Maskininlärning som verktyg för att extrahera information om attribut kring bostadsannonser i syfte att maximera försäljningspris

Sammanfattning: The Swedish real estate market has been digitalized over the past decade with the current practice being to post your real estate advertisement online. A question that has arisen is how a seller can optimize their public listing to maximize the selling premium. This paper analyzes the use of three machine learning methods to solve this problem: Linear Regression, Decision Tree Regressor and Random Forest Regressor. The aim is to retrieve information regarding how certain attributes contribute to the premium value. The dataset used contains apartments sold within the years of 2014-2018 in the Östermalm / Djurgården district in Stockholm, Sweden. The resulting models returned an R2-value of approx. 0.26 and Mean Absolute Error of approx. 0.06. While the models were not accurate regarding prediction of premium, information was still able to be extracted from the models. In conclusion, a high amount of views and a publication made in April provide the best conditions for an advertisement to reach a high selling premium. The seller should try to keep the amount of days since publication lower than 15.5 days and avoid publishing on a Tuesday.