OBJECTIVES: To demonstrate if a nationally representative panel dataset can be used to evaluate if quality of life (QoL) impacts are associated with changes in body mass index (BMI). The aim was to estimate the utility increments (or decrements) associated with weight loss (or gain) for application in economic evaluations of obesity interventions. METHODS: Data from the Household, Income and Labour Dynamics in Australia survey (HILDA) was used in the analysis.  HILDA is a household-based panel with 17,209 individuals from 6,987 households collected annually since 2001. This survey uses the SF-36 and the transformed SF-6D utility weight to capture quality of life. Currently, there are 5 waves providing information on BMI. The panel nature of the data was exploited with econometric techniques to show the effect of changes in BMI (between different BMI classification groups) on quality of life. RESULTS: The results demonstrated that being under-weight, over-weight or obese is associated with reduced quality of life. When adjusting for other explanatory variables, only the association between the obese category and diminished quality of life remained. The results from the panel data identified that only those who remain severely obese over time experience significant reductions in quality of life. Movements between other BMI categories were not associated with significant impacts on quality of life. CONCLUSIONS: Economic models that assess the cost-effectiveness of obesity interventions using cross-sectional data may overestimate the QoL gain following a reduction in BMI. This could lead to non-optimal policy oriented decisions. Population panel datasets may provide a better estimate. Using econometric techniques alongside traditional cost-effectiveness models offers a richer avenue of obtaining model inputs and more certainty in regards to quantifying gains and losses in QoL.