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What is the real market value of real estate? Combining machine learning with administrative data on property tax (IMI) and transaction tax (IMT) to estimate housing prices and housing wealth in Portugal
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Orientador(es)
Resumo(s)
Estimating real estate market value persists to be a complex problematic in public policy,
investment, and taxation, particularly in countries like Portugal, where housing is central to
both household wealth and government revenue. Traditional valuation models often rely on
limited structural or locational data, reducing their accuracy. This thesis introduces a new
approach using over a decade of administrative property and transaction tax records from the
Portuguese National Institute of Statistics to predict housing prices through machine learning.
A range of models, including random forest, gradient boosting, and traditional linear regression,
were trained on a harmonized dataset, with extensive preprocessing to handle data complexity.
Results show that non-linear ensemble models consistently outperform linear approaches, better
capturing the diverse factors influencing price formation. Key variables included tax
classification, official valuations, and location and time attributes. Model transparency was
enhanced through interpretability techniques that revealed consistent, meaningful patterns.
While not intended for precise individual price prediction, the models offer valuable insights
for macroeconomic monitoring, fiscal planning, and evidence-based housing policy in Portugal.
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
Real Estate Machine Learning Administrative Tax Data Housing Market Predictive Modeling SDG 4 - Quality education SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure SDG 11 - Sustainable cities and communities