  "homeType": "SINGLE_FAMILY",
  "daysOnMarket": 140,
  "zestimate": null,
  "rentZestimate": null,
  "taxAssessedValue": 365588,
  "priceChange": -15000,
  "priceChangeDate": "2026-10-14",
  "brokerName": "T J Lewis Real Estate",
  "photos": null,
  "detailed": false,
  "searchInput": "Austin, TX",
  "searchRank": 1,
  "scrapedAt": "2026-10-22T10:00:00.000Z"
}

Schedule it

In Apify Console, create a Task from your input and add a Schedule (for example daily). Each run writes a new dataset; integrations can push the rows to Google Sheets, Slack, a webhook or your database.