The potential of artificial neural networks in mass appraisal: the case revisited

Abstract

If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The aim of this paper is to investigate the comparative performance of an artificial neural network (ANN) and several multiple regression techniques in terms of their predictive accuracy and capability of being used within the mass appraisal industry. Design/methodology/approach -The methodology first tested that the data set had neglected non-linearity which suggested that a non-linear modelling technique should be applied. Given the capability of ANNs to model non-linear data, this technique was used along with an OLS regression model (baseline model) and two non-linear multiple regression techniques. In addition, the models were evaluated in terms of predictive accuracy and their capability of use within the mass appraisal environment. Findings -Previous studies which have compared the predictive performance of an ANN model against multiple regression techniques are inconclusive. Having superior predictive capability is important but equally important is whether the technique can be successfully employed for the mass appraisal of residential property. This research found that a non-linear regression model had higher predictive accuracy than the ANN. Also the output of the ANN was not sufficiently transparent to provide an unambiguous appraisal model upon which predicted values could be defended against objections.Research limitations/implications -The research provides an informative view as to the efficacy of ANN methodology within the real estate field. A number of issues have been raised on the applicability of ANN models within the mass appraisal environment. Practical implications -This work demonstrates that ANNs whilst useful as a predictive tool have a limited practical role for the assessment of residential property values for property tax purposes. Originality/value -The work has taken forward the debate on the usefulness of ANN techniques within the mass appraisal environment.

Improving property valuation accuracy: a comparison of hedonic pricing model and artificial neural network

“…However, in order to factor in the effect of inflation on the property prices, the sale prices of the properties were inflation adjusted to current values before the analyses. This is common in the literature, for instance, see Zurada et al (2006) and McCluskey et al (2012).…”

“…The number of hidden layer in a model could vary. However, one hidden layer has been proven to be sufficient for the modeling of property prices (McCluskey et al, 2012). As to the number of hidden neurons to be included in the hidden layer, there is no consensus in the literature (Cechin et al, 2000).…”

[Retracted] Application of BP Neural Network Model in Risk Evaluation of Railway Construction

“…The railway construction technology expert resource and investment should be integrated based on the national conditions of Asian and European countries, the factors needed to be considered for railway construction, and the principles of being scientifically, systematically, typically, and feasibly practical. The opinions of experts, market operation experts, and venture capital experts establish an evaluation index system, as shown in Figure 2 [8,9]. The risk is roughly divided into several aspects.…”

“…That is, take as few hidden layer neurons as possible. When a=0, m=4.24, take 5, and then the number (5,6,7,8,9,10,11) of hidden layer neurons is used to verify the error rate until the best error rate is obtained. Table 1 shows the error rate of each selected hidden layer neuron, and the error meets accuracy requirement (error < 5%) when the number of hidden layer neurons is 6.…”

Mass Appraisal Models of Real Estate in the 21st Century: A Systematic Literature Review

“…The ANN performs well for modeling the non-linear relationship because of its characteristics of semi-parametric regression. In addition to the basic MRA, although researchers have to face the "black box" of the ANN's structure, it is still the most popular model used in AI-based models [36][37][38][39][40][41][42][43][44][45][46][47][48][49].…”