mortgages

Error message

Deprecated function: The each() function is deprecated. This message will be suppressed on further calls in _menu_load_objects() (line 579 of /var/www/drupal-7.x/includes/menu.inc).

Separating deposit-taking from investment banking: new evidence on an old question

Published by Anonymous (not verified) on Fri, 27/11/2020 - 8:00pm in

Tags 

Banking, mortgages

Matthieu Chavaz and David Elliott

On 16 June 1933, as the nationwide banking crisis was reaching a new peak, freshly elected US President Franklin D. Roosevelt put his signature at the bottom of a 37-page document: the Glass-Steagall Act. Eight decades later, the debate still rages on: should retail and investment banking be separated, as Glass-Steagall required? In a recent paper, we shed new light on the consequences of this type of regulation by examining the recent UK ‘ring-fencing’ legislation. We show that ring-fencing has an important impact on banking groups’ funding structures, and find that this incentivises banks to rebalance their activities towards retail mortgage lending and away from capital markets, with important knock-on effects for competition and risk-taking across the wider banking system.

How does ring-fencing affect bank behaviour?

Ring-fencing came into effect at the start of 2019. The regulation requires large UK banking groups to separate their core retail banking services from their investment banking activities, in order to protect UK retail banking from shocks originating elsewhere. Unlike Glass-Steagall, ring-fencing allows banking groups to continue to run both retail and investment banks. To do so, however, these groups must house their retail deposit-taking business in a subsidiary (the ‘ring-fenced bank,’ or RFB) that is separate from the entity housing their investment banking operations (the ‘non-ring-fenced bank’, or NRFB) — as illustrated in Figure 1. As we show in the paper, this requirement implies a substantial change in the extent to which different assets across the group are funded by retail deposits. Relative to the funding mix before the reform, the retail funding share of assets that can be placed in the RFB (such as mortgages) increases by around 18 percentage points on average. Meanwhile, the retail funding share of assets housed in the NRFB (mainly wholesale and investment banking) decreases by around 45 percentage points on average.

Figure 1: Stylised balance sheet of a banking group affected by ring-fencing

The left panel illustrates the balance sheet of a fictional banking group before ring-fencing. The right panel shows how the balance sheet might change after ring-fencing. Note that, in reality, most banking groups have a much wider range of assets and liabilities than those illustrated here.

In order to understand how this change affects banks’ behaviour, we use loan-level data for the UK mortgage market and the global syndicated lending market, and analyse the lending behaviour of banking groups over the years between ring-fencing legislation being passed (2013) and implemented (2019). To isolate the impact of ring-fencing from other developments, we compare the behaviour of banking groups who face a large change in funding structure as a result of ring-fencing to those who are unaffected, or for whom the change is more modest. In addition, we compare how the behaviour of the same bank differs between loans that will sit on the balance sheet for several months or years after ring-fencing implementation (and should therefore be affected by the change in funding structure) to loans that mature before 2019 (and are therefore unlikely to be affected by the restructuring). We only aim to estimate the impact of the change in funding structure on bank lending behaviour, and do not analyse other potential impacts of ring-fencing, for example relating to compliance costs.

Our results indicate that ring-fencing encourages UK banks subject to the reform to rebalance their activities towards retail lending and away from capital markets. Specifically, banks whose funding structures are more affected by the reform reduce the interest rates they charge on domestic mortgages, leading to an increase in the quantity of their mortgage lending. Meanwhile, they reduce their provision of syndicated credit lines and underwriting services to large corporates. This rebalancing is consistent with the idea that deposit funding provides certain advantages to banks — for example, because households place a high value on the liquidity of deposits, or because of deposit insurance — and that redirecting these benefits towards consumer lending leads to a reduction in the cost of consumer credit.

When we compare the effects of ring-fencing across different mortgage products, we find that the reduction in interest rates is larger for mortgages with maturities over two years, consistent with the idea that the funding stability of retail deposits allows banks to engage in maturity transformation. In contrast, we find no evidence that the reduction in rates is larger for higher LTV mortgages, despite the fact that the cost of retail deposits is relatively insensitive to the riskiness of the bank.

What are the effects on the wider market?

Ring-fencing requirements only apply to the largest UK banks. These banks hold large market shares in the mortgage market, suggesting the potential for spillover effects on their smaller competitors. Indeed, we find that, by offering cheaper mortgages, large banks more affected by the reform gain market share, leading to an increase in mortgage market concentration. Smaller banks respond by increasing the riskiness of their lending. Specifically, in those products and geographical areas where the large banks grow more, smaller banks tend to reduce the rates on high LTV (>90%) mortgages, and increase the share of these loans in their lending portfolios.

What does this tell us about recent developments in the UK mortgage market?

Two trends in the UK mortgage market that have attracted the attention of policymakers in recent years are falls in interest rates and increases in high-LTV lending by smaller lenders. We can use our results to estimate the role of ring-fencing in contributing to these trends.

Our results suggest that the reduction in the price of large banks’ mortgages caused by ring-fencing can explain around 10% of the overall decline in mortgage spreads observed between 2013 and 2019. In line with the Bank’s December 2019 Financial Stability Report, ring-fencing has therefore contributed to the ‘price war’ in UK mortgages, without being the main driver.

Meanwhile, our estimates indicate that the indirect effect of ring-fencing on small banks can explain around 30% of the increase in high LTV mortgages in small banks’ lending portfolios between 2013 and 2019. Again, therefore, ring-fencing appears to be one of several potential drivers of this development.

What are the broader policy implications?

By redirecting the benefits of deposit funding to retail credit markets, ring-fencing reduces the cost of credit for consumers. The cheaper credit is not concentrated in the higher-risk segment of the mortgage market, limiting financial stability concerns related to rising household indebtedness. The expansion of consumer credit is mirrored by a reduction in lending to large corporates. While the net welfare effects of this rebalancing are uncertain, we find that the reduction in corporate credit is mainly focused on lending to foreign borrowers, who are less likely to be reliant on relationships with UK banks.

Our results also suggest more ambiguous impacts on the retail credit market over the longer term. First, ring-fencing appears to lead to more concentrated markets. The increased market power of large banks could lead to more expensive credit and reduced quality of service over the longer-term; alternatively, increased concentration might simply reflect less efficient banks leaving the market. Second, the increased retail focus by large UK banks could reduce their exposure to international shocks; but by encouraging smaller banks to take more risk, ring-fencing might increase their vulnerability to shocks.

Matthieu Chavaz works in the Bank’s Monetary and Financial Conditions Division and David Elliott works at Imperial College London.

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.

Following Borrowers through Forbearance

Published by Anonymous (not verified) on Wed, 18/11/2020 - 3:00am in

Tags 

mortgages

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

Following Borrowers through Forbearance

Today, the New York Fed’s Center for Microeconomic Data reported that total household debt balances increased slightly in the third quarter of 2020, according to the latest Quarterly Report on Household Debt and Credit. This increase marked a reversal from the modest decline in the second quarter of 2020, a downturn driven by a sharp contraction in credit card balances. In the third quarter, credit card balances declined again, even as consumer spending recovered somewhat; meanwhile, mortgage originations came in at a robust $1.049 trillion, the highest level since 2003. Many of the efforts to stabilize the economy in response to the COVID-19 crisis have focused on consumer balance sheets, both through direct cash transfers and through forbearances on federally backed debts. Here, we examine the uptake of forbearances on mortgage and auto loans and its impact on their delinquency status and the borrower’s credit score. This analysis, as well as the Quarterly Report on Household Debt and Credit, is based on anonymized Equifax credit report data.

The moratorium on debt service payments during the pandemic

Forbearance provides borrowers the option to pause or reduce debt service payments during periods of hardship, without borrowers’ risk of being marked delinquent. The CARES Act provided for a six-month moratorium on payments on federally guaranteed mortgages and student loans, and voluntary forbearances were offered by lenders of auto loans and credit cards, encouraged by regulators.

We first take a look at the entry into and out of forbearance, by month and by loan type, shown in the chart below. The solid bars show the number of individuals who went into forbearance on auto loans (blue) and mortgages (gold). Uptake was most brisk during April and May, when the CARES Act was rolled out and when the unemployment rate was rising sharply. In the shaded bars, we show the number of individuals who exited forbearance that month after having previously been in forbearance. A larger number of individuals have exited auto loan forbearance compared to mortgages; probably since the CARES Act provides for a long-duration, six-month (renewable to twelve-month) forbearance on mortgages, while the auto loan forbearance durations may vary by lender and are not legally required. The lines show the number of individuals in forbearance; there were 8 million individuals with an auto loan in forbearance in June, while the mortgage forbearance rate peaked in May, when about 5 million mortgage borrowers were in forbearance (note, however, that auto loans are somewhat more prevalent than mortgages).

Following Borrowers through Forbearance

Mortgage forbearance rates differed by mortgage type, as not all were covered under the CARES Act and there is substantial variation in the types of borrowers. In September 2020, 4.2 percent of Government Sponsored Entity mortgages (including Fannie Mae and Freddie Mac) were reported as in forbearance in our data, while the rate was 10.6 percent for Ginnie Mae mortgages (including Federal Housing Administration and Veterans Affairs). Among the rest of the first mortgages, including portfolio and non-agency MBS, the rate was 4.2 percent.

What type of borrowers relied on forbearance?

We find common traits among individuals who enrolled in mortgage and auto loan forbearance in the wake of the crisis. First, forborne borrowers were more likely to have lower credit scores in March – in fact, the average credit scores of these borrowers was about 40 points lower than non-forbearance participants for both loan types. The average score among auto borrowers in forbearance was 652, compared to the non-participant average of 693; for mortgage borrowers in forbearance the average score was 708, compared to the non-participant score of 754. Second, mortgage and auto loan forbearance participants had higher balances than those not. For both mortgage and auto loan forbearances, the average remaining balance was approximately 30 percent higher than those who have not taken up forbearances.

Next, we look at the performance (and credit report treatment) of loans in forbearance, shown below as the share of balances 30-89 days past due for both those who enrolled in forbearance by May 2020 (solid line) and those who have not taken up forbearance since the pandemic began (dotted line). We use the early delinquency rates as a proxy for loans that have transitioned into delinquency, and to not capture those that had already defaulted before the pandemic.

Following Borrowers through Forbearance

For both auto loans and mortgages, troubled borrowers were far more likely to opt in to forbearances, as evidenced by the higher delinquency rates of participants three months prior to the first forbearance month when we compare the two groups (left hand side of the chart). But most obvious on the chart are the sharp reductions in the share of balances that were 30-89 days past due once the forbearance began. Many lenders have marked forbearance participants who were previously delinquent as current as these borrowers had no payment due for the month. On average, delinquent borrowers whose loans were converted to “current” upon entry into forbearance saw an average 48 point increase in their credit scores (here, Equifax Risk Score 3.0). In contrast, the average credit score of borrowers who were current before the forbearance was unchanged.

The way consumers are weathering this recession has been an important policy discussion, particularly as the cash transfers to households provided by the CARES Act have ended and the forbearances are set to expire. How households will endure the “fiscal cliff” is of significant interest, and we will closely monitor the ability of borrowers to maintain debt payments, especially among borrowers rolling off forbearance as the COVID-19 crisis evolves.

Chart data

Andrew F. Haughwout

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

Donghoon Lee

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

Joelle Scally

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

Wilbert van der Klaauw
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, “Following Borrowers through Forbearance,” Federal Reserve Bank of New York Liberty Street Economics, November 17, 2020, https://libertystreeteconomics.newyorkfed.org/2020/11/following-borrower....




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.

Debt Relief and the CARES Act: Which Borrowers Benefit the Most?

Published by Anonymous (not verified) on Tue, 18/08/2020 - 9:00pm in

Rajashri Chakrabarti, Andrew Haughwout, Donghoon Lee, William Nober, Joelle Scally, and Wilbert van der Klaauw

 Which Borrowers Benefit the Most?

COVID-19 and associated social distancing measures have had major labor market ramifications, with massive job losses and furloughs. Millions of people have filed jobless claims since mid-March—6.9 million in the week of March 28 alone. These developments will surely lead to financial hardship for millions of Americans, especially those who hold outstanding debts while facing diminishing or disappearing wages. The CARES Act, passed by Congress on April 2, 2020, provided $2.2 trillion in disaster relief to combat the economic impacts of COVID-19. Among other measures, it included mortgage and student debt relief measures to alleviate the cash flow problems of borrowers. In this post, we examine who could benefit most (and by how much) from various debt relief provisions under the CARES Act.

Data and Definitions

In addition to direct stimulus to individuals and corporations, the CARES Act provides for debt forbearance (that is, a temporary break from debt service payments) for various types of loans. FHA- and GSE-backed mortgages are eligible for a 180-day forbearance period, which can be extended to 360 days, but the borrower needs to contact the mortgage servicer to request forbearance. There was also a moratorium on foreclosure for 60 days after March 18. Federal student debt borrowers can defer payments until September 30, with interest waived. This forbearance is administrative and does not have to be negotiated. The Act also suspends involuntary collections, which includes wage garnishment and the reduction of tax refunds or other federal benefits, for qualifying federal student debt borrowers who are in default. While private student debt makes up a small share (approximately 8 percent) of total outstanding student debt, our data do not enable us to differentiate between federal and private student debt. The small subset of the student debt borrowers who have only private student loans will not be eligible for CARES Act forbearance relief. For simplicity, we will consider all student debt borrowers as being eligible for student debt forbearance in this post.

To understand who may benefit (and by how much) from the mortgage and student debt relief proposed, we draw on the New York Fed’s Consumer Credit Panel—an anonymized, nationally representative sample of Equifax credit report data. Our data set for this post covers a representative 1 percent sample of the nation’s adults with credit records, showing payments, balances, and delinquencies for various types of debt, including student loans, mortgages, auto loans, and credit cards. We focus on mortgage and student debt in this post because the relief under the CARES Act pertained to these two kinds of consumer debt.

To understand who the potential beneficiaries of debt relief are, we examine differences in forbearance relief across income, age and racial lines. Specifically, we split zip codes into equal-population quartiles of median household income (pre-tax); we refer to the bottom quartile as “low income,” (with median income below $46,310) the two middle quartiles as “middle income,” and the top quartile as “high income” (with median income above $78,303). We also look at zip codes that are “majority Black,” “majority Hispanic,” “majority white,” and “mixed.” We define majority Black zip codes (neighborhoods) as those in which Black residents make up at least 50 percent of the population, and define majority Hispanic and majority white zip codes (neighborhoods) similarly. We group all other neighborhoods together into a fourth category, “mixed” neighborhoods. For all income and race data, we use the 2014-18 Five-Year American Community Survey. We investigate the extent of mortgage and student debt relief faced by each of these neighborhoods: low income, middle income, high income, majority Black, majority Hispanic, majority white, and mixed.

At the end of December 2019, the majority of borrowers (63 percent) in our sample have neither mortgage nor student debt, but 21 percent have a mortgage but no student debt and 12 percent have student debt but no mortgage. Only 4 percent of adults have both mortgage and student debt. The median student debt borrower is 34 years old while the median age of mortgagors is 51. Thus, while the student debt relief will potentially benefit younger borrowers, the mortgage relief will potentially benefit relatively older borrowers.

Who Can Benefit from CARES Act Debt Relief?

Borrowers who have student debt or mortgage debt (and hence may qualify for CARES Act debt moratoria) fall into three groups: those with student debt but no mortgage, those with mortgage but no student debt, and those with both types of debt. In the table below, we investigate what share of the adult (above 18) population in each type of neighborhood has student debt but no mortgage (column 1), mortgage but no student debt (column 2), and both mortgage and student debt (column 3), and hence will potentially be eligible for corresponding student debt and/or mortgage debt relief. Differentiating across neighborhoods by income, we find in column 1 that similar shares of the adult population will potentially be eligible for assistance from only the student debt relief provisions of the CARES Act across the three neighborhoods (18 percent), but a markedly higher share (more than double) can be eligible for mortgage relief in the high income neighborhoods relative to low income neighborhoods (column 2). The share of the adult population that may benefit from only mortgage relief is also considerably larger in middle income neighborhoods (1.6 times) than in low income neighborhoods. Column 3 reveals that the share of adult population respectively in high and middle income neighborhoods that can benefit from both the CARES Act mortgage and student debt relief is double the corresponding share in low income neighborhoods.

Differentiating by race, column 1 shows that a significantly larger share (20 percent) of the adult population in majority Black neighborhoods can be eligible for assistance from only the student debt relief provisions of the CARES Act compared to such shares in the majority Hispanic, majority white, and mixed neighborhoods. In contrast, columns 2 and 3 find that a substantially larger share in majority white neighborhoods will be potentially eligible for only mortgage relief or both mortgage and student debt relief compared to the shares in majority Black, majority Hispanic, and mixed neighborhoods.

 Which Borrowers Benefit the Most?

Is There Heterogeneity in the Expected Benefit from the CARES Act Student Debt Forbearance?

To further understand who may benefit and the extent of the potential cash flow assistance (driven by funds released by deferral of payments), we look at a neighborhood type in the table below and examine what share of the adult population in that neighborhood will be eligible for any student debt assistance and how much assistance they may receive based on their debt profile at the end of 2019. Differentiating by income, we find in the first column that a slightly higher share of the adult population in high and middle income neighborhoods can benefit from student debt relief than in the low income neighborhood. Unlike column 1 of the first table in this post, this column accounts for any student debt relief, regardless of whether the borrower holds both mortgage and student debt or holds student debt but no mortgage debt. The higher shares in this table (in contrast to the earlier table) are driven by increased incidence of borrowers who hold both student and mortgage debt in the high and middle income neighborhoods.

 Which Borrowers Benefit the Most?

Turning to the amount of potential forbearance, we find that the median scheduled monthly payments per borrower (those eligible for forbearance) in low income neighborhoods are markedly smaller than those in high income neighborhoods; at least half of the student loan borrowers in low income neighborhoods had a scheduled payment of zero before the onset of the pandemic. These may be due to a number of factors: smaller loan sizes in these neighborhoods, larger incidence of in-school deferment, or higher participation in income-driven repayment programs in these neighborhoods. In column 4, we find that the mean scheduled payment per adult (and hence the potential assistance per adult) in high income neighborhoods is more than double that in low income neighborhoods. Annualizing the payments and comparing mean scheduled payment to the median household income of the zip code the person lives in, we find that the relief is actually a higher share of median income in these low income neighborhoods, despite the smaller forbearance amount (column 5).

By race, we continue to find that majority Black zip codes have markedly higher concentrations of student debt borrowers relative to the other neighborhoods. 23 percent of the adult population of majority Black neighborhoods is eligible for student debt relief versus 14 percent in majority Hispanic and 17 percent in majority white and mixed neighborhoods. However, as in the case of low income neighborhoods, more than 50 percent of borrowers in majority Black zip codes have no regular monthly scheduled payment, and thus would not benefit from forbearance. We find in column 3 that the mean scheduled payment per borrower is higher in majority white neighborhoods and significantly lower in majority Black and majority Hispanic neighborhoods. In column 4, we find that the mean scheduled payment per adult is broadly similar across majority white, majority Black and mixed neighborhoods, while it is perceptibly lower in Hispanic neighborhoods. The difference in patterns between columns 3 and 4 is driven by the fact that majority white neighborhoods are considerably more populous than majority Black neighborhoods (column 4 of the first table in this post). Interestingly, we once again find in the last column that the potential forbearance amount will constitute a higher share of median household income in majority Black neighborhoods than in other neighborhoods. In summary, we find that larger shares of borrowers from majority Black neighborhoods can benefit from the student debt relief provision, although the expected per-borrower relief to these communities is smaller. Regardless, this relief will address a higher debt burden (as share of income) in these neighborhoods.



Understanding Heterogeneity in the CARES Act Mortgage Debt Forbearance Relief

We can repeat this analysis for mortgage debt. Remember, not all mortgages are FHA or GSE-backed and hence eligible for forbearance. The table below shows that the highest concentrations are in majority white and higher-income zip codes, as qualifying for a mortgage requires a relatively high credit score and steady stream of income. Mortgagors in high income zip codes also pay much more per month than those in other areas, indicating higher home value and mortgage balance on average. We find from column 3 that the monthly scheduled payment of mortgagors (and hence the potential forbearance amount per mortgagor) is higher for those from high income, mixed, and majority white neighborhoods, and smallest for those from low income and majority Black neighborhoods. Looking at mean scheduled payment per adult in the various neighborhoods, the indicator of average per-capita forbearance dollars to a neighborhood, once again we find that high income, majority white, and mixed neighborhoods can expect higher mortgage forbearance relief, while this relief is lowest for low income, majority Black, and majority Hispanic neighborhoods (column 4). Nevertheless, turning to the mean payment as a share of median income in the neighborhood, we find that this relief amount again constitutes higher relative debt burdens in low income, majority Black, and majority Hispanic neighborhoods, largely because of lower median income in these neighborhoods.

 Which Borrowers Benefit the Most?

To summarize, we have investigated who may benefit (and the expected forbearance amounts) from the various debt relief provisions in the CARES Act. We find that while student debt relief may be expected to reach a larger share of borrowers in majority Black neighborhoods, the dollar value of expected student debt relief per borrower will be perceptibly less in low income, majority Black, and majority Hispanic neighborhoods. Unlike student debt relief, mortgage relief may be concentrated in high income and majority white neighborhoods, both in terms of dollar amounts and share of borrowers that will be potentially assisted. It is worth emphasizing that in this post we have outlined who may benefit from the mortgage and student debt relief provisions of the CARES Act. In other words, we have focused on the supply of this relief to different neighborhoods. Who will actually benefit and the amount of relief obtained will be determined by a combination of supply and demand factors. Since, low income and majority minority neighborhoods have been affected more negatively by this pandemic, residents in these neighborhoods may have the highest take-up rate. Moreover, mortgage benefits are not automatic; mortgagors must actively seek out these benefits by contacting servicers and proving financial hardship. Thus, ultimately, who actually benefits and by how much will be determined by a combination of factors, a topic we will continue to study. This post starts the conversation by investigating the potential beneficiaries and the potential reach (in dollar terms) of the forbearance programs.

Rajashri Chakrabarti

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

Andrew Haughwout

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

Donghoon Lee

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

Joelle Scally

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

Wilbert van der Klaauw

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

How to cite this post:

Rajashri Chakrabarti, Andrew Haughwout, Donghoon Lee, William Nober, Joelle Scally, and Wilbert van der Klaauw. “Debt Relief and the CARES Act: Which Borrowers Benefit the Most?" August 18, 2020, https://libertystreeteconomics.typepad.com/libertystreetecontest/2020/08....

Additional heterogeneity posts on Liberty Street Economics:

Heterogeneity: A Multi-Part 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.

Are Financially Distressed Areas More Affected by COVID-19?

Published by Anonymous (not verified) on Mon, 17/08/2020 - 9:00pm in

Rajashri Chakrabarti, William Nober, and Maxim Pinkovskiy

Editor’s note: When this post was first published, the columns in the second table were mislabeled; the table has been corrected. (August 19, 9:30 a.m.)

Are Financially Distressed Areas More Affected by COVID-19?

Building upon our earlier Liberty Street Economics post, we continue to analyze the heterogeneity of COVID-19 incidence. We previously found that majority-minority areas, low-income areas, and areas with higher population density were more affected by COVID-19. The objective of this post is to understand any differences in COVID-19 incidence by areas of financial vulnerability. Are areas that are more financially distressed affected by COVID-19 to a greater extent than other areas? If so, this would not only further adversely affect the financial well-being of the individuals in these areas, but also the local economy. This post is the first in a three-part series looking at heterogeneity in the credit market as it pertains to COVID-19 incidence and CARES Act debt relief.

We use county-level data, on numbers of cases and deaths, compiled by the New York Times (NYT) and the New York City Department of Health (Department of Health) for our analysis. The New York Times compiles a daily series of confirmed cases and deaths for almost every county in the United States. Its data set aggregates New York City, which consists of five counties, into a single entity. To get a breakdown of deaths and cases by New York City’s boroughs, we use data from the Department of Health.

Because race and income data for affected individuals are not available in all states, we match our county-level COVID-19 data with county-level data on race, median household income, urban status, and population density from the 2014-18 five-year American Community Survey (ACS). We define percent minority as the percentage of people in a county that is Hispanic or non-Hispanic Black. We define majority-minority counties as those in which at least half the population is Hispanic or non-Hispanic Black. We split counties into equal-population quartiles of median household income; we refer to the counties that fall in the bottom quartile as “low-income” counties.

For measures of financial health, we use the New York Fed’s Consumer Credit Panel (CCP), a nationally representative sample of Equifax credit report data. Our data set for this analysis includes roughly 1 percent of the nation’s adults with credit records in anonymized form. We see their payments, balances, and delinquencies for various types of debt, including 1) auto loans, 2) mortgages, 3) credit cards, and 4) student loans. For each county and for each of these four types of loans, we compute delinquency measures that constitute the share of borrowers who are 90+ days past due on that type of loan. In addition, we create an overall delinquency measure that captures the share of borrowers in a county who are 90+ days past due on any type of loan. For each of these five delinquency measures (overall, auto, mortgage, credit card, student loan) we define high-delinquency (High DQ) counties as those in the (population-weighted) top quartile of that delinquency rate (High DQ, High Auto DQ, High Mortgage DQ, High CC DQ, High SL DQ). All analysis uses data from the fourth quarter of 2019.

The chart below presents bin-scatter plots representing the descriptive relationship between overall delinquency and neighborhood characteristics: percent minority and median household income. We find that areas with a greater level of delinquency are also those that have lower household income and larger minority populations.

Are Financially Distressed Areas More Affected by COVID-19?

Next we investigate whether high delinquency counties faced different case and death rates due to COVID-19. We find that as of mid-July, high-delinquency counties had a mean of 4.3 cases/1,000, while other counties had 2.8 cases/1,000. Death rates have been higher as well: 16/100,000 in high-delinquency counties and 10/100,000 elsewhere. To investigate whether this relationship continues to hold within communities that are relatively homogeneous in terms of income, race, urban status, and population density—factors that have been found to correlate with COVID-19 incidence—we conduct a multivariate regression analysis.

We start by regressing case rate on a dummy for high delinquency. All regressions in this post control for fixed characteristics of states, observable and unobservable. We find in column 1 of the table below that more financially vulnerable counties have had more severe COVID-19 spreads. Specifically, High DQ counties have had 3.65 more cases/1,000 than others (column 1). Given the high correlation between delinquency and household income and minority status (in the chart above), it is likely that some of this association between high delinquency and COVID-19 incidence is explained by higher minority and low-income populations in these counties.

To investigate to what extent this relationship between delinquency and case rates is accounted for by inherent characteristics of these counties (low income, majority minority status, urban status, population density), we control for these variables in column 2. We can explain some of the relationship between High DQ and case rate by the demographic factors (column 2), but even after including these variables, we find that High DQ counties still have 0.59 more cases/1,000 than counties that are not High&nbspDQ. High-delinquency counties also have more deaths after controlling for the same factors (column 3): four more per 100,000 than other counties. This analysis suggests that even if we look within low income or majority-minority or dense areas, places with higher delinquency also suffered higher COVID-19 incidence.

Next, we examine whether High DQ counties that are also low income faced a higher incidence of cases. In column 4 of the table below, we observe that high-delinquency counties that are also low-income have had a worse spread of COVID-19 than High DQ counties that are not low income—almost three more cases/1,000. In fact, increased virus spread in High DQ counties appears to be concentrated exclusively in those that are also low-income.

Are Financially Distressed Areas More Affected by COVID-19?

Next, in the table below, we examine relationships between delinquency in specific kinds of debt and COVID-19 spread. We find that counties that have high mortgage delinquency (High mortgage DQ) have higher COVID-19 incidence as captured by both case and death rates. Counties that have high student loan delinquency (High SL DQ) also have higher death rates. Counties with high auto loan delinquency (High Auto DQ) have higher case rates than those that do not have high auto loan delinquency. The High Auto DQ counties also have higher death rates, but this estimate is not statistically different from zero at conventional levels. It is worth noting that although High Mortgage DQ and High SL DQ areas were relatively adversely affected by COVID-19 incidence, and this is expected to further increase financial distress in these areas, the borrowers in these areas will potentially receive some relief from the CARES Act mortgage and student debt forbearance provisions which to some extent will ameliorate their increased financial distress. In contrast, the CARES Act does not include provisions for auto loan payment relief although these borrowers may be able to obtain some relief on a case-by-case basis by contacting the lenders. However, this relief is considerably more uncertain than CARES Act relief for student loans and mortgages. Consequently, the higher incidence of COVID-19 in High Auto DQ areas (relative to those that do not have high auto loan delinquency) may lead to larger subsequent increases in financial hardships in these areas.

Are Financially Distressed Areas More Affected by COVID-19?

What have we learned? We have seen that there is a strong relationship between COVID-19 cases and pre-COVID delinquency rates at the county level and this correlation cannot be easily explained by some known sources of heterogeneity in COVID-19, such as income, minority status, and population density. This suggests that the harms from COVID-19—the loss of life and health, the decline in employment, the destruction of businesses and the surge in medical expenses—will fall on counties particularly ill-suited to bearing them. The much higher per-capita case counts in places with high delinquency rates and low income portend a disproportionate financial impact on those who can least afford it. Why do we observe this relationship? While the precise mechanisms are beyond the scope of this work, volatility of income may be associated with both financial stress and higher risk of COVID-19, potentially because of greater reliance on essential work in such communities and corresponding difficulty of social distancing. Further research is needed to understand these factors.

Rajashri Chakrabarti<
Rajashri Chakrabarti is a senior economist in the Federal Reserve Bank of New York’s Research and Statistics Group.

William Nober was a former a senior research analyst in the Bank’s Research and Statistics Group.

Maxim Pinkovskiy
Maxim Pinkovskiy is a senior economist in the Bank’s Research and Statistics Group.

How to cite this post:

Rajashri Chakrabarti, William Nober, and Maxim Pinkovskiy, “Are Financially Distressed Areas More Affected by COVID-19?,” Federal Reserve Bank of New York Liberty Street Economics, August 17, 2020, https://libertystreeteconomics.newyorkfed.org/2020/08/are-financially-di....

Additional heterogeneity posts on Liberty Street Economics.

Heterogeneity: A Multi-Part 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.

Bitesize: The age evolution of first-time buyers

Published by Anonymous (not verified) on Tue, 11/08/2020 - 6:00pm in

Fergus Cumming and John Lewis Over the last 15 years house prices have increased and home-ownership rates have fallen. But while the *number* of first-time buyers (FTBs) has fallen – what happened to the average *age* of FTBs? Not very much… Using data on every UK mortgage from the FCA’s Product Sales Database, we pick … Continue reading Bitesize: The age evolution of first-time buyers →

Introduction to Heterogeneity Series III: Credit Market Outcomes

Published by Anonymous (not verified) on Tue, 07/07/2020 - 10:00pm in

Rajashri Chakrabarti

 Credit Market Outcomes

Average economic outcomes serve as important indicators of the overall state of the economy. However, they mask a lot of underlying variability in how people experience the economy across geography, or by race, income, age, or other attributes. Following our series on heterogeneity broadly in October 2019 and in labor market outcomes in March 2020, we now turn our focus to further documenting heterogeneity in the credit market. While we have written about credit market heterogeneity before, this series integrates insights on disparities in outcomes in various parts of the credit market. The analysis includes a look at differing homeownership rates across populations, varying exposure to foreclosures and evictions, and uneven student loan burdens and repayment behaviors. It also covers heterogeneous effects of policies by comparing financial health outcomes for those with access to public tuition subsidies and Medicare versus those not eligible. The findings underscore that a measure of the average, particularly relating to policy impact, is far from complete. Rather, a sharper picture of the diverse effects is essential to understanding the efficacy of policy.

Here is a brief look at each post in the series:

Inequality in U.S. Homeownership Rates by Race and Ethnicity1. Inequality in U.S. Homeownership Rates by Race and Ethnicity

Andrew Haughwout, Donghoon Lee, Joelle Scally, and Wilbert van der Klaauw investigate racial gaps in homeownership rates and, importantly, explore the reasons behind these differences. They find:

  • The Black-white and Black-Hispanic homeownership gaps widened after the Great Recession, markedly more so after 2015.
  • The foreclosure crisis disproportionately affected areas with majority Black or Hispanic populations.
  • Explanations for the homeownership gap may include differential effects of tightening underwriting standards across areas with a majority Black or Hispanic population versus those with a majority white population, differences in labor market outcomes across these areas during and following the Great Recession, and larger incidence of student debt in these areas.

Who Has Been Evicted and Why?2. Who Has Been Evicted and Why?

Andrew Haughwout, Haoyang Liu, and Xiaohan Zhang explore the reasons behind evictions, who is more likely to be evicted, and the possibility of owning a home and gaining access to credit following evictions. Their findings reveal:

  • Large shares of low-income households have been evicted.
  • Income or job loss and change in building ownership are important reasons behind evictions.
  • Renters with a past eviction history are less likely to have access to credit cards and auto loans.

Measuring Racial Disparities in Higher Education and Student Debt Outcomes3. Measuring Racial Disparities in Higher Education and Student Debt Outcomes

Rajashri Chakrabarti, William Nober, and Wilbert van der Klaauw investigate whether (and how) differences in college attendance rates and types of college attended may lead to student debt borrowing and default. The key takeaways include:

  • There are noticeable disparities in college attendance and graduation rates between majority white, majority Black, and majority Hispanic neighborhoods, with graduation rates the lowest in majority Black neighborhoods.
  • Students from majority Black neighborhoods are more likely to hold student debt and in larger amounts.
  • Borrowers from majority Black neighborhoods are more likely to default, and this pattern is more prominent for borrowers from two-year colleges than those from four-year colleges.

Do College Tuition Subsidies Boost Spending and Reduce Debt? Impacts by Income and Race4. Do College Tuition Subsidies Boost Spending and Reduce Debt? Impacts by Income and Race

Rajashri Chakrabarti, William Nober, and Wilbert van der Klaauw investigate the effect of tuition subsidies, specifically merit-based aid, on other debt and consumption outcomes. The main findings include:

  • Cohorts eligible for these tuition subsidies have higher credit card balances and higher delinquencies in their early-to-mid-twenties. These patterns are more evident for borrowers from low-income and predominantly Black neighborhoods.
  • Eligible cohorts are more likely to own cars (as captured by auto debt originations) in their early-to-mid-twenties. This pattern is more prominent for borrowers from low-income and predominantly Black neighborhoods.
  • The patterns indicate substitution away from student debt (as net tuition declines) to other forms of consumer debt for eligible cohorts in college-going ages, a pattern more prominent for borrowers from low-income and Black neighborhoods.

Medicare and Financial Health across the United States5. Medicare and Financial Health across the United States

Paul Goldsmith-Pinkham, Maxim Pinkovskiy, and Jacob Wallace investigate the effect of access to health insurance programs, as captured by Medicare eligibility, on financial health of individuals. They find:

  • Medicare eligibility markedly improves financial health, as captured by declines in debt in collections.
  • Access to Medicare drastically reduces geographic disparities in financial health.
  • The improvements in financial health are most evident in areas with a high share of Black, low-income, and disabled residents and in areas with for-profit hospitals.

As these posts will demonstrate in greater detail tomorrow, the average outcome doesn’t provide a full picture of credit market outcomes. There is considerable heterogeneity in different segments of the credit market both in terms of outcomes, as well as the in the effects of specific policies. Outcomes vary by a range of factors, such as differences in race, income, age, and geography. We will continue to study and write about the importance of heterogeneity in the credit market and other segments of the economy.

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

How to cite this post:

Rajashri Chakrabarti, “Introduction to Heterogeneity Series III: Credit Market Outcomes,” Federal Reserve Bank of New York Liberty Street Economics, July 7, 2020, https://libertystreeteconomics.newyorkfed.org/2020/06/introduction-to-he....

Related Reading:

Series One

Introduction to Heterogeneity: Understanding Causes and Implications of Various Inequalities

Series Two
Introduction to Heterogeneity: Labor Market Outcomes




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 do lenders adjust their property valuations after extreme weather events?

Published by Anonymous (not verified) on Mon, 06/07/2020 - 6:00pm in

Nicola Garbarino and Benjamin Guin

Policymakers have put forward proposals to ensure that banks do not underestimate long-term risks from climate change. To examine how lenders account for extreme weather, we compare matched repeat mortgage and property transactions around a severe flood event in England in 2013-14. We find that lender valuations do not ‘mark-to-market’ against local price declines. As a result valuations are biased upwards. We also show that lenders do not offset this valuation bias by adjusting interest rates or loan amounts. Overall, these results suggest that lenders do not track closely the impact of extreme weather ex-post.

Properties across the world are exposed to long-term risks of extreme weather events such as hurricanes, flooding and fires. Policymakers are increasingly concerned that financial institutions may be underestimating their exposure to climate-related risks. Yet, assessing their behaviour often requires assumptions about uncertain climate change scenarios.

In a recently published Staff Working Paper, we exploit a severe flood event to examine how lenders adjust their valuation of housing collateral using available information. The analysis focuses on the effect of a severe flooding event in England that resulted in over £1.3 billion in damages. In the winter of 2013–14, regions in the Thames catchment area and the east coast of England were hit by a combination of inland (river and surface) and coastal floods. By focusing on an actual event, rather than scenarios, we set a low bar for how lenders take climate-related risks into account.

We compare changes in lenders’ valuations (used for mortgage refinancing) against changes in sales prices for actual property transactions. If lenders ‘mark-to-market’, using available price information available at a local level, valuations should change in line with transaction prices. Instead, we find that valuations do not adjust to price declines in neighbourhoods that experience prolonged flooding. As a result, valuations are biased upwards. Also loan amounts and interest rates for mortgage refinancing remain unchanged. Increases in estimated flood risk (as opposed to actual flooding) do not appear to lead to a fall in property prices.

Relative to unaffected properties in the same geographic area (measured by the postcode district), properties in postcode units that experience prolonged flooding experience significant decreases in sales prices between 2.6% and 4.2%. Our best estimate points to a decrease of 3.3%. By contrast, these declines are not reflected in valuations for mortgage refinancing. We estimate a positive valuation bias between 2.9% and 3.2%. The net effect almost perfectly offsetting the decline in sales prices with a best estimate suggesting only a -0.4% change in valuations. In other words, valuations for refinancing for flooded properties are roughly aligned with sales prices of non-flooded properties in the same area.

Chart 1: Relative change in transaction prices and property valuations following long flood

A fall in the collateral value would result in an increase in the loan-to-value ratio. In the UK, it is the most important factor for mortgage rates and amounts. Additionally, we would expect decreases in property prices to be reflected in valuations, and result in higher interest rates and/or lower loan amounts for refinancing transactions. But since refinancing valuations appear to be unaffected by flooding or increases in flood risk, the effects on mortgage rates and loan amounts are limited.

Our results suggest that little local knowledge about flood risk is employed by lenders in valuing house prices following a flood. Lenders instead rely on house prices indexes that do not capture variation in flooding within neighbourhoods. If lenders used all available local information, changes in property valuations used for refinancing should be closely correlated with changes in actual sales prices. Chart 2 shows scatterplots of the change in property valuation vs sales price at different levels of aggregation (postcode unit, local authority, and region). The correlation between sales prices and valuations increases is low at the postcode unit level. However, it increases as we aggregate data for wider geographic units.

Chart 2: Change in property valuations versus sales price at different levels of aggregation

(a) Most granular level of aggregation: Postcode unit regions

(b) Intermediate level of granularity for aggregation: Local authority

(c) Least granular level aggregation: NUTS1 regions

Climate-related valuation bias can lead to distortions in lending quantities by relaxing credit constraints. A substantial reduction in the value of the collateral available for refinancing could force marginal borrowers to pay higher rates, use savings to make up for the difference, or potentially default on their mortgage payments. Biased collateral valuation can also sustain bank lending via their risk-based regulatory capital ratios, where collateral values are an important factor in setting regulatory requirements.

Our results indicate that the pricing of risks from extreme weather events in mortgage lending has so far been limited.

Nicola Garbarino and Benjamin Guin work in the Bank’s Policy Strategy and Implementation Prudential Policy 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.