In　this　paper,　we　introduce　a　new　class　of　heterogeneous　spatial　autoregressive　models　(heterogeneous　SAR　models)　where　the　variance　parameters　are　modeled　in　terms　of　covariates.　In　order　to　estimate　the　model　parameters,　as　well　as　their　corresponding　standard　error　estimates,　we　proposed　a　computational　efficient　MCMC　method　which　combines　the　Gibbs　sampler　with　Metropolis-Hastings　algorithm.　The　proposed　estimate　method　is　illustrated　through　numerous　simulations　and　is　applied　to　the　Boston　housing　data.