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Urban Pressures and Rental Market Affordability in Lisbon: An Interpretable Machine Learning Approach
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Resumo(s)
The housing market of Lisbon has experienced a deterioration in rental affordability over the last decade, driven by the interaction of multiple urban pressures rather than a single factor. While tourism and the expansion of short-term rentals have received substantial attention, rental affordability is also shaped by broader structural forces, including demographic change, housing stock constraints, urban amenities, and construction activity. This study applied a data-driven, interpretable machine learning framework to model advertised rental affordability at the parish-month level in Lisbon between 2016 and 2025. The analysis integrated heterogeneous data sources capturing market signals, tourism pressures, urban dynamics, and structural sociodemographic characteristics. Among the evaluated tree-based algorithms, an Augmented LightGBM model achieved superior predictive performance (R² of 0.90). Model outputs were interpreted using explainable artificial intelligence (XAI) techniques, namely SHAP values and Individual Conditional Expectation (ICE) plots. The results identify Short-Term Rental (STR) density as the dominant inflationary driver, establishing a price ceiling, while also highlighting the restrictive impact of vacant housing stock retention and the moderating potential of urban rehabilitation pipelines. Methodologically, the study bridges predictive accuracy and policy relevance. In practice, it delivers the Urban Policy Simulator, a dynamic Decision Support System (DSS) that allows municipal policymakers to simulate the financial impacts of housing policies in real-time, effectively transforming a complex machine learning pipeline into a transparent, actionable urban auditing tool.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
Palavras-chave
Urban Pressures Short-term Rentals Rental Prices Machine Learning Explainable AI Lisbon