Kijiji & RentFaster Scraper — Canada Rental Listings + Geo Data 🇨🇦🏠

One normalized, deduplicated feed of Canadian rental listings from Kijiji and RentFaster.ca, with optional nearest-amenity distances (transit, grocery, school, …) attached to every listing.

Most rental scrapers give you one site in that site's own ad-hoc shape. This Actor gives you all sources in a single schema, collapses the same unit posted to multiple sites into one row, and can tell you how far each place is from the subway — a signal no other rental scraper on Apify ships.

Why this is different

	Typical single-site scraper	This Actor
Sources	One	Kijiji + RentFaster (more coming)
Schema	Per-site, ad-hoc	One unified schema across sources
Duplicates	You dedupe yourself	Cross-source dedup built in (also_on)
Location intelligence	Lat/lng only	Nearest-amenity distances, included free

Input

Scope

Field	Type	Default	Notes
sources	array	["kijiji","rentfaster"]	Sites to scrape
cities	array	[] (all)	City names, e.g. ["Toronto","Calgary"]
maxPerCity	int	100	Cap per city, per source
dedupe	bool	true	Merge cross-source duplicates

Filters (all optional — applied cheapest-first)

Field	Type	Notes
minRent / maxRent	int	Monthly rent bounds, e.g. maxRent: 2500
minBedrooms / maxBedrooms	int	For exactly 2-bed, set both to 2. 0 = include bachelor
keywords	array	Title/description must contain ALL (case-insensitive). e.g. ["female"], ["parking","balcony"]
excludeKeywords	array	Drop if title/description contains ANY. e.g. ["no pets"]
nearAmenities	array	Keep listings within maxAmenityDistanceM of each type: subway, train, bus_stop, grocery, cafe, pharmacy, park, school, university, library, gym, hospital. Auto-enables enrichment
maxAmenityDistanceM	int	Default 800 (≈10-min walk)
nearAddress	string	A specific place to anchor on, e.g. "200 Bay St, Toronto". Geocoded, then listings kept within nearAddressRadiusM. Use this for "near my workplace"
nearAddressRadiusM	int	Default 2000

Enrichment & infra

Field	Type	Default	Notes
enrichAmenities	bool	false	Attach amenity_distances_m even without a nearAmenities filter (fast, free)
enrichRadiusM	int	1500	Amenity search radius
maxEnrich	int	200	Cap on listings enriched per run
proxyConfiguration	object	Apify Proxy on	Recommended — see below

Examples:

// minimal
{ "sources": ["kijiji", "rentfaster"], "cities": ["Toronto"], "maxPerCity": 50 }

// "2-bed under $2500 in Toronto, near a subway, female-only"
{
  "cities": ["Toronto"], "minBedrooms": 2, "maxBedrooms": 2,
  "maxRent": 2500, "nearAmenities": ["subway"], "maxAmenityDistanceM": 800,
  "keywords": ["female"]
}

// "1-bed within 2 km of my office at 200 Bay St"
{ "cities": ["Toronto"], "maxBedrooms": 1, "nearAddress": "200 Bay St, Toronto", "nearAddressRadiusM": 2000 }

"Near X" — two different mechanisms

• Near a type of place (subway, grocery, university…) → nearAmenities. Backed by OpenStreetMap; great coverage in Canadian cities.
• Near a specific place (your office, a named landmark) → nearAddress. Geocoded to one point. Amenity categories can't find a specific employer (OSM may not have "Company X"), so use nearAddress for that.

Description questions (e.g. "female only", "no pets")

The full description is always in the output. For literal phrases, use keywords / excludeKeywords (deterministic, server-side). For nuanced interpretation, let an LLM read the returned descriptions — e.g. via Apify's MCP server, Claude can run this Actor and reason over the results in chat. Note: keyword matching is literal, so it depends on how the lister phrased it, and gender-restricted whole-unit ads may run into provincial human-rights rules (shared/roommate situations are typically exempt) — that's on the data, not the filter.

Output

Each dataset item (empty fields omitted):

{
  "source": "kijiji",
  "also_on": ["rentfaster"],
  "source_id": "1700123456",
  "url": "https://www.kijiji.ca/v-apartments-condos/...",
  "title": "Bright 2BR near subway",
  "address": "123 King St W, Toronto, ON",
  "city": "Toronto",
  "province": "ON",
  "postal_code": "M5V 1J5",
  "lat": 43.6453,
  "lng": -79.3806,
  "monthly_rent": 2450,
  "bedrooms": 2.0,
  "bathrooms": 1.0,
  "sqft": 720,
  "property_type": "apartment",
  "furnished": false,
  "pet_friendly": true,
  "utilities_included": ["heat", "water"],
  "available_from": "2026-07-01",
  "description": "…",
  "amenity_distances_m": { "subway": 320, "grocery": 150, "park": 410 },
  "scraped_at": "2026-06-27T12:00:00+00:00"
}

bedrooms: 0.5 means bachelor/studio. also_on lists other sites the same unit was found on (only when dedupe is enabled).

Use it from Claude (MCP) 🤖

This Actor is built to be driven by an AI agent, not just a form. Connect Apify's MCP server to Claude (Desktop, Claude Code, or any MCP client) and Claude can run it from a plain-English request, then reason over the results — no code.

1. Add the hosted MCP server https://mcp.apify.com (OAuth), or run @apify/actors-mcp-server locally with your APIFY_TOKEN.
2. In Claude, just ask:

   "Run the Canada rentals actor for Toronto — 2-bed under $2500 near a subway — and recommend the best 3."

Claude maps that straight onto the inputs — {cities:["Toronto"], minBedrooms:2, maxBedrooms:2, maxRent:2500, nearAmenities:["subway"]} — runs the Actor, reads the dataset, and answers in chat. Because the filters (maxRent, minBedrooms, keywords, nearAmenities, nearAddress) are pushed into the Actor, Claude isn't post-filtering a huge blob — it gets a short, correct set back. The amenity_distances_m field is what lets it answer "near a subway / grocery / school" precisely instead of guessing from text.

Proxy — please read

• RentFaster.ca sits behind a Cloudflare managed challenge that fingerprints the TLS handshake, so browser-like headers alone get 403. The Actor forges a real Chrome TLS/HTTP2 fingerprint (via curl_cffi) to clear it; a residential Canadian proxy is still recommended for a clean IP.
• Kijiji rate-limits and blocks datacenter IPs aggressively.

Use Apify Proxy (residential group) for production runs. The default input already enables Apify Proxy.

Amenity enrichment

Nearest-amenity distances come from a bundled offline POI index — ~225k Canadian POIs (OpenStreetMap, via Geofabrik) shipped inside the Actor and queried in-process. No external API, no rate limits: enriching hundreds of listings takes well under a second, and it's included free (no per-listing enrichment charge). The snapshot is refreshed periodically; POIs are static infrastructure so it doesn't need to be live.

Legal & fair use

Scrapes only publicly visible listing data — no logins, no private data. You are responsible for your use of the output. Kijiji and RentFaster each have Terms of Use that restrict automated access; review them and your jurisdiction's rules, run politely (low concurrency, sensible caps), and don't redistribute in ways those terms prohibit. This Actor is provided for research and personal use.

Roadmap

• Facebook Marketplace + rentals.ca sources (best-effort; both are anti-bot).
• Listing-level change tracking (price drops, relistings).

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Built from the data pipeline behind EZrelocate, a Canada-wide rental recommender (Postgres + PostGIS + pgvector, Claude + Voyage embeddings).