This project involved the design and implementation of an end-to-end AI-driven real estate market intelligence system that automatically aggregates, structures, analyzes, and scores residential property data on a daily basis. The goal was to transform fragmented and unstructured public data into actionable insights for real estate brokers and investors.

What the System Does

The platform continuously collects and updates residential property data from multiple public sources, including online listings, land registry transaction records, court auction information, and legal databases. Incoming data is largely unstructured and written in Latvian, requiring semantic interpretation rather than simple field extraction.

An AI-based parsing layer converts raw listing text into structured records, extracting attributes such as price, floor area, address, floor level, building type, heating system, renovation state, deal type, and additional semantic indicators derived from listing language and context.

Using this structured dataset, the system applies an automated valuation methodology that estimates market value based on comparable transactions, location, building characteristics, renovation status, and floor-related factors. Properties are continuously evaluated against market expectations to identify pricing anomalies, such as underpriced or overpriced listings, as well as potential legal or transactional risk signals.

To support broker decision-making, the platform computes a motivation index (0–100) designed to prioritize properties with high actionability, reflecting factors such as pricing deviation, seller urgency indicators, and contextual risk or opportunity signals. Properties are also automatically tagged with semantic and structural flags related to condition, ownership patterns, infrastructure quality, and potential constraints.

System Architecture

The system operates as a fully automated daily pipeline, handling data ingestion, transformation, analysis, scoring, and reporting without manual intervention. Processed data is stored in a centralized operational dataset, where computed fields, valuation logic, anomaly detection, and ranking are applied consistently across thousands of listings. Daily outputs are made available for review, filtering, and comparison.

The result is a scalable PropTech intelligence platform that converts raw, unstructured real estate data into prioritized, valuation-driven insights. By combining automated data aggregation, semantic understanding, valuation logic, and scoring, the system supports faster and more informed decisions for brokers and investors, updated daily through an AI-driven workflow.