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 ...
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Dokumenttyp: | Artikel |
Erscheinungsdatum: | 2022 |
Reihe/Periodikum: | ISPRS International Journal of Geo-Information, Vol 11, Iss 125, p 125 (2022) |
Verlag/Hrsg.: |
MDPI AG
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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 |
Powered By: | BASE |
Link(s) : | https://doi.org/10.3390/ijgi11020125 |