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Do non-performing loans matter for bank lending and the business cycle in euro area countries?

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05/15/2020 | 05:15am EDT

Ivan Huljak, Reiner Martin, Diego Moccero, Cosimo Pancaro

Working Paper Series

Do non-performing loans matter for bank lending and the business cycle in euro area countries?

No 2411 / May 2020

Disclaimer: This paper should not be reported as representing the views of the European Central Bank (ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB.

Abstract

We contribute to the empirical literature on the impact of non-performing loan (NPL) ratios on aggregate banking sector variables and the macroeconomy by estimating a panel Bayesian VAR model for twelve euro area countries. The model is estimated assuming a hierarchical prior that allows for country-specic coecients. The VAR includes a large set of variables and is identied via Choleski factorisation. We estimate the impact of exogenous shocks to the change in NPL ratios across countries. The main ndings of the paper are as follows: i) An impulse response analysis shows that an exogenous increase in the change in NPL ratios tends to depress bank lending volumes, widens bank lending spreads and leads to a fall in real GDP growth and residential real estate prices; ii) A forecast error variance decomposition shows that shocks to the change in NPL ratios explain a relatively large share of the variance of the variables in the VAR, particularly for countries that experienced a large increase in NPL ratios during the recent crises; and iii) A three-year structural out- of-sample scenario analysis provides quantitative evidence that reducing banks’ NPL ratios can produce signicant benets in euro area countries in terms of improved macroeconomic and nancial conditions.

Keywords: Euro area countries, non-performing loans, panel Bayesian VAR, hierarchical priors

JEL Classification: G21, C32, C11

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Non-Technical Summary

The non-performing loan (NPL) ratio in the euro area increased from around 3% at the onset of the global nancial crisis in late 2008 to a peak of around 8% in 2014. A key driver of the substantial increase in NPL ratios was the severe and protracted recession in large parts of the euro area, which signi cantly reduced borrowers’ capacity to service their debt. At the same time, the fast increase in NPL ratios was also signi cantly inuenced by other factors, such as banks’ lending and monitoring policies and limited capacity to work-out defaulted loans. More recently, the recovery of economic activity in the euro area and the development and implementation of policies to tackle non-performing loans by the Single Supervisory Mechanism (SSM) have led to a decline in the euro area NPL ratio, which reached around 6% at the end of 2017. The evolution of the NPL ratios has been rather heterogeneous across euro area countries reecting the di erent macroeconomic conditions and diverse structural features (the eciency of legal and judicial systems, insolvency frameworks, payment culture and the level of development of distressed debt markets, among others). At the end of 2017, the NPL ratio still remained above 10% in those euro area countries most a ected by the recent economic and nancial crisis, namely Cyprus, Greece, Ireland, Italy and Portugal, while it was below 5% in countries such as Austria, Belgium, Estonia, France, Lithuania and the Netherlands.

High NPL ratios in banks’ balance sheets can adversely a ect the soundness of the banking system and its ability to lend to the real economy through three main channels. First, high non- performing loans reduce bank pro ts. They do so because they require higher provisions, they lead to lower interest income, generate higher expenses associated with their monitoring and management and lead to an increase in funding costs, as risk adverse investors are less willing to lend to institutions with a low credit quality. Second, non-performing loans feature higher risk weights, leading to higher capital needs. To maintain or boost capital adequacy, banks may thus deleverage, leading to a contraction in credit supply. Finally, the management of large NPL stocks can divert important managerial resources away from core and more pro table activities. Considering the importance of bank lending for the functioning of the euro area economy, there is a clear need to study the feedback loop between non-performing loans, bank credit and the real economy.

We contribute to the literature on the feedback loops between NPLs and the economy by estimating a panel Bayesian VAR model with hierarchical priors for twelve euro area countries for the period between the rst quarter of 2006 and the third quarter of 2017. More speci cally, the aim of our analysis is to estimate the impact of exogenous shocks to the change in NPL ratios on bank lending and the macroeconomy. Changes in NPL ratios which are unrelated to changes in the repayment capacity of borrowers (i.e., exogenous changes in NPL ratios) include, inter alia, sales of defaulted loans to investors, changes in banks’ own attitudes towards risk, write-o s, supervisory actions that incentivise banks to work out these loans (by o ering restructuring solutions to clients) and other policy initiatives which deal with NPLs’ work- outs and defaults associated with poor enforcement mechanisms. We use a Bayesian model

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because of the relatively short time-span of the available data series of our main variable of interest (NPL ratios), and because of the large number of parameters to be estimated. The adopted model allows for country-speci c coecients. This feature is especially relevant in this context as the dynamics of NPL ratios were particularly heterogeneous across countries. At the same time, the model assumes that the parameters of the VAR for individual countries share a common component across the euro area, hence ensuring an ecient use of the data. The variables included in the panel VAR are economic activity (which is a proxy for the repayment capacity of borrowers), ination, the monetary policy rate, real estate prices, bank lending volumes both to non- nancial corporations and to households for house purchase, bank lending spreads to these two sectors, the ratio of capital and reserves over total assets and the change in NPL ratios. We use the Choleski factorisation, a recursive technique commonly adopted in the literature, to identify the shock to NPLs. In particular, we use as an identi cation strategy the fact that NPLs generally do not respond within a quarter to endogenous shocks, as banks are allowed to classify a loan as non-performing only a quarter after default.

We nd that an exogenous increase in the change in NPL ratios tends to depress bank lending, widens lending spreads and leads to a fall in real GDP growth and residential real estate prices and an easing of the monetary policy rate. While the responses of the capital and reserves- to-asset ratio vary across countries, a material increase is recorded in Cyprus, Spain, Ireland, Italy, Lithuania and Portugal, due to the increase in provisions for impairments during the crisis. The results also show that the decline in bank lending to non- nancial corporations is generally more marked than the one in mortgage loans. The forecast error variance decomposition shows that shocks to the change in NPL ratios explain a relatively large share of the variance of the variables in the VAR, particularly for countries that exhibited a large increase in NPL ratios during the crisis. Finally, a three-year structural out-of-sample scenario analysis quanti es the impact of a decline in NPL ratios for Cyprus, Ireland, Spain, Italy, Greece and Portugal (the countries that exhibited the most sizable increase in NPL ratios during the crisis). The exercise provides quantitative evidence that reducing NPL ratios can produce non-negligible bene ts in terms of improved macroeconomic and nancial conditions. These results are robust to a change in the ordering of the variables in the Choleski factorisation (whereby bank loans and the NPL ratio are a ected contemporaneously by macroeconomic variables) and also to the inclusion in the VAR of the annual rate of growth in NPL volumes (instead of the NPL ratio) as rst variable.

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The non-performing loan (NPL) ratio in the euro area increased from around 3% at the onset of the global nancial crisis in late 2008 to a peak of around 8% in 2014. A key driver of the substantial growth in NPL ratios was the severe and protracted recession in large parts of the euro area, which signi cantly reduced borrowers’ capacity to service their debt. At the same time, the fast increase in NPL ratios was also signi cantly inuenced by other factors, such as banks’ lending and monitoring policies and limited capacity to work-out defaulted loans. More recently, the recovery of economic activity in the euro area and the development and implementation of policies to tackle non-performing loans by the Single Supervisory Mechanism (SSM) have led to a decline in the euro area NPL ratio, which reached around 6% at the end of 2017. The evolution of the NPL ratios has been rather heterogeneous across euro area countries reecting the di erent macroeconomic conditions and diverse structural features (e.g. the eciency of legal and judicial systems, insolvency frameworks, payment culture and the level of development of distressed debt markets, among others). At the end of 2017, the NPL ratio still remained above 10% in those euro area countries most a ected by the recent economic and nancial crisis, namely Cyprus, Greece, Ireland, Italy and Portugal, while it was below 5% in countries such as Austria, Belgium, Estonia, France, Lithuania and the Netherlands.

High NPL ratios in banks’ balance sheets can adversely a ect the soundness of the banking system and its ability to lend to the real economy through three main channels. First, high non- performing loans reduce bank pro ts. They do so because they require higher provisions, they lead to lower interest income, generate higher expenses associated with their monitoring and management and lead to an increase in funding costs, as risk adverse investors are less willing to lend to institutions with a low credit quality.1 Second, non-performing loans feature higher risk weights, leading to higher capital needs. To maintain or boost capital adequacy, banks may thus deleverage, leading to a contraction in credit supply. Finally, the management of large NPL stocks can divert important managerial resources away from core and more pro table activities.2 Considering the importance of bank lending for the functioning of the euro area economy, there is a clear need to study the feedback loop between non-performing loans, bank credit and the real economy.

The empirical literature on NPLs features three main strands which investigates the determinants of NPLs, the impact of NPLs on the real economy and the feedback loops between NPLs and the macroeconomy, respectively.

The rst strand of literature has identi ed three main groups of determinants of NPLs, namely bank level, industry-speci c and macroeconomic. The rst group includes: i) Exogenous

  • For example, Pancaro, Zochowski_ and Arnould (2020) nd that lower credit quality seems to be associated
    with higher banks’ senior bond yields.
    2Grodzicki, Laliotis, Leber, Martin, O’Brien and Zboromirski (2015), Fell, Grodzicki, Martin and O’Brien (2016a), Fell, Grodzicki, Krusec, Martin and O’Brien (2017) extensively elaborate on the challenges for the banking system stemming from the accumulation of non-performing exposures. Additionally, they illustrate macroeconomic and microeconomic policies which could be adopted to resolve this legacy issue.

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factors, such as a sudden drop in economic activity (bad luck hypothesis); ii) Poor management, which can lead to bad credit risk decisions (bad management hypothesis); iii) Low capitalisation, which can make banks prone to risk taking (moral hazard hypothesis); and iv) Scarcity of resources allocated to underwriting and monitoring loans to boost short-term eciency (skimping hypothesis). While the literature has found support for all these hypotheses, the bad management hypothesis is the most prominent one. Industry speci c drivers point mainly to the impact of competition on risk taking. On the one hand, stronger market power may drive lending rates higher, increasing the debt burden for borrowers and, thus, their bankruptcy risk (competition- stability hypothesis). On the other hand, banks with more market power have higher franchise value and, therefore, more at stake in the event of defaults, making their underwriting more prudent (competition-fragilityhypothesis). Overall, there seems to be no consensus in the literature on whether bank competition increases or decreases stability in the banking system(Beck, De Jonghe and Schepens 2013; Goetz 2018). Finally, regarding macroeconomic drivers, the literature has focused on various measures of economic activity, ination, interest rates and the exchange rate as the most relevant drivers of NPLs (Anastasiou and Tsionas 2016; Jimenez and Saurina 2006; Louzis, Vouldis and Metaxas 2012). Improved economic conditions, higher ination and lower interest rates are found to strengthen the repayment capacity of borrowers, while exchange rate depreciations are shown to increase the debt burden of foreign-exchange denominated loans for unhedged borrowers3.

The second main strand of the literature studies the impact of non-performing loans on bank lending and economic activity. This literature relied both on bank-level and country level data. For example, Balgova and Plekhanov (2016), using data for a global sample of 100 countries, quanti ed the (positive) e ects of policy-induced declines in NPLs on the real economy. The authors nd that the foregone growth due to the overhang of NPLs can be large. Accornero, Alessandri, Carpinelli and Sorrentino (2017), coupling bank level data for Italy with borrower- based information for non- nancial corporations, study the inuence of NPLs on the supply of bank credit. They nd that bank lending is impaired by the exogenous accumulation of new NPLs and the associated increase in provisions, but it is not causally a ected by the level of NPL ratios.

The literature on the determinants of non-performing loans and on the impact of non- performing loans on bank lending and the real economy has traditionally relied on single equation estimation techniques, where either NPLs or macroeconomic variables are regressed against each other and other control variables. By modelling the dynamics of each variable separately, these studies neglect the dynamic interaction and feedbacks between the changes in non-performing loans, banking and macroeconomic variables. This is a major drawback, because an exogenous increase in NPLs is likely to impair economic activity, leading to a decline in the repayment capacity of borrowers and a further increase in NPLs. As a result, a third

3The literature nds that ination generally reduces the loan servicing burden. However, it also shows that, if wages are sticky, higher ination might induce a fall in real income and, thus, cause an increase in the debt servicing burden.

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strand of literature has estimated the impact of shocks to NPLs using structural time series models where aggregate NPL ratios and economic activity are included in a VAR together with a broader set of banking and macroeconomic variables. For example, Espinoza and Prasad (2010), Nkusu (2011), De Bock and Demyanets (2012) and Klein (2013) estimate panel VAR models for various groups of countries and use country level data to investigate the feedback interactions between NPLs and macroeconomic performance.4 In addition to the expected coun- tercyclical behaviour of NPLs, all these studies nd signi cant feedback e ects from NPLs to the real economy.

We contribute to the empirical literature on the feedback e ects between NPLs, the banking sector and the macroeconomy by estimating a panel Bayesian VAR model with hierarchical priors (Jarocinski 2010). The aim of our analysis is to estimate the impact of exogenous shocks to the change in NPL ratios on bank lending and the macroeconomy. Changes in NPL ratios which are unrelated to changes in the repayment capacity of borrowers (i.e., exogenous changes in NPL ratios) include, inter alia, sales of defaulted loans to investors, changes in banks’ own attitudes towards risk, write-o s, supervisory actions that incentivise banks to work out these loans (by o ering restructuring solutions to clients) and other policy initiatives which deal with NPLs’ work-outs and defaults associated with poor enforcement mechanisms. Estimations are performed over the period from the rst quarter of 2006 to the third quarter of 2017 for twelve euro area countries, namely Austria, Belgium, Cyprus, Estonia, France, Greece, Ireland, Italy, Lithuania, Netherlands, Portugal and Spain.

The variables included in the panel VAR are economic activity (which is a proxy for the repayment capacity of borrowers), ination, the monetary policy rate, real estate prices, bank lending volumes both to non- nancial corporations and to households for house purchase, bank lending spreads to these two sectors, the ratio of capital and reserves over total assets and the change in NPL ratios. In order to disentangle the exogenous shocks to the changes in the NPL ratio, we use the Choleski factorisation, a recursive technique largely disseminated by Christiano, Eichenbaum and Evans (1999) and commonly adopted in the literature. In particular, we use as an identi cation strategy the fact that NPLs generally do not respond within a quarter to endogenous shocks, as banks are allowed to classify a loan as non-performing only a quarter after default.

We nd that an exogenous increase in the change in NPL ratios tends to depress bank lending, widens lending spreads and leads to a fall in real GDP growth and residential real estate prices. As a consequence, monetary policy rate is eased. While the responses of the capital and reserves-to-asset ratio vary across countries, a material increase is recorded in Cyprus, Spain, Ireland, Italy, Lithuania and Portugal, due to the increase in provisions for impairments recorded during the crisis. Interestingly, the results show that the decline in bank lending to non- nancial corporations is generally more marked than that in mortgage loans. These results

  • These groups of countries include the Gulf Cooperative Council (GCC) countries, a group of 26 advanced economies, a large sample of emerging markets and Central, Eastern and South-Eastern Europe (CESEE), respectively.

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are robust to a change in the ordering of the variables in the Choleski factorisation and also when including in the VAR the annual rate of growth in NPL volumes rather annual changes in NPL ratios. The forecast error variance decomposition also shows that exogenous shocks to the change in NPL ratios explain a relatively large share of the variance of the variables in the VAR, particularly for countries that exhibited a large increase in NPL ratios during the crisis. Finally, a three-year structural out-of-sample scenario analysis assesses the impact of a decline in NPL ratios for Cyprus, Ireland, Spain, Italy, Greece and Portugal, i.e. the countries that exhibited the most sizable increase in NPL ratios during the crisis. More specically, it quanties the dierential impact of a scenario where NPL ratios remain constant versus one where they are assumed to decline in line with observed recent developments. The exercise shows that reducing NPL ratios can produce non-negligible benets in terms of improved macroeconomic and nancial conditions.

Against this background, the contribution of this work to the literature is threefold. To our knowledge, this paper is the rst which studies the impact of a shock to the change in NPL ratios using a panel Bayesian VAR model which allows for country specic coecients hence capturing country specic dynamics for a large group of euro area countries with a consistent approach. This is an important contribution, given that euro area countries experienced rather heterogeneous dynamics of the NPL ratios in the considered time period due to dierent economic developments as well as key structural and institutional features. At the same time, the model assumes that the parameters of the VAR for individual countries share a common component which is compatible with the fact that euro area countries are part of a common market and share a common monetary policy. This assumption ensures an ecient use of the data. Second, this empirical analysis, thanks to the use of a Bayesian approach which allows to estimate a large number of parameters despite the relatively short time-span of the available data series for NPL ratios, benets from the inclusion in the VAR of a larger set of variables than those typically used in the literature. The use of a richer VAR allows to better characterize the feedback loop between non-performing loans, the real economy and the banking sector. In particular, the inclusion in the VAR of the capital and reserves to total asset ratio and lending spreads and the distinction between lending and spreads to non-nancial corporations and to households for house purchases are a novelty. Including the capital and reserves to total assets ratio is important because shocks to NPL ratios aect capital and provisions for impairments. Also having bank lending spreads among the endogenous variables is valuable because the exogenous shocks might lead to a re-pricing of bank loans, hence aecting the quantity of loans provided to the economy and, thus, macroeconomic conditions. Finally, this paper is the rst study which constructs and relies on a balanced panel of quarterly time series of NPL ratios for almost thirteen years and for a large number of euro area countries.

The rest of the paper is structured as follows. Section 2 presents the empirical methodology, including the econometric model and the priors adopted. The variables included in the panel VAR and the identication scheme are presented in Section 3 and 4, respectively. Section 5

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presents three sets of results: the impulse response analysis, the forecast error variance decom- position, the robustness analysis and the out of sample structural counterfactual analysis. The last section concludes.

We estimate a panel VAR model for twelve euro area countries and ten variables for the period between the rst quarter of 2006 and the third quarter of 2017.5 The twelve countries included in the analysis are Austria, Belgium, Cyprus, Estonia, France, Greece, Ireland, Italy, Lithuania, Spain, the Netherlands and Portugal. The model allows for cross-subsectional heterogeneity, hence capturing country specic dynamics. The following subsections describe in detail the methodology used to estimate the panel VAR, the variables included in the model and the adopted identication scheme.

2.1 Econometric model

We estimate the impact of shocks to real GDP growth and to the change in NPL ratios on bank lending and the economy based on the following panel V AR(p) model:

yi;t = Ci + Ai1yi;t1 + : : : + Aipyi;tp + “i;t

(1)

Where i is an individual country (i = 1; ; N), t is time (t = 1; ; T ), yi;t is a column vector of n endogenous variables (ten endogenous variables at time t are included in the model), Ci is a vector of n constants which are country specic and A1i ; : : : ; Api are matrices of coe- cients for dierent order of lags until lag p. The model we estimate allows for country-specic coecients, allowing us to capture the dierent impacts of the shocks across countries. Finally, it is assumed that the error term is normally distributed, as follows:

i;t N(0; i)

Transposing Equation (1) and expressing it in compact form, one obtains:

0 1

(A1i )0

yi;t0 = yi;t0

1 : : : yi;tp0

...

C

+ “i;t0

B

B

C

@

A

(APi )0

Stacking Equation (3) over the T periods it follows:

5The estimations in this paper were implemented relying on the BEAR toolbox and MATLAB codes developed by Dieppe, van Roye and Legrand (2016).

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0yi;0

2 1

0 yi;0

1

yi;0

1

C

yi;0

0

B ...

=

B ...

B

C

B

B

C

B

Byi;T0

C

Byi;T0

1

@

A

@

Which can be expressed as:

yi;0

1p

1 0

(Ai1)0

1

i;0

1

1

yi;0

2p

(Ai2)0

+

0i;0

2

...

C B

...

C

B ...

C

C B

C

B

C

yi;Tp0

C B

P

C

B

C

C B(Ai )0C

Bi;T0

C

A @

A

@

A

Y i = XiBi + Ei

In turn, equation (5) can be vectorised as follows:

vec(Y i) = (In Xi)vec(Bi) + vec(Ei)

Calling “i = vec(Ei) and from Equation (2) it follows that:

i N(0; i); with i = i IT

(7)

Moreover, calling i = vec(Bi), the random coecient model estimated here assumes that i can be expressed as:

With b a n2p 1 vector of parameters and assuming that bi N(0; b), it follows that:

Equations (8) and (9) imply that the coecients in the VAR will dier across countries while being drawn from the same distribution, centered around a common mean for the euro area (hence capturing similarity across country’s coecients). This model is particularly appealing because it captures a common component across countries while allowing for cross-country heterogeneity in the response to shocks. This feature is useful in our context because of the dierent dynamics exhibited by the NPL ratios in the countries included in our sample. The next sub-section describes the priors used in the paper.

2.2 The priors

The hierarchical prior adopted in this paper follows Jarocinski (2010).6 The advantage of this prior is that it treats the set of vectors i (i = 1; ; N), the residual covariance matrices i (i = 1; ; N), and the common mean and variance of the VAR coecients b and b as random variables. In particular, the hyperparameters b and b will have a hyper-prior distribution.

6The author compares impulse responses to monetary policy shocks in ve euro area countries before the EMU and in four of the newer European Union member states from central{eastern Europe.

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The hyper-prior distribution proposed by Jarocinski (2010) is diuse (improper) for b, whereas for b the functional form relies on a diagonal matrix b of dimension q q, q = Nn2p, which is inspired by the specication of the variance matrix of the Minnesota prior. In partic- ular, for parameters relating endongenous variables to their own lags, the variance is given

2

1

2

by aii

=

l3

, whereas for parameters related to cross-lag coecients the variance is dened

2

i2

2

2

as aij

=

. Because some coecients are large, while others are small, it is neces-

j2

l3

sary to scale each coecient’s variance by a factor which adjusts the size of the coecients of variables i and j. The values for i2 and j2 are obtained by tting autoregressive models by OLS for the n endogenous variables of the model (after pooling the data for all units) and then their standard deviation is computed. These standard errors capture the scale of unexpected movements in the variables. The full covariance matrix is then dened as b = (1 Iq) b. The parameter 1 captures the overall tightness of the prior for b. Note that when 1 = 0 the prior variance is null and all the coecients in i will take the value b (full pooling of the data across countries). By contrast, when 1 grows larger, coecients dier more and more across the countries in the sample and become similar to the respective single country estimates. When 1! 1 the coecients for each country are their own individual estimates and there is no sharing of information across countries. Because the number of estimated coecients in the dynamic equations diers substantially in the two cases (n2p in the pooled panel and Nn2p in the hierarchical model), it is desirable to assume an intermediate value for 1 to ensure a reasonable balance between tting individual countries’ data, on the one hand, and constraining the specication to make the estimates tighter, on the other. Hence, in order for the model to allow some degree of information sharing, Jarocinski (2010) proposes as prior for 1 an inverse Gamma distribution with very small values for the shape and scale parameters. Small parameters for the inverse Gamma distribution make the prior weakly informative, letting the data talk about the posterior common mean and variance.7 The values considered for the other two hyperparameters are those typically assumed in the literature, namelly 2 = 0:5 and 3 = 1. Finally, the prior distribution for i is simply a diuse prior. Combining the likelihood function with the priors mentioned in this sub-section one can obtain the full posterior distribution. However, this distribution does not allow for analytical derivation of the marginal posteriors, hence requiring the use of numerical methods.

  • Variables included in the panel VAR

The panel VAR includes 10 variables, which is a larger set than those typically used in the literature. This allows us to better characterise the dynamic interaction and feedback loops between non-performing loans, the real economy and the banking sector.8 In particular, the

  • In particular, the values for the shape and scale parameters are given by s20 and v20 , with s0 = v0 = 0:001.
    8For example, Espinoza and Prasad (2010) includes up to four variables, De Bock and Demyanets (2012) and

Klein (2013) include ve variables, while Nkusu (2011) includes nine variables.

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variables included are economic activity (which is a proxy for the repayment capacity of bor- rowers), ination, the monetary policy rate, real estate prices, bank lending volumes both to non- nancial corporations and to households for house purchase, bank lending spreads to these two sectors, the ratio of capital and reserves over total assets and the change in NPL ratios. Table 1 and Table 2 provide information on data sources and summary statistics, respectively.

Economic activity is measured by the annual rate of growth of real GDP (adjusted for calendar and seasonal e ects). In the case of Ireland, economic growth is computed as the annual growth rate of the nominal modi ed Gross National Income (GNI*), deated using the deator of the modi ed domestic demand (MDD).9 Ination is de ned as the annual rate of growth in the Harmonised Index of Consumer Prices (HICP) (working day and seasonally adjusted). The source of these data is Eurostat and the Irish Central Statistics Oce. The average over daily observations of the three-month Euribor rate is used as a proxy for the policy interest rate. The source of the data is the ECB Statistical Data Warehouse (SDW).

Bank lending is de ned as the annual rate of growth in bank lending to non- nancial corporations and to households for house purchase. Originally, these two variables are de ned in terms of an index of notional stocks.10 The source of these series is the MFI Balance Sheet Statistics of the ECB.11

Including bank lending spreads among the endogenous variables in the VAR is important because the exogenous shocks might lead to a re-pricing of bank loans, hence a ecting the quantity of loans provided to the economy and macroeconomic conditions. In the particular case of mortgage spreads, they may a ect the business cycle via changes in house prices, housing wealth and collateral valuations (Walentin 2014). The bank lending spreads are de ned as the di erence between bank lending rates (to households for house purchase and to non- nancial corporations) and Euribor. The lending rates used to compute the spreads are the interest rates on new business loans granted in euros, all maturities combined.12 The source is the MFI Interest Rate Statistics of the ECB.

The series of residential real estate prices is included to account for the role that real estate markets play in business cycle uctuations. This sector matters because it is a sizable sector of the real economy and rms and households own real estate properties, often used as collateral. Moreover, real estate transactions usually require credit, which is often provided by leveraged

9We use the modi ed Gross National Income (GNI*) instead of GDP because changes in the latter have become increasingly disconnected from actual trends in domestic living standards due to the sizeable distortion resulting from widespread activities of multinational companies. Instead, the GNI* attempts to control for (part of) the impact of globalisation on Irish macro-economic statistics. See Department of Finance (2018) for more details.

  1. Using notional stocks to compute the annual growth rates, rather than outstanding amounts, is important because the latter reect not only the cumulative e ect of nancial transactions but also the impact of other non-transaction related changes (e.g., instrument reclassi cation, changes in exchange rates, price uctuations and loan write-o s/write-downs, etc.). Excluding such non-transaction related changes is more meaningful for economic analysis.
  2. Data for Estonia for loans to non- nancial corporations before 2008 has been compiled by the Central Bank of Estonia and kindly shared with the authors.
  3. The exception is lending rates to non- nancial corporations in Greece, where the rates based on outstanding amounts have been used due to lack of data on new businesses.

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lenders. If borrowers default, the eects can be further amplied through a deleveraging process of the latter. As a consequence, changes in real estate prices can have large real eects and welfare implications (Hartmann 2015). Other studies that have included house prices in a VAR framework similar to ours include Bjrnland and Jacobsen (2010), Iacoviello (2005) and Meeks (2017). The residential real estate prices used in this analysis refer to new and existing dwellings for the whole country and are computed as the annual growth rate of the underlying index. The source of the data is the ECB SDW.

The ratio of bank capital and reserves over total assets is also included in the VAR. As with bank lending, this variable is dened in terms of an index of notional stocks and the source is MFI Balance Sheet Statistics of the ECB. Capital and reserves (the numerator) include total equity capital, non-distributed benets or funds and specic or general provisions against loans, securities and other types of assets. The capital and reserves to assets ratio is then computed as the ratio between this series and total assets.13 Including the capital and reserves ratio is important because institutions with larger buers are better prepared to support lending.

Finally, we include in the VAR the change in NPL ratios which is the most relevant variable in our analysis and is dened as the yearly dierence in NPL ratios. NPL ratios are dened as non-performing loans divided by total gross loans and were computed relying on several sources. The main source was the IMF Financial Soundness Indicators (FSI) database. This database provides data on the nancial health and soundness of member countries’ nancial systems since 2001. The IMF has oered guidelines to the member countries in order to improve the cross-country comparability of the data. In particular, it recommends that loans have to be classied as non-performing especially when: i) Payments of the principal and interest are past due by one quarter (90 days) or more; or ii) The interest payments equal to one quarter (90 days) interest or more have been capitalized (reinvested into the principal amount), renanced, or rolled over (that is, payment has been delayed by agreement). This guideline is based on the observation that 90 days is the horizon that is most widely used by countries to determine whether a loan is non-performing (IMF 2006).

The eective period covered by the FSI database varies across variables and countries. For most of the countries, data on NPLs dates back to the start of the global nancial crisis (2008 and 2009). In most cases, these series were extended backwards until the rst quarter of 2006 by using bank level information extracted from Bankscope. In particular, the weighted average of bank specic NPL ratios (using banks’ assets as weights) was used to construct the system wide gure for each quarter for Austria, Belgium, Estonia, Greece, Ireland, Lithuania and Portugal.

For Cyprus, France and Spain, data provided by the national central banks were used as well. In particular, the FSI NPL ratio data for Cyprus were extended backwards relying on data kindly provided by the Central Bank of Cyprus for the period between the fourth quarter of 2005 and the third quarter of 2011. For the rst three quarters of 2005, bank level information was used instead. In the case of France, the NPL ratio has been calculated by dividing the

13Data for Estonia for capital and reserves and total assets before 2008 has been compiled by the Central Bank of Estonia and shared by the ECB.

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series of Creances douteuses brutes” (available from the website of the Banque de France) and the stock of loans to the non-nancial private sector (sourced from the ECB SDW). Finally, for Spain, data provided by the national central bank were used to extend the FSI series before the fourth quarter of 2013.

The series of NPL ratios per country are displayed in Figure 1. It can be observed that the set of countries in the sample exhibits dierent dynamics in the evolution of the NPL ratios over time. In particular, there are countries where the NPL ratio increased during the crisis and decreased thereafter, but to dierent degrees and from dierent starting levels (Austria, Belgium and the Netherlands). In some cases, the NPL ratio increased signicantly during the crisis and declined also substantially afterwards (Ireland and Spain), also to levels close to those prevalent before the crisis (Estonia and Lithuania). There are also countries where the NPL ratio increased, but did not decline so far (Greece), or did only very recently (Cyprus, Italy and Portugal). Finally, in France, the NPL ratio remained overall unchanged from the beginning to the end of the sample.

Focusing on the contemporaneous correlation among the variables in the VAR (Table 3), we can observe that the change in NPL ratios (our main variable of interest) appears to be negatively and signicantly correlated with economic activity and bank lending. Also, an increase in the change in NPL ratios is signicantly associated with a widening of bank lending spreads. The Table also reports the contemporaneous correlation between the annual percentage change in NPL volumes and the variables in the VAR. This variable is described in Sub-section 5.3 and is used to perform a robustness analysis (see the discussion in that Sub-section for more details). The annual percentage change in NPL volumes is also negatively and signicantly correlated with economic activity and real estate prices. By contrast, the contemporaneous correlation with loans is positive and the one with the spreads is insignicant. Overall, these correlations appear to be lower than in the case of the changes in NPL ratios. These simple descriptive statistics may suggest that it takes time for the increase in NPL volumes to impair lending conditions.

The simple correlations between GDP growth, changes in the NPL ratios and volumes and the remaining macroeconomic and banking sector variables reported above do not allow to disentangle the source of variation of these variables. As the relation between these variables can run both ways, it is important to structurally identify the panel VAR.

We use Choleski decomposition in order to estimate the impact of changes in NPL ratios (De Bock and Demyanets 2012; Espinoza and Prasad 2010; Klein 2013). This recursive identi- cation approach implies that variables appearing earlier in the ordering are considered more exogenous than those appearing later. As such, variables that are ordered before a particular structural shock do not react to this shock on impact. Our identifying assumptions are as

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follows. First, according to the two-pillar monetary policy strategy of the ECB, the monetary policy rate is assumed to respond to a large number of indicators (Bernanke and Boivin 2003; Ciccarelli, Maddaloni and Peydro 2013; ECB 2011). Hence, we rank the monetary policy rate last in the VAR. Second, bank lending and lending spreads a ect the capital and reserves-to- asset ratio within the same quarter. This assumption reects the impact of the pro t and loss account (P&L) on capital in the same period when the result was generated. Hence, the capital and reserves-to-asset ratio is ranked before last in the system. Third, we assume that bank lending spreads move faster than macroeconomic variables (GDP and ination). Hence, we rank spreads after macroeconomic variables but before the capital and reserves-to-asset ratio. Fourth, we follow Bjrnland and Jacobsen (2010) and assume that real estate prices react to macroeconomic developments within the same quarter. Fifth, we assume that macroeconomic variables do not simultaneously react to the policy rate, while policy reacts to the macroeconomic environment simultaneously, as mentioned before. Also, we follow the standard literature on monetary policy and assume that ination is impacted simultaneously by a shock to economic activity (Bernanke and Gertler 1995; Christiano, Eichenbaum and Evans 1996). Sixth, we assume that it takes time to obtain a loan but once the loan is granted, it a ects macroeconomic variables instantaneously. Indeed, we place the macroeconomic variables (real GDP growth and ination) after the lending variables and the change in the NPL ratio. Seventh, the change in the NPL ratio is placed after the loans because a shock to loans a ects contemporaneously this ratio (through a change in its denominator). Lastly, we assume that changes in NPL ratios move slowly, meaning that GDP growth and ination a ect NPLs only with a lag. Indeed, accounting rules allow a loan to be classi ed as non-performing one quarter after the customer defaults. Hence, the change in the NPL ratio is placed before the macroeconomic variables. This ordering is similar to the ones used by Hancock, Laing and Wilcox (1995), Klein (2013) and De Bock and Demyanets (2012).

Overall, for our identi cation strategy, we use the following ordering: annual rate of growth in bank lending volumes to non- nancial corporations, annual rate of growth in bank lending volumes to households for house purchase, annual change in the NPL ratio, real GDP growth, ination rate, real estate prices, bank spreads on lending to non- nancial corporations, bank spreads on lending to households for house purchase, bank capital and reserves to assets ratio and monetary policy interest rate.

4.1 Exogenous changes in NPL ratios

As mentioned before, we are interested in estimating the impact of exogenous changes in NPL ratios on bank lending and the macroeconomy. Against this background, while the accounting rule mentioned above serves to justify the ordering of the NPL ratio among the variables in the VAR, it is useful to clarify what an exogenous change in NPL ratios can be. Overall, there are several sources of exogenous variations in NPL ratios which are unrelated to changes in the repayment capacity of borrowers.

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An exogenous change in NPL ratios can be related to the application of a new deni- tion of non-performing exposures. In particular, while the IMF makes an eort to ensure the cross-country comparability of NPL ratios, it recognises that reporting practices dier among countries (IMF 2006). For the countries in our sample, such reporting practices are aected not only by changes in the national denitions but also by the application of common reporting standards at the euro area level since 2014. In particular, the application of the Final draft technical standards on NPLs and Forbearance” by the EBA (2013) generally resulted in increases of recognised NPLs, which required banks to record additional provisions and in some cases also aected their capital positions.

Another possible exogenous change in NPL ratios is related to the transfer of non-performing loans from banks to Asset Management Companies (AMCs), which are dedicated entities that manage and workout distressed assets. The aim of these transfers is generally to cleanse banks’ balance sheets of bad loans, enabling banks to resume normal lending activities and to support a recovery in the economy. Various AMCs have been established in Europe after the nancial crisis. The rst was the National Asset Management Agency (NAMA) which was established in Ireland in 2009. The transfer of impaired assets to the National Asset Management Agency (NAMA) took place between 2009 and 2011. It took several months after it was announced by the authorities due to the necessary administrative arrangements and to the time needed to assess the value of the assets to be transferred. In November 2012, the Management Company for Assets Arising from the Banking Sector Reorganisation (SAREB) was created in Spain. In this case, the authorities had favoured alternative solutions before moving to the establishment of a system-wide AMC.14 Such AMCs resulted in signicant reductions in the level of arrears in the banking system of these two countries, although borrower behaviour or repayment ability did not change as a result of the creation of these entities.

Furthermore, supervisory actions can also have an impact on non-performing loan ratios. For example, at a national level, the supervisory authorities in Cyprus and Ireland set NPL restructuring targets to incentivise banks to accelerate the resolution of bad loans and to encourage sustainable solutions, rather than forbearance.15 More specically, the targets in Ireland referred to mortgage loans while in Cyprus they applied to the whole lending book. Also, the targets were not public in Ireland while banking system targets were published in Cyprus.

Actions by the supervisors at the euro area level also had an impact on the level of non- performing loan ratios in recent years. For example, the Comprehensive Assessment” carried out by the ECB in 2014, which consisted of an Asset Quality Review (AQR) and a Stress Test (ST), resulted in a signicant increase in the reported amount of NPLs in the euro area banking system and in the associated level of provisions for impairments (ECB 2014). Furthermore, the NPL guidance provided by the ECB to banks in 2017 and 2018 strengthened banks’ incentives to reduce their NPLs by means of write-os, restructurings or sales in secondary-markets

14For more details about AMCs in the euro area see Fell, Grodzicki, Martin and O’Brien (2016b).

15The targets involved dierent steps in the restructuring process, for example, the number of proposed sustainable restructurings, the number of concluded restructurings, etc.

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(SSM 2017, 2018). For example, it recommended the timely write-o of uncollectable loans and set up expectations regarding the assessment of bank’s levels of prudential provisions for nonperforming loans. It also recommended the setup of dedicated management units to deal with high risk clients and bad debt, which are separated from the banks’ sales units. The speed of implementation of such changes has varied from bank to bank and country to country and some countries had started to implement such recommendations beforehand. Importantly, sales of non-performing loans required the creation of secondary markets in the rst place, which usually required the adoption of legal and judicial reforms. Taken together, it is estimated that transactions in secondary markets (sales) reduced the euro area NPL ratio by 1.7 p.p. while write-o s and restructurings led to a decline of 3.2 p.p. over the period between the fourth quarter of 2016 and 2018 (ECB 2019).

Finally, another source of exogenous variation in non-performing loan ratios are the so called strategic defaults”. These are deliberate defaults which occur when solvent borrowers stop making repayments on a loan as a result of a rational nancial strategy. Strategic defaulters are unwilling, rather than unable to pay back their loan. Strategic defaults tend to occur when borrowers see other borrowers defaulting on their obligations without any immediate implication for them. They are more frequent for household mortgages and commercial real estate loans. Such behaviour is usually the reection of inecient legal systems, weak enforcement rules and bankruptcy laws, the presence of borrower protection schemes and permissive bank’s attitudes towards risk, among others. While there is wide recognition that the number of strategic defaulters” might be large in some countries, the size of the problem is dicult to quantify. However, an analysis conducted using data for corporate loans in Greece for the period 2008 to 2015 showed that one in six rms with a nonperforming loan were strategic defaulters (Asimakopoulos, Avramidis, Malliaropulos and Travlos 2016). As a result, the authors highlight the importance of distinguishing the latter from nancially distressed defaulters.

Having presented the methodology to estimate the model and the assumptions regarding the identi cation strategy, we illustrate the impact of shocks to the change in NPL ratios in twelve euro area countries relying on three sets of results. First, we present the impulse response functions for this shock. We are especially interested in estimating the size and shape of the responses of the endogenous variables. Second, we report the share of the forecast error variance for each variable and country to assess the degree by which a variable is driven by this shock. Third, we perform additional estimations to assess the robustness of the results. Finally, we implement an out of sample structural conditional forecast analysis to assess and quantify the macroeconomic and nancial bene ts stemming from a decline in NPL ratios.

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5.1 Impulse response analysis

Based on the estimated VAR model described in Equation (1), we generate the impulse responses of the endogenous variables to the structural shock for each individual country. Figures 2 and 3 report the impulse responses to a one standard deviation shock to the change in the NPL ratio for two groups of countries, namely Austria, Belgium, Cyprus, Estonia, Spain and France in the rst chart, and Greece, Ireland, Italy, Lithuania, the Netherlands and Portugal in the second. The countries are reported in the columns, while the variables are displayed in the rows. The impulse responses are plotted over a four-year horizon (16 quarters) after the shock, which is assumed to take place at time 0. The median of the accepted draws is shown together with the 16% and 84% Bayesian credibility bands.

Figure 2 and 3 show that the size of the instantaneous shock to the change in NPL ratios is stronger for those countries where the NPL ratio increased the most over the sample period, namely Cyprus, Ireland, Lithuania, Portugal, Greece, Spain and Italy (third row). The impact for these countries ranges between 0.3 and 4.3 percentage points.

The shock to the change in NPL ratios leads to a decline in bank lending which is stronger for non- nancial corporations than for households. Indeed, the annual growth of lending declines by up to 1.7 percentage points for non- nancial corporations, while it decreases by up to 1 percentage point for households.16 The relative size of these responses suggests that following a shock to the change in the NPL ratio, banks materially deleverage their balance sheets. At the same time, the impulse responses show that there is more heterogeneity in the timing of the peak response for mortgages (between four and twelve quarters after the shock) rather than for non- nancial corporations (between six and ten quarters after the shock). The shock also leads to a slight widening in both bank lending spreads (of up to about 0.3 percentage points) and to a decline in residential property prices (of up to 3.4 percentage points). For all these variables, the maximum impact is recorded for Cyprus, but strong e ects can be seen also in Ireland, Lithuania and Estonia. While the responses of the capital and reserves to asset ratio vary across countries, a material increase is recorded in Cyprus, Spain, Ireland, Italy, Lithuania and Portugal, due to the recorded increase in provisions for impairments during the crisis.

The shock to the change in the NPL ratio also leads to a decline in real GDP growth in most of the countries (by between 0.07 and 1 percentage point), between two and seven quarters after the shock. As a result of the deterioration in economic activity, monetary policy is relaxed. The response of the ination rate is rather heterogeneous across countries. These ndings are in line with those of Klein (2013) and Espinoza and Prasad (2010). These authors estimate the impact of much larger shocks, but their relative impact are comparable to ours.17 Theoretical

  1. This result is consistent with Fell, Grodzicki, Metzler and O’Brien (2018). Using bank level data, they nd that there is a negative signicant relationship between the ratio of NPLs over tier 1 capital and loan origination. This relationship appears to be stronger for lending to non-nancial corporations than for mortgages.
  2. Klein (2013) estimate that a 3 percentage point instantaneous shock to the change in the NPL ratio leads to a decline in real GDP growth of about 2 percentage point after one year. Espinoza and Prasad (2010) nd a relatively stronger impact. Indeed, they nd that a 2.3 percentage point increase in the change in the NPL ratio leads to a decline in GDP growth of about 2 percentage points.

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models also support our ndings. For example, Curdia and Woodford (2010) develop a dynamic stochastic general equilibrium model with credit frictions and nd that an increase in the loss rate of loans (i.e., the equivalent to non-performing loans in our empirical model) leads to a widening in credit spreads, a contraction in credit and also to a substantial fall in real activity. All in all, it can be observed that the shape of the responses tends to vary widely across the countries in the sample, both in terms of size and shape. This nding may be attributed to the fact that the various banking sectors experienced dierent degrees of variations in NPL ratios and at dierent times, hence generating heterogeneous responses.

5.2 Forecast error variance decomposition

In this section, we present a forecast error variance decomposition to uncover further details on the relationship among the variables included in the model. The analysis shows the share of the forecast error variance of individual variables explained by exogenous shocks to other variables. In general, we expect that shocks to the change in NPL ratios are relatively more relevant drivers of the variables in the countries where NPL ratios increased the most because these countries have been impacted by stronger and more frequent shocks to NPLs. The results of this analysis are presented in Table 4. In the Table, we report the share of the variance for each variable and each country in the panel VAR up to a 16-quarter horizon.

The shock to the change in the NPL ratio explains a non-negligible share of the variance of the variables included in the VAR. In particular, these shocks are sizable drivers of real GDP growth, explaining between 10 and 33% of the variance in Lithuania, Estonia, Ireland and Cyprus. For the remaining countries, the NPL shock still explains between 2% and 5% of the variance of real GDP growth. Regarding bank lending, the share explained is larger for corporate lending than for mortgages. For NFC lending, the NPL shock explains between 5% and 17% of the variance for Cyprus, Ireland, Lithuania, Italy and Portugal. For mortgage lending, the share is large only for Cyprus, but smaller than 3% for the remaining countries.18 For spreads (both corporate and mortgage), the share is above 10% for Cyprus, Ireland and Lithuania, and for Italy is larger than 10% only for spreads on corporate lending. For residential real estate prices, the share is large for Cyprus, Ireland and Estonia (between 12% and 56%). Importantly, we observe that shocks to the change in the NPL ratio explain more than 50% of the variance in the same variable at the end of the horizon in countries like Cyprus, Greece, Ireland, Italy, Lithuania, Portugal and Spain. This nding suggests that exogenous factors have been key drivers of changes in NPL ratios.

These ndings are broadly in line with the literature. Over long horizons (between 5 and

10 years), Espinoza and Prasad (2010), De Bock and Demyanets (2012) and Klein (2013) nd that shocks to the change in NPL ratios explain about 6%, 8% and 20% of the variance of GDP growth in their sample of countries, respectively. For the credit-to-GDP ratio (equivalent

18For comparison, Hristov, Hulsewig and Wollmershauser (2012) nd that demand shocks explain 13% of the variance of the GDP deator and 16% of lending volumes over a four year horizon in a sample of euro area countries.

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to bank lending in our model), the estimated share stands at 13% and 8% in De Bock and Demyanets (2012) and Klein (2013), respectively. For the change in the NPL ratio, Klein (2013) and De Bock and Demyanets (2012) nd that 70% and 90% of the variance is explained by the same variable, respectively.

The shock to the change in the NPL ratio explains for most countries in our model a smaller share of the variance of our variables than the literature on credit supply shocks and shocks to bank capital would suggest.19This is, however, not surprising, because the increase in NPLs has been only one of several disturbances that have in recent years a ected our sample of countries.20. However, for some countries, particularly those hardly hit by the crisis, and some variables, the share is equally important and sometimes larger than that found in the literature.

5.3 Robustness analysis

This section implements two robustness checks of the impulse-response analysis performed before to assess the reliability of our results. First, we generate impulse responses to a shock in the NPL ratio relying on a di erent ordering of the variables in the Choleski factorisation. In this new ordering, loans and NPL ratios are included in the VAR after GDP and ination and, thus, are a ected by macroeconomic variables contemporaneously. Second, we replace the changes in NPL ratios with the changes in NPL volumes and order this variable rst in the VAR.

In the rst robustness check, we relax two of the identifying assumptions made in Section

4. In particular, in that analysis we assumed that: i) It takes time to obtain a loan but once the loan is granted, it instantaneously a ects macroeconomic variables; and ii) Changes in NPL ratios move slowly, implying that GDP growth and ination a ect NPLs only with a lag. These assumptions led us to place the macroeconomic variables (real GDP growth and ination) after the lending variables and the change in the NPL ratio. By contrast, in our rst robustness check, we order loans and the NPL ratio after the two macroeconomic variables, implying a fast reply of these variables to changing macroeconomic conditions. The ordering used is as follows: real GDP growth, ination rate, annual rate of growth in bank lending volumes to non-

nancial corporations, annual rate of growth in bank lending volumes to households for house purchase, annual change in the NPL ratio, annual rate of change in real estate prices, bank lending spreads on lending to non- nancial corporations, bank lending spreads to households for house purchase, bank capital and reserves to assets ratio and monetary policy interest rate.

  1. For example, Mesonnier and Stevanovic (2017) nd that after one year, a bank capital shock accounts for some 4% of the variance of GDP growth and 11% of the variance of loan growth in the U.S. For the same country, Meeks (2012) nds that credit market shocks account for 20% of the total mean square prediction error in industrial output at a three-year horizon. Bassett, Chosak, Driscoll and Zakrajsek (2014) nd the same share at a four year horizon for credit supply shocks. Hristov et al. (2012) nd that credit supply shocks explain about 15% of the uctuations recorded in real output, 12% of loan rates and 11% of loan volumes in a sample of euro area countries over a four year horizon. However, for some countries, particularly those hardly hit by the crisis, and some variables, the share is equally important and sometimes larger than that found in the literature on credit or bank capital shocks.
  2. Other factors include, inter alia, liquidity and funding shocks, changes in bank competition, shocks to the perceived credit riskiness of borrowers and shocks to lending standards, among others.

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In the second robustness check, we replace the series of the changes in NPL ratios with the series of the changes in NPL volumes. The 90 days past due (DPD) rule discussed in Subsection 3 applies to the volume of NPLs. Indeed, as banks are allowed to classify a loan as non-performing a quarter (90 days) after a loan defaulted, we rank the NPL volume rst in the VAR, implying that it is not a ected contemporaneously by the remaining variables in the VAR. The series of NPL volumes are computed by multiplying the NPL ratios presented in Sub-section 3 by the stock of loans at the country level from the MFI Balance Sheet Statistics of the ECB, which has data for 10 of the 12 countries in our sample.21 The NPL volumes enter the VAR as annual percentage changes. In this second robustness check, we use the following ordering: the annual rate of growth in NPL volumes, real GDP growth, the ination rate, the annual rate of growth in bank lending volumes to non- nancial corporations, annual rate of growth in bank lending volumes to households for house purchase, annual rate of growth of real estate prices, bank lending spreads to non- nancial corporations, bank lending spreads to households for house purchase, bank capital and reserves to assets ratio and monetary policy rate.22

Figure 4 and Figure 5 present the results for these two extensions. Most of the results are broadly in line with those presented before, although they are somewhat weaker when assuming that loans and NPLs reacts to macroeconomic variables instantaneously. In particular, both mortgage and corporate spreads are less signi cant across countries with the new ordering. Fur- thermore, for the impact on lending, it can be observed that there are changes in the signi cance of the responses across countries (some countries with previous signi cant responses are not in- signi cant anymore and vice versa), while the total number of signi cant responses is broadly unchanged. Finally, the impact on real estate prices and real GDP growth is also unchanged across countries. Regarding the responses of the endogenous variables to a shock in NPL vol- umes, the magnitudes are not comparable with those presented before (due to the di erent scale of the change in the NPL ratio and in the NPL volumes) but results are qualitatively similar. In particular, the number of signi cant responses of corporate and mortgage spreads increases while that one of real estate prices decreases. For real GDP growth and lending, the number of signi cant responses remains broadly unchanged, but with changes for speci c countries.

All in all, this robustness analysis con rms to a large extent the results presented in Subsection 5.1. In particular, we continue to nd that banks materially deleverage their balance sheets following a shock to NPLs and the impact is stronger for non- nancial corporations than for households. The shock also leads to a decline in real GDP growth and residential property prices.

21The MFI Balance Sheet data for the loan volumes are not available for Cyprus and Estonia.

22Once the variable of interest is ordered rst in the VAR (i.e., it is assumed to be the most exogenous one), then the order of the remaining variables in the VAR is irrelevant for the calculation of response functions to a shock to that variable.

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5.4 Structural out of sample scenario analysis

This section reports the results of a structural, out-of-sample scenario analysis which assesses the impact of two dierent paths of NPL ratios over the period 2017Q4 to 2020Q3. This exercises provides a quantitative illustration of the possible benets associated with a further decline in NPL ratios in euro area countries. In this analysis, we focus on the six most relevant variables in the VAR and on the six countries that exhibited the most sizable increase in NPL ratios during the crisis, namely Cyprus, Ireland, Spain, Italy, Greece and Portugal. Under a baseline scenario, the out of sample change in the NPL ratio for each country is assumed to equal the average change recorded over the last four quarters of the historical data.23 Under an adverse scenario, the out of sample change in the NPL ratio is assumed to equal zero. In both cases, the remaining variables in the VAR are projected conditional on the assumed evolution of the change in the NPL ratio, following the methodology proposed by Antolin-Diaz, Petrella and Rubio-Ramrez (2018). In particular, the forecasts are computed assuming that only the structural shock to the change in the NPL ratio adjusts to ensure the new path for the conditioning variable.24 This structural scenario is more meaningful than the one proposed by Waggoner and Zha (1999), who assume that all the structural shocks adjust to ensure the evolution of the conditioning variable. Applied to our model, it would mean assessing the most likely set of circumstances under which the change in the NPL ratio evolves as prescribed. Instead, we are interested in computing the paths of the endogenous variables in the VAR which are consistent with a sequence of shocks to the change in the NPL ratio per se.

The observed and out of sample evolution of the change in NPL ratios for the two paths and the six countries are depicted in Figure 6. By construction, the gap between the baseline and the adverse changes in the NPL ratio depends on how strongly the variable evolved in the last four quarters of our sample. This gap is the widest for Cyprus, followed by Ireland, Portugal, Italy and then Spain and Greece. These assumptions result in dierent levels of the NPL ratio at the end of the forecast horizon. Under the baseline, the level of the NPL ratio is expected to decline (with respect to the starting point) by 10.7 percentage points in Cyprus,

9.7 percentage points in Ireland, 5 percentage points in Portugal, 3.2 percentage points in Italy and 1.6 percentage points in both Spain and Greece.

The out of sample deviation between the baseline and adverse conditional forecasts of the variables is reported in Figure 7. The countries are reported in the columns, while the variables are depicted in the rows. A positive value implies that the baseline forecast exhibits a higher value than the adverse one. The results show, as expected, that a further reduction in NPL ratios would have a positive impact on both the macroeconomic and the banking variables.

23More specically, it is assumed that the out of sample change in the NPL ratio is equal to -3.6% for Cyprus, -0.5% for Spain, -0.5% for Greece, -3.2% for Ireland, -1.1% for Italy and -1.7% for Portugal.

24Restricting the value of a variable in the VAR over a certain period of time gives rise to a system of conditions that the structural disturbances should verify. Waggoner and Zha (1999) show that the distribution of these restricted structural disturbances is normal. Jarocinski (2010) proposed a numerically more ecient method that avoids drawing the shocks from that distribution. See Dieppe et al. (2016) for more details.

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At the end of the forecast horizon, the annual rate of growth of mortgage lending under the baseline scenario is between 1.4 (Italy) and 2.9 (Ireland) percentage points higher than under the adverse scenario, while the annual rate of growth of corporate lending increases faster by between 0.9 (Spain) and 4.4 (Ireland) percentage points. Bank lending spreads are narrower, by between 0.2 and 0.6 percentage points for mortgages and by between 0.2 and 0.8 for loans to non- nancial corporations under the baseline scenario. Stronger lending and lower spreads lead to higher residential real estate prices, with annual rates of growth being between 1.6 (Italy) and 6.7 (Cyprus) percentage points higher under the baseline than the adverse. Finally, the rate of growth of real GDP is higher by between 0.5 (Italy) and 1.6 (Ireland) percentage points. Overall, this structural out-of-sample forecast illustrates that a further reduction in NPL ratios can generate signi cant medium-term economic bene ts in euro area countries.

Non-performing loan (NPL) ratios increased substantially in many euro area countries since the onset of the global nancial crisis. Despite a gradual decline from the peak in 2014, NPL ratios still remained a key problem in several euro area countries at the end of 2017. High NPL ratios can impair the stability of the banking system and its ability to lend to the real economy. Therefore, in particular, for highly bank-dependent economies such as the euro area, the necessity to deal with elevated NPL ratios is unquestionable.

Against this background, we quantify the impact of an exogenous increase in the change in NPL ratios on economic and banking sector developments in twelve euro area countries. Given the relatively short time series available for NPL ratios and the large number of parameters to be estimated, we estimate a panel Bayesian VAR model with hierarchical priors that allows for country-speci c coecients. The variables included in the panel VAR are economic activity (which is a proxy for the repayment capacity of borrowers), ination, the monetary policy rate, real estate prices, bank lending volumes both to non- nancial corporations and to households for house purchase, bank lending spreads to these two sectors, the ratio of capital and reserves over total assets and the change in NPL ratios. We estimate the impact of the shock using a Choleski factorisation approach. In particular, we use as an identi cation strategy the fact that NPLs generally do not respond within a quarter to endogenous shocks, as banks are allowed to classify a loan as non-performing only a quarter after default.

We illustrate the impact of this shock relying on three sets of results. Looking rst at the impulse response functions, we nd that an exogenous increase in the change in NPL ratios depresses bank lending, widens lending spreads and leads to a fall in real GDP growth and residential real estate prices and an easing of monetary policy. The responses of ination and capital and reserves over total assets vary across countries. However, the latter signi cantly increases in Cyprus, Spain, Ireland, Italy, Lithuania and Portugal. The reason is that this variable includes provisions for impairments which materially increased in these countries dur-

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ing the crisis. These results are robust to a change in the ordering of the variables in the Choleski factorisation (whereby bank loans and the NPL ratio are aected contemporaneously by macroeconomic variables) and also when including in the VAR the annual rate of growth in NPL volumes (instead of the NPL ratio) and order it rst in the VAR. The forecast error variance decomposition also shows that shocks to the change in NPL ratios explain a large share of the variance of the variables in the VAR, particularly for those countries that experienced a large increase in NPL ratios during the crisis. Finally, a three-year structural out of sample forecast analysis provides quantitative evidence that a further reduction of NPL ratios can produce signicant economic benets in euro area countries.

ECB Working Paper Series No 2411 / May 2020

23

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ECB Working Paper Series No 2411 / May 2020

26

Table 1: Data sources

Variable

Source

Real GDP growth

ECB SDW

Ination

ECB SDW

RRE prices

ECB SDW

Euribor

ECB SDW

Corporate loans

ECB BSI

Mortgage loans

ECB BSI

Corporate spread

ECB MIR and SDW

Mortgage spread

ECB MIR and SDW

Capital and reserves ratio

ECB BSI

Change in NPL ratio

IMF FSI, Banque de France, Banco de Espa~na,

Central Bank of Cyprus, Irish Central Statistics Oce, Bankscope

Percentage change in NPL volumes

NPL volumes are obtained by multiplying

the country-level NPL ratio

by the stock of loans from the ECB BSI

Table 2: Summary statistics

Variable

Obs

Mean

Std. Dev.

Min

Max

Real GDP growth

564

1.0

4.0

-17.5

12.0

Ination

564

1.6

1.6

-3.1

10.6

RRE prices

564

1.8

10.5

-40.3

57.5

Euribor

564

1.4

1.7

-0.3

5.0

Corporate loans

564

4.6

12.0

-20.2

67.5

Mortgage loans

564

5.7

12.3

-33.0

87.4

Corporate spread

564

2.5

1.4

0.2

6.6

Mortgage spread

564

2.3

1.1

-0.3

5.0

Capital and reserves ratio

564

12.3

10.9

2.7

68.8

Change in NPL ratio

564

0.8

3.3

-8.2

27.2

Percentage change in NPL volumes

470

23.6

60.9

-39.1

369.3

ECB Working Paper Series No 2411 / May 2020

27

2020 May / 2411 No Series Paper Working ECB

Table 3: Correlation matrix among the variables included in the panel VAR

Real GDP

Ination

RRE

Euribor

Corporate

Mortgage

Corporate

Mortgage

Capital and

Change in

Change in

growth

prices

loans

loans

spread

spread

reserves ratio

NPL ratio

NPL levels

Real GDP growth

1

Ination

0.15***

1

RRE prices

0.79***

0.26***

1

Euribor

0.15***

0.52***

0.25***

1

Corporate loans

0.35***

0.57***

0.59***

0.69***

1

Mortgage loans

0.40***

0.49***

0.62***

0.52***

0.78***

1

Corporate spread

-0.29***

-0.30***

-0.32***

-0.47***

-0.36***

-0.27***

1

Mortgage spread

-0.33***

-0.42***

-0.39***

-0.74***

-0.55***

-0.44***

0.69***

1

Capital and reserves ratio

0.01

-0.27***

-0.11**

-0.37***

-0.26***

-0.25***

0.61***

0.42***

1

Change in NPL ratio

-0.55***

-0.19***

-0.47***

-0.09*

-0.18***

-0.16***

0.39***

0.33***

0.15***

1

Percentage change in NPL volumes

-0.49***

0.17***

-0.31***

0.25***

0.17***

0.10**

-0.04

-0.08

-0.17***

0.54***

1

Note: The data sample spans from 2006Q1 to 2017Q3. (***), (**) and (*) denote statistical signi cance at the 1%, 5% and 10% levels, respectively. Data for the percentage change in NPL volumes are not available for Cyprus and Estonia.

Sources: Data constructed based on the IMF, ECB, Banque de France, Banco de Espa~na, Central Bank of Cyprus, Central Statistics Oce of Ireland and Bankscope.

Figure 1: Non-performing loan ratios

AT

4

2

0

2008 2010 2012 2014 2016

EE

6

4

2

0

2008 2010 2012 2014 2016

BE

60

4

40

2

20

0

0

2008 2010 2012 2014 2016

FR

4

40

2

20

0

0

2008 2010 2012 2014 2016

CY

2008 2010 2012 2014 2016

GR

2008 2010 2012 2014 2016

30

IE

20

20

10

10

0

0

2008 2010 2012 2014 2016

NL

20

3

2

10

1

0

0

2008 2010 2012 2014 2016

IT

30

20

10

0

2008 2010 2012 2014 2016

PT

10

5

0

2008 2010 2012 2014 2016

LT

2008 2010 2012 2014 2016

ES

2008 2010 2012 2014 2016

Note: The data sample spans from 2006Q1 to 2017Q3. The displayed NPL ratios are based on data sourced from the IMF FSI, Banque de France, Banco de Espa~na, Central Bank of Cyprus and Bankscope.

ECB Working Paper Series No 2411 / May 2020

29

Figure 2: Response to a shock to the change in the NPL ratio

loans

0.1

AT

0.1

Corporate

-0

-0.1

-0.2

-0.3

0

3

6

9

12 15

0.4

CY

0.1

-0.9

-0.4

-2.3

-0.9

0

3

6

9

12 15

EE

0.1

-0.1

-0.3

0

3

6

9

12 15

ES

0.1

-0.1

-0.2

0

3

6

9

12 15

loans

0.2

0.1

0

0.1

0

0.1

Mortgage

0.1

-0

-0.8

-0.2

-0.2

-0.1

-0.1

-0.2

-1.5

-0.5

-0.3

-0.3

ratio

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0.3

0.3

4.8

0.4

0.4

0.1

in NPL

0.1

0.1

2.4

0.2

0.2

0.1

Change

-0

-0

0

-0

-0

-0

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

growth

0.2

0.2

0.1

0

0.1

0

-0

-0

-0.4

-0.7

-0.1

-0.1

GDP

-0.2

-0.2

-1

-1.4

-0.2

-0.3

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

0.1

0.3

0

0

0

Inflation

-0

0.1

0

-0.1

-0.1

-0

-0.1

-0

-0.2

-0.2

-0.1

-0.1

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

prices

0.7

0

-0.3

-0.1

-0.1

0

0.2

-0.3

-2.2

-1.7

-0.5

-0.3

RRE

-0.3

-0.6

-4.2

-3.3

-0.9

-0.6

spread

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0.1

0.1

0.4

0.1

0.1

0.1

Mortgage

0

0

0.2

0

0

-0

-0

-0

0

-0

-0

-0.1

spread

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

0

0.4

0.1

0.1

0

Corporate

0

0

0.2

0

0

0

-0

3

6

9

-0

0

3

6

9

12 15

0

3

6

9

-0

0

3

6

9

-0

0

3

6

9

12 15

0

3

6

9

12 15

0

12 15

0

12 15

12 15

0

ratio

0.2

0.1

2.1

0.3

0.3

0.2

0.1

0.2

Capital

0

1.1

0

0.1

0

-0.1

0.1

-0.2

0

-0

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

0.1

0.1

0

0.1

0.1

Euribor

-0.1

-0

-0.2

-0.1

-0

-0

-0.1

-0.1

-0.5

-0.2

-0.1

-0.1

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

Note: The Figure reports the responses of the endogenous variables to one standard deviation shock to the change in NPL ratios. Real GDP growth, headline ination, residential real estate prices and corporate and mortgage loans are expressed in annual growth rates. The Euribor, bank lending spreads, the change in NPL ratios and the capital and reserves-to-asset ratio are expressed in percentage points. Responses are reported for 4 years (16 quarters) after the shock (assumed to take place at time 0). The median of the accepted draws is plotted together with the 16% and 84% Bayesian credibility bands.

ECB Working Paper Series No 2411 / May 2020

30

Figure 3: Response to a shock to the change in the NPL ratio (cont.)

loans

0.4

GR

0

IE

0

IT

0.1

LT

0.1

NL

0.2

PT

Corporate

-0.1

-0.7

-0.3

-0.9

-0.1

-0.2

-0.5

-1.4

-0.6

-1.8

-0.4

-0.6

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

loans

0.2

0.1

0.1

0

0

0.1

Mortgage

-0.1

-0.3

-0

-0.4

-0.2

-0.1

-0.3

-0.6

-0.2

-0.8

-0.3

-0.3

ratio

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

1.4

2.5

0.3

2.1

0.3

1.5

in NPL

0.7

1.3

0.2

1.1

0.1

0.7

Change

-0

0

0

-0

-0

-0

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

growth

0.6

0

0.1

0.1

0.1

0.1

0.2

-0.6

-0.2

-0.7

-0.1

-0.1

GDP

-0.3

-1.2

-0.4

-1.5

-0.3

-0.2

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0.1

0.1

0.1

0.3

0.1

0.1

Inflation

-0

-0

0

0.1

0

-0

-0.2

-0.1

-0

-0.2

-0

-0.1

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

prices

0.5

0.3

0.3

0.1

0.5

-0.4

-0.2

-1.4

0

-1.4

-0.2

-0.2

RRE

-0.9

-2.4

-0.3

-3

-0.6

-1

spread

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0.1

0.2

0.1

0.2

0.1

0.1

0.1

Mortgage

0.1

0.1

0.1

0

0.1

0

0

0

-0

-0.1

-0

spread

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0.1

0.2

0.1

0.2

0.1

0.1

0.1

Corporate

0.1

0.1

0.1

0

0

0

0

0

-0.1

-0

-0

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

0

3

6

9

0

3

6

9

12 15

3

6

9

12 15

12 15

12 15

0

ratio

0.9

0.9

0.2

1

0.1

0.5

Capital

0.3

0.4

0.1

0.5

0

0.2

-0.3

-0.1

-0

-0.1

-0.1

-0

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

-0

0

0.1

0

Euribor

-0

-0.1

-0.2

-0.1

-0.1

0

-0.1

-0.2

-0.3

-0.2

-0.3

-0.1

-0.2

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

Note: The Figure reports the responses of the endogenous variables to one standard deviation shock to the change in NPL ratios. Real GDP growth, headline ination, residential real estate prices and corporate and mortgage loans are expressed in annual growth rates. The Euribor, bank lending spreads, the change in NPL ratios and the capital and reserves-to-asset ratio are expressed in percentage points. Responses are reported for 4 years (16 quarters) after the shock (assumed to take place at time 0). The median of the accepted draws is plotted together with the 16% and 84% Bayesian credibility bands.

ECB Working Paper Series No 2411 / May 2020

31

Table 4: Forecast error variance decomposition (shock to the change in the NPL ratio)

NPL shock

Variable

Year

AT

BE

CY

EE

ES

FR

GR

IE

IT

LT

NL

PT

Corporate

1st

0%

0%

0%

1%

0%

0%

0%

1%

2%

1%

0%

1%

loans

2nd

1%

0%

6%

2%

0%

1%

1%

6%

5%

5%

0%

2%

3rd

1%

0%

14%

2%

1%

1%

1%

9%

6%

7%

1%

4%

4th

1%

0%

17%

2%

1%

1%

1%

10%

6%

8%

1%

5%

Mortgage

1st

0%

0%

2%

2%

1%

0%

0%

0%

0%

1%

0%

0%

loans

2nd

1%

0%

6%

2%

2%

0%

0%

1%

0%

2%

0%

1%

3rd

1%

0%

11%

2%

2%

1%

0%

1%

0%

2%

0%

1%

4th

1%

0%

14%

2%

3%

1%

1%

1%

0%

3%

0%

2%

Change

1st

81%

71%

95%

55%

87%

56%

88%

93%

80%

88%

80%

94%

in NPL ratio

2nd

61%

53%

94%

39%

71%

38%

80%

92%

66%

83%

61%

92%

3rd

51%

40%

94%

34%

63%

28%

76%

91%

58%

81%

48%

90%

4th

47%

32%

93%

33%

59%

24%

74%

91%

55%

80%

41%

89%

Ination

1st

3%

9%

12%

3%

2%

3%

2%

2%

5%

4%

1%

1%

2nd

3%

5%

14%

6%

2%

4%

3%

2%

5%

3%

1%

3%

3rd

3%

4%

15%

6%

2%

4%

3%

3%

5%

4%

1%

3%

4th

3%

4%

17%

6%

2%

4%

3%

4%

5%

4%

2%

3%

GDP growth

1st

1%

2%

14%

16%

3%

8%

2%

23%

6%

9%

2%

2%

2nd

2%

3%

28%

15%

2%

7%

2%

22%

5%

10%

4%

2%

3rd

2%

2%

33%

15%

2%

6%

2%

22%

5%

10%

4%

3%

4th

2%

2%

33%

14%

3%

5%

2%

22%

5%

10%

3%

3%

Mortgage

1st

2%

1%

8%

4%

5%

1%

4%

16%

3%

5%

1%

3%

spread

2nd

2%

1%

18%

3%

4%

1%

4%

21%

5%

10%

2%

6%

3rd

2%

1%

23%

2%

4%

1%

4%

21%

6%

11%

2%

7%

4th

2%

1%

24%

2%

4%

1%

4%

21%

7%

12%

2%

7%

Corporate

1st

1%

2%

10%

4%

5%

4%

4%

22%

11%

3%

3%

2%

spread

2nd

1%

2%

19%

5%

5%

4%

4%

32%

13%

7%

4%

4%

3rd

1%

3%

25%

5%

5%

4%

5%

36%

15%

9%

5%

6%

4th

1%

3%

29%

5%

5%

4%

6%

38%

16%

10%

5%

7%

RRE prices

1st

1%

4%

30%

11%

6%

5%

2%

25%

2%

4%

4%

3%

2nd

1%

5%

49%

11%

7%

5%

5%

36%

3%

7%

6%

6%

3rd

1%

5%

55%

11%

7%

5%

7%

40%

4%

8%

7%

8%

4th

2%

5%

56%

12%

7%

5%

8%

41%

4%

9%

6%

9%

Capital and

1st

7%

1%

15%

3%

6%

4%

1%

7%

2%

3%

3%

3%

reserves ratio

2nd

9%

1%

28%

2%

11%

6%

2%

23%

4%

7%

2%

8%

3rd

9%

1%

37%

2%

14%

8%

2%

32%

6%

10%

2%

12%

4th

9%

1%

42%

2%

15%

9%

2%

38%

8%

12%

3%

15%

Euribor

1st

2%

1%

4%

5%

1%

1%

3%

19%

6%

3%

1%

2%

2nd

2%

1%

17%

5%

1%

1%

3%

24%

9%

8%

1%

4%

3rd

2%

1%

26%

4%

1%

1%

4%

25%

10%

10%

1%

6%

4th

2%

1%

32%

4%

1%

1%

5%

26%

11%

11%

1%

8%

Note: The table reports the share of the variance of the variables in the VAR which is explained by a shock to the change in the NPL ratio over a horizon of 16 quarters. The median of the accepted draws of the variance decomposition from the posterior distribution is reported.

ECB Working Paper Series No 2411 / May 2020

32

Figure 4: Response to a shock to the change in the NPL ratio with new ordering

GDP growth Corporate loans Mortgage loans

RRE prices Mortgage spreadCorporate spread

AT

0.05

0

-0.05

0 2 4 6 8 101214

0.02

BE

0

-0.02

-0.04

-0.06

0 2 4 6 8 101214

0

CY

-0.5

0 2 4 6 8 101214

0

EE

-0.1

-0.2

-0.3

0 2 4 6 8 101214

0

ES

-0.1

-0.2

0 2 4 6 8 101214

0

FR -0.05

-0.1

0 2 4 6 8 101214

0

GR

-0.1

-0.2

0 2 4 6 8 101214

0

IE

-0.2

-0.4

0 2 4 6 8 101214

0.08

IT

0.06

0.04

0.02

0

-0.02

0 2 4 6 8 101214

0.2

LT

0

-0.2

0 2 4 6 8 101214

0

NL

-0.05

-0.1

0 2 4 6 8 101214

0

PT

-0.1

-0.2

0 2 4 6 8 101214

0.05

0

-0.05

-0.1

-0.15

0 2 4 6 8 101214

0.05

0

-0.05

-0.1

-0.15

0 2 4 6 8 101214

0.5

0

-0.5

-1

-1.5

0 2 4 6 8 101214

0.1

0

-0.1

-0.2

0 2 4 6 8 101214

0

-0.1

-0.2

0 2 4 6 8 101214

0.1

0

-0.1

0 2 4 6 8 101214

0

-0.2

-0.4

-0.6

-0.8

0 2 4 6 8 101214

0

-0.5

-1

0 2 4 6 8 101214

0

-0.1

-0.2

0 2 4 6 8 101214

0.4

0.2

0

-0.2

-0.4

-0.6

0 2 4 6 8 101214

0

-0.1

-0.2

0 2 4 6 8 101214

0.2

0

-0.2

-0.4

-0.6

0 2 4 6 8 101214

0.1

0

-0.1

0 2 4 6 8 101214

0.1

0.05

0

-0.05

0 2 4 6 8 101214

0

-0.5

-1

0 2 4 6 8 101214

0.1

0

-0.1

0 2 4 6 8 101214

0

-0.1

-0.2

0 2 4 6 8 101214

0

-0.1

-0.2

0 2 4 6 8 101214

0

-0.2

-0.4

0 2 4 6 8 101214

0

-0.2

-0.4

0 2 4 6 8 101214

0.1

0

-0.1

0 2 4 6 8 101214

0.2

0

-0.2

-0.4

0 2 4 6 8 101214

0

-0.1

-0.2

0 2 4 6 8 101214

0

-0.2

-0.4

0 2 4 6 8 101214

0.4

0.2

0

-0.2

-0.4

0 2 4 6 8 101214

0

-0.1

-0.2

-0.3

0 2 4 6 8 101214

0

-1

-2

0 2 4 6 8 101214

0

-1

-2

0 2 4 6 8 101214

0

-0.5

0 2 4 6 8 101214

0.1

0

-0.1

-0.2

0 2 4 6 8 101214

0.4

0.2

0

-0.2

-0.4

-0.6

-0.8

0 2 4 6 8 101214

0

-0.5

-1

-1.5

0 2 4 6 8 101214

0.4

0.2

0

0 2 4 6 8 101214

2

1

0

0 2 4 6 8 101214

0.1

0

-0.1

-0.2

-0.3

0 2 4 6 8 101214

0.4

0.2

0

-0.2

-0.4

-0.6

-0.8

0 2 4 6 8 101214

0.04

0.02

0

0 2 4 6 8 101214

0.04

0.02

0

0 2 4 6 8 101214

0.1

0.05

0

0 2 4 6 8 101214

0.06

0.04

0.02

0

-0.02

0 2 4 6 8 101214

0.06

0.04

0.02

0

0 2 4 6 8 101214

0

-0.05

-0.1

0 2 4 6 8 101214

0.1

0.05

0

0 2 4 6 8 101214

0.15

0.1

0.05

0

0 2 4 6 8 101214

0.04

0.02

0

-0.02

0 2 4 6 8 101214

0.05

0

-0.05

0 2 4 6 8 101214

0.02

0

-0.02

-0.04

0 2 4 6 8 101214

0.06

0.04

0.02

0

0 2 4 6 8 101214

0.01

0

-0.01

-0.02

0 2 4 6 8 101214

0.01

0

-0.01

-0.02

0 2 4 6 8 101214

0.1

0.05

0

-0.05

0 2 4 6 8 101214

0.06

0.04

0.02

0

-0.02

0 2 4 6 8 101214

0.06

0.04

0.02

0

0 2 4 6 8 101214

0.02

0.01

0

-0.01

0 2 4 6 8 101214

0.1

0.05

0

0 2 4 6 8 101214

0.1

0.05

0

0 2 4 6 8 101214

0.04

0.02

0

0 2 4 6 8 101214

0

-0.05

-0.1

0 2 4 6 8 101214

0.03

0.02

0.01

0

0 2 4 6 8 101214

0.04

0.02

0

-0.02

0 2 4 6 8 101214

Note: The Figure reports the responses of selected endogenous variables to one standard deviation shock to the change in NPL ratios. Real GDP growth, headline ination, residential real estate prices and corporate and mortgage loans are expressed in annual growth rates. The Euribor, bank lending spreads, the change in NPL ratios and the capital and reserves-to-asset ratio are expressed in percentage points. Responses are reported for 4 years (16 quarters) after the shock (assumed to take place at time 0). The median of the accepted draws is plotted together with the 16% and 84% Bayesian credibility bands.

ECB Working Paper Series No 2411 / May 2020

33

Figure 5: Response to a shock to the annual rate of change in NPL volumes

GDP growth

0.2

AT

0

-0.2

-0.4

0 2 4 6 8 101214

0.2

BE

0

-0.2

-0.4

0 2 4 6 8 101214

0.2

ES

0

-0.2

-0.4

0 2 4 6 8 101214

0.1

FR

0

-0.1

-0.2

-0.3

0 2 4 6 8 101214

0.6

GR

0.4

0.2

0

-0.2

0 2 4 6 8 101214

0

IE

-0.5

-1

0 2 4 6 8 101214

0

IT

-0.2

-0.4

0 2 4 6 8 101214

0

LT

-1

-2

0 2 4 6 8 101214

0 2 4 6 8 101214

0.2

PT

0

-0.2

-0.4

0 2 4 6 8 101214

Corporate loans Mortgage loans

RRE prices Mortgage spreadCorporate spread

0.8

0.15

0.06

0

0.6

0.5

0.1

0.04

0.4

-0.5

0

0.05

0.02

0.2

-1

0

-0.5

0

0

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

1

0

0.4

0.2

0.1

0.2

0

0

0.1

0.05

-1

-0.2

-1

-0.4

0

-2

-0.6

0

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0

0.2

0

0.15

0.05

-0.5

0

-0.5

0.1

0

0.05

-0.2

-1

-0.05

-0.4

-1

0

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0

0.2

0.2

0.1

0.04

-0.2

0

0

-0.2

-0.2

-0.4

0.05

0.02

-0.4

-0.4

-0.6

-0.6

-0.6

0

0

-0.8

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0

0.5

0.8

0.2

-0.5

0.6

0.1

-1

0

0.4

0.1

-1.5

0.2

-0.5

0

0

0

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0

0.5

0

0.2

0.15

0

0.1

-1

-0.5

-1

0.1

0.05

-1

-2

0

-1.5

-2

0

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0.6

0.8

0.08

0.08

0.06

0.4

0.6

0

0.06

0.04

0.2

0.4

0.04

-0.2

0.02

0

0.2

0.02

0

0

-0.2

-0.4

0

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0

0

0

0.4

-1

0.2

-0.5

-1

-2

0.2

0.1

-1

-2

-3

-1.5

0

0

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0

1

0.2

0.15

0.08

-0.5

0

0.1

0.06

0.5

0.04

-1

-0.2

0.05

0.02

0

-0.4

0

0

-1.5

-0.02

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0

0.4

0.6

0.1

-0.5

0.2

0.4

0.1

0

0.2

0.05

-1

-0.2

0

-0.4

0 2 4 6 8 101214

0 2 4 6 8 101214

0

0

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

Note: The Figure reports the responses of selected endogenous variables to one standard deviation shock to the annual rate of change in NPL volumes. Real GDP growth, headline ination, residential real estate prices and corporate and mortgage loans are expressed in annual growth rates. The Euribor, bank lending spreads, the change in NPL ratios and the capital and reserves-to-asset ratio are expressed in percentage points. Responses are reported for 4 years (16 quarters) after the shock (assumed to take place at time 0). The median of the accepted draws is plotted together with the 16% and 84% Bayesian credibility bands.

ECB Working Paper Series No 2411 / May 2020

34

Figure 6: Observed and assumed out-of-sample baseline and adverse change in NPL ratios for the structural scenario analysis

CY

25

20

15

10

5

0

-5

2005

2007

2009

2011

2013

2015

2017

2019

ES

10

5

0

-5

2005

2007

2009

2011

2013

2015

2017

2019

2.5

ES

2

1.5

1

0.5

0

-0.5

-1

-1.5

-2

2005

2007

2009

2011

2013

2015

2017

2019

IE

3

2

1

0

-1

-2

-3

-4

-5

-6

2005

2007

2009

2011

2013

2015

2017

2019

GR

10

8

6

4

2

0

-2

2005

2007

2009

2011

2013

2015

2017

2019

PT

6

4

2

0

-2

-4

-6

-8

2005

2007

2009

2011

2013

2015

2017

2019

Note: The data sample spans from 2006Q1 to 2017Q3. The out-of-sample assumptions for the baseline and adverse paths for the change in NPL ratios span from 2017Q4 to 2020Q3. The data are sourced from the IMF FSI, Banque de France, Banco de Espa~na, Central Bank of Cyprus and Bankscope.

ECB Working Paper Series No 2411 / May 2020

35

Figure 7: Difference in the structural scenario forecasts between the baseline and the adverse path for the main variables included in the panel VAR

CY

loans

6.00

1.00

4.00

0.50

Corporate

2.00

0.00

0.00

-0.50

1 2 3 4 5 6 7 8 9 101112

loans

2.00

2.00

1.00

Mortgage

1.00

0.00

0.00

-1.00

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

0.50

0.00

-0.50

-1.00

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

IE

IT

6.00

2.00

2.00

4.00

1.00

1.00

2.00

0.00

0.00

0.00

-1.00

-1.00

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

3.00

2.00

4.00

2.00

2.00

1.00

1.00

0.00

0.00

0.00

-2.00

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

PT

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

growth

1.50

1.00

1.50

3.00

1.50

1.50

1.00

1.00

2.00

1.00

1.00

GDP

0.50

0.50

0.50

1.00

0.50

0.50

0.00

0.00

0.00

0.00

0.00

0.00

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

10.00

2.00

4.00

6.00

2.00

4.00

prices

4.00

1.00

2.00

5.00

1.00

2.00

RRE

2.00

0.00

0.00

0.00

0.00

0.00

0.00

-1.00

-2.00

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

spread

0.00

0.00

0.00

0.00

0.00

0.50

-0.10

-0.10

Mortgage

-0.20

-0.50

-0.50

0.00

-0.20

-0.20

-0.30

-0.30

-0.40

-1.00

-1.00

-0.50

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

spread

0.00

0.00

0.00

0.00

0.00

0.20

-0.20

-0.10

-0.20

0.00

Corporate

-0.50

-0.50

-0.40

-0.20

-0.40

-0.20

-0.60

-0.30

-0.60

-1.00

-1.00

-0.40

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

Note: The gure reports the di erence between the baseline and the adverse structural scenario forecasts of the main variables in the panel VAR. Under both the baseline the adverse assumption, the forecasts for the variables in the VAR are computed assuming that only the structural shock to the change in NPL ratios adjusts to ensure the conditioning path for this variable. Real GDP growth, headline ination, residential real estate prices and corporate and mortgage loans are expressed in annual growth rates. The Euribor, bank lending spreads, the change in NPL ratios and the capital and reserves-to-asset ratio are expressed in percentage points.

ECB Working Paper Series No 2411 / May 2020

36

Acknowledgements

We thank seminar participants at the European Central Bank, the Bank of England and ADB-ECB Workshop on NPL Resolution in Asia and Europe for helpful comments and suggestions. We also thank Björn van Roye, Dejan Krusec, Lorenzo Ricci and Paolo Fioretti for useful discussions. Paola Antilici, Marija Deipenbrock, Marco Forletta and Alexandros Kouris provided excellent research assistance. We are solely responsible for any errors that remain. The findings, views and interpretations expressed herein are those of the authors and should not be attributed to the Joint Vienna Institute, the Eurosystem, the European Central Bank, its Executive Board, or its management.

Ivan Huljak

Croatian National Bank, Zagreb, Croatia; email: [email protected]

Reiner Martin

European Central Bank, Frankfurt am Main, Germany; Joint Vienna Institute, Vienna, Austria; email: [email protected]

Diego Moccero

European Central Bank, Frankfurt am Main, Germany; email: [email protected]

Cosimo Pancaro

European Central Bank, Frankfurt am Main, Germany; email: [email protected]

© European Central Bank, 2020

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All rights reserved. Any reproduction, publication and reprint in the form of a different publication, whether printed or produced electronically, in whole or in part, is permitted only with the explicit written authorisation of the ECB or the authors.

This paper can be downloaded without charge from www.ecb.europa.eu, from the Social Science Research Network electronic library or from RePEc: Research Papers in Economics. Information on all of the papers published in the ECB Working Paper Series can be found on the ECB’s website.

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ISBN 978-92-899-4054-2

ISSN 1725-2806

doi:10.2866/44778

QB-AR-20-063-EN-N

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ECB – European Central Bank published this content on 15 May 2020 and is solely responsible for the information contained therein. Distributed by Public, unedited and unaltered, on 15 May 2020 09:14:08 UTC

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Bad Credit

What is a Credit Builder Loan and Where Do I Get One?

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Your credit score plays an important role in your financial life. If you have good credit you can qualify for loans and borrow money at lower interest rates. If you don’t have a credit score or have poor credit, it can be hard to get loans and you’ll be forced to pay higher rates when you do qualify.

Building credit can be like a chicken and egg problem. If you have no credit or bad credit, you’ll have trouble getting a loan. At the same time, you need to get a loan so you have an opportunity to build credit.

 

What Is a Credit Builder Loan?

A credit builder loan is a special type of loan designed to help people who have poor or no credit improve their credit score.

In many ways, credit builder loans are less like loans and more like forced savings plans. When you get a credit builder loan, the lender places the money in a bank account that you can’t access. You then start receiving a monthly bill for the loan. As you make those payments, the lender reports that information to the credit bureaus, helping you build up a payment history. This improves your credit score.

Once you finish the payment plan, the lender will release the bank account to you and stop sending bills.

In the end, you’ll wind up with slightly less money than you paid overall, due to fees and interest charges. For example, let’s say you get a credit builder loan for $1,000, the lender may make you make a monthly payment of $90 each month for a year. After the year ends, you’ll get the $1,000 from the lender, but may pay $1,080 overall.

Why Get a Credit Builder Loan?

The main reason to get a credit builder loan is right in the name: They help you build your credit. If you don’t have any credit history or if you’ve damaged your credit by missing payments, it’s much easier to qualify for a credit builder loan than a traditional loan from a lender.

The companies offering credit builder loans take on almost no risk because they don’t give you the money until you’ve finished paying the loan, so they’re willing to approve people who have severely damaged credit.

Credit builder loans will help you build your credit history if you make your monthly payments, but you do have to pay fees and interest to do so. There are other ways to build credit that don’t require paying any money. For example, if you get a fee-free credit card and pay your balance in full each month, you’ll build credit without paying any interest or fees.

This makes credit builder loans best for people who have tried and failed to qualify for other loans and credit cards.

There is also some value in the forced savings provided by credit builder loans, but the interest and fees eat away at that savings. If saving is your goal, it’s best to use a different strategy to help you save, but if you want to save and build credit at the same time, a credit builder loan might be worth using.

Where to Find Credit Builder Loans?

There are many companies that offer credit builder loans. Each lender offers different loan terms, fees, and interest rates.

One of the top credit builder loan providers is Self. The company offers credit builder loans with payment plans as low as $25 per month, making it easy for almost anyone to afford a credit builder loan.

With Self, you can also qualify for a Visa credit card after you’ve made at least 3 payments on your credit builder loan and made $100 of progress toward paying off the loan. You can set your own credit limit, up toward the total amount of progress you’ve made on the loan.

The card doesn’t have any additional upfront costs and can help you gain experience with using a credit card. It can also help you build your credit by giving you another account to make payments on, providing you with more opportunities to build a good payment history.

Visit Self or read the full Self Review

What to Look for?

When you’re looking for credit builder loans, there are a few factors to consider.

The first thing to think about is the monthly payment. The point of a credit builder loan is to show the credit bureaus that you can make regular payments on your debts, which will help build your credit score. If a lender’s minimum payment is more than you can afford each month, you won’t be able to build your credit with that lender’s credit builder loan.

It’s also important to think about the cost of the loan. Credit builder loans often come with stiff fees and you also have to pay interest on the money you’ve borrowed, even if you don’t get access to it until you pay the loan off.

The fewer fees and the less interest you have to pay, the better. You should look very carefully at each lender’s fee structure to choose the best deal.

Finally, take some time to see how easy it is to qualify. While credit builder loans are targeted at people with bad credit, some lenders will still check your credit history and might deny your application.

If you have very bad credit, you might want to look for a lender that advertises credit builder loans with no credit check.

Alternatives to a Credit Builder Loan

Credit builder loans can be a good way to build credit for some people, but they come with interest charges and fees. There are other ways you can build credit worth considering. Some of them won’t cost any money, which may make them a better choice than a credit builder loan.

Secured Credit Cards

A secured credit card is a special type of credit card that is much easier to qualify for than a typical card.

With a secured card, you have to provide a security deposit when you open the account. The credit limit of your card will usually be equal to the deposit you provide. For example, if you want a $200 credit limit, you’ll have to give the card issuer $200 as collateral.

Because you give the lender cash to secure the card, it’s much easier to qualify for a secured credit card. The lender assumes almost no risk. Once you get the card, it works like any other credit card. You can use it to spend up to your credit limit and you’ll get a bill each month. If you pay the bill on time, you can build credit.

Many secured cards charge high interest rates and have hefty fees, but there are some fee-free options available. One great secured card is the Discover it Secured Credit Card, which has no annual fee and offers cash back rewards.

Become an Authorized User

Most credit card issuers let cardholders add other people as authorized users on their accounts. Authorized users get their own cards and can use them to spend money just like the main cardholder.

Some issuers will report account information to the credit reports of both the main cardholder and any authorized users. If you know someone that is willing to make you an authorized user on their credit card account, this may help you build your credit so you can qualify for a card of your own.

Not every issuer will report information to authorized users’ credit reports. It’s also worth keeping in mind that if you become an authorized user on a card and the cardholder stops making payments or racks up a huge balance, that will show up on your report as well, damaging your credit further. That can make this strategy risky.

Personal Loans with a Cosigner

Personal loans are highly flexible loans that you can use for almost any reason. If you need to borrow money, you can try to find someone who is willing to cosign on the loan. Having a cosigner can make it easier to qualify, even if you have poor credit, giving you a chance to build your credit score.

When someone cosigns on a loan, they’re promising to take responsibility for your debt if you stop making payments. Lenders will look at both your credit and your cosigner’s credit when you apply, so having a cosigner with strong credit can help you get the loan or reduce the interest rate of the loan.

Keep in mind that your cosigner is putting themselves at risk by cosigning on a loan. It’s even more important that you make your payments every month. If you don’t, your cosigner will have to pick up the slack.

Personal Loans without a Cosigner

Even if you have poor credit, you may be able to qualify for a personal loan designed for people that don’t have strong credit. Just keep in mind that you’ll have to pay higher fees and interest rates to compensate for your poor credit score.

If you’re looking for a personal loan and have poor credit, shopping around for the best deal becomes even more important. You can use a loan comparison site, like Fiona, to get quotes from multiple lenders so you can find the cheapest loan.

Related: Best Emergency Loans for Bad Credit

What Is the Difference Between a Credit-Builder Loan and a Personal Loan?

A personal loan is a type of loan that you can get for almost any reason, such as consolidating debts, starting a home improvement project, paying an unexpected bill, or even going on vacation. They’re offered by many lenders and banks.

A credit builder loan is less a loan and more a forced saving plan. When you get a credit builder loan, the lender doesn’t actually give you any money. Instead, it places the amount you’re borrowing in an account you can’t access. Once you finish paying the loan, the lender releases the money in that account to you.

Credit builder loans tend to be much easier to qualify for than personal loans because the lender doesn’t have to take on much risk. They’re mostly used by people who want to build or rebuild their credit score.

On the other hand, personal loans are less popular for building credit and more useful for providing funding when borrowers need cash to cover an expense.

Related: Best Prepaid Credit Cards That Build Credit

Pros and Cons of a Credit Builder Loan

Before applying for a credit builder loan, consider these pros and cons.

Pros

  • Easy to qualify for
  • Helps you build savings
  • Payments are usually small
  • Helps you build payment history

Cons

  • Not really a loan
  • Fees and interest rates can be high
  • There are cheaper alternatives to build credit

FAQs

These are some of the most frequently asked questions about credit builder loans.

Like most loans, it is possible to repay a credit builder loan ahead of schedule, but there are a few downsides to consider. One is that many lenders add an early repayment fee to their loans, so you’ll have to pay that fee if you want to get out of the credit builder loan. The other is that repaying the loan early somewhat defeats the purpose. Each monthly payment you make toward the loan helps you build your credit. If you pay the loan off early, you’ll make fewer monthly payments, which means less improvement in your credit.

Missing a payment on a credit builder loan is like missing a payment on any loan. You’ll likely owe a late fee and it will damage your credit. This is one of the reasons it’s important to make sure you can afford the monthly payment before signing up for a credit builder loan. If you can’t make your payments, the loan will wind up damaging your credit instead of helping it.

Final Thoughts

Credit builder loans can be a good way to build or rebuild your credit, but they’re not your only option. They often involve paying fees and interest, so you should search around for the best deal or look for cheaper (or free) alternatives, such as secured credit cards.



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How to lower your credit card interest rate and save money

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Why pay high interest on your credit cards when you can simply bargain a lower rate? These tips can help you save big money on your bill.

CHARLOTTE, N.C. — A lot of people have struggled to pay their bills during the COVID-19 pandemic and many have turned to credit cards so they can kick the can down the road. Now the time has come to pay it down and some of the bills are eye-popping. 

Did you know you can bargain that interest rate down and save quite a bit of money?

You could ask for a lower rate, but according to a new study, you can bargain down 10 percentage points. So, if your interest rate is 24%, it could mean paying 14% instead. That’s still high but it’s a lot better than 24% interest. 

These numbers are staggering and can be a bit overwhelming. Americans have an average credit card balance of $5,300, totaling $807 billion across 506 million credit card accounts. Why are these numbers important? Because they want to keep you spending, which means you have leverage to bargain.

“It is absolutely possible to negotiate your rate down. In fact, your chances of doing so are better than you think they are. Close to 80% surveyed said they did just that,” Matt Schultz, an industry expert with LendingTree, said. “You can save serious money, especially if your balance is bigger.”

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You have to try, and you have to keep trying, even if the lender says no. Take it higher to a manager and keep pushing. Drops of 10% are possible and that could save you hundreds, or maybe even thousands, of dollars. 

RELATED: VERIFY: Can your stimulus check be seized by banks or private debt collectors?

“So, a lot of people have bad credit, some are thankful to have it at all. Is it possible for them too? Yes, absolutely it’s possible,” Schultz said. “Credit card companies are willing to talk with you because they want to keep your business. It benefits them to lower your rate to keep their card in your wallet.”

Paying down debt is liberating. Less debt is more buying power but you must advocate for yourself. If you don’t, the card companies are just as happy to take your money at the higher rate. 


LendingTree offers these suggestions if you plan to ask for a lower rate: 

How to ask for a lower APR

Before you make the call, come armed with ammunition in the form of other offers you’ve seen at a site like LendingTree.com or that you may have received in your snail mail. Take that offer and use it to frame the conversation: 

“I’ve been a good customer of yours for a long time and I like my card. However, the APR is 25% and I’ve just been offered one with a 19% APR. Would you be able to match it?” 

As survey data shows, they’ll likely be willing to work with you, at least to some degree.

RELATED: ‘ I was very grateful’ | WCNC Charlotte breaks through red tape to help woman get money she was owed

How to ask for a waived annual fee

Before you make the call, think about what you will accept. If you ask for a fee to be waived altogether and they only offer to reduce it, is that good enough? What if they offer you extra rewards points or miles or make some other counteroffer instead of a reduced fee? And perhaps most important, what if they say no? 

As with many negotiations, you have more leverage if you’re willing to walk away, so that could be an option. However, you shouldn’t make that threat unless you’re willing to follow through with it, and you shouldn’t follow through with it unless you’ve thought about what that would mean for your credit.

How to ask for a waived late fee

Just pick up the phone and be polite. If you’re a long-time customer with good credit and this is your first offense, the odds are in your favor. In fact, some card issuers will even waive a first late fee as a matter of policy. If you’ve been late multiple times in the recent past, however, your chances probably aren’t as good. Even so, it never hurts to ask.

How to ask for a higher credit limit

Start with a number in mind based on your current limit. The average increase reported in our survey was about $1,500, but your situation will vary. If your current limit is $500, a $1,500 bump might be asking too much. However, if your current limit is $5,000, that request might be just fine. 

Think about why you’re asking for the increase — for some extra spending power or to help your credit score — and then decide what to ask for. Just remember that it’s always better to start a negotiation by asking for a little too much. That way, when you negotiate, you can give a little bit and still get what you want.


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Can A Moving Loan Help Your Relocation? Find Out Here – Forbes Advisor

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Editorial Note: Forbes may earn a commission on sales made from partner links on this page, but that doesn’t affect our editors’ opinions or evaluations.

Whether you’re relocating to another city or state, moving can be expensive. You might need money to pay for a moving van or movers, new furniture or your security deposit. If you don’t have money on hand to cover those expenses, a moving loan can help you fill in the gap.

Before you take out a relocation loan, learn what they are and how to compare your options to understand if it’s a good choice for your situation.

What Is a Moving Loan?

A moving loan—also referred to as a relocation loan—is an unsecured personal loan you can use to help cover your moving expenses. Unsecured loans don’t require you to use a personal asset to secure the loan. Because the loan is unsecured, lenders base your eligibility on factors like your credit score, income and debt-to-income (DTI) ratio. Like with other types of personal loans, you’ll have to repay your loan through fixed monthly installments.

When Should You Get a Moving Loan?

Although the answer varies based on your financial circumstances, it may make sense to get a moving loan if you can secure a good interest rate and can afford to repay the loan as promised. However, if you believe it might be hard for you to repay the loan, then it’s probably a good idea to avoid taking one out. Falling behind on payments can damage your credit score, making it harder for you to qualify for future loans.

How to Get a Moving Loan

  1. Search for lenders: To find lenders that offer relocation loans, search for the best personal loans online. A good place to start might be a lender comparison website. While there, carefully review the terms, minimum credit score requirements, fees and annual percentage range (APR) range of each lender. In addition, you can check with your local bank or credit union to see if it offers personal loans for moving.
  2. Prequalify with multiple lenders: Once you narrow down your list of the best lenders, prequalify with each one of them (if available). This allows you to see what terms and APR you might receive if approved. Make sure the lender does a soft credit check to protect your credit score from any pitfalls.
  3. Determine the amount you need to borrow: Estimate your moving or relocation expenses to see how large of a loan you need to take out. Different lenders have different minimum loan amounts. Also, some states have rules about the minimum amount you can borrow, which may affect the size of your loan.
  4. Apply for your moving loan: After you select the lender that matches your needs, complete the application process. Prepare to provide the lender with personal information, such as your income, date of birth and Social Security number (SSN). Some lenders will require you to provide W2’s, pay stubs or bank statements to confirm your income.
  5. Wait for the lender to make a loan decision: After you apply, wait for the lender to review your application. Some lenders might approve you within seconds, while others may take longer. If a lender denies your loan, ask them for an explanation. Applying with a co-borrower or co-signer, improving your credit score, reviewing your credit report for errors or requesting a smaller amount may improve your chances of approval.
  6. Sign the loan agreement and receive funds: Once approved, the lender will send you a loan agreement to sign. After you sign the agreement, the lender will most likely deposit your funds directly into your account. The time of funding varies for different lenders—some lenders can issue the funds the same day while others may take a week or longer.
  7. Repay your loan: Finally, repay your loan as promised. Making late payments or defaulting on the loan can damage your credit score. Setting up autopay is one way to ensure you’ll never miss a payment.

Pros of Moving Loans

  • Quick access to funds: If your loan application is approved, some lenders may deposit your funds into your bank account the same day or within a week.
  • Flexible loan terms: Some lenders allow you to take out personal loans for moving with loan terms as short as 12 months and as long as 84 months. A long-term loan may have a lower minimum monthly payment, which might better suit your budget. However, the downside is that you’ll pay more in interest over the life of the loan.
  • Lower interest rates than credit cards: The average interest rates for personal loans are usually lower than those for credit cards. If you have a good credit score (at least 670) and a stable income, you may be able to secure a good interest rate—an interest rate that’s lower than the national average.
  • No collateral required: Since loans for moving typically require no collateral—an asset that secures the loan—you won’t have to worry about a lender taking your asset (at least without a court’s permission).

Cons of Moving Loans

  • Fees: Some lenders charge origination fees between 1% and 8%—these fees can be a huge drawback since the lender usually subtracts them from your loan amount. Other common personal loan fees include application fees, returned check fees, late payment fees and prepayment fees.
  • Potentially high interest rates: If you have less-than-stellar credit or minimal credit history, your lender may charge you high interest rates. Some lenders have APRs above 30%.
  • Missed payments can damage your credit score: If you miss a payment or default on the loan, it can damage your credit score. This will make it more difficult for you to qualify for future loans.

Moving Loan Alternatives

If you want to avoid the potential cons of a relocation loan, consider these alternative options to help cover your moving expenses or rent.

0% APR Credit Card

Borrowers with good to excellent credit scores (at least 670) can avoid paying interest and high fees with a 0% APR credit card. These cards come with interest-free promotion periods, which can last for up to 21 months. If you pay off your balance before the promotion period expires, you won’t have to worry about paying interest. However, providers will charge interest on unpaid balances once the introductory period ends.

Family Loan

Family loans are another way to avoid paying interest or to pay minimal interest when it comes to your relocation expenses. With this option, you can also avoid the formal loan application process. The loan agreement between you and the family member should spell out the terms and conditions of the loan. Repay the loan as promised to avoid causing damage to your relationship.

Payday Alternative Loan

If you can’t qualify for a relocation loan or have trouble finding moving loans for bad credit, consider using a payday alternative loan. Some federal credit unions offer these loans, which are designed to help you avoid the high-interest charges of payday loans. You can borrow up to $2,000; loan terms range from one to 12 months and the maximum interest rate is 28%. To use this option, you must be a member of a federal credit union or be eligible for membership.

Savings

Instead of using a personal loan for moving, it might be better to use your savings, if possible. If you know how much it will cost, then create an automatic savings plan to cover most or all of your relocation expenses.

Relocation Package

If you’re moving for a new job, ask your new employer if it will cover some of your relocation expenses. Some employers offer this to employees as an incentive to accept the job offer. Even if the employer doesn’t offer this, you can ask for a relocation bonus or try negotiating a higher salary.

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