Improving Prediction Models for Mass Assessment: A Data Stream Approach Shi, Donghui Guan, Jian Zurada, Jozef Levitan, Alan 2020-01-04T07:21:32Z 2020-01-04T07:21:32Z 2020-01-07
dc.description.abstract Mass 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.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2020.130
dc.identifier.isbn 978-0-9981331-3-3
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Data, Text, and Web Mining for Business Analytics
dc.subject data stream models
dc.subject mass assessment
dc.subject prediction
dc.subject real estate properties
dc.title Improving Prediction Models for Mass Assessment: A Data Stream Approach
dc.type Conference Paper
dc.type.dcmi Text
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
435.57 KB
Adobe Portable Document Format