Singapore’s real estate market moves fast — and 99.co is right at the center of it. With the right scraper, you can access fresh listings, prices, and location data from one of the country’s most trusted property platforms.
You don’t need to manage scripts or figure out complicated platforms — we take care of everything. Whether you're looking for rental flats, resale condos, or developer launches, we build a custom scraper that extracts exactly what you need from 99.co. The data is organized, filtered, and ready for use — tailored to fit directly into your database, dashboard, or internal tools.
The 99.co scraper is designed to collect complete and well-structured property data from every listing on the platform — including:
All data is cleaned, standardized, and delivered in ready-to-use formats like CSV, Excel, or JSON — making it easy to integrate into your internal tools or workflows.
99.co offers more than just basic listing fields. When you scrape 99.co data, we can extract additional context like:
Listings often include tags like “exclusive,” “near amenities,” or “price dropped,” which we can include for deeper analysis. Even estimated mortgage info, agent ratings, and featured placement data can be captured to give your dataset more depth and business value.
99.co is a next-generation real estate platform serving users in both Singapore and Indonesia, offering a clean, data-forward experience tailored to the region’s fast-paced property markets. Listings are available in English, with pricing displayed in Singapore Dollars (SGD) or Indonesian Rupiah (IDR) depending on location. You’ll find the platform live at 99.co.
What sets 99.co apart is its user-focused interface, which includes:
With millions of active users each month, the site draws renters, buyers, and property investors, as well as developers marketing new-build projects. It’s part of the 99 Group, which also owns SRX and Rumah123, making it a major force in the Southeast Asian PropTech space.
For anyone working with housing data, market insights, or regional development tracking, 99.co is a powerful and growing source of structured real estate intelligence in the region.
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Developers
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Pages extracted
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Customized plans that grow with your data needs.
€199 / one-time
setup fee — included
€169 / mo
setup fee €499
€229 / mo
setup fee €499
€349 / mo
setup fee €499
€549 / mo
setup fee €799
99.co scraper gives you access to property listings with a level of structure and filtering that few platforms match. From MRT-line proximity tags to developer project launches and unit-level floorplans, the platform makes it easy to segment listings by lifestyle, not just location. Scraping this data helps you analyze:
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ScrapeIt builds custom solutions for property data extraction — designed for people who want results, not technical hassle. We don’t sell subscriptions or software dashboards. Instead, we focus on one thing: helping you scrape 99.co data efficiently and reliably. You get exactly what you need — already filtered, structured, and formatted for your workflow. Organized, dependable housing data, delivered without the time drain of setup or development.
Yes — we can filter by property type, lease category, or even by specific project names if needed.
We capture everything visible on the listing, including block number, street name, and even floor level if available.
Definitely. If 99.co tags the property with proximity data (like MRT lines or primary schools), we’ll include it in the export.
Yes, we extract agent names, agencies, phone numbers, and profile links — depending on what's shown publicly.
As often as you need — once a day, a few times per week, or on a custom schedule that fits your project timeline.
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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