1 research outputs found

    Property valuation with interpretable machine learning

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    Property valuation is an important task for various stakeholders, including banks, local authorities, property developers, and brokers. As a result of the characteristics of the real estate market, such as the infrequency of trades, limited supply, negotiated prices, and small submarkets with unique traits, there is no clear market value for properties. Traditionally property valuations are done by expert appraisers. Property valuation can also be done accurately with machine learning methods, but the lack of interpretability with accurate machine learning methods can limit the adoption of those methods. Interpretable machine learning methods could be a solution to this issue, but there are concerns related to the accuracy of these methods. This thesis aims to evaluate the feasibility of interpretable machine learning methods in property valuation by comparing a promising interpretable method to a more complex machine learning method that has had good results in property valuation previously. The promising interpretable method and the well-performed machine learning method are chosen based on previous literature. The two chosen methods, Extreme Gradient Boosting (XGB) and Explainable Boosting Machine (EBM) are compared in terms of prediction accuracy of properties in six big municipalities of Denmark. In addition to the accuracy comparison, the interpretability of the EBM is highlighted. The accuracy of the XGB method is better, even though there are no big differences between the two methods in individual municipalities. The interpretability of the EBM is good, as it is possible to understand, how the model makes predictions in general, and how individual predictions are made
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