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

Abstract

This study extended the use of artificial neural networks (ANNs) training algorithms in mass appraisal. The goal was to verify the comparative performance of ANNs with linear, semi-log, and log-log models. The methods were applied to a dataset of 3,232 single-family dwellings sold in Cape Town, South Africa. The results reveal that the semi-log model and the Levenberg-Marquardt trained artificial neural networks (LMANNs) performed best in their respective categories. The best performing models were tested in terms of prediction accuracy within the 10% and 20% of the assessed values, performance, and reliability ranking, and explicit explainability ranking order. The LMANNs outperform the semi-log model in the first two tests, but fail the explainability ranking order test. The results demonstrate the semi-log model as the most preferred technique due to its simplicity, consistency, transparency, locational advantage, and ease of application within the mass appraisal environment. The black box nature of the ANNs inhibits the production of sufficiently transparent estimates that appraisers could use to explain the process in legal proceedings.