Recent research has noted that in high-dimensional datasets, certain points exhibit a large k-occurrence, the number of points for which they appear in the k-nearest neighbors set of other points. This phenomenon, known as hubness, has not yet been formally modeled. In this work, we demonstrate how to compute the expected k-occurrence of points given the underlying data distribution. We present preliminary ideas on how this expectation can be used to explain hubness.