Abstract
The real estate market is a specific and imperfect field of research, and its complex structure and the presence of information gaps necessitate the use of advanced analytical tools. One such tool is simulation modeling, which has a variety of practical applications and can be used to model real-life systems characterized by a high degree of complexity and a high share of random components. In this paper, virtual data was used to simulate transactions on the local real estate market. Simulation tools were applied to generate additional information about transactions, their spatial distribution and transaction prices. The applicability of the iterative Monte Carlo approach with a standard regression model and a spatial regression model was evaluated.