An Exploration of Neural Networks and Its Application to Real Estate Valuation

Worzala, Elaine; Lenk, Margarita M.; Silva, Ana

doi:10.1080/10835547.1995.12090782

Cited by 206 publications

(90 citation statements)

References 4 publications

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“…In fact, when the smallest dataset was used, the SLR results were better than the ones obtained by the ANNs. Therefore, we agree with Worzala et al [49], who compared ANNs with traditional multiple regression models and no evidence was found demonstrating that ANNs are superior for valuation analysis. Nevertheless, our results demonstrate that they are not worse, except regarding the R 2 coefficient, where only similar results were obtained using SLR methodology when the largest dataset is used.…”

Section: Resultssupporting

confidence: 87%

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Artificial Neural Network, Quantile and Semi-Log Regression Modelling of Mass Appraisal in Housing

2021

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We used a large sample of 188,652 properties, which represented 4.88% of the total housing stock in Catalonia from 1994 to 2013, to make a comparison between different real estate valuation methods based on artificial neural networks (ANNs), quantile regressions (QRs) and semi-log regressions (SLRs). A literature gap in regard to the comparison between ANN and QR modelling of hedonic prices in housing was identified, with this article being the first paper to include this comparison. Therefore, this study aimed to answer (1) whether QR valuation modelling of hedonic prices in the housing market is an alternative to ANNs, (2) whether it is confirmed that ANNs produce better results than SLRs when assessing housing in Catalonia, and (3) which of the three mass appraisal models should be used by Spanish banks to assess real estate. The results suggested that the ANNs and SLRs obtained similar and better performances than the QRs and that the SLRs performed better when the datasets were smaller. Therefore, (1) QRs were not found to be an alternative to ANNs, (2) it could not be confirmed whether ANNs performed better than SLRs when assessing properties in Catalonia and (3) whereas small and medium banks should use SLRs, large banks should use either SLRs or ANNs in real estate mass appraisal.

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Section: Resultssupporting

confidence: 87%

“…In some papers, ANNs perform better [40][41][42][43][44][45][46]. In other papers, standard hedonic regressions perform as well as the best ANN [47][48][49]. Other papers condition the utility of neural networks to the accomplishment of certain variables.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network, Quantile and Semi-Log Regression Modelling of Mass Appraisal in Housing

2021

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“…Such data-mining methods have been considered theoretically unacceptable in many areas of economic and educational research and are often informally referred to as "going fishing." How or why, then, have they been accepted in fields such as financial analysis (Lemke, 1997), medicine (Buchman, Kubos, Seidler, & Siegforth, 1994) and real estate valuation (Worzala, Lenk, & Silva, 1995)? A partial explanation is the difference in the competitive nature of the fields.…”

Section: Balancing Deduction With Inductionmentioning

confidence: 99%

The Politics of the Production Function: The Influence of Political Bias on the Deductive Process

Baker¹

1999

Educational Policy

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This article addresses the susceptibility of the traditional theoretical frame-work of the educational production function to political influence and personal bias. Much of this influence and bias is derived form the predominantly deductive approach that is commonly used. In using this approach, researchers are driven to test the statistical significance of preconceived notions, personal or political in origin, of how the education system works. An alternative approach, using an inductive process and nonlinear algorithms, is presented. It is suggested that use of both deductive and inductive approaches will provide a more holistic picture of how schools work.

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“…This likely will accelerate the trend toward less human involvement, fewer appraisers and lower fees. Some solace for appraisers is offered by Worsala, Lenk and Silva (1995) who caution both appraisers and the banking industry. They find the use of neural network appraising presents serious questions about the reliability of computergenerated valuation conclusions compared to traditional approaches.…”

Section: Real Estate Appraisal Services Technology Implicationsmentioning

confidence: 99%