This paper treats classical as well as Bayesian model theory and how to verify spatial econometric models. Furthermore we will assign great weight to the spatial autoregressive model SAR and go into particulars about its structure and parameters. Maximum likelihood estimation is explained and used for finding the parameters of the SAR model. Monte Carlo approximation is useful in the event of a huge quantity of data and in this particular case the method approximates parts of the SAR model. The estimated parameters of the SAR model are verified using statistical hypothesis tests, confidence intervals, the Wald test and residual plots. In contradistinction to the classical approach the Bayesian approach is explained and used for finding the parameters of the SAR model. The algorithm of Markov chain Monte Carlo is a part of the Bayesian approach and the algorithm includes Gibbs sampling and Metropolis Hastings sampling alike. Most of the theory is translated into practice using data from the real estate agent HOME.