It sounds like a textbook application for multilevel models/hierarchical models/mixed models. If you have many countries, then if think about it, you do not want to include to dozens to hundreds of indicator/dummy variables for each country. If
location is similarly defined you may run to many thousands of indicators variables presenting you with difficult to organize results, and a great loss of statistical power as your degrees of freedom drop with each additional variable. If each
country has characteristics that may affect its effect on price, then you can really see the too many variables issue blowing up.
With a multilevel modeling approach you might instead treat each country as contributing its own unique variance to the constant/intercept term in your model, and the include country-level variables as predictors without having to interact them across dozens, hundreds or thousands of indicators variables.
For a good introductory read to orient you to these kinds of models, see Duncan, C., Jones, K., and Moon, G. (1998). Context, composition and heterogeneity: Using multilevel models in health research. Social Science & Medicine, 46(1):97–117.