When data exhibits heavy-tailed behavior, traditional regression approaches might be inadequate or inappropriate to model the data. In such data analyses, composite models, which are built by piecing together two or more weighted distributions at specified threshold(s), are alternative models. When data contain covariate information, composite regression models can be used. In the existing literature, there is not much work done on this topic. The only study is Gan and Valdez (2018)'s paper. In this study, a novel Lognormal-Pareto Type II composite regression model is proposed. Particle swarm optimization ( PSO ) is performed to obtain model parameters of the proposed model. The proposed model is applied to model monthly consumption expenditure and affecting factors. The data is obtained from the National Household Budget Survey, which is conducted annually by the Turkish Statistical Institute ( TurkStat ). Since the sampling design of the Household Budget Survey is stratified two-stage cluster sampling, the parameters are estimated under weighted data by updating the proposed model and PSO . Additionally, the proposed regression model performance is compared with Lognormal , Lomax , Gamma and Gamma-Pareto type II regression models. The results demonstrate that the proposed model provides an improved fit to data.