Apartments.com Scraper — US Rental Listings, Rent Comps, Floor Plans & Multifamily Property Data Extractor

The most complete Apartments.com data extraction tool on Apify. Pull live rental listings from the largest US apartment marketplace — operated by CoStar Group — with structured rent prices, total monthly costs, beds/baths/sqft, amenities, fees, pet policies, photos, phone numbers, ratings, and geocoordinates. Built for multifamily investors, prop-tech teams, rent-comp analysts, lenders, brokerages, and relocation platforms that need fresh, query-ready rental supply data without fighting CoStar's anti-bot stack.

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What This Actor Does

The Apartments.com Scraper is a production-ready Apify actor that extracts structured rental listings from Apartments.com — the flagship US rental marketplace owned by CoStar Group, the same company behind LoopNet, Homes.com, and the CoStar Suite used by 90%+ of US commercial real-estate professionals. Apartments.com aggregates multifamily complexes, condos, townhomes, and single-family rentals across virtually every metro in the United States, making it the single largest public source of US rental supply data.

The actor fetches city or URL-scoped search results, parses each listing card from the HTML and from the embedded JSON-LD CollectionPage graph, and optionally dives into each property's detail page to pull floor plans, amenity tags, fees, pet policies, photos, ratings, walk scores, and management company info. It returns one normalized JSON record per property — ready to drop into Postgres, BigQuery, Snowflake, Power BI, a CRM, or an investment-research notebook.

Each record can include:

• Property identity — Apartments.com listing ID, property name, full street address, city, state, ZIP, neighborhood, latitude/longitude
• Pricing — base rent range (rentMin/rentMax), total monthly with fees (totalMonthlyMin/totalMonthlyMax), deposit
• Unit specs — bedroom range, bathroom range, sqft range, floor plans (when detail scrape is on)
• Property metadata — property type (apartment / house / condo / townhome), year built, unit count, management company
• Lifestyle attributes — pet friendly flag + full pet policy + breed/weight restrictions, parking, lease term
• Marketing signals — rentSpecials flag + free-text promo description, ratings, review count
• Media — photo URLs, photo count, virtual tour link
• Walkability — Walk Score, Transit Score, Bike Score
• Contact — leasing phone number, listing URL

If you've ever tried to build a rent-comp dataset by hand, you already know this is a multi-week engineering project. This actor does it in minutes.

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Why scrape Apartments.com yourself when this exists?

Apartments.com is a high-value target because it is hard to scrape. CoStar invests heavily in protecting its data — they license it commercially through the CoStar API at five-figure annual contracts. DIY scrapers typically hit the wall at one of these obstacles within the first week:

• Aggressive Akamai / DataDome fingerprinting that blocks vanilla requests / axios traffic on the first hit
• HTTP 403 and 429 rate-limit walls triggered by even mild concurrency
• Markup churn — Apartments.com ships A/B tests constantly, so CSS selectors break every few weeks
• Two sources of truth per page — the visible HTML cards and a separate JSON-LD CollectionPage graph that need to be merged and de-duplicated by property name
• Pagination quirks — /2/, /3/ URL suffixes with no canonical "last page" hint; you have to detect partial pages
• Detail-page JSON-LD nesting — floor plans, fees, and pet rules live across multiple @graph nodes inside multiple <script type="application/ld+json"> blocks
• Total monthly vs. base rent confusion — the headline price excludes mandatory fees that often add $100–$400/month
• Phone numbers rendered as click-to-call tokens, not raw text
• Geocoordinates only available in JSON-LD, not in the visible card
• IP geofencing — non-US IPs get throttled or shown a stripped layout, so residential US proxies are mandatory at scale

This actor handles all of it: realistic Chrome headers, retry-with-backoff on 403/429, Apify Residential Proxy integration, JSON-LD + HTML merge, deterministic dedupe by property ID, and a stable normalized output schema that survives Apartments.com's UI refreshes.

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Quick Start

One-Click Run

1. Open the actor page on the Apify Store and click "Try for free"
2. Pick one or more cities from the Cities dropdown (e.g. Austin TX, New York NY, Chicago IL). Need a city that isn't listed? Add its slug (e.g. boise-id) in Other cities (advanced), or paste full Apartments.com search URLs into Start URLs
3. Optionally choose a Property Type, Max Monthly Rent or Minimum Bedrooms, toggle Scrape Detail Pages for floor plans/amenities/fees/photos, and set a Max Listings cap
4. Hit Start — your structured rental dataset appears in the Apify dataset view, downloadable as JSON, CSV, Excel, or HTML

API Run (Python)

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run = client.actor("haketa/apartments-com-scraper").call(run_input={
    "citySelect": ["austin-tx", "houston-tx", "dallas-tx"],
    "propertyType": "apartments",
    "maxPrice": "2500",
    "minBeds": "2",
    "scrapeDetails": True,
    "maxListings": 300,
    "maxPages": 15,
    "proxyConfiguration": {
        "useApifyProxy": True,
        "apifyProxyGroups": ["RESIDENTIAL"]
    }
})

for unit in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(
        unit["propertyName"],
        f"{unit['bedsMin']}–{unit['bedsMax']} bd",
        f"${unit['rentMin']:,}–${unit['rentMax']:,}/mo",
        unit["city"], unit["zipCode"],
    )

API Run (Node.js / TypeScript)

import { ApifyClient } from 'apify-client';

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

const run = await client.actor('haketa/apartments-com-scraper').call({
    searchUrls: [
        'https://www.apartments.com/new-york-ny/',
        'https://www.apartments.com/brooklyn-ny/under-3500/',
    ],
    rentSpecialsOnly: false,
    scrapeDetails: true,
    maxListings: 500,
    proxyConfiguration: {
        useApifyProxy: true,
        apifyProxyGroups: ['RESIDENTIAL'],
    },
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(`Pulled ${items.length} NYC + Brooklyn listings`);

API Run (cURL)

curl -X POST "https://api.apify.com/v2/acts/haketa~apartments-com-scraper/runs?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "citySelect": ["los-angeles-ca", "san-francisco-ca", "seattle-wa"],
    "propertyType": "all",
    "maxPrice": "3000",
    "scrapeDetails": false,
    "maxListings": 200
  }'

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How It Works

The actor builds a list of search URLs from your input, fetches each page with got-scraping (a rotating mobile Safari / iOS fingerprint + optional Apify Residential Proxy), parses the response with Cheerio, and merges two complementary data sources. The mobile fingerprint is deliberate: the desktop edge returns a tiny interstitial shell (HTTP 200, ~2.5 KB, zero listings), while the mobile fingerprint serves the full ~500 KB listing page with ~40 cards.

1. HTML placard cards — <article class="placard"> elements expose listing ID, street address, headline rent range, beds, deal/special badge, and detail-page URL via data-* attributes.
2. JSON-LD CollectionPage graph — every search page embeds a <script type="application/ld+json"> block containing an @graph with an ItemList of ApartmentComplex / Product / Residence entities. These supply geocoordinates, telephone, aggregate rating, review count, and a clean address object that backfills missing fields.

The actor matches the two sources by lowercased property name, dedupes by propertyId, and pushes one normalized record per property to the dataset.

If scrapeDetails: true, each property's detail page is fetched too. The detail parser pulls additional JSON-LD nodes covering floor plans (with per-floor-plan rent + sqft + bedrooms), amenity tags, fee schedule, pet policy + restrictions, parking notes, lease term, deposit, photo gallery URLs, virtual tour link, walk/transit/bike scores, and management company.

Source endpoints

Source Pattern	Example	What's there
City landing	apartments.com/{city-slug}/	Default search — all property types
Price filter	apartments.com/{city}/under-{price}/	Adds price cap filter
Bed filter	apartments.com/{city}/{n}-bedrooms/	Adds bedroom minimum
Specials	apartments.com/{city}/rent-specials/	Promotions-only feed
Type filter	apartments.com/houses/{city}/ etc.	Houses, condos, townhomes
Pagination	apartments.com/{city}/{page}/	/2/, /3/, …
Detail page	apartments.com/{property-slug}-{city-slug}/{propertyId}/	Full JSON-LD + amenity HTML

Engineering details

• Mobile Safari / iOS fingerprint — aligned User-Agent, Accept, Accept-Language and Referer; the desktop fingerprint is served a stripped interstitial, so mobile is required for full pages
• Retry-with-backoff on soft blocks, 403 and 429 — up to six attempts per URL, re-rotating the session (and proxy IP) each time; a response under 40 KB is treated as a soft block and retried
• Optional Apify Residential Proxy — strongly recommended; default config pre-selects RESIDENTIAL group
• Configurable requestDelay (default 1500 ms) + maxConcurrency (default 1) — tuned to stay under CoStar's per-IP rate-limit threshold
• JSON-LD walker — recursive scanner up to 8 levels deep, supports nested @graph, ListItem, and mentions[] structures
• Dedupe by propertyId with fallback to listingUrl then propertyName
• Auto-stop on partial pages — when a page returns fewer than 5 listings, the actor treats it as the last page and moves to the next task

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Input Parameters

Pick one or more cities from the Cities dropdown (or paste raw search URLs), then narrow the search with property type, price and bedroom filters. Leaving everything empty returns a ready-made sample of listings from popular US metros.

{
  "citySelect": ["austin-tx", "houston-tx", "denver-co"],
  "cities": ["boise-id"],
  "propertyType": "apartments",
  "maxPrice": "2000",
  "minBeds": "1",
  "rentSpecialsOnly": false,
  "scrapeDetails": true,
  "maxListings": 250,
  "maxPages": 15,
  "requestDelay": 1500,
  "maxConcurrency": 1,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"]
  }
}

Parameter reference

Parameter	Type	Default	Description
citySelect	array<string>	["austin-tx"]	Primary input. Pick one or more US cities from the built-in dropdown of 41 major metros (e.g. new-york-ny, los-angeles-ca, chicago-il, houston-tx, miami-fl, seattle-wa, washington-dc, san-francisco-ca). Leave empty for a mixed sample from popular cities.
cities	array<string>	[]	Other cities (advanced). Free-text list for any US city not in the dropdown, using the Apartments.com slug format — e.g. boise-id, madison-wi, ann-arbor-mi. Combined with your dropdown selection.
propertyType	string	"all"	One of all, apartments, houses, condos, townhomes. Applied when cities are used (ignored for raw searchUrls).
maxPrice	string (enum)	"0"	Maximum monthly rent, selected from a dropdown. Appends /under-{price}/ to search URLs. "0" = no cap. Available values: "700", "1000", "1200", "1500", "2000", "2500", "3000".
minBeds	string (enum)	"0"	Minimum bedrooms, from a dropdown. "0" = studio or any. Values: "0", "1", "2", "3", "4".
rentSpecialsOnly	boolean	false	Restrict to listings with active promotions (free month, discounted rent, etc.).
scrapeDetails	boolean	false	Visit each listing's detail page for JSON-LD enrichment (floor plans, amenities, fees, pet policy, photos, scores). Significantly increases runtime and cost.
maxListings	integer	0	Hard cap on total listings saved across all cities. 0 = unlimited. (Empty no-filter runs are auto-capped at ~100 for a fast, cheap sample.)
maxPages	integer	20	Pages crawled per city/search URL. Each page yields ~20–25 listings.
requestDelay	integer	1500	Delay between requests in ms. CoStar rate-limits aggressively — keep at or above 1500. Max 15000.
maxConcurrency	integer	1	Parallel requests. Keep at 1–2. Higher values trigger 403/429 quickly. Max 5.
searchUrls	array<string>	[]	Start URLs (advanced). Paste ready-made Apartments.com search URLs to scrape directly. When provided, the city and filter options above are ignored. Great for neighborhoods, ZIPs, and custom filter combos.
proxyConfiguration	object	Residential	Apify Proxy config. Residential proxies are strongly recommended — datacenter IPs are blocked within minutes.

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Output Schema

Every dataset record uses a single flat JSON shape so warehouse loads, BI tools, and CRMs don't need per-record branching.

Identity & location

Field	Type	Description
propertyId	string	Apartments.com canonical listing ID (e.g., 40n18nj)
propertyName	string	Property name (e.g., Edison Austin, The Hudson Yards, Pacific Plaza Apartments)
address	string	Street address (e.g., 4711 E Riverside Dr)
city	string	City (e.g., Austin, New York, Los Angeles)
state	string	Two-letter state code (TX, NY, CA, …)
zipCode	string	5-digit ZIP code
neighborhood	string	Neighborhood / submarket when detected
latitude	number	Geocoordinate (decimal degrees) — from JSON-LD
longitude	number	Geocoordinate (decimal degrees) — from JSON-LD
listingUrl	string	Canonical Apartments.com property detail URL
searchCity	string	The city slug the listing was discovered under

Property metadata

Field	Type	Description
propertyType	string	Apartment, House, Condo, Townhome (when detected)
yearBuilt	integer	Construction year (detail page)
unitCount	integer	Total units in the property (detail page)
managementCompany	string	Property manager / owner name (detail page)

Pricing & unit specs

Field	Type	Description
rentMin	number	Lowest advertised monthly rent (base)
rentMax	number	Highest advertised monthly rent (base)
totalMonthlyMin	number	Lowest total monthly cost including mandatory fees
totalMonthlyMax	number	Highest total monthly cost including mandatory fees
bedsMin	integer	Minimum bedrooms (0 = studio)
bedsMax	integer	Maximum bedrooms
bathsMin	number	Minimum bathrooms
bathsMax	number	Maximum bathrooms
sqftMin	integer	Minimum unit square footage
sqftMax	integer	Maximum unit square footage
deposit	number	Required security deposit (USD)
leaseTerm	string	Lease length(s) — e.g. 6, 9, 12 months
floorPlans	array<object>	Per-floor-plan array (name, beds, baths, sqft, rentMin, rentMax, available) — detail page
fees	object	Free-text fee schedule (application, admin, amenity, etc.) — detail page

Marketing, lifestyle & media

Field	Type	Description
availabilityStatus	string	Now Available, Coming Soon, Waitlist, etc.
rentSpecials	boolean	true if a promo banner is detected
specialText	string	Free-text promo description (e.g., 1 month free on 13-month lease)
petFriendly	boolean	true if dogs or cats are allowed
petPolicy	string	Free-text pet policy summary
petRestrictions	array<string>	Breed/weight restrictions when published
parkingInfo	string	Parking type + cost (e.g., Garage, $150/mo)
amenities	array<string>	Community + unit amenity tags (Pool, Fitness Center, In-Unit Laundry, EV Charging, …)
description	string	Full marketing description from detail page
photoCount	integer	Number of gallery images
photos	array<string>	Photo URLs
virtualTour	string	Virtual tour URL when offered
rating	number	Apartments.com aggregate rating (0–5)
reviewCount	integer	Number of resident reviews
walkScore	integer	0–100 walkability score
transitScore	integer	0–100 public transit score
bikeScore	integer	0–100 bikeability score
phoneNumber	string	Leasing office phone — present for named communities (from JSON-LD) or via detail scrape
scrapedAt	string	ISO-8601 timestamp

Example record — named apartment community (New York, NY)

Taken verbatim from a fresh verification run. Named communities also expose latitude, longitude, phoneNumber and a short description directly from the search page's JSON-LD graph:

{
  "propertyId": "1j2c5h6",
  "propertyName": "10 Halletts Point",
  "address": "10, 20, 30 Halletts Pt",
  "city": "Astoria",
  "state": "NY",
  "zipCode": "11102",
  "neighborhood": null,
  "latitude": 40.7766,
  "longitude": -73.93585,
  "propertyType": null,
  "rentMin": 4228,
  "rentMax": 4228,
  "bedsMin": 2,
  "bedsMax": 2,
  "rentSpecials": true,
  "phoneNumber": "929-552-4252",
  "description": "10 Halletts Point Apartments",
  "listingUrl": "https://www.apartments.com/10-halletts-point-astoria-ny/1j2c5h6/",
  "searchCity": "new-york-ny",
  "scrapedAt": "2026-11-01T23:43:06.268Z"
}

Example record — individual unit listing (Brooklyn, NY)

Single-unit rentals don't carry a community name, so the actor stores the street address as propertyName. These still return address, rent range, beds and the detail URL; enable Scrape Detail Pages to add geo, phone, amenities and more:

{
  "propertyId": "65t9ffv",
  "propertyName": "1515 Surf Ave",
  "address": "1515 Surf Ave",
  "city": "Brooklyn",
  "state": "NY",
  "zipCode": "11224",
  "rentMin": 2899,
  "rentMax": 3099,
  "bedsMin": 1,
  "bedsMax": 2,
  "rentSpecials": null,
  "listingUrl": "https://www.apartments.com/1515-surf-brooklyn-ny/65t9ffv/",
  "searchCity": "new-york-ny",
  "scrapedAt": "2026-11-01T23:43:06.268Z"
}

Example record — fully enriched with scrapeDetails: true

When detail scraping is on, each property's detail page backfills floor plans, amenities, fees, pet policy, photos, lease term, walk/transit/bike scores and management company. A representative enriched record looks like:

{
  "propertyId": "40n18nj",
  "propertyName": "Edison Austin",
  "address": "4711 E Riverside Dr",
  "city": "Austin",
  "state": "TX",
  "zipCode": "78741",
  "latitude": 30.2389,
  "longitude": -97.7128,
  "propertyType": "Apartment",
  "yearBuilt": 2021,
  "unitCount": 312,
  "rentMin": 1295,
  "rentMax": 2840,
  "bedsMin": 0,
  "bedsMax": 3,
  "bathsMin": 1,
  "bathsMax": 2,
  "sqftMin": 512,
  "sqftMax": 1420,
  "rentSpecials": true,
  "specialText": "1 month free on 13-month lease",
  "petFriendly": true,
  "petPolicy": "Cats and dogs welcome. 2 pets max.",
  "petRestrictions": ["No aggressive breeds", "50 lb weight limit"],
  "phoneNumber": "(512) 555-0142",
  "amenities": ["Pool", "Fitness Center", "In-Unit Laundry", "EV Charging", "Rooftop Lounge"],
  "floorPlans": [
    { "name": "S1", "beds": 0, "baths": 1, "sqft": 512, "rentMin": 1295, "rentMax": 1295 },
    { "name": "A2", "beds": 1, "baths": 1, "sqft": 720, "rentMin": 1620, "rentMax": 1620 },
    { "name": "B1", "beds": 2, "baths": 2, "sqft": 1090, "rentMin": 2310, "rentMax": 2310 },
    { "name": "C1", "beds": 3, "baths": 2, "sqft": 1420, "rentMin": 2840, "rentMax": 2840 }
  ],
  "fees": { "application": "$60", "admin": "$200", "amenity": "$25/mo" },
  "parkingInfo": "Garage, $125/mo",
  "leaseTerm": "6, 9, 12, 13 months",
  "deposit": 500,
  "photoCount": 42,
  "photos": ["https://images1.apartments.com/.../photo01.jpg", "..."],
  "rating": 4.3,
  "reviewCount": 178,
  "walkScore": 64,
  "transitScore": 51,
  "bikeScore": 72,
  "managementCompany": "Greystar",
  "listingUrl": "https://www.apartments.com/edison-austin-austin-tx/40n18nj/",
  "searchCity": "austin-tx",
  "scrapedAt": "2026-11-01T14:32:18.000Z"
}

Field coverage note: Every record always includes propertyId, propertyName, address, city, state, zipCode, listingUrl, rentMin/rentMax and bedsMin/bedsMax. Fields such as latitude, longitude, phoneNumber and description come from the search page's JSON-LD and are present for named apartment communities (a subset of each result page); detail-only fields (floorPlans, amenities, fees, petPolicy, photos, walkScore, managementCompany, yearBuilt, unitCount, …) populate when you set scrapeDetails: true. Null fields are kept in the schema so warehouse loads stay consistent.

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Property Type & Status Reference

Property type taxonomy

propertyType value	Meaning
Apartment	Multifamily rental community (most common)
House	Single-family detached rental
Condo	Condominium unit rented by owner
Townhome	Attached townhouse rental

Filter at the search level with the propertyType input parameter: all, apartments, houses, condos, townhomes.

Availability statuses

availabilityStatus	Meaning
Now Available	Units available for immediate move-in
Coming Soon	Pre-leasing future units (often new construction)
Waitlist	Fully occupied; accepting waitlist applications
Limited Availability	Few units left
Contact for Availability	No live availability published

Rent specials examples

When rentSpecials: true, the specialText field contains the promo verbatim. Common patterns:

• 1 month free on 13-month lease
• $500 off first month
• Look & Lease — $0 admin fee
• Reduced deposit for qualified applicants
• Free covered parking for 12 months

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Use Cases

Multifamily Investment Research & Acquisitions

Multifamily syndicators, REITs, and value-add investors run this actor to:

• Pull a full rent roll snapshot for every comp within a 1–3 mile radius of a target acquisition
• Compute price-per-sqft and price-per-bed comparables across submarkets to underwrite offer prices
• Track lease-up velocity at new construction by re-running weekly and diffing availabilityStatus
• Identify rent specials concentration as a soft-market signal (markets with high rentSpecials: true share are typically over-supplied)
• Benchmark target asset rent vs. market by floor plan type using the floorPlans array
• Source off-market opportunities by spotting properties listed by smaller management companies that may signal owner fatigue

Rent Comp & Pricing Analytics (Prop-Tech)

Pricing platforms, revenue-management vendors, and prop-tech startups use the data to:

• Train rent-prediction models on millions of rows of rentMin/bedsMin/sqftMin/zipCode/amenities
• Build rent index dashboards by city, ZIP, neighborhood, or property class
• Power automated comp reports that property managers receive in their inbox each Monday
• Monitor competitor pricing in real time — alert when a comp drops rent by >5%
• Feed AI underwriting copilots with the freshest possible rent signal
• Validate proprietary pricing recommendations against live market evidence

Brokerage, Leasing & Tour Generation

Apartment locators, leasing brokerages, and corporate housing providers leverage Apartments.com data to:

• Build searchable internal inventories without licensing the CoStar Suite
• Match client requirements (budget, beds, pet policy, neighborhood) against current listings instantly
• Stage tour itineraries by pulling addresses, phone numbers, and availability in one query
• Identify pet-friendly inventory for clients with dogs over 50 lbs using petRestrictions
• Spot rent specials to lead with savings in client conversations
• Track which buildings convert from "Now Available" to "Waitlist" as a soft demand signal

Relocation & Corporate Housing

Relocation management companies, HR mobility teams, and corporate-housing providers use the actor to:

• Pre-screen rentals in destination metros before an employee accepts an offer
• Build relocation packets with shortlisted comparables across price tiers
• Quote cost-of-living adjustments with hard totalMonthlyMin data — not estimated medians
• Compare neighborhoods using walkScore/transitScore/bikeScore
• Match commute zones by geocoordinate to office addresses

Lender Underwriting, Insurance & Risk

Multifamily lenders (agency, CMBS, debt funds), property insurers, and tax appeal consultants use the dataset to:

• Underwrite refi requests by validating sponsor's stated rent against scraped market rent
• Stress-test pro formas with current vacancy + concession environment in the submarket
• Identify mispriced insurance policies when scraped rent is materially above premium-baseline rent
• Support property-tax appeals with hard comparable evidence
• Monitor portfolio submarkets quarterly for early signs of rent compression
• Feed credit committees with submarket performance briefs

Market Reports & Real-Estate Journalism

Brokerages, research desks, and real-estate journalists publish recurring market reports built on this data:

• Quarterly metro rent index — median rentMin by city/ZIP, year-over-year
• Concession watch — % of listings carrying rentSpecials by metro
• New construction lease-up tracker — units coming online filtered by yearBuilt >= current_year - 2
• Pet-friendliness index — share of inventory accepting cats/dogs by city
• Amenity-of-the-month reports — which amenities (EV charging, coworking, dog wash) are spreading fastest

Location Intelligence & Site Selection

Retailers, QSR chains, fitness operators, and mobility companies use rental density as a population/demand proxy:

• Identify high-density rental ZIPs for store/site selection (renter share correlates with foot traffic)
• Spot emerging neighborhoods by tracking new-construction inventory growth
• Estimate household formation by counting unique propertyId in lease-up status
• Inform last-mile / delivery zone design with apartment-cluster maps
• Layer Walk/Transit scores onto trade-area analyses

Academic & Public-Policy Research

Housing researchers, urban planners, and policy think tanks use the dataset to study:

• Rent burden trends by metro and submarket
• Geographic patterns of affordable inventory (filter maxPrice against AMI thresholds)
• Concession patterns as market-softness indicators
• Pet-policy and source-of-income discrimination signals across jurisdictions
• Inventory mix shifts — multifamily vs. SFR vs. condo — over time

CRM Enrichment, Lead Routing & Direct Outreach

B2B vendors selling to multifamily operators (property-management software, IoT, smart locks, payment platforms) enrich their pipelines with:

• Building-level firmographics — managementCompany, unitCount, yearBuilt, location
• Targeted outbound lists filtered by property size or vintage
• Account-mapping — group properties by managementCompany to estimate operator portfolio scale
• Territory planning — geographic clustering by ZIP / metro
• Trigger-based plays — new properties appearing for the first time = new construction = sales trigger

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Sample Queries & Recipes

Recipe 1: Full Manhattan inventory under $3,000/mo, all property types

{
  "citySelect": ["new-york-ny"],
  "propertyType": "all",
  "maxPrice": "3000",
  "scrapeDetails": false,
  "maxListings": 0,
  "maxPages": 30
}

Recipe 2: 2-bedroom Austin apartments with full detail enrichment

{
  "citySelect": ["austin-tx"],
  "propertyType": "apartments",
  "minBeds": "2",
  "maxPrice": "2500",
  "scrapeDetails": true,
  "maxListings": 200
}

Recipe 3: Houses for rent across Houston, Dallas, and San Antonio

{
  "citySelect": ["houston-tx", "dallas-tx", "san-antonio-tx"],
  "propertyType": "houses",
  "minBeds": "3",
  "scrapeDetails": true,
  "maxListings": 300
}

Recipe 4: Rent-specials-only sweep across top tech metros (concession monitor)

{
  "citySelect": ["san-francisco-ca", "seattle-wa", "austin-tx", "denver-co"],
  "rentSpecialsOnly": true,
  "scrapeDetails": false,
  "maxListings": 500,
  "maxPages": 20
}

Recipe 5: Hyper-targeted neighborhood comp pull via search URLs

{
  "searchUrls": [
    "https://www.apartments.com/brooklyn-ny/williamsburg/",
    "https://www.apartments.com/chicago-il/west-loop/",
    "https://www.apartments.com/los-angeles-ca/silver-lake/"
  ],
  "scrapeDetails": true,
  "maxListings": 150
}

Recipe 6: Affordable inventory under $1,000 across Sun Belt secondary markets

Cities not in the dropdown go in the free-text cities (Other cities) field:

{
  "cities": ["el-paso-tx", "lubbock-tx", "tulsa-ok", "memphis-tn", "birmingham-al"],
  "maxPrice": "1000",
  "scrapeDetails": false,
  "maxListings": 400
}

Recipe 7: Sample run — 20 listings to validate schema before a full scrape

{
  "citySelect": ["miami-fl"],
  "maxListings": 20,
  "maxPages": 1,
  "scrapeDetails": true
}

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Integration Examples

Google Sheets

Configure an Apify schedule that runs this actor each morning. Add the "Save to Google Sheets" integration on the schedule's outputs tab — every run appends fresh rows. Use a pivot table for medianRent BY city, bedsMin.

Make.com / Zapier / n8n

Use the official Apify connector to trigger on run.succeeded. Common downstream flows:

• New listing in target ZIP → Slack alert to broker team
• rentSpecials flipped to true on a target comp → email digest
• Property dropped below $X price threshold → trigger CRM task
• Rent decreased vs. last week → push to internal pricing dashboard

Power BI / Tableau / Looker Studio

Connect Apify's REST dataset endpoint directly. Build dashboards covering:

• Median rent heatmap by ZIP
• Concession share by metro / submarket
• New construction lease-up funnel (yearBuilt >= 2024 + availabilityStatus)
• Floor-plan-level price-per-sqft comparisons
• Pet-policy distribution by city

Postgres / Snowflake / BigQuery

Use the Apify webhook integration to POST run results to your warehouse ingestion endpoint after every scheduled run. Recommended schema: one table listings, one table floor_plans (one row per floor plan, joined on propertyId).

SELECT zip_code,
       AVG(rent_min) AS avg_rent_min,
       AVG(rent_max) AS avg_rent_max,
       COUNT(*)      AS active_listings,
       SUM(CASE WHEN rent_specials THEN 1 ELSE 0 END)::float / COUNT(*) AS concession_share
FROM   apartments_com_listings
WHERE  state = 'TX' AND scraped_at >= CURRENT_DATE - INTERVAL '7 days'
GROUP  BY zip_code
ORDER  BY active_listings DESC;

Salesforce / HubSpot CRM Enrichment

Nightly Apify run → match managementCompany to your Account records → upsert unitCount, yearBuilt, latest median rent. Trigger Tasks when an Account's portfolio shows new lease-up properties (sales opportunity for property-management software).

Webhooks & Custom Pipelines

Subscribe a webhook to RUN.SUCCEEDED and post the dataset URL to your ETL worker. The actor's deterministic schema makes downstream parsing trivial.

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Major US Rental Markets at a Glance

Metro	City Slug	Why it matters
New York, NY	new-york-ny	Largest US rental market by unit count; highest median rent
Los Angeles, CA	los-angeles-ca	2nd largest renter base; complex submarket geography
Chicago, IL	chicago-il	Top-5 multifamily transaction market; deep neighborhood data
Houston, TX	houston-tx	Energy-driven rental demand; broad price spectrum
Dallas-Fort Worth, TX	dallas-tx, fort-worth-tx	One of the hottest US lease-up markets
Austin, TX	austin-tx	Tech-driven; high concession environment in 2024–2026 cycle
Miami, FL	miami-fl	Migration-driven rent growth; luxury condo rental supply
Seattle, WA	seattle-wa	Tech-heavy; high transit-score inventory
Boston, MA	boston-ma	Higher-ed and biotech demand; September lease cycle
Washington, DC	washington-dc	Government employment base; stable rent
San Francisco, CA	san-francisco-ca	Most volatile rents post-COVID; rich concession data
Denver, CO	denver-co	High new-construction pipeline; lease-up rich
Atlanta, GA	atlanta-ga	Sun Belt magnet; large SFR rental share
Phoenix, AZ	phoenix-az	Build-to-rent capital of the US
San Diego, CA	san-diego-ca	Military + biotech demand; supply-constrained
Philadelphia, PA	philadelphia-pa	Affordable East Coast comp baseline
Minneapolis, MN	minneapolis-mn	Stable rust-belt-adjacent market
Portland, OR	portland-or	Walk-score-rich; renter-friendly regulations
Nashville, TN	nashville-tn	High in-migration; lease-up heavy
Charlotte, NC	charlotte-nc	Banking center; growing multifamily pipeline

The actor works on any Apartments.com city slug — those above are simply the deepest by listing count.

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Cost & Performance

Metric	Value
Engine	HTTP fetch (got-scraping) + Cheerio + JSON-LD parsing
Runtime (city, 100 listings, no detail)	~3–6 minutes
Runtime (city, 100 listings, with detail)	~15–30 minutes
Data freshness	Live at run time
Auth required	None
Proxy required	Yes — Apify Residential strongly recommended
Concurrency	1–2 (CoStar rate-limits aggressively)
Memory footprint	256 MB minimum, 1 GB recommended for scrapeDetails: true
Pricing model	Pay-per-event — predictable per-record cost
Cost per 100 listings (no detail)	varies — typically a few cents
Cost per 100 listings (with detail)	varies — significantly higher due to extra page fetches
Output formats	JSON, CSV, Excel (XLSX), HTML, XML, RSS, JSON Lines

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Compliance, Privacy & Legal Notes

• Public data only. Every field returned by this actor comes from publicly accessible Apartments.com listing pages — the same content visible to any anonymous web visitor.
• No login, no scraped credentials. The actor does not bypass authentication, cookies, or any access control.
• No PII. Phone numbers are leasing office numbers published by property owners for marketing purposes — they are not personal numbers.
• No financial data. No payment processing, application, or tenant data is touched.
• Respect robots.txt and Apartments.com / CoStar Terms of Service. Consumers of the data are responsible for ensuring their use complies with applicable terms. The actor itself is a general-purpose research tool.
• GDPR / CCPA. Listings represent business establishments, not individuals; standard PII protections do not apply. Apply standard data-handling practices if you enrich with personal data.
• Rate considerations. Default settings (requestDelay: 1500, maxConcurrency: 1, residential proxy) are deliberately conservative to minimize impact on the source.

Important: This data must not be used for unlawful purposes including but not limited to source-of-income discrimination, fair-housing violations, scraping for resale at scale in violation of CoStar's terms, or to enable identity fraud. Consult legal counsel before resale or syndication.

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Frequently Asked Questions

How fresh is the data?

Live at run time. Apartments.com is updated continuously by property managers, so every run pulls the current snapshot. Schedule the actor hourly, daily, or weekly to maintain a history.

How many listings will I get per run?

Depends entirely on inputs. In a fresh benchmark the actor pulled 400 unique New York, NY listings in ~126 seconds (0 duplicates) before hitting the maxListings cap — roughly 18 result pages deep in a single metro. A single city with maxPages: 20 typically returns 300–500 listings; major metros (NYC, LA, Chicago) can yield 1,000+ if you raise maxPages and maxListings. Set maxListings: 0 for unlimited.

Do I need an Apartments.com login or API key?

No. The actor uses anonymous HTTP fetches against public search and detail pages. CoStar's commercial API is not required.

Why do I need residential proxies?

CoStar uses Akamai / DataDome anti-bot protection that fingerprints datacenter IP ranges within minutes. Apify Residential proxies present as real US consumer connections and are the most reliable option. Datacenter proxies will get blocked.

Will this work on the Apify Free plan?

Yes for small sample runs. Production-scale crawling (thousands of listings) typically requires a paid Apify plan because of residential proxy traffic costs.

How do I scrape only one neighborhood?

Use searchUrls and paste Apartments.com's neighborhood URL — e.g. https://www.apartments.com/brooklyn-ny/williamsburg/. The cities/propertyType/maxPrice inputs are ignored when searchUrls is set.

Can I scrape houses, condos, and townhomes — not just apartments?

Yes. Set propertyType to houses, condos, or townhomes. Apartments.com lists significant SFR and condo inventory in addition to multifamily.

What's the difference between rentMin and totalMonthlyMin?

rentMin is the headline base rent. totalMonthlyMin includes mandatory fees the property charges every month (amenity fee, trash fee, pest control, technology package, etc.). For underwriting and rent-comp accuracy, use totalMonthlyMin/totalMonthlyMax.

Does the actor return floor-plan-level pricing?

Yes when scrapeDetails: true. The floorPlans array contains one entry per published floor plan with name, beds, baths, sqft, and rent.

Why are latitude/longitude and phone empty on some records?

Those fields come from the JSON-LD CollectionPage graph that Apartments.com embeds for named apartment communities, so they populate for that subset of each result page (typically the larger, professionally managed buildings). Individual-unit listings — identified by their street address as propertyName — don't publish search-page geo/phone. To fill geo, phone, amenities, floor plans and more for every property, set scrapeDetails: true, which visits each detail page.

Are reviews and ratings included?

rating (0–5) and reviewCount are included when available (mostly with scrapeDetails: true). Individual review text is not extracted in the default schema to keep records flat — open a feature request if you need it.

Can I get historical rent data?

Not from a single run — Apartments.com only publishes current rents. To build history, schedule this actor daily and archive each dataset run; Apify stores all run datasets indefinitely on most plans.

Does the actor handle pagination automatically?

Yes. It walks /2/, /3/, … up to maxPages per task and auto-stops when it detects a partial / empty page.

Does it work for Canadian or international listings?

No — Apartments.com is US-only. For Canada use the Realtor.ca Scraper or Kijiji Scraper; for Australia use Domain.com.au Scraper; for Europe see the Immoweb and Marktplaats actors.

What if I get blocked (403 / 429) during a run?

The actor retries up to 5 times with exponential backoff on 403/429. If persistent: raise requestDelay to 3000+, drop maxConcurrency to 1, and ensure residential proxies are enabled. Avoid running multiple parallel Apartments.com jobs.

Can I schedule the actor?

Yes — Apify's built-in Scheduler supports any cron expression. Pair with the Save-to-Sheets or webhook integration for a fully automated pipeline.

Can I export to Excel, CSV, or directly to my warehouse?

Yes — JSON, CSV, Excel (XLSX), HTML, XML, RSS, and JSON Lines from the dataset view, plus REST API access for any custom pipeline.

How does this compare to the CoStar / Apartments.com commercial API?

CoStar's commercial product offers contracted data licensing, broader historical coverage, and SLA support — but starts in the high five figures annually. This actor gives you live public listing data at a pay-per-event price point, perfect for ad-hoc research, prototyping, and many production workloads.

Are agent or owner contact emails included?

No. Apartments.com does not publish leasing emails on listings — only the leasing-office phone number (when present) is returned.

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Related Apify Actors by Haketa

If you need rental, real-estate, or marketplace data beyond Apartments.com, these sibling actors cover related sources:

• Rent.com Scraper (US) — sister rental marketplace with overlapping but distinct US inventory
• Realtor.ca Scraper (Canada) — Canadian residential listings (CREA MLS feed)
• Domain.com.au Property Scraper (Australia) — Australian sale & rental listings
• Immoweb.be Belgium Property Scraper — Belgian residential & commercial property
• Zameen.com Pakistan Real Estate Scraper — Pakistan's largest property portal
• VivaReal Brazil Real Estate Scraper — Brazilian residential listings
• ZAP Imóveis Brazil Scraper — Brazilian sale & rental
• Lamudi Philippines Real Estate Scraper — Philippines property listings
• Realestate.com.kh Cambodia Scraper — Cambodian property
• OfferUp Scraper (US) — adjacent US marketplace data
• Kijiji.ca Scraper (Canada) — Canadian classifieds incl. rentals
• BBB Business Scraper — enrich property management company records

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Comparison vs. Alternatives

Approach	Setup	Freshness	Cost (1,000 listings)	Schema normalization	Anti-bot handling
This actor	< 5 minutes	Live	Pennies–dollars (pay-per-event)	Built-in	Built-in (proxies + backoff)
Manual copy-paste	Hours per city	Live	Free (labor cost)	None	N/A — manual
Custom Python + residential proxies	2–4 weeks dev	Live	Proxy + dev + infra	DIY (constantly breaks)	DIY
CoStar / Apartments.com commercial API	Weeks of contract negotiation	Live	$$$$ annual contract	Yes	Yes
Generic web-scraping SaaS	Days to configure	Live	$$ monthly subscription	Limited	Limited
Stale third-party rent reports	None	Monthly–quarterly	Subscription	Pre-aggregated only	N/A

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Why Pay-Per-Event Pricing?

• You only pay when the actor actually runs and produces records
• Cost scales linearly with how much data you consume — no monthly minimums
• Transparent line-item billing inside the Apify console
• Free to evaluate — run a 20-listing sample for pennies before committing
• Far cheaper than a CoStar enterprise contract for most non-enterprise workloads
• Predictable for finance — no surprise overage bills based on Compute Units

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Changelog

Last updated: 2026-11-01 — Verified against the live site with a fresh 400-listing New York run (0 duplicates, 100% core-field coverage) and fixed property-name extraction for individual-unit listings. See the Changelog tab for full history.

Version	Date	Notes
1.0.2	2026-11	Fixed propertyName for individual-unit listings (street address now used instead of "Unknown"); verified 400 unique NYC listings in ~126 s with 0 duplicates / 0 value bugs; README output examples regenerated from the live sample
1.0.1	2026-07	Maintenance pass — verified selectors/JSON-LD endpoints against the live site, retested output quality, structure and freshness
1.0.0	2026-05	Initial public release — HTTP + JSON-LD scrape, city/URL inputs, optional detail-page enrichment, residential proxy support, pay-per-event pricing

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Keywords

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Support

• Bug reports: Use the Issues tab on the Apify Store actor page
• Feature requests: Same place — please describe your use case and target metro
• Direct contact: Through the Apify developer profile

If this actor saves you time or unlocks a project that wasn't possible before, a 5-star rating on the Apify Store helps other multifamily investors, prop-tech teams, brokerages, and researchers discover it. Thank you.