Optimal control algorithms such as distributed model predictive control (DMPC) offer tremendous potential in reducing energy consumption of building operations. Heating, ventilation and air-conditioning (HVAC) systems which form a major part of the building operations contain a large number of interconnected subsystems. One of the challenges associated with implementing DMPC is the development of reliable models of individual subsystems for prediction, especially for large scale systems. In this paper an automated method is proposed to develop linear parametric black box models for individual building HVAC subsystems. The modeling method proposed identifies the significant inputs, and the upstream and downstream neighbors of each subsystem before performing regression analysis to determine the model parameters. Automation of the model development makes the implementation of the model-based control algorithms much more feasible. The modeling method is then verified through an EnergyPLus model, and using data of a real office building.