Effectiveness comparison of the residential property mass appraisal methodologies in the USA
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- Author(s):
- 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)
- Citation:
- Chung Chun Lin, Satish B. Mohan, (2011) "Effectiveness comparison of the residential property mass appraisal methodologies in the USA",
International Journal of Housing Markets and Analysis , Vol. 4 Issue: 3, pp.224-243, doi: 10.1108/17538271111153013 - DOI
- http://dx.doi.org/10.1108/17538271111153013
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- The fulltext of this document has been downloaded 324 times since 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:
- United States of America, Neural nets, Residential properties, Statistical methods, Housing price estimation
- Type:
Research paper - Publisher:
- Emerald Group Publishing Limited
- Copyright:
- © Emerald Group Publishing Limited 2011
Published by Emerald Group Publishing Limited
