Furnished Finder Scraper

Extract furnished rental listings from FurnishedFinder.com by city and state. Fast, reliable, and no proxy required for most runs.

Whether you're tracking the mid-term rental market, building a rental aggregator, or doing real estate research, this scraper returns clean structured data for every listing: price, location, bedrooms, amenities, photos, and more.

What It Scrapes

For every listing, you get:

• Identity — listing ID, canonical URL, title, property type (Apartment, House, Room, etc.)
• Location — city, state, latitude, longitude
• Pricing — monthly rent parsed to an integer USD value plus the raw display text, and fees such as deposit, cleaning, and pet fees
• Specs — bedrooms, bathrooms, square footage, minimum stay days
• Content — space description, neighborhood description, house rules
• Amenities — wifi, kitchen, laundry, pool, pet-friendly, and more
• Bed and bath breakdown — per-room list of bed types and bathroom types
• Photos — full-size image URLs
• Ratings — average rating and review count (when listed)
• Metadata — UTC scrapedAt timestamp on every record

Optional extras

• moreDetails flag — also attach the landlord profile: display name, profile image, username, tenure on the platform, verification badges (email, phone, ID), and a list of nearby hospitals with distances. Actual landlord email and phone are not included because FurnishedFinder gates direct contact behind a logged-in on-site messaging form.
• includeReviews flag — best-effort inclusion of individual reviews for properties where they are publicly embedded on the listing page.

Inputs

Field	Required	Description	Example
city	yes (unless startUrls)	City name to search	"Austin"
state	yes (unless startUrls)	US state — full name or 2-letter code	"Texas" or "TX"
maxItems	no	Max listings to scrape (default 20, up to 1000)	50
minPrice	no	Minimum monthly rent in USD	1000
maxPrice	no	Maximum monthly rent in USD	3000
moveInDate	no	Target move-in date (YYYY-MM-DD)	"2026-09-01"
propertyType	no	Filter by type — multi-select from room (private room) and entire_unit (whole unit). The granular type (Apartment, House, Condo, …) appears in the output propertyType field of every record regardless.	["room","entire_unit"]
moreDetails	no	Attach landlord profile fields (default false)	true
includeReviews	no	Attach reviews array when available (default false)	true
startUrls	no	Override search by passing FurnishedFinder URLs directly — search pages or /property/{id}_{unit} URLs. When set, city and state are ignored.	[{"url":"https://www.furnishedfinder.com/property/979582_1"}]

Output Example

Each dataset record is a flat JSON object. Here is a real record from a live run:

{
  "id": "979582_1",
  "url": "https://www.furnishedfinder.com/property/979582_1",
  "title": "Redriver Haven",
  "propertyType": "Apartment",
  "city": "Austin",
  "state": "Texas",
  "latitude": 30.3024655,
  "longitude": -97.7216704,
  "monthlyPrice": 2000,
  "monthlyPriceText": "$2,000/month",
  "bedrooms": 1,
  "bathrooms": 1,
  "minimumStayDays": 30,
  "spaceDescription": "Quiet, fully furnished apartment near Red River St...",
  "utilitiesIncluded": true,
  "amenities": ["washeranddryeronpremises"],
  "houseRules": [{"name": "petsNotAllowed", "description": "Not Allowed"}],
  "bedList": [{"title": "Bedroom 1", "type": "Queen Bed"}],
  "bathList": [{"title": "Bathroom 1", "type": "Private Bath"}],
  "fees": [
    {"type": "Deposit (Refundable)", "amount": 300},
    {"type": "Cleaning Fee", "amount": 100}
  ],
  "photos": [
    "https://www.furnishedfinder.com/_pdp_/979582/1/979582_1_62490947-full.png"
  ],
  "scrapedAt": "2026-04-11T12:46:26.648593+00:00"
}

Fields that cannot be extracted for a particular listing are simply omitted from that record — you will never see null values.

FAQ

Do I need a proxy? No. The scraper works with plain HTTP requests out of the box. You can still attach an Apify proxy if you hit rate limits on very large runs.

Does it use cookies or run a headless browser? No. It is a pure HTTP scraper, which makes it fast and cheap to run.

Can I scrape a single property by URL? Yes — pass it in startUrls as [{"url": "https://www.furnishedfinder.com/property/<id>_<unit>"}] and the scraper will fetch just that one listing.

Which states are supported? All 50 US states plus the District of Columbia. Enter the full name ("Texas") or the 2-letter code ("TX").

How many listings can I get per city? Up to the maxItems value (default 20, max 1000). The scraper handles FurnishedFinder's result-per-request caps internally, so you don't have to think about pagination.

What happens if a listing is missing a field? Fields that cannot be extracted for a particular listing are omitted from that record's JSON — no nulls. Your downstream pipeline always sees clean, well-typed data.

Are duplicate listings possible? No. Results are deduplicated by property and unit ID within a single run.

Does the scraper return landlord email and phone? No. FurnishedFinder does not publicly expose landlord email and phone — contacting a landlord goes through their on-site messaging form, which requires a login. The moreDetails flag still gives you the landlord's display name, profile image, verification badges (email/phone/ID verified), tenure on the platform, and a list of nearby hospitals, which is the public-facing contact information FurnishedFinder surfaces.

How fresh is the data? Every run fetches live data from FurnishedFinder at the moment the actor runs. There is no cache — each record includes a scrapedAt UTC timestamp so you always know when it was captured.

Can I filter by multiple property types at once? Yes. propertyType accepts an array. The two filter classes FurnishedFinder exposes are room (private room in a shared place) and entire_unit (whole apartment, house, or condo). To include both, pass ["room", "entire_unit"]. The granular type — Apartment, House, Condo, etc. — is returned in the output propertyType field of every record so you can filter further downstream.

Can I combine a price range with a move-in date? Yes. All filters (minPrice, maxPrice, moveInDate, propertyType) stack and are applied together.

Use Cases

• Real estate analytics — track mid-term rental prices and inventory by city
• Relocation research — find furnished rentals meeting your price and move-in criteria
• Competitive intelligence — monitor what's listed in a particular market
• Data enrichment — augment your own property database with live market listings
• Lead generation — build a list of properties by area for outreach and marketing
• Travel nursing and corporate housing — surface 30+ day stays in target hospital markets

Getting Started

1. Enter a city and state — that's all you need for a basic run.
2. Optional: add filters like minPrice, maxPrice, moveInDate, or propertyType to narrow results.
3. Optional: toggle moreDetails or includeReviews for richer output.
4. Click Start and get clean JSON listings in your dataset.