  To determine if the SuperNova Empirical Model (SNEMO) can improve Type Ia
supernova (SN Ia) standardization of several currently available photometric
data sets, we perform an initial test, comparing results with the much-used
SALT2 approach. We fit the SNEMO light-curve parameters and pass them to the
Bayesian hierarchical model UNITY1.2 to estimate the Tripp-like standardization
coefficients, including a host mass term as a proxy for redshift dependent
astrophysical systematics. We find that, among the existing large data sets,
only the Carnegie Supernova Project data set consistently provides the
signal-to-noise and time sampling necessary to constrain the additional five
parameters that SNEMO7 incorporates beyond SALT2. This is an important
consideration for future SN Ia surveys like LSST and WFIRST. Although the
SNEMO7 parameters are poorly constrained by most of the other available data
sets of light curves, we find that the SNEMO2 parameters are just as
well-constrained as the SALT2 parameters. In addition, SNEMO2 and SALT2 have
comparable unexplained intrinsic scatter when fitting the same data. When
looking at the total scatter, SNEMO7 reduces the Hubble-Lemaitre diagram RMS
from 0.148~mag to 0.141~mag. It is not then, the SNEMO methodology, but the
interplay of data quality and the increased number of degrees of freedom that
is behind these reduced constraints. With this in mind, we recommend further
investigation into the data required to use SNEMO7 and the possibility of
fitting the poorer photometry data with intermediate SNEMO-like models with
three to six components.
