Background The built environment characteristics and urban form can influence health outcomes like obesity in people living in high-income countries. However, there are few studies in megacities from middle-high income countries like Brazil in which the built environment has been modified and obesity has been growing slightly. Therefore, the objectives of this study were: 1) to describe the body mass index (BMI) and obesity in different health administrative areas in Sao Paulo; 2) to investigate the association between BMI and obesity with the places where people lived according to social and demographics variables, health variables, built environment, and family per capita income. Methods This was a cross-sectional study that used the Sao Paulo Health Survey dataset (2015) with 3,145 individuals (18 years or older). The weight and height were self-reported and was calculated the BMI. Residential locations were geocoded, types and the mix of destinations were calculated in 500m buffers. We used multilevel models to examine the association between BMI and obesity with the places where the people lived. Results The Midwest region showed the highest mean of the mix of destinations than other areas and the lowest prevalence of overweight and obesity. The BMI was higher for people that lived in North, Southeast, South, and East than Midwest after adjusted. Individuals that lived in North (OR=1.69 CI95% 1.18-2.43) and Southeast (OR=1.66 CI 95%1.17-2.37) had increased the likelihood for obesity compared with Midwest after adjusted by social and demographic variables, physical activity level, mix of destinations, and family per capita income. Conclusion This study found that individuals that lived in the North, Southeast, South, and East had higher BMI than people who lived in Midwest, and people that lived in the North and Southeast had increased the likelihood of obesity compared with the Midwest area. The place where people living can influence BMI and obesity in megacities like Sao Paulo, Brazil. Key words: Body Mass Index, Obesity, Built Environment, Multilevel analysis.