  We analyze an exhaustive data-set of new-cars monthly sales. The set refers
to 10 years of Spanish sales of more than 6500 different car model
configurations and a total of 10M sold cars, from January 2007 to January 2017.
We find that for those model configurations with a monthly market-share higher
than 0.1% the sales become scalable obeying Gibrat's law of proportional growth
under logistic dynamics. Remarkably, the distribution of total sales follows
the predictions of the Maximum Entropy Principle for systems subject to
proportional growth in dynamical equilibrium. We also encounter that the
associated dynamics are non-Markovian, i.e., the system has a decaying memory
or inertia of about 5 years. Thus, car sales are predictable within a certain
time-period. We show that the main characteristics of the dynamics can be
described via a construct based upon the Langevin equation. This construct
encompasses the fundamental principles that any predictive model on car sales
should obey.
