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題名 不動產評價之空間計量與地理統計
Spatial Econometrics and Geostatistics for Real Estate Valuation
作者 陳靜宜
Chen, Jing Yi
貢獻者 廖四郎
Liao, Szu Lang
陳靜宜
Chen, Jing Yi
關鍵詞 房價
空間自相關
空間計量學
地理統計學
克利金
共克利金
地理加權迴歸
house prices
spatial autocorrelation
spatial econometrics
geostatistics
kriging
cokriging
geographically weighted regression
日期 2012
上傳時間 2-Sep-2013 16:04:08 (UTC+8)
摘要 近年來由於地理資訊系統（GIS）的快速發展發，空間資料分析開始受到重視並在社會科學領域中逐漸扮演重要的角色。雖然一般的統計方法已在傳統資料分析上發展已久，然而它們卻不能有效地說明空間性資料，並且無法充分處理空間相依或空間異質性問題。一般而言，空間資料分析主要有兩個分派:模型導向學派與資料導向學派。本文研究目的在於應用空間統計方法合理且充分地評估房地產價值，研究方法包含地理統計(克利金和共克利金)、地理加權迴歸與空間特徵價格模型等，並且以台中市不動產資料進行實證探究。這項新的研究技術在不動產評價領域中將可提供更好的解析能力，使其在評價過程中或是不動產投資決策時，成為一個更強而有力的分析工具。
In recent years, spatial data analysis has received significant awareness and played an important role in social science because of the rapid development of Geographic Information System (GIS). Although classic statistical methods are attractive in traditional data analysis, they cannot be executed seriously for spatial data. Standard statistical techniques didn’t sufficiently deal with spatial dependence or spatial heterogeneity issues. Generally, the model-driven method and the data-driven method are mainly the two branches of the spatial data analysis. The purpose of this paper is to apply spatial statistics methods including geostatistical methods (kriging and cokiging), geographically weighted regression, and spatial hedonic price models to real estate analysis. It seems to be completely reasonable and sufficient. The real estate data in Taichung city (Taiwan) is used to carry out our exploration. These techniques give better insight in the field of real estate assessment. They can apply a good instrument in mass appraisal and decision concerning real estate investment.
參考文獻 Anselin, L. (1998). Spatial econometrics: methods and models. Kluwer, Dordrecht.
Anselin, L. (2003). GeoDa™0.9User’s Guide, CSISS.
Basu, S. and Thibodeau, T. G. (1998). Analysis of spatial autocorrelation in house prices. Journal of Real Estate Finance and Economics, 17, 61 – 85.
Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems, Journal of the Royal Statistical Society B, 36, 192-225.
Bourassa, S. C., Cantoni, E. and Hoesli, M. (2007). Spatial dependence, housing submarkets, and house price prediction. Journal of Real Estate Finance and Economics. 35, 143 –160.
Brunsdon, C., Fotheringham, A. S. and Charlton, M. E. (1996). Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical Analysis, 28 (4), 281-298.
Brunsdon, C., Fotheringham, A. S. and Charlton, M. (1998). Geographically weighted regression-modeling spatial non-stationarity. Journal of the Royal Statistical Society. Series D (The Statistician), 47(3), 431-443.
Calderón, G. F. A. (2009). Spatial regression analysis vs. kriging methods for spatial estimation. International Advances in Economic, 15, 44-58.
Cambardella, C. A., Moorman, T. B., Novak, J. M., Parkin, T. B., Karlen, D. L., Turco, R. F., et al. (1994). Field scale variability of soil properties in central iowa soils. Soil Science Society of America Journal, 58, 1501 – 1511.
Chica-Olmo, J. (1995). Spatial estimation of housing prices and locational rents. Urban Studies, 32(8), 1331-1344.
Chica-Olmo, J. (2007). Prediction of house location price by multivariate spatial method: co-kriging. Journal of Real Estate Research, 29, 91-114.
Chun, Y. and Griffith, D. A. (2013). Spatail Statistics & Geostatistics: Theory and Application for Geographic Information Science & Technology, SAGE.
Clapp, J., Dubin, R. and Rodriguez, M. (2004). Modeling spatial and temporal house price patterns: a comparison of four models. Journal of Real Estate Finance and Economics, 29(2), 167-191.
Cressie, N. (1991). Statistics for Spatial Data, New York: Wiley.
Dubin, R. A. (1998). Predicting house prices using multiple listings data. Journal of Real Estate Finance and Economics, 17, 35 – 59.
Dubin, R.A, Pace, R. K. and Thibodeau, T. G. (1999). Spatial autoregression techniques for real estate data. Journal of Real Estate Literature, 7, 79–95.
Fotheringham, A.S., Charlton, M.E. and Brunsdon, C. (2000). Quantitative Geography, SAGE.
Fregonara, E., Rolando, D. and Semeraro, P. (2012). The value spatial component in the real estate market: the Turin case study. Aestimum60, Giugno, 85-113.
Gelfand, A. E., Ecker, M. D, Knight, J. R., and Sirmans, C. F. (2004). The dynamics of location in home price. Journal of Real Estate Finance and Economics, 29(2), 149-166.
Gillen, K., Thibodeau, T. and Wachter, S. (2001). Anisotropic autocorrelation in house price. Journal of Real Estate Finance and Economics, 23, 5-31.
Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation, New York: Oxford University Press.
Johnston, K., Ver Hoef, J.M., Krivoruchko, K. and Lucas. N. (2003). Using ArcGIS Geostatistical Analyst, ESRI Press.
Kulczycki, M. and Ligas, M. (2007). Spatial Statistics for Real Estate Data, Strategic Integration of Surveying Services, Hong Kong: SAR, China.
LeSage, J. P. and Pace, R. K. (2004). Models for spatial dependent missing data. Journal of Real Estate Finance and Economics, 29 (2), 233-254.
Liu, D., Wang, Z., Zhang, B., Song, K., Li, X. and Li, J. (2006). Spatial distribution of soil organic carbon and analysis of related factors in croplands of the black soil region, northeast china. Agriculture, Ecosystems and Environment, 113, 73 – 81.
Lloyd, C. D. (2011). Local Models for Spatial Analysis, CRC Press.
Long, F. Páez, A. and Farber, S. (2007). Spatial effects in hedonic price estimation: a case study in the city of Toronto, CSpA Working Paper, McMaster University.
Matheron, G. (1963). Principle of geostatistics. Economic Geology, 58, 1246-1266.
Matthews, S. A. and Yang, T. C. (2012). Mapping the results of local statistics: using geographically weighted regression. Demographic Research, 26 (6), 151-166.
Militino, A, F., Ugarte, M. D. and García-Reinaldos, L. (2004). Alternative models for describing spatail dependencr among dwelling selling prices. Journal of Real Estate Finance and Economics, 29(2), 193-209.
Mitchell, A. (2005). The ESRI Guide to GIS Analysis, Volume 2: Spatial Measurements and Statistics, ESRI Press.
Moran, P. A. P. (1948). The interpretation of statistical maps. Biometrika, 35, 255–260.
Osland, L. (2010). An application of spatial econometrics in relation to hedonic house price modeling. Journal of Real Estate Research, 32 (2), 289-320.
Pace, R. K., Barry, R. and Simans, C. F. (1998). Spatail statistics and real estate. Journal of Real Estate Finance and Economics, 17(1), 5-13.
Pace, R. K. and LeSage, J. P. (2004). Spatail statistics and real estate. Journal of Real Estate Finance and Economics, 29 (2), 147-148.
Rosen, S. (1974). Hedonic prices and implicit markets: product differentiation in pure competition. Journal of Political Economy, 82, 34-55.
Tsutsumi, M. and Seya, H. (2008). Measuring the impact of large-scale transportation projects on land price using spatial statistical models. Paper in Regional Science, 87, 385–401.
Wackernagel, H. (1995). Multivariate Geostatistics: An Introduction with Applications, Springer.
Zhu, B., Füss, R. and Rottke, N. B. (2011). The predictive power of anisotropic spatial correlation modeling in housing prices. Journal of Real Estate Finance and Economics, 42, 542 – 565.
Yoo, E. H. and Kyriakidis, P. C. (2009). Area-to-point kriging in spatial hedonic pricing models. Journal of Geographical Systems, 11, 381-406.
描述 博士
國立政治大學
金融研究所
97352505
101
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0097352505
資料類型 thesis
dc.contributor.advisor 廖四郎zh_TW
dc.contributor.advisor Liao, Szu Langen_US
dc.contributor.author (Authors) 陳靜宜zh_TW
dc.contributor.author (Authors) Chen, Jing Yien_US
dc.creator (作者) 陳靜宜zh_TW
dc.creator (作者) Chen, Jing Yien_US
dc.date (日期) 2012en_US
dc.date.accessioned 2-Sep-2013 16:04:08 (UTC+8)-
dc.date.available 2-Sep-2013 16:04:08 (UTC+8)-
dc.date.issued (上傳時間) 2-Sep-2013 16:04:08 (UTC+8)-
dc.identifier (Other Identifiers) G0097352505en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/59307-
dc.description (描述) 博士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 金融研究所zh_TW
dc.description (描述) 97352505zh_TW
dc.description (描述) 101zh_TW
dc.description.abstract (摘要) 近年來由於地理資訊系統（GIS）的快速發展發，空間資料分析開始受到重視並在社會科學領域中逐漸扮演重要的角色。雖然一般的統計方法已在傳統資料分析上發展已久，然而它們卻不能有效地說明空間性資料，並且無法充分處理空間相依或空間異質性問題。一般而言，空間資料分析主要有兩個分派:模型導向學派與資料導向學派。本文研究目的在於應用空間統計方法合理且充分地評估房地產價值，研究方法包含地理統計(克利金和共克利金)、地理加權迴歸與空間特徵價格模型等，並且以台中市不動產資料進行實證探究。這項新的研究技術在不動產評價領域中將可提供更好的解析能力，使其在評價過程中或是不動產投資決策時，成為一個更強而有力的分析工具。zh_TW
dc.description.abstract (摘要) In recent years, spatial data analysis has received significant awareness and played an important role in social science because of the rapid development of Geographic Information System (GIS). Although classic statistical methods are attractive in traditional data analysis, they cannot be executed seriously for spatial data. Standard statistical techniques didn’t sufficiently deal with spatial dependence or spatial heterogeneity issues. Generally, the model-driven method and the data-driven method are mainly the two branches of the spatial data analysis. The purpose of this paper is to apply spatial statistics methods including geostatistical methods (kriging and cokiging), geographically weighted regression, and spatial hedonic price models to real estate analysis. It seems to be completely reasonable and sufficient. The real estate data in Taichung city (Taiwan) is used to carry out our exploration. These techniques give better insight in the field of real estate assessment. They can apply a good instrument in mass appraisal and decision concerning real estate investment.en_US
dc.description.tableofcontents Chapter1 RESEARCH MOTIVATION AND PURPOSE 1
Chapter 2 RELATED LITERATURE 5
Chapter 3 STUDY AREA DESCRIPTION 8
Chapter 4 GEOGRAPHICALLY WEIGHTED REGRESSION APPROACH 15
4.1 Linear Regression (Global Model) 15
4.2 Geographically Weighted Regression (Local Model) 16
4.3 Empirical Analysis 20
4.3.1. Results of Global (OLS) Model 21
4.3.2. Results of Local (GWR) Model 22
4.4 Summary 24
Chapter 5 MEASUREMENTS OF SPATIAL AUTOCORRELATION AND SPATIAL MODELS 25
5.1 Spatial Autocorrelation 25
5.2 Spatial Lag Model (SLM or SAR) 27
5.3 Spatial Error Model (SEM) 27
5.4 Empirical Analysis 28
5.5 Summary 34
Chapter 6 GEOSTATISTICAL APPROACH 35
6.1. Semi-Variogram 36
6.2. Ordinary Kriging 38
6.3 Cokriging 40
6.4 Understanding the Dynamical Changes in House Price in the Study Area 41
6.5 Predict House Price in 2012 42
6.6 Empirical Analysis 42
6.7 Summary 56
Chapter7 CONCLUSIONS 58
REFERENCES 62
APPENDIX 1 66
APPENDIX 2 70
APPENDIX 3 71		zh_TW
dc.format.extent 7213868 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0097352505en_US
dc.subject (關鍵詞) 房價zh_TW
dc.subject (關鍵詞) 空間自相關zh_TW
dc.subject (關鍵詞) 空間計量學zh_TW
dc.subject (關鍵詞) 地理統計學zh_TW
dc.subject (關鍵詞) 克利金zh_TW
dc.subject (關鍵詞) 共克利金zh_TW
dc.subject (關鍵詞) 地理加權迴歸zh_TW
dc.subject (關鍵詞) house pricesen_US
dc.subject (關鍵詞) spatial autocorrelationen_US
dc.subject (關鍵詞) spatial econometricsen_US
dc.subject (關鍵詞) geostatisticsen_US
dc.subject (關鍵詞) krigingen_US
dc.subject (關鍵詞) cokrigingen_US
dc.subject (關鍵詞) geographically weighted regressionen_US
dc.title (題名) 不動產評價之空間計量與地理統計zh_TW
dc.title (題名) Spatial Econometrics and Geostatistics for Real Estate Valuationen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) Anselin, L. (1998). Spatial econometrics: methods and models. Kluwer, Dordrecht.
Anselin, L. (2003). GeoDa™0.9User’s Guide, CSISS.
Basu, S. and Thibodeau, T. G. (1998). Analysis of spatial autocorrelation in house prices. Journal of Real Estate Finance and Economics, 17, 61 – 85.
Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems, Journal of the Royal Statistical Society B, 36, 192-225.
Bourassa, S. C., Cantoni, E. and Hoesli, M. (2007). Spatial dependence, housing submarkets, and house price prediction. Journal of Real Estate Finance and Economics. 35, 143 –160.
Brunsdon, C., Fotheringham, A. S. and Charlton, M. E. (1996). Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical Analysis, 28 (4), 281-298.
Brunsdon, C., Fotheringham, A. S. and Charlton, M. (1998). Geographically weighted regression-modeling spatial non-stationarity. Journal of the Royal Statistical Society. Series D (The Statistician), 47(3), 431-443.
Calderón, G. F. A. (2009). Spatial regression analysis vs. kriging methods for spatial estimation. International Advances in Economic, 15, 44-58.
Cambardella, C. A., Moorman, T. B., Novak, J. M., Parkin, T. B., Karlen, D. L., Turco, R. F., et al. (1994). Field scale variability of soil properties in central iowa soils. Soil Science Society of America Journal, 58, 1501 – 1511.
Chica-Olmo, J. (1995). Spatial estimation of housing prices and locational rents. Urban Studies, 32(8), 1331-1344.
Chica-Olmo, J. (2007). Prediction of house location price by multivariate spatial method: co-kriging. Journal of Real Estate Research, 29, 91-114.
Chun, Y. and Griffith, D. A. (2013). Spatail Statistics & Geostatistics: Theory and Application for Geographic Information Science & Technology, SAGE.
Clapp, J., Dubin, R. and Rodriguez, M. (2004). Modeling spatial and temporal house price patterns: a comparison of four models. Journal of Real Estate Finance and Economics, 29(2), 167-191.
Cressie, N. (1991). Statistics for Spatial Data, New York: Wiley.
Dubin, R. A. (1998). Predicting house prices using multiple listings data. Journal of Real Estate Finance and Economics, 17, 35 – 59.
Dubin, R.A, Pace, R. K. and Thibodeau, T. G. (1999). Spatial autoregression techniques for real estate data. Journal of Real Estate Literature, 7, 79–95.
Fotheringham, A.S., Charlton, M.E. and Brunsdon, C. (2000). Quantitative Geography, SAGE.
Fregonara, E., Rolando, D. and Semeraro, P. (2012). The value spatial component in the real estate market: the Turin case study. Aestimum60, Giugno, 85-113.
Gelfand, A. E., Ecker, M. D, Knight, J. R., and Sirmans, C. F. (2004). The dynamics of location in home price. Journal of Real Estate Finance and Economics, 29(2), 149-166.
Gillen, K., Thibodeau, T. and Wachter, S. (2001). Anisotropic autocorrelation in house price. Journal of Real Estate Finance and Economics, 23, 5-31.
Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation, New York: Oxford University Press.
Johnston, K., Ver Hoef, J.M., Krivoruchko, K. and Lucas. N. (2003). Using ArcGIS Geostatistical Analyst, ESRI Press.
Kulczycki, M. and Ligas, M. (2007). Spatial Statistics for Real Estate Data, Strategic Integration of Surveying Services, Hong Kong: SAR, China.
LeSage, J. P. and Pace, R. K. (2004). Models for spatial dependent missing data. Journal of Real Estate Finance and Economics, 29 (2), 233-254.
Liu, D., Wang, Z., Zhang, B., Song, K., Li, X. and Li, J. (2006). Spatial distribution of soil organic carbon and analysis of related factors in croplands of the black soil region, northeast china. Agriculture, Ecosystems and Environment, 113, 73 – 81.
Lloyd, C. D. (2011). Local Models for Spatial Analysis, CRC Press.
Long, F. Páez, A. and Farber, S. (2007). Spatial effects in hedonic price estimation: a case study in the city of Toronto, CSpA Working Paper, McMaster University.
Matheron, G. (1963). Principle of geostatistics. Economic Geology, 58, 1246-1266.
Matthews, S. A. and Yang, T. C. (2012). Mapping the results of local statistics: using geographically weighted regression. Demographic Research, 26 (6), 151-166.
Militino, A, F., Ugarte, M. D. and García-Reinaldos, L. (2004). Alternative models for describing spatail dependencr among dwelling selling prices. Journal of Real Estate Finance and Economics, 29(2), 193-209.
Mitchell, A. (2005). The ESRI Guide to GIS Analysis, Volume 2: Spatial Measurements and Statistics, ESRI Press.
Moran, P. A. P. (1948). The interpretation of statistical maps. Biometrika, 35, 255–260.
Osland, L. (2010). An application of spatial econometrics in relation to hedonic house price modeling. Journal of Real Estate Research, 32 (2), 289-320.
Pace, R. K., Barry, R. and Simans, C. F. (1998). Spatail statistics and real estate. Journal of Real Estate Finance and Economics, 17(1), 5-13.
Pace, R. K. and LeSage, J. P. (2004). Spatail statistics and real estate. Journal of Real Estate Finance and Economics, 29 (2), 147-148.
Rosen, S. (1974). Hedonic prices and implicit markets: product differentiation in pure competition. Journal of Political Economy, 82, 34-55.
Tsutsumi, M. and Seya, H. (2008). Measuring the impact of large-scale transportation projects on land price using spatial statistical models. Paper in Regional Science, 87, 385–401.
Wackernagel, H. (1995). Multivariate Geostatistics: An Introduction with Applications, Springer.
Zhu, B., Füss, R. and Rottke, N. B. (2011). The predictive power of anisotropic spatial correlation modeling in housing prices. Journal of Real Estate Finance and Economics, 42, 542 – 565.
Yoo, E. H. and Kyriakidis, P. C. (2009). Area-to-point kriging in spatial hedonic pricing models. Journal of Geographical Systems, 11, 381-406.		zh_TW