Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/13411
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Dimopoulos, Thomas | - |
dc.contributor.author | Tyralis, Hristos | - |
dc.contributor.author | Bakas, Nikolaos P. | - |
dc.contributor.author | Hadjimitsis, Diofantos G. | - |
dc.date.accessioned | 2019-03-31T18:44:24Z | - |
dc.date.available | 2019-03-31T18:44:24Z | - |
dc.date.issued | 2018-11-29 | - |
dc.identifier.citation | Advances in Geosciences, 2018, Vol. 45, pp. 377-382 | en_US |
dc.identifier.issn | 16807340 | - |
dc.description.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. | en_US |
dc.format | en_US | |
dc.language.iso | en | en_US |
dc.relation.ispartof | Advances in Geosciences | en_US |
dc.rights | © Author(s). | en_US |
dc.subject | Accuracy assessment | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Numerical model | en_US |
dc.subject | Price determination | en_US |
dc.subject | Regression analysis | en_US |
dc.subject | Urban housing | en_US |
dc.title | Accuracy measurement of Random Forests and Linear Regression for mass appraisal models that estimate the prices of residential apartments in Nicosia, Cyprus | en_US |
dc.type | Article | en_US |
dc.collaboration | Neapolis University Pafos | en_US |
dc.collaboration | Cyprus University of Technology | en_US |
dc.collaboration | Hellenic Air Force | en_US |
dc.subject.category | Computer and Information Sciences | en_US |
dc.journals | Open Access | en_US |
dc.country | Cyprus | en_US |
dc.country | Greece | en_US |
dc.subject.field | Natural Sciences | en_US |
dc.publication | Peer Reviewed | en_US |
dc.identifier.doi | 10.5194/adgeo-45-377-2018 | en_US |
dc.relation.volume | 45 | en_US |
cut.common.academicyear | 2018-2019 | en_US |
dc.identifier.spage | 377 | en_US |
dc.identifier.epage | 382 | en_US |
item.fulltext | With Fulltext | - |
item.languageiso639-1 | en | - |
item.grantfulltext | open | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.cerifentitytype | Publications | - |
item.openairetype | article | - |
crisitem.journal.journalissn | 1680-7359 | - |
crisitem.journal.publisher | Copernicus | - |
crisitem.author.dept | Department of Civil Engineering and Geomatics | - |
crisitem.author.dept | Department of Civil Engineering and Geomatics | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.orcid | 0000-0002-2684-547X | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
Appears in Collections: | Άρθρα/Articles |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
adgeo-45-377-2018.pdf | Fulltext | 1.73 MB | Adobe PDF | View/Open |
adgeo-45-377-2018-supplement.pdf | Supplement | 603.74 kB | Adobe PDF | View/Open |
CORE Recommender
SCOPUSTM
Citations
24
checked on Nov 6, 2023
Page view(s)
575
Last Week
0
0
Last month
33
33
checked on Mar 14, 2025
Download(s)
172
checked on Mar 14, 2025
Google ScholarTM
Check
Altmetric
Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.