Advertisement

Letters in Spatial and Resource Sciences

, Volume 9, Issue 1, pp 93–101 | Cite as

A spatial hedonic model application of variance function regression to residential property prices in Beijing

  • Yue Zhang
  • Robert G. Cromley
  • Dean M. Hanink
Original Paper
  • 442 Downloads

Abstract

Variance function regression incorporates a novel method of residual analysis that should be of interest in spatial modeling. The method is a two part regression: one for the conditional mean, which is a standard regression, and one for the conditional variance, which is estimated from the residuals of the initial regression. The method is briefly described and then applied hedonic models of residential real estate prices in Beijing, using both a metropolitan area sample and a smaller sample of the district of Chaoyang. As expected the results generally indicate that distance to Tiananmen Square is a powerful predictor of residential real estate prices in Beijing. That distance effect is fully encompassed in the mean structure of a regression model. However, other variables, such as the age of an apartment block have price effects that are revealed fully only when the conditional variance is modeled as well.

Keywords

Regression residuals Residual variance Spatial models  Hedonic models Beijing real estate 

JEL Classification

C4 R21 P250 

References

  1. Ahlfeldt, G.: If Alonso was right: modeling accessibility and explaining the residential land gradient. J. Reg. Sci. 51, 318–338 (2011)CrossRefGoogle Scholar
  2. Anselin, L., Bera, A.K., Florax, R., Yoon, M.J.: Simple diagnostic tests for spatial dependence. Reg. Sci. Urban Econ. 26, 77–104 (1996)CrossRefGoogle Scholar
  3. Anselin, L.: Spatial externalities, spatial multipliers, and spatial econometrics. Int. Reg. Sci. Rev. 26, 153–166 (2003)CrossRefGoogle Scholar
  4. Breusch, T.S., Pagan, A.R.: The Lagrange multiplier test and its applications to model specification in econometrics. Rev. Econ. Stud. 47, 239–253 (1980)CrossRefGoogle Scholar
  5. CITC Real Estate: http://www.citiccapital.com/r_estate.html (2013). Accessed 16 July 2014
  6. Cook, R.D., Weisberg, S.: Diagnostics for heteroscedasticity in regression. Biometrika 70, 1–10 (1983)CrossRefGoogle Scholar
  7. Ding, C., Zhao, X.: Land market, land development and urban spatial structure in Beijing. Land Use Policy 40, 83–90 (2014)CrossRefGoogle Scholar
  8. Engle, R.F.: Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50, 987–1007 (1982)CrossRefGoogle Scholar
  9. Fan, J., Yao, Q.: Efficient estimation of conditional variance functions in stochastic regression. Biometrika 85, 645–660 (1998)CrossRefGoogle Scholar
  10. Haggett, P., Cliff, A.D., Frey, A.: Locational analysis in human geography. In: Volume 2 Locational Methods, 2nd edn. Halsted Press, New York (1977)Google Scholar
  11. Hanink, D.M., Cromley, R.G., Ebenstein, A.Y.: Spatial variation in the determinants of house prices and apartment rents in China. J. Real Estate Financ. Econ. 45, 347–363 (2012)CrossRefGoogle Scholar
  12. Homelink: http://beijing.homelink.com.cn/ (2013). Accessed 16 July 2014
  13. Hua, X., Sun, L., Borgia, D.: The influence of fundamental factors on Chinese residential real estate prices: a unique data panel study. http://ssrn.com/abstract=2022682 (2012). Accessed 9 July 2014
  14. LeSage, J., Pace, R.K.: Introduction to Spatial Econometrics. CRC Press, Boca Raton (2009)CrossRefGoogle Scholar
  15. Osland, L.: An application of spatial econometrics in relation to hedonic house price modeling. J. Real Estate Res. 32, 289–320 (2010)Google Scholar
  16. Pace, R.K., Gilley, O.W.: Using the spatial configuration of the data to improve estimation. J. Real Estate Financ. Econ. 14, 333–340 (1997)CrossRefGoogle Scholar
  17. Páez, A.: Recent research in spatial real estate hedonic analysis. J. Geogr. Syst. 11, 311–316 (2009)CrossRefGoogle Scholar
  18. Sirmans, G.S., Macpherson, D.A., Zietz, E.N.: The composition of hedonic pricing models. J. Real Estate Lit. 13, 3–43 (2005)Google Scholar
  19. Western, B., Bloome, D.: Variance function regressions for studying inequality. Sociol. Methodol. 39, 293–326 (2009)CrossRefGoogle Scholar
  20. Western, B., Rosenfeld, J.: Unions, norms, and the rise of US wage inequality. Am. Sociol. Rev. 76, 513–537 (2011)CrossRefGoogle Scholar
  21. Wu, W., Dong, G.: Valuing the “green” amenities in a spatial context. J. Reg. Sci. (2013). doi: 10.1111/jors.12099
  22. Wu, J., Gyourko, J., Deng, Y.: Evaluating conditions in major Chinese housing markets. Reg. Sci. Urban Econ. 42, 531–543 (2012)CrossRefGoogle Scholar
  23. Xu, D. Smith, T.E.: Location-dominant housing markets: evidence from Beijing. http://ssrn.com/abstract=2378535 (2013). Accessed 9 July 2014
  24. Zheng, H., Yang, Y., Land, K.C.: Variance function regression in hierarchical age-period-cohort models: applications to the study of self-reported health. Am. Sociol. Rev. 76, 955–983 (2011)CrossRefGoogle Scholar
  25. Zheng, S., Wu, J., Kahn, M.E., Deng, Y.: The nascent market for “green” real estate in Beijing. Eur. Econ. Rev. 56, 974–984 (2012)CrossRefGoogle Scholar
  26. Zheng, S., Kahn, M.E.: Does government investment in local public goods spur gentrification? Evidence from Beijing. Real Estate Econ. 41, 1–28 (2013)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Yue Zhang
    • 1
  • Robert G. Cromley
    • 1
  • Dean M. Hanink
    • 1
  1. 1.Department of GeographyUniversity of ConnecticutStorrsUSA

Personalised recommendations