Incremental Hoeffding Tree Regressors (HTR) are powerful non-linear online learning tools. However, the commonly used strategy to build such structures limits their applicability to real-time scenarios. In this paper, we expand and evaluate Quantization Observer (QO), a feature discretization-based tool to speed up incremental regression tree construction and save memory resources. We enhance the original QO proposal to create multi-branch trees when dealing with numerical attributes, creating a mix of interval and binary splits rather than binary splits only. We evaluate the multi-branch and strictly binary QO-based HTRs against other tree-building strategies in an extensive experimental setup of 15 data streams. In general, the QO-based HTRs are as accurate as traditional HTRs, incurring one-third of training time at only a fraction of the memory resource usage. The obtained numerical multi-branch HTRs are shallower than the strictly binary ones, significantly faster to train, and they keep predictive performance similar to the traditional incremental trees.