doi: 10.4304/jsw.6.9.1672-1679
Applying Unascertained Theory, Principal Component Analysis and ACO-based Artificial Neural Networks for Real Estate Price Determination
- 1 Hebei University of Engineering, Handan, P.R.China
- 2 School of Civil Engineering, Hebei University of Engineering, Handan, P.R.China
Abstract
Real estate industry is both capital-intensive, highly related industries and industries essential to provide the daily necessities. However, the real estate pricing models and methods of research rarely receive the critical attention and development it deserves. In this paper, we present a multi-resolution approach for the determination of the real estate pricing. The proposed method firstly utilizes unascertained theory to describe and quantity the price indices of the real estate, then principal component analysis (PCA) were introduced in to eliminate the real estate pricing indices having the relativities and overlap information. The representative indices from principal component analysis process substitute for the primary indexes. Thus subjective random problem in choosing indices can be avoided. Finally, Using ACO-based artificial neural networks, real estate pricing was analyzed and the results show that this method is more convenient and practical compared with the traditional one.
Keywords
- unascertained theory
- principal componen
How to Cite
Wanqing Li and Huawang Shi, "Applying Unascertained Theory, Principal Component Analysis and ACO-based Artificial Neural Networks for Real Estate Price Determination," Journal of Software, vol. 6, no. 9, pp. 1672-1679, 2011. https://doi.org/ 10.4304/jsw.6.9.1672-1679
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).