Rentcafe Scraper

Extract listing data from RentCafe pages in a structured dataset. Collect core property information such as rent ranges, bedroom and bathroom ranges, area, availability signals, and listing links. This scraper is built for market research, lead generation, and rental inventory monitoring.

Features

• Flexible URL support — Works with RentCafe listing and search page URL patterns.
• Automatic context discovery — Detects API context from page/network responses without manual token input.
• Rich property coverage — Capture pricing ranges, bedroom and bathroom ranges, area, and listing metadata.
• Clean dataset output — Saves records without null-only fields for easier downstream use.
• Deduplicated records — Removes duplicates across captured listing payloads.
• Proxy-ready runs — Supports proxy configuration for stable production scraping.

Use Cases

Rental Market Research

Track apartment inventory and pricing by city to understand market positioning and rent distribution.

Lead List Building

Build structured property datasets with URLs, addresses, and contact-ready metadata.

Competitor Monitoring

Monitor listing updates and compare available inventory ranges across target markets.

Data Pipelines

Feed clean listing records into dashboards, spreadsheets, and automation workflows.

Input Parameters

Parameter	Type	Required	Default	Description
startUrl	String	No	https://www.rentcafe.com/apartments-for-rent/new-york-city-ny/	Main RentCafe URL to scrape.
url	String	No	—	Optional alias for startUrl.
results_wanted	Integer	No	20	Maximum number of records to save.
max_pages	Integer	No	1	Safety cap for pagination attempts.
proxyConfiguration	Object	No	Apify Proxy Residential	Proxy settings for reliability.

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Output Data

Each dataset item can include:

Field	Type	Description
property_name	String	Name of the apartment property.
property_id	Number	Property identifier when available.
property_code	String	Property code when available.
address	String	Street address.
city	String	City of the listing.
state	String	State of the listing.
zip_code	String	Postal code.
full_address	String	Combined address value.
site_url	String	Property site URL.
detail_url	String	Property details URL.
phone	String	Contact phone if available.
latitude	Number	Latitude coordinate.
longitude	Number	Longitude coordinate.
min_bedrooms	Number	Minimum listed bedrooms.
max_bedrooms	Number	Maximum listed bedrooms.
min_bathrooms	Number	Minimum listed bathrooms.
max_bathrooms	Number	Maximum listed bathrooms.
min_rent	Number	Minimum listed rent.
max_rent	Number	Maximum listed rent.
min_area_sqft	Number	Minimum listed area in sqft.
max_area_sqft	Number	Maximum listed area in sqft.
available_units_count	Number	Availability or waitlist count when present.
amenities	Array	Amenities list when available.
image_url	String	Listing image URL.
specials_available	Boolean	Special offers flag when available.
is_fully_occupied	Boolean	Occupancy signal when available.
featured_property	Boolean	Featured listing signal when available.
source_page_url	String	Page URL where the data was captured.
source_api_url	String	Source response URL for captured data.
scraped_at	String	ISO timestamp when the record was extracted.

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Usage Examples

Basic Run

{
	"startUrl": "https://www.rentcafe.com/apartments-for-rent/new-york-city-ny/",
	"results_wanted": 20
}

Different RentCafe URL Type

{
	"startUrl": "https://www.rentcafe.com/houses-for-rent/us/ca/san-diego/",
	"results_wanted": 40,
	"max_pages": 2
}

Alias URL Field

{
	"url": "https://www.rentcafe.com/apartments-for-rent/chicago-il/",
	"results_wanted": 25,
	"max_pages": 2
}

Sample Output

{
	"property_name": "Example Towers",
	"property_id": 123456,
	"property_code": "p000123",
	"address": "100 Example Ave",
	"city": "New York",
	"state": "NY",
	"zip_code": "10001",
	"full_address": "100 Example Ave, New York, NY, 10001",
	"site_url": "https://www.exampletowers.com/",
	"detail_url": "https://www.rentcafe.com/apartments-for-rent/example-towers/",
	"min_bedrooms": 1,
	"max_bedrooms": 3,
	"min_bathrooms": 1,
	"max_bathrooms": 2,
	"min_rent": 2450,
	"max_rent": 4290,
	"min_area_sqft": 620,
	"max_area_sqft": 1380,
	"available_units_count": 12,
	"source_page_url": "https://www.rentcafe.com/apartments-for-rent/new-york-city-ny/",
	"source_api_url": "https://api.example.com/search",
	"scraped_at": "2026-03-28T10:30:22.119Z"
}

Tips for Best Results

Use Stable Listing URLs

• Use valid city listing URLs from RentCafe.
• Start with one URL, then scale to multiple URLs.

Keep QA-Sized Runs Fast

• Use results_wanted: 20 for quick verification runs.
• Increase record count for production jobs as needed.

Prefer Residential Proxies

• Residential proxies improve reliability on protected targets.
• Keep default proxy settings for stable cloud runs.

Integrations

Connect extracted data with:

• Google Sheets — Share rental datasets with teams.
• Airtable — Build searchable inventory tables.
• Make — Trigger downstream workflows.
• Zapier — Send listing updates to business apps.
• Webhooks — Push results to custom endpoints.

Export Formats

• JSON — Best for APIs and automation.
• CSV — Ideal for spreadsheet analysis.
• Excel — Business reporting and sharing.
• XML — Legacy workflow compatibility.

Frequently Asked Questions

Why is the dataset empty?

This usually means the source did not return listing data for your run settings. Confirm URL validity and use residential proxy settings.

Do I need to provide an API token?

No. The actor discovers API context automatically from page and network data.

How many records can I collect?

You can increase results_wanted as needed, depending on source availability.

Does the actor remove null values?

Yes. Output records are saved with null and empty fields removed.

Support

For issues or feature requests, open a discussion in your Apify actor management workflow.

Resources

• Apify Documentation
• Apify API Reference
• Schedules

Legal Notice

This actor is intended for legitimate data collection and analytics workflows. You are responsible for complying with applicable laws, website terms, and data usage policies.