Machine learning as a tool for improved housing price prediction : the applicability of machine learning in housing price prediction and the economic implications of improvement to prediction accuracy
dc.contributor.advisor | Andersson, Jonas | |
dc.contributor.author | Wolstad, Henrik I W. | |
dc.contributor.author | Dewan, Didrik | |
dc.date.accessioned | 2021-04-27T07:51:16Z | |
dc.date.available | 2021-04-27T07:51:16Z | |
dc.date.issued | 2020 | |
dc.identifier.uri | https://hdl.handle.net/11250/2739783 | |
dc.description.abstract | This thesis investigates whether non-linear machine learning algorithms can produce more accurate predictions of Norwegian housing prices compared to linear regression models. We find that the non-linear XGBoost algorithm increases out-of-sample prediction accuracy by 8.5% in terms of Root Mean Squared Error compared to the linear model used by Statistics Norway. Using additional property-specific and macroeconomic variables such as coordinates, common debt, story, inflation rate and interest rate, we find that a non-linear Stacked Regression model improves out-of-sample prediction accuracy by 39.52% in terms of Root Mean Squared Error compared to a linear model. | en_US |
dc.language.iso | eng | en_US |
dc.subject | business analytics | en_US |
dc.subject | financial economics | en_US |
dc.title | Machine learning as a tool for improved housing price prediction : the applicability of machine learning in housing price prediction and the economic implications of improvement to prediction accuracy | en_US |
dc.type | Master thesis | en_US |
dc.description.localcode | nhhmas | en_US |
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Master Thesis [4210]