MULTICOLLINEARITY IN REGRESSION MODELS WITH MULTIPLE DISTANCE MEASURES*

ABSTRACT. In a polycentric urban context, several urban nodes may exert an influence over land rents. Where this is the case, regression analysis to explain land rents should employ distance variables corresponding to each of the urban nodes. However, these distance measures may be highly intercorrelated, thereby posing a problem of “spatial multicollinearity.” This paper demonstrates that problems arising from spatial multicollinearity can be avoided or substantially lessened by carefully selecting the geographic domain from which observations are drawn.