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Non-Stationary Semivariogram Analysis Using Real Estate Transaction Data

  • Simon, Arnaud
  • Srikhum, Piyawan
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    Geostatistical model is one of spatial statistical methodologies used for correcting spatial autocorrelation problem. To apply this model, two common assumptions should be made to allow global homogeneity: spatial continuity and spatial stationary. In different fields of research such as geography, environmental science and computer science, they usually take into account a violation of the second assumption (spatial stationary) but no article works under non-stationary condition in real estate research fields. This article is probably a first attempt to examine the violation of stationary assumption, in term of time and space, using transaction prices, from 1998 to 2007, of Parisian properties situated 5 kilometers around Arc de Triomphe. By comparing estimated 1-year semivariogram to 10-years semivariogram function, we found evidence of non-time-stationary. Likewise, non-spatial-stationary problem was detected by segmenting data in 90 degrees rotating windows. Our results show that we should not compute a common variogram for all parts of the region of interest.

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    File URL: http://basepub.dauphine.fr/xmlui/bitstream/123456789/5692/2/eres2010_314_pres_Srikhum_NON-STATIONARY_SEMIV.pptx
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    Paper provided by Paris Dauphine University in its series Economics Papers from University Paris Dauphine with number 123456789/5692.

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    Date of creation: Jun 2010
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    Handle: RePEc:dau:papers:123456789/5692
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