Apartments.com is the largest rental listings network in the United States, with millions of active units across apartments, condos, townhouses, and single-family rentals. The data on the site — rent ranges, bedroom counts, pet policies, amenities, square footage, and direct landlord contact details — is the backbone of how renters, investors, and proptech teams understand the U.S. rental market.
Unlike narrower rental aggregators, Apartments.com pulls inventory from professionally managed buildings and independent landlords, which makes it one of the few sources where you can see both Class A apartment communities and small multi-family listings in a single feed. That breadth is exactly why so many teams want it as a structured data source.
Property managers, multifamily investors, and relocation firms track average rents by bedroom count, neighborhood price trends, and inventory velocity across hundreds of zip codes. A studio in Austin renting at $1,650 looked unusual six months ago and is now the median — but you only see that pattern if you can extract listings continuously and run your own time-series on top of them.
Consumer-facing rental tools aggregate Apartments.com listings alongside Zillow and Trulia so renters can compare prices, amenities, and commute times in one dashboard. Without a structured feed, these apps either run thin (a single source) or fall back on manual data entry that goes stale within days.
HR teams and relocation specialists pull real-time rental data to build city guides and calculate cost-of-living differentials for new hires. When an employee is relocating from Denver to Charlotte next quarter, the relocation package needs to reflect what 2-bedroom apartments actually rent for this month, not last year's BLS averages.
B2B SaaS companies enrich their own datasets with Apartments.com price signals for valuation models, yield analysis, and cap rate estimation. A rental price index that updates weekly is dramatically more useful than one that updates quarterly — and the only way to refresh weekly is to extract listings programmatically.
Apartments.com is a high-traffic commercial property and the operator invests heavily in keeping unauthorized scrapers out. Building a reliable extractor in 2026 is a real engineering project, not a weekend script.
Apartments.com listings are rendered through client-side JavaScript. The raw HTML returned by a simple HTTP request is mostly empty — rent prices, bedroom counts, photos, and amenities are populated only after the page's JavaScript executes against the site's internal APIs. Any scraper that does not run a real browser context misses essentially all of the data you actually want.
The site uses multiple layers of bot detection: TLS fingerprinting, browser environment checks, behavioral signals, and challenge-response gates that fire when traffic looks automated. Default headless browser configurations are flagged within a handful of requests. Maintaining session quality at scale — across thousands of zip codes and hundreds of thousands of listings — is a moving target as detection methods evolve.
Search results are paginated and the site applies soft caps on how many listings any given query can return. To extract a complete picture of a metropolitan area, scrapers have to decompose the search by neighborhood, price band, or bedroom count and then deduplicate the results — which adds orchestration complexity on top of the extraction itself.
A "listing" on Apartments.com can be a single unit, a building with dozens of floor plans, or a property management portfolio. Rent fields appear sometimes as a single number, sometimes as a range, sometimes as "Call for price". Bedroom counts can be a fixed integer, a range ("1-3 bd"), or text like "Studio". Normalizing this into clean structured records that downstream systems can use is non-trivial.
Rather than build all of this from scratch, you can use our Apartments.com Scraper on Apify. It handles the rendering, anti-bot, pagination, and normalization — you give it a location and a few filters, and you get back clean JSON records.
The actor is pay-per-result, which means you only pay for successful extractions. There are no monthly minimums, no proxy management, no headless browser maintenance.
The input is a flat JSON object. Only location is required — the rest are optional filters that apply on top of Apartments.com's own search.
{
"location": "Chicago, IL",
"maxResults": 50,
"minRent": 1500,
"maxRent": 3000,
"minBedrooms": 1,
"petFriendly": true
}Key input parameters:
location — City and state, "City, ST" format, or a 5-digit ZIP code. Examples: "Chicago, IL", "Austin TX", "10001".maxResults — Maximum number of listings to return. Default is 50.minRent / maxRent — Monthly rent filter in USD (optional).minBedrooms / maxBedrooms — Bedroom count filter (optional).petFriendly — Boolean. Returns only pet-friendly listings when set to true.propertyType — One of "apartment", "house", "condo", "townhouse", "studio" (optional).Each result is a flat JSON record with both raw display strings (for direct rendering) and parsed numeric fields (for filtering and analysis). Here is a representative example:
{
"listingId": "p1234567890",
"url": "https://www.apartments.com/presidential-towers-chicago-il/p1234567890/",
"name": "Presidential Towers",
"propertyType": "apartment",
"address": "555 W Madison St",
"city": "Chicago",
"state": "IL",
"zip": "60661",
"minRent": 1738,
"maxRent": 2400,
"rentText": "$1,738 - $2,400/mo",
"bedrooms": "Studio-2 bd",
"phone": "(312) 555-0198",
"imageUrl": "https://images1.apartments.com/i2/abc123/103/image.jpg",
"scrapedAt": "2026-04-30T14:22:11Z"
}| Field | Type | Description |
|---|---|---|
listingId | string | Apartments.com internal listing identifier |
url | string | Canonical listing URL |
name | string | Property name (e.g. "Presidential Towers") |
propertyType | string | apartment, house, condo, townhouse, or studio |
address | string | Street address |
city | string | City name |
state | string | 2-letter state code |
zip | string | 5-digit ZIP code |
minRent | integer | Parsed minimum monthly rent in USD |
maxRent | integer | Parsed maximum monthly rent in USD |
rentText | string | Rent range as displayed on the listing |
bedrooms | string | Bedroom range or label (e.g. "1-3 bd") |
phone | string | Listed contact phone number |
imageUrl | string | Primary listing image URL |
scrapedAt | string | ISO 8601 timestamp when the record was extracted |
The Apartments.com Scraper runs on Apify's pay-per-result model at $0.004 per listing. You pay only for successful extractions — failed requests are not billed.
| Volume | Cost | Use case |
|---|---|---|
| 250 listings | $1.00 | Spot check for a single neighborhood |
| 1,000 listings | $4.00 | City-wide snapshot for a metro |
| 10,000 listings | $40.00 | Multi-metro market research run |
| 100,000 listings | $400.00 | National rental dataset, refreshed monthly |
Apartments.com is one of the richest rental data sources in the U.S. market, but extracting it reliably means navigating JavaScript rendering, anti-bot detection, paginated search caps, and field normalization — all of which need ongoing maintenance as the site evolves.
For teams building rental analytics tools, comparison apps, relocation services, or proptech platforms, the Apartments.com Scraper handles the hard parts and returns clean JSON records with parsed rent ranges, bedroom counts, addresses, and contact details.
If you do not have an Apify account yet, you can sign up free and use the free monthly credits to run a test batch before committing to volume. Try the Apartments.com Scraper on Apify →