Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/137044


Title: 鄰里環境與公共設施對房價與租金之影響 - 以臺北市為例
The Influence of Neighborhood Environment and Public Facilities on House Prices and Rents: A Case Study of Taipei City
Authors: 徐慶萱
Hsu, Ching-Hsuan
Contributors: 江穎慧
林士淵

Chiang, Ying-Hui
Lin, Shih-Yuan

徐慶萱
Hsu, Ching-Hsuan
Keywords: 實價登錄
租賃住宅市場
鄰里環境
公共設施
地理加權迴歸
The Information of Actual Price Registration of Real Estate
Rental Housing Market
Neighborhood Environment
Public Facilities
Geographic Weighted Regression Model
Date: 2021
Issue Date: 2021-09-02 17:35:51 (UTC+8)
Abstract: 隨著就業機會和人口漸趨集中於都市,居住需求也開始逐漸擴大,購屋與租屋族群日益增加,根據臺北市地政局針對房價與租金分布進行統計,發現價格分布趨勢並未呈現連動,推測租買族群在選擇居住地點之考量條件可能有所差異,故對價格之影響程度也有所不同。回顧文獻發現鄰里環境與公共設施為其重點考量因素,故將價格影響因素主要分為前述兩個面向進行探討。

本研究使用2019年內政部實價登錄資料與591租屋網成交案件,保留建物類型為公寓及電梯大樓為研究樣本。以等量分配法(Quantile)將各里平均單價分組,劃分為高房價低租金鄰里與低房價高租金鄰里,再運用地理加權迴歸模型(Geographically Weighted Regression,GWR),分析鄰里環境與公共設施對於前述各鄰里之價格影響差異,並以地理資訊系統(Geographic Information System,GIS)視覺化展示各項特徵之空間分布情形。

研究結果顯示,在價格分布方面,高房價地區未必為高租金地區,除信義區例外。在鄰里環境特徵與公共設施特徵對價格影響差異方面,高房價低租金鄰里,以所得、住宅竊盜案件數、空氣品質指標、與大學距離,共4項特徵最常對價格影響產生差異;低房價高租金鄰里,以住宅竊盜案件數、噪音、空氣品質指標、與捷運站距離、與醫院距離、與公園距離,共6項特徵最常對價格影響產生差異。值得注意的是,不論前者或後者分組之鄰里,住宅竊盜案件數及空氣品質指標此兩項特徵皆會造成分組過後之價格產生影響差異。
As employment opportunities and population tend to be concentrated in cities, housing demand has gradually expanded, and the number of buyers and renters is increasing. According to the statistics of the distribution of house prices and rents by the Taipei City Lands Bureau, it is found that the trend of price distribution does not show a link. The rent-buyers may have different considerations in choosing a place to live, so the degree of influence on the price is also different. Reviewing the literature found that the neighborhood environment and public facilities are the key consideration factors, so the price influencing factors are mainly divided into the aforementioned two aspects for discussion.

This study uses the real-price registration data of the Ministry of the Interior and the transaction cases of 591 renting a house in 2019. The types of buildings are apartments and elevator buildings as the research samples. The average unit price of each mile is grouped by the Quantile method, and divided into neighborhoods with high housing prices and low rents and neighborhoods with low housing prices and high rents. Then, using the geographic weighted regression model (GWR), the neighborhood environment and public facilities are analyzed for The price of the neighborhood affects the difference, and the geographic information system (GIS) is used to visualize the spatial distribution of various characteristics.

The research results show that in terms of price distribution, areas with high housing prices may not necessarily be areas with high rents, with the exception of Xinyi District. In terms of the difference between the environmental characteristics of the neighborhood and the characteristics of public facilities, the neighborhoods with high housing prices and low rents, including income, number of residential theft cases, air quality indicators, and distance from universities, have the most common impact on prices. In neighborhoods with high housing prices, the number of residential thefts, noise, air quality indicators, distance from the MRT station, distance from the hospital, and distance from the park, six characteristics most often have a difference in price effects. It is worth noting that regardless of the neighborhood in the former or the latter group, the two characteristics of the number of residential theft cases and the air quality index will cause the price difference after the grouping.
Reference: 一、 中文參考文獻
1.李如君,1997,「台北地區住宅租金水準之研究」,國立政治大學地政系碩士論文:台北。
2.李春長、游淑滿、張維倫,2012,「公共設施、環境品質與不動產景氣對住宅價格影響之研究─兼論不動產景氣之調節效果」,『住宅學報』,21(1):67-87。
3.李馨蘋、劉代洋,1999,「租賃住宅市場租金之影響因素」,『中華管理評論』,2(1)。
4.林祖嘉、林素菁,1994,「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』,1:21-45。
5.林素菁,2004,「台北市國中小明星學區邊際願意支付之估計」,『住宅學報』,13(1):15-34。
6.林楨家、黃志豪,「臺北捷運營運前後沿線房地屬性特徵價格之變化」,『運輸計劃季刊』,32(4):777-800。
7.陳冠位、李永展,2007,「台南市民生活品質滿意度之研究」,『建築學報』,60:1-26。
8.陳章瑞,2012,「以地理加權迴歸模型之空間分析探討都市公園之寧適效益」,『造園景觀學報』,19(1):17-46。
9.彭建文,2004,「台灣出租住宅市場與自有住宅市場價格調整關係之研究」,『都市與計劃』,31(4):391-412。
10.彭建文、李美杏、陳冠儒,2020,「台灣地區居住滿意度影響因素之實證分析」,『都市與計劃』,47(3):243-270。
11.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」,『運輸計劃季刊』,38(3):275-296。
12.楊宗憲、蘇倖慧,2011,「迎毗設施與鄰避設施對住宅價格影響之研究」,『住宅學報』,20(2):61-80。
13.楊國柱、顏愛靜,2004,「殯葬設施設置鄰避衝突之分析:以交易成本理論為觀點」,『中國行政評論』,14(1):27-58。
14.溫在弘,2015,空間分析:方法與應用,台北:雙葉書廊。
15.蔡宛蓉,2013,「運用地理加權迴歸方法探討都市土地混合使用空間分佈之影響因素」,國立成功大學都市計劃學系碩士論文:台南市。

二、 英文參考文獻
1.Brunsdon, C., Fotheringham, A. S., & Charlton, M., 2002, “Geographically weighted summary statistics – a framework for localized exploratory data analysis”, Computers Environment and Urban Systems, 26(6), 501-524.
2.Cavanaugh, J. E., 1997, “Unifying the derivations for the Akaike and corrected Akaike information criteria”, Statistics & Probability Letters, 33(2), 201-208.
3.Ceccato, V., & Wilhelmsson, M., 2020, “Do crime hot spots affect housing prices? ”, Nordic Journal of Criminology, 21(1), 84-102.
4.Cohen, J.P. & Coughlin, C.C., 2008, “Spatial Hedonic Models Of Airport Noise, Proximity, And Housing Prices”, Journal of Regional Science, 48(5), 859-878.
5.Cui, N., Gu, H., 1, Shen, T. & Feng. C., 2018, “The Impact of Micro-Level Influencing Factors on Home Value: A Housing Price-Rent Comparison”, Sustainability, 10(12), 1-23.
6.Dai, X., Bai, X., & Xu, M., 2016, “The influence of Beijing rail transfer stations on surrounding housing prices”, Habitat International, 55, 79-88.
7.Dirick, L., Claeskens, G., & Baseens, B., 2015, “An Akaike information criterion for multiple event mixture cure models”, European Journal of Operational Research, 241(2), 449-457.
8.Follain, J. R., Malpezzi, S., 1980, “Dissecting Housing Value and Rent”, Washington, DC: The Urban Institute.
9.Fotheringham, A. S., Charlton, M., & Brunsdon, C., 1998, “Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis”, Environment and planning A, 30(11), 1905-1927.
10.Halvorsen, R. & Palmquist, R., 1980, “The Interpretation of Dummy Variables in Semilogarithmic Equations”, American Economic Review, 70(3), 474-475.
11.Hoshino, T., & Kuriyama, K., 2010, “Measuring the benefits of neighborhood park amenities: application and comparison of spatial hedonic approaches”, Environmental and Resource Economics, 45(3), 429-444.
12.Hu, L., He, S.; Han, Z., Xiao, H., Su, S., Weng, M.; & Cai, Z., 2019, “Monitoring housing rental prices based on social media: An integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policies”, Land Use Policy, 82, 657-673.
13.Hui, E.C.M., Chau, C.K., Pun, L. & Law, M.Y., 2007, “Measuring the neighboring and environmental effects on residential property value: Using spatial weighting matrix”, Building and Environment, 42(6), 2333-2343.
14.Kim, K., & Lahr, M.L., 2014, “The impact of Hudson–Bergen light rail on residential property appreciation”, Regional Science, 93, S79-S97.
15.Lancaster, K.J.,1966, “A New Approach to Consumer Theory”, Journal of Political Economy, 74 (2), 132-157.
16.Leung, K.M., & Yiu, C.Y., 2019, “Rent determinants of sub-divided units in Hong Kong”, Journal of Housing and the Built Environment, 34, 133-151.
17.Li, H.; Wei, Y.D.; & Wu, Y., 2019, “Analyzing the private rental housing market in Shanghai with open data”, Land Use Policy, 85, 271-284.
18.Malpezzi, S., 2003, “Hedonic pricing models: A selective and applied review, In: Housing Economies and Public Policy”, Maclennan, D., Sullivan, T. O., and Gibbs, K. (ed.), Oxford: Blackwell Sciences, 67-74.
19.Malpezzi, S., OZanne, L & Thibodeau, T., 1980, “Characteristic prices of housing in fifty-nine metropolitan areas”, Washington, DC: The Urban Institute.
20.McCord, M., Davis, P.T., Haran, M., McGreal, S., & McIlhatton, D., 2012, “Spatial variation as a determinant of house price : Incorporating a geographically weighted regression approach within the Belfast housing market” , Journal of Financial Management of Property and Construction, 17 (1), 49-72.
21.Oshan, M., & Fotheringham, A, S., 2018, “A Comparison of Spatially Varying Regression Coefficient Estimates Using Geographically Weighted and Spatial-Filter-Based Techniques” Geographical Analysis, 50(1), 53-75.
22.Pan, Q., Pan, H., Zhang, M., & Zhong, B., 2014, “Effects of rail transit on residential property values: Comparison study on the rail transit lines in Houston, Texas, and Shanghai, China”, Transportation Research, 2453(1), 118-127.
23.Rosen, S., 1974, “Hedonic prices and implicit markets : product differentiation in pure competition”, Journal of Political Economy, 8(1), 35-55.
24.Sirmans, G.S., Macpherson, A.D., & Zietz, N.E., 2005, “The Composition of Hedonic Pricing Models”, Journal of Real Estate Literature, 13(1), 1-44.
25.Trojanek, R., & Gluszak, M., 2018, “Spatial and time effect of subway on property prices”, Journal of Housing and the Built Environment, 33, 359–384.
26.Wang, D., & Li, S.M., 2004, “Housing Preferences in a Transitional Housing System: The Case of Beijing, China”, Environment and Planning A, 36(1), 69-87.
27.Wang, K., Wolverton., & Marvin, L., (ed.), 2002, Real Estate Valuation Theory, Boston:Kluwer.
28.Wittowsky, D., Hoekveld, J., Welsch, J., & Steier, M., 2020, “Residential housing prices: impact of housing characteristics, accessibility and neighbouring apartments – a case study of Dortmund, Germany”, Urban, Planning and Transport Research, 8(1), 44-70.
29.Zhang, S.; Wang, L.; & Lu, F., 2019, “Exploring Housing Rent by Mixed Geographically Weighted Regression : A Case Study in Nanjing”, International Journal of Geo-Information, 8(10), 431.
Description: 碩士
國立政治大學
地政學系
108257028
Source URI: http://thesis.lib.nccu.edu.tw/record/#G0108257028
Data Type: thesis
Appears in Collections:[地政學系] 學位論文

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