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Household debt and consumption revisited

Published by Anonymous (not verified) on Wed, 01/09/2021 - 6:00pm in

Philip Bunn and May Rostom

The academic literature finds that the build-up of household debt before the 2008 financial crisis is linked to weaker consumption afterwards. But there is wider debate over the mechanisms at play. One strand of literature emphasises debt overhang acting through the level of leverage. Others find it was over-optimism acting through leverage growth. In this post, we revisit our previous analysis on leverage and consumption in the UK using synthetic cohort analysis. The correlation between leverage measures and their link to other macroeconomic variables mean it’s challenging to tease out their effects. Yet we find that whilst both mechanisms played a role, there is evidence that debt overhang linked to a tighter credit constraints was the bigger driver.

In the UK, the ratio of household debt to income rose from about 85% in 1997 to almost 150% in 2007, with much of that increase accounted for by increases in mortgage debt (Chart 1). For most of this period, household consumption growth was close to its historical average – there was no sharp acceleration in spending – and inflation was low and stable. When the crisis hit, consumption fell sharply. How might the build-up in debt have affected households’ consumption response in the wake of the GFC?

Chart 1: Household debt to income ratio rose sharply prior to the financial crisis while consumption growth was close to average

Academics have put several hypotheses forward to explain this relationship. For this post, we examine two of them: pre-crisis overoptimism, and debt overhang. These two hypotheses are not necessarily mutually exclusive. In fact, both are likely relevant, but not all animals are equal, and some are more important than others.

The over-optimism hypothesis

One view argues that households adjusted their expectations downwards. In this view, prior to the crisis, some households were buoyed by looser credit conditions and rapid growth in house prices increased the amount of collateral homeowners could borrow against. Households who felt positive about the future, could have also been optimistic about their future income, and would have been comfortable to increase leverage quickly. The more optimistic households had to revise down their future income expectations and also cut back their spending by more than others during the GFC, and so this correlates with the change in leverage.

In one paper, Andersen et al (2016) find a strong correlation between the increase in pre-crisis leverage and Danish spending patterns during the recession, but less so for the level. Similarly, in an aggregate-level cross-country comparison, Broadbent (2019) shows that growth in debt between 2005 and 2007 was a better predictor of the economic downturn than the level. However, none of these studies specifically refer to the UK.

The debt overhang hypothesis

A second hypothesis relates to the size of outstanding debt. Households who were highly leveraged going into the crisis faced binding borrowing constraints once credit conditions tightened, limiting their ability to refinance or borrow more. For the UK, this was important: during the GFC many mortgagors were on two-year fixed rates that were refinanced often. This ‘debt overhang’ may have caused these households with higher levels of leverage to cut back spending more. Indeed, a number of studies point to the importance of the level of pre-crisis leverage in explaining the weakness of consumption growth (Dynan (2012) and Baker (2018)), with debtors having higher marginal propensities to consume (Mian et al (2013)).

Credit constraints may also play a role here. Prior to the crisis, mortgage products with loan to value (LTV) ratios greater than 90% were common in the UK, and in some cases offered loans greater than the value of the property – the most infamous example being Northern Rock’s ‘Together’ mortgage at 125% LTV. When the crisis hit, those high LTV products disappeared (Chart 2). This reduction in credit availability will also have been amplified by falls in house prices, which will have raised a household’s outstanding LTV ratio for a given level of debt. UK house prices fell by up to 13% between 2007 and 2009. Taking these two facts together, any household going into the crisis with an outstanding LTV above 75% would have struggled to refinance their mortgage or take on additional debt. LTV ratios are primarily associated with the level of debt. We estimate that around 15% of mortgagors were in this position. These were primarily young households: the average age was 35, and five out of six were younger than 45.

Chart 2: There were very few mortgages with LTVs above 90% during the financial crisis

Revisiting the debate: micro evidence for the UK

We revisit our previous analysis (Bunn and Rostom (2015)) on the comovement between leverage and consumption during the GFC, to consider the role of these different measures and associated hypotheses. For the UK, household-level panel data containing both debt and consumption around the GFC are not available, so we track groups of households, or cohorts, over time using the well-established methodology of Deaton (1985). Nevertheless, our results tell a plausible story.

One challenge with this exercise is that all measures of debt we examine are well correlated – this means it’s hard to definitively conclude which leverage measures are driving this effect. For example, those with high levels of debt to income often also saw strong growth in their leverage (Chart 3).

Chart 3: Measures of the growth and level of leverage are well correlated

Table A reports the results of our reduced-form regressions for the growth in household spending over the financial crisis on different debt measures as explanatory variables (full details in the technical appendix). We also control for other factors such as income growth, wealth and household composition.

Table A: Regressions for household spending during the financial crisis with different debt metrics

Looking at levels of leverage, column 1 shows that groups of households who went into the crisis with high loan to incomes (LTI) made larger cuts in spending during it. In column 2, the level of LTI is replaced with the change in LTI between 2003/04 and 2006/07. Again this is significant, showing that groups of households who experienced earlier rapid growth in debt also made larger reductions in spending. Putting these two debt measures in together in column 3, the coefficient on both falls, but only the change in LTI remains statistically significant. This result is the same if average LTI of a cohort is replaced by the percentage of high LTI households within each cohort and is consistent with the findings of Andersen et al (2016) for Denmark.

But this picture is incomplete because it abstracts from credit constraints. In column 4, we include a measure of the percentage of households in each cohort with a pre-crisis LTV above 75%. This measure, which is based on the level of leverage, aims to capture credit-constrained households. This metric also has a negative and statistically significant relationship with consumption growth during the financial crisis.

However, when we add the change in LTI in column 5, the coefficients on both the change in LTI and percentage of credit constrained households remain significant. Both coefficients are smaller than when they are included on their own. This relationship during the crisis is not seen in earlier periods when credit conditions were looser (see column 6 for one such example). 

As well as the statistical significance of the estimated coefficients, it is important to also consider their economic significance. Chart 4 shows that the magnitudes of spending cuts associated with debt during the GFC implied by all five equations are similar (the black diamonds), at just under 2% of aggregate private consumption. However, they differ on how to apportion it (the coloured bars). In equations 1, 2 and 4 only one channel is included by definition). In equation 5 – the only equation with two statistically significant debt measures – the percentage of credit constrained households accounts for 60% of the total effect, and the increase in debt in the run up to the crisis accounts for the remaining 40%.

Chart 4: Size of spending cuts associated with debt

Conclusion

What can these empirical results tell us about the co-movement between debt and consumption during the financial crisis in the UK? The strength of the correlation between the different measures of leverage make it challenging to conclude the mechanism at play and to definitively prove causation. Nevertheless, they do support an important role for debt overhang, driven by a tightening in credit conditions, and typically captured by the level of leverage. And the role of credit constraints here is supported by the significant relationship between the percentage of households with an LTV ratio above 75% going into the crisis, and cuts in consumption during it.

There can also be more than one explanation, and we do find some weaker support for overoptimism too, although it is curious there was little sign of a large pre-crisis consumption boom in the macro data. In the run up to the GFC, aggregate consumption growth was close to its historical average and nothing like the boom of the late 1980s, implying that any such pre-crisis effects were probably modest.

Technical appendix

The data used on the regressions in Table A are described in more detail in Bunn and Rostom (2015) but the key points are summarised below:

Sample definition: Equations are estimated using cohort data where cohorts are defined by single birth year of the household head and mortgagor/non-mortgagor status. Only households where the head is aged 21–69 are included. The specification reported in equation 1 differs from the equivalent regression reported in Bunn and Rostom (2015) as cohort cells with insufficient observations, after calculating lagged changes in debt, are dropped.

Data sources: Living Costs and Food Survey for all variables except LTV and measures of wealth. For equations 1 to 5, LTV and wealth are from the Wealth and Assets survey, and from the British Household Panel Survey for equation 6.

Additional controls: All equations also include controls for income growth, changes in household composition and growth in housing and financial wealth and a constant. Equation 6 does not include a control for financial wealth due to data availability. 

Variable definitions: ∆lnC is the log change in non-housing consumption, LTI is the ratio of outstanding mortgage LTI ratio, ∆LTI is the change in the ratio of outstanding mortgage LTI ratio, LTV share>75% is the percentage of households in each cohort with an outstanding LTV ratio of more than 75%. For equations 1 to 5 period t is 2009/10 and t-1 is 2006/07. ∆LTIt-1 represents the change between 2003/04 and 2006/07. For equation 6 period t is 2006/07 and t-1 is 2003/04. ∆LTIt-1 represents the change between 2000/01 and 2003/04.

Philip Bunn works in the Bank’s Structural Economics Division and May Rostom works in the Bank’s Monetary Policy Outlook Division.

If you want to get in touch, please email us at bankunderground@bankofengland.co.uk or leave a comment below.

Comments will only appear once approved by a moderator, and are only published where a full name is supplied. Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.

Forbearance Participation Declines as Programs’ End Nears

Published by Anonymous (not verified) on Wed, 01/09/2021 - 6:54am in

Tags 

Credit, mortgages

The Federal Reserve Bank of New York’s Center for Microeconomic Data today released its Quarterly Report on Household Debt and Credit for the second quarter of 2021. It showed that overall household debt increased at a quick clip over the period, with a $322 billion increase in balances, boosted primarily by a 2.8 percent increase in mortgage balances, a 2.2 percent increase in credit card balances, and a 2.4 percent increase in auto balances. Mortgage balances in particular were boosted by a record $1.22 trillion in newly originated loans. Although some borrowers are originating new loans, struggling borrowers remain in forbearance programs, where they are pausing repayment on their debts and creating an additional upward pressure on outstanding mortgage balances.

This analysis, as well as the Quarterly Report on Household Debt and Credit are based on the New York Fed’s Consumer Credit Panel (CCP), a 5 percent representative sample of consumer credit reports from Equifax, which we have used for monitoring the health of the household balance sheet and the uptake of forbearance since the arrival of the pandemic. While pandemic related mortgage forbearances were declining over time, thanks to a recovery of economic activity and stimulus payments, there were some increases in inflows to mortgage forbearance not directly related to the pandemic.

When the state of Texas and some neighboring states were hit with severe weather in winter of 2021, almost 70 percent of borrowers lost power, and many borrowers experienced damage to their properties, producing additional mortgage forbearances. Texas is a big state, enough to produce fluctuations in the forbearance rate for the United States overall. Thus, in the chart below, we break out these effects separately, we exploit the richness in our forbearance data to break out these effects separately. We observe that there has been a steady outflow of borrowers in forbearance as the pandemic has begun to wane, with just 2.7 percent of mortgages still in forbearance at the end of June 2021 (when we include storm-related forbearances, the national rate is 3.3 percent).

Forbearance participation, entry, and exit

Source: New York Fed Consumer Credit Panel / Equifax.

In the map below, we look at the forbearance participation rates of all states, beyond Texas. Texas and Oklahoma rank the highest, both having been affected by the storms: it is unlikely that this reflects forbearances associated with the CARES Act and more likely are weather related. The map reveals substantial geographic differences in the participation in pandemic mortgage forbearance programs, with states in the Southeast having the highest lingering forbearance rate, excepting the Carolinas. This dispersion likely captures a combination of the severity of the pandemic’s local economic impact and differences in the borrowers’ awareness, interest in, and access to these programs.

Percent of Mortgages in Forbearance by State, June-2021

Source: New York Fed Consumer Credit Panel / Equifax.

Finally, we look at the composition of borrowers who remain in forbearance. During the course of the past fifteen months, 9.3 million mortgages were in forbearance at some point. However, only 21 percent of those mortgages, or just under 2 million, remained in forbearance as of the end of June 2021. As we described in our series in May (see Related Reading below), the remaining pool of forborne borrowers has a notably lower average credit score compared to participants a year ago. The chart below examines the pool of mortgages in forbearance by the borrowers pre-pandemic credit score. A year ago, 32 percent of mortgages in forbearance were associated with prime borrowers with credit scores of 760 or higher. But the composition has shifted, and that share has shrunk to 25 percent, with 39 percent of the remaining mortgages in forbearance held by borrowers with credit scores under 620. This is especially remarkable when we consider the overall pool of mortgages skews much more toward the prime–only 18 percent of mortgages outstanding are associated with subprime borrowers.

We find similar, although less pronounced, results when we look at the same share broken out instead by the borrowers’ neighborhood income. Although we do not directly observe income on credit reports, we are able to group our borrowers using the average income of their zip code using data from the IRS Statistics of Income. Mortgages held by borrowers living in the lowest income neighborhoods, comprise only 16 percent of the pool of outstanding mortgages but were 21 percent of the mortgages in forbearance at the end of June. This is consistent with the finding that uptake of mortgage forbearance is correlated with the mortgage origination credit score.

Subprime Borrowers Are Much More Likely to Be In Forbearance, and More Likely to Remain

Source: New York Fed Consumer Credit Panel / Equifax.

Looking forward, those borrowers remaining in forbearance are the borrowers that will be most likely to struggle when these policies are lifted. Federally backed mortgages are eligible for up to eighteen months of forbearance, depending on when the initial forbearances started. With this limit in effect, many of the borrowers remaining in forbearance will face the limits of this leniency in the coming 3-4 months, when they must either resume payment or will be pressured to sell their homes to avoid delinquencies and consequent foreclosures. It appears that the CARES Act and other policy supports to households has been largely successful at avoiding widespread damage to household balance sheets, with little evidence of increasing delinquency in our Quarterly Report. It is unclear, however, how the unwinding of these programs in a still recovering economy will impact mortgage borrowers across different income and credit score groups and geographic locations.

Chart data

Andrew Haughwout is a senior vice president in the Federal Reserve Bank of New York’s Research and Statistics Group.

Donghoon Lee is an officer in the Bank’s Research and Statistics Group.

Joelle Scally is a senior data strategist in the Bank’s Research and Statistics Group.

Wilbert van der Klaauw is a senior vice president in the Bank’s Research and Statistics Group.

How to cite this post:

Andrew F. Haughwout, Donghoon Lee, Joelle Scally, and Wilbert van der Klaauw, “Forbearance Participation Declines as Programs’ End Nears,” Federal Reserve Bank of New York Liberty Street Economics, August 3, 2021, https://libertystreeteconomics.newyorkfed.org/2021/08/forbearance-partic...

Related Reading

Keeping Borrowers Current in a Pandemic
What Happens during Mortgage Forbearance?
Small Business Owners Turn to Personal Credit
What’s Next for Forborne Borrowers?

Disclaimer

The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Richard Werner: A Whistle-Stop Tour Of Modern Banking

Published by Anonymous (not verified) on Fri, 27/08/2021 - 3:02pm in

Professor Werner discusses a whole a range of banking issues including the Weimar Republic, central bank digital currencies, credit creation, the war on cash, crypto, gold, the date of the next crash and personal sovereignty.

The post Richard Werner: A Whistle-Stop Tour Of Modern Banking appeared first on Renegade Inc.

Richard Werner: A Whistle-Stop Tour Of Modern Banking

Published by Anonymous (not verified) on Fri, 27/08/2021 - 3:02pm in

Professor Werner discusses a whole a range of banking issues including the Weimar Republic, central bank digital currencies, credit creation, the war on cash, crypto, gold, the date of the next crash and personal sovereignty.

The post Richard Werner: A Whistle-Stop Tour Of Modern Banking appeared first on Renegade Inc.

Mortgage Rates Decline and (Prime) Households Take Advantage

Published by Anonymous (not verified) on Fri, 13/08/2021 - 1:20am in

Andrew F. Haughwout, Donghoon Lee, Joelle Scally, and Wilbert van der Klaauw

LSE_2021_HDC_mortgage_scally_460_art

Today, the New York Fed’s
Center for Microeconomic Data
reported that household debt balances increased by $206 billion in the fourth quarter of 2020, marking a $414 billion increase since the end of 2019. But the COVID pandemic and ensuing recession have marked an end to the dynamics in household borrowing that have characterized the expansion since the Great Recession, which included robust growth in auto and student loans, while mortgage and credit card balances grew more slowly. As the pandemic took hold, these dynamics were altered. One shift in 2020 was a larger bump up in mortgage balances. Mortgage balances grew by $182 billion, the biggest quarterly uptick since 2007, boosted by historically high volumes of originations. Here, we take a close look at the composition of mortgage originations, which neared $1.2 trillion in the fourth quarter of 2020, the highest single-quarter volume seen since our series begins in 2000. The Quarterly Report on Household Debt and Credit and this analysis are based on the New York Fed’s Consumer Credit Panel, which is itself based on anonymized Equifax credit data.

In the first chart, we look at a figure direct from the Quarterly Report: total mortgage originations by credit score band. Here, the boom in originations is starkly visible—originations to the highest credit score borrowers rose sharply during 2020. (We use the Equifax Risk Score 3.0). The origination volume in the fourth quarter of 2020 just surpassed the previous high, from 2003, when a dip in mortgage interest rates prompted a boom in mortgage refinancing. Although these two bumps in mortgage originations are similar in magnitude, the composition is quite different; 71 percent of originations in the fourth quarter of 2020 went to borrowers with credit scores over 760, while in the third quarter of 2003, only 31 percent of those new mortgages went to the most creditworthy borrowers. Researchers have concluded that the 2003 refi boom had long-running consequences, contributing to over-leveraged balance sheets as home prices fell.

LSE_2021_HDC-mortgage_scally_ch1-03

Purchases or Refis?

Refinances do tend to go to higher-credit-score borrowers, compared to purchase mortgages—this is because those who are refinancing already have a mortgage that they’ve been repaying for some time and are building their credit history. Thus, this high volume of superprime mortgage originations suggests we are in the midst of another refinance boom. And homeowners are taking advantage of the mortgage interest rates at historic lows, even taking some cash out with their refinanced mortgages. In the following chart, we break out mortgage originations into purchase and refinances. The origination boom in 2020 is owing to a confluence of high refinance originations as well as high purchase originations—in fact, in nominal terms, the level of purchase originations nears that seen in 2006. As for the increasing level of purchase originations, it’s consistent with high levels of home sales paired with increasing prices. Overall, refinance originations in 2020 were at their highest level since 2003. Although about 15 percent below the 2003 level in terms of nominal aggregate debt, some 7.2 million mortgages were refinanced in 2020, which was less than half the 2003 count (15 million!).

LSE_2021_HDC-mortgage_scally_ch2-02

Who’s Buying? Looking Into Purchase Originations

Although it is difficult to compare the volume of purchase originations with the levels seen in 2005-06 due to differences in home prices, the trend was unmistakably increasing this year, and to a high level. In the following chart, we break purchases out into three groups: (1) first-time homebuyers, or those whose origination marks the first mortgage on their credit report, shown in blue; (2) repeat buyers – individuals who are making a purchase that is not their first, shown in red; and (3) second home buyers and investors—those who have a new origination with one or more first mortgages already on their credit report. The boom years preceding the Great Recession—which similarly saw a brisk pace of mortgage originations, had the three lines at roughly equal volumes for several quarters. And then as house prices began to decline and the market began to crash, it was first-time buyers who were able to come in at a higher rate—incentivized by first-time homebuyer credits in the Housing and Economic Recovery Act, as well as declining interest rates and home prices. But during the slow recovery in originations in the past eight years, the first time and repeat buyer lines have essentially tracked each other, while second home and investor purchases have trailed, only picking up slightly in the past year.

LSE_2021_HDC-mortgage_scally_ch3-01

2020 Vintage Remains Very High Quality

As we describe above, the bulk of newly originated mortgages are going to borrowers with the highest credit score. But is this because of the volume of refinance originations? In the final chart, below, we look at the median credit score of newly originated mortgages, by type. Refinanced mortgages typically go to higher score borrowers than purchase mortgages, due to their established repayment histories. However, when we break out purchasers into first-time and repeat buyers, we see that the median credit scores are quite close for refinancers and repeat buyers, while the first-time buyers have lower credit scores, and always have. The median credit score of refinancers and repeat buyers was just below 800 at the end of 2020, about 60 points higher than that of first-time buyers. With a look to the series history, new mortgages are more prime—for even first-time buyers, median credit scores have slowly drifted up since 2002-06, when they hovered in the high 600s.

LSE_2021_HDC-mortgage_scally_ch4-02

Cha-Ching! Cashing Out Home Equity When the Rates Make Sense

With refinances picking up in 2020, we look to see whether consumers are cashing out, and drawing against their home equity. In the chart below, we show the quarterly cashout refinance volume, which is calculated as the difference between the borrowers’ new mortgages and the mortgages that the refinances replaced. Keeping in mind that the data we present here are not adjusted for inflation, cashout refinance volume is still notably smaller than what was seen between 2003-06. However, we do see a notable increase in cashout refinance volumes, which spiked in the fourth quarter of 2020 and show no sign of abating. Homeowners withdrew $188 billion in home equity over the course of 2020. One important note though—although this is a big increase, it’s also associated with lower average cashout amounts. Borrowers who refinanced in 2019 withdrew, on average, an additional $49,000; while borrowers who refinanced in 2020 withdrew, on average, $27,000. The median cashout withdrawal in 2020 was only $6,700, suggesting that at least half of the refinancers borrowed only enough additional funds to cover the closing costs on the new mortgage. Still, a substantial minority of refinancers took out some cash from their property, which they can use to fund consumption or other investment opportunities, including home improvements. Meanwhile, borrowers who did not choose to take out extra cash saw an average savings of $200 on their monthly mortgage payment, improving their monthly household cash flow.

LSE_2021_HDC-mortgage_scally_ch5-02

What To Look For Going Forward

Support for households who have suffered job and income losses has been a key feature of the CARES Act, through both the direct financial transfers to households as well as the moratoria on Federally backed mortgages and student loans. Meanwhile, many households are taking advantage of the historically low mortgage interest rates that are a consequence of the supportive monetary policy. It will be interesting to see whether households will maintain these high rates of home purchases and refinances into 2021 and more generally how households will adjust their balance sheets depending, in part, on whether and how long forbearances continue on payments on federally backed mortgages and student loans.

Chart data

Haughwout_andrewAndrew F. Haughwout is a senior vice president in the Federal Reserve Bank of New York’s Research and Statistics Group.

Lee_donghoonDonghoon Lee is an officer in the Federal Reserve Bank of New York’s Research and Statistics Group.

Scally_joelleJoelle Scally is a senior data strategist in the Bank’s Research and Statistics Group.

Vanderklaauw_wilbertWilbert van der Klaauw is a senior vice president in the Bank’s Research and Statistics Group.

How to cite this post:

Andrew F. Haughwout, Donghoon Lee, Joelle Scally, and Wilbert van der Klaauw, “Mortgage Rates Decline and (Prime) Households Take Advantage,” Federal Reserve Bank of New York Liberty Street Economics, February 17, 2021, https://libertystreeteconomics.newyorkfed.org/2021/02/mortgage-rates-dec....

Related Reading

Household Debt Balances Increase as Deleveraging Period Concludes

Interactive: Household Debt and Credit Report

CMD: Housing Market

Disclaimer

The views expressed in this post are those of the author and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the author.

How Does U.S. Monetary Policy Affect Emerging Market Economies?

Published by Anonymous (not verified) on Wed, 04/08/2021 - 3:15am in

Ozge Akinci and Albert Queralto

How Does U.S. Monetary Policy Affect Emerging Market Economies?

The question of how U.S. monetary policy affects foreign economies has received renewed interest in recent years. The bulk of the empirical evidence points to sizable effects, especially on emerging market economies (EMEs). A key theme in the literature is that these spillovers operate largely through financial channels—that is, the effects of a U.S. policy tightening manifest themselves abroad via declines in international risky asset prices, tighter financial conditions, and capital outflows. This so-called Global Financial Cycle has been shown to affect EMEs more forcefully than advanced economies. It is because higher U.S. policy rates have a disproportionately larger impact on rates in EMEs. In our recent research, we develop a model with cross-border financial linkages that provides theoretical foundations for these empirical findings. In this Liberty Street Economics post, we use the model to illustrate the spillovers from a tightening of U.S. monetary policy on credit spreads and on the uncovered interest rate parity (UIP) premium in EMEs with dollar-denominated debt.

Real Effects of U.S. Monetary Policy on EMEs

We start by estimating the effects of U.S. monetary policy on EMEs, using a structural vector autoregressive model (SVAR) model including EME GDP and U.S. variables such as GDP, inflation, unemployment, capacity utilization, consumption, investment, and the federal funds rate for the period 1978:Q1-2008:Q4. The key identification assumption in the SVAR model is that the only variable that the U.S. monetary policy shock affects contemporaneously is the federal funds rate.

The results are shown the chart below. The red line in each panel indicates the point estimates of the impulse response functions, while the gray dotted lines mark the corresponding 95 percent probability bands. The blue line shows the predictions of our model, where we calibrate the larger economy to the United States, and take the smaller economy to represent a bloc of EMEs, such as the Asian or the Latin American EMEs. Starting with the U.S. economy, the model captures the dynamic response of U.S. output to a U.S. monetary policy shock remarkably well. A monetary policy innovation that raises the U.S. federal funds rate by 100 basis points induces U.S. output to fall around 0.50 percent at the trough, very close in magnitude to those implied by our model.

How Does U.S. Monetary Policy Affect Emerging Market Economies?

We next turn to the spillovers to emerging markets. In response to the same shock, EME output falls around 0.45 percent at the trough, broadly comparable in magnitude to the decline in U.S. GDP, and remains below its baseline path well after the effect of the shock on interest rates is gone. Our model captures both the magnitude and the persistence of the response of EME output reasonably well, although the model-implied EME output response is somewhat less sluggish than the SVAR-implied one.

Disentangling Channels of Spillovers

Having shown that the model’s predictions on the spillovers of a U.S. monetary policy shock on EMEs are plausible, we next use it to disentangle channels through which these shocks transmit to EMEs. Our model predicts two channels of transmission: the trade channel and the financial channel. The chart below displays how much EME GDP would be affected through each channel.

How Does U.S. Monetary Policy Affect Emerging Market Economies?

The trade channel operates through a fall in EME exports due to lower U.S. demand. This effect is partially offset by EME exports becoming cheaper as the dollar appreciates. Overall, EME output declines by about 0.10 percent relative to baseline due to the trade channel.

The financial channel operates through lower investment spending due to both rising credit spreads and larger UIP risk premia (shown in the lower left and lower right panels of the chart, respectively). Note that UIP risk premia are defined as the difference between the required return by global investors for lending to EMEs (adjusted for expected exchange rate changes) and the return on U.S. safe assets. To highlight the amplification role played by the deviations from UIP, we first shut down this channel (by assuming that UIP holds at all times and that there is no dollar debt in EME balance sheets), and show the predicted effects by the gold lines in the four panels of the chart. The drop in EME asset prices following the U.S. rate hike works to initiate losses in EME borrowers’ balance sheets. Weaker EME balance sheets then give rise to higher domestic lending spreads, making credit more expensive for EME borrowers and triggering declines in investment, and ultimately slowing economic activity. This is the standard financial accelerator effect typically present in models with credit market frictions, causing EME output to fall by an additional 0.15 percent below baseline.

Our model adds an additional amplification mechanism based on the interaction between balance sheets and external financing conditions. Now, the EME’s exchange rate depreciation following the U.S. rate hike causes additional losses in EME borrowers’ balance sheets (over and above the effects of the drop in EME asset prices). This occurs due to the presence of some dollar debt on the balance sheet of these borrowers. Because the assets held by EME borrowers are denominated in the local currency, the depreciation of the local currency against the dollar that occurs in the wake of the U.S. tightening raises the real burden of the dollar debt, thus reducing borrowers’ net worth further. In equilibrium, a weakening of local balance sheets widens the deviation from UIP, which in turn is accommodated via a depreciation of the EME currency against the dollar. Because local balance sheets are partly mismatched, a weaker local currency then feeds back into balance sheet health, further weakening it. The result is sharply amplified declines in the value of EME currency and investment, more than offsetting the positive effect of depreciation on EME exports, that brings total decline in EME output to 0.45 percent relative to baseline.

Testing the Link between UIP Deviations and Financial Stress

The model features a time-varying UIP risk premium that increases with the domestic lending spreads in EMEs. We test this prediction of the model using data from Korea and present the results in the table below.

How Does U.S. Monetary Policy Affect Emerging Market Economies?

The second column shows our results, where we regress the change in real exchange rate on the changes in the interest differential and the corporate bond spread. We find that the coefficient on the spread is highly statistically significant, and the presence of the spread improves the fit considerably. In the third and fourth columns, we include an indicator variable for the crisis periods, which takes unity in the months 1998:8–1999:3 and 2008:9–2009:3, and zero otherwise, and a measure of global risk aversion (proxied by the VIX), respectively. As shown, the coefficient on the spread continues to be significant, lending support to the mechanism we propose in the model.

In sum, we present a model where the effects of a U.S. monetary policy shock on EMEs are amplified due to UIP premia that are correlated with domestic lending spreads, consistent with the evidence. Our research provides theoretical foundations for the Global Financial Cycle that shows monetary contractions in the United States lead to tightening of foreign financial conditions, and for more recent findings that show these effects are larger in EMEs than in advanced economies.

Ozge Akinci
Ozge Akinci
is a senior economist in the Federal Reserve Bank of New York’s Research and Statistics Group.

Albert QueraltoAlbert Queralto is a principal economist at the Board of Governors of the Federal Reserve System.

How to cite this post:

Ozge Akinci and Albert Queralto, “How Does U.S. Monetary Policy Affect Emerging Market Economies?,” Federal Reserve Bank of New York Liberty Street Economics, May 17, 2021, https://libertystreeteconomics.newyorkfed.org/2021/05/how-does-us-moneta....

Disclaimer

The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Banking the Unbanked: The Past and Future of the Free Checking Account

Published by Anonymous (not verified) on Fri, 30/07/2021 - 8:18am in

Stein Berre, Kristian Blickle, and Rajashri Chakrabarti

 The Past and Future of the Free Checking Account

About one in twenty American households are unbanked (meaning they do not have a demand deposit or checking account) and many more are underbanked (meaning they do not have the range of bank-provided financial services they need). Unbanked and underbanked households are more likely to be lower-income households and households of color. Inadequate access to financial services pushes the unbanked to use high-cost alternatives for their transactional needs and can also hinder access to credit when households need it. That, in turn, can have adverse effects on the financial health, educational opportunities, and welfare of unbanked households, thereby aggravating economic inequality. Why is access to financial services so uneven? The roots to part of this problem are historical, and in this post we will look back four decades to changes in regulation, shifts in the ownership structure of retail financial services, and the decline of free/low-cost checking accounts in the United States to search out a few of the contributory factors.

The Unbanked
The economics of banking do not incentivize the delivery of financial services to those who may need them most. Since profits at retail banks are driven heavily by the size of the balances their customers deposit or borrow, product offerings and marketing efforts are disproportionately focused on clients with higher levels of income and wealth. Checking accounts display the characteristics of a good that has a high degree of income elasticity for lower-income households. In other words, a checking account is a good that lower-income households may choose to forego if household budgets are tight (as during a spell of unemployment); meanwhile, checking accounts have near-universal penetration among wealthier households. For a subset of consumers, estimated by the FDIC in 2019 to be just over 5 percent of the population, basic account access remains lacking, particularly among those with irregular incomes, less documentation, and a lack of credit history.

Significantly, almost half of this unbanked group previously had bank accounts and now choose not to have one. Reported factors in this decision include distrust of banks, high fees, or large minimum balances. Although the size of the unbanked population has been falling over time, it tends to go up and down with the state of the economy and level of employment, having recently peaked at 8.2 percent of U.S. households in 2011 and risen again as the result of the pandemic, underscoring that for poorer households, checking accounts are often a good they choose to forego when income falls.

Those without bank accounts still need financial services, including money transfer and credit. According to research by Mehrsa Baradaran on low-earners in How the Other Half Banks, expenditure on financial services by unbanked households could amount to as much as 10 percent of their income. Often, this spending is due to high interest rates (on such services as payday loans), wire fees (especially for those providing or receiving remittances), or overdraft/transaction fees on transaction accounts.

Low-cost, low-minimum bank accounts were once widely available in the United States. In the 1960s and 1970s, an era of regulated deposit pricing, banks competed for new depositors through aggressive advertising and incentives (the proverbial toaster) and offered low-fee checking accounts with low or no minimum balance requirements. Customers with larger balances cross-subsidized those with smaller balances, since the cost of servicing smaller accounts was often greater than the revenues they generated. The repricing of this era was concurrent with the Savings and Loan Crisis and the associated declines in financial inclusion and access to low-cost financial services products have continued to this day.

This post looks at some historical factors that accompanied changes in the supply of checking accounts to low- and moderate-income households in the United States. Part of this change in supply was structural, as institutions that had been founded to serve low- and middle-income communities either changed strategy or were acquired. Other changes happened at a product and customer level as banks reduced the cross-subsidies from higher-balance customers to lower-balance ones through minimum balance requirements, increased account fees, and overdraft charges.

Comparative Market Structure
All countries face challenges in achieving full financial inclusion, but the United States is relatively unusual among richer countries in the size of its unbanked and underbanked population. In neighboring Canada, for example, more than 99 percent of the population has access to a basic transaction account, including 98 percent of the poorest 40 percent of the population. Japan, Germany, Singapore, and the countries of Scandinavia show similar levels of access. The FDIC’s 2019 Survey of Household Use of Banking and Financial Services, in contrast, showed that about a quarter (23.3 percent) of U.S. households with annual incomes of less than $15,000 were unbanked, as were 10.4 percent of households earning between $15,000 and $30,000.

The United States is also somewhat unusual among wealthier countries in the scale of its not-for-profit banking sector. The United States does not have a universal Giro- or Postbank, as in much of Europe or Japan, and although it does have a vibrant credit union sector (accounting for about 8 percent of all banking assets), the mutual banking sector is smaller than in many other OECD nations.

This was not always the case. As recently as the 1970s, mutuals (thrifts, mutual savings and loans [S&Ls], and affinity-based mutuals) accounted for half of the residential lending market and commanded a significant share of the deposit market. A large number of these mutual banks were founded in the late 19th and early 20th centuries with the goal of ensuring financial inclusion for the working poor, at a time in which banking services were provided primarily to the well-to-do. Their missions were sometimes rooted in communitarian self-help and sometimes in paternalist philanthropy. Mutual savings and loans were present in most parts of the country and savings banks were once particularly prevalent in the cities and towns of the northeastern United States. Fraternal and friendly societies, including provident societies, also provided financial services as part of lodge, club, or church membership.

FRED data show the rapid decline of mutual banks in the U.S. banking market, both in terms of their number and their assets. In the chart below, the rapid fall in the deposit share of mutuals between 1985 and 1995 is particularly apparent. Many of these organizations still exist, but are no longer mutual-based, having demutualized or been acquired by for-profit commercial firms and migrated upmarket along with their customer base.

 The Past and Future of the Free Checking Account

While traditional mutuals have declined, there has been a revival since the 1980s in credit unions. Like the mutual savings banks and savings and loans, credit unions have a mutual structure, more akin to a not-for-profit, usually linked to some form of employer, professional affiliation, or local geography. While credit unions have grown their deposit
base at nearly 7 percent per annum on average since the 1980s, nominal growth of other mutuals (savings banks, S&Ls) has been negligible.

Over this same period (since 1989), commercial banks have grown in scale and scope, with total deposits growing from $2 trillion to over $17 trillion today. The acquisition wave that began in the 1980s has been associated with a commercial banking sector that is dominated by regional and national banking networks, which market themselves based on brand and convenience and do not generally compete on price in the market for retail deposits. The top four banking groups now account for more than 35 percent of U.S. commercial bank deposits.

The Great Repricing
Many factors came together in the 1970s and 1980s as banks began differentiating between higher- and lower-balance customers. Because deposit markets in this era were still very local and data on retail deposit pricing was just beginning to be widely available, we rely here on press and other primary sources’ descriptions from that era. In exploring why price differentiation grew in the 1980s and 1990s, we describe some of those factors, but we think of these as correlates, not proof of causality.

Through their exposure to short-term interest rates, money market mutual funds offered savers higher rates and greater protection against inflation than demand deposit accounts could offer, as they were subject to Regulation Q restrictions on the payment of interest. To compete, banks launched a high-interest account with limited transactions allowed per month, the NOW Account (Negotiated Order of Withdrawal). These products were approved for rollout nationwide in 1980 by an act of Congress and a 5 percent interest rate cap was lifted in 1986.

These products were widely popular with customers, but much less profitable on average than checking accounts had previously been. In the years following the introduction of the NOW account, banks searched for ways by which they could still offer interest on checking accounts without losing revenue. Banks instituted higher fees on lower balance customers and relatively high account minimums to ensure break-even for this new product. These balances (typically ranging on the order of $1,000 minimum balance at commercial banks, and around $300 at thrifts, such as savings banks and S&Ls) were publicly justified by bankers arguing that higher minimum balances reflected the increased break-even point on interest-paying deposits.

There was extensive writing about these new accounts both within the press and in the burgeoning industry of saver-oriented consumer reporting. In 1983, the New York Times reported that account fees had more than doubled since the start of the decade and minimum balances for fee waivers, as high as $2,500 in some local markets, had been introduced. A few years later, Sheshunoff, a leading publisher of deposit product comparisons, reported that transaction fees on checking accounts nearly doubled yet again in many of the markets it covered between 1985 and 1988.

In parallel, as changes in the deposit market intensified, mutual banks came under a long and painful period of financial stress on their lending portfolios. The causes and impacts of the waves of bank failures triggered by the Savings and Loan Crisis are covered in an extensive literature of their own. Many mutuals failed, converted, or were acquired in this period, but their deposits and branch infrastructure still made them attractive acquisition targets.

Throughout the 1980s and early 1990s, through both voluntary and arranged mergers, struggling mutuals were among the first retail players to be consolidated in the two and a half decades of retail banking consolidation that followed. The loss of the mutuals reduced the options available to low-balance customers, particularly in the regional markets of the northeast where they had historically been strongest. Mutuals tended to have lower minimum balance requirements and lower fees. Studies from the period also show that commercial banks with large branch networks also tended to pay lower rates on deposits than either community banks or mutuals did.

Due in no small part to increased computing power and growing sophistication in cost-accounting, customer-level profit segmentation became a lens through which banks have looked at retail customers, not only in banking but across a wide range of industries since the late 1980s. Banks needed to know what products to target at which customers and where the break-even points would be on each product, leading to changes in the way retail banks were managed at the customer level. A bibliography of some of this literature can be found here.

With profitability segmentation, bank clients could be divided into “A,” “B,” and “C” tiers (“A” being the most potentially lucrative, but the terms will vary by firm) based on current balances and potential future profit. Higher balance “A” customers tend to cover more than their allocated share of bank expenses and make a large contribution to bank profits; customers in the “B” and “C” tiers tend to make a marginal or negative contribution. Consequently, lower tier clients saw an increase in account service and overdraft fees, so that they would be more profitable on a stand-alone basis. In more recent years, higher overdraft charges have continued this trend of imposing fees to make lower-balance customers more profitable.

There may be signs of change in the market. Fintech firms are increasingly offering low-cost checking account options in a marketplace less tied to costly brick-and-mortar infrastructure. As of this posting, some major banks have also begun announcing an end to some or all overdraft fees on retail accounts (although other fees will likely stay in place). This may or may not be a longer-term trend, but it would go some way toward addressing one of the most clear-cut cases of product pricing impacting low-balance customers.

The following post in this series will dive more deeply into the dynamics of account overdraft fees and their implications for inequality.

Summing Up
The great repricing of bank products that began four decades ago has been associated with profound changes in financial inclusion. Compared to that earlier era, there are fewer institutions offering low-minimum, low-fee accounts and retail deposit and transaction products are more differentiated based on balances, with the associated fees hitting lower-balance customers hardest. As a result, a substantial number of low- and middle-income consumers choose to be unbanked to avoid fees, a figure that rises when the economy is not performing well. Loss of access to a transaction account can have knock-on effects in terms of inequality, particularly through the mechanism of credit access, banking relationships, and credit histories. Greater availability of low-cost checking accounts, by this logic, would translate into higher rates of inclusion in what is arguably the cornerstone product for inclusion in the economy.

If the past is prelude, this leads us to think about alternative futures. Will traditional banks increasingly forego fees and allow greater cross-subsidization between customer segments? As many credit unions offer increasingly liberal terms for membership, will they help fill the gap for financial inclusion? Alternatively, will technology fill the void, enabling banks or fintechs to serve clients that are currently high-cost much more cheaply so that there are very few “C” customers left? Or perhaps, as was the case a century ago, it is time for mission-driven organizations to think again about mutual banking?

Stein BerreStein Berre is a senior vice president in the Federal Reserve Bank of New York’s Supervision Group.

Kristian BlickleKristian Blickle is an economist in the Bank’s Research and Statistics Group.

Rajashri ChakrabartiRajashri Chakrabarti is a senior economist in the Bank’s Research and Statistics Group.

How to cite this post:
Stein Berre, Kristian Blickle, and Rajashri Chakrabarti, “Banking the Unbanked: The Past and Future of the Free Checking Account,” Federal Reserve Bank of New York Liberty Street Economics, June 30, 2021, https://libertystreeteconomics.newyorkfed.org/2021/06/banking-the-unbank....
Additional Posts in this Series
Hold the Check: Overdrafts, Fee Caps, and Financial Inclusion
Credit, Income, and Inequality

Related Reading
Once Upon a Time in the Banking Sector: Historical Insights into Banking Competition
The ‘Banking Desert’ Mirage
Economic Inequality: A Research Series

Disclaimer
The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Credit, Income, and Inequality

Published by Anonymous (not verified) on Fri, 30/07/2021 - 8:10am in

Credit, Income, and Inequality

Access to credit plays a central role in shaping economic opportunities of households and businesses. Access to credit also plays a crucial role in helping an economy successfully exit from the pandemic doldrums. The ability to get a loan may allow individuals to purchase a home, invest in education and training, or start and then expand a business. Hence access to credit has important implications for upward mobility and potentially also for inequality. Adverse selection and moral hazard problems due to asymmetric information between lenders and borrowers affect credit availability. Because of these information issues, lenders may limit credit or post higher lending rates and often require borrowers to pledge collateral. Consequently, relatively poor individuals with limited capital endowment may experience credit denial, irrespective of the quality of their investment ideas. As a result, their exclusion from credit access can hinder economic mobility and entrench income inequality. In this post, we describe the results of our recent paper which contributes to the understanding of this mechanism.

Access to Credit Has a Positive Effect on the Income of Small Business Owners
In our paper, we study how banks’ credit decisions (credit acceptance or rejection) affect applicants’ income and its distribution in a developed economy. We identify this effect using a unique data set of business loan applications to a single large European bank during the time period 2002-16. Our focus is on loan applications from small and micro enterprises (with total assets less than EUR 10 million) that are majority-owned by individuals and do not have a credit relationship with another regulated bank. For these applicants, the bank decides whether to grant loans based on a credit score cutoff rule. Specifically, each applicant is assigned a credit score at the time of the loan application, which consists of an internal rating constructed by the bank. Then, credit is granted to applicants with credit scores above the cutoff, and denied otherwise. We exploit the cutoff rule as a source of variation in the bank’s credit decision. Our approach builds on the idea that applicants whose credit scores are around the cutoff share approximately the same traits, including similar incomes. Thus, we quantify the effect of a loan origination on individual income by comparing changes in the income of accepted and denied applicants, who prior to the bank’s credit decision have similar credit scores.

Our main finding is summarized by the chart below, which shows applicants’ income five years after the loan decision against the credit score. The chart reveals a clear upward shift in applicants’ income around the cutoff, with marginally accepted applicants experiencing higher incomes than marginally rejected applicants. In general, our analysis shows that access to credit has a positive effect on the income of small business owners. Specifically, being approved for a loan implies an increase in the recipient’s income of approximately 6 percent one to three years after the loan decision, and an increase of 11 percent five years after.

Credit, Income, and Inequality

We next investigate the economic channels behind the observed positive impact of a loan origination on individual income. We document that firms of accepted applicants use the borrowed funds to make investments and expand their business, ultimately experiencing higher profitability and growth rates compared to firms of rejected applicants. We also show that the effect of credit access on income is more pronounced when we compare marginally accepted and rejected applicants whose credit score is positively affected by the soft information held by the bank (for example, on the quality of the investment opportunities of the firm). Overall, these results suggest that credit provision to small businesses is pivotal to foster entrepreneurship and economic mobility.

Our Findings Support a Negative Finance-Inequality Nexus
A natural implication of our key findings is that the income distribution of applicants around the cutoff changes in response to the bank’s credit decisions. Using two standard aggregate measures of income inequality (the Gini coefficient and the Theil index), we document a tighter income distribution among accepted applicants and a wider income distribution among rejected applicants.

We next examine how credit origination affects the income distribution in the economy more generally. First, we show that loan approval has a stronger effect on applicants’ future income in low-income regions compared to high-income regions, thus potentially affecting the income distribution within and across geographical areas. Second, we exploit the Great Recession to analyze how an economic crisis and associated credit crunch affect the credit-income relationship. We show that the positive effect of credit access on individual income is somewhat stronger during the crisis period, when small business owners are more credit-constrained. Overall, these results are in line with the theory pointing to a negative relationship between credit availability and income inequality.

Our findings have two key and interrelated economic implications. First, credit decisions strongly affect applicants’ future income and its subsequent dynamics, altering lifetime income expectations and realizations. Second, credit decisions exert substantial effects on the income distribution. In general, our study supports the idea that extending credit to individuals with good investment ideas improves economic mobility and reduces income inequality.

Manthos Delis is a professor of financial economics at Montpellier Business School.

Fulvia FringuellottiFulvia Fringuellotti is an economist in the Federal Reserve Bank of New York’s Research and Statistics Group.

Steven Ongena is a professor of banking at the University of Zurich.

Additional Posts in this Series

Banking the Unbanked: The Past and Future of the Free Checking Account
Hold the Check: Overdrafts, Fee Caps, and Financial Inclusion

Related Reading
Economic Inequality: A Research Series

How to cite this post:
Manthos Delis, Fulvia Fringuellotti, and Steven Ongena, “Credit, Income, and Inequality,” Federal Reserve Bank of New York Liberty Street Economics, July 1, 2021, https://libertystreeteconomics.newyorkfed.org/2021/07/credit-income-and-....

Disclaimer
The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Consumer Credit Demand, Supply, and Unmet Need during the Pandemic

Published by Anonymous (not verified) on Tue, 27/07/2021 - 5:56am in

Tags 

Credit, mortgages

Jessica Lu and Wilbert van der Klaauw

Consumer Credit Demand, Supply, and Unmet Need during the Pandemic

It is common during recessions to observe significant slowdowns in credit flows to consumers. It is more difficult to establish how much of these declines are the consequence of a decrease in credit demand versus a tightening in supply. In this post, we draw on survey data to examine how consumer credit demand and supply have changed since the start of the COVID-19 pandemic. The evidence reveals a clear initial decline and recent rebound in consumer credit demand. We also observe a modest but persistent tightening in credit supply during the pandemic, especially for credit cards. Mortgage refinance applications are the main exception to this general pattern, showing a steep increase in demand and some easing in availability. Despite tightened standards, we find no evidence of a meaningful increase in unmet credit need.

Demand and Supply of Household Credit during a Recession

How would one generally expect credit flows to consumers to change during a recession? To the extent that households perceive declines in income and wealth as permanent, they may lower current consumption and demand for debt. Similarly, an increase in uncertainty can depress consumption and increase precautionary or “buffer-stock” saving, reducing demand for credit. On the other hand, during recessions households may optimally respond to a temporary negative income shock by drawing on new and existing credit in order to smooth consumption.

On the supply side, access to credit may become more difficult during recessions due to a rise in lender risk aversion or a reduction in collateral values. Lenders may be concerned that a recession and higher unemployment could trigger a sharp rise in loan defaults, and this concern may cause them to tighten credit standards. With lenders requiring higher credit scores, more income documentation, and reduced credit limits on new and existing loans, households may find it harder to obtain credit when they most need it.

While we expect these factors to play similar roles in the current recession, the pandemic and the public health and policy responses resulted in an economic downturn with different characteristics and dynamics than prior recessions. The recession was unique in the speed with which it took hold, the types of workers it affected, and the nature and size of the policy response. In an economy with reduced consumption opportunities (especially services) due to lockdowns, a huge decline in employment was accompanied by a steep decline in spending, but also a jump in the personal savings rate to a record high, as personal income surged in response to policy measures.

Using the SCE to Measure Demand for Credit

Our analysis draws on data from the Survey of Consumer Expectations (SCE) Credit Access Survey. The survey has queried respondents every four months since October 2013 about their experiences and expectations of applying for and obtaining credit. Our data differ from existing data sources in several important ways. First, unlike existing sources such as the Senior Loan Officer Opinion Survey (SLOOS), our survey captures realized and expected credit demand as reported by consumers instead of lenders. Second, we measure credit applications and rejections separately for different types of consumer loans, including mortgages, auto loans, and credit cards. Third, besides realized and expected credit demand, we identify “discouraged credit seekers”—respondents who report not applying for credit over the past twelve months despite needing it, because they believed they wouldn’t be approved.

In this post, we introduce a new, more forward-looking gauge of unmet need based on a new measure of latent credit demand. Specifically, starting in October 2016 we began asking respondents for the probability that they will apply for credit over the next twelve months if their application is guaranteed to be approved. Our new measure of unmet need is then defined as the difference between their answer to this question and their reported unconditional probability of applying over the next twelve months.

Application and Rejection Rates

The overall share of respondents who reported applying for any type of credit over the past year shows a v-shaped pattern after the onset of the pandemic, as shown in the chart below. After the share of those who reported applying fell from 45.6 percent in February 2020 to 39.2 percent in June and to a series low of 34.6 percent in October, we see a robust rebound in February 2021 to an application rate of 44.8 percent, just below its pre-pandemic value. We see a similar pattern for credit card application rates, which fell from 26.3 percent in February 2020 to 15.7 percent in October, though with a smaller rebound in February 2021 to 19.4 percent, still well below pre-pandemic levels. Requests for credit card limit increases (not shown) evolved comparably. Auto loan and mortgage application rates (not shown) were relatively more stable and in February 2021 were just above their year-ago levels.

The big outlier in these reported trends is a dramatic increase among mortgagors in mortgage refinance applications during the pandemic, with many mortgage loan borrowers taking advantage of low interest rates. In February 2021, 24.6 percent of mortgagors reported applying for a refinance over the past twelve months, compared to only 10.8 percent a year earlier.


Consumer Credit Demand, Supply, and Unmet Need during the Pandemic

Among those who applied for any type of credit, the share reporting a rejection has increased monotonically since the onset of the pandemic, from 14.2 percent in February 2020 to 18.5 percent in February 2021. As shown in the next chart, the increase was relatively modest and within the range of changes observed in our series’ history. The rejection rate for credit card applicants shows a somewhat more noticeable increase, rising from a series low of 9.7 percent in February 2020 to a series high of 26.3 percent a year later—a 170 percent increase. Rejections of requests for credit limit increases (not shown) follow the same trajectory—reaching a new high of 40.3 percent in February 2021. The reported rejection rates for auto loan and mortgage applicants (not shown) follow less of a clear pattern over the past year.

Rejections reported by mortgage refinance applicants initially declined, but then rebounded somewhat in February 2021 to 12.2 percent, remaining below the 15.8 percent reading a year earlier. The steep increase in demand and some easing in available credit is consistent with the general strength of the housing market and historically high levels of home equity. These findings from our survey are largely consistent with indicators of credit demand and credit tightening reported by loan officers in the SLOOS.


Consumer Credit Demand, Supply, and Unmet Need during the Pandemic

When distinguishing respondents by age and credit score, we find that the patterns in application and rejection rates described above are largely broad-based across age and credit score groups, with two exceptions. First, the February 2021 rebound in application rates is generally much larger for respondents with self-reported credit scores between 680 and 760, aside from mortgage refinances, where the largest increase is among those with scores of 760 or higher. Second, the increase in the reported overall rejection rate during the pandemic was by far the largest for those with low credit scores (680 or below).

Two New Measures of Unmet Credit Demand

Turning now to our two alternative measures of latent credit demand (or unmet need), we first consider our backward-looking measure (shown in the left panel below). The share of discouraged borrowers, which we define as respondents who reported not applying for a loan (despite having a need for it) because they expected their application to be rejected, initially remained stable at 6.9 percent from February to June of 2020, rose slightly to 7.2 percent in October, and then fell to 6.1 percent in February 2021. Interestingly, this overall pattern masks a decline in borrower discouragement among those aged 40 and under and 60 and over, with the rate for the middle-age group increasing somewhat from 6.1 percent in February 2020 to 9.1 percent a year later. Similarly, we see an increase in discouraged borrowing among those with credit scores of 680 and below (from 21.6 percent to 28.1 percent), while seeing a slight decrease for those with higher scores.


Consumer Credit Demand, Supply, and Unmet Need during the Pandemic

Finally, we turn to our second alternative measure of latent credit demand, our forward-looking measure, available since October 2016. We first find that expected credit applications, both unconditional and conditional upon guaranteed acceptance, declined following the onset of the pandemic and more recently have experienced a considerable rebound. The initial decline in expected demand, even under guaranteed acceptance, substantiates our earlier finding of a general decrease in credit demand during the first eight months of the pandemic. Considering the difference between the two expected application rates—our indicator of unmet credit need—we find, as expected, that the gap is persistently positive, indicating a lower unconditional expected application rate due to a non-negative probability that the application will be rejected.

The panel on right above shows a simple unweighted average of the gap for mortgage, credit card, auto loan, credit limits, and mortgage refinance applications. After the onset of the pandemic, there is a slight decline in unmet credit need across all loan types, with the important exception of mortgage refinance applications. In contrast to our backward-looking measure of unmet credit (which does not distinguish between loan types), here for most loan types we find the decline in unmet need to be the largest for those with low credit scores (under 680). For mortgage refinancing (not shown), we see instead a slight rise among mortgagors in unmet need from 6.1 percent in February 2020 to 7.3 percent in June and 9.1 percent in October. However, by February 2021 the gap had narrowed to 5.5 percent. As was the case for the backward-looking measure, the increase in unmet need for refinancing is the largest for those with lower credit scores (below 680).

Conclusion

In this post, we used new data from the SCE Credit Access Survey to study the evolution of consumer credit demand and supply during the COVID-19 recession. We find that credit demand dropped sharply during most of 2020, especially for credit cards, with a modest rebound observed by February 2021. We also observe a modest increase in credit rejection rates during 2020, especially for credit cards. Despite this credit tightening and increased unemployment, we see no meaningful increase in discouraged borrowing and unmet credit need. Mortgage refinancing is the main exception to this general pattern, showing a steep increase in demand and some increase in unmet need, especially for those with lower credit scores.

These overall results likely evince the impact of large fiscal interventions, including stimulus checks and expanded unemployment insurance benefits, which have enabled many households to pay down debt, especially credit card debt, and increase their saving. Even among those with low credit scores, while unmet credit needs remain formidable, instead of an increase in expected unmet need we actually see a decline for the group for which unmet need is typically high. The only clear evidence of an increase in unmet credit needs is found for mortgage refinancing, where lower credit score mortgagors have been less able to take advantage of the low-rate environment.

Jessica Lu is a senior research analyst in the Federal Reserve Bank of New York’s Research and Statistics Group.

Wilbert van der KlaauwWilbert van der Klaauw is a senior vice president in the Bank’s Research and Statistics Group.

How to cite this post:

Jessica Lu and Wilbert van der Klaauw, “Consumer Credit Demand, Supply, and Unmet Need during the Pandemic,” Federal Reserve Bank of New York Liberty Street Economics, May 20, 2021, https://libertystreeteconomics.newyorkfed.org/2021/05/consumer-credit-de....

Disclaimer

The views expressed in this post are those of the author and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the author.

Keeping Borrowers Current in a Pandemic

Published by Anonymous (not verified) on Tue, 27/07/2021 - 5:56am in

Andrew Haughwout, Donghoon Lee, Joelle Scally, and Wilbert van der Klaauw

Keeping Borrowers Current in a Pandemic

Federal government actions in response to the pandemic have taken many forms. One set of policies is intended to reduce the risk that the pandemic will result in a housing market crash and a wave of foreclosures like the one that accompanied the Great Financial Crisis. An important and novel tool employed as part of these policies is mortgage forbearance, which provides borrowers the option to pause or reduce debt service payments during periods of hardship, without marking the loan delinquent on the borrower’s credit report. Widespread take-up of forbearance over the past year has significantly changed the housing finance system in the United States, in different ways for different borrowers. This post is the first of four focusing attention on the effects of mortgage forbearance and the outlook for the mortgage market. Here we use data from the New York Fed’s Consumer Credit Panel (CCP) to examine the effects of these changes on households during the pandemic.

Background: Who Qualifies for COVID-19 Mortgage Forbearance?

Initially, under the CARES Act, borrowers with federally backed mortgages could request up to twelve months of forbearance, made up of two 180-day periods, if they experienced financial hardship because of COVID-19. Several agencies have subsequently granted extensions. Specifically, borrowers with mortgages backed by the government-sponsored enterprises (GSEs) Fannie Mae and Freddie Mac can request up to two additional three-month extensions (for a maximum of eighteen months of total forbearance) if they were in an active forbearance plan as of February 28, 2021, while borrowers with mortgages backed by the Department of Housing and Urban Development/Federal Housing Administration (HUD/FHA), the U.S. Department of Agriculture (USDA), or the Department of Veterans Affairs (VA) can enroll in forbearance until June 30, 2021, and receive up to eighteen months of total forbearance. At the same time, the CARES Act (section 4013) eased the accounting treatment of pandemic-related modifications for loans in bank portfolios, and the federal banking agencies released guidance to that effect in early April 2020.

How Does Forbearance Work?

Widespread forbearance is a new policy, although similar programs have previously been rolled out on a smaller scale in the wake of natural disasters. Typically, the missed payments will be added to the end of the loan; for a borrower in the first year of a thirty-year mortgage, a forbearance thus amounts to a twenty-nine-year interest-free loan of the forborne amount. These forbearances are safe loans in part because they are incentive-compatible: in order to preserve their housing equity, borrowers must resume payments when they are able. (Note that renters, with no equity in their property, do not have strong incentives to pay back forborne rent payments, making the provision of relief to renters more difficult than to owners.)

Who Entered Forbearance?

As we reported back in November, large numbers of mortgage forbearances began to appear on credit reports in April 2020, and by May 2020, 7 percent of mortgage accounts were in forbearance. By June, however, exits from forbearance began to outweigh entries, and the number of mortgages in forbearance began a slow decline. The following chart shows that by March 2021, the overall forbearance rate had fallen to 4.2 percent, accompanied by reductions in both entries and exits, suggesting a relatively stable group of borrowers in forbearance for a relatively long period of time. In fact, of the 2.2 million mortgages still in forbearance in March 2021, 1.2 million entered forbearance in June 2020 or earlier. (In March, the inflows into forbearance are likely affected by additional payment relief offered in Texas as a response to the effects of the winter storm there.)

Keeping Borrowers Current in a Pandemic

These dynamics—a sharp rise in April and May, followed by a slow decline through the summer and fall—are common across most types of mortgages, but FHA borrowers were considerably more likely to take up mortgage forbearance initially, and have remained in the program longer. As of March 2021, more than 11 percent of FHA borrowers remain in forbearance, as shown below.

Keeping Borrowers Current in a Pandemic

What accounts for the higher forbearance rates for FHA borrowers? FHA borrowers are much more likely to be first-time home buyers and to live in lower-income areas. About 41 percent of FHA borrowers live in neighborhoods with average annual household income below $50,000, compared to 22 percent for GSE borrowers.

With this context, it’s perhaps not surprising to find that forbearance rates rose most, and were most persistent, in lower average income zip codes. As shown in the next chart, forbearance rates in the poorest quartile of zip codes approached 10 percent in May and June 2020 and remain above 5.5 percent at the end of March 2021.

Keeping Borrowers Current in a Pandemic

The likelihood of forbearance falls steadily as borrower credit score (measured at the date of mortgage origination) rises, and it is far higher for loans that were delinquent in March 2020; see the next chart. Indeed, forbearance rates remain near 40 percent for borrowers who were delinquent on their mortgages pre-pandemic. The higher rates of mortgage forbearance in poorer areas and among FHA borrowers is consistent with the uneven impact that COVID-19 and the accompanying recession have had on different segments of the population. Mortgage forbearance has been an important policy tool to mitigate the impact of these challenges faced by less-advantaged households.

Keeping Borrowers Current in a Pandemic

Since housing costs are typically one of the largest household expenses, it isn’t surprising that mortgage forbearance offers very substantial cash flow relief to the households that take it up. The table below provides details on the payment relief that different forbearance participants received. (As we show in a companion post, being enrolled in forbearance isn’t quite the same as receiving cash flow relief.) We estimate this relief using the average payment that was due prior to enrolling in forbearance for those who were enrolled in forbearance as of March 2021. (These figures have been very stable since March 2020, so we don’t show the changes over time here.)

Keeping Borrowers Current in a Pandemic

As the table shows, the average monthly cash flow relief associated with a mortgage forbearance is somewhat different across different mortgage types and grows sharply as neighborhood income rises. Indeed, aggregate cumulative payments skipped by borrowers from the poorest 25 percent of neighborhoods are about 38 percent of those skipped in top-quartile neighborhoods.

All told, in absolute dollar terms, mortgage forbearance has brought the most benefit to the highest-income areas. This is due to a combination of high homeownership and relatively expensive mortgage payments in these areas, which more than offsets the considerably higher incidence of forbearance in lower-income areas. Still, the high rates of forbearance take-up on FHA loans and in poorer zip codes makes clear that these programs have been an important lifeline to less-advantaged households.

Conclusion

We find that mortgage forbearance has been an important policy tool to mitigate the impact of the pandemic and has become a fairly common phenomenon since it became widely available last year. After an initial rapid rise to over 7 percent, the share of mortgages in forbearance has slowly declined and stood at just over 4 percent in late March 2021. Forbearance has been more common for FHA borrowers and mortgagors from poorer neighborhoods, as well as those who were already delinquent in March 2020. In a separate post, we look at how being in forbearance affects borrowers, and continue to look at the distribution of those effects.

Andrew F. HaughwoutAndrew F. Haughwout is a senior vice president in the Federal Reserve Bank of New York’s Research and Statistics Group.

Donghoon LeeDonghoon Lee is an officer in the Bank’s Research and Statistics Group.

Joelle ScallyJoelle Scally is a senior data strategist in the Bank’s Research and Statistics Group.

Wilbert van der KlaauwWilbert van der Klaauw is a senior vice president in the Bank’s Research and Statistics Group.

How to cite this post:

Andrew F. Haughwout, Donghoon Lee, Joelle Scally, and Wilbert van der Klaauw, “Keeping Borrowers Current in a Pandemic,” Federal Reserve Bank of New York Liberty Street Economics, May 19, 2021, https://libertystreeteconomics.newyorkfed.org/2021/05/keeping-borrowers-....

Additional Posts in This Series

What Happens during Mortgage Forbearance?

Small Business Owners Turn to Personal Credit

What’s Next for Forborne Borrowers?

Related Reading
Economic Inequality: A Research Series

Press Briefing

Keeping Borrowers Current in a Pandemic

Disclaimer

The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

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