Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/227605 
Erscheinungsjahr: 
2020
Schriftenreihe/Nr.: 
FORLand-Working Paper No. 22 (2020)
Verlag: 
Humboldt-Universität zu Berlin, DFG Research Unit 2569 FORLand "Agricultural Land Markets - Efficiency and Regulation", Berlin
Zusammenfassung: 
In this paper, we apply Ridge Regression, the Lasso and the Elastic Net to a rich and reliable data set of condominiums sold in Berlin, Germany, between 1996 and 2013. We their predictive performance in a rolling window design to a simple linear OLS procedure. Our results suggest that Ridge Regression, the Lasso and the Elastic Net show potential as AVM procedures but need to be handled with care because of their uneven prediction performance. At least in our application, these procedures are not the "automated" solution to Automated Valuation Modeling that they may seem to be.
Schlagwörter: 
Automated valuation
Machine learning
Elastic Net
Forecastperformance
JEL: 
R31
C14
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Dokumentart: 
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