Portugal Rental Investment Intelligence

Investors compare short-term rental markets on intuition and scattered listings. Whether a purchase in a given Portuguese region actually pays back depends on occupancy, nightly price, expenses, and financing, variables that live across platforms and are hard to combine into one view.

The goal was a quantitative decision-support system, not a descriptive report: expected annual revenue, break-even occupancy, monthly cash flow, mortgage sustainability, years to repay a €150,000 investment, and how scaling to multiple properties changes the outcome.

• Custom ingestion pipeline for Airbnb, Booking, and Vrbo listing snapshots
• Medallion architecture (Bronze / Silver / Gold) with geographic filtering by coordinates and municipality
• Occupancy estimated from availability, (365 − availability_365) / 365, across conservative, base, and optimistic scenarios
• XGBoost models for occupancy and nightly price, versioned with benchmark metadata
• Financial modeling: revenue, expenses, cash flow, and a €150,000 mortgage simulation with interest-rate sensitivity
• Cross-market comparison across the Lisbon coast, Porto, and the Douro wine-tourism region, with a dashboard output

A repeatable investment-intelligence system that turns scattered listing data into a financial view an investor can act on, comparing regions and modeling payback instead of guessing. It spans the full path MCC works across: ingestion, data engineering, machine learning, financial modeling, and a decision-ready interface.