Variable　selection　has　played　a　fundamental　role　in　regression　analysis.　Spatial　autoregressive　model　is　a　useful　tool　in　econometrics　and　statistics　in　which　context　variable　selection　is　necessary　but　not　adequately　investigated.　In　this　paper,　we　consider　conducting　variable　selection　in　spatial　autoregressive　models　with　a　diverging　number　of　parameters.　Smoothly　clipped　absolute　deviation　penalty　is　considered　to　obtain　the　estimators.　Moreover　the　dimension　of　the　covariates　are　allowed　to　vary　with　sample　size.　In　order　to　attenuate　the　bias　caused　by　endogeneity,　instrumental　variable　is　adopted　in　the　estimation　procedure.　The　proposed　method　can　do　parametric　estimation　and　variable　selection　simultaneously.　Under　mild　conditions,　we　establish　the　asymptotic　and　oracle　property　of　the　proposed　estimators.　Finally,　the　performance　of　the　proposed　estimation　procedure　is　examined　via　Monte　Carlo　simulation　studies　and　a　data　set　from　a　Boston　housing　price　is　analyzed　as　an　illustrative　example.