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篇名 學校特徵與空間距離對周邊房價之影響分析-以台北市為例
卷期 42:2
並列篇名 Analyzing the Effect of School Characteristics and Geographical Distance on Local Housing Price- The Case of Taipei City
作者 林忠樑林佳慧
頁次 215-271
關鍵字 額滿學區特徵價格法傾向分數配對法房屋價格full enrollment school districthedonic pricing methodpropensity score matchinghousing priceEconLitTSSCI
出刊日期 201406
DOI 10.627/TER.2014.42.2

中文摘要

本文主要在探討學校特徵與空間距離對周邊房價之影響,並進一步 探討在不同空間距離下, 學校特徵對房價之影響程度有何異同。根 據2007年到2009年的內政部「房地產交易價格」與臺北市政府「不 動產數位資料庫」之房屋交易資料, 本文發現額滿學校學區對周邊 房屋總價具有正向顯著的影響,房屋座落地點距離學校愈遠而其房 屋交易價格愈高, 但學校距離對交易房價之影響效果隨著距離學校 愈遠而呈現遞減的現象; 另外, 我們發現捷運站的交通便利性為提 升房價的重要因素。針對不同空間距離討論發現, 學校特徵對房價 之影響效果會隨著距離學校愈遠而呈現遞減的趨勢;而交通便利性 對非額滿學區的房價提升有顯著的影響效果。為了降低文獻中的樣 本選擇偏誤之可能性, 本文應用傾向分數配對方法, 以配對後的房 屋樣本進行分析亦未出現相反的證據,位於額滿學區的房價是相對 高於位於非額滿學區的房價。最後, 在房屋座落在共同學區的交易 資料當中, 我們發現額滿學校與空間距離因素對房價之影響效果並 不顯著, 房屋本身的特徵為主要影響房價之決定因素。

英文摘要

This paper analyzes the effects of school characteristics and geographical distances on local housing prices, and further discusses these effects within different geographical areas. According to the housing price data from the years 2007 to 2009, merged together from the Department of Land Administration and Taipei Real-estate Database, we find that the housing price is higher if the house is located in a full enrollment school district and the magnitude of this effect is decreasing with geographical distance from the school. In addition, we also find that the distance from the house location to a Mass Rapid Transit (MRT) station plays an important role in increasing the local housing price. As for the analysis of the different geographical areas, the results also show that the magnitude of the effect of school characteristics on the housing price is decreasing with the distance from the house location to the school. We then find that the proximity to anMRT station raises the local housing price and the effect is only significant in the non-full enrollment school districts. Moreover, to reduce the sample selection bias, we use the Propensity Score Matching (PSM) method to analyze the impact of a full enrollment school district on the housing price by using the matching data and we do not find conflicting results. Finally, the empirical results suggest that the housing’s own characteristics are the most important factors to affect the local housing price in multi-school districts.

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