Realtor.ca Listings & Price Changes Scraper

What is Realtor.ca Listings & Price Changes Scraper?

Realtor.ca Listings & Price Changes Scraper returns public Canadian MLS listings from Realtor.ca as a dataset, one row per listing, searched by city, map area, Realtor.ca URL or MLS number. Each row has the MLS number, CAD list price, structured address, coordinates, bedrooms, bathrooms, building type, photo URLs, listing agent, brokerage and price-change fields. Optional detail enrichment adds public remarks and price history, and monitor mode compares prices across scheduled runs. Investors, agents, proptech teams and AI agents use it to track Canadian inventory and price drops.

Why property teams use this Realtor.ca scraper

• Four ways to search. A city name, an exact mapBoundingBox, Realtor.ca startUrls, or a list of mlsNumbers.
• Clean numbers. price is a number in CAD next to the display string, and lat/lon place each listing on a map.
• Listing contact on the row. agentName, agentPhone and brokerageName come with every listing.
• Price-drop tracking you can trust. monitorMode stores a snapshot in your own key-value store and fills previousPrice, newPrice, priceChangeDetected and monitorDiff on the next run.
• Filters that match the site. transactionType, propertyGroup, minPrice, maxPrice, minBeds, minBaths, keywords and sortBy.

Best fit for this Actor

• Best fit for Canadian residential and commercial listings on Realtor.ca, for sale or for rent.
• Use city for a quick market pull and mapBoundingBox for a precise neighbourhood.
• Use mlsNumbers with enrichDetails when you already have a shortlist and want remarks and history.
• For US markets, use Zillow Search Scraper with the same workflow.
• To price a rental against nearby comparables, then pass the address to Rental Price Analyzer — Rentometer Alternative.

What people and AI agents ask this Actor to do

List the newest condos for sale in downtown Toronto under 700,000 dollars. Tell me every day which Vancouver listings dropped their price. Get the details and remarks for these three MLS numbers.

Request	Input to send
City market pull	{"city": "Vancouver", "maxPrice": 900000, "minBeds": 2, "maxResults": 100}
Exact neighbourhood	{"mapBoundingBox": {"LatitudeMin": 43.63, "LatitudeMax": 43.68, "LongitudeMin": -79.43, "LongitudeMax": -79.35}}
Rentals in a city	{"city": "Calgary", "transactionType": "for-rent"}
MLS shortlist with remarks	{"mlsNumbers": ["C13846184"], "enrichDetails": true}
Daily price-drop alert	{"city": "Toronto", "monitorMode": true, "onlyPriceChanges": true} on a daily schedule

Example workflow: from a neighbourhood map to a price-drop list

A small investment fund starts with a map box around Toronto's Annex and University neighbourhoods and maxPrice set to 1,100,000. The first run returns every matching listing with price, bedrooms, bathrooms, buildingType and listingUrl, and with monitorMode on it saves a price snapshot. They schedule the same input daily with onlyPriceChanges on, so each morning's dataset holds only listings whose price moved, with previousPrice and newPrice. They compare those against their target yield, export the shortlist, and feed the MLS numbers back with enrichDetails for remarks before calling the listing agents.

Input

Provide one of city, mapBoundingBox, startUrls or mlsNumbers.

{
  "mapBoundingBox": {
    "LatitudeMin": 43.63,
    "LatitudeMax": 43.68,
    "LongitudeMin": -79.43,
    "LongitudeMax": -79.35,
    "ZoomLevel": 13
  },
  "transactionType": "for-sale",
  "propertyGroup": "residential",
  "sortBy": "date-desc",
  "maxResults": 25,
  "enrichDetails": false,
  "monitorMode": false
}

Field	Type	What it controls
city	string	Canadian city name, for example Toronto or Calgary
mapBoundingBox	object	LatitudeMin, LatitudeMax, LongitudeMin, LongitudeMax, optional ZoomLevel
startUrls	array	Realtor.ca map search URLs or listing URLs
mlsNumbers	array	MLS reference numbers for direct lookups
transactionType	string	for-sale or for-rent. Default for-sale
propertyGroup	string	residential or commercial. Default residential
sortBy	string	date-desc, date-asc, price-asc, price-desc
minPrice, maxPrice	integer	Price range in CAD
minBeds, minBaths	integer	Minimum bedrooms and bathrooms
keywords	string	Address fragment, building name or other keyword
maxResults	integer	Cap on dataset rows. Default 5 for a quick sample
enrichDetails	boolean	Add remarks, extra photos and public price history
monitorMode, onlyPriceChanges, monitorStoreName	mixed	Cross-run price tracking settings
proxyConfiguration	object	Apify Proxy; Residential Canada by default

Output

Field	What it contains
mlsNumber, propertyId, listingUrl	Listing identity and public URL
price, priceDisplay	List price in CAD as a number and as shown
address	street, city, province, postalCode, text
lat, lon	Coordinates
bedrooms, bathrooms, propertyType, buildingType, sqft	Property details when published
photoUrls, photoCount	Listing photos
agentName, agentPhone, brokerageName	Listing agent and brokerage
transactionType, status, listedDate	Listing state as Realtor.ca reports it
publicRemarks, priceHistory	Filled with enrichDetails when Realtor.ca publishes them
priceChangeDetected, previousPrice, newPrice, priceChangeDate, priceChangeLabel, monitorDiff	Price-change signals
source, scrapedAt	Data route and collection time

{
  "mlsNumber": "C13846098",
  "propertyId": "30347209",
  "listingUrl": "https://www.realtor.ca/real-estate/30347209/7-473-dupont-street-toronto-annex",
  "transactionType": null,
  "price": 1074900,
  "priceDisplay": "$1,074,900",
  "address": {
    "street": "7 - 473 DUPONT STREET",
    "city": "Toronto (Annex)",
    "province": "Ontario",
    "postalCode": "M6G1Y6",
    "text": "7 - 473 DUPONT STREET|Toronto (Annex), Ontario M6G1Y6"
  },
  "lat": 43.6727215,
  "lon": -79.4149301,
  "bedrooms": 2,
  "bathrooms": 2,
  "propertyType": "Single Family",
  "buildingType": "Apartment",
  "sqft": 130.06322,
  "photoUrls": [
    "https://cdn.realtor.ca/listings/TS639263952302970000/reb82/highres/8/c13846098_1.jpg"
  ],
  "photoCount": 1,
  "agentName": "Jordan Example",
  "agentPhone": "(416) 555-0142",
  "brokerageName": "RE/MAX HALLMARK BIBBY GROUP REALTY",
  "listedDate": "639264096302930000",
  "status": "1",
  "publicRemarks": null,
  "priceChangeDetected": false,
  "previousPrice": null,
  "newPrice": 1074900,
  "priceChangeDate": null,
  "priceChangeLabel": null,
  "priceHistory": [],
  "monitorDiff": null,
  "source": "property-search",
  "scrapedAt": "2026-10-01T01:38:33.453Z"
}

Pricing

This Actor uses Pay per event plus Apify platform usage. Each run charges one Actor Start event, one Listing scraped event per listing row saved, and one Listing detail enriched event per listing when enrichDetails succeeds. Platform usage, including the Residential Canada proxy, is billed on top at Apify's standard rates. The live Pricing tab on this page shows the current event prices.

Worked example: a city pull with maxResults at 100 costs up to 100 Listing scraped events plus one Actor Start event. Turning on enrichDetails for the same run adds up to 100 Listing detail enriched events. With onlyPriceChanges on, only the listings whose price moved are saved and billed.

API example

curl -X POST "https://api.apify.com/v2/acts/khadinakbar~realtor-ca-listings-scraper/run-sync-get-dataset-items?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"city": "Toronto", "maxPrice": 700000, "maxResults": 25}'

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")
run = client.actor("khadinakbar/realtor-ca-listings-scraper").call(run_input={
    "city": "Vancouver",
    "minBeds": 2,
    "maxResults": 50,
})
for listing in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(listing["mlsNumber"], listing["price"], listing["address"]["text"])

Use with AI agents (MCP)

Connect the Actor through Apify MCP in Claude, ChatGPT or Cursor and ask:

Find two-bedroom condos for sale in downtown Toronto under 900,000 dollars. Return price, address, bathrooms and the listing link, cheapest first.

The agent sends city, maxPrice, minBeds and sortBy set to price-asc, then reads rows back with get-dataset-items. It should cite listingUrl and scrapedAt for each listing. Each saved row is one Listing scraped event, so agents should set maxResults to the number of listings the user wants to see and turn on enrichDetails only for a shortlist.

Best results

Starting situation	Helpful action	Expected outcome
You want a precise neighbourhood	Copy the map extent from a Realtor.ca map URL into startUrls or mapBoundingBox	Listings inside that exact area
You want price drops	Turn on monitorMode and schedule the same input	Price-change fields from the second run onward
You need remarks and history	Run mlsNumbers with enrichDetails on	Fuller rows for a shortlist
You want a quick sample	Keep maxResults at 5	A low-cost preview of the data
You track several cities	Give each city its own monitorStoreName	Separate snapshots per market

Builder's note

I built monitor mode around a snapshot in your own key-value store because I wanted price changes you can trace to a real earlier observation, not a guessed history. I learned that people search Realtor.ca by place far more than by keyword, so the Actor accepts a city name, a map box or a pasted map URL and turns each into the same search.

FAQ

Does it cover US listings?

It covers Canadian listings on Realtor.ca. For US homes, use a Zillow Actor.

When are previousPrice and monitorDiff filled?

From the second run with monitorMode on and the same monitorStoreName, once a snapshot exists to compare against.

Why is publicRemarks empty?

Remarks and price history come from listing details. Turn on enrichDetails to fill them when Realtor.ca publishes them.

Responsible use

This Actor reads public listing information from Realtor.ca. Agent names and phone numbers are business contact details; use them in line with Canadian anti-spam law, privacy laws such as PIPEDA, and Realtor.ca's terms of use, and honor opt-outs. REALTOR® and Realtor.ca are trademarks of their owners, and this independent Actor is not affiliated with them. For questions, open a ticket in the Issues tab.