Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/63869

Improving Prediction Models for Mass Assessment: A Data Stream Approach

File Size Format  
0104.pdf 435.57 kB Adobe PDF View/Open

Item Summary

Title:Improving Prediction Models for Mass Assessment: A Data Stream Approach
Authors:Shi, Donghui
Guan, Jian
Zurada, Jozef
Levitan, Alan
Keywords:Data, Text, and Web Mining for Business Analytics
data stream models
mass assessment
prediction
real estate properties
Date Issued:07 Jan 2020
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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/63869
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.130
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Data, Text, and Web Mining for Business Analytics


Please email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.

This item is licensed under a Creative Commons License Creative Commons