Zillow Property Details Scraper

Export public Zillow property-detail records from known home URLs, ZPIDs or a supplied listing dataset. Retain source-attributed property facts and optional photos or history, with explicit success and diagnostic statuses. Listing prices and estimates are source observations rather than independent valuations or verified current property conditions.

Quick start

Start with one known detail URL, a one-property cap and only the optional arrays you need. The existing Sonoma example is:

{
  "propertyUrls": [
    { "url": "https://www.zillow.com/homedetails/17199-Park-Ave-Sonoma-CA-95476/15800416_zpid/" }
  ],
  "maxItems": 1,
  "includePhotos": false,
  "includeHistory": true,
  "connectionMode": "automatic"
}

Prefer a video guide? This short Apify walkthrough shows you how to set up and run an Actor. It uses a different Actor, but the setup steps are the same.

Ready-to-run examples

• Export Zillow Property Details by URL: Fetch one public Zillow detail URL with bounded spend.
• Look Up Zillow Home Details by ZPID: Look up one home from its Zillow property ID.
• Export Zillow Property Photos and Facts: Fetch one home with its available photo array enabled.

For larger enrichments, pass a small batch of propertyUrls, or set searchResultsDatasetId to a dataset containing url, detailUrl, hdpUrl, canonicalUrl, or zpid fields.

What you get

Successful property rows include input provenance, workKey, identity, address and core facts. Optional schools, photos, history and listing attribution are included when available. Unavailable properties are explained in the run summary. Extraction failures retain diagnostic rows and make the run fail without discarding useful records.

Use zpid and canonicalUrl to join details to your own property list. For historical analysis, retain each source-provided history entry rather than treating an empty array as proof that no transactions occurred. To compare your own snapshots, keep source and scrapedAt because a cached result is not a fresh observation.

Who is it for?

• Real estate investors and analysts who need repeatable property facts, pricing signals, and comparable-home exports.
• Brokerages and agents enriching lead lists or saved searches with current public detail-page fields.
• Proptech and data teams chaining Zillow search/listing datasets into normalized property-detail records.
• Automation builders feeding Zillow detail data into CRMs, alerts, spreadsheets, dashboards, or AI agents.

Input settings

Setting	JSON key	Purpose
Zillow property URLs	propertyUrls	Public home-detail URLs, as shown above.
ZPIDs	zpids	Known property IDs, for example ["15800416"].
Source dataset	searchResultsDatasetId	Enrich URL or ZPID rows from an accessible dataset.
Maximum properties	maxItems	Cap unique properties, from 1 to 1,000.
Photos and history	includePhotos, includeHistory	Include available optional arrays.
Connection	connectionMode	Automatic by default, or Direct without paid proxy fallback. See field help for costs and limitations.
Retry and timeout limits	maxRetries, requestTimeoutSecs	Advanced request limits.
Custom proxy settings	proxyConfiguration	Optional managed or customer-pool override. See field help.

Output data

The existing row illustrates historical Sonoma source values rather than a current asking price or verified valuation:

{
  "input": "https://www.zillow.com/homedetails/17199-Park-Ave-Sonoma-CA-95476/15800416_zpid/",
  "inputType": "url",
  "workKey": "zpid:15800416",
  "status": "success",
  "source": "html_cache",
  "zpid": "15800416",
  "canonicalUrl": "https://www.zillow.com/homedetails/15800416_zpid/",
  "homeStatus": "FOR_SALE",
  "address": { "streetAddress": "17199 Park Ave", "city": "Sonoma", "state": "CA", "zipcode": "95476" },
  "price": 995000,
  "beds": 3,
  "baths": 2,
  "livingArea": 1520,
  "priceHistory": [],
  "errorCode": null,
  "scrapedAt": "2026-07-13T00:00:00.000Z"
}

Field	Description
input, inputType, workKey	Original input, its origin, and a stable deduplication/resume key.
status	success, blocked, fetch_error, or parse_error. Historical datasets may also contain not_found.
source	Structured live source, or persistent_cache when a recent successful public record is used after all live routes are blocked.
zpid, canonicalUrl	Zillow property ID and normalized detail URL.
homeStatus, propertyType	Listing status and property type when available.
address	Structured address object from Zillow payloads.
price, zestimate, rentZestimate	Listing and estimate values when exposed.
beds, baths, livingArea, lotSize, yearBuilt	Core home facts.
description, facts	Listing description and detailed facts object.
agent, broker	Listing attribution data when present.
priceHistory, taxHistory	Optional history arrays controlled by includeHistory.
schools, photos, nearbyHomes	Optional public detail-page arrays.
missingFields, warnings, errorCode, errorMessage, diagnostics, scrapedAt	Quality and troubleshooting metadata.

Download your results

Open your run's dataset, choose Export and select JSON, CSV or Excel. JSON keeps nested fields together. CSV and Excel are useful when you want to review the records in a spreadsheet.

See Apify's download and export guide for the available formats and field options.

Pricing

The start event is a one-time run fee. The item event applies to each complete successful property row, including a clearly labelled cached record.

A successful row requires a ZPID, usable address and multiple core facts. Unavailable inputs and failure diagnostics have no item charge. A started run can still incur the start fee and usage costs without returning a successful property.

See the live Pricing tab for current rates and discounts. Platform usage can also apply, and a successful cached record remains a successful row rather than a fresh live lookup.

Tips for best results

• Start small: Use maxItems: 1 until the selected URLs produce the fields you need.
• Use detail pages: Search result pages are better handled by a Zillow listings actor. This Actor is for property details.
• Keep inputs focused: Split very broad enrichment jobs into smaller runs.
• Expect diagnostics: Zillow may return access-denied pages. Those are saved as free diagnostic rows for review.
• Check the collection time: A persistent_cache result can be up to six hours old. Its original scrapedAt and warning explain the age.
• Use retries deliberately: The default retries temporary failures twice per source. Lower maxRetries for quick probes or raise it for small reliability-focused jobs.

Limits and caveats

• Coverage: Zillow may vary detail-page payloads by property type, geography, and access controls.
• Freshness: Source data can change after a run finishes.
• Access controls: Zillow can block collection. Blocking or incomplete required extraction fails the run and retains useful records and diagnostics.
• Empty results: If all requested sources report a property unavailable, it produces no dataset row. RUN_SUMMARY explains the affected property. This does not prove the property is private or deleted.
• Partial runs: Processing is bounded to five minutes, including shutdown reserve. A buyer charge limit can stop further properties with PARTIAL_BUDGET. Other unfinished extraction fails with partial results retained.
• Success contract: A row is only marked and charged as successful when it contains a ZPID, a usable address, and multiple core property facts.

Optional schools, photos, agent attribution and history can be absent or change. A public listing or cached record does not independently verify ownership, current availability, contact authority or property condition. Inspect row status and source before using a record in a customer-facing report.

PENDING_WORK reports unfinished inputs. A separate new run does not automatically import another run's storage. Review RUN_SUMMARY and retain existing results before requesting more work.

Automation and integrations

Save your settings for next time

Once you have an input that works for you, save it as an Apify task. This keeps your settings together so you can use them again without filling in the form from scratch.

Run on a schedule

Need fresh results regularly? You can schedule the Actor or a saved task to run at a time that suits you.

Each scheduled run collects the data again. If you want to compare results or highlight changes, add that step in your own workflow.

Send results to your other tools

Use n8n, Make or Zapier to connect this scraper to the rest of your workflow. For example, you could pass its results to an AI assistant or add records to a spreadsheet. Follow the Apify integration guides for your chosen tool.

For a custom connection, an Apify webhook can notify your app when a run finishes or fails. Your app can then fetch the results or handle the failure.

API usage

Want to collect this data from your own app or automation? The examples below send input to the scraper and show how to collect its results.

The Node.js and Python examples wait for the run to finish before reading its results. The cURL example starts the run and returns its details. Wait for that run to finish, then use its defaultDatasetId to fetch the results.

For setup instructions, see Apify's JavaScript client guide, Python client guide and Actor runs API reference.

cURL

curl -X POST "https://api.apify.com/v2/acts/fetch_cat~zillow-property-details-scraper/runs?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"zpids":["15800416"],"maxItems":1}'

Node.js

import { ApifyClient } from "apify-client";

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor("fetch_cat/zillow-property-details-scraper").call({
  propertyUrls: [{ url: "https://www.zillow.com/homedetails/17199-Park-Ave-Sonoma-CA-95476/15800416_zpid/" }],
  maxItems: 1,
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("fetch_cat/zillow-property-details-scraper").call(run_input={
    "zpids": ["15800416"],
    "maxItems": 1,
})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

MCP and AI agents

You can ask your AI assistant to collect data with Zillow Property Details Scraper and help you work with the results. The scraper returns the data, and your assistant can then summarize it, compare records or prepare notes.

Connect your assistant through the Apify MCP server. MCP gives your assistant a way to find this scraper, run it and read the results. Follow the official setup and sign-in guide for your app.

Easier setup with an Apify plugin

If you use a supported coding assistant, the official Apify plugin can set up the connection for you. It connects your assistant to Apify and includes instructions for common tasks. Follow the guide for your particular app.

Use Apify AI in Console

Prefer to keep everything in Apify? Open Apify AI in Console and ask it to find Fetch Cat's Zillow Property Details Scraper. You can describe the data you want and ask questions about the results.

Apify AI is currently in beta. It asks for confirmation before starting an Actor, and normal run charges apply.

Choose your assistant

• Claude and Claude Code (Anthropic): Apify setup guide.
• ChatGPT and Codex (OpenAI): Apify setup guides.
• Grok Bot (xAI): Apify plugin guide.
• Gemini CLI (Google): MCP server setup guide. Use the server URL below with the HTTP configuration supported by Gemini CLI.

Connect just this scraper

To keep your setup focused on this scraper, add this server configuration to a compatible MCP client:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com?tools=fetch_cat/zillow-property-details-scraper"
    }
  }
}

Sign in to Apify when your client asks. If your client uses a Bearer token instead, follow its setup guide and keep the token in its secure configuration. Configuration formats vary by app, so use the linked guide if your client needs a different format.

Use Apify's general server

Already connected to the general Apify MCP server at https://mcp.apify.com? You can use it to find this scraper without adding another server. Ask your assistant to use Zillow Property Details Scraper by Fetch Cat, or give it the identifier fetch_cat/zillow-property-details-scraper.

Your assistant can check the available settings and retrieve the results from the returned run. If the run is still working, it should follow that run rather than start it again. Both connection options use Apify's hosted service.

Example requests

• “Look up Zillow property 15800416 with its available price and tax history, then summarize the recorded changes.”
• “Get the details for this Sonoma home and make a short facts table: https://www.zillow.com/homedetails/17199-Park-Ave-Sonoma-CA-95476/15800416_zpid/.”
• “Retrieve the available photos for Zillow property 15800416 and list their links alongside the property's address and collection time.”

Normal Apify charges apply. Check the live Pricing tab before asking your assistant to run a larger batch.

Pay with x402

If your AI agent has a funded wallet, it can pay for Apify usage through x402. This experimental option lets an agent use Apify without a conventional account.

Apify AGI provides a prepaid API token with a spending cap after payment. Your agent can use that token to run this scraper through the Apify API or an authenticated MCP connection.

Some MCP setups also support paying for each request directly. That is a different payment option, so check the official guide for its availability.

Follow the official guide for wallet setup, supported payment options and current terms. Apify handles the payments. Keep wallet keys and payment tokens in your agent's secure configuration.

Other ways for agents to pay

Apify also supports MPP for prepaid access through AGI and Skyfire for eligible Actors. If your agent already uses one of these, see the agentic payment guide to choose the right setup.

FAQ

Can I export results?

Yes. Apify datasets support JSON, CSV, Excel, XML, RSS, and API access.

Why are some fields empty?

Some properties do not expose every field publicly. The Actor leaves unavailable fields empty and lists important gaps in missingFields.

What happens when Zillow blocks a property?

A block is not an empty property result. The run fails if required extraction remains incomplete, while preserving successful rows and free diagnostics. Check the run summary before requesting the missing properties again.

Can I call it through an API or AI assistant?

Yes. Use the API and MCP examples above with known property URLs, IDs or a source dataset. Listing search belongs to the related Zillow Property Listings Scraper.

Are unsuccessful lookups charged as results?

No item event applies to unavailable properties or failure diagnostics. The start event and platform usage can still apply. See Pricing above.

Related Actors

• Zillow Property Listings Scraper — Collect public Zillow property search listings with available prices, locations and listing links.
• Website Screenshot Generator — Capture public page screenshots for visual records or manual comparison, without automatic change detection.
• Rightmove Property Listings Scraper — Collect UK Rightmove search listings, rather than full individual property pages.
• Idealista Scraper — Collect public Idealista listings from search or property URLs in Spain, Portugal and Italy.
• ImmobilienScout24 Property Listings Scraper — Collect German regional ImmobilienScout24 search listings, rather than full property details.

Support and feedback

For custom additions or integrations, email hello@fetchcat.dev.

Found an error or have a feature request? Open an issue with the run URL, a non-sensitive input example and what you expected.

Need help sharing it? See Apify’s guide to sharing a run when reporting an issue. Sharing a run can give the developer access to its input, logs and results, so use a run that contains no sensitive data. Please leave out API tokens, passwords, cookies, private documents and personal data. Use a public sample URL or a redacted example instead.

Tried this scraper? We'd love to hear how it worked for you. Leave a review to help others choose the right tool and help us improve it. Your feedback means a lot to us.

Made with care for curious people, AI agents and the possibilities in data. ♥