By　incorporating　temporal　effects　into　the　geographically　weighted　regression　(GWR)　model,　an　extended　GWR　model,　geographically　and　temporally　weighted　regression　(GTWR),　has　been　developed　to　deal　with　both　spatial　and　temporal　nonstationarity　simultaneously　in　real　estate　market　data.　Unlike　the　standard　GWR　model,　GTWR　integrates　both　temporal　and　spatial　information　in　the　weighting　matrices　to　capture　spatial　and　temporal　heterogeneity.　The　GTWR　design　embodies　a　local　weighting　scheme　wherein　GWR　and　temporally　weighted　regression　(TWR)　become　special　cases　of　GTWR.　In　order　to　test　its　improved　performance,　GTWR　was　compared　with　global　ordinary　least　squares,　TWR,　and　GWR　in　terms　of　goodness-of-fit　and　other　statistical　measures　using　a　case　study　of　residential　housing　sales　in　the　city　of　Calgary,　Canada,　from　2002　to　2004.　The　results　showed　that　there　were　substantial　benefits　in　modeling　both　spatial　and　temporal　nonstationarity　simultaneously.　In　the　test　sample,　the　TWR,　GWR,　and　GTWR　models,　respectively,　reduced　absolute　errors　by　3.5%,　31.5%,　and　46.4%　relative　to　a　global　ordinary　least　squares　model.　More　impressively,　the　GTWR　model　demonstrated　a　better　goodness-of-fit　(0.9282)　than　the　TWR　model　(0.7794)　and　the　GWR　model　(0.8897).　McNamara＇s　test　supported　the　hypothesis　that　the　improvements　made　by　GTWR　over　the　TWR　and　GWR　models　are　statistically　significant　for　the　sample　data.