Developments in loans to enterprises in Germany since the start of the monetary policy tightening cycle

Article from the Monthly Report

Over the past few years, growth in loans granted by German banks to domestic non-financial corporations has been characterised by unusually strong ups and downs. Although monetary policy responded to the inflationary surge that began in 2021 with substantial tightening, loan growth initially accelerated further to just under 14 % by October 2022. From the fourth quarter of 2022, however, net lending declined sharply; most recently, credit growth was close to zero. This development was reflected across all categories of banks, maturities and economic sectors. 

This article presents model-based analyses to establish whether such developments in lending were out of the ordinary when viewed alongside economic and interest rate developments, and examines the role played by loan supply and loan demand factors. 

It is apparent that the government support loans granted to selected energy suppliers in 2022 were a key factor in the initially observed increase in loan growth. In addition, the sharp rise in input and storage costs in 2022 increased the need for short-term financing across the corporate sector. 

The downturn in net lending began at the end of that year. Bundesbank models suggest that the decline in loan growth and the current stagnation are mainly attributable to the adverse macro environment, which was characterised in particular by a slowdown in economic activity, high inflation rates and rising interest rates. Banks responded to the rapid, steep rise in financial market interest rates by raising lending rates accordingly. Additionally, they tightened their lending policies in view of the increased credit risk on the corporate side. However, in our models, there is no sign of a conspicuous restriction of credit supply based on purely bank-related factors. This result is consistent with the continued stability of the German banking system.

The adverse macro environment and heightened uncertainty about the economic and geopolitical outlook, for their part, dampened private investment. This reduced the financing needs of non-financial corporations for equipment and buildings. Moreover, many enterprises already had sufficient internal financing at their disposal, not least as a result of high profits in recent years. Although the decline in loan demand caused by these factors was relatively strong, it was largely in line with historical patterns overall.

The Bundesbank’s current forecast for loans to non-financial corporations anticipates a gradual recovery over the course of 2024. The German economy, which is slowly regaining momentum according to the Bundesbank’s June Forecast for Germany, plus the gradual reversal of interest rate hikes by monetary policymakers currently anticipated in the financial markets, should bolster loan growth. Major bank-side restrictions on lending are not expected at present. However, the projected credit growth rate is rising only slowly, as enterprises tend to use their remaining internal funds first during an economic upturn. In addition, the initially still difficult investment environment is dampening loan demand. 

1 Stocktake

The strong and rapid rise in euro area inflation, which began in 2021, required a decisive monetary policy response. The euro area has been hit by various supply and demand shocks in recent years. Increased demand for goods, also bolstered by extensive economic policy measures, coincided with pandemic-related production and supply bottlenecks. This drove commodity prices, transport costs, and producer and consumer prices up sharply. The further rise in commodity prices brought about by the Russian war of aggression against Ukraine exacerbated the already high price pressures. Finally, the normalisation of demand for services following the lifting of mobility and contact restrictions contributed to the price increase. 1 Euro area inflation reached a record high of 10.6 % in October 2022. In order to ensure price stability over the medium term, the ECB Governing Council decided at the end of 2021 to move away from the low interest rate environment of previous years and tighten monetary policy. It ended its purchase programmes and raised key interest rates by a total of 450 basis points in rapid steps between July 2022 and September 2023. Having held key interest rates steady for nine months following this period, the Governing Council lowered them by 25 basis points in June 2024. At the same time, it stressed the data dependence of its future monetary policy path. 

Weaker economic activity and higher interest rates weighed increasingly on net lending 2 in Germany. The dampening of loan dynamics was intentional on the part of monetary policymakers. 3 A significant degree of monetary policy impulses are typically transmitted via the banking system. Banks’ own increased financing costs are passed through to their customers in the form of higher lending rates. As a result, loan demand on the part of non-financial corporations 4 and households is lower. They invest less, and economic growth weakens. This leads, with a certain time lag, to a weakening of inflationary pressures. 

At the start of the monetary policy tightening cycle, however, growth in corporate lending picked up significantly, declining steeply only from the end of 2022. The annual growth rate of loans issued by banks in Germany to domestic non-financial corporations accelerated significantly between 2014 and 2019, reaching 5 % at the end of 2019. 5 After a brief slowdown due to the coronavirus pandemic, loan growth picked up again, reaching its highest level since 1992, at just under 14 %, in October 2022 (see Chart 3.1). Only then was the changed macro environment reflected in loans to enterprises: their annual rate of growth declined rapidly and has been hovering around 0 % since the fourth quarter of 2023. Such strong ups and downs in the rate of loan growth had only previously been observed during German reunification and the global financial crisis in 2008. 

Loans granted by German banks to domestic non-financial corporations* by category of banks
Loans granted by German banks to domestic non-financial corporations* by category of banks

2 Developments in lending and macroeconomic environment

Empirical models can be used to analyse the extent to which the ups and downs of loan growth line up with economic and interest rate developments. The steep upward and downward movement of loan growth and the current weak lending dynamics raise the question of whether lending to non-financial corporations reacted more strongly to the changed macro environment than would have been expected in view of past correlations. To answer this question, actual loan growth since the start of the interest rate tightening period can be compared with hypothetical developments derived from model relationships. Chart 3.2 presents the outcome of such an exercise on the basis of a forecasting model that describes loan developments using the investment ratio, the yield spread between long-term corporate and government bonds, the lending rate and the development of credit standards set by banks. As outlined in the supplementary information on loan forecasts at the end of this article, the model is initially estimated for the period up to the end of 2021. Loan developments are subsequently forecast for the period from the beginning of 2022 to the current end using the estimated model relationships and the actual development of the macro environment. 

On balance, although loan growth was relatively weak from mid-2023 onwards, it remained broadly in line with the estimated historical pattern. Chart 3.2 compares the actual growth in loans to enterprises (black line) with hypothetical developments (blue line) calculated on the basis of the forecast simulation described above since the start of the interest rate tightening cycle. The results of the forecast simulation are naturally subject to great uncertainty, and this is depicted by a forecast range. Generally speaking, actual loan developments progress largely in line with this uncertainty band. That said, the individual fluctuations in actual loan developments are more substantial than projected by the model: for instance, actual loan growth in 2022 is greater to begin with, lying slightly above the uncertainty band in the second quarter of the year. The decline in growth from autumn 2022 onwards is also steeper than forecast; in the third and fourth quarters of 2023, actual growth is below the uncertainty band. However, the ups and downs in loan growth were significantly amplified by aid loans from the KfW (Kreditanstalt für Wiederaufbau) to selected energy suppliers (see the following chapter). Adjusting the loan series for this one-off factor, actual loan growth as a whole remains within the uncertainty band (grey line).

Conditional forecast of the annual growth rate of loans to non-financial corporations in Germany*
Conditional forecast of the annual growth rate of loans to non-financial corporations in Germany*

A second empirical model also reinforces the assessment that lending to enterprises has, on the whole, followed the historical pattern over the past two years. The forecasting model used above forms the basis of the Bundesbank’s regularly produced loan forecasts. It focuses on forecasting quality and is therefore parsimonious. By comparison, the macro-financial Bayesian VAR model described in the second supplementary information section at the end of the article comprises a larger number of monetary, financial and real economic data. It can thus be used for analyses of a greater number of questions, including the identification of different economic shocks and their effects. In the simulation described above, this larger model produces comparable results, reinforcing the conclusion that loans to enterprises have largely developed in line with historical patterns over the past two years and that lending dynamics are not unusually weak at the current end.

3 Loan dynamics increased on the back of one-off factors up to October 2022

Sharply higher commodity prices as well as input and storage costs heightened the German corporate sector’s borrowing needs. A surprisingly strong recovery in demand for goods exacerbated pandemic-related supply and production bottlenecks. As a result, commodity prices, transport and material costs rose sharply. German enterprises were hit particularly hard by these developments. In addition, the unavailability of key intermediate products and materials caused disruptions in production and sales. This led to an increase in storage costs for unfinished products. On balance, demand for short-term loans to finance inventories and working capital rose sharply. 6 An evaluation of individual firm data from the Bundesbank’s representative survey of German firms (Bundesbank Online Panel Firms (BOP-F)) 7 also illustrates the relationship between supply bottlenecks and lending: the share of firms that anticipate both decreased access to necessary intermediate inputs in the next year and an increase in their need for credit financing rose significantly up to mid-2022 (see Chart 3.3).

Factors influencing developments in short-term loans
Factors influencing developments in short-term loans

Extensive government aid loans to energy suppliers were another key factor in the steep rise in loan growth in the 2022 summer half-year. Sharply higher gas and electricity prices, combined with hedging transactions and other commitments, caused considerable financial difficulties for some energy suppliers. In order to safeguard the energy supply in Germany, the KfW granted exceptionally high loans to a number of selected companies in the energy sector on behalf of the Federal Government. 8 Looking at all loans to enterprises on aggregate, this is evidenced by the steep increase in the contribution made by the category of banks “Banks with special, development and other central support tasks,” to which the KfW belongs, to the annual growth rate of loans to enterprises; this category’s contribution to growth rose by 4½ percentage points in 2022 (see Chart 3.1). In November 2022, it accounted for around one-third of the growth rate of all loans to enterprises. 

The after-effects of the extensive fiscal policy support measures taken in the early stages of both the coronavirus pandemic and of a highly expansionary monetary policy also boosted lending in 2022. 9  The empirical macro-model described in the supplementary information on the macro-financial Bayesian VAR model at the end of this article decomposes the fluctuations in credit growth around a long-term equilibrium into the contributions made by economically interpretable exogenous impulses, i.e. structural shocks. The analysis attributes the acceleration of credit growth from the end of 2021 onwards to what are, from the perspective of the model, unexpectedly strong developments in macroeconomic demand (positive aggregate demand shocks) and an unusually accommodative monetary policy (expansionary monetary policy shocks) following the first few quarters of the coronavirus pandemic (Chart 3.17 in this supplementary information). Other structural economic shocks, by contrast, played no major role in loan developments. The aggregate demand shocks are, at least in part, likely to reflect the extensive pandemic-related fiscal policy support measures taken as well as, later on, the expansionary effect of pent-up demand during the pandemic. The model results suggest that the decline in credit growth from mid-2022 onwards is partly due to the gradual end of the supportive effect of the demand shocks and the tightening of monetary policy over the course of 2022.

The monetary policy tightening initiated at the end of 2021 likewise boosted demand for bank loans in the short term. In December 2021, the Governing Council of the ECB decided to discontinue net asset purchases in response to the rapid and strong rise in inflation in the euro area. This change in monetary policy led to a significant rise in capital market interest rates. By contrast, the monetary policy impulse affected banks’ lending rates with a certain time lag, as was to be expected based on historical patterns. 10 The fact that interest rates responded at different speeds was an incentive for enterprises with access to the capital market to increasingly finance themselves via loans rather than debt securities. In addition, loan demand was strengthened by the fact that, to secure interest rates, some enterprises took out still relatively cheap medium and long-term bank loans earlier than planned in anticipation of a rise in lending rates.

4 Subsequent phase of weakening loan dynamics

The interplay of tighter lending conditions and a decline in loan demand drove the slowdown in loan dynamics in the corporate sector observed from autumn 2022 onwards. Banks responded to the slowdown in economic activity and the turnaround in monetary policy interest rates resulting from the acceleration in inflation by gradually tightening their lending conditions. Enterprises, for their part, demanded fewer loans. However, given the sharp decline in loan growth and the relatively weak loan dynamics compared with the model forecast, the question arises as to whether this is driven more by loan supply or loan demand. A strong tightening in loan supply carries the risk that weak lending and the economic downturn could inadvertently be mutually reinforcing. Overly weak loan demand could also trigger such a macro-financial feedback loop if it is based on excessive corporate debt, and if investment that is, in fact, profitable is put on hold. This chapter explores the question based on various statistics and model results.

4.1 Higher lending rates and tighter lending conditions

Given the sharp rise in key monetary policy interest rates, it was to be expected that banks would raise their lending rates and tighten their lending conditions – and that is in line with monetary policymakers’ intentions. The question is, however, whether banks’ reaction was mainly due to the more adverse macro environment or whether it was notably reinforced by purely bank-related factors, such as capital or liquidity constraints. The latter could trigger major feedback effects between the financial system and developments in the real economy, which would dampen the economic slowdown further. Such a development would go beyond the usual intended effects of monetary policy and would therefore be undesirable. 

The rise of interest rates in the financial markets led banks to increase lending rates on loans to enterprises significantly – but within the expected range. After the ECB started raising its key policy rates in July 2022, interest rates in the money and capital markets continued to rise with each upward revision of rate hike expectations, with corresponding repercussions for banks’ financing costs. The banks, in turn, responded by tightening lending rates (see Chart 3.4). Empirical models based on historical relationships show that changes in market interest rates are generally almost fully reflected in lending rates on loans to enterprises, albeit with a certain delay. 11 Although interest rates in the financial markets began to decline somewhat again in November 2023, the aggregate lending rate broadly remained at its high level until May 2024, thus continuing to have a dampening effect on lending. 

 Interest rate for bank loans to non-financial corporations in Germany*
 Interest rate for bank loans to non-financial corporations in Germany*

The data collected in the Bank Lending Survey (BLS) suggest that banks also made their lending conditions more restrictive. Since the first quarter of 2022, the banks questioned in the BLS have continuously tightened their credit standards for loans to enterprises, i.e. their internal loan approval criteria (see Chart 3.5). The intensity of the tightening has eased somewhat since the second half of 2023. While the scale of the overall tightening was considerable, it was much less pronounced than the restrictive adjustments made during the global financial crisis. In addition, banks applied stricter standards to their credit terms and conditions, for example by increasing margins or imposing more stringent collateral requirements. 

Germany: change in credit standards for loans to enterprises and contributing factors*
Germany: change in credit standards for loans to enterprises and contributing factors*

According to BLS data, the main reason for the tightening of credit standards was the increase in credit risk on the corporate side. In particular, respondents to the survey stated that the economic situation and outlook had deteriorated, in their assessment. Furthermore, they noted that industry-specific and enterprise-specific factors and a deterioration in borrowers’ creditworthiness had contributed to the tightening (see the supplementary information entitled “Interaction between credit risk, bank credit supply policy and loan negotiations”). In addition, the surveyed banks reported a decline in their risk tolerance. By contrast, bank-related factors affecting the cost of funds and balance sheet constraints for banks contributed only marginally to the tightening of credit standards for loans to enterprises overall, according to the surveyed bank managers.

Supplementary information

Interaction between credit risk, bank credit supply policy and loan negotiations

Disaggregated data illustrate that the deteriorated macro environment has led to a particularly pronounced increase in credit risk for construction firms and real estate firms as well as for smaller enterprises. Data from the analytical credit dataset (AnaCredit), the BLS and the Bundesbank’s BOP-F survey suggest that, during the period of monetary policy tightening, banks adjusted their lending policy depending on the borrowers’ economic sector and firm size. As regards credit risk, according to AnaCredit the average probability of default of firms in the construction and real estate sectors as well as of smaller enterprises, weighted across all credit claims in the respective segments, rose more sharply in 2023 than the probability of default of all firms (see Chart 3.6). However, compared to the average of all firms, it does not appear to be exceptionally high thus far. In the construction and real estate sectors, the increased credit risk can be explained in terms of the turnaround in the real estate market, which, in both sectors, led to an above-average deterioration in gross value added and to a rise in the number of corporate insolvencies. Small businesses are likely to be less able to circumvent the currently adverse business environment owing to the less diversified nature of their sources of income and a deterioration in access to capital markets. 

Credit risk of German banks' loans to domestic non-financial corporations*
Credit risk of German banks' loans to domestic non-financial corporations*

Banks reflected the elevated credit risk in part by tightening their lending policies. Empirical models based on historical interrelationships show that, as from June 2023, banks raised their lending rates somewhat more steeply than would have been expected based on the models. This is probably because banks increased their risk premia against a backdrop of weak economic growth and an uncertain corporate outlook. In addition, the BLS banks cited industry-specific and firm-specific factors as additional reasons for tightening their credit standards. The surveyed banks reported tightening their credit standards more strongly for, in particular, construction firms and real estate firms since mid-2022 than for those in other economic sectors (see Chart 3.7). For small and medium-sized enterprises, however, the BLS banks did not report an unusually severe tightening of credit standards.

Germany: Changes in credit standards across the main economic sectors*
Germany: Changes in credit standards across the main economic sectors*

Mirroring the BLS, firms responding to the Bundesbank’s BOP-F survey have reported a deterioration in loan negotiations since the beginning of the period of monetary policy tightening. For the entire corporate sector, the BOP-F survey shows that the share of firms that have received loans either at reduced amounts or at less favourable conditions, or whose loan negotiations ended without a deal, has gone up since the beginning of the period of monetary policy tightening (see Chart 3.8). 1 For ease of comparison, it makes sense to merge the six possible responses from which firms can choose when assessing their loan negotiations into a single indicator. For each loan category, the indicator is calculated by the sum of the number of weighted responses “Loan approved, conditions as desired” multiplied by + 1, the number of weighted responses “Loan approved but for a loan amount reduced and/or less favourable conditions” multiplied by − 0.5, and the number of weighted responses “Loan negotiations concluded without a deal” multiplied by − 1. 2  

Outcome of loan negotiations*
Outcome of loan negotiations*

In the BOP-F survey, construction firms 3 assessed the results of their loan negotiations as being increasingly worse, including in comparison with other economic sectors. Whereas, according to the loan negotiation indicator, the construction sector’s bargaining position was better than average at the end of 2021, it has increasingly deteriorated since then (see Chart 3.9). Most recently, the indicator value was below the average of all economic sectors.

On the other hand, data from the BOP-F survey do not suggest that the tightening of the credit supply has hit micro and small enterprises disproportionately hard. Since the beginning of the period of monetary policy tightening, the loan negotiation indicator for firms in each of the individual size classes has shown a fundamentally similar trajectory, albeit at different levels (see Chart 3.9). This suggests that, for structural reasons, loan negotiations for micro and small enterprises lead to results that are, in their view, less favourable than for medium-sized or large enterprises. During the period of monetary policy tightening, the levels of loan negotiation indicators calculated by enterprise size shifted markedly downward. On balance, however, a disproportionately strong tightening of banks’ lending to micro and small enterprises cannot be inferred from the indicator.

Loan negotiation indicator*
Loan negotiation indicator*

Footnotes
  1. The share of firms that negotiated loans in the survey quarter has remained relatively stable at just under 15 % since the beginning of the period of monetary policy tightening.
  2. The weighted responses of firms whose loan negotiations had not yet been concluded at the time of the survey are entered into the indicator with a factor of zero. For the purpose of calculating the indicator value, the sum of the individual weighted responses, multiplied by the relevant factors, is reported per quarter and loan category as a percentage of the weighted number of firms that negotiated loans in the respective quarter. 
  3. Separate data on firms providing residential and other real estate services are not collected in the BOP-F survey. 

Enterprises did not perceive this tightening of loan supply as exceptionally strong. In the corresponding surveys conducted by the ifo Institute and the Bundesbank (BOP-F), enterprises have noted a significant deterioration in the financing environment. However, the financing environment is not considered to be noticeably difficult when compared with previous periods and other obstacles to business activity (such as a shortage of skilled workers or insufficient demand).

The banks surveyed as part of the BLS do not regard balance sheet constraints on the part of banks as currently playing a major role in lending to enterprises, which is consistent with the overall sound health of the German banking sector. Before the start of the monetary policy tightening cycle, there was little indication that banks’ balance sheet constraints played a relevant role in lending. 12 Looking at developments since the start of the tightening, there is scant evidence to date that this assessment has changed. The German banking system’s liquidity situation remains favourable and the sector’s CET1 ratio has continued to rise in trend terms. 13 All in all, banks’ net interest income has likewise increased so far. That said, value adjustments were made on the securities held as a result of the rise in interest rates, weighing on profitability in 2022. 14 At the same time, only a moderate amount of credit risk has materialised so far. Although the share of non-performing loans to enterprises on German banks’ books has risen since mid-2022, it remains at a very low level. 15 Overall, the current situation in the banking system in Germany can be seen as stable with no indications of significant restrictions in the supply of loans due to bank-related factors. 

Empirical models also provide no evidence of significant restrictive loan supply shocks beyond the impact of the weakened macro environment. The last two items of supplementary information at the end of the article present two empirical macro models that allow the identification of loan supply shocks. The first approach uses the medium-sized macro model already described in Chapter 2, which applies monetary, financial and real economic data to identify various economic shocks relevant to lending (see the supplementary information on the macro-financial Bayesian VAR model). The second model examines lending in a smaller model framework, using the BLS banks’ assessment of loan supply and loan demand to identify the shocks (see the supplementary information on the quantitative importance of loan supply and loan demand). The models thus use different information but both come to the conclusion that purely bank-related factors were not a major factor in the sharp slowdown in loan growth from autumn 2022 onwards and during the subsequent period of very low growth. 

Rather, the model-based decomposition of loan growth suggests that the sharp decline in loan supply and loan demand from autumn 2022 onwards is mainly attributable to the weakened macro environment. Using observations from the BLS and other relevant variables, the model presented in the supplementary information on the quantitative importance of loan supply and loan demand decomposes loan growth into the impact of the macro environment and additional loan supply or loan demand effects. The model results suggest that the sharp decline in loan growth from the fourth quarter of 2022 onwards can be explained mainly by the deterioration in the macroeconomic environment (see Chart 3.18 in this supplementary information). The model also identifies consistently negative loan demand shocks from the end of 2020; their size and thus their quantitative significance for credit growth are relatively small, however. 

4.2 Weakening of loan demand, mainly due to the deterioration in the macro environment

The weakness of loan demand resulted from the interplay of several factors. The fact that the model presented in the supplementary information on the quantitative importance of loan supply and loan demand points to the existence of negative loan demand shocks is consistent with the forecast simulations presented in Chapter 2, showing that loan dynamics were relatively weak from mid-2023 onwards. The following section describes the individual factors that have dampened enterprises’ demand for loans. We will first demonstrate the importance of macroeconomic developments for the corporate sector as a whole. Second, we will examine the role of alternative financing options in the corporate sector and take a look at enterprises and their economic conditions at the disaggregated level. On the whole, it is clear that the decline in loan demand was also largely driven by the weakened macro environment. 

The BLS provides a good overview of the drivers of loan demand: the surveyed banks attribute the decline in loan demand from autumn 2022 onwards mainly to the dwindling willingness to invest and the rise in interest rates. Furthermore, there was evidence from several banks surveyed in the BLS that uncertainty about the economic outlook in Germany and geopolitics in general also dampened enterprises’ demand for loans. 16 BLS banks believe that the weakening of loan demand in the current tightening cycle is far more pronounced than during the global financial crisis and than in 2012 and 2013, when loan dynamics cooled in the wake of the sovereign debt crisis (see Chart 3.10). 

Germany: change in demand for loans to enterprises and key factors
Germany: change in demand for loans to enterprises and key factors

The declining willingness to invest should be viewed against the backdrop of the effects of the deteriorating macro environment, which are hitting the corporate sector in Germany comparatively hard. 17 The sharp rise in energy costs and weak foreign demand weighed on industrial output throughout the euro area, but in Germany the energy price shock hit the industry-based economy with its high dependence on imported energy commodities particularly hard. The weakness in global trade was also felt more strongly in Germany owing to firms’ strong focus on exports. Heightened geopolitical tensions also exacerbated enterprises’ uncertainty with regard to future energy supplies and their cost, as well as to disruptions to international trade links. There was also uncertainty about the challenges the green and digital transformations and demographic change pose to enterprises and policymakers. 

Moreover, the strong rise in the interest rate level also led to a postponement of investment on aggregate. In December 2023, the enterprises surveyed by the ifo Institute reported that they had reduced their originally planned investments by 8.4 % on average over the past one and a half years due to the higher interest rate level. 18 However, these adjustments were not evenly distributed across the corporate sector: rather, it emerged that 80 % of the surveyed enterprises did not adjust their investment plans at all. By contrast, enterprises that chose to adjust their investment plans typically reduced them on a larger scale. The results of the Bundesbank BOP-F survey are consistent with this. According to this survey, around 15 % of the enterprises surveyed reduced their spending on innovation by 50 % or more in 2022 and 2023 as a result of the ECB’s interest rate hikes. In addition, the responses suggest that the effect increased the more debt an enterprise had outstanding (see Chart 3.11). More highly indebted enterprises also disproportionately chose to put their investments on hold. 19 This can be explained by the higher debt service and, as a consequence, the increasing relevance of interest rate changes in the event of new borrowing or interest rate adjustments.

Postponement of investment and innovation by level of corporate debt
Postponement of investment and innovation by level of corporate debt

Demand for bank loans was also weakened by the fact that firms were, on aggregate, able to resort to significant liquidity buffers as alternative financing. The non-financial corporate sector entered the tightening period with sufficient liquidity buffers, not least as a result of extensive government coronavirus aid. A significant portion of these buffers are still available at present. In addition, non-financial corporations were able to generate large profits on aggregate in 2022 and probably also in 2023. They managed to expand their profit margins in an environment of strong demand – which was in part driven by catch-up effects after the pandemic – and supply-side restrictions such as supply bottlenecks. Parts of the corporate sector generated a turnover increase that went well beyond the lagged increase in employee compensation. 20 In isolation, these profits strengthened the internal financing options of the enterprises concerned. Since the beginning of 2022, these have, on aggregate, been largely sufficient to finance the rather weak investment in recent years (see Chart 3.12). 

Non-financial corporations in Germany: Use of debt and internal financing
Non-financial corporations in Germany: Use of debt and internal financing

Furthermore, the repayment of short-term loans granted in large volumes during the energy crisis in 2022 also dampened lending. 21 As tensions in the energy markets subsided, the need for government support for selected energy suppliers steadily declined. A large proportion of the loans granted were repaid in a timely manner, with the last of this energy support assistance being repaid by the end of the first quarter of 2024. 22 In addition, global supply chain problems have eased since mid-2022 and input and storage costs have again declined. The need for short-term funding normalised across the corporate sector as a result, leading to above-average short-term loan redemptions in 2023. 

The identification of the deteriorated macro environment as the main driver of loan weakness fits with the broad-based decline in lending. Since the end of 2022, banks’ net lending has weakened across all categories of banks, loan maturities and economic sectors (see Charts 3.1, 3.3 and 3.13). Although there were individual loan categories in which the decline was less pronounced, the general downward trend affected all categories. A similar picture emerges for enterprises’ overall external financing: from the end of 2022, German enterprises not only took out fewer loans from German banks, but they also significantly reduced their borrowing via loans from other sectors as well as debt securities, shares and other equity.

External financing of non-financial corporations in Germany
External financing of non-financial corporations in Germany

Although loan demand weakened broadly, a disaggregated view of loan data shows that individual economic sectors were affected in quite different ways. Loans to the manufacturing sector declined particularly sharply, especially for the manufacture of machinery and equipment and chemicals industry sectors. This is in line with the slowdown in industrial activity in Germany.

By contrast, the changed interest rate environment hit nominal demand for loans in the construction and real estate sector 23 less substantially than expected. The sharp rise in construction and funding costs triggered a cyclical turnaround in the real estate market after an upswing of many years; the annual growth rate of real private construction investment fell into negative territory in 2021 and remained there until the end of 2023. In line with this, BLS banks have observed an above-average decline in demand for loans among construction and real estate firms since mid-2022. However, the negative impact of the interest rate reversal on loan demand in the construction and real estate sector was offset by a sharp rise in construction costs. For example, the increase in material and labour costs in the construction sector was particularly strong during this period, even compared with the costs relevant to other gross fixed capital formation. This can be seen in the gap between real and nominal construction investment growth (see Chart 3.14). The sharp rise in prices kept nominal construction investment at a relatively high level until 2023, which was reflected in the nominal loan volumes demanded by the sector. As a result, the volume of loans to the construction and real estate sector fell less sharply than in other sectors of the economy. 24  

Gross fixed capital formation in the private sector* in Germany
Gross fixed capital formation in the private sector* in Germany

5 Conclusion and outlook

Despite the interest rate reversal, loan growth accelerated in 2022 due to government aid loans to energy suppliers and other one-off factors. Although monetary policy responded to the inflationary surge with substantial tightening, lending to non-financial corporations initially continued to pick up strongly until October 2022. One key factor was the government support loans granted to selected energy suppliers in 2022 as a result of Russia’s war against Ukraine. In addition, the sharp rise in input and storage costs in 2022 increased the need for short-term funding across the corporate sector. Further positive impulses came in the aftermath of fiscal and monetary policy support measures from the coronavirus pandemic. 

In line with the deteriorated economic environment and the monetary policy-induced rise in interest rates, the downturn in net lending began at the end of 2022. Model-based analyses indicate that the decline in loan supply and loan demand was mainly due to the adverse macro environment and was generally consistent with historical patterns. In particular, persistently weak private investment and high funding costs were the main factors behind the weakening of loan demand. Other factors were the – on aggregate – sufficient supply of internal financing in the corporate sector and heightened uncertainty with regard to potential geopolitical conflicts and how structural problems are dealt with in Germany, which dampened firms’ willingness to invest. The significant tightening of lending conditions can be explained by the rapid and strong rise in interest rates and the cyclically induced increase in credit risk on the corporate side. The German banking system remains in a stable state. Model results also provide no evidence of a significant reduction in the supply of loans beyond the impact of the adverse macro environment due to bank-related factors affecting cost of funds and bank-related balance sheet constraints. 

A gradual rebound in lending is expected over the course of this year. In recent months, banks’ net lending to firms has largely stagnated. New lending business, i.e. newly concluded loan agreements, excluding redemptions, recently saw slightly stronger lending. The banks surveyed in the BLS observed – for the first time since 2022 – an increase in demand for loans to enterprises in the second quarter of 2024, which they expect to continue in the third quarter. Our current forecast for loans to enterprises also expects a recovery starting from the second half of 2024; however, the projected increase in the annual growth rate is slow (see the supplementary information on loan forecasts). 

The projected gradual increase in lending is based on the assumption of a gradual improvement in the macro environment. After around two years of economic weakness, the German economy is slowly regaining momentum. 25 The expected increase in foreign demand should stimulate the export industry. Private consumption is likely to recover. In addition, the current Forecast for Germany is based on the assumption that a gradual reversal of interest rate hikes will have a positive impact on firms’ financing environment. Nevertheless, tangible growth impulses from private investment are not expected until 2026. It is therefore to be assumed that net lending will only rise again slowly. In addition, the currently elevated uncertainty amongst enterprises with regard to future underlying economic conditions and structural change is likely to persist for the time being. 

Empirical analyses suggest that demand for loans will not rise immediately as the economy improves. In the past, loan growth mostly lagged behind GDP growth and investment by between two and six quarters. 26 This lag can be explained by the role of internal financing: in the economic upturn, enterprises can initially cover their funding needs from internally generated funds. Therefore, there is often a delay before they begin to apply for loans. As German enterprises still have significant internal financing resources on aggregate, loan growth is likely to pick up only gradually, even in the economic upswing.

The robust financial situation in the German banking sector currently does not suggest that major bank-related restrictions will dampen the recovery in lending. Should the economic environment brighten up, this would also have a positive impact on future credit risk developments. Credit risks currently appear to be significant only in certain areas of commercial real estate financing. In addition, according to financial accounts data, the German corporate sector’s debt situation can still be assessed as sound. A falling interest rate level would gradually improve the credit risk situation in commercial real estate in particular. 

Supplementary information

Loan forecasts

Forecasts of future loan developments

In order to allow a broader assessment of lending dynamics, the Bundesbank regularly conducts loan forecasts for the coming quarters. These forecasts are based on two different variants of a parsimonious Bayesian vector autoregressive (BVAR) model in levels using the approach in Giannone et al. (2015) 1 and focusing on forecast performance. In addition to loans to non-financial corporations, the first model variant includes the investment ratio (defined as the ratio of private fixed investment to GDP), the yield spread between long-term corporate and government bonds, and the long-term interest rate on loans to enterprises. The second model variant additionally includes the cumulative BLS standards in the model. 2 The BVAR is specified with four lags. Both the selection of the model variables and the determination of the lag structure were carried out using a forecast evaluation. 

The model parameters are estimated up to the available data end, currently the first quarter of 2024. Forecasts are then conducted for the following quarters for each point in time. These are based on the Bundesbank’s forecast of economic activity in Germany, which is published every June and December. 3 All projections in this article are made using data available on 21 May 2024. Chart 3.15 shows the loan forecasts based on these data. The grey and blue paths represent the two model variants used here, with and without taking account of BLS standards. Both estimation variants suggest a slow recovery in lending dynamics from the second half of 2024 onwards. However, the projected increase in loan growth is gradual, with annual growth rates ranging from 2 % to 3 % for medium forecast horizons (in 2025). At the same time, the very broad uncertainty bands in some cases reflect the considerable uncertainty associated with the forecasts. Overall, however, they are in line with current expectations regarding the economic outlook. 4  

Conditional forecast of annual growth rate of loans to non-financial corporations in Germany
Conditional forecast of annual growth rate of loans to non-financial corporations in Germany

Forecast of hypothetical loan developments

The forecasting model can also be used for hypothetical simulations, i.e. for simulations of loan developments derived from model relationships. Chapter 2 of the main article compares the loan growth observed since the beginning of 2022 with hypothetical loan developments calculated on the basis of the variant of the forecasting model which also includes BLS standards. To this end, the model described above is estimated for the period from the beginning of 1991 to the end of 2021. For the period from the beginning of 2022, loan developments are then forecast based on historical relationships between the model variables and the actual development of the explanatory variables up to the current data end. The forecasts are therefore conditional on the realisations of the investment ratio, the yield spread, the lending rate and the BLS standards. In order to show the uncertainty associated with the forecast, the uncertainty band (2.5th to 97.5th percentile) is shown alongside the median.

This reveals that actual loan growth over the period under review was broadly in line with the model forecast, although there were some deviations in individual quarters. In Chart 3.2 in Chapter 2, the black line represents actual credit growth. The blue line shows the median distribution of the conditional forecasts. 5 The shaded area depicts the dispersion of the simulated forecast distribution and reflects the uncertainty associated with the forecasts. The grey line represents the loan growth rate adjusted for KfW aid loans. 6 As these loans were very large in volume and mostly had fairly short terms, they temporarily led to the fairly large ups and downs in actual lending. As a short-term government support measure, they were exogenous in nature and thus represented a one-off effect on lending. Adjusted for this one-off effect, loan growth remained within the uncertainty band throughout the simulation period and was therefore compatible with the model relationships prevailing before 2022. 

Footnotes
  1. For a description of the approach, see Giannone et al. (2015).
  2. The data for the BLS credit standards are only available from the fourth quarter of 2002. In order to allow a longer estimation period from 1991 onwards, the BLS standards are extended backwards using information on firm insolvencies, see Deutsche Bundesbank (2022b). 
  3. See Deutsche Bundesbank (2024b). For the calculation of conditional forecasts, see Bańbura et al. (2015).
  4. See Deutsche Bundesbank (2024b).
  5. To better illustrate this, the forecasts of loan levels are then converted into annual growth rates.
  6. See the supplementary information in Chapter 3.

 

Supplementary information

Analysis of loans to non-financial corporations in Germany using a macro-financial Bayesian VAR model

Developments in loans to non-financial corporations in Germany are investigated using a vector autoregressive (VAR) model that describes the interaction of a number of real economic and financial variables. The model contains quarterly observations of ten variables: it comprises real gross domestic product (GDP) in Germany and the rest of the euro area, the Harmonised Index of Consumer Prices (HICP) in Germany and the rest of the euro area, loans to non-financial corporations in Germany, the lending rate (new business), the German contribution to M3, and the yield on German government bonds with a residual maturity of five years. In order to capture both standard and non-standard monetary policy measures, the shadow interest rate of Geiger and Schupp (2018) is used as monetary policy indicator and extended into the past or present with the interest rate on overnight loans in the interbank market (EONIA or €STR). To control for possible spillover effects from US monetary policy, the model also contains the yields of US Treasuries with a residual maturity of five years. 1 All variables enter the model with five lags, interest rates as decimal numbers, and the other variables in log levels. 2  

Comparing the distribution of the conditional simulation with actual loan growth provides indications as to whether loan growth has deviated noticeably during the simulation period from the estimated relationships in the model. In addition to the actual annual growth rate of loans to enterprises, Chart 3.16 also shows selected percentiles of the probability distribution of conditional simulations of the loan growth rate, starting in the first quarter of 2022. These simulations are based on the model parameters estimated for the period from the beginning of 1999 to the end of 2021 and are conditional on actual developments in real GDP and HICP in Germany and the rest of the euro area, government bond yields and the monetary policy indicator up to the end of the first quarter of 2024. 3  

At the end of 2023 and the beginning of 2024, loan growth does not deviate much from the simulations after having exceeded them markedly in 2022. Actual loan growth clearly exceeds the simulation from the second to the fourth quarter of 2022 and then drops to the lower end of the simulated distribution. 4 In the last two quarters, it is close to the 25th percentile of the simulated distribution. Both the upward and downward deviation can be partly attributed to KfW’s special loans and their subsequent repayment. According to the analysis, the dynamics of loans to enterprises in Germany do not appear unusual overall conditional on the macroeconomic environment. 5  

Identifying structural economic shocks allows the fluctuations in loan growth to be decomposed into their economically interpretable determinants. Comparing the actual loan growth rate with the conditional simulations does not allow direct conclusions to be made as to which economic determinants cause observed deviations, as the simulations are based on the reduced form of the VAR model. Fluctuations in loans to enterprises around their long-term trend are the result of structural shocks that can be interpreted economically. These are contained as linear combinations in the residuals of the VAR model and can be identified using additional assumptions. In the present analysis, identification is based on sign restrictions, i.e. on theoretically substantiated assumptions about the sign with which certain variables react to structural shocks. 6 Five structural shocks are identified: an aggregate demand shock, an aggregate supply shock, a loan supply shock, a monetary policy shock and a money demand shock. The identification assumptions are based on those in Deutsche Bundesbank (2023b) and are summarised in Table 3.1. The analysis takes into account the fact that the Eurosystem’s monetary policy responds to economic developments in the euro area as a whole and not only in Germany. In order to sufficiently capture the Eurosystem’s monetary policy response function, the model includes real GDP and the HICP not only of Germany but also of the rest of the euro area. 7 When identifying the monetary policy shock, it is assumed that, following an increase in the monetary policy interest rate, real GDP and HICP will fall not only in Germany but also in the rest of the euro area. 8 For the other shocks, the variables for the rest of the euro area are not subject to any restrictions. Whether or not the other shocks are specific to Germany or impact the euro area as a whole thus remains undetermined by the analysis. For this analysis, the estimation period is extended until the first quarter of 2024.

The model attributes the acceleration of loan growth from the end of 2021 and its subsequent decline to the impact of aggregate demand shocks and monetary policy shocks. Chart 3.17 shows the decomposition of the deviations of the loan growth rate from a hypothetical scenario in which no shocks would have occurred from 2019 onwards into the contributions of the identified structural shocks. “Other” summarises the contributions of the remaining five unidentified and thus uninterpretable shocks. 9 In the phase of accelerating loan growth from mid-2021 onwards, loan growth is positively impacted by aggregate demand shocks and expansionary monetary policy shocks. 10 According to the analysis, the decline in loan growth from mid-2022 onwards is partly due to the expiry of the supportive effect of these shocks. The results do not provide any evidence to suggest that loan supply shocks have a significant role in loan growth over the period under review. Unlike the results for the euro area in Deutsche Bundesbank (2023a), money demand shocks were likewise not a major factor during the COVID-19 pandemic in Germany. 11

Table 3.1: Sign restrictions for shock identification
VariableShock
Aggregate demandAggregate supplyLoan supplyMonetary policyMoney demand
Germany 
Real GDP

+

-

+

-

-

HICP

+

+

.

-

-

Loans

+

.

+

-

.

Lending rate

+

.

-

+

.

Government bond yield

.

.

.

.

+

Money supply (M3)

+

.

+

-

+

Euro area
Shadow interest rate/money market interest rate

+

+

+

+

-

Real GDP (excluding Germany)

.

.

.

-

.

HICP (excluding Germany)

.

.

.

-

.

Other
US Treasury yield

.

.

.

.

.

The restrictions apply to the period in which the shock occurs. A dot indicates that no assumption has been made for that variable regarding the direction of the effect of the shock described in the corresponding column.

 

Conditional forecast of the annual growth rate of loans to non-financial corporations*
Conditional forecast of the annual growth rate of loans to non-financial corporations*

 

Shock decomposition of the annual growth rate of loans to non-financial corporations
Shock decomposition of the annual growth rate of loans to non-financial corporations

 

Footnotes
  1. The model is broadly similar to that used in Deutsche Bundesbank (2020), but additionally contains the German contribution to M3 to allow a comparison of the role of money demand shocks during the COVID-19 pandemic with the results for the euro area in Deutsche Bundesbank (2023a).
  2. The estimation is conducted using the Bayesian approach of Giannone et al. (2015). Modelling of the increased variance during the COVID-19 pandemic follows Lenza and Primiceri (2022).
  3. The simulation approach follows Bańbura et al. (2015).
  4. Compared with the simulation in Chart 3.2, the distribution of the simulations is wider, especially towards the end of the simulation period. This is due, amongst other factors, to the increased estimation uncertainty associated with the larger number of variables.
  5. However, episodes with relatively high inflation rates, as in the simulation period, play only a comparatively small role in the estimation period. This could lead to the model not correctly reflecting the relationships between the variables in a regime with elevated inflation rates and a significant role of supply-side shocks.
  6. See Arias et al. (2018).
  7. See Mandler and Scharnagl (2020).
  8. It is also assumed that the coefficients of GDP and HICP are positive in the monetary policy indicator equation, i.e. that the Eurosystem responds to an increase in GDP or the price level with a more restrictive monetary policy. See Arias et al. (2019).
  9. The analysis provides statistical distributions of the contributions of the shocks and the deviation of the actual loan growth rate from the hypothetical model simulation without shocks (unconditional forecast). The chart represents the median of each of these distributions. A difference between the black line, the median of the difference in the unconditional forecast and the sum of the median contributions of the shocks can be explained by the fact that the median of a sum is not necessarily equal to the sum of the medians of the summands.
  10. The estimated model equation for the monetary policy indicator would have required a much more expansionary monetary policy in response to the sharp decline in GDP during the COVID-19 pandemic in the first quarter of 2020 than was actually the case. A monetary policy easing as predicted by the model would probably not have been feasible at all, owing to the effective lower bound and limits on potential monetary policy purchase programmes. The model therefore diagnoses a restrictive monetary policy shock at this point in time, followed by a somewhat less pronounced expansionary monetary policy shock in the second quarter, when GDP partially recovered. In the shock decomposition, the effect of the first, restrictive monetary policy shock prevails throughout 2020. For more information, see footnote 35 in Deutsche Bundesbank (2023a).
  11. This also applies to the decomposition of the other variables, in particular M3. By contrast, the results pertaining to the role of aggregate demand shocks and monetary policy shocks are similar to those for the euro area in Deutsche Bundesbank (2023a).

 

Supplementary information

The quantitative importance of loan supply and loan demand for loan growth

A model-based analysis is necessary in order to identify loan supply and demand as drivers of loan growth. The approach described below merges information on the dynamics of loan volumes from the balance sheet statistics of the monetary financial institutions (MFIs) with data on average interest rates for (new) loans from the MFI interest rate statistics as well as with BLS data. 1 The analysis also takes into account the macroeconomic environment (including monetary policy), which has a bearing on developments in the loan market. This model can be used to show the observed annual growth rate of loans to non-financial corporations as the sum of all unexpected changes in loan supply and demand in the past – i.e. shocks – as well as the development of exogenous variables and an unidentified residual component. Unlike in the supplementary information on the macro-financial Bayesian VAR model, this approach specifically aims to map the unexpected changes in loan growth to one of the two sides of the market. 

The VAR model presented here focuses on the relationships between loan growth, the lending rate, the change in credit standards according to the BLS (BLS standards) and the change in loan demand according to the BLS (BLS demand). It also takes into account the effects of various exogenous variables, namely the rates of change in gross domestic product and the consumer price index, as well as a short-term money market rate (three-month EURIBOR). These variables capture the effects of the macroeconomic environment on the loan market. These effects can influence the identification of unexpected changes in loan supply and demand, which means that they should be excluded as far as possible from a model that is, nonetheless, parsimoniously parametrised. The coefficients and residuals are estimated using the ordinary least squares (OLS) method over the period from the first quarter of 2003 to the first quarter of 2024. 2

The residuals of the loan growth equation are decomposed into supply and demand effects as well as into an unidentified remainder component. This is done by defining a combination of zero and sign restrictions that parts of the residuals must meet in order to be attributed to the shock in question; see Table 3.2. The restrictions reflect the definition of loan supply and demand, supplemented by the assumed response of the BLS variables. As a case in point, a tightening of the BLS standards is identified as a loan supply shock only if it exceeds the level expected according to the model and coincides with an unexpected decline in loan growth and a rise in the lending rate. The four endogenous variables of the model can now be represented as a linear combination of all shocks that occurred up to the respective point in time and exogenous influences; see Chart 3.18. “Macroeconomic environment” refers here to the universe of exogenous effects. These include the contribution made by government assistance loans to energy suppliers in 2022, which initially biased aggregated loan growth upwards and then, at end-2023/early 2024, downwards. The contribution of the unidentified shocks shown in the chart reflects the effects of the residual components that do not correspond to the schema described above. 

According to the model decomposition, after the peak economic impact of the coronavirus pandemic, both the increase in loan growth up to mid-2022 and the subsequent decline were mainly driven by the macroeconomic environment, which, in the specification chosen, also includes monetary policy. In addition, unexpected changes in loan growth mainly reflect particularly weak loan demand. This weakness is likely to have been driven in part by factors that cannot be adequately factored into this small model. They include, amongst other things, the repayment of loans taken out by firms during the pandemic to bridge delays in incoming payments or for precautionary reasons. However, firms’ uncertainty about the longer-term consequences of the war in Ukraine, particularly regarding energy prices, is also likely to have contributed to the relatively persistent dampening effect on loan demand. 

Unexpected loan supply restrictions on loans to enterprises cannot be identified for Germany. According to the model results, loan supply shocks supported loan growth, especially during the first phase of the coronavirus pandemic. The contribution of loan supply shocks is also positive rather than negative for the subsequent phase of initially rising and subsequently falling inflation rates, even though the positive contributions shown in the chart are not statistically significant throughout. The decomposition for the period considered here does not provide any evidence of significant dampening loan supply effects, which would have been caused, for example, by bank-related factors.

Table 3.2 Identifying zero and sign restrictions
VariableShock
Loan supplyLoan demand
Loan growth

-

+

Lending rate

+

+

BLS standards

+

0

BLS demand

0

+

The restrictions apply to the period in which the shock occurs. A positive value of the “BLS standards” variable reflects a net tightening of credit standards.

 

BLS-based historical decomposition of the growth rate of loans to non-financial corporations in Germany
BLS-based historical decomposition of the growth rate of loans to non-financial corporations in Germany

 

Footnotes
  1. The modelling follows the procedure outlined in Deutsche Bundesbank (2023h).
  2. For the estimation, loan growth was adjusted for the contribution of KfW loans to non-financial corporations in Germany, as these represent a distorting one-off effect starting from the second half of 2022, in the wake of the energy crisis. In addition, the peak economic impact of the coronavirus pandemic in 2020 was excluded by disregarding the five observations from the first quarter of 2020 to the first quarter of 2021 when calculating the coefficient estimators and the covariance matrix; see Schorfheide and Song (2021).

List of references

Arias, J., D. Caldara and J. Rubio-Ramírez (2019), The systematic component of monetary policy in SVARs: An agnostic identification procedure, Journal of Monetary Economics, Vol. 101, pp. 1‑13.

Arias, J., J. Rubio-Ramírez and D. Waggoner (2018), Inference based on structural vector autoregressions with sign and zero restrictions: Theory and applications, Econometrica, Vol. 86(2), pp. 685‑720.

Bańbura, M., D. Giannone and M. Lenza (2015), Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections, International Journal of Forecasting, Vol. 31(2015), pp. 739‑756.

Best, L., B. Born and M. Menkhoff (2024), Wie passen Unternehmen ihre Investitionen an die gestiegenen Zinsen an?, ifo Schnelldienst digital, No 3/2024, 7 March 2024. 

Boddin, D., M. Köhler and P. Smietanka (2023), Bundesbank Online Panel – Firms (BOP-F), Data Report 2023‑07, Deutsche Bundesbank, Research Data and Service Centre.

Deutsche Bundesbank (2024a), The global disinflation process and its costs, Monthly Report, July 2024.

Deutsche Bundesbank (2024b), Forecast for Germany: German economy slowly regaining its footing – outlook up to 2026, Monthly Report, June 2024.

Deutsche Bundesbank (2023a), From the monetary pillar to the monetary and financial analysis, Monthly Report, January 2023, pp. 15‑51. 

Deutsche Bundesbank (2023b), BVAR model for estimating the effects of macroeconomic shocks on growth in gross domestic product, loans and the money supply and on the inflation rate, Monthly Report, January 2023, pp. 25‑28. 

Deutsche Bundesbank (2023c), Developments in bank interest rates in Germany during the period of monetary policy tightening, Monthly Report, June 2023, pp. 39‑62.

Deutsche Bundesbank (2023d), Financial Stability Review 2023.

Deutsche Bundesbank (2023e), The performance of German credit institutions in 2022, Monthly Report, September 2023, pp. 87‑125.

Deutsche Bundesbank (2023f), Germany as a business location: selected aspects of current dependencies and medium-term challenges, Monthly Report, September 2023, pp. 15‑35.

Deutsche Bundesbank (2023g), German enterprises’ profitability and financing during the 2022 energy crisis, Monthly Report, December 2023, pp. 55‑71.

Deutsche Bundesbank (2023h), Identifying loan supply and loan demand using the Bank Lending Survey, Monthly Report, January 2023, pp. 41‑43. 

Deutsche Bundesbank (2022a), Monetary policy in a prolonged period of low interest rates – a discussion of the concept of the reversal rate, Monthly Report, March 2022, pp. 17‑36.

Deutsche Bundesbank (2022b), The macroeconometric model of the Bundesbank revisited, Deutsche Bundesbank, Technical Paper, No 01/2022. 

Deutsche Bundesbank (2020), The upswing in loans to enterprises in Germany between 2014 and 2019, Monthly Report, January 2020, pp. 13‑44.

Deutsche Bundesbank (2015), Results of a wavelet analysis examining the relationship between lending to non-financial corporations and real economic activity in Germany, France, Italy and Spain, Monthly Report, September 2015, pp. 20‑22.

Deutsche Bundesbank (2011), German banks’ lending to the domestic private sector since summer 2009, Monthly Report, September 2011, pp. 59‑78.

European Central Bank (2024), The euro area bank lending survey – First quarter of 2024. 

Geiger, F. and F. Schupp (2018), With a little help from my friends: Survey-based derivation of euro area short rate expectations at the effective lower bound, Deutsche Bundesbank Discussion Paper No 27/2018.

Giannone, D., M. Lenza and G. Primiceri (2015), Prior selection for vector autoregressions, Review of Economics and Statistics, 97(2), pp. 436‑451.

Kreditanstalt für Wiederaufbau (2024), KfW promotional business volume in the first quarter of 2024: return to normality after years of crisis, press release of 8 May 2024.  

Kreditanstalt für Wiederaufbau (2023), Financial Report 2022.

Lenza, M. and G. Primiceri (2022), How to estimate a vector autoregression after March 2020, Journal of Applied Econometrics, Vol. 37, No 4, pp. 688‑699.

Mandler, M. and M. Scharnagl (2020), Bank loan supply shocks and alternative financing of non-financial corporations in the euro area, The Manchester School, Vol. 88 (S. 1), pp. 126‑150.

Schorfheide, F. and D. Song (2021), Real-time forecasting with a (standard) mixed-frequency VAR during a pandemic, National Bureau of Economic Research, Working Paper, No w29535. 

Footnotes
  1. See Deutsche Bundesbank (2024a).
  2. The term “lending” is used synonymously with “net lending” in this article.
  3. For a more detailed explanation, see Deutsche Bundesbank (2023a).
  4.  The term “non-financial corporations” comprises both non-financial corporations and quasi-corporations; it is used synonymously with “enterprises” in this article.
  5. See Deutsche Bundesbank (2020).
  6. This is also in line with the data provided by banks in the Bank Lending Survey (BLS); see Chart 3.10. 
  7. For information on the structure of the BOP-F survey, see Boddin et al. (2023).
  8. These funds were mainly needed for replacement gas purchases and to fulfil short-term margin call requirements in energy trading. The loans were also used to finance gas procurement, ensuring that gas storage facilities were filled to a specific level. See Kreditanstalt für Wiederaufbau (2023).
  9. See also Deutsche Bundesbank (2023b).
  10. For more detailed information, see Deutsche Bundesbank (2023c).
  11. See Deutsche Bundesbank (2023c).
  12. See Deutsche Bundesbank (2022a).
  13. See Deutsche Bundesbank (2023d).
  14. These adjustments were only partly reflected in banks’ profitability and capital levels. Banks absorbed a significant part of the value adjustments by reducing hidden reserves or accumulating unrealised losses so that they did not have an impact on profit or equity capital. See Deutsche Bundesbank (2023d, 2023e).
  15. See Deutsche Bundesbank (2023d).
  16. See European Central Bank (2024).
  17. See Deutsche Bundesbank (2023f).
  18. See Best et al. (2024). 
  19. However, the number of more highly indebted enterprises is not high: only just over 3 % of the enterprises questioned in the BOP-F survey reported that the residual debt of all bank loans outstanding at the time of the survey amounted to 70 % or more of their total assets in the fourth quarter of 2023. Just over 50 % of the enterprises surveyed reported that they currently have no bank loans on their balance sheets.
  20. See Deutsche Bundesbank (2023g).
  21. The net lending considered in this article reflects the balance of newly granted loans and loan redemptions.
  22. See Kreditanstalt für Wiederaufbau (2024).
  23. In this article, the construction and real estate sector refers to the following economic sectors: construction, housing enterprises and other real estate activities.
  24. The slower decline in lending to the construction and real estate sector also supported developments in other loan sub-categories. For example, the lending business of savings banks and credit unions benefited from the fact that they traditionally have a strong presence in this business segment. At the same time, its business structure means that the construction and real estate sector is predominantly funded over the long term. This contributed to long-term lending declining less sharply during the period of interest rate tightening than in the shorter segments.
  25. See Deutsche Bundesbank (2024b).
  26. See Deutsche Bundesbank (2015, 2011).