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Non-Stationary Semivariogram Analysis Using Real Estate Transaction Data

dc.contributor.authorSimon, Arnaud
dc.contributor.authorSrikhum, Piyawan
dc.date.accessioned2011-02-10T11:13:04Z
dc.date.available2011-02-10T11:13:04Z
dc.date.issued2010
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/5692
dc.language.isoenen
dc.subjectReal estate transactionen
dc.subjectnon-stationaryen
dc.subjectspatial autocorrelationen
dc.subjectgeostatistical modelen
dc.subject.ddc332en
dc.subject.classificationjelC49en
dc.subject.classificationjelG19en
dc.titleNon-Stationary Semivariogram Analysis Using Real Estate Transaction Dataen
dc.typeCommunication / Conférence
dc.description.abstractenGeostatistical 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.en
dc.description.sponsorshipprivateouien
dc.subject.ddclabelEconomie financièreen
dc.relation.conftitleERES 2010en
dc.relation.confdate2010-06
dc.relation.confcityMilanen
dc.relation.confcountryItalieen


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