Journal of Geographical Systems

, Volume 11, Issue 4, pp 357–380 | Cite as

Hedonic approaches based on spatial econometrics and spatial statistics: application to evaluation of project benefits

  • Morito TsutsumiEmail author
  • Hajime Seya
Original Article


This study discusses the theoretical foundation of the application of spatial hedonic approaches—the hedonic approach employing spatial econometrics or/and spatial statistics—to benefits evaluation. The study highlights the limitations of the spatial econometrics approach since it uses a spatial weight matrix that is not employed by the spatial statistics approach. Further, the study presents empirical analyses by applying the Spatial Autoregressive Error Model (SAEM), which is based on the spatial econometrics approach, and the Spatial Process Model (SPM), which is based on the spatial statistics approach. SPMs are conducted based on both isotropy and anisotropy and applied to different mesh sizes. The empirical analysis reveals that the estimated benefits are quite different, especially between isotropic and anisotropic SPM and between isotropic SPM and SAEM; the estimated benefits are similar for SAEM and anisotropic SPM. The study demonstrates that the mesh size does not affect the estimated amount of benefits. Finally, the study provides a confidence interval for the estimated benefits and raises an issue with regard to benefit evaluation.


Benefits evaluation Spatial hedonic approach Spatial error model Spatial process model Anisotropy 

JEL Classification

C21 R19 



The authors would like to thank Prof. Haruo Ishida and Prof. Atsushi Yoshida of the University of Tsukuba and Prof. Yasuhisa Hayashiyama of Tohoku University for their useful comments on the study. In addition, the authors are grateful to anonymous referees for their valuable comments and suggestions on our previous drafts. Of course, they are not responsible for the manner in which the comments and suggestions were used. This study was supported by a Grant-in-Aid for Scientific Research (B) (20560485).


  1. Anselin L (1988) Spatial econometrics: methods and models. Kluwer Academic Publishers, DordrechtGoogle Scholar
  2. Anselin L (2006) Spatial econometrics. In: Mills T, Patterson K (eds) Palgrave handbook of econometrics: vol 1, econometric theory. Palgrave Macmillan, Basingstoke, pp 901–969Google Scholar
  3. Anselin L, Bera A (1998) Spatial dependence in linear regression models with an introduction to spatial econometrics. In: Ullah A, Giles DE (eds) Handbook of applied economic statistics. Marcel Dekker, New York, pp 237–289Google Scholar
  4. Anselin L, Le Gallo J (2006) Interpolation of air quality measures in hedonic house price models, spatial aspects. Spat Econom Anal 1(1):31–52CrossRefGoogle Scholar
  5. Anselin L, Lozano-Gracia N (2009) Spatial hedonic models. In: Patterson K, Mills TC (eds) Palgrave handbook of econometrics, vol 2. Palgrave Macmillan, New YorkGoogle Scholar
  6. Arbia G (2006) Spatial econometrics: statistical foundations and applications to regional convergence. Springer, BerlinGoogle Scholar
  7. Banerjee S, Carlin B, Gelfand A (2004) Hierarchical modeling and analysis for spatial data. Chapman & Hall/CRC, Boca RatonGoogle Scholar
  8. Can A (1992) Specific and estimation of hedonic housing price models. Reg Sci Urban Econ 22(3):453–474CrossRefGoogle Scholar
  9. Chilès JP, Delfiner P (1999) Geostatistics. Modeling spatial uncertainty. Wiley, New YorkGoogle Scholar
  10. Cressie N (1985) Fitting variogram models by weighted least squares. Math Geol 17(5):563–586CrossRefGoogle Scholar
  11. Cressie N (1993) Statistics for spatial data: revised edition. Wiley, New YorkGoogle Scholar
  12. Deng M (2008) An anisotropic model for spatial processes. Geogr Anal 40(1):26–51CrossRefGoogle Scholar
  13. Dubin RA (1992) Spatial autocorrelation and neighborhood quality. Reg Sci Urban Econ 22(3):432–452CrossRefGoogle Scholar
  14. Haining R (1978) The moving average model for spatial interaction. Trans Pap Inst Br Geogr, New Ser 3(2):202–225CrossRefGoogle Scholar
  15. Inoue R, Kigoshi N, Shimizu E (2007) Visualization of spatial distribution and temporal change of land prices for residential use in Tokyo 23 wards using Spatio-Temporal Kriging. In Proceedings of 10th international conference on computers in urban planning and urban management, 63: CD-ROM (
  16. Kanemoto Y (1988) Hedonic prices and the benefits of public projects. Econometrica 56(4):981–989CrossRefGoogle Scholar
  17. Kelejian HH, Robison DP (1995) Spatial correlation: a suggested alternative to the autoregressive model. In: Anselin L, Florax RJ (eds) New directions in spatial econometrics. Springer, Berlin, pp 75–95Google Scholar
  18. Kim CW, Phipps TT, Anselin L (2003) Measuring the benefits of air quality improvement: a spatial hedonic approach. J Environ Econ Manage 45(1):24–39CrossRefGoogle Scholar
  19. LeSage JP, Fischer MM (2008) Spatial growth regressions: model specification, estimation and interpretation. Spat Econom Anal 3(3):275–304CrossRefGoogle Scholar
  20. LeSage JP, Pace RK (2008) Introduction to spatial econometrics. Chapman & Hall/CRC, Boca RatonGoogle Scholar
  21. Militino AF, Ugarte MD, Garcîa-Reinaldos L (2004) Alternative models for describing spatial dependence among dwelling selling prices. J Real Estate Finance Econ 29(2):193–209CrossRefGoogle Scholar
  22. Neill HR, Hassenzahl DM, Assane DD (2007) Estimating the effect of the air quality: spatial versus traditional hedonic price models. South Econ J 73(4):1088–1111Google Scholar
  23. Páez A, Long F, Farber S (2007) Moving window approaches for hedonic price estimation: an empirical comparison of modeling techniques. Urban Stud 45(8):1565–1581CrossRefGoogle Scholar
  24. Pior MY, Shimizu E (2001) GIS-aided evaluation system for infrastructure improvements: focusing on simple hedonic and Rosen’s two-step approaches, Computers. Environ Urban Syst 25(2):223–246CrossRefGoogle Scholar
  25. Rosen S (1974) Hedonic prices and implicit market, product differentiation in pure competition. J Polit Econ 82(1):34–55CrossRefGoogle Scholar
  26. Schabenberger O, Gotway CA (2005) Statistical methods for spatial data analysis. Chapman & Hall/CRC, Boca Raton Google Scholar
  27. Small KA, Steimetz S (2006) Spatial hedonics and the willingness to pay for residential amenities, Economics Working Paper, No. 05–06-31. University of California, IrvineGoogle Scholar
  28. Tsutsumi M, Seya H (2008) Measuring the impact of large-scale transportation projects on land price using spatial statistical models. Paper Reg Sci 87(3):385–401CrossRefGoogle Scholar
  29. Tsutsumi M, Shimizu E, Ide H, Fukumoto J (1999) On regularization methods for regression analysis in the presence of spatially correlated errors: application to hedonic regression of land price. J East Asia Soc Transportation Stud 3(4):87–95Google Scholar
  30. Valente JWu, Gelfand A, Sirmans CF (2005) Apartment rent prediction using spatial modeling. J Real Estate Res 27(1):105–136Google Scholar
  31. Zimmerman DL (1993) Another look at anisotropy in geostatistics. Math Geol 25(4):453–470CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.Department of Policy and Planning SciencesUniversity of TsukubaTsukuba, IbarakiJapan
  2. 2.PASCO CorporationMeguro, TokyoJapan

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