Isolating Spatial from A-spatial Components of Housing Attributes Using Kriging Techniques
||Des Rosiers, Francois; Thériault, Marius, Villeneuve, Paul-Y and Kestens, Yan
||Isolating Spatial from A-spatial Components of Housing Attributes Using Kriging Techniques
||8th European Real Estate Society Conference (26-29 June 2001) Alicante, Spain
||The current piece of research is an attempt to isolate spatial from a-spatial components of housing attributes, using kriging techniques. While hedonic models have long proved their usefulness as an analytical device, previous research has shown that substantial portion of price variability remains unexplained (Anselin and Can 1986; Dubin and Sung 1987; Can 1993; Dubin 1998). The great deal of neighbourhood factors needed to adequately account for submarket specifics (Adair, Berry and McGreal 1996 & 1998) raises methodological issues linked to the presence of excessive multicollinearity between model attributes, as well as to structural heteroskedasticity and spatial autocorrelation among residuals; all of these are detrimental to the stability of regression coefficients (Dubin 1988; Anselin and Rey 1991; Can and Megbolugbe 1997; Basu and Thibodeau 1998; Pace, Barry and Sirmans 1998; Des Rosiers and Thériault 1999). Among the issues that need be addressed, spatial dependence - a current feature of property markets - deserves substantial research efforts. Indeed, several situations involve the presence of spatial autocorrelation, thereby violating the basic assumptions underlying hedonic models. For instance, while liveable area will partly mirror size features that are exclusive to a given piece of property, it also reflects neighbourhood characteristics which are common to all houses in the same market segment. Consequently, implicit prices of housing attributes may prove to be both biased and unstable (Can 1990 & 1993, Dubin 1998). In this paper, kriging techniques are used to address the problem. Once spatial dependence has been identified for each housing descriptor, relevant variables are then split into their spatial and a-spatial components; this results in two sets of unbiased coefficients being calibrated. Model residuals are also processed via kriging. In so doing, space-related influences on prices can be isolated for each attribute and their consistency assessed over space and time as well Using the principal component method (PCA) with a Varimax rotation, some 49 initial variables are thus being reduced to only six significant factors – four neighbourhood and two access principal components – which are in turn integrated in the hedonic equation as complex independent variables. Findings clearly suggest that factor analysis is highly efficient at sorting out access and neighbourhood dimensions. In particular, the educational profile of local residents as well as access to regional and local services emerge as powerful price determinants.
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