The overall purpose of this paper is to suggest a new technique for creating interesting regression- and model trees. Interesting models are here defined as models that fulfill some domain dependent restriction of how variables can be used in the models. The suggested technique, named ReReM, is an extension of M5 which can enforce variable constraints while creating regression and model trees. To evaluate ReReM, two case studies were conducted where the first concerned modeling of golf player skill, and the second modeling of fuel consumption in trucks. Both case studies had variable constraints, defined by domain experts, that should be fulfilled for models to be deemed interesting. When used for modeling golf player skill, ReReM created regression trees that were slightly less accurate than M5’s regression trees. However, the models created with ReReM were deemed to be interesting by a golf teaching professional while the M5 models were not. In the second case study, ReReM was evalu ated against M5’s model trees and a semi-automated approach often used in the automotive industry. Here, experiments showed that ReReM could achieve a predictive performance comparable to M5 and clearly better than a semi-automated approach, while fulfilling the constraints regarding interesting models.