How to Use Real Estate Web Scraping to Gain Valuable Insights
Real estate web scraping: a powerful tool for data collection and analysis. Learn how to choose the right data collection method and benefit from real estate web scraping
Metrocuadrado hosts Colombia’s largest collection of real estate listings — and we make that data work for you. With our scraping service, you get property listings, prices, and location details collected directly from the platform, structured and ready for use. We focus on the data that matters to you — clearly formatted and easy to integrate.
You don’t need to build your own tools or rely on partial exports — we handle the entire extraction process for you. Whether you're interested in residential rentals, commercial listings, or newly built homes, we create a scraper that targets exactly what matters. With our solution, you can scrape Metrocuadrado data effortlessly — property details are collected, cleaned, and delivered in a structured format, ready for integration into your internal systems, research, or marketplace.
Our Metrocuadrado scraper pulls detailed, multi-layered property listings directly from the platform. It captures all key elements visible to users — titles, full addresses with neighborhoods and cities, listing prices in COP, and property types ranging from apartments to commercial spaces. We also gather metadata unique to Metrocuadrado, such as building age, stratum level (estrato), property condition, number of garages, and administration fees when shown. Listing descriptions, agent names, contact numbers, square meters, and publication dates are all included. Every detail is delivered in a consistent structure — ready for analysis, enrichment, or integration into your system.
Metrocuadrado listings often carry more than just surface-level data. When you scrape Metrocuadrado data, you can extract elements like interactive map positions, neighborhood tags, estimated mortgage values, and property features such as floor level, building age, or energy efficiency. Some listings also include time-on-market indicators, branding from specific agencies, and links to virtual tours. These additional insights bring more depth to your analysis and allow for smarter filtering, lead scoring, or data modeling.
Metrocuadrado is Colombia’s most established real estate portal, covering listings across all regions — from Bogotá and Medellín to coastal towns and rural zones. Operating entirely in Spanish and displaying prices in Colombian Pesos (COP), metrocuadrado.com serves millions of monthly users looking for homes, offices, land, and commercial properties.
The platform hosts listings from both individual sellers and licensed real estate professionals, offering everything from city apartments to warehouses and farmland. What makes it stand out is its integrated set of tools — including mortgage calculators, financing guides, and custom map-drawing search features — designed to give users full control over how they explore the housing market.
Whether browsing on desktop or mobile, users benefit from a fast, intuitive experience tailored to local needs. For analysts, developers, and housing platforms, Metrocuadrado is one of the most reliable and current sources of real estate data in Colombia.
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Pages extracted
Hours saved for our clients
Customized scraping setup for Metrocuadrado — faster and cheaper than building a solution from scratch.
Data limits (rows): up to 10%
Iterations: up to 3
Custom requirements: Yes
Data lifetime: up-to-date
Data quality checks: Yes
Delivery deadline: 1-2 working days
Output formats: CSV, JSON, XLSX
Delivery options: e-mail
Metrocuadrado offers detailed, high-volume listings that reflect real-time activity across Colombia’s dynamic property market. Using a Metrocuadrado scraper, you can unlock direct access to this rich dataset — allowing you to monitor price fluctuations by city, district, or neighborhood, compare offerings across residential and commercial property types, and track how individual agencies and developers position and market their listings over time. This kind of data extraction is especially valuable for building localized housing databases, powering automated listing feeds, and running advanced analytics tied to geographic zones, construction types, or budget segments. With broad national coverage and frequent updates, a Metrocuadrado scraper provides a clear, data-driven view into supply and demand trends, helping you anticipate market movements and make smarter decisions.
Learn how to use web scraping to solve data problems for your organization
Real estate web scraping: a powerful tool for data collection and analysis. Learn how to choose the right data collection method and benefit from real estate web scraping
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Working with housing data shouldn’t be complicated. At ScrapeIt, we handle the full extraction process for you — no tools to install, no technical setup required. You let us know what kind of property information you’re looking for on Metrocuadrado, and we provide clean, organized data that’s ready to use in your system. It’s a simple way to get exactly what you need, without wasting time.
We deliver everything in your preferred format — CSV, Excel, or JSON — ready to plug into your tools or reports.
Absolutely. We can filter the data by region, city, or even down to individual neighborhoods, so you only get listings relevant to your focus area.
Yes — we can set up regular scraping intervals to make sure you’re always working with fresh, current listings.
We collect full listing details including price, address, property type, size, features, and more — all tailored to what you need.
Not at all. We handle everything on our end — you just receive the data, ready to use.
1. Make a request
You tell us which website(s) to scrape, what data to capture, how often to repeat etc.
2. Analysis
An expert analyzes the specs and proposes a lowest cost solution that fits your budget.
3. Work in progress
We configure, deploy and maintain jobs in our cloud to extract data with highest quality. Then we sample the data and send it to you for review.
4. You check the sample
If you are satisfied with the quality of the dataset sample, we finish the data collection and send you the final result.
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