Skip Tracing Scraper

You have a property address but no idea who owns it. Public listing sites show the house, not the person behind it, and pulling owner names one address at a time through a paid lookup tool gets slow and expensive fast. This scraper closes that gap. Paste a US property address and it matches the parcel, returns the owner and any co-owner on record, and bundles a profile of the property plus an equity snapshot. Run a single address or drop in a whole list and let it work through them.

What you get

Each address you submit comes back as one row with a steady shape. When a piece of data is missing it returns null rather than disappearing, so your columns line up the same way every time you load the results into a sheet or CRM. Each row carries:

• Owner contacts — ownerName and coOwnerName straight from the property record
• Property profile — propertyProfile, a nested object with the parcel's structural and address details
• Equity snapshot — equitySnapshot, a nested object with estimated value and equity figures
• Run context — the original searchAddress, a collectedAt timestamp, and an errorMessage field that tells you why a row came back empty

Quick start

1. Hit Try for free and open the input form.
2. Add one or more US property addresses to Property addresses, one per line.
3. Pick a proxy type (the default is fine for most runs).
4. Press Start, then export as JSON, CSV, Excel, or XML once the run finishes.

Use cases

• Real estate investors — turn a shortlist of target properties into a contact list of owners to approach directly
• Wholesalers and flippers — find the decision-maker behind a distressed or off-market house
• Title and escrow teams — confirm the owner of record before opening a file
• Property service providers — reach homeowners for roofing, solar, or renovation outreach
• Market researchers — pair owner and equity data with addresses to study a neighborhood's ownership mix
• Lead generation — build a clean owner-contact dataset from a list of addresses you already hold

Input

Field	Type	Required	Description
propertyAddresses	array of strings	Yes	US property addresses to trace, one per entry. Include house number, street, city, state, and ZIP for the best match.
proxyConfiguration	object	No	Proxies used for lookup requests. Defaults to Datacenter (Anywhere); switch to Residential if lookups get blocked.

Example input

{
    "propertyAddresses": [
        "812 Maplewood Ave, Charlotte, NC 28203",
        "27 Birchfield Ln, Austin, TX 78745"
    ],
    "proxyConfiguration": {
        "useApifyProxy": true,
        "apifyProxyGroups": ["RESIDENTIAL"]
    }
}

Output

Every address you submit produces one row. Every field is always present — values that could not be found come back as null, so your dataset stays rectangular and easy to parse.

Example output

{
    "searchAddress": "812 Maplewood Ave, Charlotte, NC 28203",
    "ownerName": "Marcus T Holloway",
    "coOwnerName": "Diane R Holloway",
    "propertyProfile": {
        "bedrooms": 3,
        "bathrooms": 2,
        "livingArea": 1840,
        "yearBuilt": 1996,
        "propertyType": "Single Family"
    },
    "equitySnapshot": {
        "estimatedValue": 412000,
        "estimatedEquity": 268500,
        "lastSalePrice": 215000,
        "lastSaleDate": "2014-08-19"
    },
    "collectedAt": "2026-06-29T12:00:00.000000+00:00",
    "errorMessage": null
}

Output fields

Field	Type	Description
searchAddress	string	The property address you submitted for this lookup
ownerName	string	Name of the primary owner on record
coOwnerName	string	Name of a second owner on record, when the property is jointly held
propertyProfile	object	Structural and parcel details for the matched property
equitySnapshot	object	Estimated value and equity figures tied to the property
collectedAt	string	ISO 8601 timestamp of when the record was captured
errorMessage	string	Reason the address failed; null on success

Tips for best results

• Write addresses in full. House number, street, city, state, and ZIP give the matcher the most to work with and cut down on wrong or empty results.
• Start with a handful. Run five or ten addresses first to confirm the output fits your pipeline before loading hundreds.
• Switch to Residential if you get blocked. Datacenter clears most lookups; residential helps when you see repeated failures in the errorMessage field.
• Check errorMessage before assuming a miss. A populated errorMessage tells you whether the address simply had no match or whether the request itself failed.
• Expect nested objects. propertyProfile and equitySnapshot come back as JSON objects, so flatten them if your destination needs one value per column.

How can I use property owner data?

How can I use the Skip Tracing Scraper to find a property owner from an address? Paste the address into Property addresses and run it. The scraper matches the parcel and returns the owner and any co-owner on record, along with a property profile and equity snapshot, so a bare address becomes a real contact you can act on.

How can I skip trace a list of addresses for real estate leads? Load your whole list into Property addresses, one per line, and start the run. Each address comes back as a single row with owner names and property data, giving you a clean owner-contact dataset you can drop straight into a CRM or outreach sequence.

How can I check who owns a house before reaching out? Submit the property address and read the ownerName and coOwnerName fields. You confirm exactly who holds the property, plus its estimated value and equity, before spending time on outreach to a wholesaling, listing, or service prospect.

How can I enrich property records with owner and equity data? If you already keep a table of addresses, feed them in and the scraper returns matching owner names, a structural property profile, and an equity snapshot for each. Join the results back to your records by searchAddress to fill in the ownership and value picture.

Is it legal to scrape data?

Our actors are ethical and do not extract any private user data, such as email addresses or private contact information. They only extract what the user has chosen to share publicly. We therefore believe that our actors, when used for ethical purposes by Apify users, are safe.

However, you should be aware that your results could contain personal data. Personal data is protected by the GDPR in the European Union and by other regulations around the world. You should not scrape personal data unless you have a legitimate reason to do so. If you're unsure whether your reason is legitimate, consult your lawyers.

You can also read Apify's blog post on the legality of web scraping.

Support

Questions, feature requests, or a field you'd like added? Reach out at data.apify@proton.me and we'll get back to you.