Camacho, MáximoRamallo, SalvadorRuiz, Manuel2022-10-032022-10-032022-10-032022-10-032021-05-06https://revistas.pucp.edu.pe/index.php/economia/article/view/23752/22670https://repositorio.pucp.edu.pe/index/handle/123456789/186820In this paper, we assess the drivers of office rental prices in the municipality of Madrid with a sample of 4,721 offices in March, 2020. The estimation was performed using the decision tree approach, which was built with a random forest algorithm. This technique allows us to capture the strong nonlinear component in the relation between price and its drivers, mainly geospatial location. Through a stratified analysis, we find out that the willingness to pay high rent in the center of Madrid is a feature of particular relevance to medium-sized offices. For diferent reasons, we also find out some office clusters located far from the city center with high rent for both large and small offices.application/pdfenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0Spatial economicRandom forestNonlinearOfficesinfo:eu-repo/semantics/articlehttps://purl.org/pe-repo/ocde/ford#5.02.01https://doi.org/10.18800/economia.202101.002