论文信息 - Applying Unascertained Theory, Principal Component Analysis and ACO-based Artificial Neural Networks for Real Estate Price Determination

Applying Unascertained Theory, Principal Component Analysis and ACO-based Artificial Neural Networks for Real Estate Price Determination

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.

Wanqing Li | Huawang Shi

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