Introduction
In Canada’s fast-moving real estate market, staying ahead of competitors requires precise, timely data. A realty firm operating in Toronto, Vancouver, and Calgary struggled to benchmark listings accurately and identify pricing trends. To solve this, they partnered with ArcTechnolabs to access Real Estate Property Review Datasets through Web Scraping of real estate listings. Leveraging ArcTechnolabs’ Real Estate Data Scraping Services , the goal was to enable smarter pricing, faster positioning, and a data-driven strategy across platforms like Realtor.ca, Zillow Canada, and Centris.ca.
Client Overview
A mid-sized real estate firm operating in Toronto, Vancouver, and Calgary was struggling to accurately position their listings in an increasingly competitive market. They wanted a competitive intelligence solution to track how similar properties were being priced, described, and promoted across platforms like Realtor.ca, Zillow Canada, and Centris.ca. They partnered with ArcTechnolabs to develop a data-driven strategy using scraped real estate listings for benchmark analysis and pricing optimization.
The Challenge
The firm struggled with a lack of real-time competitor visibility and faced several critical data challenges. There was no centralized source of competitor listing data across different provinces, making comparisons difficult. Manual data collection from public listings was time-consuming and prone to errors. As a result, they often missed emerging trends in seasonal pricing, promotions, and keyword usage. The absence of structured insights limited their ability to justify their own pricing to sellers. Additionally, they lacked geospatial intelligence on popular or undervalued neighbourhoods, which impacted decision-making and strategic planning. To overcome these challenges, the company needed access to accurate and timely Real Estate Property Review Datasets .
ArcTechnolabs Solution:
ArcTechnolabs provided a tailored listing intelligence feed by scraping data from key platforms such as Realtor.ca, Zillow Canada, Centris.ca, and select local MLS websites across various provinces. The solution captured detailed property data points including listing ID, property type, price, square footage, and price per square foot. It also included information on the number of bedrooms and bathrooms, year built, listing agent, brokerage, and street-level location with geo-coordinates. Additional data covered days on market, property features and descriptions, images and virtual tour links, neighborhood classification, and indicators like open house status or sold tags, where available.
Sample Data Snapshot
| Handle | Platform | Category | Followers | ER% | Region | Viral Index | Language |
|---|---|---|---|---|---|---|---|
| @itsme_shweta | Fashion | 1.2M | 5.4% | Delhi NCR | 8.9 | Hindi | |
| @techguyrahul | YouTube | Tech | 3.1M | 4.8% | Bengaluru | 9.3 | English |
| @desifoodbyte | Food | 850K | 6.2% | Mumbai | 8.6 | Hinglish | |
| @dancemaddy | TikTok* | Dance | 1.9M | 7.1% | Kolkata | 9.1 | Bengali |
*TikTok banned in India; historical and Instagram Reel backup available
Client Testimonial
“ArcTechnolabs gave us a real competitive edge in a crowded real estate market. Their Real Estate Property Review Datasets and listing scraping solution gave our team visibility we never had before—across pricing, location trends, and feature comparisons. With timely, structured data, we could justify our pricing strategies to clients and move faster than the competition.”
— VP, Market Intelligence, Canadian Realty Firm
Conclusion
ArcTechnolabs helped a top digital agency unlock new-age influencer intelligence for India—resulting in smarter campaigns, better targeting, and higher engagement. With structured datasets, regional insight, and trend forecasting, the agency could predict virality, act fast, and win big—across every Indian metro and niche.
