Zillow Detail Scraper

Zillow Detail Scraper ⚡

Scraping Zillow property details one-by-one is painfully slow and doesn’t scale when you need clean, structured real estate data. Zillow Detail Scraper pulls property details directly from Zillow URLs or addresses, so you can automate extraction for Zillow listing data extraction at speed. It’s ideal for teams working on Zillow property data scraper workflows like Zillow listing scraper runs, Zillow details scraper projects, and Zillow lead generation scraper research. This actor is built for marketers, data analysts, and researchers who want reliable Zillow listing data extraction without manual copy-paste. In one run, you can process a queue of properties and optionally extract all building units so your dataset is ready to analyze.

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See the Data: Sample Output

Here's a real record from a single run:

{
  "status": "success",
  "url": "https://www.zillow.com/homedetails/4-Huff-Dr-Greenville-SC-29611/49339396_zpid/",
  "zpid": "49339396",
  "address": "4 Huff Dr, Greenville, SC 29611",
  "buildingUnits": [
    {
      "unitNumber": "1",
      "is_for_sale": false,
      "is_recently_sold": true
    }
  ],
  "listing_sub_type": {
    "is_pending": false,
    "is_forAuction": false
  },
  "error_message": null
}

Below is how to think about the output fields you’ll receive in the dataset:

Field	Type	What It Tells You
status	string	Indicates whether a record was successfully scraped for your run.
url	string	The Zillow URL processed for this record, useful for traceability.
error_message	string | null	Details why a record failed (or null when successful).
zpid	string | number	The Zillow property identifier, handy as a stable key for joins and deduping.
address	string	The resolved property address associated with the scraped details.
buildingUnits	array	When building-unit extraction is enabled, this holds unit-level data.
units	array	Alternative unit container that may appear depending on the parsed property structure.
listingSubType / listing_sub_type	object	Captures listing-type flags in a normalized form for easier filtering.
is_pending	boolean	A normalized listing status indicator you can use to filter for pipeline stage.
is_forAuction	boolean	Helps identify auction-related listings in your Zillow real estate data scraper exports.
is_FSBO	boolean	Indicates whether the listing is categorized as FSBO (normalized key).
is_newHome	boolean	Indicates if the listing is marked as a new home (normalized key).

Export your full dataset as JSON, CSV, or Excel from the Apify dashboard.

Note: The exact set of Zillow fields depends on what’s present on the property page. This actor pushes the parsed property data as-is (with one normalization step for listingSubType → listing_sub_type).

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Setting It Up

Drop this into your input.json and you're ready to go:

{
  "startUrls": [
    { "url": "https://www.zillow.com/homedetails/4-Huff-Dr-Greenville-SC-29611/49339396_zpid/" }
  ],
  "addresses": [
    "123 Main St, New York, NY"
  ],
  "propertyStatus": "Sold",
  "extractBuildingUnits": "All",
  "datasetId": ""
}

Parameter	Required	What It Does
startUrls	⬜	Zillow property detail URLs to scrape (Zillow home detail URLs).
addresses	⬜	Addresses that the actor turns into Zillow property links and then scrapes.
propertyStatus	⬜	Lets you indicate which status you’re targeting for properties from your startUrls (defaults to Sold).
extractBuildingUnits	⬜	Controls whether the actor extracts unit details from a building page (defaults to All).
datasetId	⬜	If you have another Apify dataset containing Zillow URLs, you can provide its ID and this actor will read URLs from it and process them.

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What It Does

Zillow Detail Scraper automatically retrieves property details from Zillow URLs (or from addresses you provide), and writes each result to your Apify dataset.

Scrapes Zillow property details from URLs or addresses

You can supply direct Zillow property detail URLs via startUrls, or pass human-readable addresses and let the actor resolve them to Zillow pages and extract details. This makes it a practical Zillow property data scraper when your sources come in different formats.

Extracts building units when you need them

With extractBuildingUnits, you can control whether the actor extracts unit-level records from building pages. When enabled, unit objects are pushed to the dataset individually, which is ideal for Zillow listing data extraction where each unit needs to be analyzed separately.

Normalizes listing sub-type flags for easier filtering

The actor converts listingSubType into a normalized listing_sub_type object using a mapping (for example, isPending becomes is_pending, isFSBO becomes is_FSBO, and more). This helps turn Zillow details scraper output into clean filters for your pipeline.

Supports bulk runs from existing Apify datasets

If you already ran a separate discovery step and saved results to an Apify dataset, you can provide datasetId. The actor reads items from that dataset and processes any Zillow URLs it finds, streamlining Zillow listing scraper workflows.

Includes resilience when pages are blocked or fail

The actor uses a browser-based approach to access Zillow pages and includes retry logic with graceful handling. If property data can’t be found or access is blocked, the actor logs the failure and moves on rather than silently stopping.

Overall, Zillow Detail Scraper helps you turn Zillow details into an analysis-ready dataset—fast enough for Zillow web scraping tool-style workflows, without manual scraping labor.

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Why Zillow Detail Scraper?

There are plenty of ways to pull data from Zillow—here’s why Zillow Detail Scraper stands out.

Built for unit-level extraction, not just page-level metadata

If you’re working on building-level inventories, the extractBuildingUnits option lets you extract all building units (or narrower subsets) so you can analyze at the right granularity for Zillow rent estimate scraper-style datasets.

Clean, dataset-ready output with consistent normalization

The actor pushes structured records to the dataset and normalizes listingSubType into listing_sub_type. That means less time transforming Zillow listing scraper chrome extension–style exports into something your team can filter quickly.

Designed for reliability at scale

It uses retries and fallbacks for resilience, and it’s built to keep going through a queue. That’s especially important for Zillow real estate data scraper runs where you might have dozens or hundreds of targets in one job.

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Real-World Use Cases

Here's how different teams put Zillow Detail Scraper to work:

Real Estate Marketing Teams
A marketing team collects a list of target properties for campaigns and needs consistent detail fields fast. They feed property URLs into Zillow Detail Scraper, export the dataset, and quickly build segments like “Sold” vs “For Rent” while optionally including building units for multi-unit analysis.

Lead Generation Specialists
A lead gen workflow starts with a property list, but outreach requires structured context to prioritize. They run Zillow Detail Scraper on URLs pulled from their prior Apify discovery dataset, then filter results for the right listing types using the normalized listing_sub_type fields.

Data Analysts Building Property Inventories
An analyst needs to join property details with other datasets and wants stable keys and unit-level structure. By enabling building unit extraction, they produce a dataset where unit records can be matched downstream for reporting and modeling.

Research Teams Monitoring Housing Market Signals
Researchers compare property outcomes over time and need repeatable extraction runs. They use property status input to guide what they’re targeting, keep results in Apify for refresh cycles, and export JSON/CSV/Excel for their analysis pipelines.

Automation Developers and Integrators
A developer integrates property detail scraping into an automation pipeline. They trigger Zillow Detail Scraper with inputs that come from another system, then pass dataset outputs to downstream steps via export or API access—keeping the process consistent for each scheduled run.

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How to Run It

No code required. Here's how to get your first results in under 5 minutes:

1. Open the actor on Apify — go to the actor page on console.apify.com.
2. Enter your inputs — add Zillow property detail URLs in startUrls and/or provide addresses. Use propertyStatus and set extractBuildingUnits to what you need.
3. Optional: reuse a previous dataset — if you have a dataset of Zillow URLs, paste its ID into datasetId.
4. Configure proxy settings (if prompted) — keep the run reliable for larger batches by using Apify Proxy support.
5. Start the run and watch the live log — monitor progress as each Zillow Detail Scraper target is processed.
6. Open the Dataset tab — results are written to your dataset as the actor processes each record.
7. Export in your preferred format — export JSON, CSV, or Excel from the Apify dataset tab.

The whole setup takes under 5 minutes — results start appearing within seconds of launch.

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Export & Integration Options

Once your data is collected, Zillow Detail Scraper fits directly into your existing workflow.

You can export your dataset as JSON, CSV, or Excel from the Apify dashboard (from the dataset where the actor writes results). This is great for analysis-ready Zillow property data scraper exports.

For automation and pipelines, you can integrate via API access to pull results programmatically and connect downstream workflows. If you use Apify automations or no-code tools like Zapier/Make, you can route exported or fetched dataset items into CRMs and data stores.

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Pricing

Zillow Detail Scraper runs on Apify, which includes a free tier — no credit card needed to start. On Apify, free tier usage is provided as $5 platform credits on sign-up, which should be enough for several real test runs.

For heavier workloads, scaling is typically pay-as-you-go based on Actor compute units (CUs) without monthly lock-in. Check the Apify pricing page for the latest details and plan options, then start with a small run and scale when you need to.

Start free at apify.com — scale up when you need to.

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Reliability & Limitations

What We Handle	How
Blocks and access issues	Retries with resilience and skips failed targets after attempts.
Building page complexity	Supports extractBuildingUnits to extract unit details when available.
Output consistency	Pushes parsed records to the dataset and normalizes listingSubType into listing_sub_type.
Queue processing	Continues through multiple targets and records progress in logs.
Proxy reliability (high level)	Works with Apify Proxy support for more reliable scraping.

Limitations: This actor relies on public web page content and may fail if a page is blocked or if property data isn’t available in the expected rendered content. It won’t provide any data that isn’t accessible from the Zillow page content available during the run. For enterprise-scale runs or custom needs, we can help.

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Frequently Asked Questions

Is there a free plan?

Yes—Apify provides a free tier with platform credits so you can run Zillow Detail Scraper for testing without a credit card.

Do I need to log in or create an account on Zillow?

No. Zillow Detail Scraper is designed to scrape publicly accessible property pages using the inputs you provide.

How accurate is the extracted data?

Accuracy depends on what Zillow provides on each property page at the time of scraping. The actor returns what it can parse from the page content and writes the parsed result to your dataset.

How many results can I get per run?

You can process a queue of property targets per run depending on your Apify compute capacity. For larger batches, plan multiple runs if you want better control over retries and runtime.

How fresh is the data?

The data freshness matches when each property URL is processed during your run. If listings change frequently, you’ll want to rerun to refresh results.

Is this legal? Does it comply with GDPR / CCPA?

The actor works with publicly available data. Whether your use of the data complies with GDPR, CCPA, and applicable regulations is your responsibility—always follow relevant privacy and platform rules.

Can I export to Google Sheets or Excel?

Yes. You can export your dataset as JSON, CSV, or Excel from the Apify dashboard, then import it into Google Sheets or Excel as needed.

Can I schedule this to run automatically?

Yes. In Apify, you can schedule actor runs so Zillow Detail Scraper executes automatically based on a cron-like schedule using your chosen inputs.

Can I access results via the API?

Yes. You can access the dataset results programmatically using Apify API patterns for retrieving dataset contents for your Zillow listing scraper workflows.

What happens when the actor encounters an error?

If property data can’t be extracted for a given URL, the actor logs the failure and continues processing the remaining targets. Failed records aren’t pushed as successful results, and the run won’t stop just because one property failed.

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Get Help & Use Responsibly

Got a question about Zillow Detail Scraper or a feature you'd like added? Reach out at dataforleads@gmail.com. We can help with practical setup questions (like batching URLs vs addresses) and we actively maintain this actor based on user feedback—tell us what you want to scrape next (for example, tighter building-unit filtering for Zillow details scraper use cases).

Publicly available data: Zillow Detail Scraper is designed to work with publicly available data on Zillow. It does not access private accounts, login-gated pages, or password-protected content. You’re responsible for complying with GDPR, CCPA, and any applicable platform terms when using or storing the extracted data. For data removal requests, contact dataforleads@gmail.com. Use responsibly, ethically, and only for lawful purposes.