Spatiotemporal　autocorrelation　and　nonstationarity　are　two　important　issues　in　the　modeling　of　geographical　data.　Built　upon　the　geographically　weighted　regression　(GWR)　model　and　the　geographically　and　temporally　weighted　regression　(GTWR)　model,　this　article　develops　a　geographically　and　temporally　weighted　autoregressive　model　(GTWAR)　to　account　for　both　nonstationary　and　auto-correlated　effects　simultaneously　and　formulates　a　two-stage　least　squares　framework　to　estimate　this　model.　Compared　with　the　maximum　likelihood　estimation　method,　the　proposed　algorithm　that　does　not　require　a　prespecified　distribution　can　effectively　reduce　the　computation　complexity.　To　demonstrate　the　efficacy　of　our　model　and　algorithm,　a　case　study　on　housing　prices　in　the　city　of　Shenzhen,　China,　from　year　2004　to　2008　is　carried　out.　The　results　demonstrate　that　there　are　substantial　benefits　in　modeling　both　spatiotemporal　nonstationarity　and　autocorrelation　effects　simultaneously　on　housing　prices　in　terms　of　R2　and　Akaike　Information　Criterion　(AIC).　The　proposed　model　reduces　the　absolute　errors　by　31.8%　and　67.7%　relative　to　the　GTWR　and　GWR　models,　respectively,　in　the　Shenzhen　data　set.　Moreover,　the　GTWAR　model　improves　the　goodness-of-fit　of　the　ordinary　least　squares　model　and　the　GTWR　model　from　0.617　and　0.875　to　0.914　in　terms　of　R2.　The　AIC　test　corroborates　that　the　improvements　made　by　GTWAR　over　the　GWR　and　the　GTWR　models　are　statistically　significant.　2014　©　2014　Taylor　&　Francis.