Detecting the spatially non-stationary relationships between housing price and its determinants in China: Guide for housing market sustainability

Sustainability (Switzerland)

Volumn 9, Issue 10, 2017, Pages

  Mou, Yanchuan ^a,c   He, Qingsong ^b,c   Zhou, Bo ^a  

^a SICHUAN UNIVERSITY   (China)

^b WUHAN UNIVERSITY   (China)

^c University of North Carolina at Chapel Hill   (United States)

Author keywords

Chinese cities; Geographically weighted regression; Housing price; Spatial non stationarity; Sustainability

Indexed keywords

DEMAND ANALYSIS; ECONOMIC CONDITIONS; HOUSING MARKET; HOUSING POLICY; REGRESSION ANALYSIS; SPATIAL VARIATION; SUSTAINABILITY; SUSTAINABLE DEVELOPMENT; URBAN AREA; URBANIZATION;

CHINA;

EID: 85031414371     PISSN: None     EISSN: 20711050     Source Type: Journal    
DOI: 10.3390/su9101826     Document Type: Article

References (52)

1. Shih, Y.; Li, H.; Qin, B. Housing price bubbles and inter-provincial spillover: Evidence from China. Habitat Int. 2014, 43, 142-151
2. Li, C.; Gibson, J. Spatial price differences and inequality in China: Housing market evidence. Work. Pap. Econ. 2013, 6, 13
3. Wang, Y.P. Urban housing reform and finance in China a case study of Beijing. Urban Aff. Rev. 2001, 36, 620-645
4. Chen, J.; Guo, F.; Wu, Y. One decade of urban housing reform in China: Urban housing price dynamics and the role of migration and urbanization, 1995-2005. Habitat Int. 2011, 35, 1-8
5. Shi, W.; Chen, J.; Wang, H. Affordable housing policy in China: New developments and new challenges. Habitat Int. 2016, 54, 224-233
6. Zhang, L.; Hui, E.C.; Wen, H. Housing price-volume dynamics under the regulation policy: Difference between Chinese coastal and inland cities. Habitat Int. 2015, 47, 29-40
7. Manning, C.A. Intercity differences in home price appreciation. J. Real Estate Res. 1986, 1, 45-56
8. Wang, Z.; Zhang, Q. Fundamental factors in the housing markets of China. J. Hous. Econ. 2014, 25, 53-61
9. Wang, Y.;Wang, D.;Wei, Y. Spatial differentiation patterns of housing price and housing price-to-income ratio in China's cities. In Proceedings of the 2013 21st International Conference on Geoinformatics, Kaifeng, China, 20-22 June 2013
10. Wang, Y.;Wang, S.; Li, G.; Zhang, H.; Jin, L.; Su, Y.;Wu, K. Identifying the determinants of housing prices in China using spatial regression and the geographical detector technique. Appl. Geogr. 2017, 79, 26-36
11. Gardiner, B.; Martin, R.; Sunley, P.; Tyler, P. Spatially unbalanced growth in the British economy. J. Econ. Geogr. 2013
12. Rothenberg, J. The Maze of Urban Housing Markets: Theory, Evidence, and Policy; University of Chicago Press: Chicago, IL, USA, 1991
13. Anselin, L. Lagrange multiplier test diagnostics for spatial dependence and spatial heterogeneity. Geogr. Anal. 1988, 20, 1-17
14. Leung, Y.; Mei, C.; Zhang, W. Statistical tests for spatial nonstationarity based on the geographically weighted regression model. Environ. Plan. A 2000, 32, 9-32
15. Brunsdon, C.; Fotheringham, A.S.; Charlton, M.E. Geographically weighted regression: A method for exploring spatial nonstationarity. Geogr. Anal. 1996, 28, 281-298
16. Zhang, H.L.; Zhang, J.; Lu, S.; Cheng, S.; Zhang, J. Modeling hotel room price with geographically weighted regression. Int. J. Hosp. Manag. 2011, 30, 1036-1043
17. Bitter, C.; Mulligan, G.F.; Dall Erba, S. Incorporating spatial variation in housing attribute prices: A comparison of geographically weighted regression and the spatial expansion method. J. Geogr. Syst. 2007, 9, 7-27
18. Fotheringham, A.S.; Charlton, M.; Brunsdon, C. Measuring Spatial Variations in Relationships with Geographically Weighted Regression; Springer: Berlin/Heidelberg, Germany, 1997; pp. 60-82
19. Park, N. Estimation of Average Annual Daily Traffic (AADT) Using GeographicallyWeighted Regression (GWR) Method and Geographic Information System (GIS). Available online: http://digitalcommons.fiu. edu/dissertations/AAI3128612/(accessed on 6 October 2017)
20. Hu, X.; Waller, L.A.; Al-Hamdan, M.Z.; Crosson, W.L.; Estes, M.G., Jr.; Estes, S.M.; Quattrochi, D.A.; Sarnat, J.A.; Liu, Y. Estimating ground-level PM2.5 concentrations in the southeastern US using geographically weighted regression. Environ. Res. 2013, 121, 1-10
21. Du, H.; Mulley, C. Relationship between transport accessibility and land value: Local model approach with geographically weighted regression. Transp. Res. Rec. J. Transp. Res. Board 2006, 1997, 197-205
22. Zhang, H.; Qi, P.; Guo, G. Improvement of fire danger modelling with geographically weighted logistic model. Int. J. Wildland Fire 2014, 23, 1130-1146
23. Waller, L.A.; Zhu, L.; Gotway, C.A.; Gorman, D.M.; Gruenewald, P.J. Quantifying geographic variations in associations between alcohol distribution and violence: A comparison of geographically weighted regression and spatially varying coefficient models. Stoch. Environ. Res. Risk Assess. 2007, 21, 573-588
24. Chow, G.C.; Niu, L. Demand and supply for residential housing in urban China. J. Financ. Res. 2010, 44, 1-11
25. Zou, G.L.; Chau, K.W. Determinants and sustainability of house prices: The case of Shanghai, China. Sustainability 2015, 7, 4524-4548
26. Zhang, H.; Li, L.; Hui, E.C.-M.; Li, V. Comparisons of the relations between housing prices and the macroeconomy in China's first-, second-and third-tier cities. Habitat Int. 2016, 57, 24-42
27. Wang, Y.; Jiang, Y. An Empirical Analysis of Factors Affecting the Housing Price in Shanghai. Asian J. Econ. Model. 2016, 4, 104-111
28. Li, Q.; Chand, S. House prices and market fundamentals in urban China. Habitat Int. 2013, 40, 148-153
29. Yue, S.; Hongyu, L. Housing Prices and Economic Fundamentals: A Cross City Analysis of China for 1995-2002. Econ. Res. J. 2004, 6, 78-86
30. Wen, H.; Goodman, A.C. Relationship between urban land price and housing price: Evidence from 21 provincial capitals in China. Habitat Int. 2013, 40, 9-17
31. Weixing, X.; Hongjin, L. Supply-Demand Framework and Effects of Macro-Adjustment on the Housing Market. Econ. Rev. 2007, 3, 110-115. (In Chinese)
32. Rongrong, R.; Hongyu, L. The influence mechanism of land supply on housing price: A case study of Beijing. Price Theory Pract. 2007, 10, 40-41. (In Chinese)
33. Chi-man Hui, E. An empirical study of the effects of land supply and lease conditions on the housing market: A case of Hong Kong. Prop. Manag. 2004, 22, 127-154
34. Mei, H.; Fang, H. A Study on the Real Estate Price Forecast Model in the Midwest of China-Based on Provincial Panel Data Analysis. Available online: https://link.springer.com/chapter/10.1007/978-981-10-1837-4_45 (accessed on 6 October 2017)
35. Min, H.; Quigley, J.M. Economic Fundamentals in Local Housing Markets: Evidence from U.S. Metropolitan Regions. J. Reg. Sci. 2006, 46, 425-453
36. Poterba, J.M.;Weil, D.N.; Shiller, R. House price dynamics: The role of tax policy and demography. Brook. Pap. Econ. Act. 1991, 1991, 143-203
37. Tingjun, Z. On the rigid demand of housing. Econ. Theory Bus. Manag. 2009, 2009, 16-21. (In Chinese)
38. Murie, A.; Niner, P.; Watson, C. Housing Policy and the Housing System; Allen and Unwin: London, UK, 1976
39. Goodman, A.C. Following a panel of stayers: Length of stay, tenure choice, and housing demand. J. Hous. Econ. 2003, 12, 106-133
40. Zheng, J. Study on the Effects of Land Supply Mode and Land Supply Amount on Real Estate Price; Zhejiang University: Hangzhou, China, 2008. (In Chinese)
41. Yu, L. Chinese City and Regional Planning Systems; Ashgate Publishing, Ltd.: Farnham, UK, 2014
42. Henneberry, J. Transport investment and house prices. J. Prop. Valuat. Invest. 1998, 16, 144-158
43. Wu, F. Sociospatial differentiation in urban China: Evidence from Shanghai's real estate markets. Environ. Plan. A 2002, 34, 1591-1615
44. Hui, E.C.; Ho, V.S. Does the planning system affect housing prices? Theory and with evidence from Hong Kong. Habitat Int. 2003, 27, 339-359
45. Wu, W.; Zhang, L. Comparison of spatial and non-spatial logistic regression models for modeling the occurrence of cloud cover in north-eastern Puerto Rico. Appl. Geogr. 2013, 37, 52-62
46. Benjamini, Y.; Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 1995, 57, 289-300
47. Da Silva, A.R.; Fotheringham, A.S. The multiple testing issue in geographically weighted regression. Geogr. Anal. 2016, 48, 233-247
48. Matthews, S.A.; Yang, T. Mapping the results of local statistics: Using geographically weighted regression. Demogr. Res. 2012, 26, 151
49. Ying, L. Spatial Heterogeneity and the Difference of Regional Fluctuation in Housing Price-Based on the Geographically Weighted Regression. J. Cent. Univ. Financ. Econ. 2010, 11, 80-85
50. Chen, J.; Zheng, T. The analysis of the role of foreign investments in housing market development in urban China. Price World 2008, 3, 36-37
51. Sun, F. Introduction to the Sustainable Land Use in Tibet Autonomous Region. Nat. Resour. Econ. China 2008, 21, 13-15. (In Chinese)
52. Tian, L.; Ma, W. Government intervention in city development of China: A tool of land supply. Land Use Policy 2009, 26, 599-609