Conditional density estimation (CDE) models can be useful for many statistical applications, especially because the full conditional density is estimated instead of point estimates, revealing more information about the uncertainty of random variables. In this paper, we propose a new methodology called Odds Conditional Density Estimator (OCDE) to estimate conditional densities in a supervised learning scheme. The main idea is that it is very difficult to estimate p{x,y} and p{x} in order to have the conditional density p_{y|x}, but by introducing an instrumental distribution, we transform the CDE problem into a problem of odds estimation, or similarly, training a binary probabilistic classifier. We demonstrate how OCDE works using simulated data and then test its performance against other known state-of-the-art CDE methods in real data. Overall, OCDE is competitive compared with these methods in real datasets.