Recent developments and macroprudential policy update

Austrian economy experiences second year of recession in 2024

The inflation shock is coming to an end, and HICP inflation is to fall below 3% in 2024. HICP inflation peaked at 11.6% in January 2023 and has since steadily declined, reaching 1.8% in September – a level last seen in early 2021. The drop in HICP inflation from 2023 to 2024 has been driven by all major components of the index, particularly industrial goods (excluding energy), energy and food. According to the OeNB’s latest forecasts, the annual average HICP inflation rate is expected to fall from 7.7% in 2023 to 2.9% in 2024. However, disinflation is anticipated to slow in subsequent years due to the expiration of fiscal measures in the energy sector. The OeNB projects HICP inflation to be 2.3% in 2025 and 2.2% in 2026.

Table 1: OeNB September 2024 outlook for Austria – main results  

	2023	2024	2025	2026
	Annual change in % (real)
Gross domestic product (GDP)	–0.7	–0.7	1.0	1.5
Harmonised Index of Consumer Prices (HICP)	7.7	2.9	2.3	2.2
HICP excluding energy	7.8	3.8	2.6	2.2
	%
Unemployment rate (national definition)	6.4	7.1	7.5	7.3
Source: 2023: Statistics Austria; 2024 to 2026: OeNB September 2024 outlook.

Austria’s budget deficit will be higher than 3%, which is why the country is likely to face an excessive deficit procedure. The ongoing recession, coupled with declining inflation, is causing a further worsening of public finances. Without additional corrective measures, the budget deficit will exceed the 3% target in 2024 and in the coming years. This increases the likelihood of the European Commission initiating an excessive deficit procedure against Austria.

The ECB lowered its deposit rate to 3.25% in October 2024. Inflation in the euro area has fallen more quickly than anticipated at the start of the year. According to the ECB’s September forecast, inflation is projected to reach 2.2% in 2025 and 1.9% in 2026. In response, the ECB Governing Council decided to lower interest rates in June, September and October by 25 basis points each, marking the start of an easing cycle.

The Austrian banking sector’s profitability and capitalization remain strong, but risks from commercial real estate loans intensify

Despite ongoing quantitative tightening, the liquidity position of the Austrian banking sector has improved and remained comfortable. Recent trends in Austrian banks’ funding and liquidity positions have continued in the first half of 2024. Banks have responded to the continued reduction of excess reserves in the Eurosystem (quantitative tightening) by further substituting their cash and reserve holdings with government and covered bond holdings. The banking sector managed to keep its overall liquidity risk metrics fairly constant: The liquidity coverage ratio, for instance, which measures the amount of high-quality liquid assets – like reserves or government bonds – that banks hold against expected short-term outflows in a liquidity stress scenario, stood at 174% on a consolidated level as of mid-2024, up 1.1 percentage points from end-2023 and up 9.2 percentage points year on year. On the liability side, the relative importance of sight deposits, which markedly decreased as interest rate hikes started in mid-2022, has stabilized in the first half of 2024. This could be a consequence of the narrowing interest rate differential between term and sight deposits due to recent interest rate cuts.

The Austrian banking system earned a EUR 7.0 billion profit in the first half of 2024, which is only slightly below the record set in the same period of 2023. Net interest income, which makes up around two-thirds of all income, was up by 4%, while fees and commissions recorded only a marginal decline. Consequently, operating income increased to EUR 18.8 billion in the first half of 2024. As operating costs rose in line with income – with noticeable reductions in contributions to resolution and deposit guarantee funds but higher impairments on participations – the operating profit stayed flat at EUR 9.1 billion. The cost-to-income ratio was almost unchanged at 51%. As risk provisioning rose by 12% to EUR 0.9 billion, and tax payments increased by 13% to EUR 1.7 billion, the profit of the Austrian banking system fell slightly to EUR 7.0 billion. This translated into a return on assets of 1.2%, some 8 basis points lower than in the first half of 2023, but still highlighting banks’ strong profitability.

The Austrian banking sector’s capitalization further increased in the first half of 2024, and large Austrian banking groups improved their capitalization compared to their competitors in the SSM. With the tailwind of high profitability, Austrian banks further increased their capitalization in the first half of 2024. Although risk-weighted assets grew by EUR 14 billion, the retention of profits raised the common equity tier 1 (CET1) ratio by a further 10 basis points to 17.7%. The leverage ratio – which is not risk-weighted – improved to 8.5%. Compared to the EU banking sector, the Austrian banking sector is well capitalized with a CET1 ratio 130 basis points above the average. Besides, the CET1 ratio of large Austrian banking groups in the SSM increased to 16.3%, some 50 basis points above the average of their SSM peers.

As for macroprudential capital requirements, the buffer for other systemically important institutions (O-SII) addresses risks that large systemic banks pose to the financial system. ¹ The buffer requires systemic banks in Austria to hold additional CET1 capital and thereby lowers their probability of failure. On an annual basis, the OeNB identifies banks that are systemically important and thus have to hold more capital. In October 2024, Austria’s Financial Market Stability Board (FMSB) recommended removing a temporary limit on O-SII buffer rates that had been in place since 2022 due to uncertainty over Russia’s war against Ukraine, increased energy prices and high inflation. As a result of this final phase-in step, four large banks will see their buffer requirements increase slightly.

In order to create a stronger link between systemic importance and buffer rates, the FMSB recommended increasing the number of buckets in the O-SII assessment methodology. ² Banks that are systemically important but at the lower end of the distribution will be required to hold a buffer of 0.45% (after taking the overlap with the systemic risk buffer (SyRB) into account). Currently, two banks fall into this category and will see a slight reduction in their O-SII buffers. At the other end of the spectrum, banks exceeding a high threshold will need to hold a buffer of 2.2% (after taking the overlap with the SyRB into account). This bucket is deliberately left empty and ensures that if the largest banks further increase their systemic importance, they do so with a commensurate increase in resilience.

Table 2: Allocation of scores to buffer levels  

Previously				New
Bucket	O-SII buffer	O-SII buffer	Scores	Bucket	O-SII buffer	O-SII buffer	Scores
	(pre-overlap)	(post-overlap)			(pre-overlap)	(post-overlap)
	% of CET1 capital				% of CET1 capital
				Bucket 1	0.5	0.45	Only additional indicators and <275
Bucket 1	1.0	0.90	275–636	Bucket 2	1.0	0.90	275–636
Bucket 2	1.5	1.30	637–999	Bucket 3	1.5	1.30	637–999
Bucket 3	2.0	1.75	≥1,000	Bucket 4	2.0	1.75	1,000–3,399
				Bucket 5	2.5	2.20	≥3,400
Source: OeNB.
Note: Changes in bold. “Pre-overlap” means prior to adjusting for overlap with systemic risk buffer; “post-overlap” means after adjustment.

The share of lending at variable interest rates continued to decline. In the first half of 2024, new borrowers in Austria sought low interest rate risk. Consequently, the share of variable rate loans in total new loans continued to decline to around 40% for households and three-quarters for companies. The decline was especially pronounced in mortgage lending, where only one in five new loans had a variable interest rate. However, the overall share of variable rate loans in Austria is still above the European average. Supervisors therefore continue to closely monitor further developments.

The deterioration of credit quality continued in the first half of 2024, albeit at a noticeably slower pace. Triggered by major bankruptcies in the construction and CRE sector, Austrian banks’ overall loan quality started to deteriorate in late 2023. This trend continued in the first half of 2024, albeit at a noticeably slower pace. In recent quarters, the decline in banks’ credit quality was more pronounced in Austria than in other European countries.

As of mid-2024, the consolidated nonperforming loan (NPL) ratio of the Austrian banking sector stood at 2.7%, well above the all-time low of around 2% marked two years ago. Banks’ risk provisioning in the first half of 2024 did not, however, keep up with the increase in NPLs. This means that the NPL coverage ratio, i.e. the ratio of loan loss provisions to NPLs, fell to 40%, as old NPLs with higher coverage were written off and the volume of new NPLs still continues to grow. Compared to Austrian banks’ peak NPL ratio of nearly 7% in 2015, however, the current level is still moderate.

A further increase in forbearance points to a continued deterioration in credit quality. Forbearance involves granting concessions to borrowers who are unlikely to repay their loans under the current terms and conditions, with the aim to return borrowers to a sustainable repayment path. It can take the form of refinancing or restructuring the loan or modifying the terms and conditions. Forborne loans are a leading indicator of future credit quality. In Austria, the share of forborne loans in total loans increased from 1.7% at the end of 2022 to 2.3% as of mid-2024.

Borrower-based measures for residential real estate (RRE) lending in Austria (the KIM-V) are effective. ³ Data for the first half of 2024 show a further increase in the share of sustainable loans ⁴ , from 80% to 84%. Moreover, a difference-in-differences estimation shows that the introduction of the KIM-V is associated with a reduction of the NPL ratio for RRE loans, thus effectively contributing to financial stability (see the special topic in this report entitled “From part of the problem to part of the solution: evaluating the effectiveness of borrower-based measures in Austria”). This contributed to a relatively stable credit quality of RRE lending, where NPL ratios remain at 1.1%. ⁵ In the interest of administrative simplification, the KIM-V was amended for a second time in July 2024. The indicator-specific exemptions were abolished and only the 20% institution-related exemption remains in place. The key role of the KIM-V has also been emphasized internationally: S&P Global Ratings positively highlighted the regulation in their Banking Industry Country Risk Assessment for Austria ⁶ and acknowledged that exemptions remained largely unused. The rating agency also confirmed that the decline in new lending was the result of increased financing costs, and not brought about by the KIM-V. This remained true throughout the first half of 2024, as close to EUR 500 million in exemption volume continued to be available. The share of banks that used less than half of their available exemption volume increased from 46% in the second half of 2023 to 62% in the first half of 2024. Given that the KIM-V has its legal sunset date on June 30, 2025, the OeNB is currently evaluating if borrower-based measures remain necessary to address systemic risks from the RRE sector.

CRE loan woes have intensified in the course of 2024. CRE loans have been under scrutiny by Austrian and international supervisors for several years now. Since interest rates started to increase in 2022, the vulnerabilities of this sector’s funding, which rested on increasing real estate values and low interest rates, have come to the fore. The number of defaults of real estate companies ⁷ has increased, as have nonperforming CRE loans on banks’ balance sheets (see chart 5). CRE loan loss provisions have increased as well, but to a lesser extent. Accordingly, CRE loans’ coverage ratios have decreased, while real estate values – another cushion to protect banks from losses in the event of defaults – have been under pressure as well.

This report features a special topic on systemic risks from CRE loans in Austria. It finds that a further deterioration of the economy and of real estate valuations, as experienced in past crises, could lead to CRE loan losses that are not covered by regulatory (“pillar 1”) and microprudential (“pillar 2”) requirements. The FMSB has concurred with this assessment and found that potential losses from CRE loans, in the event of a further deterioration of the economic environment, can pose an increased risk to financial stability in Austria. After its 42nd meeting, the FMSB therefore recommended that Austria’s Financial Market Authority (FMA) set a sectoral SyRB of initially 1% for risk-weighted exposures to domestic nonfinancial corporations of the ÖNACE 2025 sectors “M.68 real estate activities,” “F.41 construction of buildings” and “F.43 specialised construction activities” as of July 1, 2025. As limited-profit housing associations do not pose a systemic risk due, among other things, to their markedly lower probabilities of default, the FMSB recommended excluding them from the scope of the sectoral SyRB.

The importance of Austrian banks’ foreign business continued to grow. With EUR 530 billion in foreign assets as of June 2024, 43% of Austrian banks’ business was located abroad (see chart 6), mainly within the EU. The most important foreign markets are Czechia, Germany and Slovakia, accounting for 40% of all foreign business. While most banks’ business is done locally, either in Austria or via subsidiaries in host countries, one-fifth of all business occurs across borders.

The total assets of Austrian banking subsidiaries in Central, Eastern and Southeastern Europe (CESEE) surpassed EUR 300 billion in mid-2024, ⁸ with more than 80% located in EU member states. Six countries continue to be dominant, as Czechia accounts for 38%, Slovakia and Romania make up 15% and 12%, respectively, followed by Hungary, Russia and Croatia with shares of less than 10% each. As shown in chart 7, growth was strong during the COVID-19 pandemic but lost steam over the last two years. One of the reasons was tighter monetary policy, but as inflationary pressures are decreasing in the region and central banks start cutting rates, it will be important to monitor loan growth.

Profits of Austrian banking subsidiaries in CESEE reached a new high of EUR 3.1 billion in the first half of 2024, driven by higher net interest income and a marginal provision release. Net interest income of Austrian subsidiaries in CESEE rose to EUR 4.5 billion (+11% year on year), boosted by moderate asset growth (+3%) and an expansion of the net interest margin to 3.1% (+22 basis points). At the same time, fees and commissions income fell by 13% to EUR 1.8 billion. Consequently, operating income rose by 2% to EUR 6.6 billion. ⁹ As operating costs declined by 3%, driven by staff cost that fell despite ongoing wage pressures, the subsidiaries’ operating profit reached EUR 3.8 billion (+6% year on year). Risk costs were very benign in the first half of 2024, as EUR 26 million of credit risk provisions were released, compared to a buildup of more than EUR 300 million in the same period of 2023.

Credit quality of Austrian banking subsidiaries in CESEE remains at historically good levels, as reflected in an NPL ratio for total loans of just 1.9%, a 65% coverage of NPLs (both stable year on year) and a noticeable increase in the share of stage 1 loans, i.e. loans with no significant increase in credit risk since initial recognition (see chart 8). All these trends resulted in a half-year profit of EUR 3.1 billion, up 15% from last year. As of mid-2024, the aggregate CET1 ratio of Austrian banks’ CESEE subsidiaries stood at 20% and their loan-to-deposit ratio was 71%. These solid levels reflect past efforts by banks and supervisors to make local banking systems more resilient by increasing the subsidiaries’ risk-bearing capacity and ensuring a balanced refinancing structure. ¹⁰

As for macroprudential capital requirements, the SyRB addresses structural systemic risks, such as the domestic banking sector’s specific ownership structures and its high exposure to emerging economies in Europe. ¹¹ Disruptions in the whole or in parts of the Austrian financial system may entail severe negative consequences for the entire financial system and the real economy. In 2024, the SyRB was evaluated according to a biennial assessment plan. As it was found that the major structural systemic risks identified in the previous assessment from 2022 continue to exist, the FMSB recommended keeping SyRB rates unchanged. All previously identified banks will have to maintain a SyRB of 0.5% to 1.0%. Additionally, one institution was identified as a SyRB bank for the first time (with a rate of 0.5%). ¹²

Recommendations by the OeNB

• Continue to safeguard or, where appropriate, further strengthen their capital position by exercising restraint regarding profit distributions.
• Adhere to sustainable lending standards for residential as well as commercial real estate (CRE) financing and prepare for stricter supervisory requirements for CRE loans.
• Ensure adequate risk management practices, especially a commensurate coverage of NPLs by risk provisions and a conservative valuation of collateral.
• Ensure sustainable profitability by maintaining cost discipline while investing in information technologies as well as in protection against cyber risks and the impact of climate change.

The impact of the digital euro on Austrian banks from a financial stability perspective

Manuel Gruber, Christoph Siebenbrunner, Alexander Trachta, Christian Wipf ¹⁶

We study the impact the introduction of the digital euro might have on Austrian banks from a financial stability perspective. The premise is that the digital euro will not bear interest and will be subject to a holding limit. More specifically, we analyze (1) the impact on Austrian banks’ liquidity positions in a liquidity stress scenario and (2) long-run profitability effects on banks’ net interest income and income from payment services. With respect to liquidity risk, we find substantial effects only for extreme scenarios and high holding limits. For instance, at a holding limit of 3,000, the most extreme stress scenario we consider results in outflows of 9.0% of total retail deposits into the digital euro. Besides, 7.4% of banks (accounting for 4.2% of the sample’s total assets) would breach the regulatory liquidity coverage ratio (LCR) threshold of 100%. Smaller banks are disproportionately affected because they have a larger share of retail funding, which leads to higher outflows. The picture is similar with respect to the long-run effects on banks’ net interest income. In a similarly extreme scenario as above, we estimate that the digital euro would cause interest income losses and a drop in the aggregate sample return on equity (RoE) of 51 basis points – the aggregate sample RoE is 14.9% – at a holding limit of 3,000. Smaller banks and less capitalized banks would be affected more strongly. In a more realistic scenario, the effects are substantially lower, with 1.0% of total retail deposits outflowing and the aggregate sample RoE dropping by 5 basis points. Lower holding limits effectively contain adverse outcomes both with respect to interest income losses and liquidity risk. As to the effect on payment services income, it is harder to arrive at reliable estimates given a lack of suitable bank-level data and high uncertainty about the digital euro’s impact on transaction volumes and fees of retail current accounts and about how digital euro transactions and account management will be remunerated. In a tentative estimation, we find an aggregate sample RoE effect of around 26 basis points. By determining the remuneration of the digital euro, the central bank can effectively control the magnitude of this effect. Overall, we conclude that the introduction of a digital euro would not pose a threat to the stability of the Austrian banking system provided the digital euro is subject to a carefully designed holding limit and remuneration model. From a purely financial stability perspective, low holding limits would be preferable to higher ones.

JEL classification: E42, G21

Keywords: central bank digital currencies, digital euro, financial stability, substitution of bank deposits, liquidity, profitability, business models

We study these concerns, using bank-level data from Austrian banks. ¹⁷ We first analyze the impact the digital euro would have on Austrian banks’ liquidity risk in a stress scenario in section 1. Then, in section 2, we assess the long-run profitability effects of the digital euro, in particular on banks’ net interest income and income from payment services, abstracting from initial implementation costs. A particular emphasis lies on bank heterogeneity because the impact of the digital euro most likely depends on a bank’s business model, e.g. on its share of financing from retail deposits.

Consistent with the current proposals of the ECB, ¹⁸ we model the digital euro as a digital alternative to cash, which does not bear interest and can be held by households only. Households can hold digital euro up to a certain holding limit set by the ECB. Their digital euro accounts are directly linked with their other payment accounts to automatically top up the digital euro account up to the holding limit. This “(reverse) waterfall approach” allows households to transact any amount in digital euro, independent of the holding limit.

In the following sections, we focus on a baseline scenario characterizing the most likely outcome and a maximal scenario that captures the extreme but very unlikely upper bound for outcomes. The baseline scenario is mainly calibrated by using the Deutsche Bundesbank’s Survey on Consumer Expectations of 6,000 households in Germany presented in Bidder et al. (2024). This survey contains information on how households plan to use the digital euro both in normal and in crisis times.

1 Liquidity effects

Table 1: Descriptive sample statistics  

Total assets, EUR billion	Household sight deposits, EUR billion	Salary/pension accounts, EUR million	Accounts per bank (median)	Account size (mean), EUR
727.3	191.9	5.75	5,200	33,400
Source: OeNB.

We consider a systemic liquidity stress scenario, where e.g. due to a sudden loss of confidence in the banking sector, Austrian salary and pension account holders abruptly transfer deposits up to the holding limit from their deposit accounts to a digital euro wallet. We model the deposit outflow for bank i in such a liquidity crisis as follows

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Table 2: Calibration of the baseline and the maximal scenario liquidity effects  

	Baseline	Maximal	Description/Source
d€uptake_crisis	0.6	1	Bidder et al. (2024)
holding limit	(200, 5,000)	(200, 5,000)
d€holdings	1,000	0
Source: OeNB.

In the baseline scenario, d€uptakecrisis is the upper bound of Bidder et al. (2024), who find that in a crisis event 56% of respondents would adopt the digital euro. In the maximal scenario, we assume that all account holders adopt the digital euro. We also assume that digital euro adopters intend to hold EUR 1,000 in digital euro in the baseline scenario before the crisis, while in the maximal scenario they hold no digital euro. We motivate the low intended digital euro holdings with the “reverse waterfall approach” explained above and the unremunerated nature of the digital euro. Note that the maximal scenario means that households hold no digital euro before the crisis but in the crisis they all transfer deposits up to the holding limit into the digital euro.

Table 3: Deposit outflows under liquidity stress: baseline vs. maximal scenario  

	Baseline scenario			Maximal scenario
Holding limit	Deposit outflow, EUR billion	% of total assets	% of deposits	Deposit outflow, EUR billion	% of total assets	% of deposits
200	0.0	0.0	0.0	1.1	0.2	0.6
1,000	0.0	0.0	0.0	5.7	0.8	3.0
3,000	6.9	0.9	3.6	17.2	2.4	9.0
5,000	13.8	1.9	7.2	28.7	4.0	15.0
Source: OeNB.

Table 3 shows deposit outflows in the two scenarios. In the baseline scenario, deposit outflows increase linearly from holding limits above 1,000 (since households already hold 1,000 digital euro before the crisis) to EUR 13.8 billion or 7.2% of total sight deposits at a 5,000 holding limit. In the maximal scenario, deposit outflows increase linearly to EUR 28.7 billion or 15% of total sight deposits at a holding limit of 5,000.

Do banks have enough liquid assets to withstand these outflows? To answer this question, we calculate banks’ liquidity coverage ratios (LCR). In other words, we compare the high-quality liquid assets (HQLA) banks hold to their outflows due to the liquidity crisis outi,crisis plus their other net liquidity outflows (NLO). ²⁰

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2 Profitability effects

2.1 Net interest income (NII)

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Table 4: Calibration of the baseline and the maximal scenario NII effects  

	Baseline	Maximal	Description/Source
d€uptake_normal	0.5	1	Bidder et al. (2024)
d€holdings	1,000	5,000
deposit_sub	0.64	1	Bidder et al. (2024)
funding_advantage	1.61%	1.61%	Average interest spread between new term deposits and household sight deposits in Austria 2003–2008
Source: OeNB.

In the digital euro survey of the Deutsche Bundesbank (Bidder et al., 2024), 46% of households responded they would adopt the digital euro in normal times. We take the upper bound of this and – similar to the liquidity part – assume 100% adoption in the maximal scenario. d€holdings are calibrated as in the liquidity section. However, note that high digital euro holdings map into high outflows here, while in the liquidity section high digital euro holdings implied low outflows. Thus, we choose EUR 5,000 for the maximal scenario here (and as a robustness exercise we also consider 3,000 or 5,000 intended digital euro holdings in the baseline scenario). The substitution parameter is derived as follows: In Bidder et al. (2024), digital euro adopters project to hold 21.1% of their liquid portfolio in digital euro (a share similar to cash) while reducing their deposit share by 13.4 percentage points to this end. Thus, deposit_sub is 0.134/0.21=0.64. Note that NII losses are zero if digital euro holders completely substitute digital euro holdings for cash, i.e. deposit_sub is zero. Finally, the funding advantage banks lose with deposit outflows is calibrated to the period before the very low or negative interest rate period with deposit rates stuck at the zero lower bound. The value is close to Austrian banks’ average net interest margin in 2023 (1.53%).

Table 5 shows the deposit outflows and NII losses in the two scenarios. We also express NII losses relative to tier 1 bank capital, thus capturing the effect on the return on equity (RoE). In the baseline scenario, the aggregate deposit outflows amount to EUR 1.8 billion, which results in an NII loss of EUR 29 million and an RoE effect of 5 basis points, which is very small compared to the aggregate RoE (14.9%) in the sample. ²¹ In the maximal scenario, the effects are more material. Deposit outflows here exactly correspond to the outflows in the maximal crisis scenario above. This is because, with intended digital euro holdings of 5,000, the holding limit always binds and deposit outflows equal the holding limit, as was the case in the liquidity crisis scenario. Also note that the holding limit effectively contains the more material effects in the maximal scenario. Choosing a 3,000 holding limit instead of a 5,000 holding limit reduces the RoE effect from 86 basis points to 51 basis points.

Table 5: Deposit outflows and NII losses: baseline vs. maximal scenario  

	Baseline scenario			Maximal scenario
Holding limit, EUR	Deposit outflow, EUR billion	NII loss, EUR million	RoE effect, basis points	Deposit outflow, EUR billion	NII loss, EUR million	RoE effect, basis points
200	0.4	6	1	1.1	19	3
1,000	1.8	29	5	5.7	93	17
3,000	1.8	29	5	17.2	278	51
5,000	1.8	29	5	28.7	463	86
Source: OeNB.

Chart 2 identifies banks that are particularly affected by NII losses. In the baseline scenario, no bank has an RoE effect above 100 basis points. We only have significant effects in the maximal scenario, where the share of banks with an RoE effect above 100 basis points increases approximately linearly from a holding limit around 1,500. At a 3,000 holding limit, 23.1% of banks (22.6% of the sample’s total assets) have an RoE effect above 100 basis points, while at a 5,000 holding limit, 59.3% of banks (37.3% of the sample’s total assets) have an RoE effect above 100 basis points. As in the liquidity section, the NII losses rather affect small banks but not as strongly as was the case with effects on the LCR above.

2.2 Net payment services income (NPI)

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Table 6: Calibration of the baseline and the maximal scenario NPI effects  

	Baseline	Maximal	Description
NPI	1.1	1.1	Aggregate NPI of sample banks in EUR billion
x	0.16	0.32	16% (32%) of transactions move to the digital euro
R’D/RD	0.95	0.90	Profitability of transactions with sight deposits decreases by 5% (10%)
Source: OeNB.

To estimate the NPI, we proceed as follows: We use the average end-of-year 2020–2023 NPI of the banks for which NPI data are available (EUR 2.16 billion) and set this number in relation to the average end-of-year sum of sight deposits from households, nonfinancial firms and the government from 2020 to 2023. ²³ This yields 0.57, which means that Austrian banks on average earn 0.57 cent per euro of (transactional) sight deposits. Assuming this also holds for the household deposits of the banks in our sample, we arrive at an aggregate NPI of EUR 1.1 billion. To define how many transactions move into the digital euro (x), we follow up on the reasoning in the NII section. There, we assumed in the baseline scenario that 50% of account holders adopt the digital euro. We further assume that the share of deposit substitutions we assumed there (64%) also holds for transactions and, finally, we assume that holders of a digital euro wallet split their transactions 50-50 between sight deposits and digital euro, which yields 0.16. For the maximal scenario, we double this to 32% outflows. Finally, the decrease in the profitability of transactions with sight deposits of 5% (10%) reflects the idea that the introduction of the digital euro also increases competition in the conventional NPI business with sight deposits and puts these margins under pressure.

Table 7: NPI losses: baseline vs. maximal scenario  

	Baseline scenario		Maximal scenario
Digital euro remuneration	NPI loss, EUR million	RoE effect, basis points	NPI loss, EUR million	RoE effect, basis points
0.47	140	26	262	49
0.28	173	32	329	61
Source: OeNB.

Chart 3 shows the NPI losses for different digital euro remuneration levels Rd€/RD. Values like 0.5 mean that transactions in digital euro are remunerated at 50% of the current return on sight deposits. In table 7 we provide reasonable parameters for the remuneration of digital euro transactions, following the idea that the remuneration of digital euro transactions should target the return of the most efficient providers of transactions. ²⁴ This yields NPI losses ranging from EUR 140 to 173 million (EUR 262 to 329 million) and RoE effects of 26 to 32 basis points (49 to 61 basis points) in the baseline (maximal) scenario. As with holding limits, carefully calibrating the digital euro remuneration prevents extreme profitability effects for the banking sector.

References

Auer, S., N. Brazoli, G. Ferrerim, A. Ilari, F. Palazzo and E. Rainone. 2024. CBDC and the banking system. Banca d’Italia Occasional paper 829.

Bellina, M. and L. Cales. 2023. Bank profitability and central bank digital currency. JRC Working Papers in Economics and Finance 2023/06.

Bidder, R., T. Jackson and M. Rottner. 2024. CBDC and banks: Disintermediating fast and slow. Deutsche Bundesbank Discussion Paper 15/2024.

ECB. 2024. Progress on the preparation phase of a digital euro. June.

Meller, B. and O. Soons. 2023. Know your (holding) limits: CBDC, financial stability and central bank reliance. ECB Occasional Paper Series No 326.

Annex

Table A1: Sample statistics for small, medium-sized and large banks  

	Small banks	Medium-sized banks	Large banks
Share of banks, %	79.9	16.5	3.6
Share of total assets, %	15.6	25.0	59.4
Share of accounts, %	27.6	27.4	45.0
Share of deposits, %	22.3	27.9	49.8
Accounts per total assets, EUR million	14.0	8.7	6.0
Share of household deposits in total assets, %	37.9	29.6	22.1
Source: OeNB.
Note: Small banks are defined as banks with total assets up to EUR 1 billion, medium-sized banks’ total assets range from EUR 1 billion to EUR 10 billion, while large banks’ total assets amount to more than EUR 10 billion.

Systemic risks from commercial real estate lending of Austrian banks

Marcel Barmeier, David Liebeg ²⁵ , Sebastian Rötzer ^{26 , 27}

The commercial real estate (CRE) market in Austria – and many other countries – has been under stress at least since interest rate increases began in 2022. Consequently, the evaluation of financial stability risks in the CRE segment is of high relevance for supervisory authorities and policymakers. This study contributes to this goal by providing an integrated approach to gauge systemic risks associated with CRE financing. Combining macroeconomic information with data on the loan, firm and bank level, we estimate the effect of adverse macroeconomic conditions on CRE loan portfolios of Austrian banks. We find that in an adverse scenario, nonperforming loan (NPL) ratios could increase to levels seen in international historical crises and a sizable share of bank capital could be depleted. Thus, we conclude that CRE loans, in the event of a further deterioration of the economic environment, pose an increased risk to financial stability in Austria. This is in line with the assessment that Austria’s Financial Market Stability Board (FMSB) made in its 41st meeting.

JEL classification: G01, G21, G28, R33

Keywords: commercial real estate, systemic risk, financial stability

We start our paper with a short introduction on insights from past CRE crises, then present the data available for our systemic risk assessment, describe our empirical strategy, lay out the scenarios we use and the results we obtain, and finally conclude.

1 Insights from CRE crises of the past

Crowe et al. (2013), while concluding that CRE crises are difficult to pin down due to their interweaving with residential real estate (RRE) crises, find that CRE played an important role in the savings and loans crisis in the US in the early 1980s, in Japan from the late 1980s onward, in the Nordic crisis and in Australia in the early 1990s, in the Asian crisis in the late 1990s, and in Ireland and Spain in the late 2000s. Davis and Zhu (2011) employ a similar sample but add France, the UK, and the United States in the late 1980s and early 1990s to the list of property crisis episodes. Ellis and Naughtin (2010) add Australia, the UK and the US to the list of countries with CRE crises during the global financial crisis of the late 2000s. Herring and Wachter (1999) offer deeper insights into the CRE crises of the early 1980s in the US, the early 1990s in Japan and Sweden, and the late 1990s in Thailand. The Danish Systemic Risk Council (2023) finds that in the crisis of the late 2000s, lending to the corporate sector “real estate activities” has given rise to significant impairment charges for credit institutions.

While international CRE price data are comparably more widely available, data for CRE credit risk indicators, such as nonperforming loans (NPLs) and loan loss provisions from historical CRE crises, are scarce. In their overview, Deghi et al. (2021) show that CRE prices tend to reach their troughs roughly two years after their peaks, dropping by 20% to 56% in that time span. Some price drops last longer and are deeper – Ireland saw a decline by about two-thirds over five years. Ellis and Naughtin (2010) demonstrate that CRE price drops are usually larger than RRE price drops. As for risk indicators, the US Federal Deposit Insurance Corporation (FDIC) offers time series data on loan portfolio performance that are available for all FDIC-insured institutions from 1984 onward and for various aggregates of real estate loans. ²⁸ Noncurrent rates ²⁹ of US real estate loans increased from 0.9% in Q1 2007 to 7.6% in Q1 2010. In construction and development loans, there was an increase from 1% to 16.8% in the same time span. Banco de Espana’s BIEST ³⁰ dataset offers time series data on real estate loan quality from 1992 onward. The NPL ratio in Spain increased from 0.7% in Q4 2007 to 34.3% in Q4 2013 for loans to the construction sector. In loans to the real estate activities sector, the ratio rose from 0.7% to 38% in the same period of time. The loan loss provisions ratio for loans to the construction sector increased from 0.3% to 18% during this six-year period. Finally, Danmarks Nationalbank has provided us with data on loan loss provisions for real estate activities loans: They grew from 0.5% in 2007 to 9.4% in 2013.

2 Data

One particularity of the Austrian CRE segment is the importance of limited-profit housing associations (GBVs). ³³ GBVs account for 22% of domestic CRE loans, and the largest share is in the “real estate activities” sector. The risk structure of GBVs differs significantly from profit-oriented debtors (non-GBVs). As of year-end 2023, the NPL ratio stood at 3.7% for profit-oriented debtors, whereas it was 0% for GBVs. ³⁴ Furthermore, we also use information from the Bureau van Dijk SABINA database to investigate the balance sheet structure of CRE companies. As of year-end 2021, CRE companies had on average a lower capitalization than companies from other sectors irrespective of profit orientation (see chart 2). ³⁵ However, compared to profit-oriented debtors in the CRE sector, the number of GBVs with negative equity is much lower and close to zero. Moreover, with respect to the availability of liquid assets, we find that GBVs generally have stronger liquidity positions (cash and bank balances) than non-GBVs. Since better capitalization and liquidity as well as legal provisions effectively shield GBVs from market stress, we treat them separately in our assessment of systemic risks in the CRE segment.

3 Empirical strategy

3.1 Step I – Projecting CRE companies’ probability of financial distress

We use a two-step approach to map changes in macroeconomic state variables into shifts in probabilities of firm balance sheet stress. First, using data from the Banque de France’s BACH database, we estimate the impact of changes in macroeconomic variables on within-country aggregate revenue changes in the construction and real estate sectors. In the second step, following the micro-simulation approach of Ryan et al. (2023), we employ Bureau van Dijk SABINA data on firm-level balance sheets in the construction and real estate sectors to assess liquidity and solvency stress for a large number of historically plausible shocks to revenues and interest rates. In determining the thresholds for a corporation to fail, we rely on Guth et al. (2020) and Puhr and Schneider (2021): A firm is insolvent when either its cash and bank reserves are below –10% (e.g. bank lines are overdrawn) or its equity ratio is below –30%. ³⁷ Taken together, the combined sensitivities obtained from these two exercises allow us to project any macroeconomic scenario, be it historical or hypothetical, into a shift of borrowers’ probabilities of solvency or liquidity stress. ³⁸ To ensure that our projection method returns only real probabilities, we constrain output values of stressed borrower PDs, i.e. base PDs from the lender-borrower level data plus shifts, to the interval of [0.05%, 100%].

Taken together, let βNACE denote a vector of sectoral sensitivities to the vector of macroeconomic changes, ∆Z, including the particular element ∆NFCrate for the change in corporate lending rates. Let αS and αR be the sensitivities obtained from the micro-simulation in the second step and let PSSi and ShareVariablei be the ex ante probability of stressed sales by firm i and its share of variable rate loans. Then the projected probabilities of CRE companies’ financial distress in our model are given by

3.2 Step II – Simulation of banks’ portfolio losses from real estate financing

For each borrower in distress, the process of distress resolution, as illustrated in step II of figure 1, may lead to economic default if the proceeds from a collateral sale cannot cover the cost of debt following Harrison and Mathew (2008) and Górnicka and Valderrama (2020). Any resulting losses are then collected at the bank portfolio level. This approach allows us to track measures like probability of economic default, loss given default and NPL ratios not only on the individual bank-level but also for within-bank subportfolios, such as lending to each of the individual economic sectors considered, as well as to distinguish between profit-oriented debtors and GBVs.

In the implementation of our simulation methodology, we place particular emphasis on the consistency of the macroeconomic channels between the projection method in step I and the resolution of credit risk in step II. That is, the same shocks that drive up distress probabilities of firms in the broader real estate sector also reduce collateral value and increase ex ante loan NPVs. This is to maintain the intuitive perspective on how this sector accumulates a systemic risk to financial stability: It is the twofold dependency of debt servicing capacity and collateral value on real estate prices and interest rates that makes the real estate sector, and thereby its lenders, particularly vulnerable to adverse shifts in the macroeconomic environment.

4 Macroeconomic scenarios

Table 1: Macroeconomic scenarios: cumulative change over three years  

	Baseline	Adverse
	%
Real GDP	+4.1	–5.0
Risk free and corporate rate	Unchanged from YE 2023	Unchanged from YE 2023
CRE prices	+2.4	–28.4
RRE prices	+4.1	–33.2
Source: OeNB, ESRB, authors’ compilation.

Following the usual approach in stress testing, we use a three-year horizon, which is also backed by the experiences made in historic CRE crises that roughly lasted from two to six years peak to trough. In line with the static balance sheet assumption, we assume that the banks’ balance sheets do not change over time. The adverse scenario of our stress simulation assumes a severe stagflation: a period of negative GDP growth associated with elevated levels of inflation that hinders central banks from lowering their policy rates and a materialization of accumulated risks in the real estate sector that leads to an extended fall in property prices.

Cumulative GDP growth from 2024 to 2026 is 4.1% in the baseline scenario and –5.0% in the adverse scenario. CRE property prices rise by 2.4% in the baseline scenario and fall by 28.4% in the adverse scenario; RRE property prices increase by 4.1% and decline by 33.2%, respectively. ⁴⁰

5 Results

In the adverse macroeconomic scenario, the estimated PDs and LGDs for CRE loans increase significantly. While the PD is 0.3% is the baseline scenario, it increases to 16.4% in the adverse scenario. LGDs triple from 12% in the baseline scenario to 35.3% in the adverse scenario (see chart 3).

Consequently, the increase in PDs also leads to a higher share of NPLs. We find that our estimated NPL ratios in the adverse scenario are in the range of historical CRE crises: During those crisis periods, the NPL ratio stood at 8%–17% in the United States ⁴¹ and 34%–38% in Spain. ⁴²

Increases in PDs and LGDs subsequently also increase banks’ expected credit losses. In total, bank losses are estimated to amount to 13% of total CET1 capital in the adverse scenario. One third of capital headroom, i.e. the difference between CET1 capital and the required CET1 capital for fulfilling the overall capital demand (OCD), ⁴³ could be depleted in the adverse scenario. To put the number in another perspective, estimated bank losses could be larger than the average annual bank profits between 2019 and 2023 (see chart 4). Virtually all losses (98%) stem from profit-oriented debtors (non-GBVs), thus confirming the risk-mitigating character of limited-profit housing associations in Austria. This observation is supported by the distribution of losses across banks. Banks with a high share of GBV financing have lower average losses.

In a systemwide CRE crisis, various factors that are not incorporated in our model would influence the severity of the crisis. While banks’ operating profits could mitigate some effects, various aspects could amplify the impact of the crisis. These include an increase in bank funding costs, interbank contagion effects as well as negative spillovers to other industries. In our view, these observations confirm the systemic nature of risks associated with CRE financing.

6 Conclusions

References

Crowe, C., G. Dell’Ariccia, D. Igan and P. Rabanal. 2013. How to deal with real estate booms: Lessons from country experiences. In: Journal of Financial Stability 9(3). 300–319.

Danish Systemic Risk Council (Det Systemiske Risikoråd). 2023. Activation of a sector-specific systemic risk buffer for corporate exposures to real estate companies. Recommendation.

Davis, E. P. and H. Zhu. 2011. Bank lending and commercial property cycles: Some cross-country evidence. In: Journal of International Money and Finance 30(1). 1–21.

Davydenko, S. A. 2007. When do firms default? A study of the default boundary. Mimeo.

Deghi, A., J. Mok and T. Tsuruga. 2021. Commercial Real Estate and Macrofinancial Stability During COVID-19. IMF Working Paper 2021/264.

Ellis, L. and C. Naughtin. 2010. Commercial Property and Financial Stability – An International Perspective. In: Reserve Bank of Australia Bulletin. June. 25–30.

ECB. 2024. Macroprudential Report – Technical Annex. June.

ESRB. 2016. Recommendation of the European Systemic Risk Board of 31 October 2016 on closing real estate data gaps (ESRB/2016/4).

ESRB. 2019. Recommendation of the European Systemic Risk Board of 21 March 2019 amending Recommendation ESRB/2016/14 on closing real estate data gaps (ESRB/2019/3).

ESRB. 2023. Vulnerabilities in the EEA commercial real estate sector. January.

ESRB. 2024. Adverse scenario for the 2024 European Insurance and Occupational Pensions Authority’s insurance sector stress test exercise.

Górnicka, L. and L. Valderrama. 2020. Stress Testing and Calibration of Macroprudential Policy Tools. IMF Working Paper 2020/165.

Guth, M., C. Lipp, C. Puhr and M. Schneider. 2020. Modeling the COVID-19 effects on the Austrian economy and banking system. In: Financial Stability Report 40. OeNB. 63–86.

Harrison, I. and C. Mathew. 2008. Project TUI: A structural approach to the understanding and measurement of residential mortgage lending risk. Reserve Bank of New Zealand.

Herring, R. J. and S. Wachter. 1999. Real Estate Booms and Banking Busts: An International Perspective. The Wharton School – Financial Institutions Centre Paper Nr. 99-27.

Liebeg, D. and M. Liegler. 2022. Systemic risks of commercial real estate funding in Austria. In: Financial Stability Report 44. OeNB. 45–67.

Puhr, C. and M. Schneider. 2021. Have mitigating measures helped prevent insolvencies in Austria amid the COVID-19 pandemic? In: Monetary Policy & The Economy Q4/20–Q1/21. OeNB. 77–110

Ryan, E., B. Jarmulska, G. De Nora, A. Fontana, A. Horan, J. H. Lang, M. Lo Duca, C. Moldovan and M. Rusnák. 2023. Real estate markets in an environment of high financing costs. In: ECB Financial Stability Review. November 2023.

From part of the problem to part of the solution: evaluating the effectiveness of borrower-based measures in Austria

Marcel Barmeier, Selin Johanna Scheuerer ⁴⁵

Evaluating macroprudential policies is key to ensuring that measures are implemented effectively. Borrower-based measures were introduced in Austria in August 2022 via the so-called KIM-V regulation that defines sustainable lending standards for residential real estate (RRE) financing. In our evaluation, we provide evidence on how effective these measures have been so far in addressing systemic risks in Austria’s RRE sector. Based on data for lending standards, we find that the KIM-V has halved the share of new lending with a debt service-to-income ratio (DSTI) above 40%. In addition, by applying estimations in a difference-in-differences setting, we find that the ratio of nonperforming loans (NPLs) of RRE loans has decreased by up to 0.5 percentage points since mid-2022. Our findings support the literature, which shows that borrower-based measures effectively reduce systemic risks in the housing sector.

JEL classification: G21, G28, R31

Keywords: borrower-based measures, KIM-V, financial stability, residential real estate

1 Borrower-based measures and financial stability

2 Literature review

To measure borrower resilience, the target variable is often a single credit risk indicator, e.g. the PD, which is regressed on loan and borrower characteristics. Examples include de Haan and Mastrogiacomo (2020), who find that in Denmark limits to the loan-to-value ratio (LTV) and DSTI reduce the probability of nonperformance of loans, which encompasses arrears, foreclosures and defaults. If the LTV (DSTI) is 10 percentage points higher, the probability of nonperformance of loans increases by 0.19 (0.75) percentage points. Galán and Lamas (2019) corroborate the main insights for Spain, emphasizing that income-based measures are more robust determinants of mortgage default than LTV limits. Nier et al. (2019) show for Romania that if the DSTI limit of 40% had been implemented earlier, the PD would have been lowered by approximately 23% in comparison to the case without BBMs. Catapeno et al. (2021) rely on an agent-based model to assess the effectiveness of potential BBMs in Italy. They acknowledge that BBMs reduce the probability of mortgage default but find negligible effects for the Italian market. The TUI ⁴⁹ -model developed by Górnicka and Valderrama (2020) is another method to estimate effects on credit risk indicators. It was for instance applied to Switzerland (Maslova et al., 2022) and Austria (Górnicka and Valderrama, 2020) to measure the effectiveness of various theoretical DSTI, DTI and LTV limits. For Austria, the PD decreased from 3.9% to 2.2% in an adverse macroeconomic scenario thanks to a DSTI limit of 40% combined with an LTV limit of 80%.

With respect to lender resilience, the literature directs attention to risk measures on the level of individual institutions. Gross and Población (2017) developed a structural micro-macro model which combines household information of the Household Finance and Consumption Survey with macroeconomic and bank-level data. Household resilience is indicated by PD, LGD as well as the expected loss. Any change in these variables subsequently affects banks’ capital position via the mortgage portfolios. The model has been applied in a cross-country context, e.g. by Giannoulakis et al. (2023) or Ampudia et al. (2021), but also for individual countries, e.g. Slovakia (Jurča et al., 2020). Giannoulakis et al. (2023) find that the median capital ratio across countries implementing BBMs increases by up to 1 percentage point compared to no policy intervention. Some researchers construct their own bank-level risk measures to evaluate the impact of BBMs. The target variables are typically based on data from stock markets as well as banks’ financial statements. Meuleman and Vander Vennet (2020) distinguish between individual bank risk and risk from the linkage with the financial system. They find that BBMs are most effective in lowering banks’ individual risk. In other words, a unit increase (tightening) of their self-constructed index for BBMs on average reduces risk by 4.2 percentage points. They do not find a statistically significant effect on the linkage component. Belkhir et al. (2023) add that DSTI and LTV limits are effective in reducing banks’ expected capital shortage in a crisis – but only in combination with an inflation-targeting regime. Altunbas et al. (2018) find that asset class measures, which encompass DSTI, LTV and credit growth limits and limits on the exposure to the housing sector, reduce (increase) the expected default frequency ⁵⁰ for the average bank by 0.15 (0.66) percentage points when tightened (eased).

To summarize, the literature finds support for the effectiveness of BBMs in addressing systemic risks, measured by indicators evaluating borrower and lender resilience. This gives authorities well-founded arguments to apply BBMs. However, as national specificities play an important role for the effectiveness of BBMs, national characteristics should be considered.

3 Effectiveness of BBMs in Austria

3.1 Data

In Austria, data on lending standards, i.e. on DSTI, DTI, LTV, LTC and maturity, are available from 2011 onward. From 2011 until 2020, banks reported their lending standards as part of the “Hypothekarkreditumfrage” (HKU) ⁵² . Starting from 2020, reporting standards were amended and reporting via “VERA H – Private Wohnimmobilienfinanzierung” (VERA-H) ⁵³ related to RRE lending became legally binding. For Germany, we rely on data that are provided by a loan brokerage platform to the Deutsche Bundesbank (Ausschuss für Finanzstabilität, 2024). As the DSTI is the most relevant indicator for debtors’ ability to repay their loans, we discuss its development in more detail. ⁵⁴

Chart 1 shows the average volume-weighted DSTI for new lending in Germany and Austria. Although interest rates increased gradually from July 2022, the average DSTI for new lending in Austria remained below 30%, whereas in Germany the DSTI peaked at 33.5% in the first half of 2022. While the average DSTI in Austria also increased slightly between the first half of 2022 and the second half of 2023, the reduction in the share of loans with a DSTI above 40% dampened the overall increase in the DSTI (chart 2). In the first half of 2022, 16% of the new lending volume was issued with a DSTI above 40%; in the second half of 2023, this percentage dropped to 8%. The improvement of the DSTI and other lending standards (see the annex) is a first indication of the effectiveness of the KIM-V.

To gauge the loan quality of banks’ RRE portfolio, we consider the NPL ratio for RRE loans. The NPL ratio is corrected for loans that are past due more than one year. ⁵⁵ Chart 3 shows the development of the median corrected NPL ratio on an unconsolidated level for significant institutions from Germany and Austria since June 2020. ⁵⁶ While in Austria and Germany the NPL ratio remained relatively constant up to the introduction of the KIM-V, the NPL ratio increased in Germany from mid-2022 onward, namely from 0.3% to 0.5% in March 2024. In Austria, the NPL ratio stood at 1.1% in June 2022 and March 2024.

When evaluating the effect of the KIM-V on the NPL ratio, we need to consider that improved lending standards do not immediately reduce defaults in the stock. ⁵⁷ Thus, the direct increase in the NPL ratio recorded by German vs. Austrian banks should be considered as part of general fluctuations. Only the persistent increase in the NPL ratio in Germany relative to Austria might be attributable to the KIM-V. Since other confounding factors might have played a role, we continue our analysis with an econometric approach to estimate the causal relationship between the introduction of the KIM-V in Austria and the evolution of nonperforming loans.

3.2 Empirical strategy

Since the assignment of banks into a treatment or control group is crucial, we rely on two alternative approaches. First, as the KIM-V was introduced only in Austria, we draw on bank-level data from Germany to build a control group (baseline specification). ⁵⁸ As the banking sectors in Austria and Germany are alike (e.g. high degree of bank competition, large number of banks), German banks are most suitable to serve as a control group when we estimate the effects of the implementation of the KIM-V in Austria.

Second, we classify Austrian banks into a treatment and a control group based on their standards for new lending prior to the introduction of the KIM-V (robustness specification). ⁵⁹ Chart 4 shows the distribution of Austrian banks with respect to their share of new lending with a DSTI above 40% in 2020 and 2021, i.e. before the KIM-V was introduced. The control group comprises banks that had a below-median share of new lending with a DSTI above 40%, while banks with an above-median share of new lending with a DSTI above 40% make up the treatment group. ⁶⁰ Given the numerous exemptions to the KIM-V, banks with a low share of new lending with a DSTI above 40% did not need to change their lending standards significantly once BBMs were introduced. ⁶¹

The econometric validity of the difference-in-differences approach rests on critical assumptions. Most importantly, the method assumes that the NPL ratios of treated and non-treated banks have parallel trends in the absence of the KIM-V (“parallel trends assumption”). While this is generally not testable, the pre-KIM-V trends provide an indication. Chart 3 shows for the baseline specification that the evolution of the median NPL ratios were fairly parallel for banks from Austria and Germany before the treatment. In June 2020 and June 2022, the median NPL ratios corresponded for banks in both countries, with the German NPL ratio standing at 0.3% and the Austrian one at 1.1%. As a further assumption for the difference-in-differences approach, the composition of the control and the treatment group should not change over time (“time-invariant composition assumption”). This assumption would be violated in the baseline specification if banks endogenously changed their headquarters between Austria and Germany in response to the introduction of the KIM-V. However, this has not been observed in the Austrian and German banking markets.

To estimate the effects of BBMs on the credit quality in Austria, the following two-way fixed effects model in its baseline specification will be estimated: ⁶²

(1)

3.3 Results

Columns (1) and (2) show that the introduction of BBMs in Austria is associated with a 0.5-percentage-point decrease in the NPL ratio of Austrian banks compared to German banks. With respect to the robustness specification, we find that the KIM-V reduced the NPL ratio of Austrian banks that were relatively more exposed to the regulation by 0.1 percentage points compared to Austrian banks that were relatively less exposed (columns (3) and (4)). The results are confirmed when we consider bank control variables.

The results need to be interpreted with caution. While we are confident that the KIM-V reduced the NPL ratio for RRE loans, the magnitude is of greater uncertainty. This is shown by the relatively large difference between the estimated coefficients in the baseline and robustness specifications, which indicates the importance of choosing an appropriate control group. Furthermore, a reduction of the NPL ratio in the range of 0.1 to 0.5 percentage points may appear small. However, given that the KIM-V has only addressed a portion of the RRE loan volume currently outstanding ⁶⁶ , this would translate into a significantly lower NPL ratio for loans granted since August 2022. When we factor in the unfavorable macroeconomic developments since then (e.g. rising interest rates), it could seem unrealistic for RRE loans granted since mid-2022 to have a very low NPL ratio.

Table 1: Estimation results for the effectiveness of the KIM-V  

	Baseline specification		Robustness specification
Dependent variable	NPL ratio		NPL ratio
	(1)	(2)	(3)	(4)
BBM x Time	–0.0046**	–0.0045**	–0.0008**	–0.0013*
	(0.00002)	(0.00006)	(0.00001)	(0.00010)
Bank controls	No	Yes	No	Yes
Time-fixed effects	Yes	Yes	Yes	Yes
Country-/group-fixed effects	Yes	Yes	Yes	Yes
Observations	1,472	1,371	1,222	1,083
R2	0.05263	0.17945	0.04423	0.31703
Source: OeNB.
Note: Clustered standard errors are in parentheses. Significance codes: *** = 0.01, ** = 0.05, * = 0.1.

4 Concluding remarks

Given that BBMs such as the KIM-V address only the new lending volume, it can take many years for their full effect to unfold with respect to borrower and lender resilience. Many member countries in the Single Supervisory Mechanism therefore regard BBMs as a structural measure in the nature of a backstop (Lang et al., 2022).

References

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Ampudia, M., M. Lo Duca, M. Farkas, G. Pérez-Quirós, M. Pirovano, G. Rünstler and E. Tereanu. 2021. On the effectiveness of macroprudential policy. ECB Working Paper Series 2559/2021.

Ausschuss für Finanzstabilität. 2024. Elfter Bericht an den Deutschen Bundestag zur Finanzstabilität in Deutschland.

Belkhir, M., S. Ben Naceur, B. Candelon, W. Gyu Choi and F. Mugrabi. 2023. Macroprudential policy and bank systemic risk: does inflation targeting matter? IMF Working Paper Series 23/119.

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Annex

Results of the first dynamic balance sheet stress test in the ARNIE framework

Christoph Siebenbrunner ⁶⁷ , Martin Hafner-Guth, Philipp Weiss, Claus Puhr

Stress tests have become an important element of the supervisory review process for banks and an important tool for financial stability analysis. Including balance sheet dynamics substantially improves stress tests by reducing the need for implicit assumptions, thereby making them more realistic and enabling more flexible analyses. After years of work on the dynamic balance sheet stress testing model and its integration into the larger OeNB stress testing infrastructure (ARNIE), this paper presents the first dynamic balance sheet exercise by the OeNB, conducted in parallel with the annual static balance sheet stress testing exercise. The dynamic balance sheet model predicts that, in the baseline scenario, capital ratios stay relatively flat, showing that banks grow their balance sheets instead of hoarding capital. The aggregate CET1 ratio in the baseline scenario increases from 17.6% to 18%, with average annualized credit growth of 3.8%, compared to an increase to 19% in the static exercise. In the adverse scenario, credit growth at the system level slows down to practically zero over the course of the scenario horizon, but it does not turn negative because well-capitalized banks grow their balance sheets, gaining market share from capital-constrained banks that have to engage in deleveraging. The result is that growth of better-capitalized banks effectively compensates for deleveraging pressures from undercapitalized banks. The average annualized credit growth in the adverse scenario is 1.1%, leading to a CET1 ratio that is 0.4 percentage points lower than in the static exercise. This lower CET1 ratio is only true in the aggregate; granular results show a clear difference between undercapitalized banks, which undergo substantial deleveraging, and well-capitalized banks, which continue growing their balance sheets. We discuss these results and present an outlook for the future development of our dynamic balance sheet model.

JEL classification: G21, G28

Keywords: stress test, financial stability

The static balance sheet (SBS) assumption implies that banks cannot change their business model in reaction to shocks, and no additional steps are taken by banks to offset the adverse macroeconomic developments. In practice, it means that the size and composition of the balance sheet remains unchanged throughout the exercises’ horizon – assets and liabilities maturing over time are replaced with instruments similar to those at the start of the exercise. While such a simplification helps to ensure that individual banks will generate results that are roughly consistent and comparable to one another, it comes with numerous drawbacks. The simplifying approach does not take into account management interventions to respond to the shocks, thus preventing banks from reacting to adverse market conditions, most notably by deleveraging.

To address these weaknesses, supervisory authorities have sought to add dynamic elements to their stress tests for nearly a decade, longer in some cases. According to a survey of 31 authorities and 54 banks conducted by the Basel Committee on Banking Supervision, nearly half of supervisors use an SBS approach, but only one in five banks used this approach for their internal risk management (BCBS, 2017). Furthermore, top-down stress tests conducted by all major supervisory authorities, including the ECB, Fed, SNB, BoE and BoJ, already use some form of dynamic balance sheet (DBS) approach in their stress testing methodologies. This ranges from a simple proportional credit growth in line with projected industry-wide loan and asset growth to more elaborate modeling of optimized portfolio structure and allowing for management actions (Baudino et al., 2018).

At the OeNB, top-down stress tests were initially inspired by early works of Elsinger et al. (2006) on risk assessment in banks. They initially focused on financial stability with an aim to quantify systemic risk rather than to assess individual institutions (Boss et al., 2006). Due to the short observation period of one quarter, the implicit SBS assumption played a minor role. But even the subsequent multiperiod extensions of the approach used by the OeNB that resulted in the development of the Applied Risk, Network and Impact assessment Engine (ARNIE) and incorporated contagion analysis and solvency-liquidity feedback still relied on the SBS assumption (Feldkircher et al., 2013). In line with the aforementioned international efforts to consider more dynamic elements in stress tests and a more general push to make stress tests more macroprudential, while further enhancing the usefulness of stress tests for microprudential purposes, creating a DBS extension for ARNIE has become a priority of stress test development work at the OeNB.

In this paper, we present the first results of this ongoing work. First, we describe the scenario and results of the OeNB’s first DBS stress test. Second, we compare the results to those of an SBS calculation given the same underlying scenario. Then we discuss the impact on results and its implications for our analyses. We conclude by providing a brief outlook on our next steps.

1 Scenarios and results

1.1 Scenarios

1.2 Results

Moving from SBS to DBS has a substantially larger impact on REAs than on CET1 capital: In the adverse scenario, REAs are 10.6% higher in 2026 than the starting value in the DBS configuration, compared to an increase of 5.8% in the SBS configuration, a delta of 4.8 percentage points. In the baseline scenario, REAs are practically flat in the SBS configuration at –0.1% compared to +7.0% for DBS, a delta of 7.2 percentage points (numbers do not add up due to rounding). By comparison, the delta for CET1 capital is only 1.7 percentage points in the baseline scenario and 0.6 percentage points in the adverse scenario. A stronger reaction of REAs than CET1 capital to credit growth is to be expected, as REAs directly increase when new loans are granted, whereas capital only grows over time, through the positive P&L contribution of profitable businesses.

Chart 3 shows a breakdown of various drivers explaining the overall difference in the aggregate CET1 ratio between the DBS and SBS configurations. In line with the above discussion, REAs are the most important driver of differences between the results of the DBS and SBS exercises: REA changes drive the aggregate CET1 ratios by 118 basis points lower in the DBS configuration than in the SBS configuration in the baseline scenario, and by 73 basis points lower in the adverse scenario. These changes are partly offset by changes to income components, which – considered in isolation – increase the CET1 ratio in the DBS configuration by 89 basis points in the baseline scenario and by 29 basis points in the adverse scenario, as compared to the SBS configuration. Among those income components, net interest income (NII) is the most important driver. This is not a surprise, given that credit growth directly affects interest income. Taxes, dividends and other effects are another important driver in the baseline scenario, with an impact of –66 basis points. These are primarily driven by higher tax and dividend payments, which are both direct results of the higher income. With regard to credit risk provisions and participation income, the difference between the DBS and SBS configurations is comparatively small.

Chart 4 shows aggregate credit growth in the Austrian banking system for both scenarios. Credit growth is an output of the DBS model and hence only applies in the DBS configuration. Projections for credit growth take into account historical growth rates, macroeconomic influences as well as bank-level profitability and capitalization constraints. Credit growth is relatively consistent in the baseline scenario, with an average annualized growth rate of 3.8%, and drops substantially over time in the adverse scenario, with an average growth rate of 1.1%. These credit growth projections are somewhat high compared to recent credit growth rates due to two main reasons: First, credit growth in Austria has historically been high, which is still reflected in the model. Second, the baseline projection, which dates from 2023, is generally too optimistic compared to actual outcomes in 2024 – for credit growth and other variables as well. The decline in credit growth in the adverse scenario is driven both by macroeconomic variables as well as higher bank losses due to the stress scenario, which limit system-wide growth capacity due to capitalization constraints. Without these capitalization constraints, average annual credit growth would have been 1 percentage point higher in the adverse scenario. In the baseline scenario, capitalization constraints were not binding at the aggregate level but for some banks because shortfalls by capitalization constraints were compensated by other well-capitalized, profitable banks which could benefit by increasing their market shares. The same effect of well-capitalized, profitable banks growing their market share at the expense of capital-constrained, loss-making banks was also observed in the adverse scenario.

Table 1 shows average values for selected result drivers in the baseline and adverse scenarios for both configurations as well as the 2023 starting values for comparison. Variables are based on definitions developed explicitly for the stress tests, which may differ from other definitions used in this report. We see that the net interest margin (NIM) decreases in all scenarios compared to 2023. This is not surprising given the very benign environment for banks and is due to the asymmetric response of interest income and expenses to the interest rate increases throughout 2022 and 2023. A lower NIM in both scenarios reflects both a normalization, as interest expenses catch up with higher interest rate levels compared to pre-2022, and a decline in interest rates. The higher cost of risk in the baseline scenario is driven by an expected uptick in insolvencies, following low default rates in the (post)-COVID era. These changing circumstances are also reflected in higher cost-to-income ratios in both scenarios. Dynamic bank reactions partially offset this development, but only slightly.

Table 1: Selected results of the static and dynamic balance sheet stress tests  

	2023	SBS		DBS
		Baseline	Adverse	Baseline	Adverse
	%
Net interest margin	2.15	1.73	1.52	1.70	1.52
Cost of risk	–0.27	–0.40	–1.55	–0.39	–1.54
Cost-to-income ratio	48	61	75	58	73
	EUR billion
Loan stock	1,307	1,307	1,307	1,406	1,346
Source: Authors’ calculations.

2 Discussion and outlook

We observe that the decline in credit growth in the adverse scenario is less marked compared to other exercises. We focus on the exercise of Cappeletti et al. (2024) in our comparison because it is the most relevant benchmark in our opinion, being a relatively recent exercise by the European Central Bank covering the euro area banking system with a scenario horizon from 2023 to 2025. They project an annualized aggregate credit growth of –3.4% in the adverse scenario, compared to +1.1% in our results. These differences can be explained by various factors, including:

1. Different scope of the data: Our exercise only covers the Austrian banking market, and the credit growth model has been calibrated on historical data stretching back to 1998. Credit growth in the Austrian banking sector has been substantially positive over the whole 1998–2023 period, leading to generally high estimates. It seems likely that the difference between the Austrian market and the whole euro area, as well as potentially different time frames used in the model calibration will explain part of this observed difference.
2. Different scenarios: The exercise by Cappeletti et al. (2024) is based on data up to 2022 with a scenario horizon from 2023 to 2025, i.e. starting one year before our scenario. Different scenarios may account for some differences but given the overall comparable magnitude of the economic shock in both scenarios, this seems likely a less important contributor.
3. Model differences: The analysis of Cappeletti et al. (2024) is based on the BEAST model by Budnik et al. (2023), while our DBS exercise is based on our own OeNB DBS model, for which a publication will follow later. Compared to the BEAST model, our model emphasizes the ability of the banking system to compensate for the deleveraging needs of some banks, through the channel of other banks growing faster, capitalizing on the opportunity to gain market share. These compensation effects mean that credit growth slows down to effectively zero in the adverse scenario, but it does not turn negative. Combined with slower, but still positive, growth at the beginning of the scenario horizon, this leads to a slightly positive average credit growth rate of 1.1% in the adverse scenario. This positive credit growth is also responsible for the lower aggregate CET1 ratio in the adverse scenario. We stress that this lower CET1 ratio is only true in the aggregate. The granular picture shows a clear differentiation between well-capitalized banks, which grow faster and gain market share, and undercapitalized banks, for which we observe deleveraging at a scale comparable to, or exceeding, the results of the exercise of Cappeletti et al. (2024). These compensating effects seem likely to be the most important driver explaining the differences between our results and those of Cappeletti et al. (2024).

References

Basel Committee on Banking Supervision (BCBS). 2017. Supervisory and bank stress testing: range of practices.

Basel Committee on Banking Supervision (BCBS). 2018. Stress testing principles.

Baudino, P., R. Goetschmann, J. Henry, K. Taniguchi and W. Zhu. 2018. Stress-testing banks – a comparative analysis. FSI Insights on policy implementation 12.

Borio, C., M. Drehmann and K. Tsatsaronis. 2012. Stress-testing macro stress testing: does it live up to expectations? BIS Working Papers 369.

Boss, M., G. Krenn, C. Puhr and M. Summer. 2006. Systemic Risk Monitor: A Model for Systemic Risk Analysis and Stress Testing of Banking Systems. In: Financial Stability Report 11. OeNB. 83–95.

Budnik, K. B., J. Groß, G. Vagliano, I. Dimitrov, M. Lampe, J. Panos, S. Velasco, L. Boucherie and M. Jancokova. 2023. BEAST: A model for the assessment of system-wide risks and macroprudential policies. ECB Working Paper Series 2855.

Cappeletti, G., I. Dimitrov, C. Le Grand, L. Naruševičius, A. Nunes, J. Podlogar, N. Röhm and L. Ter Steege. 2024. 2023 macroprudential stress test of the euro area banking system. ECB Occasional Paper Series 347.

Drehmann, M. 2008. Stress tests: Objectives, challenges and modelling choices. In: Riksbank Economic Review 2/2008. 60–92.

Elsinger, H., A. Lehar and M. Summer. 2006. Risk Assessment for Banking Systems. In: Management Science. 52(9). 1301–1314.

Feldkircher, M., G. Fenz, R. Ferstl, G. Krenn, B. Neudorfer, C. Puhr, T. Reininger, S. W. Schmitz, M. Schneider, C. Siebenbrunner, M. Sigmund and R. Spitzer. 2013. ARNIE in Action: The 2013 FSAP Stress Tests for the Austrian Banking System. In: Financial Stability Report 26. OeNB. 100–118.

Interconnections between the Austrian banking sector and debt securities markets

Roberto Moshammer, Michael Nawaiseh ⁶⁸

Banks use debt securities markets to finance and refinance their activities and to manage liquidity. Debt securities offer many advantages regarding liquidity management, earnings stability and regulatory compliance. Yet, they also harbor risks in times of economic stress, as seen in the past in connection with bank collapses in the USA. Effective risk management and supervision are important to ensure that interconnected relationships do not compromise financial stability. It is therefore of great benefit if the requirements for issuing and holding financial instruments rest on a harmonized regulatory environment such as that provided by the European Union (EU). At the end of 2023, the majority of debt instruments issued by the Austrian banking sector were held by counterparties from the EU and the euro area. The securities on the banks’ books were also predominantly issued by counterparties from these regions. Refinancing from the EU and the euro area is crucial for the Austrian banking sector, as the strict European regulatory framework effectively reduces banks’ risk profiles. We use empirical data to show the most important trends over recent years (2017–2023). Austrian banks’ debt instruments grew significantly, with the volume of debt securities holdings increasing by 12.3% and that of debt securities issues surging by 50.6%. The counterparty composition shifted on both sides of the balance sheet. For one thing, debt securities issued by monetary financial institutions (MFIs) grew in prominence. For another, Austrian counterparties reduced their holdings, which was offset by a rise in EU and euro area counterparties. The changes highlight the deepening interconnection of banks with debt securities markets. This is addressed by both microprudential and macroprudential supervisory measures. In addition, the greater geographical diversity of counterparties, which are mainly from the EU and the euro area, contributes to a broader distribution of risk.

JEL classification: G11, G15, G20, G21, G23

Keywords: securities holdings statistics, banks securities holdings, bank securities portfolios, portfolio investment, debt securities, banking statistics, Eurosystem

1 Size, relevance and recent developments of the Austrian banking sector and the Austrian bond market

2 Developments relating to debt securities on the asset side of the Austrian banking sector since 2017

At the end of 2017, Austrian banks held EUR 97.9 billion in debt securities ⁷⁴ , which accounted for around 80% of banks’ entire securities portfolio ⁷⁵ . By end-2023, banks’ bond holdings had increased by 12.4% since 2017, to EUR 109.9 billion. However, this growth was not linear, with fluctuations throughout the years. Notable increases occurred in 2020 and 2023, while other years saw declines. Especially by year-end 2019, the total volume of debt securities had declined to EUR 91.6 billion (chart 2). The decrease was largely driven by low bond yields, as during that period many bonds carried fixed interest rates ⁷⁶ , and matured bonds were often not replaced. Besides, the reduction was ascribable to a shift toward more profitable lending opportunities, spurred by the low interest rate environment. Another factor were cautious investment strategies in response to global and EU market uncertainties such as the upcoming Brexit in 2020. Regulatory considerations likewise contributed to the decline. Moreover, government and corporate debt securities offered lower yields, which had an impact on risk-weighted assets. In 2020, the COVID-19 pandemic led to a significant economic shift. Price slumps on international financial markets and the economic downturn both in the EU and in Austria prompted comprehensive monetary and fiscal measures. The ECB measures, like the TLTROs and the PEPP, played a pivotal role as they included large-scale purchases of government and corporate bonds. The PEPP increased market liquidity, incentivizing banks to hold more bonds to use as collateral. Consequently, Austrian banks increased their holdings of debt securities to EUR 101 billion in 2020, and that level remained almost stable throughout 2021. In 2022, when interest rates began to rise and the COVID-19 crisis subsided, the market volume fell to EUR 96.7 billion. Yet in 2023, bond volumes reached a new high of nearly EUR 110 billion, driven by improved yields and banks’ strategic responses to rising borrowing costs and tighter liquidity conditions.

Analyzing the composition of Austrian banks’ portfolios reveals the following: Throughout the 2017–2023 period, government bonds and bonds issued by monetary financial institutions (MFIs) ⁷⁷ played a prominent role. They accounted for 90% of the total volume of debt securities in 2017, and for 88.7% in 2023, thus staying almost stable. As evident in chart 3, their respective relevance shifted somewhat over time. While the total volume of government bonds (around EUR 51 billion) remained relatively stable, their share in the portfolio declined from 52.8% to 46.4%. By contrast, MFI bonds rose from EUR 36.4 billion in 2017 to EUR 46.4 billion in 2023, with their share in the portfolio increasing from 37.2% to 42.3%. The reduction in government bonds was primarily driven by a continuous decline in the volume of Austrian government bonds between 2017 and 2022 (–48.5%), caused mainly by the search for higher yields in a low interest rate environment (chart 4). During this period, Austrian government bonds offered lower yields compared to those of other euro area countries, such as Spain, which prompted investors to seek more attractive alternatives. In 2022, Austrian government bonds plunged by 20.3%, as the termination of the ECB’s APP and PEPP reduced demand for government debt securities. ⁷⁸ The ECB’s key interest rate hikes in 2022 and 2023 led to a sharp drop in the market value of all types of bonds, especially those with longer maturities. Despite these reductions, 2023 saw a 13.8% increase in total debt securities holdings, driven largely by banks’ need to maintain liquidity buffers. Non-EU government bonds, particularly from the USA, also gained prominence, growing by 18.5% in 2023. ⁷⁹

By the end of 2023, Austrian banks’ securities portfolios largely consisted of debt securities issued by governments (46.4%) and MFIs (42.3%). EU issuers accounted for over 84% and euro area issuers for 67.9% of the portfolios. Euro area government bonds accounted for 32.5% and euro area MFIs accounted for 30.2%, while EU government bonds and EU MFIs each reached 38% ⁸⁰ .

As can be seen in chart 5, in 2023, the most important countries of origin were Austria (33.4%), followed by Germany (8.1%), France (7.1%) and Spain (4.2%). The share of Austrian debt securities, while still the largest component of banks’ securities portfolios, declined significantly from 2017 to 2023, namely from 48% to 33.4%. This reduction is primarily linked to the substantial decrease in Austrian government bond holdings, which dropped from EUR 28.9 billion in 2017 to EUR 15.9 billion by end-2023. Moreover, the importance of MFI bonds grew considerably during the same period, rising by 27.5%. Notably, the increase in Austrian MFI bonds, from EUR 13.1 billion to EUR 18.5 billion (+41.2%), partially offset the decline in government bonds. The rise in MFI bonds highlights the growing interconnectedness between Austrian banks and their EU counterparts through debt securities, which reinforces contagion risk. This risk is mitigated by macroprudential measures such as the capital buffer regime. Additionally, the reduction in Austrian government bonds points to a broader trend of regional diversification. The share of EU and euro area (excluding Austria) debt securities in Austrian banks’ portfolios increased from 41.6% and 28.1% in 2017 to 50.6% and 34.5% in 2023, respectively.

3 Developments relating to debt securities issued by Austrian banks since 2017

The sectoral analysis (chart 7) revealed that in 2017 MFIs held the largest share of Austrian banks’ debt securities (23.7%), followed closely by nonbank financial institutions (22.3%) and households and nonfinancial corporations (19.9%). Notably, one-quarter of Austrian bank bonds was held by investors outside the EU, which led to data gaps concerning most of those positions. ⁸² As a result, in 2017, for 22.6% of the bonds no detailed information regarding the counterparty was available beyond the country of origin. An additional 11.5% were held by other counterparties, such as governments. From 2017 to 2023, the composition shifted consistently toward a higher share of MFI holders, which had reached nearly one-third (32.7%) by end-2023. The distribution became more distinct, with 24.6% unclassified, 23.2% held by nonbank financial institutions, 13.7% by households and nonfinancial corporations and 5.8% by other counterparties. Especially from 2017 to 2019, households significantly reduced their holdings of bank debt securities (–21.9%). At the same time, MFIs increased their holdings of bank debt securities by 19.4%, while the holdings of nonfinancial corporations remained almost stable. This general trend persisted until the end of 2023. The “other” section also saw a decline, which was offset by MFIs.

A closer look at the EU holders of Austrian bank debt securities reveals that in 2017 counterparties within the EU and the euro area held a substantial portion of Austrian banks’ debt securities. EU holders accounted for 74.8% or EUR 90.9 billion and euro area holders for 66.1% or EUR 80.5 billion (chart 8). Over time, this composition shifted slightly, with the shares of EU and euro area counterparties adjusting to 72.1% and 69.8% in 2023. This change is partly attributable to the Brexit in 2020. The UK’s withdrawal from the EU reduced the proportion of EU-based holders. Nevertheless, the EU – particularly the euro area – remains the most significant region in terms of holders of Austrian debt securities.

As to the debt securities issued by Austrian banks (chart 9), a breakdown of holders by region was as follows in 2017: Austria (41.5%), followed by Germany (15.9%), the UK (5.3%), the USA (4.9%) and Luxembourg (3.2%). Over time, this regional composition shifted notably, with the share of Austrian holders shrinking significantly. In 2023, Austrian counterparties held just 29%, while Germany’s share had increased to 24%, that of the USA to 6.5%, that of the UK to 5.2% and that of Luxembourg to 5%. The 12.5 percentage point decline in Austrian holders was nearly offset by an 8.1 percentage point rise in German holders, which reflected a notable redistribution of bond holdings across regions.

In summary, between 2017 and 2023, the volume of debt securities issued by Austrian banks surged by 50.6%, reaching an impressive EUR 183.2 billion, which underscored banks’ increasing reliance on bonds as a financing tool. This growth was accompanied by a significant shift in counterparty composition, with MFIs rising in prominence to account for nearly one-third of holders in 2023. This trend highlights the deepening interconnection among banks via debt securities markets, mirroring developments on the asset side. Furthermore, the data illustrate the ongoing integration of the Austrian banking sector into the euro area and broader EU financial system, which is evident on both sides of the balance sheet. Austrian counterparties’ declining importance on the liability side mirrors trends on the asset side. But from 2017 to 2023, the shares of EU and euro area holders (excluding Austria) rose from 33.2% and 24.6% to 43.1% and 40.8%, respectively.

4 Risks and regulatory environment relating to banks’ debt securities

1. Interest rate risk, which is the risk that changing interest rates will negatively affect the market value of bonds. This effect is more pronounced, the longer the bond maturity is.
2. Credit risk, which is the risk that the issuer of a bond will default and fail to make interest rate payments or repay the principal. If the creditworthiness of the issuer is deteriorating, losses can occur. Credit risk is typically higher for corporate bonds than for government bonds.
3. Liquidity risk arises when bonds cannot be sold quickly in periods of market stress. If a bank faced sudden withdrawals, bonds could be sold with losses amid unfavorable market conditions.
4. Contagion risk, which refers to the risk that one bank’s financial distress may spread to other institutions. Such transmission can occur through debt securities, particularly if counterparty risks arise among financial institutions.

5 Summary and concluding remarks

References

Affinito, M., G. Albareto and R. Santioni. 2022. Purchases of sovereign debt securities by banks during the crisis: The role of balance sheet conditions. In: Journal of Banking and Finance 138(2022). 105575.

Barbiero, F., M. Boucinha and L. Burlon. 2021. TLTRO III and bank lending conditions. In: ECB Economic Bulletin, Issue 6/2021.

Boermans, M. 2022. A literature review of securities holdings statistics research and practitioner’s guide. DNB Working Paper No 757.

Schober-Rhomberg, A., A. Trachta and M. Wicho. 2019. Nonbank financial intermediation in Austria – developments since 2008. In: Financial Stability Report 38. OeNB. 74–86.

Schober-Rhomberg, A., A. Trachta and M. Wicho. 2021. Nonbank financial intermediation in Austria – an update. In: Financial Stability Report 42. OeNB. 57–70.

ECB. 2017–2023. Annual Reports. https://www.ecb.europa.eu/press/annual-reports-financial-statements/annual/html/index.en.html

Annex

Main changes of the EU regulatory environment relating to the bond market since 2017

1. MIFID II (implemented in January 2018)

2. Prospectus Regulation (effective since July 2019) ⁸⁶

3. Benchmark Regulation (effective since January 2018) ⁸⁷

4. Sustainable Finance Disclosure Regulation (effective since 2021) ⁸⁸

5. Implementation of the EU Green Bond Standard (2023) ⁸⁹

These regulatory improvements have made Austria’s bond market more transparent, sustainable and accessible, aligning it with broader European and global financial standards. All these enhancements aiming at investor protection, market integrity and sustainable finance have contributed to the Austrian bond market’s growth and resilience, which are key in the face of global and economic challenges.

Macroeconomic indicators for Austria

Table A1: Bank lending  

	2019	2020	2021	2022	2023	H1 23	H1 24
	%
Loan growth (year on year): households	4.2	3.6	5.3	3.5	–1.9	–0.3	–1.4
Loan growth (year on year): residential real estate	6.1	5.5	6.9	5.0	–2.4	0.0	–2.5
Loan growth (year on year): corporations	6.2	5.0	8.7	9.2	2.7	6.6	1.5
	% of total loans
Share of variable rate loans (outstanding): households	65	60	57	51	45	57	39
Share of variable rate loans (outstanding): corporations	70	69	67	67	66	87	77
Share of variable rate loans (new lending): households	51	46	47	59	51	50	40
Share of variable rate loans (new lending): corporations	82	77	86	85	78	82	75
Source: OeNB.

Economic indicators

Selected economic measures

Interest rates and exchange rates

Consumer prices

Economic sector breakdown of households

Economic sector breakdown of nonfinancial corporations

Property market

Table A2: Debt ratios  

	2019	2020	2021	2022	2023	H1 23	H1 24
	%
Household debt (relative to net disposable income)	92	95	94	89	81	83	77
Corporate debt1 (relative to gross operating surplus2)	471	466	476	457	473	447	536
Source: OeNB.
1 Short- and long-term loans, money and capital market instruments.
2 Including mixed income of the self-employed.

Indicators for the Austrian banking sector

Table A3: Consolidated banking data  

	2019	2020	2021	2022	2023	H1 23	H1 24
	EUR billion
Total assets	1,032	1,136	1,197	1,200	1,216	1,232	1,243
Loans	744	752	787	814	819	836	844
Shares and debt instruments	137	143	147	155	173	170	181
Cash balance and deposits at central banks	75	164	186	161	152	157	148
Deposits by nonbanks	615	656	686	709	717	717	734
Deposits by credit institutions	101	102	106	106	113	131	129
Debt instruments issued	150	153	152	163	195	186	204
Profit	6.7	3.7	6.1	9.8	12.6	7.3	7.0
Operating income	25.0	24.8	25.8	31.7	37.0	18.3	18.8
Operating costs	16.7	16.5	16.8	18.9	18.1	9.2	9.6
Operating profit	8.3	8.2	9.0	12.8	18.9	9.1	9.1
Risk costs	1.0	3.7	1.4	2.9	3.9	0.8	0.9
Key ratios	%
Common equity tier 1 (CET1) ratio	15.6	16.1	16.0	16.5	17.6	16.6	17.7
Leverage ratio	7.6	7.4	7.7	8.0	8.4	7.9	8.5
Return on assets (annualized)	0.7	0.4	0.6	0.9	1.1	1.3	1.2
Cost-to-income ratio	67	67	65	60	49	50	51
Nonperforming loan (NPL) ratio1	2.2	2.4	2.1	2.1	2.6	2.0	2.7
Coverage ratio	49	49	48	46	40	45	40
Liquidity coverage ratio (LCR)2	146	181	176	163	173	164	174
Net stable funding ratio (NSFR)2	n.a.	n.a.	135	131	134	134	136
Source: OeNB.
1 As of 2020, the NPL ratio excludes cash balances at central banks and other demand deposits.
2 Historical data calculated using the March 2024 banking sample at the highest consolidation level.

Table A4: Unconsolidated banking data1  

	2019	2020	2021	2022	2023	H1 23	H1 24
	EUR billion
Total assets	885	974	1.024	1.014	1.011	1.023	1.026
Loans	654	669	700	730	704	716	717
Shares and debt instruments	94	95	93	104	130	126	133
Cash balance and deposits at central banks	50	123	141	102	97	104	98
Deposits by nonbanks	444	474	496	505	516	510	523
Deposits by credit institutions	166	217	240	213	174	201	173
Debt instruments issued	137	140	140	160	190	184	197
Profit	4.8	2.7	6.5	5.0	12.3	6.6	5.4
Operating income	19.7	19.3	21.2	23.7	26.2	12.9	14.1
Operating costs	14.2	13.6	14.2	14.0	11.6	5.8	7.3
Operating profit	5.5	5.7	6.9	9.7	14.6	7.1	6.8
Risk costs	0.23	2.47	–0.42	3.63	1.1	–0.1	0.7
Key ratios	%
Return on assets (annualized)	0.6	0.3	0.7	0.5	1.2	1.3	1.1
Cost-to-income ratio	72	71	67	59	44	45	52
Nonperforming loan (NPL) ratio (Austria)	2.2	2.0	1.8	1.7	2.4	1.8	2.6
Coverage ratio (Austria)2	61	68	70	74	62	71	60
Liquidity coverage ratio (LCR)	142	174	171	155	168	158	166
Net stable funding ratio (NSFR)	n.a.	n.a.	129	124	127	127	129
Source: OeNB.
1 As of 2023 and due to reporting changes, comparability to previous years’ data is limited.
2 Total loan loss provisions as a percentage of NPLs in domestic business.

Table A5: CESEE subsidiaries  

	2019	2020	2021	2022	2023	H1 23	H1 24
	EUR billion
Total assets	223	234	271	279	288	294	303
Loans	161	165	186	184	188	196	202
Shares and debt instruments	38	42	48	49	55	55	59
Cash balance and deposits at central banks	18	22	30	39	39	36	35
Deposits by nonbanks	167	178	205	211	214	220	223
Deposits by credit institutions	22	16	18	18	17	22	21
Debt instruments issued	5	11	15	12	19	16	18
Profit	2.8	1.9	3.0	5.2	5.5	2.7	3.1
Operating income	8.4	8.2	8.9	12.8	12.7	6.5	6.6
Operating costs	4.4	4.4	4.6	5.1	5.5	2.9	2.8
Operating profit	4.1	3.8	4.3	7.7	7.2	3.6	3.8
Risk costs	0.5	1.3	0.5	1.0	0.3	0.3	–0.0
Key ratios	%
Return on assets (annualized)	1.3	0.8	1.2	1.9	1.9	1.9	1.9
Cost-to-income ratio	52	54	52	40	43	44	42
Nonperforming loan (NPL) ratio1	2.4	2.6	2.2	2.1	2.0	1.9	1.9
Coverage ratio	67	67	64	64	64	63	65
Source: OeNB.
1 As of 2020, the NPL ratio excludes cash balances at central banks and other demand deposits.

Structural indicators

Table A6: Financial stress indicators  

	2019	2020	2021	2022	2023	H1 23	H1 24
	Indicator value
Austrian financial stress indicator (AFSI)	–0.72	–0.57	–0.66	0.67	–0.29	0.02	–0.46
Composite indicator of systemic stress (CISS)	0.02	0.10	0.05	0.33	0.06	0.26	0.05
Source: OeNB, ECB.

Indicators for other financial intermediaries in Austria

Mutual funds

Pension funds

Insurance corporations

Overview of the macroprudential stance in Austria