Approximate Bayesian Inference for Spatial Econometrics Models Roger S. Bivand, Virgilio Gomez-Rubio, Havard Rue In this work we explore the use of the Integrated Laplace Approximation (INLA) for Bayesian inference in some widely used models in Spatial Econometrics. Bayesian inference often relies on very computationally intensive simulation methods, such as Markov Chain Monte Carlo. However, when only marginal inference is needed, INLA provides a fast and accurate estimate of the posterior marginals of the parameters in the model. We have compared the results provided by these Spatial Econometrics models to those obtained with a more general class of Generalized Linear Models with random effects. In these models, spatial autocorrelation is modelled by means of correlated Gaussian random effects. We also discuss a procedure to extend the class of models that the R-INLA software can fit. This approach is based on conditioning on (i.e., fixing) one or more parameters so that the resulting models can be fitted with R-INLA. By repeating this for different values of the fixed parameters we can use Bayesian Model Average methods to provide the posterior marginal of the parameters of interest. This is particularly interesting to fit models with complex spatial autocorrelation structures. Finally, we discuss the use of all these models on two data sets based on housing prices in Boston and the probability of business re-opening in New Orleans in the aftermath of hurricane Katrina.