In the context of the SustainCity project (www.sustaincity.eu), three European cities (Brussels, Paris and Zurich) will be modelled using the land use microsimulation platform UrbanSim. This platform relies on various models interacting with each other, to predict long-term urban development. The aim of this paper is to provide some econometric insight into this process. A common set of notation and assumptions are first defined, and the more common model structures (linear regression, multinomial logit, nested logit, mixed MNL and latent variable models) are described in a consistent way. Special treatments and approaches that are required due to the specific nature of the data in this type of applications (i.e. involving very large number of alternatives, and often exhibiting endogeneity, correlation, and (pseudo-)panel data properties) will also be discussed. For example, importance sampling, spatial econometrics, Geographically Weighted Regression (GWR) and endogeneity issues will be covered. Applications and specific options of the following models: (i) household location choice model, (ii) jobs location/firmography, (iii) real estate price model, and (iv) land development model, will be demonstrated using examples from the on-going case studies in Brussels, Paris and Zurich. Finally, lessons learnt in relation to the econometric models from these on-going case studies will be summarized.