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Abstract
The aim of this study was to measure the effect of spatial characteristics on housing prices and to integrate an interpolation and regression model in terms of spatially predicting housing price values. In this paper, housing price is investigated by taking into consideration distance to city centre, transportation arteries and coasts, in addition to housing and neighbourhood characteristics as control variables. This investigation is conducted in two stages: firstly by the utilization of multiple regression analysis, and then by an interpolation technique which is generated to predict the spatial pattern of housing price on a continuous surface in order to test the reliability and consistency of the regression model. The results reveal that housing prices are significantly affected by spatial determinants referred to as the distance variables. By conducting a residual analysis from the regression model, housing price values are analysed and visualized in a continuous map which is globally consistent with the housing markets in Istanbul.