The financial fragility of the bottom 50% of US households.

1. about the financial health of the bottom 50% of U.S. households
MODERN TIMES
THE FINANCIAL FRAGILITY OF HOUSEHOLDS
READ Enterprises
December 2019
Authors
Alberto Furger & Oliver Olbort
Co-Founders of READ Enterprises

2. Foreword
As we approach the end of the year, the U.S. economy is in relatively good shape, but
slowing global growth forecasts are clouding the outlook. Meanwhile, phase one of
the trade deal between the U.S. and China, which is expected to be signed off in early
2020, will see an export increase of U.S. products to China over the next two years
with Beijing having agreed to purchase American-made goods worth $200B.
While growth will slow, market participants expect that a recession will be avoided,
absent of unforeseen shocks in the market. On the monetary front, the Federal
Reserve has cut interest rates three times this year and can be expected to make
further cuts to stimulate economic growth in the year ahead.
Consumer spending accounts for approximately two-thirds of U.S. economic activity.
The 50-year low unemployment rate and overall strong consumer sentiment support
the belief of a reasonably good outlook for consumer spending in 2020.
That said, uncertainty such as geopolitical conflicts increased in 2019 and made the
economy vulnerable to unexpected shocks which could cause a broader disruption
of economic activity. Uncertainty also means that it is reasonable to expect a more
defensive investment climate, particularly for industries that could be exposed to
policy changes.
While we will not speculate on the likelihood of such ‘external’ scenarios or the
impact on U.S. dollar developments that could lead to an increase in dollar-
denominated debt defaults in emerging markets, this report takes a more in-depth
look at the state of affairs of consumer financial health in the U.S.
Based on our research, we have reasons to believe that a large proportion of U.S.
consumers are financially 'mud in the neck,' and only slight deteriorations in
economic activities could have significant financial implications to the bottom 50% of
the population. In our view, the potential ripple effect poses a systematic risk to the
U.S. economy and beyond and is, in our view, largely underestimated and
disregarded.
While consumer sentiment is currently still sending strong signals of confidence, we
believe it does not need much, and the situation can change very quickly. With the
presidential elections coming up in late 2020, any change in consumer optimism
earlier in the year might have political consequences as from a historical perspective,
presidents tend to win re-elections when consumer confidence is high.
I take the opportunity to thank everyone that has been supporting READ in 2019, and
I wish you and your families and friends a joyful festive season.
Alberto Furger
Co-Founder & CEO

3. Introduction
READ has been focusing on understanding and analyzing the financial health of
households since its inception in 2017.
TransUnion has recently published its 2020 consumer credit forecast, which largely
provides a positive outlook on the expected credit activity of U.S. consumers.
While we acknowledge the potential for further consumer credit growth, we are
concerned about an increasing systemic fragility given the decrease in financial
health of a growing portion of the population.
In this report, we aim to share our view and provide supporting indicators of why we
believe that the bottom 50% of the population's sensitivity to market shocks is
growing.
On the other hand, we would like to share opportunities we believe could strengthen
the financial system and decrease financial vulnerabilities over time.
Living through our digital age, we have access to data points inconceivable only two
decades ago. As such, we not only need to discuss data collection fundamentally but
also how to complement the economic metrics we have applied so far.
We hope this report will stimulate the discussion around the financial health of
households in 2020 and beyond.

4. The illiquid assets of the bottom 50%
Figure 1
Figure 2
Figure 2: The top asset holdings are real estate with 53% and consumer durables
with 19%, which compares to liabilities of 41% in mortgage debt and 29% in
consumer debt.
Nominal net assets, therefore, stand at 2%; however, if we consider mortgage loan-
to-value restrictions (usually set at a maximum of 85%), the adjusted asset-to-debt
ratio becomes negative (-6%), which means that no "liquidity reserve" is available, or
with other words, consumers cannot further leverage their asset base.
Figure 1: The overall debt-to-asset ratio currently stands at roughly 73%. At first
sight, the declining tendency might suggest to be driven by deleveraging of home
mortgages. However, we have reason to assume that the decrease is rather driven by
increasing housing prices (Figure 4). Another indicator which speaks against a
deleverage is the elevated level of consumer-debt-to-consumer-durables ratio. In the
coming sections we will further elaborate why the declining debt-to asset ratio is not
a sign of improving financial health.

5. Figure 3
Figure 3: Looking at the remaining assets and liabilities, we find pension entitlements
with 10% at the top, followed by "other assets" with 9%. Corporate equity accounts
for 8% and private business for 3%.
While pension assets, in theory, could be used to offset debt, the purpose is
obviously to secure retirement income, and therefore, in our view, should not be
considered as collateral. Similar to pension assets, we believe that private business
assets are income-relevant and should not be collateralized for personal
consumption purposes.
As such, in our thinking, we should eliminate 13% of assets since they harm the
current or future income situation of the household. In addition, "other assets" have
declined, and it is difficult to assess how liquid these assets are.
As such, we remain with 8% to 17% of positive adjusted net assets in this asset
category versus negative adjusted net assets of -6% for real estate and consumer
durables (see figure 2) and -3% for "other loans", resulting in a real “liquidity cushion”
of between -1% to 8%.
Figure 4: Housing prices increased in total by 14% from their peak in 2006. For every
1% increase in housing prices, the adjusted net assets for the bottom 50% of
households improved by 0.53%. In comparison, for the 50th to 90th percentile of
households, that hold more than three times more real estate assets, the same
housing price increase resulted in an adjusted net asset adjustment of only 0.33%.

6. In other words, the bottom 50% of households' adjusted net assets are more elastic
to price changes compared to the 50th to 90th percentile. The reason for the
increased sensitivity to price volatility is two-fold; first, real estate assets have a
higher weighting in the asset mix of the bottom 50%, and the bottom 50% has a
higher loan-to-value ratio, i.e., their gearing is higher.
The Wilshire 5000 Total Market Full Cap Index almost tripled since its height in 2007.
Any 1% index movement has a 0.08% impact on the adjusted net assets of both the
bottom 50% as well as the 50th to 90th percentile of households.
While the upper 50th to 90th percentile holds in absolute terms more than five times
more assets in this asset category compared to the bottom 50%, the impact is
relatively speaking the same, if we consider that little gearing is applied to this asset
class.
Figure 4
Figure 5: We are concerned about the sharp increase in additional liabilities over the
last three years of the bottom 50% of the population, which now roughly holds one-
third of the total liabilities, especially given the high sensitivity to price fluctuations,
and therefore, the high impact on adjusted net asset values. Besides, asset
valuations for both real estate assets and listed equity have now exceeded the 2007
levels.
If we take the analogy of Icarus from Greek mythology and the idiom "do not fly to
close to the sun," we know that the higher they rise, the deeper they fall. In very
recent history, we vividly recall what the 2008/9 financial crisis meant for millions of
Americans, the economy in the U.S., and the global ripple effect it caused.
As we go through economic cycles, "asset bubbles" are a "natural" phenomena, and
are usually fueled by over-leveraging as the root cause. Eventually, critical threshold

7. levels are reached, and it comes to a correction. The question is not if, but when,
these threshold levels are reached, and more importantly, who will pay the price for
the correction.
We believe, given our understanding and interpretation of available data, that the
impact of market corrections on the bottom 50% of U.S. households will be
disproportionate.
Figure 5
Figure 6/7: During the financial crisis, we have seen rapid asset devaluations. The
time of deleveraging stretched over eight years for the bottom 50% and has been
more than equalized in the past three years. In contrast, the assets and liabilities for
the 50th to 90th percentile moved synchronously.
Figure 6

8. Conclusion
For the bottom 50% of the population, in particular, the adjusted net assets, as we
define them in this report, and therefore the embedded liquidity cushion, wears thin
and makes this socio-economic group particularly perceptible to market volatility.
Furthermore, we see an acceleration in uncollateralized credit exposure. While on
the one hand, “quick money” supports short-term liquidity, it also increases overall
debt levels and therefore reinforces the financial fragility of the bottom 50% of
households.
Figure 7

9. We recognize limitations of comparing the bottom 50% income group with the
bottom 50% net worth group. While we can draw inference from the net income
about the net worth, the reverse is unlikely to be the case.
Figure 8: Based on a recent financial health survey, 85% of people with household
incomes of less than $60,000 are either financially “just” coping or vulnerable, i.e.
they are on the brink of serious cash flow constraints. Given that the median income
in the U.S. is $63,000 and for our further argumentation, we assume that 85% of the
bottom 50% income group substantially overlaps with the bottom 50% wealth group.
The income and savings situation of the bottom 50%
Figure 8
Figure 9: The average spending propensity to consume suggests that households up
to the 5th decile of the income distribution are not able to save in the best case, and
are spending beyond their means in the worst case. Figure 9 also illustrates that the
majority of “asset” spending is allocated to shelter, durables, and vehicles.
Figure 9

10. Figure 10: The real median income has increased by approx. 8.6% in total over the
last decade, or 0.78% on average p.a. Our view regarding the financial fragility,
however, remains intact. Despite the increase in real incomes, we believe that the
gains have been more than off-set by negative savings rates and increased debt
levels. We argue that looking only at the purchasing power adjusted income increase
would paint a misleading picture, because in our view, we need to consider the
overall financial exposure, and in particular, debt exposure to determine financial
health.
Figure 10
Figure 11: The Census Bureau has redesigned its questionnaire since prior research
identified limitations related to health insurance content in the CPS ASEC. The
research suggested that improvements to the questionnaire would produce higher
quality estimates of health insurance coverage. “Without the adjustment, income in
2018 is significantly higher than all years shown before 2017. However, with the
adjustment, it is higher than all the pre-2017 years except 2007, 2000 and 1999”, CB
stated.
The adjustment has to be taken with a pinch of salt because the assumption requires
that the data improvements would have been identical in all years, which becomes
less likely to be accurate the further we move away from the date of the redesigned
questionnaire.
However, the takeaway is that the real median income might not have increased
substantially beyond the 2007 level, as it would seem that this amount might be
higher than initially anticipated.

11. Figure 11
Figure 12: Another perspective is to look at the bottom 40th and 50th percentiles,
which during the last decade have not seen any real median income improvements.
Figure 12

12. Figure 13
Figure 13: If we compare the average income with the spending distribution, we can
see that the bottom 30% (with median incomes below $37k p.a., see Figure 12) are
spending beyond their means. The question is, why? Either 1) their spending
behavior differs from the average, 2) the weightings of goods and service in their
consumer basket are different, or 3) higher interest rate spreads for lower credit
ratings explain the difference. This analysis goes beyond the scope of this report, but
the fact remains; the bottom 30% for sure, and possibly the bottom 50%, are either
not or only just covering their cost-of-living.
Conclusion
With the variations in spending patterns between different income percentiles, the
data suggests negative saving rates of the bottom 50% (Figure 14). On this basis, we
believe the overall financial standing for the bottom half of US households did not
improve.
Figure 14

13. The fastest-growing debt category is personal loans, and while it accounts only for
$300B out of $4T in total consumer debt, according to Experian, this debt category
has seen a whopping 11% year-on-year growth rate.
Personal loans are considered a last-ditch solution during financially challenging
times, and we believe it is an important indicator concerning the financial health of
households. In addition, and based on research conducted by LendingTree, we know
that over 60% of personal loans are used to pay for other personal debt.
Figure 15: We recognize that the Federal Fund Rates do not influence the overall
debt exposure of households and mostly follow the M2/M3 Money Stock
development. This correlation is insofar important that the price of money, i.e., the
interest rate is hardly relevant for debt expansion.
Interest rates, margins & delinquencies
Figure 15
Figure 16: The influence of the level of Federal Fund Rates on consumer loans and
mortgages shows a low correlation during a rate cut and a high correlation if rates
are increased. We believe that the probability of an interest rate cut on the level of
consumer loans is minimal. We note that the mortgage rate market is different, at
least for as long as housing prices continue to rise.

14. Figure 16
Figure 17: Collateralized debt mostly follows delinquency rates as well as underlying
asset prices. Uncollateralized (consumer) debt, however, does not. In fact, despite
mostly stable delinquency rates since 2016 on consumer loans, the net interest
margin of lenders has increased.
Figure 17
Conclusion
Based on our findings, we do not think that Federal interest rate cuts will be passed
on to uncollateralized consumer debt borrowers, neither proportionately, nor in real-
time. While, from a historical perspective, we face low Federal interest rate levels,
and despite standard economic indicators (unemployment, stock market, housing
prices) are showing a “rosy” picture, lenders seem to price in

15. higher default probabilities.
Figure 18: Also, the net interest margins (NIM) of U.S. commercial banks have
declined and are relatively low, although the NIM of U.S. banks is twice as high
compared to banks in APAC. Nevertheless, consumers might face resistance from
banks that would allow for much “discretionary” interest rate reduction. We have
reasons to believe that mainly the bottom 50% of U.S. households will not be able to
benefit from a Federal interest rate cuts given their consumer liability share.
Figure 18
Figure 19
Figure 19: The chart shows the high exposure of the bottom half of U.S. households
to consumer debt. In summary, in a scenario of reduced economic activity in 2020
and beyond, which would ultimately lead to higher unemployment rates, and in
combination with thin liquidity reserves, we fear that delinquency rates could quickly
balloon, triggering a "domino effect" that would further accelerate the downward
economic spiral.

16. In the past three years we have analyzed the credit scoring system in the U.S. and
conclude that the current model is both incomplete as well as inaccurate when it
comes to the assessment of the probability of default risk of borrowers, in particular,
the younger generation (Gen X & Gen Y).
Figure 20: We note that the current credit scoring system is de facto uncorrelated to
income groups. Given the substantial difference in adjusted net assets and,
therefore, liquidity levels, the current credit scoring distribution seems at the very
least disquieting.
Credit ratings
Figure 20
Instead, the correlation of credit scores is very much skewed towards increasing age
(see Figure 21), and to a lesser degree, to net worth (see Figure 22).
Prima facie, this makes sense given that income patterns (see Figure 24) and net
worth, both being a function of age, at least statistically speaking. However, the credit
score distribution (see Figure 20) suggests that well over 50% of low income earners
have a high credit score. That stands in stark contrast to the previously referred to
financial health survey which found only 15% of this income class are financially
healthy.
Figure 21 Figure 22

17. Figure 23 Figure 24
Based on the current system (see Figure 21), the bulk of the high credit scores are
distributed between the age of 50 to 70+ which represent 36% (see Figure 23) of the
population. This is no surprise given that the emphasis of the current credit scoring
system is on credit history. However it raises the question whether the aging of the
high credit scores contributes to the financial fragility of households.
On the one hand, households in the age groups 49 and below face higher interest
costs, although the fact that they are earning an active income and have lower risks
of mortality and disability.
On the other hand, households in the age group 50 and above have only limited or
no time left to earn an active income, and are more dependent on their asset base,
which makes them more vulnerable to market shocks.
The 2010 OECD publication "Pensions and the Crisis" states:
"For people near retirement, however, investment losses in private pension funds, public
pension reserves, and other savings may not be recouped. Even postponing their
retirement may allow them to offset only part of their loss. Declines in account balances in
private pensions in the U.S. were most significant for the 45-54-year-old age group,
ranging from a loss of around 18% for people with short tenures to 25% for more
extended periods of coverage. The degree to which the crisis affects current pensioners
depends on the composition of their old-age income."
The fact that over the past ten years, liabilities for the age group above 50 (Figure 25)
increased, gives reasons to concern. While in this report, we will not further analyze
older-age debt composition, we believe it is an important area to be carefully
observed. Given that the average spending propensity is negatively correlated to
income and net worth (the latter up to the 7th income percentile), the liability levels
of older households clearly point to an accelerating liquidity need in the past years.

18. Conclusion
The current credit scoring system is strongly skewed toward credit history. It,
therefore, favors the older demographic, making it difficult for younger generations
to obtain credit at reasonable interest rates.
The system also seems to have promoted increased borrowing of the age group 50+,
which could backfire during times of market corrections, given their relatively high
dependency on assets.
Besides, we note that the increased mortality and disability risk of the 50+ segment
seems not captured in their credit rating.
Figure 25

19. We believe that the bottom 50% of U.S. households on average is over-leveraged and
has not enough reserves to withstand even a slight economic downturn or market
shock. We furthermore expect that the liquidity constraints will accelerate in 2020,
leading to an increased likelihood of default, which could trigger an economic
downward spiral. While we do not speculate on the likelihood, degree or timing of an
economic slowdown (although market participants expect a moderate slowdown), we
note that if and when it should happen, we see a high probability that the bottom
50% will be affected more significantly than during the financial crisis of 2008/9. The
bottom 50% of U.S. households has increased their debt levels, has hardly any
liquidity reserves, and their real income just covers their cost-of-living in the best
case and is insufficient in the worst case.
In addition, the fundamental changes in the employment market are noticeable
already today: While technology has been increasing productivity, automation is
replacing an increasing number of workers and, therefore, jobs. It is estimated that
"freelancers” will make up for the majority of the workforce in the U.S. by 2027
(Figure 26).
Summary
Figure 26
According to research from PwC, robotics, for example, will replace 28% of the
current workforce while simultaneously create 35% new job opportunities. The
challenge is that the skills of the replaced workers do not match the skills required
for the new opportunities. We, therefore, believe it is important to broaden our
definition of “work” in order to counter-steer expected job losses due to automation.
Besides, we need to better price in negative and positive externalities that have been
largely disregarded up until today.

20. We know that in most nations, including the U.S., household consumption is the
largest GDP contributing factor (see Figure 27). We believe that if households are
financially healthy, we can assume there is a high chance that the economy overall is
healthy, too.
Figure 27
That said, up until now, the majority of economists have measured the importance of
households based on consumption. We believe it is essential to explore the macro-
economic impact of households beyond consumption as, in our view, many socio-
economic activities performed by households are not factored in appropriately.
We believe it is in everyone's best economic interest to ensure that the bottom 50%
of households become financially healthy. As such we see an opportunity to
complement the “consumption-based financing only” model with a “productivity-
based financing” solution, making it simpler for households to start micro-activities
and generate additional income.
“Give a man a fish and you feed him for a day; teach a man to
fish and you feed him for a lifetime.”

21. References
Business Insider:
https://markets.businessinsider.com/news/stocks/u-s-consumers-expected-to-maintain-strong-credit-activity-in-
2020-1028758080
Federal Reserve:
https://www.federalreserve.gov/releases/z1/dataviz/dfa/distribute/table/#quarter:119;series:Liabilities;demograp
hic:networth;population:1,3,5,7;units:levels
https://www.federalreserve.gov/econres/notes/feds-notes/are-income-and-credit-scores-highly-correlated-
20180813.htm
FRED St.Louis:
https://fred.stlouisfed.org/
DQYDJ (Don't Quit Your Day Job):
https://dqydj.com/average-median-top-household-income-percentiles/
https://dqydj.com/correlation-of-income-and-net-worth-america-2016/
Financial Health Pulse 2019:
https://finhealthnetwork.org/programs-and-events/u-s-financial-health-pulse/
Federal Reserve Bank of San Francisco:
https://www.frbsf.org/economic-research/publications/economic-letter/2015/june/income-redistribution-policy-
economic-stimulus/
Census:
https://www.census.gov/library/publications/2019/demo/p60-266.html
https://www.census.gov/library/stories/2019/09/us-median-household-income-not-significantly-different-from-
2017.html
Deloitte:
https://www2.deloitte.com/us/en/insights/economy/spotlight/economics-insights-analysis-08-2019.html
Bloomberg:
https://www.bloomberg.com/opinion/articles/2019-11-29/inequality-doesn-t-show-up-in-u-s-spending-statistics
CISION PR Newswire:
https://www.prnewswire.com/news-releases/lendingtree-analysis-reveals-how-personal-loan-purposes-vary-by-
states-and-credit-scores-300786802.html
Valuepenguin:
https://www.valuepenguin.com/average-credit-score
Wallethacks:
https://wallethacks.com/average-net-worth-by-age-americans/
Census Reporter:
https://censusreporter.org/profiles/01000us-united-states/
OECD:
https://www.oecd-ilibrary.org/docserver/9789264077072-5-en.pdf?
expires=1576612431&id=id&accname=guest&checksum=3249E2FA2E484DB8AE06E44C9A792538
Federal Reserve Bank of New York:
https://www.newyorkfed.org/medialibrary/interactives/householdcredit/data/pdf/HHDC_2019Q3.pdf
Upwork:
https://www.upwork.com/press/2017/10/17/freelancing-in-america-2017/
PWC:
https://www.pwc.com/hu/hu/kiadvanyok/assets/pdf/impact_of_automation_on_jobs.pdf

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About READ
READ Enterprises Holding LLC (“READ”) is a FinTech startup focusing on the financial
health of households. READ has developed a consumer credit rating methodology
and a peer-to-peer pension model.
READ is a Delaware-registered privately owned company. The READ name and logo
are trademarks of READ Enterprises Holding LLC. All rights reserved.