A regression tree method for count data called CORE is introduced. Besides a Poisson regression, a count regression such as negative binomial, hurdle, or zero-inflated regression which can accommodate over-dispersion and/or excess zeros is fitted at each node. Likelihood function is used to guide the selection of split variables and split sets. We then use node deviance in the tree pruning process to avoid overfitting. CORE is free of variable selection bias. It is shown to have an edge over the existing methods in the simulation and real data studies.