Case Study: Property Data Scraping from Zoopla
The Challenge
Our client, a real estate analytics firm, needed accurate and up-to-date residential and commercial property data from Zoopla — including property type, price, location, agent details, and availability. Manual data collection was slow and prone to inconsistencies, making it difficult to perform market comparisons and generate insights.
Hurdles We Faced
- Dynamic Pages: Zoopla listings load dynamically, requiring smart crawling logic.
- Anti-Bot Restrictions: Rate limits and IP blocking during large-scale scraping.
- Data Diversity: Residential and commercial listings had different structures and fields.
- Frequent Updates: Prices and availability changed daily, demanding timely refreshes.
Our Step-by-Step Approach
Requirement Analysis:
Defined key attributes — property type, location, price, agent info, and listing status.
Proxy-Based Scraping:
Used rotating proxies and randomized requests to scrape high-volume data without detection.
Custom Parser & Data Cleaning:
Developed a parser to handle dynamic elements and standardized extracted data for both residential and commercial properties.
Automated Scheduling:
Implemented daily scraping jobs with error handling to keep listings continuously updated.
Results & Impact
- Automated collection of property listings from Zoopla with high accuracy.
- Delivered a unified, clean dataset ready for analytics and dashboards.
- Reduced manual data effort and improved reporting efficiency.
Our Services & Expertise
At Ascendance Solutions, we specialize in:
- Real Estate Data Scraping & Aggregation
- Proxy-Based Automation for Dynamic Websites
- Data Cleaning, Structuring & Analytics Integration
📧 Connect with us at connect@ascendancesolutions.com to automate property data collection for real estate insights.
