Spatial Determinants of Real Estate Appraisals in The Netherlands: A Machine Learning Approach

With the rapidly increasing house prices in the Netherlands, there is a growing need for more localised value predictions for mortgage collaterals within the financial sector. Many existing studies focus on modelling house prices for an individual city; however, these models are often not interesting for mortgage lenders with assets spread out all over the country. That is why, with the current abundance of national geospatial datasets, this paper implements and compares three hedonic pricing models (linear regression, geographically weighted regression, and extreme gradient boosting—XGBoost)... Mehr ...

Verfasser: Evert Guliker
Erwin Folmer
Marten van Sinderen
Dokumenttyp: Artikel
Erscheinungsdatum: 2022
Reihe/Periodikum: ISPRS International Journal of Geo-Information, Vol 11, Iss 125, p 125 (2022)
Verlag/Hrsg.: MDPI AG
Schlagwörter: real estate values modelling / housing market / housing price / real estate appraisals / hedonic model / extreme gradient boosting / Geography (General) / G1-922
Sprache: Englisch
Permalink: https://search.fid-benelux.de/Record/base-29172152
Datenquelle: BASE; Originalkatalog
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Link(s) : https://doi.org/10.3390/ijgi11020125