隨都市發展與不同類型大眾軌道運輸系統興建，致都會區房價快速飆漲，然個人可支配所得及實質購買力卻呈日益減少趨勢，尤對經濟及社會弱勢族群言，基於沉重負擔，迫於由購屋轉向租屋，然我國租賃市場於不動產市場本佔比不高，過去政府住宅政策重心亦以自有住宅為主，雖近十餘年住宅租賃補貼預算及受惠戶逐年增加，現有制度能否滿足弱勢家戶需求仍待驗證。
本研究以第一個興建並營運輕軌系統的高雄輕軌作為研究，資料來源以「內政部不動產交易實價查詢服務網」之租賃資料，蒐集輕軌第一階段行經之行政區，2018年至2022年等5年之時間序列資料，合計818個樣本，以特徵價格理論及最小平方法（OLS）迴歸進行實證分析，探討房屋特徵對住宅租賃價格之影響。
實證結果，房屋特徵變數，如屋齡、廳（房）數量和面積大小對租賃價格有顯著影響，其餘控制變數如管理組織、附屬傢具和鄰近捷運站也對租賃價格有一定影響，惟具體影響方向因模型而異，故需進一步評估其影響；於重要變數距離因素部分，於所有模型中，房屋與輕軌站距離的變化對租賃價格具有顯著影響。通常情況下，距離輕軌站500公尺內房屋租金最低，距離輕軌站500至1000公尺的房屋租金最高，顯示輕軌站區域內有正面顯著影響，亦同時有負面顯著影響。
於政策討論部分，指出現行住宅補貼實施政策存在：補助政策對象重複集中未妥善分配、評點標準及補助分級缺乏綜合考量，與未依租屋者實際需求考量制定政策等問題。本文依據實證分析結果，建議政府參考各行政區租金差異，根據租屋家戶的可負擔租金能力，建立各行政區「租金可負擔基準」，另參考社會住宅坪數與附近區域概等坪數樣本平均租金差異，提出和實行更為精準的分配策略，旨在最大化所有族群的利益，確保所有人都能享有基本的住宅權利。

With the development of urban areas and the construction of various types of mass transit systems, housing prices in metropolitan areas have soared rapidly. However, personal disposable income and real purchasing power have been steadily decreasing. This trend is particularly concerning for economically and socially disadvantaged groups. Due to the heavy burden, many people have been forced to shift from homeownership to renting. However, the rental market in our country has a relatively low share in the real estate market. In the past, government housing policies have mainly focused on homeownership. Although the budget for rental subsidies and the number of beneficiaries have increased in the past decade, whether the existing system can meet the needs of vulnerable households remains to be seen.
This study focuses on the Kaohsiung Light Rail, which was the first light rail system constructed and operated in Taiwan. The rental data for this study is sourced from the "Real Estate Transaction Price Inquiry Service" provided by the Ministry of the Interior. We collected five years of time series data from 2018 to 2022 for the administrative districts through which the first phase of the light rail system passes, resulting in a total of 818 samples. Using the hedonic price theory and ordinary least squares (OLS) regression, we conducted empirical analysis to investigate the impact of various housing characteristics on rental prices.
The empirical results show that housing characteristics such as age, number of rooms, and floor area have a significant impact on rental prices. Other control variables such as management organization, furnishings, and proximity to subway stations also have some influence on rental prices, although the specific direction of influence varies depending on the model. Therefore, further evaluation of their impact is needed. In terms of the distance factor, which is an important variable, the change in distance between housing and light rail stations has a significant impact on rental prices in all models. Generally, rental prices are lowest for houses within 500 meters of the light rail station and highest for houses located between 500 and 1000 meters away, indicating both positive and negative significant effects within the light rail station area.
Regarding policy discussion, it is pointed out that the current implementation of housing subsidy policies has issues such as concentrated distribution of subsidy recipients, lack of comprehensive consideration in evaluation criteria and subsidy levels, and failure to formulate policies based on the actual needs of renters. Based on the empirical analysis results, this paper suggests that the government should refer to the differences in rental prices among administrative districts and establish "rent affordability standards" for each district based on the rental affordability of tenant households. Furthermore, by considering the average rental price differences between social housing units and neighboring areas with similar floor areas, a more precise distribution strategy can be proposed and implemented. The aim is to maximize the benefits for all groups and ensure that everyone can enjoy the basic right to housing.

論文摘要
Abstract
致謝
目錄
圖目錄
表目錄
第壹章 緒論.....1
第一節 研究動機與目的.....1
第二節 高雄輕軌沿革與現況.....3
第三節 中央、直轄市、縣（市）政府住房補助政策.....4
第貳章 文獻回顧 .....6
第一節 影響不動產價格文獻分析.....6
第二節 不動產價格受大眾運輸影響文獻分析.....9
第三節 特徵價格理論.....11
第參章 資料介紹、迴歸模型設定與變數說明.....13
第一節 資料介紹與迴歸模型設定.....13
第二節 變數說明.....15
第肆章 實證結果分析.....18
第一節 敘述統計分析.....18
第二節 實證結果分析.....19
第伍章 結論與建議.....29
第一節 結論.....29
第二節 政策建議.....34
第三節 研究限制與建議.....37
參考文獻.....39

尹國典（2013）:“大台北地區房地產價格指數連動性分析”臺灣大學經濟學研究所學位論文。
李泓見、張金鶚、花敬群（2006），台北都會區不同住宅類型價差之研究，台灣土地研究，9(1)，頁63-87。
李宗澤（2015）:“房地合一稅實施前後對不動產價格與交易量之影響－以臺北市為例”國立臺灣師範大學全球經營與策略研究所學位論文。
李沛宸（2019）:“影響臺灣房地產價格因素之探討 -以臺灣各都市為例”國立政治大學行政管理碩士學程學位論文。
林祖嘉、林素菁 （1993）。台灣地區環境品質與公共設施對房價與房租影響之分析。住宅學報，第1期，頁21-45。
林國民（1996）:“高雄市自有住宅特徵價格之研究”國立成功大學都巿計畫學類學位論文。
林禎家、黃志豪（2003）。台北捷運營運前後沿線房地屬性特徵價格之變化。運輸計畫季刊，32(4)，頁777-800。
林祖嘉、黃麗蓉（2014）。嫌惡性風水對商用不動產價格影響之研究。住宅學報，23(1)，頁51-72.
黃倧賦（2014）:“總體經濟與台灣房地產價格之非線性研究”淡江大學財務金融學系學位論文。
黃郁涵（2015）:“資本流入對不動產價格之非線性探討－日本實證”淡江大學財務金融學系學位論文。
馮郁婷（2015）:“總體經濟對房地產價格之非線性影響－台北、香港之比較”淡江大學財務金融學系學位論文。
彭建文、楊宗憲、楊詩韻（2009）。捷運系統對不同區位房價影響分析-以營運階段為例。運輸計畫季刊，38(3)，頁275-296。
廖四郎、陳靜宜（2013）。高速鐵路的時空效應對房屋價格的影響－以高雄市為例。住宅學報，第22卷，第1期，頁25-54。
劉振誠（1986）:“住宅價格影響因素之研究－以台北市松山、中山、大安、古亭區為例”國立中興大學都市計畫研究所學位論文。
劉廷揚、王蓉莉、蘇政宏（2000）。高雄市消費者購屋決策行為之研究。中華民國住宅學會第九屆會論文集，頁123-136。
薛惠珍（1989）:“台北市新興地區居住環境品質之研究－忠孝東路四段之實例探討” 國立政治大學地政研究所學位論文。
鄭羽竣（2014）:“工作可及性對集合住宅租賃價格的影響”臺灣大學地理環境資源學研究所學位論文。
謝博明、黃于禎（2021）。從家戶租屋需求觀點探討台灣租金補貼與租賃住宅政策。公共事務評論，第 19 卷，第2期，頁21-45。
戴國正、張金鶚、江穎慧（2012）。大眾捷運系統對房價影響效果再檢視。住宅學會第二十屆年會研討會。
Dewees, D. N. （1976.）, “The Effect of a Subway Improvement on Residential Property Values in Toronto”, Journal of Urban Economics, Vol. 3, 1976, pp.357-369.
Guo, Y., Liu, H., Wang, D., & Xu, X.（2019）. Factors Affecting Housing Prices in China: An Empirical Study Based on the ARDL Model. Sustainability, 11(21), 6073.
Jaffe, A. J. and Bussa, R. G.（1977）. Using a simple model to estimate market rents: a case study, Appraisal journal. 45(1): 7-13.
Lancaster, K.（1966）. A new approach to consumer theory. Journal of Political Economics, 74,132-157.
Lerman, S.（1977）﹒Location, Housing, Automobile Ownership, and Mode toWork: A Joint Choice Model﹒Transportation Research Record, 610(1), 6-11.
Lin, C. C. S.（1993）. The relationship between rents and prices of owner-occupied housing in Taiwan. The Journal of Real Estate Finance and Economics, 6(1), 25-54.
Liu, Y., & Mei, J.（2020）. A Comprehensive Review of Factors Influencing Housing Prices. Journal of Real Estate Literature, 28(2), 193-217.
Marks, D. (1984). The effect of rent control on the price of rental housing: A hedonic approach. Land Economics, 60(1), 81-94.
Quan, D.C. and Titman, S.（1999）. Do real estate prices and stock prices move together? An international analysis”, Real Estate Economics, 272, 183-207.
Rosen, S.（1974）. Hedonic prices and implicit markets: Product differentiation in pure competition. The Journal of Political Economy, 82(1), 34-55.
Ryan, S,（1999）, "Property Values and Transportation Facilities: Find the Transportation-Land Use Connection";Joumal of Planning Literature, 13(4): 412-427.
Sirmans, G. S., Sirmans. C. F., and. Benjamin J. D.（1989）. Determining apartment rent: the value of amenities, services, and external factors, Journal of Real Estate Research. 4(2): 33-44.
Stull, W. J.（1971） “Community Environment, Zoning, and the Market Value of SingleFamily Homes,” Journal of Law and Economics. 18(2): 535-557.
Waugh, R. V.（1928）. Quality factors influencing vegetables prices. Journal of Farm Economics,10, 185-196.
Waddell P., B. J. L. Berry and I. Hoch,（1993）. “Residential property values in a multinodal urban area: New evidence on the implicit price of location” The Journal of Real Estate Finance and Economics. 7:117-141.