Please use this identifier to cite or link to this item:
Title: Accuracy measurement of Random Forests and Linear Regression for mass appraisal models that estimate the prices of residential apartments in Nicosia, Cyprus
Authors: Dimopoulos, Thomas 
Tyralis, Hristos 
Bakas, Nikolaos P. 
Hadjimitsis, Diofantos G. 
Keywords: Accuracy assessment;Machine learning;Numerical model;Price determination;Regression analysis;Urban housing
Category: Computer and Information Sciences
Field: Natural Sciences
Issue Date: 29-Nov-2018
Publisher: Copernicus GmbH
Source: Advances in Geosciences, 2018, Volume 45, Pages 377-382
Journal: Advances in Geosciences 
Abstract: The purpose of this article is to examine the prediction accuracy of the Random Forests, a machine learning method, when it is applied for residential mass appraisals in the city of Nicosia, Cyprus. The analysis is performed using transaction sales data from the Cyprus Department of Lands and Surveys, the Consumer Price Index of Cyprus from the Cyprus Statistical Service and the Central Bank of Cyprus' Residential Index (Price index for apartments). The Consumer Price Index and the price index for apartments record quarterly price changes, while the dependent variables for the computational models were the Declared and the Accepted Prices that were conditional on observed values of a variety of independent variables. The Random Forests method exhibited enhanced prediction accuracy, especially for the models that comprised of a sufficient number of independent variables, indicating the method as prominent, although it has not yet been utilized adequately for mass appraisals.
ISSN: 16807340
DOI: 10.5194/adgeo-45-377-2018
Rights: © 2018 Author(s).
Type: Article
Appears in Collections:Άρθρα/Articles

Files in This Item:
File Description SizeFormat
adgeo-45-377-2018.pdfFulltext1.73 MBAdobe PDFView/Open
adgeo-45-377-2018-supplement.pdfSupplement603.74 kBAdobe PDFView/Open
Show full item record

Citations 50

checked on Jul 31, 2019

Page view(s)

Last Week
Last month
checked on Nov 17, 2019


checked on Nov 17, 2019

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.