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Decoding the spatial dynamics of sales and rental prices in a high-pressure Portuguese housing market: a random forest approach for the Lisbon Metropolitan Area

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Resumo:Sales and rental prices were analysed at parish level using random forest regression for the Lisbon Metropolitan Area. Three dependent variables (new sales, new rents, and all rents) and a set of independent variables/associated factors were used, including location, building/dwelling characteristics, socioeconomic features, and tourism. This geographically-based approach aims not to predict housing prices, but to identify relevant factors associated with sales/rents, ranking their importance. The temporal dimension is also explored by comparing new and all existing rents. The results revealed similarities and differences between housing submarkets. New sales and new rents had similar spatial patterns and dynamics but were different from that of all rents, with different regulations over time. Strong associations were found between the dependent variables and the population's social status and urban quality. However, while location was more strongly related to new sales and new rents, revealing a greater dependence on the current dynamics of the housing market, socioeconomic features were more closely related to all rents, expressing the urban and demographic dynamics of recent decades. Different associated factors prevail inside and outside the Lisbon municipality. The results contribute to a better understanding of housing submarkets and the relationships between sales/rents and associated factors.
Autores principais:Leal, Miguel
Outros Autores:Carreiras, Marina; Alves, Sónia
Assunto:Housing Real estate Sales prices Rental prices Random forest Machine learning
Ano:2025
País:Portugal
Tipo de documento:artigo
Tipo de acesso:acesso aberto
Instituição associada:Universidade de Lisboa
Idioma:inglês
Origem:Repositório da Universidade de Lisboa

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