Macroprudential Diagnostics No. 9
The macroprudential diagnostic process consists of assessing any macroeconomic and financial relations and developments that might result in the disruption of financial stability. In the process, individual signals indicating an increased level of risk are detected based on calibrations using statistical methods, regulatory standards or expert estimates. They are then synthesised in a risk map indicating the level and dynamics of vulnerability, thus facilitating the identification of systemic risk, which includes the definition of its nature (structural or cyclical), location (segment of the system in which it is developing) and source (for instance, identifying whether the risk reflects disruptions on the demand or on the supply side). With regard to such diagnostics, instruments are optimised and the intensity of measures is calibrated in order to address the risks as efficiently as possible, reduce regulatory risk, including that of inaction bias, and minimise potential negative spillovers to other sectors as well as unexpected cross-border effects. What is more, market participants are thus informed of identified vulnerabilities and risks that might materialise and jeopardise financial stability.
1. Identification of systemic risks
Total systemic risk exposure has remained moderately high (Figure 1). Structural weaknesses of the financial and non-financial sectors are unchanged from the previous assessment (Macroprudential Diagnostics No. 8), with favourable developments continuing in the financial sector due to an improvement in asset quality, increase in solvency and lower dependency on external financing.
Figure 1 Risk map, second quarter of 2019
Note: The arrows indicate changes from the risk map in the first quarter of 2019 published in Macroprudential Diagnostic No. 8 (July 2019).
Continued economic growth and favourable financing conditions have alleviated the structural vulnerabilities of domestic non-financial sectors. Despite the economic slowdown in the second quarter of 2019 caused by decreases in goods exports and capital investments, real economic activity growth is expected to accelerate in the remaining part of the year, which will have a favourable effect on debt indicators. General government and private sector debt to GDP ratios could thus continue declining. The still high level of debt is a major structural risk, even more so as it includes high shares of external debt and foreign currency-indexed debt. Household sector debt increased slightly to 33.4% of GDP at mid-year, but has remained lower than the EU average. However, non-financial corporate debt, although reduced, has remained quite high (81.9% of GDP, on an unconsolidated basis). Along with economic growth, the currently favourable international market conditions have also contributed to improved debt sustainability. Still, the economy’s exposure to changes in financing conditions will remain high as long as the debt level is high.
Structural weaknesses in the financial sector are still assessed as moderately high. Despite the favourable impact of a continuing decrease in the share of non-performing loans and the increase of their coverage on the financial sector’s stability, Croatia has remained among the EU countries with the highest share of non-performing loans in total bank loans. In addition, the long-standing growth trend in the share of kuna and fixed interest rate household financing has had a positive impact on currency and interest rate induced credit risk, with the stability and solvency of banks (measured by the Z-index) underpinned by the high level of capitalisation and an increase in current earnings. The banking sector’s continuing consolidation increases market concentration, which could limit the degree of competition in the system, accelerating and stimulating the spillover of any difficulties experienced by one bank to the whole banking system. However, the sector’s resilience has been strengthened by the gradual exit of banks with poorer asset quality and performance indicators from the market. In addition, a higher sectoral concentration can help enhance the cost effectiveness of banks through economies of scale. Also, bank balance sheet concentration in terms of exposure to groups of connected clients and the government sector continues to be high. However, the sector's structural vulnerabilities have been mitigated by the increasing diversification of financing, i.e., a greater reliance on domestic, diffused sources and a lower reliance on foreign owners.
The cyclical vulnerabilities of the financial sector are assessed as low. Value adjustment expenses measured in relation to bank assets are at their lowest level since the pre-crisis period, a typical characteristic of the late stage of the credit cycle. High bank liquidity levels and declining interest rates have boosted transaction account deposits. With the maturity of liabilities shortened as a result and an increase in general-purpose and housing loans with increasingly longer initial maturities, the assets and liabilities maturity gap has grown. The factors alleviating the impact of a decrease in the net interest margin on bank profitability in the last two years have included lower value adjustment expenses and unit administrative costs, with the latter due to continuing market consolidation and the digitalisation of business operations.
Short-term risk continues to be assessed as very low. The financial stress index, showing current developments in the Croatian financial market, has increased slightly from the previous report. However, this can be attributed solely to the shallowness of the money and bond markets, where new government bond issues and short-term interbank financing led to a rise in the indicators of interest rate differentials, i.e., increased the measure of volatility (Figure 2). The country’s risk perception is reduced, with CDS spreads on Croatian government bonds standing at all-time lows, and August saw government borrowing through issuing foreign currency-indexed kuna T-bills at a negative interest rate.
Figure 2 Croatian index of financial stress and the contributions of individual markets
The currently favourable financing conditions facilitate household debt servicing, but a prolonged period of low interest rates could result in an increase in vulnerabilities.
The negative consequences of such trends include a further increase in financial and real assets prices, which raises the risk of a sudden and steep fall in prices. The debt of the household sector has been on the increase, driven mainly by the growth of general-purpose cash loans, most of which are non-collateralised loans maturing in five or more years. A number of banks continue to grant such loans applying more lenient criteria than when granting housing loans, thus not acting in compliance with the CNB Recommendation. The increase in general-purpose lending could partly be linked with real estate investments, as may be concluded from the extension of the initial maturities of loans, whose individual amounts are relatively high. This, and the growth of real estate prices cause the accumulation of credit risk, which can materialise in the case of a contraction of economic activity and growth in the unemployment rate. Especially vulnerable are households with below average incomes and no savings, as, despite lower interest rates, their debt servicing burden is not alleviated due to the increasingly high amounts of loans granted. Box 1, Components and distribution of net household assets in Croatia, gives an overview of the household distribution of income, savings and all other components of real and financial assets.
The non-financial corporations sector has recorded positive trends due to a lower debt burden and capital growth. The growth of capital and gross operating surplus has been reducing debt burden and corporate debt indicators. Specifically, a continuing decrease in interest rates and good business results have reduced the solvency and liquidity risks of the corporate sector. However, there are still uncertainties looming over the future business results of the Fortenova Group (former Agrokor) and legal issues related to the settlement reached. In addition, adverse demographic and migration trends weigh down on the future business performance of corporations, already faced with a qualitative and quantitative labour shortage and increased labour costs.
2. Potential risk materialisation triggers
The main potential triggers for risk materialisation in Croatia lie in external developments. The escalation of protectionism, that is, the so-called trade war between China and the USA, has increased geopolitical uncertainties. Risks in the EU are further aggravated by the continuing uncertainty surrounding Brexit and fiscal sustainability in some large member states (Italy, France, Spain). The German economic slowdown, so far evident in manufacturing, poses another risk to the European and Croatian economies, which could spill over through trade channels as well as through investments, tourism and EU funds.
Given the start of monetary easing in the EU and USA, interest rates could remain low. As anticipated, euro area economic growth has decelerated, and is expected to stand at 1.5% in the medium term, with significant negative risks and an inflation rate remaining below the target of 2%. Under such circumstances, the ECB continues pursuing an expansionary monetary policy, introducing a two-tier system to penalise excess liquidity in order to lessen the consequences of low or negative interest rates threatening bank profitability. As regards the USA, the yield spread between the ten-year and two-year bond almost disappeared by the end of August, and such a yield-curve flattening trend indicates uncertainty regarding future trends, i.e., an increased risk of recession, which may also be triggered by a stronger Chinese economic slowdown.
Domestic factors are at the moment less likely to trigger a contraction of the domestic economy and unemployment growth capable of leading to credit risk materialisation. However, despite positive macroeconomic projections, there are still uncertainties regarding future economic growth, which will increasingly depend on the possibilities of increasing the labour participation rate. The favourable condition of public finances, reflected in the currently low country risk premium, could be deteriorated by the accumulation of additional arrears in the health sector, a sharp increase in expenditures on wages and various transfers in the pre-election period, legal actions and international arbitrations (e.g. the legal action taken by banks regarding the conversion of Swiss franc-denominated loans, the MOL legal action and the trade unions’ legal action about the implementation of collective agreements) and, in the long term, the anticipated changes to be introduced to the pension system following the Government's announcement renouncing the key elements of the last year's reform. As regards the financial sector, some banks are exposed to an additional risk stemming from the indefinite amount of total costs of anticipated consumer legal actions related to the pronouncement of the contracted variable interest rate and the currency clause for loans granted in Swiss francs null and void.
3. Recent macroprudential activities
3.1. Continued application of the countercyclical capital buffer rate for the Republic of Croatia for the fourth quarter of 2020
On the basis of a new quarterly analytical assessment of the development of cyclical systemic risks, the CNB has announced that the countercyclical capital buffer rate of 0% will continue to be applied in the fourth quarter of 2020. Specifically, the economic slowdown in the second quarter of 2019 was accompanied by a slightly decelerated growth in placements to the private sector, measured by both stocks and transactions. This led to a further decrease in the standardised relative debt indicator (the ratio of total placements to nominal annual GDP), which remained below its long-term trend, with the result that the credit gap calculated on the basis of this ratio has remained negative. The specific relative debt indicator, i.e. the ratio of domestic credit institutions’ loans to the non-financial sector to seasonally adjusted quarterly GDP, continued to decrease in the second quarter, and the credit gap calculated on the basis of this indicator is also negative. As other important indicators, such as credit growth dynamics, real estate price developments or current account balance trends also do not point to risks of excessive credit growth, corrective interventions on the part of the Croatian National Bank are still not necessary.
3.2. Continued application of the structural systemic risk buffer
At mid-2019, the CNB carried out a regular biennial review of the requirement to maintain the structural systemic risk buffer, confirming the need for the continued application of the structural systemic risk buffer at the previously set rates for two types of credit institutions. Credit institutions that have their head office in the Republic of Croatia are required to maintain the structural systemic risk buffer rate of 1.5% and 3% of their total risk exposure amount (as defined in Article 3 of the Decision on the application of the structural systemic risk buffer, Official Gazette 78/2017).
An analysis of the structural elements of financial stability and systemic risk in the economy shows that structural vulnerabilities and systemic risk exposure have remained at a moderately high level (see chapter 1 Identification of systemic risks). Despite the alleviation of structural imbalances, brought about by the continued economic growth, both public and private sector debt as well as external imbalances have remained substantial, exceeding those in other CEE countries, which makes the domestic economy vulnerable to possible changes in financing conditions in international markets. In addition, adverse demographic and migration trends have a negative effect on the Croatian economy’s growth potential and the sustainability of debt of all sectors. Despite having been in decline for several years due to economic growth and the mentioned migration trends, Croatia has continued to have a noticeably higher unemployment rate than other CEE countries. In the financial sector, which is stable and well-capitalised, the continued consolidation of credit institutions has led to an increase in the already high concentration, which significantly exceeds the EU average, making the system sensitive to potential vulnerabilities of individual banks.
3.3. Recommendations of the European Systemic Risk Board (ESRB)
3.3.1. Recommendation of the European Systemic Risk Board amending Recommendation ESRB/2016/14 on closing real estate data gaps (ESRB/2019/3)
In August 2019, the ESRB published the Recommendation amending Recommendation on closing real estate data gaps, adopted in late 2016, in order to harmonise data required for the assessment and monitoring of risks to financial stability associated with real estate markets. The amendments, aimed at facilitating the establishment of a harmonised data collection system at the EU level, were adopted because soon after the adoption of Recommendation ESRB/2016/14 it became obvious that most EU countries would find harmonisation difficult due to a large number of missing data and their various definitions. Some of the important amendments include a recommendation to the European Commission to establish a common minimum framework for EU statistics on the physical commercial real estate market (such as the price index, rental index, vacancy rates, etc.), to amend certain definitions and indicators for the monitoring of the residential and commercial real estate markets for the purpose of harmonisation with definitions from existing EU regulations (AnaCredit, CRD/CRR), and to extend initial deadlines for the delivery to the ESRB and the Council of interim reports on the information already available or expected to be available (from end-2018 to end-2019) and for the delivery of final reports (from end-2020 to end-2021 for financial indicators for the commercial real estate market and to 2025 for some countries’ physical market indicators; the deadline for the residential real estate market remained end-2020).
The Recommendation and amendments thereto were discussed at the sessions of the Financial Stability Council and at the meetings of the representatives of Croatian institutions engaged in data collection (the Croatian National Bank, the Ministry of Finance, the Croatian Financial Services Supervisory Agency and the Croatian Bureau of Statistics). The conclusion was that more effort should be put into the collection of missing data on the real estate market. With this aim in view, the CNB is setting up a new system for the collection of granular data on consumer lending conditions, which will enable the monitoring of risks associated with the residential real estate market in line with the requirements set forth in the Recommendation. The activities on the collection of data on the commercial real estate market are planned to be linked with future data collection pursuant to the requirements of the European Reporting System (Finrep, AnaCredit, Eurostat).
3.3.2. Implementation of Recommendation ESRB/2012/2 on funding of credit institutions
The CNB carried out a regular assessment of the funding plans of credit institutions for the end of 2018, delivered by significant credit institutions in Croatia pursuant to the Decision on the reporting of funding plans (Official Gazette 76/2015). The monitoring and assessment of the feasibility of funding plans are in compliance with the Recommendation on funding of credit institutions (ESRB/2012/2) and take into account the Guidelines of the European Banking Authority in order to improve the risk assessment of funding and liquidity sources as well as the impact of the implementation of these financing plans on the flow of credit to the real economy (for more details, see Macroprudential Diagnostics No. 1, chapter 3.4.4).
The assessment shows that at present there are no risks related to the unsustainability of the funding or liquidity structures or an adverse impact on credit to the real economy. Banks are planning to continue meeting their funding needs primarily by relying on their clients’ deposits, with most of household deposits to remain covered by the deposit insurance system. They are also planning to partly fund the flow of credit to the private sector by the existing excess liquidity. This, and the continued growth of private sector deposits, will enable banks to continue deleveraging against parent banks and further reduce (deposit and lending) interest rates. Although such bank strategies tend to lead to a decrease in short-term liquidity indicators, they are expected to remain above the prescribed minimum because of the currently high liquidity levels. Banks’ reliance on public sector sources is mostly accounted for by CBRD credit support to the real sector. Their share in total liabilities is therefore not significant, and, what is more, banks do not rely on innovative financial instruments. Given the above, banks do not see any objective impediments on the supply side precluding the planned credit flow and expect lending to the private sector to continue to rise.
Macroprudential policy implementation in other European Economic Area countries
Due to their strong credit growth having continued for some time, coupled with real estate price growth, an increasing number of EEA countries have embarked on macroprudential policy measures aimed at alleviating related systemic risks, and the countries already using these measures have additionally tightened them. The most actively applied measure, i.e. the non-zero countercyclical capital buffer rate, was in September 2019 applied by ten countries (ranging from 0.25% in France to 2% in Norway and Sweden). They were in October joined by Bulgaria, which applied the rate of 0.5%, having already announced that it would raise it to 1% in 2020, and three more countries will apply the non-zero rate in the first half of the following year: Luxembourg (0.25%), Belgium (0.5%) and Germany (0.25%). In addition, Sweden and Norway will increase the countercyclical buffer rate to the already announced 2.5% by the end of 2019, while Denmark, the Czech Republic and Slovakia announced in the summer that they would raise it to 1.5% and 2% respectively as of mid-2020.
The structural systemic risk buffer was in September applied by 16 EEA counties, with the rates ranging from 0.5% to 3% (Figure 3). The United Kingdom activated the structural systemic risk buffer for the first time in late July, applying it to ring-fenced bodies and large housing savings banks whose assets exceeded GBP 175bn. The prescribed rate ranges from 1% do 3%, depending on the amount of assets of a particular institution, and is currently applied to the five largest banking groups and one housing savings bank. The measure was introduced to contain systemic risk to the financial sector and the real economy that could arise owing to the operational problems of these large institutions and to mitigate a potential contraction in credit to households and non-financial corporations. In Hungary, the requirement to maintain the structural systemic risk buffer was, after a new review, revoked for a bank that had been required to maintain it in the previous period. The amount of the prescribed and annually reviewed buffer rate depends on the level of the so-called problematic exposures secured by real estate pursuant to Pillar 1 capital requirement, with the exception of credit institutions having exposures of such kind lower than HUF 5bn.
Figure 3 Distribution of the application of the systemic risk buffer and rates applied in EEA countries
With a view to increasing banks’ resilience to systemic risks associated with the real estate market, Estonia and Finland have announced the implementation of macroprudential measures pursuant to Article 458 of Regulation (EU) No 575/2013 (hereinafter: Regulation) related to an average risk weight floor for real estate-secured exposures for credit institutions using internal rating based approaches for the calculation of own funds. As macroprudential measures under Article 458 of the Regulation cover a period of two years, which can be extended, Finland, due to the continued increased level of systemic risk stemming from the fast growth of housing loans to already debt-burdened households, announced that it would extend for one year the application of the 15% average risk weight floor for residential real estate-secured exposures, applied since the beginning of 2018 and due to expire at the end of this year. Estonia, on the other hand, due to strong cyclical pressures and a surge in housing loans to households, introduced an average risk weight floor (also amounting to 15%) for the first time for precautionary reasons, for retail real estate-secured exposures to Estonian residents.
Table 1 Overview of macroprudential measures by EU member states, Iceland and Norway
Notes: The measures listed are in line with Regulation (EU) No 575/2013 on prudential requirements for credit institutions and investment firms (CRR) and Directive 2013/36/EU on access to the activity of credit institutions and the prudential supervision of credit institutions and investment firms (CRD IV). The definitions of abbreviations are provided in the List of Abbreviations at the end of the publication. Green indicates measures that have been added since the last version of the table. For CCB, green marks the rates applied or announced after 1 July 2019.
Disclaimer: of which the CNB is aware.
Sources: CNB, ESRB and notifications from central banks and websites of central banks as at 26 September 2019.
For more detailed data, see: https://www.esrb.europa.eu/national_policy/html/index.en.html.
Table 2 Implementation of macroprudential policy and overview of macroprudential measures in Croatia
Note: The definitions of abbreviations are provided in the List of abbreviations at the end of the publication.
Box 1 Components and distribution of net household assets in Croatia
In mid-2017, the Croatian National Bank carried out the first Household Financial and Consumption Survey (HFCS). The results of the survey have already been used in the analyses published in CNB’s publications this year (Financial Stability No. 20 and Macroprudential Diagnostics No. 8). The survey, carried out on a sample of households in Croatia, contains detailed data on the real and financial assets of households, their liabilities, incomes and consumption as well as various socio-demographic characteristics. Before this survey, the assets of Croatian households could be analysed only on the basis of aggregate data sources, such as financial accounts containing data on total financial assets and liabilities, while there was no adequate source for the analysis of household real assets. In addition, the available aggregate sources of data did not contain information on the distribution of assets and liabilities among households and underlying inequalities. Household inequality in Croatia used to be analysed only using data on incomes as data on the distribution of assets and liabilities were not available. This Box uses the results of the survey to describe the main types of real and financial assets and analyse total household net assets and their distribution. The Box concludes with a discussion on the implications of inequalities in the distribution of net assets for central bank measures and policies.
- Main components of household assets
The structure of the assets and liabilities of households in Croatia, represented in Table 1, shows that 98% of Croatian households own some kind of assets (real or financial). Real assets account for 97% of the total asset value and financial assets for the remaining 3%. However, when interpreting these figures it is important to note that the survey strongly underestimates the value of financial assets, as the value of household-owned financial assets shown by financial account statistics is approximately seven times higher. However, other data collected by the survey that allow for comparison with other data sources (e.g. the socio-demographic characteristics of households, the total income value and the share of household main residence ownership) are in line with the figures recorded in alternative data sources (Jemrić and Vrbanc). Unfortunately, some data, such as data on the value of real assets, lack data sources with which they could be compared.
The most significant component of real assets is main residence, owned by 85% of households, which is considerably above the EU average. Specifically, a comparison with data collected in the EU during the second wave of the survey in 2013 shows that an average of 62% of EU households own a main residence. Household financial assets are very homogenous. The bulk is accounted for by deposits, primarily current account deposits, owned by 81% of households. The survey also collected detailed data on the liabilities of Croatian households (described in Financial Stability No. 20, Box 3) required for the assessment of total household net assets (the sum of all types of assets net of total household liabilities). Table 1 shows a more detailed overview of household assets and liabilities and their median and mean value per household.
Table 1 Components of household assets and liabilities
Notes: Gross assets are calculated as the sum of real and financial assets. Net assets equal the amount of gross assets net of household liabilities. The survey has been harmonised among EU member states and its values are expressed in euros.
Sources: HFCS and authors’ calculations.
- Inequality in the distribution of household net assets
Data collected by the survey can be used to analyse the distribution of household net assets, which shows that 5% of the poorest households have almost no assets (Figure 1.a). Above the 5th percentile the value of net assets gradually increases up to the 70th percentile. Above the 70th percentile, the increase becomes more pronounced, especially at the distribution tail above the 90th percentile. In addition, the values of various types of assets are unequally distributed among households, with financial assets and liabilities concentrated among wealthier households. Figure 1.b therefore shows inequality in the distribution of various types of assets using the Lorenz curve. Inequality in asset distribution is much more pronounced as regards financial assets than real assets, which is typical of countries with a high share of household main residence ownership, that is, a large prevalence of real asset ownership among households. Such marked inequality in the distribution of financial assets is consistent with the results presented in Financial Stability No. 16, Box 3, in which the Lorenz curve shows transaction savings and time deposits of natural persons in the Republic of Croatia in 2014. Furthermore, a comparison of the distribution of net assets and incomes among households suggests that inequality in the distribution of net assets is more pronounced than inequality in the distribution of incomes.
Figure 1.a Distribution of real and financial assets and liabilities, in thousand euro
Sources: HFCS and authors’ calculations.
Figure1.b Lorenz curve for real, financial and net assets and income
Sources: HFCS and authors’ calculations.
An analysis of the main socio-demographic characteristics of the household reference person, such as the educational level, age or labour market status, presented in Figure 2, shows that the educational level can be related to the value of net assets, so that households with highly educated reference persons have the largest share (30%) of persons with net asset value in the highest, 5th quintile. The share of persons in the highest asset quintile increases in proportion with the reference person’s age and decreases slightly once the reference person retires. As regards the labour market status, the share of self-employed persons is the largest in the highest asset quintile, with over 50% of the self-employed belonging to the fifth net asset quintile. Over 50% of households in which the reference person has the labour status “other”, primarily referring to non-active persons who have left the labour market, are in the lowest net asset quintile.
Figure 2 Socio-demographic characteristics of households and quintiles of net assets
Sources: HFCS and authors’ calculations.
The educational level, labour market status and age are also connected with the level of household income, this income being a determinant of the value of net assets, which can be approximated by the savings from current income accumulated through time and increased by intergenerational transfers and gifts (for a detailed discussion, see Du Caju P., 2016). The interconnection between income level and inequality in the distribution of net assets among households is shown in Figure 3.a. Households with the highest income (in the highest income quintile) are also among the wealthiest: 40% of them are in the highest net asset quintile. Households in the lowest income quintile most often own net assets of low value. However, the share of low income households with a high value of net assets is also not negligible (17% of households are in the lowest income quintile and the highest asset quintile). The literature offers several explanations of why some households are in the lowest income quintiles and the highest asset quintiles: a high share of pensioners in the first income quintile, who, despite having low current incomes, have accumulated a considerable amount of assets, or a potential impact of intergenerational transfers that are not related to the income level. Still, a detailed decomposition of data has shown that these explanations do not apply to Croatian households. Specifically, in the survey carried out in Croatia, among a large number of households that responded that they did not have income of any kind, and that have zero gross annual income (7%), there are some that have large values of assets. Given that the total annual gross income comprises employment income, rent, income from financial assets, pensions, social transfers or any other sources of income, this result indicates that the actual value of data presented in the responses was deliberately omitted. Figure 3.b therefore shows the distribution of assets and incomes for the households whose annual gross income exceeds EUR 1 300 (a one-person household at a minimum receives HRK 800 per month, the amount of the guaranteed minimum benefit). However, even when households whose gross annual income is lower than EUR 1 300 are excluded from the sample, there are still households with very low incomes and high net asset values. This is why other factors that may influence inequality in the distribution of net assets are also examined.
Figure 3 Joint distribution of income and net assets
- Total b) Annual gross income of households above EUR 1,300
Sources: HFCS and authors’ calculations.
Recent research (Piketty, T., 2013, Zucman, G., and T. Piketty, 2015) suggests that there is a growing influence of intergenerational transfers on inequality in the distribution of net assets. As to the acquisition of main residence, Figure 4.a shows that households in the lowest deciles of net assets include a very small share of those that have bought or inherited the main residence and a prevailing share of those without a main residence, which is not surprising as this is the most valuable asset determinant. The average share of households that have inherited or bought a residence increases in other deciles and remains the same in various deciles of assets (around 30% and 60%). Figure 4.b shows that households that rent or use the main residence are among the poorest, while the share of households owning the main residence increases from 5% among households with the lowest net assets to 95% among households whose net assets are the highest.
Figure 4.a Way of acquiring the household main residence and deciles of net assets
Sources: HFCS and authors’ calculations.
Figure 4.b Household main residence – tenure status and deciles of net assets
Sources: HFCS and authors’ calculations.
Due to the marked regional heterogeneity and price differences of the Croatian real estate market (for more information, see Financial Stability No. 20), the geographic location of a residence also has a significant effect on the value of total net household assets. Figure 5.a shows that on the Adriatic Coast and in the City of Zagreb more than 50% of households can be grouped among the 40% wealthiest households, while the share of such households in Eastern Croatia is lower than 20%. The poorest municipalities in Eastern Croatia are those with over 60% of households classified in 40% of households with the lowest value of net assets at the level of Croatia. Figure 5.b presents a further breakdown of inequality among various geographical locations in Croatia: areas below the slope of 45 degrees in each observed percentile of assets have net asset values that are lower than the value of the sample of the whole of Croatia. For example, a household in the 50th percentile according to the net asset value in the municipalities of Eastern Croatia is in the 30th percentile according to the net asset value on the level of Croatia. In contrast, a household in the 50th percentile according to the net asset value in a geographical area comprising municipalities on the Adriatic Coast is in the 65th percentile according to the net asset value on the level of Croatia.
Figure 5.a Regional heterogeneity of households with regard to the value of net assets
Figure 5.b Comparison of net asset percentiles for households in the region and at the level of Croatia
Sources: HFCS and authors’ calculations.
Notes: The geographic location the Adriatic Coast includes: the Primorje-Gorski Kotar County, Zadar County, Šibenik-Knin County, Split-Dalmatia County, Istra County and Dubrovnik-Neretva County. The geographic location East Croatia includes: the Sisak-Moslavina County, Karlovac County, Bjelovar-Bilogora County, Virovitica-Podravina County, Požega-Slavonia County, Brod-Posavina County, Osijek-Baranja County and Vukovar-Srijem County. The geographic location Central Croatia includes: the County of Zagreb, Krapina-Zagorje County, Varaždin County, Koprivnica-Križevci County and Međimurje County.
In conclusion, the analysis shows that there is inequality in the distribution of assets among Croatian households. Real assets are relatively widespread among households and have a considerably higher share than in other EU countries: 85% of households own the main residence, which accounts for the largest portion of the household net asset value. However, the value of total net assets of households varies, depending on their socio-demographic characteristics, income, owner-occupancy and the geographical location of a particular household. The greatest inequality is observed in the possession of financial assets: these assets are owned only by some households. The analysis of the distribution of assets among households is especially important in the context of the assessment of effects of certain monetary and macroprudential policy measures, which may further deepen the existing inequalities. For example, macroprudential policy measures aimed at the alleviation of systemic risks arising from the fast growth of household lending and the growth of real estate prices may be focused both on bank capital and on loan users. An example of such measures is the limitation of the maximum loan or loan installment amount relative to the debtor’s income or assets. These measures are likely to affect the redistribution of income and wealth in a society, which has to be taken into account in their formulation. This is the reason why they are often introduced with exemptions for first-time buyers, the buyers of real estate in underdeveloped areas or buyers of residential real estate (in contrast with the buyers of real estate to let).
Financial stability is characterised by the smooth and efficient functioning of the entire financial system with regard to the financial resource allocation process, risk assessment and management, payments execution, resilience of the financial system to sudden shocks and its contribution to sustainable long-term economic growth.
Systemic risk is defined as the risk of an event that might, through various channels, disrupt the provision of financial services or result in a surge in their prices, as well as jeopardise the smooth functioning of a larger part of the financial system, thus negatively affecting real economic activity.
Vulnerability, in the context of financial stability, refers to structural characteristics or weaknesses of the domestic economy that may either make it less resilient to possible shocks or intensify the negative consequences of such shocks. This publication analyses risks related to events or developments that, if materialised, may result in the disruption of financial stability. For instance, due to the high ratios of public and external debt to GDP and the consequentially high demand for debt (re) financing, Croatia is very vulnerable to possible changes in financial conditions and is exposed to interest rate and exchange rate change risks.
Macroprudential policy measures imply the use of economic policy instruments that, depending on the specific features of risk and the characteristics of its materialisation, may be standard macroprudential policy measures. In addition, monetary, microprudential, fiscal and other policy measures may also be used for macroprudential purposes, if necessary. Because the evolution of systemic risk and its consequences, despite certain regularities, may be difficult to predict in all of their manifestations, the successful safeguarding of financial stability requires not only cross-institutional cooperation within the field of their coordination but also the development of additional measures and approaches, when needed.
On 1 January 2019, structural reform requirements came into effect in the United Kingdom for banks with more than GBP 25bn of retail deposits. These banks were required to separate, in terms of financing, operation and organisation, the provision of core retail services (deposit-taking, payment operations and financing households and small businesses) from other banking activities (investment banking and international financial market trading) in order to reduce risks and increase the retail banking’s resilience to shocks originating in other areas of operation or in global financial markets. Banks’ core retail activities that have been separated in this way are known as ring-fenced bodies. ↑
The Central Bank of Hungary defines these problematic exposures as the sum of non-performing and restructured gross loans secured by commercial real estate and the gross value of domestic commercial real estate held for sale on credit institutions' balance sheets. ↑
The Box shows the preliminary results of a working paper by M. Kunovac: What affects the net wealth of households in Croatia? ↑
The Household Financial and Consumption Survey was conducted in coordination with the European Central Bank. The European Central Bank had already coordinated two HFCS waves in some EU member states, the first one in the period from 2008 to 2010 and the second one in 2013. As Croatia joined the EU in July 2013, the Croatian National Bank (CNB) joined the third wave of the survey, in 2017, when data on net household assets for 2016 were collected. The survey was carried out in cooperation with Ipsos agency and the Croatian Bureau of Statistics (CBS) on a gross sample of 4 070 households. The realised sample comprised 1 357 households, which means that the response rate was 33%. Jemrić, I., and I. Vrbanc (CNB working material) provide a detailed sampling procedure, a description of the survey, a summary overview of the main results and their comparison with the results of the previous waves of the survey. ↑
For more details, see Nestić, D. (2005), Rubil, I. (2013), Rubil, I., P. Stubbs, and S. Zrinščak (2018), based on the Household Budget Survey of the CBS. ↑
An interesting example is provided by Austria, where inequality in the distribution of financial assets is lower than inequality in the distribution of real assets, as shown by the low share of main residence ownership (45%). For more details, see Fessler P., P. Lindner, and M. Schurz (2019). ↑
The survey probably overestimates inequality in the distribution of income as its sample comprises 7% of households that responded that they had no income of any kind and that their annual gross income is zero. EU-SILC 2016 data suggest a slightly lower income inequality distribution among Croatian households (EU SILC has a Gini income coefficient of 0.3 and the HFCS 0.5). ↑