We exploit the multiplicative structure of Pólya Tree priors for density and differential entropy estimation in $p$-dimensions. We establish: (i) a representation theorem of entropy functionals and (ii) conditions on the parameters of Pólya Trees to obtain Kullback-Leibler and Total Variation consistency for vectors with compact support. Those results motivate a novel differential entropy estimator that is consistent in probability for compact supported vectors under mild conditions. In order to enable applications of both results, we also provide a theoretical motivation for the truncation of Univariate Pólya Trees at level $3 \log_2 n $.