Location is the most important strategic decision in retailing. The location problem is markedly
complex and multicriteria. One of the key factors to consider is the so-called balanced tenancy —i.e.,
the degree to which neighboring businesses complement each other. There are several network-based
methodologies that formalize the notion of balanced tenancy by capturing the spatial interactions between
different commercial sectors in cities. Some of these methodologies provide indices that have been successfully used as input features in location recommendation systems. However, from a predictive perspective,
it is still unknown which of the indices provides best results. In this work, we analyze the performance of six
of these indices on a set of nine Spanish cities. Our results show that the combined use of all of them in an
ensemble model such as random forest significantly improves predictive accuracy. In addition, we explore
the effect of knowledge transfer between cities from two different perspectives: 1) quantify how much the
quality of solutions degrades when the balanced tenancy of a city is explored through the indices obtained
from another city; 2) investigate the interest of network consensus approaches for knowledge transfer in
retailing.
