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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/66062

    Title: 顧客需求導向對都會區不動產特徵價格影響之研究;Customer-oriented weight determination for assessing house price in urban areas using SIP clustering approach
    Authors: 李宣佑;Lee,Hsuan-yu
    Contributors: 營建管理研究所
    Keywords: 不動產;房價;顧客導向;群啟發;分群;智慧型計算;特徵價格法;環境特徵;real estate;housing price;customer oriented;clustering;intelligence computation;hedonic feature approach;environmental attribute
    Date: 2014-07-08
    Issue Date: 2014-10-15 17:28:04 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 不動產之選擇及成交價格受顧客偏好影響,估價人員通常依循自身之經驗為顧客決定不動產特徵與其相關權重,對於擁有不同喜好之顧客可能導致不合理之估價。本研究之目的即為根據顧客需求導向,測定對不動產價格有影響之特徵與其權重。根據文獻回顧與專家建議選定39項特徵,並透過台灣不動產交易資料庫蒐集成交案例資料,囊括台北市近3年間之成交案例5000筆,藉由GIS量測完成39項環境特徵之建立。而後利用群啟發演算法進行分群,共產出5群並各別進行獨立分析,分析結果可彙整為以下發現:1.各群皆代表一顯著之需求取向,顧客主要需求可區分為5種:教育、交通、醫療、休閒與購物。2.各群間之特徵權重皆有所不同。3.各群中警局為共通之嫌惡設施,對房價具有顯著之負影響。4.銷售時間對銷售價格具有正面之影響,即銷售時間愈長價格愈高。;Customer preference affects selection and transaction price for real estate. Appraisers by their own experience usually select housing attributes and their corresponding weights for customers, creating possible impropriate assessment for those with different favorites. The research objective is to determine attributes and the corresponding weights that affect house pricing based on customer preference. Literature review and expertise qualified 39 attributes, followed by the house transactions collected from the Taiwan transaction database of real estate. A total of 5000 sets in the most prosperous urban area of Taiwan in recent 3 years are randomly selected. Each set contains 39 GIS calibrated environmental attributes suggested by citations and expertise. The swarm-inspired projection (SIP) algorithm is employed for clustering, yielding 5 clusters for a close-up analysis. The findings form the analysis can be summarized as: 1. each cluster presents a significant preference. There are 5 major customer preferences of leisure, education, transportation, medical care, and shopping convenience. 2. The weight of any attribute is mostly different from that in the other clusters. 3. The common hostile attribute among 5 clusters is police station and has significantly negative impact to housing price. 4. Sale time has positively impact to sale price. The longer sale time is the higher sale price presents.
    Appears in Collections:[營建管理研究所 ] 博碩士論文

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