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dc.contributor.advisorAndersson, Jonas
dc.contributor.authorWolstad, Henrik I W.
dc.contributor.authorDewan, Didrik
dc.date.accessioned2021-04-27T07:51:16Z
dc.date.available2021-04-27T07:51:16Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/11250/2739783
dc.description.abstractThis 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.isoengen_US
dc.subjectbusiness analyticsen_US
dc.subjectfinancial economicsen_US
dc.titleMachine 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 accuracyen_US
dc.typeMaster thesisen_US
dc.description.localcodenhhmasen_US


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