Empirical applications of the spatial theory of elections typically rely on the discrete choice framework to arrive at probabilistic voting models. Whereas in the classic model voter choice is solely a function of spatial proximity, neo-Downsian models also incorporate voter-specific nonpolicy attributes, which are represented by sociodemographic characteristics. One prominent line of such probabilistic models, Schofield’s Valence Model, additionally includes party valences into voter utility functions. The model rests on the estimated party intercepts to measure the valence advantages empirically. The party intercepts are ordered based on their values, and then this valence ranking is used further to predict equilibrium locations. The paper demonstrates that this measurement strategy does not provide unique results in fully specified models due to central properties of discrete choice models and the specific nature of party intercepts in these models. Drawing on a simple example based on mass election surveys from Germany, it is shown that the valence ranking, the crucial factor to investigate how valence differences affect the nature of spatial competition, is highly sensitive to arbitrary coding decisions. As a consequence, it is impossible to represent valence with the constants and to infer something substantial from the resulting valence ranking.