Improving Prediction Models for Mass Assessment: A Data Stream Approach

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dc.contributor.authorShi, Donghui
dc.contributor.authorGuan, Jian
dc.contributor.authorZurada, Jozef
dc.contributor.authorLevitan, Alan
dc.date.accessioned2020-01-04T07:21:32Z
dc.date.available2020-01-04T07:21:32Z
dc.date.issued2020-01-07
dc.description.abstractMass appraisal is the process of valuing a large collection of properties within a city/municipality usually for tax purposes. The common methodology for mass appraisal is based on multiple regression though this methodology has been found to be deficient. Data mining methods have been proposed and tested as an alternative but the results are very mixed. This study introduces a new approach to building prediction models for assessing residential property values by treating past sales transactions as a data stream. The study used 110,525 sales transaction records from a municipality in the Midwest of the US. Our results show that a data stream based approach outperforms the traditional regression approach, thus showing its potential in improving the performance of prediction models for mass assessment.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2020.130
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/63869
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData, Text, and Web Mining for Business Analytics
dc.subjectdata stream models
dc.subjectmass assessment
dc.subjectprediction
dc.subjectreal estate properties
dc.titleImproving Prediction Models for Mass Assessment: A Data Stream Approach
dc.typeConference Paper
dc.type.dcmiText

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