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JACIII Vol.22 No.7 pp. 1099-1103
doi: 10.20965/jaciii.2018.p1099
(2018)

Short Paper:

Research on Mass Real Estate Evaluation Mode Based on BP Neural Network Model

Ke Ma*, Yichuan Zhang*, and Zhongxuan Yang**

*Henan Institute of Science and Technology
Hualan Avenue, Xinxiang City, Henan 450007, China

**School of Economics and Management, Systems and Industrial Engineering Technology Research Center, Zhongyuan University of Technology
No.1 Huaihe Road, Longhu Economic Development Zone, Longhu, Xinzheng, Zhengzhou, Henan 450007, China

Received:
April 25, 2018
Accepted:
June 1, 2018
Published:
November 20, 2018
Keywords:
BP neural network, real estate evaluation, real estate cost
Abstract
Research on Mass Real Estate Evaluation Mode Based on BP Neural Network Model

The training error curve of the model

With the rapid development of the real estate market, real estate evaluation is becoming more and more important and active. The real estate is now evaluated according to the expertise and experience of the appraiser. The evaluation results are often influenced by the subjective randomness of the evaluation personnel and the complicated and changeable environmental factors. It is not only a professional technology, but also a complicated art. Therefore, how to improve the scientific, accuracy and efficiency of real estate evaluation has become an important issue that needs to be studied and solved in the real estate evaluation industry. This paper takes mass real estate evaluation system as the research object, adopts the BP neural network to research the design principles of the evaluation system and the design method of the model, and designs and develops the mass intelligent evaluation system to improve the intelligence, scientific, accuracy and credibility of the evaluation system.

Cite this article as:
K. Ma, Y. Zhang, and Z. Yang, “Research on Mass Real Estate Evaluation Mode Based on BP Neural Network Model,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.7, pp. 1099-1103, 2018.
Data files:
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Last updated on Dec. 07, 2018