Furnished Finder Rental Scraper

Extract furnished monthly rental listings from Furnished Finder in a structured dataset. Collect availability, pricing, property details, amenities, coordinates, and listing links for research, monitoring, and market analysis. Built for recurring runs with clean output and deduplicated listing IDs.

Features

• City-level extraction - Collect rental listings from Furnished Finder housing pages.
• Rich listing fields - Save pricing, beds, baths, amenities, and location coordinates.
• Deduplicated output - Keep only unique listings by listing_id.
• Clean dataset - Removes null-only values to keep output compact and usable.
• Fast start defaults - QA-safe defaults for quick test runs.

Use Cases

Rental Market Monitoring

Track furnished rental availability and pricing changes across target cities.

Lead Generation

Build lists of active listings with direct URLs and listing IDs for outreach workflows.

Competitive Analysis

Compare unit types, amenities, and price ranges across neighborhoods.

Internal Dashboards

Feed structured rental data into BI tools for trend analysis and reporting.

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Input Parameters

Parameter	Type	Required	Default	Description
startUrls	Array	No	Austin URL list	Optional list of housing URLs.
results_wanted	Integer	No	20	Maximum number of unique listings to save.
max_pages	Integer	No	1	Number of URL path variants to attempt (/2, /3, ...).
proxyConfiguration	Object	No	{"useApifyProxy": false}	Proxy settings for browser requests.

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Output Data

Each dataset item can contain:

Field	Type	Description
listing_id	String	Unique Furnished Finder listing identifier.
title	String	Listing title.
url	String	Listing detail page URL.
source_url	String	Source housing URL used for extraction.
place	String	Encoded place slug.
city	String	City name.
state	String	State code.
formatted_place_name	String	Human-readable place label when available.
search_id	String	Search identifier from listing payload.
search_session_id	String	Search session identifier.
sort_variant_id	String	Sorting/availability variant identifier.
property_type	String	Property category (house, apartment, condo, etc.).
property_type_class	String	Property class grouping.
available_on	String	Availability text from listing feed.
is_available_now	Boolean	Whether listing is currently available.
is_fully_available	Boolean	Full availability indicator when present.
minimum_stay_days	Number	Minimum stay duration in days.
rent_amount	String	Displayed rent amount.
rent_currency	String	Rent currency code.
bedrooms	Number	Bedroom count.
bathrooms	Number	Bathroom count.
total_sleeps	Number	Total sleep capacity.
laundry_type	String	Laundry setup text.
amenities	Array	Amenity tags.
latitude	Number	Approximate latitude.
longitude	Number	Approximate longitude.
description	String	Listing description when provided.
photos	Array	Listing photo URLs.
featured_photo	String	Main featured photo URL.
best_matched_availability_window	Object	Best matched date window.
future_availability_window	Object	Future date window.

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Usage Examples

Basic Austin Run

{
  "startUrls": [
    { "url": "https://www.furnishedfinder.com/housing/us--tx--austin" }
  ],
  "results_wanted": 20
}

Multiple Start URLs

{
  "startUrls": [
    { "url": "https://www.furnishedfinder.com/housing/us--tx--austin" },
    { "url": "https://www.furnishedfinder.com/housing/us--tx--dallas" }
  ],
  "results_wanted": 60,
  "max_pages": 2
}

Proxy-Enabled Run

{
  "startUrls": [
    { "url": "https://www.furnishedfinder.com/housing/us--tx--austin" }
  ],
  "results_wanted": 50,
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}

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Sample Output

{
  "listing_id": "974043_1",
  "title": "Charming Central Austin Studio, Pool, Close to UT & Downtown",
  "url": "https://www.furnishedfinder.com/property/974043_1",
  "source_url": "https://www.furnishedfinder.com/housing/us--tx--austin",
  "place": "us--tx--austin",
  "city": "Austin",
  "state": "TX",
  "property_type": "Apartment",
  "property_type_class": "entire_unit",
  "available_on": "Available",
  "is_available_now": true,
  "minimum_stay_days": 30,
  "rent_amount": "$1,675/month",
  "rent_currency": "USD",
  "bedrooms": 1,
  "bathrooms": 1,
  "total_sleeps": 2,
  "laundry_type": "In Complex W/D",
  "amenities": ["wifi", "utilitiesIncluded", "parking"],
  "latitude": 30.2997635,
  "longitude": -97.7331021,
  "featured_photo": "https://staticproperties.furnishedfinder.com/974043/1/974043_1_62341674-full.jpeg"
}

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Tips for Best Results

Start With Real Housing URLs

Use direct city housing URLs to get stable results quickly.

Keep Test Runs Small

Use results_wanted: 20 for quick validation before larger runs.

Use Proxy When Needed

If your run gets unstable in certain regions, enable proxy settings.

Schedule Recurring Runs

Run daily or weekly to monitor availability and price changes.

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Integrations

Connect your output with:

• Google Sheets - Build live rental trackers.
• Airtable - Create searchable listing databases.
• Make - Automate downstream workflows.
• Zapier - Trigger actions from new listings.
• Webhooks - Send listing data to your own endpoint.

Export Formats

• JSON - Best for APIs and app pipelines.
• CSV - Spreadsheet-friendly analysis.
• Excel - Team reporting and sharing.
• XML - Legacy or enterprise integrations.

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Frequently Asked Questions

How many listings can I collect?

You can request as many as needed with results_wanted, but output depends on available unique listings.

Can I scrape multiple cities in one run?

Yes. Provide multiple entries in startUrls.

Why are some fields missing on some listings?

Some listings do not provide every field. Missing values are removed automatically from output.

Can I monitor listing changes over time?

Yes. Schedule recurring runs and compare by listing_id.

Does the actor deduplicate listings?

Yes. The actor keeps unique items by listing_id.

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Support

For issues or feature requests, use the Apify Actor issue tracker or contact through Apify Console.

Resources

• Apify Documentation
• Apify API Reference
• Apify Schedules

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Legal Notice

This actor is provided for lawful data collection use cases. You are responsible for complying with website terms, local laws, and applicable data regulations.