Instruments based on resonance are widely used in the forest industry to predict modulus of elasticity (MOE) and segregate logs of varying quality for different end uses for fast growing softwoods such as Pinus radiata D. Don. Predictions of MOE, made using resonance instruments, often assume a constant green density, ρg, of 1,000 kg m–3. However, little research has been done to test the robustness of this assumption. The objective of this research was to describe changes in predictive precision of MOE as ρg is increasingly well characterised. Longitudinal measurements of velocity, V, and ρg taken from eighty 17-year old unthinned P. radiata trees growing at two sites in Chile were used to calculate MOE. Predictions of MOE were then made by substituting measurements of ρg for values predicted by the following models (i) Model 1 - assuming a constant ρg of 1,000 kg m−3, (ii) Model 2 - using the mean tree ρg of 914 kg m−3, (iii) Model 3 - using a model with fixed effects to account for the mean longitudinal variation in ρg, (iv) Model 4 - inclusion of previous terms and random effects to account for tree level variation and (v) Model 5 - inclusion of previous effects (in model 4) and a random quadratic term. Differences in MOE determined from measurements of ρg and the five predictions of ρg were expressed as both a percentage difference, (D) and an absolute percentage difference (Da) to assess precision and bias. At the tree level, values for mean D and Da (in brackets) were −9.9 (10.4)%, −0.459 (5.49)%, −0.262 (4.15)%, −0.045 (0.232)% and −0.0406 (0.189)%, for Models 1, 2, 3, 4 and 5, respectively. At the log level, considerable longitudinal bias in D was evident for Model 1 where over-prediction of MOE was greatest between relative heights of >0.1–0.4, with D reaching maximum values of −33.8% between relative heights of >0.1–0.2. Assuming constant ρg can result in substantial error in estimates of MOE using acoustic instruments particularly when predictions are made at the log level. The mixed effects modelling approach described here demonstrates a useful method for characterising variation in ρg allowing more accurate estimates of MOE to be made using acoustic methods.