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Analysis of Pricing Factors in Airbnb Listings in the Porto Metropolitan Area: A Multiscale Geographically Weighted Regression Analysis
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Resumo(s)
The rapid growth of platforms like Airbnb has reshaped urban housing dynamics, especially in
tourist destinations such as the Porto Metropolitan Area (PMA). Most existing studies rely on
global models that overlook spatial variations in price determinants, limiting the effectiveness
of place-based public policies. To address this gap, we applied Multiscale Geographically
Weighted Regression (MGWR) to 9,914 Airbnb listings in the PMA, calibrating eight key
variables: location and cleanliness ratings, 60-day availability (quadratic term), number of
beds², bedrooms² and bathrooms², density of points of interest (POIs) and a tourism
accessibility index generated via PCA. The model explained R² = 0.67 of price variation.
Cleanliness had a consistently positive effect across the PMA, acting as a universal quality
signal. In contrast, availability, bathrooms and amenity density operated at highly local scales,
reflecting demand-specific patterns. Structural attributes showed intermediate elasticities,
varying with local market competition. We conclude that MGWR is superior in capturing
spatial price dynamics. Policy recommendations include: (i) promoting longer stays in central
areas to ease housing pressure; and (ii) encouraging tourism diversification in peripheral
municipalities. The proposed model offers a practical framework for designing spatially
calibrated interventions in short-term rental markets.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Business Intelligence
Palavras-chave
Airbnb MGWR Short-term rental prices Porto Metropolitan Area Spatial determinants SDG 8 - Decent work and economic growth SDG 11 - Sustainable cities and communities SDG 17 - Partnerships for the goals