Machine learning with explainability or spatial hedonics tools? An analysis of the asking prices in the housing market in Alicante, Spain
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Título: | Machine learning with explainability or spatial hedonics tools? An analysis of the asking prices in the housing market in Alicante, Spain |
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Autor/es: | Rico-Juan, Juan Ramón | Taltavull de La Paz, Paloma |
Grupo/s de investigación o GITE: | Reconocimiento de Formas e Inteligencia Artificial | Economía de la Vivienda y Sector Inmobiliario (ECOVISI) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos | Universidad de Alicante. Departamento de Análisis Económico Aplicado |
Palabras clave: | Forecasting housing prices | Hedonic tools | Machine Learning | Models’ explainability |
Área/s de conocimiento: | Lenguajes y Sistemas Informáticos | Economía Aplicada |
Fecha de publicación: | 13-ene-2021 |
Editor: | Elsevier |
Cita bibliográfica: | Expert Systems with Applications. 2021. https://doi.org/10.1016/j.eswa.2021.114590 |
Resumen: | Two sets of modelling tools are used to evaluate the precision of housing-price forecasts: machine learning and hedonic regression. Evidences on the prediction capacity of a range of methods points to the superiority of the random forest as it can calculate real-estate values with an error of less than 2%. This method also ranks the attributes that are most relevant to determining housing prices. Hedonic regression models are less precise but more robust as they can identify the housing attributes that most affect the level of housing prices. This empirical exercise adds new knowledge to the literature as it investigates the capacity of the random forest to identify the three dimensions of non-linearity which, from an economic theoretical point of view, would identify the reactions of different market agents. The intention of the robustness test is to check for these non-linear relationships using hedonic regression. The quantile tools also highlight non-linearities, depending on the price levels. The results show that a combination of techniques would add information on the unobservable (non-linear) relationships between housing prices and housing attributes on the real-estate market. |
URI: | http://hdl.handle.net/10045/111958 |
ISSN: | 0957-4174 (Print) | 1873-6793 (Online) |
DOI: | 10.1016/j.eswa.2021.114590 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1016/j.eswa.2021.114590 |
Aparece en las colecciones: | INV - GRFIA - Artículos de Revistas INV - ECOVISI - Artículos de Revistas |
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Rico-Juan_Taltavull_2021_ESWA.pdf | 1,73 MB | Adobe PDF | Abrir Vista previa | |
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