It was determined that most EU countries face the problem of housing affordability, housing overcrowding and the financial burden caused by general housing costs. It was established that the financing of social and affordable housing is an actual direction of the state financial policy, its main tools in the field of providing social and affordable housing were analysed using the example of EU countries. Strengths and weaknesses, opportunities, and threats for the construction of affordable housing were identified using a SWOT analysis. The need to resolve the dilemma between the ethical side and business motivation when investing in social and affordable housing is substantiated.
Dominant trends of interdisciplinary research, which are expressed within four formed clusters, have been identified. At the same time, attention is paid to the goals of sustainable development, in close connection with which the financing of social and affordable housing is being studied, in particular SDG 1 “Poverty reduction”, SDG 3 “Strong health and well-being”, SDG 4 “Quality education”, SDG 8 “Decent work and economic growth” and SDG10 “Reducing inequality”
The causal relationship, its nature and strength between the country’s economic growth and budget expenditures on housing within the framework of social protection and housing development are analysed in the context of achieving SDG8. Correlation analysis (Pearson / Spearman, depending on the previous results of the Shapiro-Wilk test) confirmed the relationship between budgetary spending on housing within social protection and GDP growth per capita in 22 out of 27 EU countries, including 14 countries with positive dependence, mainly average strength and a 1-year lag (or without a time lag). In turn, the relationship between budget expenditures for the development of the housing stock in the block of expenditures for housing and utilities and the growth of GDP per capita was determined in 26 of the 27 countries of the sample, which include 17 with a positive direction, high (8 countries) and average (9 countries) level of connection strength and a 1–2-year lag. Using the results of the Granger causality test, public social spending on housing affects the growth of GDP per capita in 14 EU countries, and public spending on housing development is the cause of GDP growth per capita in 17 EU countries. Based on the construction of the Arellano–Bover / Blundell–Bond dynamic system linear regression model of panel data assessment, it was determined with a probability of 91% that a 1% increase in budget expenditures for housing development in the section of the housing and communal services expenditure block will contribute to the growth of GDP per capita by 2.38% with a time lag of 3 years.
The impact of mortgage lending, subsidized rent, and budget housing costs on the level of housing overcrowding and the population’s resilience to socially determined diseases in the context of achieving SDG3 was confirmed and evaluated on the basis of correlation-regression analysis, taking into account time lags and building a system dynamic model for evaluating panel data for 27 EU countries for 2012-2021 using Pearson / Spearman methods, Arellano-Bover / Blundell-Bond model, Shapiro-Wilk test and STATA 18 tools. Reduction of housing overcrowding helps to reduce the mortality rate from mental and behavioural disorders – in 59%; tuberculosis – in 56%; AIDS, viral hepatitis, diabetes, pneumonia – in 44% of EU countries. The increase in the share of homeowners with a mortgage or home loan contributes to decrease in housing overcrowding in 74%; shares of tenants on preferential terms or free of charge – 59%; the share of budget expenditures for the development of the housing stock within the limits of housing and communal services expenditures – 56%; the share of budget expenditures on housing as a direction of social protection is in 45% of EU countries. At the same time, a 1% increase in the share of homeowners on mortgage terms will contribute to a decrease in the level of housing overcrowding by an average of 0.42% with a time lag of 3 years (with a probability of 94%), the share of tenants on preferential terms or free of charge – by 0.75% with a time lag of 2 years, the share of budget expenditures for housing development – by 4.17% with a time lag of 2 years. On the other hand, an increase in the share of budget expenditures on housing as a direction of social protection by 1% leads to an increase in the level of housing overcrowding by an average of 1.51% with a time lag of 2 years (with a probability of 81%).
