Real Estate Data Platform Overhaul

Case Study: Enabling Smarter Property Investment Decisions

Client

A real estate analytics startup developing a platform to support property investors and first-time homebuyers with financial projections and market insights.

Engagement Overview

The client approached us to modernize and extend their platform, which had grown from a basic MVP into a core business tool. Their infrastructure and development setup were limiting their ability to scale, integrate third-party data, and deliver long-term forecasting. Over a two-year collaboration, we rebuilt their stack, automated data flows, and implemented property valuation forecasting tools—all while improving security, reliability, and speed of delivery.

Platform Modernisation

We migrated the platform from a legacy stack to a modular, modern architecture.

• Approach: Full transition to a LEMP-based architecture (Linux, Nginx, MySQL, Node.js, Python).
• Infrastructure: Moved infrastructure from a fragmented AWS setup to a leaner, maintainable environment.
• Improvements: Implemented SSL, optimized the database, and built custom internal tools to support ongoing analytics.

Data Aggregation & ETL

We developed automated pipelines to consolidate market and property data from various public and private sources.

• Data Types: Historical price trends, valuations, rental performance, and property-level characteristics.
• ETL: Scheduled ingestion of external data into a centralized warehouse, with frequency tuned to each source's update cycle.
• Outcome: A consistent, clean dataset supporting internal analytics and platform functionality.

Forecasting Engine

We delivered a forecasting model to support long-term property value projections.

• Model Type: Time-series forecasting driven by property features and geolocation data.
• Deployment: Exposed via Python integration within the backend services.
• Flexibility: The model can be swapped or retrained with different data subsets depending on the investment strategy.

Frontend & Business Tools

Beyond backend and data architecture, we contributed to critical user-facing improvements.

• Frontend: Developed core UI features and graphical components to visualize market trends and investment scenarios.
• Automation: Created newsletter automations and internal dashboards for client insights and customer engagement tracking.
• Collaboration: Worked directly with executive leadership, ensuring priorities were aligned with business goals.

Technologies Used

Node.js, Python, MySQL, Angular, Azure, Nginx, Linux (LEMP stack). Custom-built ETLs, time-series forecasting, secure web architecture.

Collaboration Style

This has been a long-term collaboration, supporting rapid iteration and steady platform growth. We've acted as an extension of their internal team—designing, developing, and delivering features aligned with their mission to make property investment more data-driven and accessible.