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

Chung Chun Lin and Satish B. Mohan

International Journal of Housing Markets and Analysis, 2011, vol. 4, issue 3, 224-243

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: United States of America; Neural nets; Residential properties; Statistical methods; Housing price estimation (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eme:ijhmap:v:4:y:2011:i:3:p:224-243

DOI: 10.1108/17538271111153013

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