預售市場住房價格之決定因素 : 以臺北市中正區為例

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Title:	預售市場住房價格之決定因素 : 以臺北市中正區為例
Other Titles:	Decisive factors of housing price for pre-sale market : illustrated with Chung-Chang District of Taipei City
Authors:	林郁庭;Lin, Yu-Ting
Contributors:	淡江大學會計學系碩士在職專班張寶光;黃台心;Chang, Bao-guang;Huang, Tai-Hsin
Keywords:	住房價格;預售市場;結構體市場;Housing price;pre-sale market;structure market
Date:	2012
Issue Date:	2013-04-13 11:31:20 (UTC+8)
Abstract:	本研究採取較具同質性的區位建案做為選樣之對象，並以預售市場做為實證之依據，同時參考文獻及建設業實務常用的住房價格決定因素，採用公開發行且長期間的資料庫來進行實證分析，主要發現如下：首先，以台北市的中正區之樣本言，由單因子變異數分析結果可知，包括國中學區、公園休憩、自然景觀、樓高標準、房車比等五個因素均是具顯著影響住房價格的因素。至於未具顯著影響的因素包括建築面積、近捷運火車站、交通動線、建築型式、基地條件、熱鬧商圈、基地鄰路、鄰市場、鄰高架橋、車位型式等十個。其次，無論係由具統計顯著性的決定因素取出，或以建設業業界常用的影響因素進行分析，均未發現各雙因子對住房售價具交互影響的作用。推測其可能原因甚可能與未在估計模式中放入控制變數或樣本數尚不足有關。本文研究結果或可供消費者及業界管理者決策參考，並填補過去文獻的缺口。 This study has employed construction projects of locations with rather homogeneity as its subjects of sampling, while pre-sale market is based on as its foundation of substantiation. Besides, it has take reference of decisive literatures and decisive factors of housing price frequently used by construction business, and exploited database that has been publicly released for long period of time to conduct substantiation analysis. And the primary findings are: first of all, take the same of Chung-chang District of Taipei City for illustration, it is learned from the results of one-way analysis of variance that five factors as academic district of junior high school, park for recreation, natural landscape, standard of building height, and ratio of apartment to car park are considered to be prominent factors that affect housing price. As for factors not considered as prominent effect, they include ten respective factors as construction size, proximity to MRT and train station, movement route of transportation, construction style, site conditions, hustling commercial district, site proximity to road , proximity to market, proximity to fly-over bridge, and mode of car park. In addition, whether if it is taken out from statistically prominent decisive factor or to be conducted of analysis from the frequently impact by the world of construction business, effect of cross-impact is not found from each of the two-factor on housing, and it is speculated that the possible reason might be related to the fact that controlling factors are not planted or the amount of samples is not sufficient in the estimation model. The result of this study might perhaps provide itself to consumers and the management of the trade as reference of decision making, and fill up the gap that is found short within the literatures.
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