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

• Published in: International journal of housing markets and analysis Vol. 4; no. 3; pp. 224 - 243
• Main Authors: Chun Lin, Chung, Mohan, Satish B.
• Format: Journal Article
• Language: English
• Published: Bingley Emerald Group Publishing Limited 09-08-2011
• Subjects: Accuracy; Brain; Housing; Housing prices; Human error; Neural networks; Neurons; Real estate appraisal; Residential areas; Studies; Variables; United States > US; Housing price estimation; Statistical methods; Neural nets; United States of America; Residential properties
• ISSN: 1753-8270; 1753-8289
• DOI: 10.1108/17538271111153013

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.