Improving Prediction Models for Mass Assessment: A Data Stream Approach

Shi, Donghui
Guan, Jian
Zurada, Jozef
Levitan, Alan
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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.
Data, Text, and Web Mining for Business Analytics, data stream models, mass assessment, prediction, real estate properties
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