20.500.12556/RUL-114444 Estimating the performance of random forest versus multiple regression for predicting prices of the apartments The goal of this study is to analyse the predictive performance of the random forest machine learning technique in comparison to commonly used hedonic models based on multiple regression for the prediction of apartment prices. A data set that includes 7407 records of apartment transactions referring to real estate sales from 2008-2013 in the city of Ljubljana, the capital of Slovenia, was used in order to test and compare the predictive performances of both models. Apparent challenges faced during modelling included (1) the non-linear nature of the prediction assignment task; (2) input data being based on transactions occurring over a period of great price changes in Ljubljana whereby a 28% decline was noted in six consecutive testing years; and (3) the complex urban form of the case study area. Available explanatory variables, organised as a Geographic Information Systems (GIS) ready dataset, including the structural and age characteristics of the apartments as well as environmental and neighbourhood information were considered in the modelling procedure. All performance measures (R$^2$ values, sales ratios, mean average percentage error (MAPE), coefficient of dispersion (COD)) revealed significantly better results for predictions obtained by the random forest method, which confirms the prospective of this machine learning technique on apartment price prediction. random forest OLS hedonic price model PCA Ljubljana strojna metoda učenja naključni gozd metoda najmanjših kvadratov hedonski cenovni model analiza glavnih komponent stanovanja trg nepremičnin Ljubljana true true true Angleški jezik Slovenski jezik Članek v reviji 2020-02-28 12:50:14 2020-02-28 12:59:32 2022-08-26 03:50:37 0000-00-00 00:00:00 2018 0 0 16 str. iss. 5, art. 168 Vol. 7 May 2018 0000-00-00 Zaloznikova Objavljeno apc 0000-00-00 0000-00-00 0000-00-00 004.8:332.85(497.451.1)(049.5) 2220-9964 10.3390/ijgi7050168 8417121 18678550 RAZ_Ceh_Marjan_2018.pdf RAZ_Ceh_Marjan_2018.pdf 1 8644ACDA66D6133A3D28B582A7131500 2fcf12bcc725677323ee8ba643367b468b758e221c6ad0aabb40d557b8ab0027 88ded7ef-24e3-11ec-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=149224 http://www.mdpi.com/2220-9964/7/5/168 0 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=126636 Fakulteta za gradbeništvo in geodezijo 0 0 0