Foreclosed Property Scraper

Finding foreclosures the hard way means paying a monthly fee to some niche real estate portal or digging through county records by hand. This scraper skips all of that. Tell it a ZIP code or a state and it pulls back the homes currently in foreclosure (or recently sold off) as clean, structured rows: asking price, full street address, beds and baths, square footage, agent contacts, coordinates, photos, and more. One run gives you a foreclosure dataset you can drop straight into a spreadsheet or a model.

What you get

Every property comes back as one flat row, so the columns line up when you load results into a sheet or database. The fields fall into a few groups:

• Identity and status — propertyId, listingId, listingSlug, listingStatus, isForeclosure, plus lifecycle flags like isPending, isContingent, isAuction, isNewListing, isPriceReduced
• Price and dates — askingPrice, salePrice, priceCutAmount, listedDate, soldDate
• Property details — propertyKind, propertySubKind, bedroomCount, bathroomCount, interiorSqft, lotSqft, yearBuilt, garageSpaces, hasMatterport
• Location — streetAddress, cityName, stateName, stateCode, postalCode, lat, lng, countyName, countyFips, streetViewUrl
• Media — coverPhotoUrl, photoCount, photoLinks
• Agents and source — primaryAgentName, primaryAgentEmail, primaryAgentPhones, secondaryAgentName, brandingName, sourceName, openHouses

Quick start

1. Press Try for free to open the input form.
2. Type a location into at least one of ZIP code, City, or State abbreviation.
3. Add any filters you want, such as price range, bedroom count, or property type.
4. Set a Total listings cap to control spend, pick a proxy, then press Start.
5. When the run finishes, export the data as JSON, CSV, Excel, or XML.

Use cases

• Wholesaling — pull a fresh foreclosure list for one ZIP code and reach out to distressed sellers first
• Comparable sales — export recently sold foreclosures to build a market analysis without retyping anything
• Lead generation — collect listing agent names, emails, and phone numbers for bank-owned homes
• Market monitoring — schedule a daily run to catch new foreclosures the moment they hit your target areas
• Investment screening — filter by price, beds, square footage, and year built to shortlist deals that fit your model

Input

Field	Type	Required	Description
postalCode	string	One of postalCode, cityName, stateCode, or addressLine	Single 5-digit US ZIP code, the tightest location filter. Example 33139.
stateCode	string	One of postalCode, cityName, stateCode, or addressLine	Two-letter US state code, best paired with another filter. Prefilled FL.
cityName	string	One of postalCode, cityName, stateCode, or addressLine	Exact city name; pair with stateCode for clean results. Prefilled Miami.
streetName	string	Optional	Limit results to homes on one named street, e.g. Ocean Drive.
addressLine	string	Optional	One precise street address line.
propertyTypes	array of strings	No	Keep only chosen property categories, e.g. single_family, condo.
featureKeywords	array of strings	No	Filter by amenities such as pool, waterfront, central_air.
bedsMin / bedsMax	integer	No	Lowest and highest bedroom counts to allow.
bathsMin / bathsMax	integer	No	Lowest and highest bathroom counts to allow.
listPriceMin / listPriceMax	integer	No	Asking price range in USD.
soldPriceMin / soldPriceMax	integer	No	Sold price range in USD.
soldDateFrom / soldDateTo	string	No	Closed-sale date window, written YYYY-MM-DD.
sqftMin / sqftMax	integer	No	Interior living area range in square feet.
lotSqftMin / lotSqftMax	integer	No	Lot size range in square feet.
yearBuiltFrom / yearBuiltTo	integer	No	Construction year range.
hoaFeeMax	integer	No	Largest monthly HOA fee to allow.
excludeHoa	boolean	No	Return only homes with no HOA fee. Default false.
openHouseFrom / openHouseTo	string	No	Open-house date window, written YYYY-MM-DD.
pageSize	integer	No	Listings fetched per request. Default 150.
startOffset	integer	No	Position where pagination begins. Default 0.
resultsLimit	integer	No	Hard cap on total listings collected. Default 3000.
runTimeoutSeconds	integer	No	Whole-run time budget in seconds. Default 360.
requestTimeoutSeconds	integer	No	Per-request time budget in seconds. Default 45.
proxyConfiguration	object	No	Proxies used for requests. Defaults to US datacenter.

Example input

{
    "stateCode": "FL",
    "cityName": "Miami",
    "propertyTypes": ["single_family", "multi_family"],
    "listPriceMax": 750000,
    "bedsMin": 2,
    "resultsLimit": 200,
    "proxyConfiguration": {
        "useApifyProxy": true,
        "apifyProxyCountry": "US"
    }
}

Output

Each foreclosed home becomes one flat JSON row. Every declared field is present on a normal row, and unavailable values come back as null or empty so your dataset stays rectangular.

Example output

{
    "listingAttribution": true,
    "propertyId": "9482736150",
    "listingId": "2967451308",
    "listingSlug": "742-Evergreen-Ter_Miami_FL_33139_M94827-36150",
    "listingStatus": "for_sale",
    "askingPrice": 389000,
    "salePrice": null,
    "priceCutAmount": 15000,
    "listedDate": "2026-05-18",
    "soldDate": "",
    "propertyKind": "single_family",
    "propertySubKind": "",
    "bedroomCount": 3,
    "bathroomCount": "2",
    "interiorSqft": 1640,
    "lotSqft": 5200,
    "yearBuilt": 1998,
    "garageSpaces": 2,
    "streetAddress": "742 Evergreen Terrace",
    "cityName": "Miami",
    "stateName": "Florida",
    "stateCode": "FL",
    "postalCode": "33139",
    "lat": 25.7907,
    "lng": -80.1300,
    "countyName": "Miami-Dade County",
    "countyFips": "12086",
    "streetViewUrl": "https://maps.googleapis.com/maps/api/streetview?location=25.7907,-80.1300",
    "coverPhotoUrl": "https://ap.rdcpix.com/example/cover-2x.jpg",
    "photoCount": 24,
    "photoLinks": [
        "https://ap.rdcpix.com/example/photo-1.jpg",
        "https://ap.rdcpix.com/example/photo-2.jpg"
    ],
    "hasMatterport": false,
    "isComingSoon": false,
    "isNewListing": true,
    "isPriceReduced": true,
    "isForeclosure": true,
    "isNewConstruction": false,
    "isPending": false,
    "isContingent": false,
    "isAuction": false,
    "isFractionallyOwned": false,
    "sourceId": "MIAMI",
    "sourceName": "Miami Association of Realtors",
    "sourceType": "mls",
    "sourceListingId": "A11234567",
    "showContactAgent": true,
    "marketType": "for_sale",
    "leadType": "advantage",
    "isVaEligible": false,
    "productList": ["co_broke"],
    "productBrandName": "essentials",
    "primaryAgentType": "seller",
    "primaryAgentName": "Maria Alvarez",
    "primaryAgentEmail": "maria.alvarez@example.com",
    "primaryAgentOffice": "Coastal Realty Group",
    "primaryAgentPhones": ["3055550142"],
    "secondaryAgentType": null,
    "secondaryAgentName": null,
    "secondaryAgentEmail": null,
    "secondaryAgentOffice": null,
    "secondaryAgentPhones": [],
    "brandingName": "Coastal Realty Group",
    "searchPromotions": [],
    "communityPromotions": [],
    "openHouses": [
        {
            "start_date": "2026-06-07T17:00:00Z",
            "end_date": "2026-06-07T20:00:00Z",
            "description": "Saturday open house",
            "time_zone": "America/New_York"
        }
    ],
    "collectedAt": "2026-06-29T12:00:00.000000+00:00",
    "errorMessage": null
}

Output fields

Field	Type	Description
listingAttribution	boolean	Source attribution flag from the listing feed
propertyId	string	Stable internal identifier for the property
listingId	string	Identifier of the specific listing
listingSlug	string	URL slug for the listing
listingStatus	string	Current state, such as for_sale or sold
askingPrice	integer	Current listed asking price in USD
salePrice	integer	Final closing price in USD, on sold homes only
priceCutAmount	integer	How far the asking price has been reduced, in USD
listedDate	string	ISO 8601 date the listing went live
soldDate	string	ISO 8601 date the sale closed, when applicable
propertyKind	string	Top-level property type, e.g. single_family
propertySubKind	string	Finer property classification
bedroomCount	integer	Number of bedrooms
bathroomCount	string	Consolidated bathroom count
interiorSqft	integer	Interior living area in square feet
lotSqft	integer	Outdoor lot size in square feet
yearBuilt	integer	Year the home was built
garageSpaces	integer	Number of garage spaces
streetAddress	string	Full street address line
cityName	string	City the property sits in
stateName	string	Full state name
stateCode	string	Two-letter US state code
postalCode	string	ZIP or postal code
lat	number	Latitude of the property
lng	number	Longitude of the property
countyName	string	County the property falls under
countyFips	string	County FIPS code
streetViewUrl	string	Link to a street-level map preview
coverPhotoUrl	string	Direct link to the headline photo
photoCount	integer	How many photos accompany the listing
photoLinks	array	Direct image URLs for the property
hasMatterport	boolean	True when a Matterport 3D tour is attached
isComingSoon	boolean	True when marked coming soon
isNewListing	boolean	True when flagged as a new listing
isPriceReduced	boolean	True when the price recently dropped
isForeclosure	boolean	True for active foreclosure listings
isNewConstruction	boolean	True when newly built
isPending	boolean	True when the sale is pending
isContingent	boolean	True when the sale is contingent
isAuction	boolean	True when sold at auction
isFractionallyOwned	boolean	True when ownership is fractional
sourceId	string	Identifier of the originating feed
sourceName	string	Name of the originating feed, e.g. the local MLS
sourceType	string	Type of the originating feed
sourceListingId	string	Listing ID in the source feed
showContactAgent	boolean	Whether a contact-an-agent action is offered
marketType	string	Lead market classification
leadType	string	Lead type for the listing
isVaEligible	boolean	True when flagged Veterans United eligible
productList	array	Distribution products attached to the listing
productBrandName	string	Brand name tied to the products
primaryAgentType	string	Type of the primary representative
primaryAgentName	string	Name of the primary representative
primaryAgentEmail	string	Email for the primary representative
primaryAgentOffice	string	Office handling the primary representative
primaryAgentPhones	array	Phone numbers for the primary representative
secondaryAgentType	string	Type of the secondary representative
secondaryAgentName	string	Name of the secondary representative
secondaryAgentEmail	string	Email for the secondary representative
secondaryAgentOffice	string	Office handling the secondary representative
secondaryAgentPhones	array	Phone numbers for the secondary representative
brandingName	string	Affiliated agent branding label
searchPromotions	array	Search-level promotions attached to the listing
communityPromotions	array	Community-level promotions attached to the listing
openHouses	array	Scheduled open house entries with times and notes
collectedAt	string	ISO 8601 timestamp of when the row was captured
errorMessage	string	Reason a row failed; null on success

Tips for best results

• Always set a location. Give the scraper at least one of postalCode, cityName, stateCode, or addressLine, or it has nothing to search against.
• Start small. Drop resultsLimit to 50 or so on your first run to confirm the fields match your pipeline before pulling thousands.
• Combine cityName with stateCode. A city on its own can match across several states; pairing the two keeps results tight.
• Switch proxy tiers if requests start failing. US datacenter clears most runs; move to residential if you hit repeated errors.
• Raise requestTimeoutSeconds toward 60 to 90 on slower proxies, and give runTimeoutSeconds more headroom for large states.
• Use the sold-date filters for comps. Set soldDateFrom and soldDateTo to gather recently closed foreclosures for a market analysis.

How can I use foreclosure property data?

How can I use the Foreclosed Property Scraper to build a wholesaling lead list? Enter a target ZIP code or city and the scraper returns every active foreclosure there, each with the street address, asking price, beds and baths, and the listing agent's name, email, and phone. Sort by priceCutAmount or listedDate to surface motivated sellers, then export the rows straight into your CRM.

How can I find recently sold foreclosures for a comparable market analysis? Set soldDateFrom and soldDateTo to the window you want and the scraper pulls closed foreclosure sales with salePrice, interiorSqft, yearBuilt, and location fields. Group the results by neighborhood or ZIP to price your own offers against what actually traded.

How can I scrape bank-owned home listings across a whole state? Pass a stateCode such as FL, raise resultsLimit, and the scraper paginates through the available inventory and hands back one row per home. Filter by propertyTypes, price range, or bedroom count to keep only the distressed deals that match your buy box.

How can I monitor new foreclosures automatically? Save your filters and schedule the run on Apify to repeat daily. Each run captures collectedAt and the lifecycle flags like isNewListing and isForeclosure, so you can diff against yesterday's dataset and flag fresh distressed listings as they appear.

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.