Determination of Real Estate Price Based on Principal Component Analysis and Artificial Neural Networks

被引：4
|
作者：
Shi, Huawang [1 ]
机构：
[1] Hebei Univ Engn, Sch Civil Engn, Handan, Peoples R China
来源：
ICICTA: 2009 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION, VOL I, PROCEEDINGS | 2009年
关键词：
principal component analysis (PCA); artificial neural networks; real estate; price determination; GENETIC ALGORITHMS; FUZZY-LOGIC; DESIGN;
D O I：
10.1109/ICICTA.2009.83
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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. This paper utilizes the principal components analysis method of multi-dimensional statistical analysis and artificial neural networks to determine the price of real estate. By using principal component method to process a number of listed real estate pricing indices. Firstly, the index system of accident risk was established. Then principal component analysis was applied to eliminate the indexes having the relativities and overlap information. Finally, based on historical data and artificial neural networks, a new real estate pricing models was established. The experiment results show that this method is effective and precise.
引用
收藏
页码：314 / 317
页数：4
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