Estimating the performance of random forest versus multiple regression for predicting prices of the apartments

Čeh, Marjan (avtor), Kilibarda, Milan (avtor), Lisec, Anka (avtor), Bajat, Branislav (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: random forest, OLS, hedonic price model, PCA, Ljubljana
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FGG - Fakulteta za gradbeništvo in geodezijo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2018
• Št. strani: 16 str.
• Številčenje: Vol. 7, iss. 5, art. 168
• PID: 20.500.12556/RUL-114444
• UDK: 004.8:332.85(497.451.1)(049.5)
• ISSN pri članku: 2220-9964
• DOI: 10.3390/ijgi7050168
• COBISS.SI-ID: 8417121
• Datum objave v RUL: 28.02.2020

Gradivo je del revije

• Naslov: ISPRS international journal of geo-information
• Skrajšan naslov: ISPRS int. j. geo-inf.
• Založnik: MDPI
• ISSN: 2220-9964
• COBISS.SI-ID: 18678550

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
• Začetek licenciranja: 02.05.2018

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: strojna metoda učenja, naključni gozd, metoda najmanjših kvadratov, hedonski cenovni model, analiza glavnih komponent, stanovanja, trg nepremičnin, Ljubljana

Projekti

• Financer: Drugi - Drug financer ali več financerjev
• Številka projekta: 451-03-3095/2014-09/34