Comprehensive Data Collection and Analysis of UK Real Estate

Development and implementation of a system for large-scale data collection on real estate properties in the United Kingdom from various online platforms, including Zoopla, Airbnb, and Unihomes. The project aims to create an extensive database for subsequent real estate market analysis.

Key Challenges:

• Data Source Heterogeneity: Each target website had a unique structure and data presentation format, requiring an individual approach to information extraction.
• Data Format Diversity: The need to process various types of server responses, including HTML pages and JSON responses, which complicated the parsing and data normalization process.
• Overcoming Protection Mechanisms: Developing strategies to bypass web scraping protection systems such as Cloudflare, as well as addressing the issue of request rate limiting from a single IP address.

Implementation Stages:

1. Preliminary Analysis:
   • Detailed study of the target websites' structure.
   • Analysis of network interactions between client and server.
   • Investigation of mobile application APIs where applicable.
2. Database Design:
   • Development of an optimized database schema in PostgreSQL for efficient storage and subsequent analysis of collected information.
3. Scraper Development:
   • Creation of high-performance web scrapers in Python without using browser emulation tools to minimize overhead.
   • Implementation of a modular architecture to ensure flexibility and scalability of the solution.
4. Implementation of Protection Bypass Measures:
   • Development of algorithms to bypass Cloudflare and other automated data collection protection systems.
   • Implementation of a proxy server rotation system to distribute load and reduce the risk of blocking.
   • Implementation of a request rate limiting strategy for each proxy server to avoid detection.
5. Testing and Optimization:
   • Conducting comprehensive performance and reliability testing of scrapers.
   • Optimization of data collection algorithms to increase efficiency and reduce load on target services.
6. Deployment and Automation:
   • Configuration of server infrastructure for continuous system operation.
   • Setup of a task scheduler to automate the data collection process.

Additional Project Aspects:

• Data Normalization: Development of algorithms to bring heterogeneous data to a unified format, ensuring consistency and ease of analysis.
• Address Analysis and Matching: Creation of a system to identify and link real estate objects across different data sources, enhancing the completeness and reliability of collected information.
• Data Analytics: Conducting preliminary analysis of collected data to identify trends and patterns in the UK real estate market.

Project Outcomes:

The developed system effectively collected, processed, and analyzed large volumes of real estate data from multiple sources, overcoming technical and methodological challenges. This resulted in the creation of a unique database providing valuable information for analyzing the UK real estate market.