A Semiparametric spatial model is used as it allows nonlinear estimation of both mean and variance.
A Bayesian approach is used for inference via a Markov Chain Monte Carlo sampling scheme. A distinct advantage of using the Bayesian approach is the incorporation of prior information in the inferential process. The prior is updated with arrival of information. In the real world, the modeller should have some idea of the outcome before the modelling process begins. Finite sample inference can be obtained and is more accurate than asymptotic approximation. In the case of the real estate market, transaction data are finite due to infrequent trading. Estimation is done via posterior distributions which factor in the variability of estimators and therefore have improved confidence intervals.
Spatial variables such as longitude and latitude are modelled via the construction of a bivariate thin plate spline. These two variables provide powerful lens for capturing the effect of demographic factors and for borrowing and lending information in neighbouring suburbs. Demographic factors and 1 trends are just as important as economic factors in determining demand for residential housing and they are also included in the model.