| 摘要: | 本研究旨在探討桃園機場捷運通車對青埔地區住宅價格之影響，並進
一步分析房屋特徵與周邊環境變數對房價的關聯性。近年來，桃園市青埔
地區因重大交通建設與開發案陸續推動，吸引大量投資與人口移入，房地
產市場熱度日增，尤以機場捷運於2017 年正式通車後，該區住宅價格變動
情形更受到關注。
本研究以2013 年至2024 年間青埔地區實價登錄資料為研究樣本，地
理範圍涵蓋機場捷運A17（領航站）、A18（高鐵桃園站）與A19（桃園體
育園區站）三站周邊地區，僅納入住宅用途且樓高十層以上之電梯大樓交
易資料，以確保樣本一致性。研究方法採用特徵價格模型（Hedonic Pricing Model），並輔以描述統計、相關係數與共線性檢定，評估房屋內部屬性（如坪數、樓層、格局、屋齡、車位數）與外部因素（如捷運站、高鐵站、棒球場等）對房價的影響。
實證結果顯示，機場捷運通車對青埔地區住宅價格具有顯著正向影響，
平均漲幅約11%。此外，高鐵站、捷運站、管委會、樓層與客廳數亦對房
價具正向顯著影響；而車位數與屋齡則呈負向顯著關係。部分設施如加油
站與建物面積則未顯現統計顯著性。;This study investigates the impact of the Taoyuan Airport MRT operation on housing prices in the Qingpu area of Taoyuan City, with a particular focus on
the effects of housing characteristics and surrounding environmental variables.
In recent years, the Qingpu district has experienced rapid development due to major infrastructure projects and transportation improvements. Following the official launch of the Airport MRT in 2017, housing price fluctuations in this area have drawn increasing attention from both scholars and the real estate market. Using transaction data from Taiwan’s Actual Price Registration system
between 2013 and 2024, this study focuses on properties located within the vicinity of three MRT stations: A17 (Linghang), A18 (Taoyuan HSR Station),and A19 (Taoyuan Sports Park). The dataset includes only residential elevator
buildings with ten or more floors to ensure consistency across samples. The study employs the Hedonic Pricing Model to analyze the influence of both internal housing attributes (such as floor area, age, number of rooms, number of parking
spaces, and floor level) and external environmental factors (such as proximity to MRT stations, HSR stations, and baseball stadiums on housing prices.Descriptive statistics, correlation analysis, and multicollinearity tests are also
conducted.
The empirical results indicate that the operation of the Airport MRT has a
significantly positive effect on local housing prices, with an average increase of
approximately 11%. In addition, proximity to HSR stations, MRT stations,property management organizations, number of lobbies, and higher floor levels all contribute positively and significantly to housing prices. Conversely, housing
age and the number of parking spaces are negatively correlated with price.
Variables such as gas stations and floor area show no statistically significant
effect.
This study provides empirical insights for policymakers, urban planners,and real estate practitioners, offering a valuable reference for future urban transportation development and housing market evaluation. |
