1 Stocktake
2 Developments in lending and macroeconomic environment
3 Loan dynamics increased on the back of one-off factors up to October 2022
4 Subsequent phase of weakening loan dynamics
4.1 Higher lending rates and tighter lending conditions
Interaction between credit risk, bank credit supply policy and loan negotiations
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. 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. Separate data on firms providing residential and other real estate services are not collected in the BOP-F survey.
4.2 Weakening of loan demand, mainly due to the deterioration in the macro environment
5 Conclusion and outlook
Loan forecasts
Forecasts of future loan developments
Forecast of hypothetical loan developments
For a description of the approach, see Giannone et al. (2015). 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). See Deutsche Bundesbank (2024b). For the calculation of conditional forecasts, see Bańbura et al. (2015). See Deutsche Bundesbank (2024b). To better illustrate this, the forecasts of loan levels are then converted into annual growth rates. See the supplementary information in Chapter 3 .
Analysis of loans to non-financial corporations in Germany using a macro-financial Bayesian VAR model
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). 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). The simulation approach follows Bańbura et al. (2015). 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. 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. See Arias et al. (2018). See Mandler and Scharnagl (2020). 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). 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. 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). 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).
The quantitative importance of loan supply and loan demand for loan growth
The modelling follows the procedure outlined in Deutsche Bundesbank (2023h). 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
See Deutsche Bundesbank (2024a). The term “lending” is used synonymously with “net lending” in this article. For a more detailed explanation, see Deutsche Bundesbank (2023a). The term “non-financial corporations” comprises both non-financial corporations and quasi-corporations; it is used synonymously with “enterprises” in this article. See Deutsche Bundesbank (2020). This is also in line with the data provided by banks in the Bank Lending Survey ( BLS ); see Chart 3.10. For information on the structure of the BOP-F survey, see Boddin et al. (2023). 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). See also Deutsche Bundesbank (2023b). For more detailed information, see Deutsche Bundesbank (2023c). See Deutsche Bundesbank (2023c). See Deutsche Bundesbank (2022a). See Deutsche Bundesbank (2023d). 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). See Deutsche Bundesbank (2023d). See European Central Bank (2024). See Deutsche Bundesbank (2023f). See Best et al. (2024). 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. See Deutsche Bundesbank (2023g). The net lending considered in this article reflects the balance of newly granted loans and loan redemptions. See Kreditanstalt für Wiederaufbau (2024). In this article, the construction and real estate sector refers to the following economic sectors: construction, housing enterprises and other real estate activities. 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. See Deutsche Bundesbank (2024b). See Deutsche Bundesbank (2015, 2011).