台灣已逐步邁入高齡化社會，為保障高齡族群經濟與生活需求，「以房養老」
逐漸成為政府社會福利政策之一環，採「政府出資、銀行代辦」方式進行，將自
身擁有之房地產委托不動產估價師估價，依據鑑價結果每月領取抵押款，因此建
構房價估計模型，輔助不動產估價師進行房屋鑑價，以及提供辦理民眾快速且具
參考性質之估價工具，將成為一重要課題。而從過往文獻得知，住宅周邊之生活
機能、交通方便性等因素會對房價產生正面影響，然過往文獻於建構房價估算模
型時，經常以建物與附近設施其座標兩點間之直線距離作為模型因子，而於實際
情況中住宅與迎毗設施之道路距離並不一定等於直線距離，故本研究將住宅至捷
運、百貨公司、大型公園、鄰里公園、運動中心、明星國中及明星國小之距離替
換成實際步行距離，運用 Python 搭配 Google Map Distance Matrix API 計算座標
兩點間之實際步行距離，並採用全部輸入、前進選擇、後退刪除及逐步迴歸四種
方法選取模型因子，同時建構直線與實際距離兩種多元迴歸分析模型，接續使用
交叉驗證之方式將樣本資料分成測試與訓練資料反覆訓練模型後，比較直線與實
際距離模型於四種迴歸方式之綜合績效指數(SI)後，實際距離模型於四種迴歸方
式之表現皆優於直線距離模型。

Taiwan has become an aging society. To fulfill older adults’ economic and
everyday needs, a system of reverse mortgage has been developed and has become an
essential element of social welfare policies in Taiwan. This mortgage system is funded
by the government and managed by banks. The mortgage plan entails valuing the real
estate of potential beneficiaries by real estate appraisers; according to the valuation
results, decisions are made regarding the monthly payments. Therefore, constructing a
property valuation model to help real estate appraisers in executing property appraisals
and to provide a convenient reference tool for people undergoing this process is crucial.
Research has suggested that factors including favorable neighborhood amenities and
high accessibility to transportation facilities positively affect house prices. Studies have
constructed property valuation models using the linear distance between the coordinates
of a building and those of surrounding facilities as the model factors; however, in reality,
the walking distance between a house and a nearby amenity is not necessarily
equivalent to the linear distance between such two objects. Therefore, this study
converted the distances between a house and its surrounding amenities—namely a
subway station, department store, large park, neighborhood park, sports center, and
prestigious junior high schools and elementary schools—into actual walking distances.
The program for calculating the actual walking distance between two coordinate points
was developed using the Python programming language along with the Google Maps
Distance Matrix API. Model factors were determined using four approaches: all
selection, forward selection, backward elimination, and stepwise regression methods.
Two multiple regression analysis models were constructed for evaluating linear and
actual walking distances. These models were then trained using cross-validation
techniques, with the original sample data divided into testing and training datasets. The
III
performance levels of the two models in terms of the four regression approaches were
evaluated using the synthesis index. The results revealed that in all four regression
approaches, the model used for evaluating actual walking distances outperformed that
used for evaluating linear distances.

[1] M. A Boyle, K. Kiel, A Survey of House Price Hedonic Studies of the Impact of
Environmental Externalities, 2000.
[2] J. A Weber, The Reverse Mortgage and Its Ethical Concerns, 2006.
[3] T.K. Bauer, S.T. Braun, M. Kvasnicka, Nuclear power plant closures and local
housing values: Evidence from Fukushima and the German housing market,
Journal of Urban Economics 99 (2017) 94-106.
[4] S.E. Black, Do Better Schools Matter? Parental Valuation of Elementary
Education*, The Quarterly Journal of Economics 114 (2) (1999) 577-599.
[5] D.M. Brasington, Which Measures of School Quality Does the Housing Market
Value?, The Journal of Real Estate Research 18 (3) (1999) 395-413.
[6] T.S. Breusch, A.R. Pagan, A Simple Test for Heteroscedasticity and Random
Coefficient Variation, Econometrica 47 (5) (1979) 1287-1294.
[7] K. Burningham, NIMBYism A2 - Smith, Susan J, International Encyclopedia
of Housing and Home, Elsevier, San Diego, 2012, pp. 127-130.
[8] Y.-H. Chiang, T.-C. Peng, C.-O. Chang, The nonlinear effect of convenience
stores on residential property prices: A case study of Taipei, Taiwan, Habitat
International 46 (Supplement C) (2015) 82-90.
[9] T.-L. Chin, K.W. Chau, A critical review of literature on the hedonic price model,
International Journal for Housing Science and Its Applications 27 (2) (2003)
145-165.
[10] Thomas A. Downes, Jeffrey E. Zabel, The impact of school characteristics on
house prices: Chicago 1987–1991, Journal of Urban Economics 52 (1) (2002)
1-25.
[11] M. Espey, The Effects of Airport Noise on Residential Property Values, 2000.
64
[12] A. Field, Discovering Statistics Using SPSS, SAGE Publications, 2009.
[13] B. Geng, H. Bao, Y. Liang, A study of the effect of a high-speed rail station on
spatial variations in housing price based on the hedonic model, Habitat
International 49 (Supplement C) (2015) 333-339.
[14] D. George, P. Mallery, SPSS for Windows step by step : a simple guide and
reference, 17.0 update, 10th ed. ed., Boston : Allyn & Bacon, 2010.
[15] S.M. Goldfeld, R.E. Quandt, Some Tests for Homoscedasticity, Journal of the
American Statistical Association 60 (310) (1965) 539-547.
[16] F. Gravetter, L. Wallnau, Essentials of statistics for the behavioral sciences,8th
ed, Belmont, CA: Wadsworth., 2014.
[17] P.A. Groothuis, G. Miller, Locating Hazardous Waste Facilities: The Influence
of NIMBY Beliefs, American Journal of Economics and Sociology 53 (3) (1994)
335-346.
[18] D.R. Haurin, D. Brasington, School Quality and Real House Prices: Inter- and
Intrametropolitan Effects, Journal of Housing Economics 5 (4) (1996) 351-368.
[19] R.H.R. Holzmann, Old-Age Income Support in the 21st Century, The World
Bank, 2005.
[20] Z. Huang, R. Chen, D. Xu, W. Zhou, Spatial and hedonic analysis of housing
prices in Shanghai, Habitat International 67 (Supplement C) (2017) 69-78.
[21] P. Hubbard, NIMBY A2 - Kitchin, Rob, in: N. Thrift (Ed.), International
Encyclopedia of Human Geography, Elsevier, Oxford, 2009, pp. 444-449.
[22] C.Y. Jim, W.Y. Chen, Value of scenic views: Hedonic assessment of private
housing in Hong Kong, Landscape and Urban Planning 91 (4) (2009) 226-234.
[23] K.J. Lancaster, A New Approach to Consumer Theory, Journal of Political
Economy 74 (2) (1966) 132-157.
[24] S.H. Lin, P.J. Hsieh, Book Review: Kline, R. B. (2005). Principles and Practice
65
of Structural Equation Modeling (2nd ed.). New York: Guilford. 366 pp., $40.50
paperback, ISBN 978-1-57230-690-5, 2010.
[25] J.J. McCluskey, G.C. Rausser, Hazardous waste sites and housing appreciation
rates, Journal of Environmental Economics and Management 45 (2) (2003) 166-
176.
[26] S.L.U. Miroslav Havranek (CUEC), AriRabl (ARMINE), Milan Scasny
(CUEC), Timothy Taylor(UBATH), Harry Walton (UBATH), Xingshu
Zhao(CASS), Assaad Zoughaib (ARMINE) Final report on waste management
externalities in EU25 and report on disamenity impacts in the UK 2009.
[27] M. Rivas Casado, J. Serafini, J. Glen, A. Angus, Monetising the impacts of
waste incinerators sited on brownfield land using the hedonic pricing method,
Waste Management 61 (2017) 608-616.
[28] L.H. Rong, Y.M. Sun, The analysis of second-hand housing price influencing
factors based on hedonic model and WEB information, 4th International
Conference on Civil Engineering, Architecture and Building Materials,
CEABM 2014, May 24, 2014 - May 25, 2014, Vol. 587-589, Trans Tech
Publications Ltd, Haikou, China, 2014, pp. 2285-2289.
[29] S. Rosen, Hedonic Prices and Implicit Markets: Product Differentiation in Pure
Competition, Journal of Political Economy 82 (1) (1974) 34-55.
[30] V.v. Szeliski, S.L. Horner, C.F. Roos, A.T. Court, The Dynamics of automobile
demand. Statement of the problem, S.L. Horner. Factors governing changes in
domestic automobile demand, C.F. Roos and Victor von Szeliski. Hedonic price
indexes with automotive examples, A.T. Court. Significance of the findings,
S.M. Dubrul, General motors Corporation, New York, 1939.
[31] W. Trochim, The Research Methods Knowledge Base, 2007.
[32] S. W. Hamilton, G. M. Schwann, Do High Voltage Electric Transmission Lines
66
Affect Property Value?, 1995.
[33] W. Wang, G.-J. Tong, H.-R. Zhang, Empirical analysis on the housing price in
Harbin City based on hedonic model, 17th International Conference on
Management Science and Engineering, ICMSE 2010, November 24, 2010 -
November 26, 2010, IEEE Computer Society, Melbourne, VIC, Australia, 2010,
pp. 1659-1664.
[34] H.-Z. Wen, S.-H. Jia, X.-Y. Guo, Hedonic price analysis of urban housing: An
empirical research on Hangzhou, China, Journal of Zhejiang University:
Science 6 A (8) (2005) 907-914.
[35] H. White, A Heteroskedasticity-Consistent Covariance Matrix Estimator and a
Direct Test for Heteroskedasticity, Econometrica 48 (4) (1980) 817-838.
[36] J. Wu, M. Wang, W. Li, J. Peng, L. Huang, Impact of Urban Green Space on
Residential Housing Prices: Case Study in Shenzhen, Journal of Urban Planning
and Development 141 (4) (2015) 05014023.
[37] W. Xuyu, The application of SPSS in empirical research of housing hedonic
price, 2011 International Conference on Multimedia Technology, 2011, pp.
3262-3265.
[38] H. Zhang, M. Zhang, Environment hedonic price analysis: Evidence from Jilin
city, 2010 2nd International Conference on Communication Systems,
Networks and Applications, ICCSNA 2010, Vol. 1, IEEE Computer Society,
2010, pp. 354-356.
[39] 內政部, 不動產說明書應記載及不得記載事項, 內政部, 2013.
[40] 內政部地政司, 不動產估價技術規則, 內政部, 2013.
[41] 李春長, 游淑滿, 張維倫, 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) (2012) 67-87.
[42] 杜宇璇, 宋豐荃, 曾禹瑄, 葛仲寧, 陳奉瑤, 台灣特徵價格模型之回顧分
67
析, 土地問題研究季刊 12 (2) (2013) 44-57.
[43] 林左裕, 楊博翔, 逆向房屋抵押貸款在臺灣推行之需求分析, 住宅學報
20 (1) (2011) 109-125.
[44] 林祖嘉, 黃麗蓉, 嫌惡性風水對商用不動產價格影響之研究, 住宅學報
23 (1) (2014) 51-72.
[45] 林素菁, 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)
(2004) 15-34.
[46] 林英彥, 不動產估價, 2004.
[47] 楊宗憲, 蘇倖慧, 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) (2011) 61-80.
[48] 江穎慧, 莊喻婷, 張金鶚, 臺北市公共自行車場站對鄰近住宅價格之影響,
運輸計劃季刊 46 (4) (2017) 399-428.
[49] 洪得洋, 林祖嘉, 臺北市捷運系統與道路寬度對房屋價格影響之研究, 住
宅學報 (8) (1999) 47-67.
[50] 邱皓政, 量化研究與統計分析, 2013.