A fast and efficient computational model based on the method of proper orthogonal decomposition , POD, is developed to predict indoor airflows. This model has been applied successfully to a canonical office room, which is mechanically ventilated and air conditioned. The results suggest that the model can be applied quickly and efficiently to predict the indoor velocity and temperature distributions inside the office, for conditions other than those used in forming the base cases of the POD scheme, with a reliability of R2 > 0.98. Results also suggest that the POD models can be applied to sensor placement problems and to real-time indoor airflow control. The indoor flow conditions are obtained in seconds compared to the typical CFD run times of hours to tens of hours.