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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/36172

Title: 以特徵價格法探討影響房價之因子 -以新北市板橋區為例
Using Hedonic Pricing Approach to Analyze the Factors Influencing Housing Price: A Case Study on Banciao District of New Taipei City
Authors: Ming-Han Li
李明翰
Contributors: NTOU:Institute of Applied Economics
國立臺灣海洋大學:應用經濟研究所
Keywords: 板橋區;特徵價格;Box-Cox轉換;分量迴歸
Banciao;hedonic price;Box-Cox transformation;quantile regression
Date: 2012
Issue Date: 2013-10-07T03:04:19Z
Abstract: 由於國內住宅市場的資訊並不流通,以致市場上出現的資訊相當混亂,使一般購屋民眾相當困擾。不同的時間、住宅特徵、環境特徵,對住宅價格皆有一定的影響,但其影響為何?本研究選用新北市中人口最多的板橋區做為研究對象,透過內政部不動產交易服務網所提供之房屋交易資料,及應用地圖軟體找出影響房價之環境因子,運用特徵價格法(Hedonic Price Method,HPM)作為研究方法,藉此評估影響新北市板橋區房價之因素。迴歸模型分成兩部分,首先以Box-Cox轉換模型進行分析,找出適合的板橋區房價迴歸模式;接著,建立分量迴歸之特徵價格模型,以了解房價在不同分量時影響因子的差異。 研究結論發現,建坪、屋齡、車位及巷弄等特徵變數都有顯著的影響,顯示消費者在購屋時對於房屋本身特徵的偏好。距離圖書館、捷運站、火車站之距離最能影響提升房屋的價格,因此瞭解公共設施的可及性會對房價帶來正面的效益;但是,與醫院及公園綠地體育場之距離,則會為房價帶負面的效益,反而降低了房屋的價格。季節對於房屋成交總價較高的民眾沒有顯著影響,但對於房屋成交總價較低的50%的購屋民眾有顯著的影響,其影響如下:對2010年第4季而言,2011年第1季的房屋成交總價大約高出6.28%到9.44%之間,2011年第2季則大約高出3.57%到14.02%之間,2011年第3季大約高出4.97%到14.25%之間。
Domestic property market transaction data are not transparent; as a result, data presented in the market are rather chaotic, which caused quite a disturbance for the individual property buyer. What will different transaction timing, property characteristic and surrounding characteristic do toward property pricing? This research will choose Banciao area of New Taipei City, the most populated area, as the research candidate, using property transaction data provided by Ministry of Internal Affair Property Transaction Service Network and electronic map application, to find out what affect property pricing. This research will use hedonic price method (HPM) as research method to determine what are the factors affect the property pricing in New Taipei City Banciao Area. Regression Model is divided into two parts, first the research will conduct analysis based on Box-Cox transformation Model, to identify the most appropriate regression model for Banciao area property pricing; second, it will build quantile regression of characteristic pricing model, to understand what are the factors affect property pricing. In conclusion, property size, property age, parking availability and adjacent to tight alley or not are noticeable factors, which proves that property buyer has his/her own preference of the property characteristic when making a purchase. Closer to library, MRT station and train station will push the property pricing higher, which proves that public infrastructure will provide a positive impact toward property pricing; however, closer to hospital and closer to park/ green field / arena will have a negative impact toward the property pricing and push the property pricing lower. Different transaction month will have little impact toward the higher transaction amount property, but will have a greater impact toward bottom 50 percentile of transaction amount property. And the impacts are followed: compared with Q4/2010 data, Q1/2011 property transaction total amount are approximately 6.28%~9.44% higher, Q2/2011 are 3.57%~14.02% higher, Q3/2011 are 4.97%~14.25% higher.
URI: http://ethesys.lib.ntou.edu.tw/cdrfb3/record/#G0019935015
http://ntour.ntou.edu.tw/handle/987654321/36172
Appears in Collections:[應用經濟研究所] 博碩士論文

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