Integrated Index for the Economic Assessment of the Construction Sector

Abstract

This article substantiates the necessity of developing a composite index for a comprehensive assessment of the economic condition of Ukraine’s construction sector, particularly in the context of destabilizing factors such as the abrupt loss of a significant portion of the population and national territory due to ongoing military aggression. The study provides an in-depth analysis of the current state of the industry, which is undergoing a phase of accelerated recovery following substantial wartime losses. In 2024, the volume of completed construction works amounted to UAH 204.7 billion, reflecting an 80% increase compared to 2022, although still below pre-war levels.

To identify the key determinants influencing the sector’s economic dynamics, the research employs basic correlation and linear regression analyses of selected macroeconomic and sector-specific indicators. The Ordinary Least Squares (OLS) method was used to test the hypothesis of a single dominant predictor for the construction output index. However, the findings indicate the absence of such a factor and reveal significant multicollinearity among the variables. A key observation is that the multifactor regression model demonstrates substantially greater explanatory power (coefficient of determination of 0.814 versus 0.33 for the single-factor model), thereby reinforcing the rationale for a multidimensional analytical approach.

To enhance the robustness of the analysis, a methodological framework for data preprocessing was developed. This includes harmonizing the frequency of observations through linear interpolation of annual data into quarterly intervals, normalizing all indicators relative to a base period (December 2015), and adjusting for territorial changes by scaling data according to the area of Ukrainian-controlled regions.

The study justifies the feasibility and relevance of constructing a composite index that integrates macroeconomic, sectoral, and conflict-specific destabilizing variables. Such an index could serve as a practical and adaptive tool for evidence-based decision-making in public policy, strategic planning, and investment management. Future research directions include improving the quality and granularity of input data, expanding the set of predictive variables (e.g., labor market indicators, credit availability, investment flows, and business sentiment), and testing alternative methodologies for index construction, including nonlinear regression and machine learning techniques.