Estimates of soil hydraulic conductivity (K) and air permeability (ka) at given soil-water potentials are often used as reference points in constitutive models for K and kaas functions of moisture content and are, therefore, a prerequisite for predicting migration of water, air, and dissolved and gaseous chemicals in the vadose zone. In this study, three modeling approaches were used to identify the dependence of saturated hydraulic conductivity (KS) and air permeability at −100 cm H2O soil-water potential (ka100) on soil physical properties in undisturbed soil: (i) Multiple regression, (ii) ARIMA (autoregressive integrated moving average) modeling, and (iii) State-space modeling. In addition to actual soil property values, ARIMA and state-space models account for effects of spatial correlation in soil properties. Measured data along two 70-m-long transects at a 20-year old constructed field were used. Multiple regression and ARIMA models yielded similar prediction accuracy, whereas state-space models generally gave significantly higher accuracy. State-space modeling suggested KSat a given location could be predicted using nearby values of KS, ka100and air-filled porosity at −100 cm H2O soil-water potential (ε100). Similarly, ka100could be predicted from nearby values of ka100and ε100. Including soil total porosity in the state-space modeling did not improve prediction accuracy. Thus, macro-porosity (ε100) was the key porosity parameter for predicting both KSand ka100in undisturbed soil.