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Effectiveness comparison of the residential property mass appraisal methodologies in the USA

Chung Chun Lin (Department of Civil, Structural and Environmental Engineering, State University of New York at Buffalo, Buffalo, New York, USA)
Satish B. Mohan (Department of Civil, Structural and Environmental Engineering, State University of New York at Buffalo, Buffalo, New York, USA)

International Journal of Housing Markets and Analysis

ISSN: 1753-8270

Article publication date: 9 August 2011

Abstract

Purpose

Quite a few statistical and artificial neural network (ANN) models have been developed for the mass appraisal of the real estate by the municipalities. The purpose of this paper is to report the results of a research conducted to compare the prediction accuracy of the three most used models: multiple regression model, additive nonparametric regression, and ANN.

Design/methodology/approach

The three models were developed using the housing database of a town with 33,342 residential houses. In this database, the cutoff point for higher priced homes was $88 per square foot of living area.

Findings

The research confirmed that using statistical and ANN models are reliable and cost‐effective methods for mass appraisal of residential housing.

Originality/value

It was found that any of the three models can be used, with similar accuracy, for lower and medium‐priced houses, but the ANN is considerably more accurate for higher priced houses.

Keywords

Citation

Chun Lin, C. and Mohan, S.B. (2011), "Effectiveness comparison of the residential property mass appraisal methodologies in the USA", International Journal of Housing Markets and Analysis, Vol. 4 No. 3, pp. 224-243. https://doi.org/10.1108/17538271111153013

Publisher

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Emerald Group Publishing Limited

Copyright © 2011, Emerald Group Publishing Limited