Academic journal article The Journal of Real Estate Research

Impact of Artificial Neural Networks Training Algorithms on Accurate Prediction of Property Values

Academic journal article The Journal of Real Estate Research

Impact of Artificial Neural Networks Training Algorithms on Accurate Prediction of Property Values

Article excerpt

(ProQuest: ... denotes formulae omitted.)

There have been concerted efforts worldwide to deal with problems of inconsistencies that sometimes occur in mass appraisal assessment. These challenges can sometimes be the result of appraisers' intuitiveness and the approach or methods employed. The intuition of an appraiser is predicated on the foreknowledge of market reactions relating to different classes of properties over time. The intuition is what influences the appraiser(s) choice of method for estimation of property values. Income, market, and cost approaches are the most widely used appraisal methods for determining the market values of residential properties. These methods are used for single and mass appraisal until obvious limitations are observed. The limitations relate to subjectivity in dealing with a number of properties, delay in reporting value estimates to clients, and an insufficient number of comparable properties. In mass appraisal predictions, sophisticated technology is required to comparatively evaluate a number of properties (McGreal, Adair, McBurney, and Patterson, 1998). Technology is employed to complement the appraisers' efforts in appraising values, reduce their workload, and increase the precision of estimates.

Several high-tech methods including supportive vector machines, hedonic regression models, expert systems, fuzzy logic, and artificial neural networks are used in mass appraisal. The hedonic regression models have been the most extensively used techniques in modeling property prices among practitioners and academics (Zurada, Levitan, and Guan, 2011). The hedonic regression models are used to advise mortgage lenders, local tax authorities, dissolved companies etc. on the market values of properties. However, despite their widespread use, the methods have a number of shortcomings including an inability to handle specification error exacerbated by nonlinearity, multicollinearity, and functional form (Do and Grudnitski, 1992; Worzala, Lenk, and Silva, 1995. The shortcomings led to the emergence of a number of propositions towards the use of non- or semi-parametric regression techniques in mass appraisal. Artificial neural networks (ANNs) are among the techniques designed to remediate the obvious limitations of the hedonic regression models. Pioneering works in the field include Borst (1991, 1995), Do and Grudnitski (1992), Tay and Ho (1992), Worzala, Lenk, and Silva (1995), and McCluskey (1996). The model is designed to handle the complex nonlinear relation that exists in data without the many parametric restrictions that are found in statistical techniques.

Numerous elements and processes are required for the smooth operation of the ANNs. Training the ANNs is one of such process that is fundamental to the achievement of desired results. The purpose of the training phase is to reduce a cost function usually defined as sum squared error (SSE), mean squared error (MSE) or root mean squared error (RMSE) between the actual and the predicted property sale values by adjusting weights and biases. There are a number of training algorithms including back propagation (BP), Levenberg-Marquardt (LM), Powell-Beale conjugate gradient (PBCG), and scaled conjugate gradient (SCG). The most frequently used training algorithm in the mass appraisal of properties is the BP, first developed by Werbos (1974) and popularized for multilayer perceptron by Rumelhart, Hinton, and Williams (1986). Most of the studies involving the ANNs utilize the BP training algorithm (Borst, 1991; Do and Grudnitski, 1992; Tay and Ho, 1992; Borst, 1995; Worzala, Lenk, and Silva, 1995; McCluskey, 1996; Lenk, Worzala, and Silva, 1997; McGreal, Adair, McBurney, and Patterson, 1998; Nguyen and Cripps, 2001; Limsombunchai, Gan, and Lee, 2004; Peterson and Flanagan, 2009; McCluskey et al., 2012; McCluskey, et al., 2013). Additionally, there has been no definite conclusion on the superiority of the ANNs over the hedonic regression models in most of these studies. …

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