Hybrid Genetic-Based Support Vector Regression with Feng Shui Theory for Appraising Real Estate Price

Chih H. Wu, Chi-Hua Li, I-Ching Fang, Chin-Chia Hsu, Wei-Ting Lin, Chia-Hsiang Wu
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引用次数: 11

Abstract

In this paper, we proposed a novel house prediction model that integrated hybrid genetic-based support vector regression (HGA-SVR) model and Feng Shui theories for developing a high accuracy appraising real estate price system in Taiwan. In Taiwan, Feng Shui theory applies in choosing good days, divination and house selection. From the past researches, many factors might affect the real estate price which are the announced land values, the building room age, building total number of floor, the transportation condition and surrounding environment of house etc. However, few studies have been considered the Feng Shui effects in appraising real estate price. Therefore, the present study pioneers in applying Feng Shui theories for developing a high accuracy real estate price prediction system with back-propagation neural network(BPN), fuzzy neural network (FNN) and Hybrid Genetic-based SVR (HGA-SVR) to compare.Our results obtained from the comparison between two house price models with various artificial neural network models. By comparing the accuracy with various network architectures, the result demonstrates that HGA-SVR is the best network architecture and the Feng Shui model has a better performance in BPN, FNN and HGA-SVR. Our house price prediction system discovers some real estate price much higher than the reasonable prices. This result shows these unreasonable price needs adjusting to become more reasonable to conform the housing market.
基于混合遗传的支持向量回归与风水理论的房地产价格评估
本研究以台湾为研究对象,提出一种结合混合遗传支持向量回归(HGA-SVR)模型与风水理论的房屋预测模型,以建立一个高精度的房地产价格评估系统。在台湾,风水理论适用于选好日子、占卜和选房子。从以往的研究来看,影响房地产价格的因素有公示地价、建筑房龄、建筑总层数、房屋的交通状况和周边环境等。然而,很少有研究考虑风水在房地产价格评估中的作用。因此,本研究率先应用风水理论,以反向传播神经网络(BPN)、模糊神经网络(FNN)和基于遗传的混合支持向量回归算法(HGA-SVR)进行比较,开发了一个高精度的房地产价格预测系统。我们的结果是通过对两种房价模型与各种人工神经网络模型的比较得出的。通过与各种网络结构的准确率比较,结果表明HGA-SVR是最好的网络结构,并且风水模型在BPN、FNN和HGA-SVR中具有更好的性能。我们的房价预测系统发现一些房地产价格远远高于合理价格。结果表明,这些不合理的价格需要调整,以适应住房市场。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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