The Dutch heating transition involves changing the heating systems of eight million buildings to a sustainable alternative by 2050. Many heating system technologies are available, but deciding which systems are cheapest for all these buildings is a difficult question to answer. Local policymakers are increasingly making use of heating transition models that estimate the feasibility and costs of systems in municipal neighbourhoods. The applicability of these models is limited by the degree of uncertainty about the future as well as the complexity in communicating the model results to policymakers. Sensitivity Analysis (SA) is a tool with which the most influential model uncertainties can be identified, quantified and communicated. So far, limited energy transition model studies have extensively used this method. A case study of SA on the CEGOIA heating transition model was performed to fill this gap and evaluate SA’s value. CEGOIA calculates the costs of a variety of heating systems and optimizes the allocation of scarce energy carriers such as green gas and hydrogen to find the lowest societal costs. Sensitivities of eight heating system options were analysed in different archetypical neighbourhood contexts using Fractional Factorial analysis, the Method of Morris and the Sobol’ Method. Out of an initial set of 953 parameters, a subset of less than a dozen highly influential variables – consistent between neighbourhoods of different physical characteristics – was identified for each heating system option. High sensitivities indicate that changing the value of a parameter leads to a large change in total costs. These sets, therefore, describe exactly what uncertainties are crucial to evaluating what heating system is the cheapest possible solution. Variables in these sets include, but are not limited to, the price and infrastructure costs of electricity and gas, heating installation costs and insulation costs. Interviews with other heating transition model owners further illustrated that the use of systematic SA as done in this analysis is not the norm. Besides results and insights from the CEGOIA SA, further applications for SA in heating transition modelling is postulated to be able to improve the modelling process, as well as better, understand complex model dynamics. One recommendation is, therefore, to include SA as part of the toolkit for the large heating transition models currently being used in the Netherlands. The main barrier for doing so with CEGOIA is the computational time of the model, which limited the number of parameters that could be evaluated as well as the SA techniques that could be used. Still, a more systematic analysis of sensitivities in heating transition models will provide insights that ultimately aid Dutch policymakers in making robust decisions.