Hippocampal place cells encode space through phase precession, whereby neuronal spike phase progressively advances during place-field traversals. What neural constraints are essential for achieving efficient transfer of information through such phase codes, while concomitantly maintaining signature neuronal excitability? Here, we developed a conductance-based model for phase precession within the temporal sequence compression framework, and defined phase-coding efficiency using information theory. We recruited an unbiased stochastic search strategy to generate thousands of models, each with distinct intrinsic properties but receiving inputs with identical temporal structure. We found phase precession and associated efficiency to be critically reliant on neuronal intrinsic properties.Despite this, disparate parametric combinations with weak pair-wise correlations resulted in models with similar high-efficiency phase codes and similar excitability characteristics.Mechanistically, the emergence of such parametric degeneracy was dependent on two factors.First, the dependence of phase-coding efficiency on individual ion channels was differential and variable across models. Second, phase-coding efficiency manifested weak dependence independently on either intrinsic excitability or synaptic strength, instead emerging through synergistic interactions between synaptic and intrinsic properties. Despite these variable dependencies, our analyses predicted a dominant role for calcium-activated potassium channels in regulating phase precession and coding efficiency. Finally, we demonstrated that a change in afferent statistics, manifesting as input asymmetry, introduces an adaptive shift in the phase code that preserved its efficiency. Our study unveils a critical role for neuronal intrinsic properties in achieving phase-coding efficiency, while postulating degeneracy as a framework to attain the twin goals of efficient encoding and robust homeostasis.