Non-parametric approaches are being used in various fields to address classification type problems, as well as to estimate continuous variables. One type of the non-parametric lazy learning algorithms, a k-Nearest Neighbor (k-NN) algorithm has been applied to estimate water retention at –33 and –1500 kPa matric potentials and saturated hydraulic conductivity. Performance of the algorithm has subsequently been tested against estimations made by a neural network (NNet) model, developed using the same data and input soil attributes. We developed an ensemble of predictions by performing multiple randomized subset-selection from the main data set and performing subsequent calculations on each of the data subsets. We used a hierarchical set of inputs using soil texture, bulk density and organic matter content to avoid possible bias towards one set of inputs, and also varied the sizes of data subsets used to develop the NNet models and to run the k-NN estimation algorithms. Different ‘design-parameter' settings, analogous to model parameters have been optimized. The k-NN technique showed little sensitivity to potential sub-optimal settings in terms of how many nearest soils were selected and how those were weighed while formulating the output of the algorithm, as long as extremes were avoided. The optimal settings were, however, dependent on the size of the development/reference data set. The non-parametric k-NN technique performed mostly equally well with the NNet models, in terms of root-mean-squared residuals and mean residuals. Gradual reduction of the data set size from 1600 to 100 resulted in only a slight loss of accuracy for both the k-NN and NNet approaches. The k-NN technique is a competitive alternative to other techniques to develop PTFs because no re-development of PTFs is needed as new data become available, while the user would not have to compromise estimation accuracy.