RentHop NYC Apartment Rentals Scraper: 14 Data Fields per Record

Each record carries 14 output fields including listingId, address, price, bedrooms, bathrooms, zipCode, latitude, longitude, and photoUrl. At $5.00 per 1,000 results on the free plan, this Actor collects live New York City rental listings directly from public RentHop search pages. It is designed for real estate analysts, market researchers, and developers powering NYC rental search applications. It is not for anyone needing coverage outside the five NYC boroughs or direct tenant contact phone numbers, which the extracted records do not contain.

Try it: open RentHop NYC Apartment Rentals Scraper on Apify, sign in on the free plan and run the prefilled example.

Can you try RentHop NYC Apartment Rentals Scraper before paying?

Yes. Apify's free plan includes $5.00 of prepaid usage every month and asks for no credit card. At $0.005 per result, that covers up to 1,000 results of RentHop NYC Apartment Rentals Scraper a month, before run-start charges and platform usage.

The example request further down caps maxPrice at 4,000, so a first run returns at most 4,000 results and costs at most $20.00 in result charges. That is enough to see the real shape of the data before deciding anything.

RentHop NYC Apartment Rentals Scraper was last updated on 2026-08-05. It is one of 1,724 Actors CrawlerBros publishes on Apify, which together have 700,263 lifetime public runs and an average rating of 4.63 out of 5 across 416 reviews.

What does it cost to run RentHop NYC Apartment Rentals Scraper?

Each result costs $0.005 on Apify's free plan, which is $5.00 per 1,000 results. Starting a run is charged separately at $0.005 per GB of Actor memory. Apify also bills the platform usage each run consumes, at the rates of your Apify plan, on top of these charges.

The maxItems control has the largest effect on your total bill because result charges apply to each dataset item written. Adjusting filters like minPrice and maxPrice narrows listing criteria, but maxItems directly bounds the volume of written items. To inspect listing structure before committing budget, run your initial query with maxItems set to a low value like 5.

How do you run RentHop NYC Apartment Rentals Scraper from the API?

None of its 9 controls is strictly required, so the defaults below produce a valid run on their own. The payload below uses the schema's own prefilled values, so it runs as written once you substitute your API token.

Call the synchronous endpoint to start a run and receive dataset items in one request:

curl -X POST "https://api.apify.com/v2/acts/crawlerbros~renthop-scraper/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"minPrice":1500,"maxPrice":4000,"bedrooms":"any","bathrooms":"any","sortBy":"hopscore","maxItems":50}'

The same run from Python, using the official client:

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_TOKEN>")

run_input = {
  "minPrice": 1500,
  "maxPrice": 4000,
  "bedrooms": "any",
  "bathrooms": "any",
  "sortBy": "hopscore",
  "maxItems": 50
}

run = client.actor("crawlerbros~renthop-scraper").call(run_input=run_input)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

And from Node.js:

import { ApifyClient } from 'apify-client'

const client = new ApifyClient({ token: '<YOUR_APIFY_TOKEN>' })

const input = {
  "minPrice": 1500,
  "maxPrice": 4000,
  "bedrooms": "any",
  "bathrooms": "any",
  "sortBy": "hopscore",
  "maxItems": 50
}

const run = await client.actor('crawlerbros~renthop-scraper').call(input)
const { items } = await client.dataset(run.defaultDatasetId).listItems()
console.log(items)

The synchronous endpoint holds the connection open until the run finishes, which is convenient for small batches and wrong for large ones. For anything long running, start the run asynchronously and poll, or attach a webhook, so a dropped connection does not cost you the results.

Which RentHop NYC Apartment Rentals Scraper inputs matter, and which can you skip?

The minPrice, maxPrice, bedrooms, and neighborhood controls shape your core dataset. On your first run, leave keyword empty and avoid checking excessive amenity options to prevent over-filtering before you understand listing density.

• minPrice (integer): Minimum monthly rent.
• maxPrice (integer): Maximum monthly rent.
• bedrooms (string): Filter by bedroom count. Default: "any".
• neighborhood (string): Optional case-insensitive substring filter over NYC neighborhood/borough names (e.g. 'Williamsburg', 'Manhattan', 'Astoria').
• bathrooms (string): Filter by minimum bathroom count. Default: "any".
• amenities (array): Optional filters matching RentHop's own search checkboxes (pet policy, unit/building amenities, listing type).
• keyword (string): Optional free-text keyword(s) RentHop matches against listing descriptions (e.g. 'wifi', 'balcony').
• sortBy (string): How RentHop orders results. Default: "hopscore".
• maxItems (integer): Maximum number of listings to return. Default: 50.

Fixed-choice controls: bedrooms accepts any, studio, 1 (1 Bedroom), 2 (2 Bedrooms), 3 (3 Bedrooms), 4+ (4+ Bedrooms); bathrooms accepts any, 1 (1+ Bath), 2 (2+ Bath), 3 (3+ Bath), 4+ (4+ Baths); sortBy accepts hopscore (RentHop Quality Score), price.

What does RentHop NYC Apartment Rentals Scraper return?

Output records supply structured rental data including exact street addresses, coordinate pairs, listing URLs, and bedroom counts. They do not contain landlord email addresses, direct agent phone numbers, or historical lease records.

• listingId, address, neighborhoods, zipCode, city, state
• price (single price) or priceMin/priceMax (for listings quoted as a range)
• bedrooms, bathrooms
• latitude, longitude
• url - direct link to the listing
• photoUrl
• scrapedAt

These are the documented fields. Optional ones can be empty on a given record, so measure how often each field your deliverable depends on is populated across a real sample before automating the handoff.

How do you build the workflow end to end?

Open RentHop NYC Apartment Rentals Scraper and work through these in order. Each step ends with something to check, so a bad configuration surfaces on a small run rather than a scheduled one.

1. Run an initial execution with maxItems set to 5 to confirm structure without high usage.
2. Inspect the returned JSON dataset to confirm that records contain listingId, address, and price.
3. Set neighborhood to a target substring like Williamsburg or Manhattan to narrow geographic scope.
4. Select specific options in amenities, such as noFee or dogsAllowed, to isolate target unit types.
5. Configure sortBy to hopscore for quality ranking or price for ordered cost evaluation.
6. Increase maxItems to your desired dataset size, keeping within the 1 to 1000 input range.
7. Execute the run and verify that output items contain valid numerical values in price or priceMin.
8. Export your dataset and confirm that latitude and longitude fields carry valid coordinates.

How do you apply it? Three worked playbooks

These are RentHop NYC Apartment Rentals Scraper's own documented use cases, each worked through as an operating pattern rather than a description.

Use case 1: Track no-fee listings

Outcome: Track new no-fee apartment listings in a specific NYC neighborhood

Configure: Set neighborhood to "Williamsburg", amenities to ["noFee"], maxItems to 50, and sortBy to "hopscore".

Working method: Execute a search for your target neighborhood with the noFee amenity option selected. Compare returned listingId records against your database to identify newly active listings.

Deliverable: A dataset of active no-fee listings containing address, price, and photoUrl details.

Stop condition: Stop if schedule runs return zero listings due to an invalid neighborhood substring.

Use case 2: Borough market research

Outcome: Build a rent-price dataset by borough/neighborhood for market research

Configure: Set minPrice to 1500, maxPrice to 5000, neighborhood to "Manhattan", sortBy to "price", and maxItems to 1000.

Working method: Execute a broad query across a targeted price range and borough. Aggregate the returned price, priceMin, priceMax, and zipCode fields to calculate average rent benchmarks.

Deliverable: A structured dataset containing rent, bed, and bath data across NYC zip codes.

Stop condition: Stop if returned items fail to populate valid price or priceMin numbers.

Use case 3: Power apartment tool

Outcome: Power a relocation or apartment-hunting tool with live NYC inventory

Configure: Set bedrooms to "1", bathrooms to "1", maxItems to 100, and sortBy to "hopscore".

Working method: Run scheduled queries targeting specific bedroom and bathroom counts. Ingest latitude, longitude, address, and photoUrl into your application database.

Deliverable: A refreshed JSON dataset formatted for ingestion into listing portals or map displays.

Stop condition: Pause integration if url values fail to point to valid renthop.com listing endpoints.

What breaks, and how do you design around it?

• The url (listing page) and photoUrl (image) fields point directly to renthop.com/photos.renthop.com, which are fronted by a Cloudflare JS challenge. These links open normally in a real web browser, but a plain HTTP client (curl, most scripts/integrations without a JS-capable browser) will receive an HTTP 403 challenge response instead of the listing page/photo. This is the same protection the actor itself works around internally with a real headless browser; if your workflow needs to fetch these URLs programmatically, use a headless-browser client too.

When accessing output photoUrl or listing url links programmatically, standard HTTP clients receive HTTP 403 challenge responses. To fetch these resources downstream, process the links using a headless browser environment capable of executing JavaScript challenges.

When should you not use RentHop NYC Apartment Rentals Scraper?

Do not use this Actor if you need rental inventory outside the five boroughs of New York City. For broader US rental market coverage, use Apartments.com Rental Scraper or ApartmentFinder Scraper instead.

What should you check before trusting the output?

• Check that records missing price contain valid numeric values in priceMin and priceMax.
• Verify that latitude and longitude are not null for geo-mapping workflows.
• Confirm that returned neighborhood strings match your input neighborhood substring filter.
• Stop execution if listingId is empty or missing across returned records.
• Validate that url values begin with expected renthop.com address paths.

None of this proves a record is correct. It gives a scheduled RentHop NYC Apartment Rentals Scraper run defined points where it should stop instead of quietly passing bad data downstream.

Frequently asked questions

How much does it cost to scrape RentHop listings?

Results cost $0.005 per result, which is $5.00 per 1,000 results on the free plan. Apify's free plan includes $5.00 of monthly usage with no credit card required, covering up to 1,000 results.

Can this Actor scrape cities outside New York City?

No. This Actor is strictly scoped to New York City. RentHop server-renders full listing data for NYC search pages, but non-NYC city pages require client-side JavaScript/XHR calls to populate results.

Why do some records contain priceMin and priceMax instead of price?

Listings offered with a flexible rent range populate priceMin and priceMax instead of price. Standard listings with fixed monthly rent populate the single price field.

Does using this scraper require a RentHop account?

No. The Actor collects public search results directly from RentHop.com without requiring account credentials, API keys, or login cookies.

How accurate are the bedroom and price filters?

The Actor re-checks price, bedroom count, and minimum bathroom count against each listing's parsed data before writing it to the dataset, ensuring output records match your requested filters.

Where to go next

When you are ready to run it, open RentHop NYC Apartment Rentals Scraper on Apify; the free plan covers up to 1,000 results a month.

Start with the RentHop NYC Apartment Rentals Scraper Actor page for the current input schema, pricing tier, and run history.

Other Actors we maintain for related data:

• PadMapper Scraper: Scrape PadMapper.com apartment and rental listings.
• Apartments.com Rental Scraper: Extract apartment rental listings from Apartments.com including property name, address, rent range, beds/baths, sqft, amenities, neighborhood, walk/transit scores, and more.
• Apartments.com Scraper: Scrape rental listings from Apartments.com with search by location, bedroom count, price, property type, and amenities.
• NYC Business License Scraper: Search New York City's official business license registry - find active and historical licensed businesses by name, category, borough, license status, or phone number.
• CozyCozy Scraper: Scrape lodging listings from CozyCozy's public catalog pages - hotels, vacation rentals, hostels, apartments, and more, across 13 English-language markets, with price, rating, review count, geo-coordinates, and address.
• ApartmentFinder Scraper: Scrape ApartmentFinder.com - one of the largest US apartment rental platforms.
• Apartment List Scraper: Scrape Apartment List - the US apartment rental marketplace.
• Spotahome Mid-Term Rentals Scraper: Scrape Spotahome.com - the mid/long-term furnished rental marketplace covering 92 cities across Europe, the UAE and Turkey.

Related guides:

• PadMapper Scraper: 3 Practical Use Cases
• Apartments.com Rental Scraper: Practical Use Cases and Guide
• Spotahome Mid-Term Rentals Scraper: 92 Cities Covered (2026)
• Guide to Furnished Finder Data Extraction for Rental Markets

Resources

• Actor documentation, input schema, and pricing: verified against the published Actor on 2026-09-30.

• Actor last updated by its maintainers on 2026-08-05.

• Run outcome figures cover the 30 day public window ending 2026-09-30.

• RentHop NYC Apartment Rentals Scraper on Apify