Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/19124
DC FieldValueLanguage
dc.contributor.authorDimopoulos, Thomas-
dc.contributor.authorBakas, Nikolaos P.-
dc.date.accessioned2020-10-12T05:20:44Z-
dc.date.available2020-10-12T05:20:44Z-
dc.date.issued2019-06-27-
dc.identifier.citationSeventh International Conference on Remote Sensing and Geoinformation of the Environment, 2019, 18-21 March, Paphos, Cyprusen_US
dc.identifier.isbn978-151063061-1-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/19124-
dc.description.abstractA recent study in property valuation literature, indicated that the vast majority of researchers and academics are focusing on Mass Appraisals rather than on further developing the existing methods. Researchers are using a variety of mathematical models from the field of Machine Learning and Artificial Neural Networks, which are applied to real estate valuations, with high accuracy. On the other hand, it appears that the professional valuers do no use those sophisticated models on their daily practice, using essentially the traditional 5 methods. At that point, authors deal with the ethical question that arises and that is whether those models can replace the judgment of the individual valuer. As in many other aspects of scientific research, and in particular in artificial intelligence applications, human intelligence is still dangerous to be replaced by machine intelligence (like i.e. the self-driving cars). Despite the fact that those models are proved to be extremely accurate in academic test cases, in real-world applications, they cannot be used without the audit of an experienced valuer. The aim of this work is to investigate the capabilities of such models and how they can be used in order to improve valuer's work.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.rights© SPIEen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAlgorithmsen_US
dc.subjectArtificial intelligenceen_US
dc.subjectAVMen_US
dc.subjectCAMAen_US
dc.subjectMass appraisalsen_US
dc.subjectMathematical modelsen_US
dc.titleArtificial intelligence for mass appraisals of residential properties in Nicosia: Mathematical modelling and algorithmic implementationen_US
dc.typeConference Papersen_US
dc.collaborationNeapolis University Pafosen_US
dc.collaborationCyprus University of Technologyen_US
dc.subject.categoryCivil Engineeringen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.relation.conferenceInternational Conference on Remote Sensing and Geoinformation of the Environmenten_US
dc.identifier.doi10.1117/12.2538430en_US
cut.common.academicyear2018-2019en_US
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.openairetypeconferenceObject-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.languageiso639-1en-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
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