Visual Scenario Development Platform Used to Model Real Estate Market Processes

Introduction. The real estate market is a key sector of the economy with high price dynamics, dependence on macroeconomic factors and complexity of forecasting. Traditional analysis methods require a lot of time and resources, which limits their application. Using low-code platforms allows you to reduce the cost of developing models and make analysis tools accessible to specialists without advanced programming skills. The purpose of the study is to demonstrate the use of KNIME to predict the value of real estate and their classification. In addition, it is necessary to assess how accurate the models are and how useful they are in practice. Materials and Methods. Real estate market processes were modeled using KNIME Analytics Platform for visual scenario development. Real estate data is collected using the Cyanparser Python library, and regression analysis and data visualization methods are implemented in KNIME Analytics Platform. Results. Linear and polynomial regression of real estate prices according to specified parameters is constructed, clusterization of real estate objects and visualization of the results are performed. Clustering revealed three groups of objects correlating with location and infrastructure. Discussion and Conclusion. KNIME has confirmed its effectiveness as a low-code tool for analyzing the real estate market. The materials of the article can be useful for understanding the dynamics of the real estate market and forecasting its future trends.