It is widely known that pre-electoral polls often suffer from nonsampling errors that pollsters try to compensate for in final estimates by means of diverse ad hoc adjustments, thus leading to well-known house effects. We propose a Bayesian hierarchical model to investigate the role of house effects on the total variability of predictions. To illustrate the model, data from pre-electoral polls in Italy in 2006, 2008 and 2013 are considered. Unlike alternative techniques or models, our proposal leads: (i) to correctly decompose the different sources of variability; (ii) to recognize the role of house effects; (iii) to evaluate its dynamics, showing that variability of house effects across pollsters diminishes as the date of election approaches; (iv) to investigate the relationship between house effects and overall prediction errors.
