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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/117440
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/117440


    Title: 應用大數據於杭州市房地產價格模型之建立
    The Application of Big Data Analytics on Real Estate Price Model of Hangzhou
    Authors: 郁嘉綾
    Yu, Cia-Ling
    Contributors: 鄭宇庭
    郭訓志

    Cheng, Yu-Ting
    Kuo, Hsun-Chih

    郁嘉綾
    Yu, Cia-Ling
    Keywords: 房地產估價
    大數據
    神經網絡
    混合模型
    Appraisal of real estate
    Big data
    Neural network
    Mixed model
    Date: 2018
    Issue Date: 2018-06-01 17:33:57 (UTC+8)
    Abstract: 互聯網的發展與近年來數據平台受到公私部門重視,資訊的取得與流通變得便捷,中國房地產文化目前有別於台灣,尚無實價登錄機制且地域面積廣大,傳統估價模型可能無法直接應用,面對房地產背後眾多的影響因素,本研究將預測建模目標放在泡沫化尚不嚴重且較具有潛力的中國新一線城市杭州市,自新浪二手房網爬取杭州市房地產數據,並自國家統計局取得各地區行政支出數據,作為實證分析資料。結合自動程序爬蟲抓取數據、統計分析與機器學習方法,期望對中國房地產建立一混合非監督式與監督式學習之模型。
    在分群結果之後建構模型採用之技術為C5.0、三層CHAID、五層CHAID與Neural Network,挑選出最適合的模型為使用混合模型後的C5.0決策樹方法,達到降低變數維度亦提升或達到相當的預測準確率的雙贏目標,模型中行政地區、面積、總樓層為最頻出現的重要變數。
    另外透過集群分析於行政支出的應用,發現2016年度杭州市投入的行政支出集中於余杭區、蕭山區、濱江區,成為賣屋及購屋者的第二項決策標準。
    In recent years, with the growth of the Internet and the importance of data platform on public sector and private sector. Getting and sharing information are made easily. The culture of real estate in China is different from Taiwan. For instance, there is no actual house price registration system. Furthermore, traditional estimate model may not be directly applicable to China which has the vast geographical area of the mainland. There are many factors to influence house price model. This study focus on Hangzhou city. Because the burst of real estate bubbles is not serious as first-tier cities and it is one of new first-tier cities in China. The research data were crawler from Sina second-hand housing website and National Bureau of Statistics. By using auto web crawler skill, statistical analysis, and machine learning method to build a real estate model in China, which was combining unsupervised learning method with supervised learning method.
    After clustering Hangzhou second-hand housing data, this study used C5.0, three layers Chi-Square Automatic Interaction Detector(CHAID), five layers CHAID, and Neural Network(NN). The study goal are both reducing dimension and getting better forecast accuracy. Choosing clustering- C5.0 model as appropriate house price model to achieve win-win situation after comparing final result. Administrative region, area, and total floor are the top three high frequency influential factors.
    Applying Clustering Analysis to administrative expenses data in Hangzhou, the study found that the government resource focus on Yuhang, Xiaoshan, and Binjiang. It can be the second decision-making criterion for house sellers and house buyers.
    Reference: 一、中文文獻
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    二、英文文獻
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    2.Kong, F., H. Yin & N. Nakagoshi (2007). Using GIS and landscape metrics in the hedonic price modeling of the amenity value of urban green space: A case study in Jinan City(China), Landscape and Urban Planning, Vol. 79, No. 3-4, pp. 240-252.
    3.Lee, T. S. & I. F. Chen (2005). A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines. Expert Systems with Applications, Vol. 28, No. 4, pp. 743-752.
    4.Liang, X., H. S. Zhang, J. G. Mao & Y. Chen (2009). Improving option price forecasts with neural networks and support vector regressions. Neurocomputing, Vol. 72, No. 13-15, pp. 3055-3065.
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    11.Zhang, R., Q. Du, J. Geng, B. Liu & Y. Huang (2015). An improved spatial error model for the mass appraisal of commercial real estate based on spatial analysis: Shenzhen as a case study. Habitat International, Vol. 46, pp. 196-205.
    Description: 碩士
    國立政治大學
    統計學系
    105354007
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0105354007
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

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