Guide to Furnished Finder Data Extraction for Rental Markets

The most efficient way to extract data from Furnished Finder is to use a specialized scraper that targets their public GraphQL search endpoint. This method allows you to bypass the standard 72-listing limit per search by automatically subdividing price ranges to capture up to 1000 listings per city. By configuring specific city and state inputs, you can retrieve structured JSON data including monthly rent, amenities, coordinates, and landlord verification status without manual browsing.

Practical use cases

Use case 1: Real estate analytics

Outcome: A comprehensive dataset of mid-term rental inventory to calculate average price per bedroom and market saturation in specific US cities.

Question to answer: What is the current median monthly rent for entire units versus private rooms in a specific metropolitan area?

Configure: Set the city and state fields for your target market. Ensure propertyType is set to both room and entire_unit to capture the full market spectrum. Set maxItems to 1000 to ensure the broadest possible sample size.

Working method: Run the Furnished Finder Scraper using the GraphQL-backed search mode. The scraper will automatically slice the price range to overcome the site's pagination limits. Once the data is collected, use the monthlyPrice integer field and the propertyType string (which identifies if a listing is an Apartment, House, or Condo) to perform a comparative analysis.

Deliverable: A CSV or JSON dataset containing monthlyPrice, bedrooms, bathrooms, and propertyType for all available listings in the target city.

Stop condition: The run finishes when the maxItems limit is reached or all unique listings in the specified city have been deduplicated and saved.

Use case 2: Relocation research

Outcome: A filtered list of move-in ready furnished properties that meet specific budget and timeline requirements for corporate or medical travelers.

Question to answer: Which properties are available for move-in on a specific date within a $2,000 to $3,500 monthly budget?

Configure: Input the moveInDate in YYYY-MM-DD format. Set minPrice to 2000 and maxPrice to 3500. Use the moreDetails toggle to include nearby hospital information, which is critical for travel nursing assignments.

Working method: Execute the scraper with the stacked filters. The actor will apply the price and date constraints directly to the search request. It will then perform best-effort property-page enrichment to pull in the nearbyHospitals list and minimumStayDays to ensure the properties allow for the required duration.

Deliverable: A curated list of property URLs, photos, and space descriptions, including the scrapedAt timestamp to verify data freshness.

Stop condition: The process stops once the scraper has checked all listings matching the price and date criteria up to the maxItems limit.

Use case 3: Landlord verification and outreach

Outcome: A directory of active mid-term rental landlords and property managers for service offerings or market outreach.

Question to answer: Who are the most active or verified landlords in a specific region, and what is their platform tenure?

Configure: Enable the moreDetails flag to extract landlord profile data. Provide a list of specific property URLs in the startUrls field if targeting known high-value listings, or use city/state inputs for a broader territory search.

Working method: The scraper navigates to individual property pages to extract the landlord's display name, profile image, and verification badges. It specifically looks for email, phone, and ID verification statuses. While direct contact info is gated, the landlord object provides the public-facing identity and tenure on the platform.

Deliverable: A lead list containing landlord names, their verification status, and the url of their active listings.

Stop condition: The run completes after the scraper attempts to enrich the base listing records with the optional landlord details for the requested number of items.

Operational workflow

1. Define geographic scope: Identify the city and state. Use the full state name or the two-letter ISO code.
2. Set extraction depth: Determine if you need basic search results (fastest) or enriched details (requires visiting individual property pages). Toggle moreDetails accordingly.
3. Configure proxy settings: Use the default Apify Proxy (datacenter) for standard runs. If you encounter blocks or empty results, switch to residential proxies to navigate Cloudflare protections.
4. Execute and monitor: Start the Furnished Finder Scraper. Monitor the logs for any Cloudflare challenges; the actor will automatically attempt to use a headless browser fallback if the GraphQL endpoint is restricted.
5. Validate output: Review the dataset for the id and url fields to ensure unique records. Check the scrapedAt field to confirm the data reflects the live site state.

Roles and actors

• Furnished Finder Scraper: The primary execution engine that handles request rotation, price-slice subdivision, and data parsing. It manages the transition between GraphQL API calls and headless browser fallbacks.
• Apify Proxy: The infrastructure component that provides the IP rotation necessary to avoid detection by Cloudflare. It supports both datacenter and residential IP types.
• Data Analyst: The individual responsible for configuring the minPrice, maxPrice, and propertyType filters to align the extraction with business requirements.

Data quality and limits

• Pagination limits: Furnished Finder typically limits search results to 72 items. The scraper bypasses this by splitting the search into multiple price-based requests, allowing for up to 1000 items.
• Field availability: Data is extracted on a best-effort basis. If a landlord has not provided a square footage value or a neighborhood description, those specific fields are omitted from the JSON record rather than being returned as null.
• Contact information: Direct landlord email and phone numbers are not publicly accessible and are not included in the output. The scraper provides verification badges and profile names instead.
• Geographic focus: The tool is designed specifically for the US market, supporting all 50 states and the District of Columbia.

Frequently asked questions

Do I need to configure a proxy? Yes, a proxy is required because the target site uses Cloudflare. The scraper has Apify datacenter proxies enabled by default, which works for most tasks. For very large volumes, residential proxies are recommended to maintain high success rates.

How many items can I scrape per city? You can scrape up to 1000 items per city. The scraper automatically handles the technical challenge of Furnished Finder's 72-item display limit by using price-range subdivision to find more listings.

Are duplicate listings removed automatically? Yes. The scraper deduplicates results within a single run based on the property and unit ID, ensuring that you do not pay for or store the same listing twice in one dataset.

What happens if optional details are missing? If a listing is missing optional data like reviews or specific fees, the scraper simply omits those keys from the JSON object. This ensures the resulting dataset is clean and free of unnecessary null values.

Can I target single properties directly? Yes. You can bypass the city/state search by providing specific property URLs in the startUrls input. This is useful for monitoring specific high-interest listings for price changes or availability.

Resources

• Furnished Finder Scraper Actor Page
• Official Furnished Finder Website
• Apify Proxy Documentation