English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113303/144284 (79%)
Visitors : 50830010      Online Users : 739
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/76238


    Title: 以區位價值波面提升大量估價精度之研究 -以條件式殘差擬合變數為核心
    The Research of Refining Mass Appraising by the Concept of Location Value Response Surface
    Authors: 李智偉
    Lee, Chih Wei
    Contributors: 陳奉瑤
    Chen, Fong Yao
    李智偉
    Lee, Chih Wei
    Keywords: 大量估價
    區位價值波面
    特徵價格模型
    估價
    Mass Appraisa
    Location Value Response Surface
    Hedonic Model
    Appraisal
    Date: 2014
    Issue Date: 2015-07-01 14:57:56 (UTC+8)
    Abstract: 現行不動產大量估價主要以特徵價格模型為基礎進行價格之預估,而常以鄰里、轄區或次市場虛擬變數或是與特定公共設施之距離作為控制區位價值之變數。惟僅以次市場變數之係數或是距離特定公共設施距離之係數衡量樣本之區位價值,則因係數之僵化性弱化或低估區位對不動產價格之影響,導致大量估價模型之精度難以突破。
    本研究以區位價值波面之概念建立條件式殘差擬合變數,從空間角度評估各樣本之區位價值並以量化數值呈現各樣本區位價值之高低,在細膩處理區位價值下模型之預估能力相對提升。實證結果顯示,整體模型之絕對誤差平均值為10.1%,而10%、20%誤差命中率達62.9%、87.9%,相對優於過去研究之模型預估能力;另外,經過區域侷限性測驗發現,條件式殘差擬合變數修正模型不受次市場之侷限,對於是否劃分模型次市場已不影響模型之預估能力,且經由實證發現,當實價登錄樣本愈趨豐富時,模型之預估能力將更加提升,值得作為後續建立大量估價模型之參考。
    Hedonic model is the most commonly-used tool for real estate mass appraisal, and neighborhoods, districts or sub-market dummies or the distance from the specific public facilities are the common variables used to control the value of location in the model. However, controlling the location value by these ways leads to the coefficient rigidities, making it possible to underestimate the value of the location.
    This research sets up the conditional-selected residual fitting variable by the concept of location value response surface, and estimates the location value from the spatial perspective. The result shows that the MAPE of the model is 10.1%, and the hit-rate of 10% and 20% come to 62.9% and 87.9%, having significant improvement compared with the past studies. Besides, by the confinement test of sub-market, it has been proved that the CRF modified model successfully gets rid of confinement from the sub-market, and whether dividing sub-markets or not no longer affects the prediction capability of the model. Another test giving us new images that, when the train data gets richer as time goes, the prediction capability of the model gets higher as well.
    Reference: 一、中文參考文獻
    王群猛,2013,「銀行聚集與不動產價格之關係-以台北市辦公商圈為例」,國立政治大學地政學系碩士論文。
    江穎慧,2009,「不動產價格之估值認知與調整 – 估價行為、大量估價與估值機率之研究」,國立政治大學地政學系博士論文。
    林子欽、李汪穎、陳國華,2011,「公寓建物之折舊估算與房屋稅負」,『都市與計劃』,38(1):31-46。
    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』,1:21-45。
    林祖嘉、馬毓駿,2007,「特徵方程式大量估價法在台灣不動產市場之應用」,『住宅學報』,16(2):1-22。
    林楨家、黃至豪,2003,「台北捷運營運前後沿線房地屬性特徵價格之變化」,『運輸計劃季刊』,32(4):777-800。
    陳力綸,2013,「以地理加權迴歸改進住宅次市場劃分之研究」,國立政治大學地政學系碩士論文。
    陳奉瑤、梁仁旭,2014,『不動產估價理論』,第三版:台北。
    陳奉瑤、李智偉,2015,「不動產估價業發展概況」,2015台灣地區房地產年鑑。
    陳奉瑤、楊依蓁,2007,「個別估價與大量估價之準確性分析」,國立政治大學地政學系碩士論文。
    張怡文,2006,「特徵價格法在住宅大量估價模型中的延伸—分量迴歸之應用」,國立政治大學地政學系碩士論文。
    楊宗憲、蘇幸慧,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。
    廖咸興、張芳玲,1997,「不動產評價模式特徵價格法與逼近調整法之比較」,『住宅學報』,5:17-35。
    廖彬傑,2012,「應用克利金法劃分地價區段之研究」,國立政治大學地政學系碩士論文。
    蔡爾逸,2012,「應用支撐向量機(SVM)於都市不動產價格預測之研究」,國立中央大學營建管理研究所碩士論文。
    戴國正,2012,「大眾捷運系統對房價影響效果之再檢視」,國立政治大學地政學系碩士論文。
    龔永香,2007,「客觀標準化不動產估價之可行性分析─ 市場比較法應用於大量估價」,國立政治大學地政學系碩士論文。
    二、外文參考文獻
    Basu, S., & Thibodeau, T. G. (1998). Analysis of spatial autocorrelation in house prices. The Journal of Real Estate Finance and Economics, 17(1), 61-85.
    Besner, C. (2002). A spatial autoregressive specification with a comparable sales weighting scheme. Journal of Real Estate Research, 24(2), 193-212.
    Bourassa, S. C., Cantoni, E., & Hoesli, M. (2007). Spatial dependence, housing submarkets, and house price prediction. The Journal of Real Estate Finance and Economics, 35(2), 143-160.
    Bowen, W. M., Mikelbank, B. A., & Prestegaard, D. M. (2001). Theoretical and empirical considerations regarding space in hedonic housing price model applications. Growth and change, 32(4), 466-490.
    Chou, Y.-H. (1997). Exploring spatial analysis in geographic information systems Exploring Spatial Analysis in Geographic Information Systems: OnWord Press.
    D’Amato, M. (2010). A location value response surface model for mass appraising: An “iterative” location adjustment factor in Bari, Italy. International Journal of Strategic Property Management(3), 231-244.
    D’Amato, M., & Siniak, N. (2012). Mass Appraisal Modelling in Minsk: Testing different Models Location sensitive. Aestimum, 735-743.
    Dewees, D. N. (1976). The effect of a subway on residential property values in Toronto. Journal of Urban Economics, 3(4), 357-369.
    Dubin, R., Pace, R. K., & Thibodeau, T. G. (1999). Spatial autoregression techniques for real estate data. Journal of Real Estate Literature, 7(1), 79-96.
    Eckert, J. K., Gloudemans, R. J., & Almy, R. R. (1990). Property appraisal and assessment administration (Vol. 130): International Association of Assessing Officers Chicago.
    Eichenbaum, J. (1989). Incorporating location into computer-assisted valuation. Property Tax Journal, 8(2), 151-169.
    Eichenbaum, J. (1995). The location variable in world class cities: lessons from cama valuation in New York city. Journal of Property Tax Assessment and Administration, 1(3), 46-60.
    Englund, E. J. (1993). Spatial simulation: environmental applications. Environmental Modeling with GIS, 432-437.
    Fik, T. J., Ling, D. C., & Mulligan, G. F. (2003). Modeling spatial variation in housing prices: a variable interaction approach. Real Estate Economics, 31(4), 623-646.
    Freeman III, A. M. (1979). Hedonic prices, property values and measuring environmental benefits: a survey of the issues. The Scandinavian Journal of Economics, 154-173.
    Gallimore, P., Fletcher, M., & Carter, M. (1996). Modelling the influence of location on value. Journal of Property Valuation and Investment, 14(1), 6-19.
    González, M. A. S., Soibelman, L., & Formoso, C. T. (2005). A new approach to spatial analysis in CAMA. Property Management, 23(5), 312-327.
    Hembd, J., Infanger, Craig L. (1981). An application of trend surface analysis to a rural-urban land market. Land Economics, 57(3), 303:320.
    Hoffmann, J., & Lorenz, A. (2006). Real estate price indices for Germany: past, present and future. Present and Future (November 30, 2006).
    Kevin, K., & YU, S. M. (2001). Using response surface analysis in mass appraisal to examine the influence of location on property values in Hong Kong.
    Lancaster, K. J. (1966). A new approach to consumer theory. The journal of political economy, 132-157.
    Legendre, P. (1993). Spatial autocorrelation: trouble or new paradigm? Ecology, 74(6), 1659-1673.
    McCluskey, W. J., Deddis, W. G., Lamont, I. G., & Borst, R. A. (2000). The application of surface generated interpolation models for the prediction of residential property values. Journal of Property Investment & Finance, 18(2), 162-176.
    O’Connor, P. (1982). Locational valuation derived directly from the real estate market with the assistance of response surface techniques. Lincoln Institute of Land Policy.
    O’Connor, P. M., & Eichenbaum, J. (1988). Location value response surfaces: the geometry of advanced mass appraisal. Property Tax Journal, 7(3), 277-296.
    Parker, C. (1981). Trend surface and the spatio-temporal analysis of residential land-use intensity and household housing expenditure. Land Economics, 323-337.
    Rosen, S. (1974). Hedonic prices and implicit markets: product differentiation in pure competition. The journal of political economy, 34-55.
    Sirmans, S. G., Macpherson, D. A., & Zietz, E. N. (2005). The composition of hedonic pricing models. Journal of Real Estate Literature, 13(1), 1-44.
    Thériault, M., Des Rosiers, F., Villeneuve, P., & Kestens, Y. (2003). Modelling interactions of location with specific value of housing attributes. Property Management, 21(1), 25-62.
    Ward, R., Guilford, J., Jones, B., Pratt, D., & German, J. (2002). Piecing Together Location: Three Studies by the Lucas County R&D Section. Assessment Journal, 21-53.
    Ward, R. D., Weaver, J. R., & German, J. C. (1999). Improving CAMA models using geographic information systems/response surface analysis location factors. Assessment Journal, 6, 30-39.
    Wilhelmsson, M. (2002). Spatial models in real estate economics. Housing, Theory and Society, 19(2), 92-101.
    Description: 碩士
    國立政治大學
    地政研究所
    101257026
    103
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0101257026
    Data Type: thesis
    Appears in Collections:[地政學系] 學位論文

    Files in This Item:

    File SizeFormat
    702601.pdf1453KbAdobe PDF2117View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback