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The participation of residents plays a key role in residential energy saving strategies because they make decisions on how to operate building heating and cooling systems. Eco-feedback is an effective tool to motivate energy conserving behaviours (ECBs) by providing information on energy efficiency and associated benefits. The main purpose of this study is to develop an online data-driven building energy model to evaluate heating and cooling-related behaviour changes for eco-feedback design in a multifamily residential building. A grey-box state-space model is presented that is updated with realtime data using a particle filter approach. The model accounts for the evolution of parameters and captures the unobserved inter-unit heat transfer without modelling the whole building thermal network through sequential Bayesian update. The model is developed and validated using data collected in an actual multifamily residential building