Craigslist Housing Scraper

Scrape Craigslist housing listings across US cities — apartments, houses, rooms & shared. Plain HTTP, no browser, no proxy needed (Craigslist's 2026 search and detail pages are fully server-rendered).

Features

• Price filters — minPrice / maxPrice applied server-side (Craigslist filters before you pay for results)
• Keyword filter — titleKeyword matches listing titles client-side (Craigslist's search page ignores keyword params) and filters before detail fetches, so targeted runs use less compute
• Multi-category — apartments (cat=apa), the full housing feed (houses + rooms + shared, cat=hhh), or all
• New-listings monitoring — dedupeAgainstDatasetId skips listings already in a previous run's dataset and tags everything is_new=true. Combine with an Apify Schedule for a daily "new listings only" feed
• Multi-city search — scrape several areas in one run
• Detail page extraction — per listing: address, neighborhood, beds, baths, sqft, full description, all photos, amenities, reply URL, post ID, posted date
• Derived fields — price_num (numeric rent) and price_per_sqft for analysis
• Fast & cheap — CheerioCrawler, 256MB, no browser; a typical 100-listing detail run finishes in ~1–2 minutes

Input

Field	Type	Default	Description
locations	string[]	["San Francisco, CA"]	Cities/areas to scrape
category	string	apartments	apartments | housing | all
minPrice	number	0	Minimum rent, server-side (0 = no minimum)
maxPrice	number	0	Maximum rent, server-side (0 = no maximum)
maxResults	number	100	Stop after this many listings (0 = no limit)
titleKeyword	string	—	Only listings whose title contains this keyword (client-side; filters before detail fetches)
fetchDetails	boolean	true	Visit detail pages for enriched data
detailDelaySecs	number	1	Delay between detail page visits
dedupeAgainstDatasetId	string	—	Dataset ID(s) of previous runs (comma-separated or array); matched post_ids are skipped
apiToken	string	—	Optional Apify API token for reading cross-actor datasets (needed when deduping against a dataset created by a different actor/task)

Example: filtered search

{
  "locations": ["San Francisco, CA"],
  "category": "apartments",
  "minPrice": 1000,
  "maxPrice": 4000,
  "titleKeyword": "pool",
  "maxResults": 50
}

Example: daily new-listings monitor (set as an Apify Schedule)

{
  "locations": ["New York, NY", "Los Angeles, CA"],
  "category": "housing",
  "minPrice": 1500,
  "dedupeAgainstDatasetId": "<yesterday-run-dataset-id>",
  "apiToken": "apif..."
}

Each day you get only listings that weren't in the previous run's dataset, all tagged is_new: true. (Cloud-verified: a 100-listing baseline run followed by a deduped run returned 20 new listings with zero overlap.)

Output

Each listing includes:

Field	Source	Example
title	Search	"Bright 2 Bedroom Apartment"
price	Search / Detail	"$2,455"
price_num	Derived	2455
beds / baths / sqft	Detail	"2" / "1" / "729"
price_per_sqft	Derived	3.37
neighborhood	Search / Detail	"Mission District"
link	Search	"https://www.craigslist.org/view/d/..."
thumbnail	Detail	image URL
address	Detail	"123 Main St, San Francisco"
latitude / longitude	Detail	37.76 / -122.42
post_id	Detail	"7947327024"
posted	Detail	"2026-07-15T..." (ISO)
photos	Detail	["url1", "url2", ...]
attributes	Detail	["hardwood", "laundry", ...]
description	Detail	"Full description text..."
listing_type	Detail	"apartment"
rent_period	Detail	"monthly"
reply_url	Detail	"https://.../reply/..."
contact_info / contact_websites	Detail	phone/email/URL mentions from description
has_contact	Detail	true
is_new	Dedupe	true (only set when dedupeAgainstDatasetId produced matches)
location / area	Input	"San Francisco, CA" / "sfbay"
platform	System	"craigslist"
scraped_at	System	ISO timestamp

Use Cases

• New-listing alerts — daily schedule + dedupeAgainstDatasetId = "new listings only" feed
• Filtered market research — minPrice/maxPrice + category for a specific rental segment
• Price tracking — run weekly, store price_num + price_per_sqft over time
• Competitive analysis — monitor rental supply in target neighborhoods
• Data pipeline — feed structured housing data into analytics or ML models

Performance & Limitations

• No browser (CheerioCrawler, 256MB) — Craigslist's 2026 pages are fully server-rendered
• One search page per area+category contains all results (no pagination/scroll needed)
• Actual phone/email behind Craigslist's JS-gated "show contact" — we extract the reply URL plus any phone/email/URL mentions from the description text
• Craigslist's 2026 UI collapsed the old category codes: only cat=apa (apartments) and cat=hhh (housing superset) are housing feeds. The old fsbo/rom codes now point to general for-sale or jobs — do not expect them to return housing.
• Some listings lack specs (no sqft, no address) — those fields will be null

💬 Quick Start for AI Assistants

Copy and paste this into ChatGPT, Claude, or another AI assistant:

---

You are helping me use the "Craigslist Housing Scraper" on Apify (actor ID: udJ1lSFPz264RxA17). It extracts housing listings (apartments, houses, rooms, shared) from craigslist.org across US cities.

Input fields:

• locations: string array, e.g. ["San Francisco, CA", "New York, NY"]
• category: "apartments" (default) | "housing" (houses+rooms+shared) | "all"
• minPrice / maxPrice: numbers, server-side rent filter (0 = no filter)
• titleKeyword: string, only listings whose title contains it (client-side)
• maxResults: number, max listings to return (0 = no limit)
• fetchDetails: boolean, visit detail pages for photos, descriptions, amenities
• detailDelaySecs: number, seconds between detail page visits (default 1)
• dedupeAgainstDatasetId: string dataset ID(s), skip already-seen listings, tag is_new
• apiToken: optional Apify API token to read cross-actor datasets

Output fields per listing: title, price, price_num, beds, baths, sqft, price_per_sqft, neighborhood, link, thumbnail, address, latitude, longitude, post_id, posted, photos[], attributes[], description, listing_type, rent_period, reply_url, contact_info[], contact_websites[], has_contact, is_new, location, area, platform, scraped_at.

Help me with the right input, output processing, or troubleshooting.

📋 Changelog

Version	Date	Changes
0.6	2026-09-12	Added titleKeyword client-side filter (Craigslist ignores search_title; filtered before detail fetches to save compute). Live daily schedule demo (NYC + LA, housing, $1,500+) with verified new-listings dedupe
0.5	2026-09-12	Filters + monitoring: minPrice/maxPrice (server-side), category (apartments/housing/all), dedupeAgainstDatasetId + apiToken (new-listings dedupe, is_new flag), price_num + price_per_sqft fields
0.4	2026-08-31	Rewrote for Craigslist 2026 DOM; switched PlaywrightCrawler → CheerioCrawler (no browser, 256MB) — cut compute ~16×
0.3	2026-07-15	Fixed reply_url extraction — button.reply-button with data-href attribute
0.2	2026-07-15	Added contact extraction — reply_url, contact_info, contact_websites, has_contact
0.1	2026-07-15	AI assistant prompt block, enriched README, actor title keywords
0.0.11	2026-07-15	Fixed beds/baths extraction from spec line; studio (0BR) + decimal baths (2.5BA) support
0.0.7	2026-07-14	Detail page extraction — photos, descriptions, amenities, address, listing type, rent period
0.0.1	2026-07-13	Initial release — search result extraction, multi-city support

Pricing

Pay per event — $1.00 per 1,000 results ($0.001 per listing in the dataset).

Support & Feedback

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