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題名 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作者 李智偉
Lee, Chih Wei貢獻者 陳奉瑤
Chen, Fong Yao
李智偉
Lee, Chih Wei關鍵詞 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日期 2014 上傳時間 1-七月-2015 14:57:56 (UTC+8) 摘要 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.參考文獻 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. 描述 碩士
國立政治大學
地政研究所
101257026
103資料來源 http://thesis.lib.nccu.edu.tw/record/#G0101257026 資料類型 thesis dc.contributor.advisor 陳奉瑤 zh_TW dc.contributor.advisor Chen, Fong Yao en_US dc.contributor.author (作者) 李智偉 zh_TW dc.contributor.author (作者) Lee, Chih Wei en_US dc.creator (作者) 李智偉 zh_TW dc.creator (作者) Lee, Chih Wei en_US dc.date (日期) 2014 en_US dc.date.accessioned 1-七月-2015 14:57:56 (UTC+8) - dc.date.available 1-七月-2015 14:57:56 (UTC+8) - dc.date.issued (上傳時間) 1-七月-2015 14:57:56 (UTC+8) - dc.identifier (其他 識別碼) G0101257026 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/76238 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 地政研究所 zh_TW dc.description (描述) 101257026 zh_TW dc.description (描述) 103 zh_TW dc.description.abstract (摘要) 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。 zh_TW dc.description.abstract (摘要) 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. en_US dc.description.tableofcontents 第一章 緒論 1第一節 研究動機與目的 1第二節 研究方法與範圍 4第三節 研究流程 7第二章 文獻回顧 9第一節 特徵價格模型 9第二節 國內特徵價格模型於大量估價之應用 12第三節 區位價值波面 15第四節 小結 20第三章 研究設計 23第一節 模型設計 23第二節 研究分析 29第三節 資料說明與處理 31第四章 實證與分析 45第一節 模型預估能力比較 45第二節 模型區域次市場侷限性測試 51第三節 模型穩定性測試 56第四節 模型空間樣本數量與預估能力測試 60第五節 小結 63第五章 結論與建議 65第一節 結論 65第二節 建議 66參考文獻 69 zh_TW dc.format.extent 1488506 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0101257026 en_US dc.subject (關鍵詞) 大量估價 zh_TW dc.subject (關鍵詞) 區位價值波面 zh_TW dc.subject (關鍵詞) 特徵價格模型 zh_TW dc.subject (關鍵詞) 估價 zh_TW dc.subject (關鍵詞) Mass Appraisa en_US dc.subject (關鍵詞) Location Value Response Surface en_US dc.subject (關鍵詞) Hedonic Model en_US dc.subject (關鍵詞) Appraisal en_US dc.title (題名) 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 zh_TW dc.title (題名) The Research of Refining Mass Appraising by the Concept of Location Value Response Surface en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) 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. zh_TW
