In the last decades, property prices have attracted both policy-makers and economists. In several countries, houses are the most valuable asset for a household. Thus, the needs to find the determinants of houses prices have never decreased. Through spatial econometric models, several economists (Kolbe et al., 2012 ; Koschinsky et al., 2012) have confirmed that the key role of housing location in determining house prices. In addition, taking account of possible spatial latent structures ensures that estimated coefficients are unbiased. However, considering the spatial latent structures is not enough, some economists (Iacoviello, 2002 ; Robstad, 2017) underline that temporal effects also play a key role in determining house prices since house markets have cycles. In addition, some spatial datasets are consisted of spatial data at different time points. Therefore, neglecting the temporal dimension of spatial data may lead to the coefficients biased. Through an empirical case study, this paper addresses the possible spatial and temporal biases. We estimate hedonic housing models based on apartments sold in Ajaccio, Corsica between 2008 and 2013. We choose the INLA-SPDE (Integrated nested Laplace approximation-Stochastic Partial Differential Equations (Blangiardo and Cameletti, 2015)) approach to take spatial effects and temporal effects into account simultaneously. Estimation results confirm that in our case, ignoring the spatial and temporal effects results in serious problems. Meanwhile, we suggest several model specifications and attempt to choose the best model from them.