REGIONAL ASYMMETRY OF STATE-SUPPORTED MORTGAGE LENDING IN UKRAINE UNDER WARTIME CONDITIONS

Abstract

Introduction. The full-scale Russian-Ukrainian war has caused unprecedented destruction of Ukraine’s housing stock and significantly deepened regional disparities. The state program “eOselya”, launched in October 2022 as a universal tool for affordable mortgage lending, has become one of the key mechanisms of state support for the housing sector.

Purpose. The aim of the article is to assess the regional asymmetry in the distribution of loans under the “eOselya” program and to substantiate directions for its mitigation in order to enhance spatial convergence under wartime conditions.

Materials and Methods. The empirical basis of the study consists of official data from Ukrfinzhytlo as of April 29, 2026 (25,535 loans totaling UAH 44.5 billion). To quantify the unevenness of distribution, the coefficient of variation, Gini coefficient, Theil index, Atkinson indices, and Herfindahl-Hirschman index were used. Methods of comparative, structural, and spatial analysis were applied.

Results. The study revealed a high level of regional asymmetry: the capital region (Kyiv city and Kyiv oblast) concentrates 50.5% of all loans, while frontline regions (Zaporizhzhia, Mykolaiv, Kirovohrad, Sumy, and Kharkiv oblasts) account for less than 1% each. The Gini coefficient stands at 0.524, the Theil index at 0.541, and the coefficient of variation at 1.38. The main factors of asymmetry are security risks, population migration, differences in solvency, and reduced liquidity of real estate in high-risk areas.

Conclusions and Prospects. To increase the effectiveness of the “eOselya” program, the authors propose transitioning to an adaptive regionally sensitive model. This includes regionally differentiated support parameters, mechanisms for insuring war risks, expansion of the program’s functional purpose (including reconstruction of damaged housing), risk-sharing instruments with banks, and the development of a comprehensive guarantee system. Further research should focus on evaluating the program’s multiplier effect across different types of regions and its impact on housing prices, developer activity, and migration decisions.