The Appraisal Model of Real Estate Project Based on PCA and BP Neural Network

2011 ◽  
pp. 316-320
Author(s):  
Hui Zhao
Keyword(s):  
Neural Network ◽  
Real Estate ◽  
Bp Neural Network ◽  
Real Estate Project

Hangzhou Housing Demand Forecasting Model Based on BP Neural Network of Genetic Algorithm Optimization

2014 ◽  
Vol 587-589 ◽  
pp. 37-41 ◽  
Author(s):  
Yi Hua Mao ◽  
Meng Bo Zhang ◽  
Ning Bo Yao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Real Estate ◽  
Housing Market ◽  
Bp Neural Network ◽  
Housing Demand ◽  
Forecasting Model ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
The Impact

Hangzhou, the capital of Zhejiang province and a famous scenic tourist city in China, goes at the forefront of the country for its high real estate prices, which hold a very important position of orientation to pricing in the real estate markets of the Yangtze River Delta region and of the whole country as well. The price trend of Hangzhou's real estate is even related to the sustainable development of the city. This paper uses the macro data on the housing market in Hangzhou during 1999-2012 to establish a forecasting model which is based on BP neural network of genetic algorithm optimization. With MATLAB software exploited for programming and simulation, the prediction made by the model about the housing demand in Hangzhou and the subsequent re-examination show that the model has high precision. But due to the impact of the national macro-control policies on housing market, the predictive value of some years may fluctuate to a certain extent.

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

2018 ◽  
Vol 22 (7) ◽  
pp. 1099-1103 ◽  
Author(s):  
Ke Ma ◽  
Yichuan Zhang ◽  
Zhongxuan Yang ◽  
◽  
Keyword(s):  
Neural Network ◽  
Real Estate ◽  
Bp Neural Network ◽  
Evaluation System ◽  
Rapid Development ◽  
Real Estate Market ◽  
The Real ◽  
The Real Estate ◽  
Scientific Accuracy ◽  
The Evaluation System

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.

Research on Selecting Real Estate Project Based on PCA and RBF Neural Network

2011 ◽  
Vol 225-226 ◽  
pp. 162-165
Author(s):  
Hui Zhao ◽  
Li Ming Chen
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Real Estate ◽  
Rbf Neural Network ◽  
Principal Component ◽  
Component Analysis ◽  
Basic Function ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Project

A new method based on the integration of principal component analysis (PCA) and radial basic function (RBF) neural network is put forward for selecting the real estate project. Firstly, principal component analysis (PCA) is used to reduce the evaluation index dimensions. And then, radial basic function (RBF) neural network is used to evaluate the real estate projects. In order to grasp this method better, finally, the paper provides a case to demonstrate the application of this method in selecting the real estate project. The case has shown that the method applied to select the real estate project is feasible and reliable.

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