In this contribution we establish hedonic pricing models integrating spatial effects. Based on prices for more than 52,000 residences in the Vienna region, which are arranged by postal code, we specify a hedonic regression model which explains the price structure in this sample. For this purpose we use explanatory variables such as the buildingís year of construction, its total floor space, the number of rooms, the floor the domicile is located on, the existence of a garage or parking area, the existence of an elevator, balcony or terrace as well as its current condition. However, not only the marginal effects of individual characteristics that contribute to the value of a building are estimated but also spatial effects. We therefore go further to integrate spatial effects by employing both geoadditive and spatial autocorrelation (SAR) models. In the geoadditive models, the postal code serves as a location variable. The spatial effect captured by this variable serves as a surrogate for variables not collected in the sample. In the SAR-models, a weighting matrix reflects spatial interaction (spatial autocorrelation) and spatial structure (spatial heterogeneity). The model can then be interpreted in terms of spatial spillovers in supply and demand for real estate. The results obtained in this study can for example be applied to project development and used for investment decisions in order to derive the future return of a building with certain characteristics in a specific district.