Annotation

Annotation: Comprehensive assessment of the investment attractiveness of land plots and the calculation of the socio-economic effects of residential development projects represent a complex scientific challenge requiring a formalized approach. This article presents a research-based framework for constructing an intelligent model to support urban planning decisions, aimed at integrating several classes of methods into a unified system. The purpose of the study is to create a scientifically grounded instrument that formalizes the process of selecting land plots and adjusting the parameters of large-scale residential development, while simultaneously accounting for investment efficiency and social effects. To achieve this goal, the following tasks were addressed: development of an ontology-based domain description using the OWL language and the RDFLib library; construction of an integrated evaluation of investment attractiveness of land plots using the Analytic Hierarchy Process (AHP); calculation of technical and economic indicators based on regulatory requirements; financial analysis including Net Present Value (NPV), Internal Rate of Return (IRR), and profit; and numerical optimization of social effects using Sequential Least Squares Programming (SLSQP) and trust-constr methods from the SciPy library. The proposed three-tier architecture includes modules for ontology-driven data representation, multi-criteria evaluation, and financialeconomic analysis with optimization. The prototype was tested on two contrasting land plots, which confirmed the hypothesis that moderate reductions in developer profit can be accompanied by significant increases in social and infrastructural performance indicators. The results demonstrate the applicability of intelligent methods for achieving balanced urban planning solutions and emphasize their significance for integrating social and economic interests in the implementation of complex residential development projects.