The purpose of this paper is to estimate econometric models with sample and prior information. Prices of land property for residential development in Szczecin are modeled (the price level was determined for 2018). Modeling property prices only based on sample data generates numerous problems. Transaction databases from local real estate markets often contain a small number of observations. Properties are frequently similar, which results in low variability of property characteristics, and thus – low efficiency of parameter estimators. In such a situation, the impact of some features cannot be estimated from the sample data. As a solution to this problem, the paper proposes econometric models that consider prior information. This information can be, for example, in the form of property feature weights proposed by experts. The prior information will be expressed in the form of stochastic restrictions imposed on the model parameters. In the simulation experiment, the predictive power of mixed estimation models is compared with two kind of models: OLS models and model with only prior information. It turned out that mixed estimation results are superior with regard to formal criteria and predictive abilities.