This paper addresses the possible problem related to using strictly spatial modelling techniques for spatial data poole d over time. For these data, such as real estate, the spatial dimension is present, but subject to constraints related to temporal dimension. Three empirical examples are presented to investigate the impact of neglecting the temporal dimension in spatial analysis and to show how such an approach overestimates the pattern of spatial dependence, and overestimates the spatial autoregressive coefficient estimated. If generalized to all other empirical applications, this conclusion may have important considerations if one tries to measure the effect of extrinsic amenities on house prices.