We　propose　a　fully　Bayesian　estimation　approach　for　partially　linear　varying　coefficient　spatial　autoregressive　models　on　the　basis　of　B-spline　approximations　of　nonparametric　components.　A　computational　efficient　MCMC　method　that　combines　the　Gibbs　sampler　with　Metropolis-Hastings　algorithm　is　implemented　to　simultaneously　obtain　the　Bayesian　estimates　of　unknown　parameters,　as　well　as　their　standard　error　estimates.　Monte　Carlo　simulations　are　used　to　investigate　the　finite　sample　performance　of　the　proposed　method.　Finally,　a　real　data　analysis　of　Boston　housing　data　is　used　to　illustrate　the　usefulness　of　the　proposed　methodology.