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      Property valuation by machine learning for the Norwegian real estate market

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          Summary

          We train a machine learning model on a large data set for predicting property values in the Norwegian real estate market. Our model is a gradient boosted regression tree. The data set is the largest market data set of properties in Norway considered in the research literature. We achieve state of the art accuracy. The novelty of our work lies in the fact that we use a minimal feature set in our model, and we have the largest data set in the research literature. Moreover, we have used only freely and publicly accessible data which are simple to obtain. Our data set covers most property types: freehold houses, apartments, rentals and cabins. This shows that useful estimation models with high accuracy can be built with quite simple resources.

          Abstract

          We train a machine learning model on large data set for predicting property values in the Norwegian real estate market. Our model is a gradient boosted regression tree. The data set is the largest market data set of properties in Norway considered in the research literature. We achieve state of the art accuracy.

          A large scale market data set of real estate properties is collected from sales and rental ads on publicly accessible internet sites. The property advertisements show property features and appraisal values made by real estate brokers. We train a gradient boosted regression tree model on selected features of the data set. This is a multivariate regression model built with supervised learning. We do 5-fold cross validation to assess the accuracy and robustness of the model.

          The gradient boosted regression tree models are already known to give the best prediction accuracy on real estate price valuations. We achieve state of the art pre- diction accuracy using a minimal feature set and only publicly and freely available sales advertisement data.

          The novelty of our work lies in the fact that we use a minimal feature set in our model, and we have the largest data set in the research literature, and moreover we have used only freely and publicly accessible data which are simple to obtain. This shows that useful estimation models with high accuracy can be built with quite simple resources.

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          Author and article information

          Journal
          ScienceOpen Preprints
          ScienceOpen
          13 September 2021
          Affiliations
          [1 ] Askin, Norway
          Author notes
          Article
          10.14293/S2199-1006.1.SOR-.PP0TP9I.v1

          This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

          The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

          Applied probability & Statistics, Data mining statistics, Machine learning, Data analysis, Econometrics, Trading & Market microstructure

          prediction, real estate, property valuation, machine learning, automated valuation model, price, artifical intelligence, house price prediction, property price prediction

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