Price and Spatial Distribution of Office Rental in Madrid: A Decision Tree Analysis

• Autores: Camacho, Máximo, Ramallo, Salvador, Ruiz, Manuel
• Formato: artículo
• Fecha de Publicación: 2021
• Institución: Pontificia Universidad Católica del Perú
• Repositorio: PUCP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.pucp.edu.pe:20.500.14657/178311
• Enlace del recurso: http://revistas.pucp.edu.pe/index.php/economia/article/view/23752/22670; https://doi.org/10.18800/economia.202101.002
• Nivel de acceso: acceso abierto
• Materia: Spatial economic; Random forest; Nonlinear; Offices; https://purl.org/pe-repo/ocde/ford#5.02.01

In 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.