Artificial Neural Networks and the Mass Appraisal of Real Estate

Gang Zhou, Yicheng Ji, Xiding Chen, Fangfang Zhang

Abstract


With the rapid development of computer, artificial intelligence and big data technology, artificial neural networks have become one of the most powerful machine learning algorithms. In the practice, most of the applications of artificial neural networks use back propagation neural network and its variation. Besides the back propagation neural network, various neural networks have been developing in order to improve the performance of standard models. Though neural networks are well known method in the research of real estate, there is enormous space for future research in order to enhance their function. Some scholars combine genetic algorithm, geospatial information, support vector machine model, particle swarm optimization with artificial neural networks to appraise the real estate, which is helpful for the existing appraisal technology. The mass appraisal of real estate in this paper includes the real estate valuation in the transaction and the tax base valuation in the real estate holding. In this study we focus on the theoretical development of artificial neural networks and mass appraisal of real estate, artificial neural networks model evolution and algorithm improvement, artificial neural networks practice and application, and review the existing literature about artificial neural networks and mass appraisal of real estate. Finally, we provide some suggestions for the mass appraisal of China's real estate.


Keywords


artificial neural networks; back propagation neural network; machine learning algorithms; mass appraisal of real estate; tax base valuation

Full Text:

PDF



International Journal of Online and Biomedical Engineering (iJOE).ISSN: 1861-2121
Creative Commons License
Indexing:
Web of Science ESCI logo Engineering Information logo INSPEC logo DBLP logo ELSEVIER Scopus logo EBSCO logo Ulrich's logoGoogle Scholar logo Microsoft® Academic Search