Reinforced Bank Climate Vulnerability: Evidence from Hurricanes * Yao Lu
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Reinforced Bank Climate Vulnerability: Evidence from
*
Hurricanes
Yao Lu
Cornell University
August, 2022
Preliminary. Please do not cite without consent.
Abstract
I study how risk-taking facilitated by delayed loss recognition contributes to banks’ vul-
nerability to hurricanes. Banks delay the recognition of long-term loan losses caused
by hurricanes, which leads to banks’ increased risk-taking and vulnerability. Specif-
ically, banks that delay more in loss recognition shift more lending to the riskier,
hurricane-affected regions and suffer from more losses and failures in the long term.
After experiencing more hurricanes, banks delay even more to increase risk-taking in
response to a new hurricane. In this vicious cycle, the increased vulnerability after past
hurricanes incentivizes banks to take on more risks after future hurricanes, reinforc-
ing banks’ vulnerability. This vicious cycle poses an increasing threat to the banking
system when hurricanes expand into broader areas, as hurricanes hitting previously
unexpected areas have a stronger effect on banks’ delay in loss recognition.
*I thank Philip Berger, Jianfei Cao, Hans Christensen, John Gallemore, João Granja, Anya Kleymenova,
Christian Leuz, Miao Liu, Shirley Lu, Maximilian Muhn, Valeri Nikolaev, Thomas Rauter, Haresh Sapra,
Douglas Skinner, Christopher Williams (discussant), and seminar participants at the University of Chicago
and the 2022 CAPANA Conference for helpful comments and suggestions. I benefited greatly from institu-
tional insights that were provided by Robert Esquivel (SBA), Stuart Hinson (NOAA), and the OpenFEMA
Team. I am grateful to Toshi Tokuyama for excellent research assistance. All errors are my own. Email:
yao.lu@cornell.edu.1 Introduction
Understanding banks’ vulnerability to climate change is critical for global bank regulators’
recent efforts to incorporate climate risks into their analyses of financial stability.1 As pointed
out by the Federal Reserve’s Financial Stability Framework, analyzing climate risks involves
assessing “not only potential climate shocks, but also whether climate change might make
the financial system more vulnerable in ways that could amplify these shocks” (Brainard,
2021; Board of Governors, 2020). One important factor affecting banks’ climate vulnerability
in the long term concerns how banks adjust their risk-taking behaviors in response to severe
climate events. If banks adapt to climate hazards by reducing (increasing) climate-related
risk-taking, they are likely to mitigate (reinforce) their long-term vulnerability to these
hazards. To better understand banks’ vulnerability to climate risks, I investigate banks’
risk-taking behaviors after experiencing hurricanes. I provide evidence that banks increase
risk-taking through delayed loss recognition in response to hurricanes, and that this effect
is stronger when banks have experienced more hurricanes and when they are hit by less
expected hurricanes.
I focus on banks’ risk-taking through the channel of loss recognition because the timeliness
with which banks recognize expected loan losses plays an important role in their risk appetite,
especially after disasters such as hurricanes. Banks recognize expected loan losses by setting
aside a provision (an income statement expense) that contributes to the allowance for future
loan losses (a balance sheet contra-asset). Delaying loss recognition increases reported capital
and reduces bank transparency, which create incentives and opportunities for increased risk-
taking (Bushman and Williams, 2015; Lu and Nikolaev, 2022).
Delaying the reporting of expected losses becomes a particularly convenient and justifi-
able tool for risk-taking after hurricanes, because regulators often give affected banks more
1
For example, the Federal Reserve’s 2020 financial stability report includes a chapter on climate risk for
the first time, stating that banks should “have systems in place that appropriately identify, measure, control,
and monitor” climate risks (Board of Governors, 2020). Meanwhile, the ECB launched its first climate risk
stress test in 2020, which examines the impact of climate events on 2,000 Eurozone banks over a 30-year
period using data from four million firms worldwide.
1flexibility in financial reporting to reduce their regulatory burden and to encourage their
“recovery lending” to the riskier, affected regions. Even without an intent, banks can delay
loss recognition if they cannot reasonably predict long-term loan losses caused by hurricanes.
Such delay can nevertheless lead to increased risk-taking, as the delay distorts information
about credit losses and weakens both market discipline and banks’ assessment of their own
risks. Despite these tendencies for delay, banks also have incentives to be timely in loss
recognition after hurricanes. This is because timely loss recognition helps banks take early
corrective actions, which can strengthen banks’ resilience to hurricanes in the long run.
Because there are reasons for both delayed and timely loss recognition, how banks change
their loss recognition after hurricanes, or how this change affects banks’ risk-taking and
vulnerability, is unclear ex ante. I investigate these questions in three steps. First, I examine
how timely banks recognize long-term loan losses caused by hurricanes. Then, I investigate
whether delaying expected loss recognition facilitates risk-taking after hurricanes. Finally, I
explore how these effects change when banks become more experienced with hurricanes and
when banks are hit by less expected hurricanes.
My main analyses on banks’ post-hurricane risk-taking behavior rely on a measure of the
delay in recognizing expected loan losses. A delay in recognizing realized losses could result
from future losses that are unpredictable at the time of provisioning, instead of from banks’
lack of prediction ability or incentives to delay. Thus, to study banks’ influence on provision
timeliness, it is more meaningful to use a benchmark that captures expected losses, which are
defined as losses that can be reasonably predicted using information available at the time
of provisioning. Expected loss recognition is also the focus of current financial reporting
standards for banks (e.g., CECL).
I use the measure of expected loss overhang (OV H), developed by Lu and Nikolaev
(2022), as a proxy for the extent of the delay in expected loss recognition. OV H is calculated
by subtracting reported allowance from an out-of-sample estimate for long-term cumulative
expected losses (ExpLoss). Since the model that estimates ExpLoss does not use hurricane-
2related predictors, ExpLoss does not reflect hurricanes’ impact on loan losses. As a result,
after controlling for ExpLoss, OV H should decrease following a hurricane shock if a bank
recognizes at least some incremental long-term losses caused by the hurricane. In other
words, reported allowance should increase (decrease) at a faster (slower) speed in response
to the increase (decrease) in ExpLoss following a hurricane shock. Therefore, OV H is a
useful tool to detect a bank’s provisioning for the long-term loan losses from hurricanes.
In my first set of analyses, I examine hurricanes’ impact on loan losses and how timely
banks recognize these losses. A given bank’s hurricane exposure is measured based on the sum
of hurricane-related property damage in affected counties weighted by the bank’s mortgage
market share in each of the counties. I find that hurricane exposure reduces banks’ loan
losses in the first two years following the quarter of exposure. This effect is stronger for
hurricane-affected regions that received more flood insurance claims, SBA disaster loans,
and/or FEMA assistance, which are the three primary sources of disaster assistance. These
results are consistent with the findings from prior research that, in the short run, disaster
assistance more than offsets the loan losses caused by natural disasters.2 While disaster aid
provides temporary relief, hurricanes cause decade-long damages to the local economy and
local governments’ functions (Boustan, Kahn, Rhode, and Yanguas, 2020; Jerch, Kahn, and
Lin, 2020), which impair bank performance in the long run. Indeed, I find that bank losses
and failures surge four to seven years after a hurricane shock.3
Although banks enjoy a “grace period” in the short term after hurricanes, during which
loan losses are pushed back, they do not take advantage of it to build up allowance for
increased losses in the long term.4 I find that both allowance and ExpLoss are slow in
2
Prior research finds that both household debt and foreclosures decline after hurricanes (Billings, Gal-
lagher, and Ricketts, 2019; Gallagher and Hartley, 2017; Overby, 2007), and that default and foreclosure
decrease more after more severe wildfires (Issler, Stanton, Vergara-Alert, and Wallace, 2020). These papers
attribute the reductions in debt, default, and foreclosures to disaster assistance.
3
This is similar to the pattern of loan losses after weather disasters documented by Blickle, Hamerling,
and Morgan (2021). Blickle, Hamerling, and Morgan (2021) study all types of weather disasters and use
a different disaster exposure measure and sample period than my study. The authors find an increase in
post-disaster loan losses three to five years after weather disasters for all banks except for those with all
branches in a single county.
4
During my sample period, banks follow the incurred loss model, which requires the recognition of losses
3capturing post-hurricane loan losses, as they both decline in the first two years following
a hurricane shock before increasing four to five years after the event. Since ExpLoss is
estimated using non-hurricane-related information, the fact that allowance follows the same
pattern as ExpLoss suggests that banks behave as if they do not use hurricane information
in making their provisioning decisions. In my primary test on banks’ provisioning timeliness,
I find that, after controlling for ExpLoss, OV H increases in the year following a hurricane
shock. This result suggests that banks not only fail to recognize the incremental losses from
hurricanes, but also delay more in recognizing expected losses unrelated to hurricanes (i.e.,
ExpLoss) in response to a hurricane shock.
My second set of analyses investigate whether delaying expected loss recognition facil-
itates post-hurricane risk-taking. I find that banks that delay more in the year following
a hurricane shock provide more recovery lending to affected regions. Recovery lending is
measured as the mortgages issued to hurricane-affected counties in the year following the
hurricane shock. In addition, I find that recovery lending increases the mortgage prices
charged by delaying banks, which suggests that shifting lending to hurricane-affected re-
gions allows banks to issue more expensive and riskier loans. While delaying banks increase
recovery lending, they do not increase their total loan growth.5 The unchanged total lending
suggests that delaying loss recognition does not help increase banks’ lending capacity after
hurricanes. Instead, the fact that delaying banks increase their exposure to riskier mortgages
in hurricane-affected regions supports the hypothesis that the delay facilitates risk-shifting.6
This increased risk-taking weakens banks’ long-term stability after hurricanes. Specifically,
only when they become probable (typically, losses that are plausible over the next year). In practice, however,
banks have considerable discretion in provisioning for long-term losses. The fact that the average reported
allowance for loan losses is more than three times that of annual net charge-offs (Table 1) suggests that
banks reserve for a significant portion of long-term losses.
5
Cortés and Strahan (2017) find that only small banks (i.e., banks with assets below $2 billion) reallocate
lending from other regions to affected regions to fund recovery lending after natural disasters due to their
external financing constraints. Larger banks do not reduce lending to unaffected regions. Since my sample
includes all banks, the finding that delaying banks reallocate lending to hurricane-affected regions is more
likely to be the result of risk-shifting than that of financial constraints.
6
If banks delay recognition to increase lending, the slack in reported capital would lead to an increase
in total lending. However, if banks delay for risk-shifting purposes, the slack would be used to absorb the
higher expected losses from riskier asset portfolios.
4the more a bank delays loss recognition in the year following a hurricane, the greater loan
losses and higher probability of default that bank will face four years after the hurricane.
My last set of analyses explore how banks’ behaviors change when they have experienced
more hurricanes and when they are hit by unexpected hurricanes. Studying the role of
hurricane experience in banks’ post-hurricane behaviors reveals how banks’ adaptation to
hurricanes affects their vulnerability in the long term. I find that banks that have experienced
more hurricanes delay more in the year following a hurricane shock. Hurricane experience
also strengthens the effect of delaying loss recognition on recovery lending and on long-term
loan losses. These results suggest that banks are more likely to increase risk-taking through
delayed loss recognition after experiencing more hurricanes. This is potentially because
past hurricanes undermine banks’ resilience, resulting in underperformance and stronger
risk-taking incentives after a new hurricane. Consistent with this conjecture, I find that
banks with more hurricane experience report lower profitability in response to hurricanes.7
In this vicious cycle, past hurricanes reinforce banks’ vulnerability to future hurricanes by
undermining their discipline in loss recognition and risk-taking.
How important is this vicious cycle to the banking system as a whole? As most bank
portfolios are outside currently hurricane-prone areas, this is an important question that
can broaden the implications of the above findings. Since hurricanes are expanding into
previously unexpected areas due to climate change (Studholme, Fedorov, Gulev, Emanuel,
and Hodges, 2022), more banks could enter the vicious cycle if they react to unexpected
hurricanes with significant risk-taking. Therefore, I investigate banks’ risk-taking through
delayed loss recognition in response to unexpected hurricanes. A bank is defined to experience
an unexpected hurricane in a quarter when over half of its hurricane exposure in that quarter
comes from counties that experienced no more than two hurricanes in my sample period.
I find that banks delay more in loss recognition after unexpected hurricanes and that the
7
In contrast to banks’ increased risk exposure to hurricanes, insurance companies pull back coverage in
regions that experience heightened flood or fire risks. See, for example,
https://www.nytimes.com/2019/08/20/climate/fire-insurance-renewal.html?emc=rss&partner=
rss and https://www.reinsurancene.ws/renre-could-pull-back-in-florida-if-rates-stay-low-says-ceo/.
5delay contributes more to recovery lending. Additional analysis suggests that the delay is
likely due to banks’ inability to predict losses from these highly unexpected shocks. These
findings suggest that the vicious cycle poses an increasing threat to the banking system
when hurricanes expand into broader areas and kick-start the risk-taking responses for more
banks.
So far, I emphasize banks’ risk-taking incentives and their inability to predict loan losses
as the key drivers of their delayed loss recognition. One alternative explanation, however,
is that banks in my sample period follow the incurred loss model (ILM), which prevents
them from recognizing long-term, uncertain losses after hurricanes. To test this possibility,
I examine banks’ disclosure of hurricane-related loan losses in Form 8-K filings. As Bischof,
Laux, and Leuz (2021) pointed out, listed banks need to file Form 8-K to disclose additional
loan losses that are not recognized in their financials, as long as the likelihood of such losses
is more than “remote.” The threshold for disclosure is much lower than that for recognition.
Hence, if banks delay the recognition of post-hurricane losses only because of the ILM,
they can and should disclose these potential losses in 8-K filings. I find that banks hit by
hurricanes rarely make such disclosures. Therefore, banks’ reporting incentives and inability
to predict, instead of the ILM, are more likely to explain their delayed loss recognition after
hurricanes.
My paper contributes to research on the climate risks of financial institutions. Some prior
studies examine the impact of natural disasters on bank behaviors, such as lending (Cortés
and Strahan, 2017; Koetter, Noth, and Rehbein, 2020), risk-taking (Schüwer, Lambert, and
Noth, 2019), and mortgage pricing and provision (Garbarino and Guin, 2021). Other papers
examine how the economic impact of natural disasters varies with bank characteristics, such
as the ability to set deposit rates locally (Dlugosz, Gam, Gopalan, and Skrastins, 2021),
bank structure and capital (Schüwer, Lambert, and Noth, 2019), and the presence of local
lenders (Cortés, 2014). I add to this literature by documenting delayed loss recognition as
a key channel through which banks increase their risk-taking and climate vulnerability after
6climate hazards, such as hurricanes. A related paper by Chamberlain, Vijayaraghavan, and
Zheng (2019) focuses on banks’ timeliness in recognizing short-term, realized losses after
hurricanes, while my paper studies the timeliness in recognizing long-term, expected losses.
More importantly, my paper investigates how delaying expected loss recognition facilitates
banks’ risk-taking and thus undermines banks’ resilience to hurricanes.
My paper is also related to research on the timeliness of loan loss provisioning. Ex-
isting evidence suggests that delayed provisioning restricts banks’ lending capacity, incen-
tivizes risk-shifting, and undermines financial stability (Beatty and Liao, 2011; Bushman
and Williams, 2012, 2015; Kim, 2021). In addition, research finds that banks were slow to
recognize future losses during the 2007-2009 financial crisis, and that the delayed recognition
was primarily driven by reporting incentives (Bischof, Laux, and Leuz, 2021; Huizinga and
Laeven, 2012). My paper complements these studies by documenting a similar pattern of
delayed loan loss recognition after a different crisis, i.e., hurricanes, and by investigating the
roles of reporting incentives and prediction ability in contributing to this delay.
2 Background
2.1 US banks’ climate risk from hurricanes
According to the Federal Reserve’s financial stability monitoring framework, climate risks
threaten financial stability from two aspects: increasing financial shocks and increasing fi-
nancial system vulnerability (Board of Governors, 2020). Specifically, extreme climate events
can lead to more frequent and severe financial shocks by quickly changing (or revealing new
information about) the value of bank assets or economic conditions. Meanwhile, the opacity
of banks’ exposure and the uncertainty about the timing and intensity of climate hazards
increase the vulnerability of the financial system. Financial system’s vulnerability to climate
hazards amplifies the financial shocks of these extreme events.
Among all types of climate hazards, hurricanes contribute the most to banks’ climate
7risk. This is because of hurricanes’ devastating effect (which causes financial shocks) and
unpredictable nature (which leads to financial vulnerability). According to the National
Oceanic and Atmospheric Administration (NOAA), hurricanes contributed over 53% of the
$1.9 trillion total loss caused by major natural disasters (each costing more than $1 billion) in
the US from 1980 to 2020 and caused the highest per event losses with the greatest number
of death per year (Smith, 2021). Figure 1 reports similar results based on the property
damage sample used in this paper. Hurricanes and related flooding contributed the most
property losses (over $331 billion) caused by natural disasters from 1990 to 2019 (Panel A).
Over 3,700 hurricane events were recorded over the past 30 years (Panel B), and these events
have the second highest per event losses among all types of hazards, second only after the
extremely rare earthquakes (Panel C).
Hurricanes not only cause significant damages, but are also highly unpredictable. Weather
patterns that determine hurricane landfalls are only predictable when the hurricane is within
several days of making a landfall. Because of this, NOAA does not provide seasonal hur-
ricane landfall predictions.8 Figure 2 shows that hurricanes can influence a wide range of
coastal areas in the US (Panel A and B), but the specific location of their landfalls and the
corresponding damages in a year are quite random and vary considerably from year to year
(Panel C).
2.2 The role of provisioning in banks’ post-hurricane performance
The recognition of expected loan losses plays an important role in banks’ lending, risk-
taking, and stability, which are important performance metrics for post-hurricane banks.
Delaying expected loss recognition builds an overhang of unrecognized expected losses. The
overhang inflates the reported capital and degrades bank transparency, which give banks the
opportunity to increase risk-taking and lending (Bushman and Williams, 2012, 2015; Akins,
Dou, and Ng, 2017; Lu and Nikolaev, 2022). However, since the loss overhang depletes banks’
8
See, for example, NOAA 2021 Atlantic Hurricane Season Outlook: https://www.cpc.ncep.noaa.gov/
products/outlooks/hurricane.shtml
8future capital, delaying loss recognition can lead to increased losses and failures in the future
(Harris, Khan, and Nissim, 2018; Lu and Nikolaev, 2022) and can reduce lending capacity
in future crises (Beatty and Liao, 2011; Kim, 2021).
In my setting, delaying expected loss recognition after hurricanes can help banks in-
crease lending to hurricane-affected regions, but it can also facilitate risk-shifting behaviors.
Originating new loans and restructuring terms on existing loans provide critical financial
support to hurricane-affected households and businesses. As a result, some commentators
argue that delaying loss recognition after hurricanes benefits disaster recovery by facilitating
banks’ recovery lending. However, lending in hurricane-affected regions also increases banks’
exposure to riskier borrowers, adding to their financial vulnerability in the long run. Timely
loss recognition, on the other hand, disciplines banks’ post-hurricane risk-taking behaviors
and strengthens their resilience to future hurricanes.
The timeliness in expected loss recognition depends on banks’ ability to predict future
losses (Bhat, Ryan, and Vyas, 2019; Yang, 2021) and their reporting incentives (Bischof,
Laux, and Leuz, 2021; Huizinga and Laeven, 2012; Liu and Ryan, 1995, 2006). If a bank
is unable to predict the long-term increase in loan losses after hurricanes, the bank is likely
to delay the recognition of these losses. This delay is especially likely when a bank is hit
by unexpected hurricanes. A delay can also result from banks’ risk-taking incentives after
hurricanes. These incentives grow stronger when past hurricane experience has undermined
a banks’ stability and profitability. The incurred loss model (ILM), which dictates banks’
provisioning during my sample period, is another explanation for delays in expected loss
recognition (Dugan, 2009). However, because all banks follow this standard throughout my
sample period, the ILM cannot explain the changes in banks’ timeliness relative to each
other, which are the focus of this paper. My additional analysis also rules out the ILM as
the primary reason for banks’ delayed loss recognition after hurricanes.
92.3 Disaster relief and bank loan losses after hurricanes
Both the federal government and banks provide multiple forms of disaster relief after hurri-
canes (and other natural disasters) to reduce affected areas’ economic losses. By supporting
hurricane victims, this financial assistance also mitigates the short-term impact of hurricanes
on bank losses from several channels.
The main sources of financial support from federal agencies are flood insurance claims
from the National Flood Insurance Program (NFIP), low-interest-rate disaster loans pro-
vided by the Small Business Administration (SBA), and direct cash grants from the Federal
Emergency Management Agency (FEMA) (Drexler, Granato, and Rosen, 2019). The NFIP
flood insurance is required for federally guaranteed mortgages in the 100-year floodplain.
The insurance provides residential coverage up to $250,000 for buildings and up to $100,000
for contents. Affected households without flood insurance or those needing additional aid are
often first referred to the SBA for its disaster loans. Homeowners can borrow up to $200,000
to replace or repair their primary residence and up to $40,000 to replace or repair personal
property. Interest rates are as low as 1.25% for homeowners and renters, with terms up to
30 year.9 As a last resort, FEMA provides financial assistance in the form of grants that do
not need to be repaid, with a cap of $37,900 for housing assistance and $37,900 for other
needs assistance as of 2021 (86 FR 63046).10
Banks provide important financial relief in addition to the federal assistance to borrow-
ers affected by hurricanes. A primary form of bank assistance is to grant a “grace period,”
typically ranging from 30 days to 12 months, during which banks reduce or suspend mort-
gage payment, suspend foreclosures, and/or waive penalties or late fees for borrowers with
9
The interest rate will not exceed 4% for applicants unable to obtain credit elsewhere and will not exceed
8% for applicants who can obtain credit elsewhere.
10
These financial supports are economically meaningful. For example, NFIP made an estimated $8.92
billion insurance payouts to about 92,000 Texans after Hurricane Harvey (FEMA, 2019), which suggests a
$97,000 average payout per affected resident. To put the average number into context, the average amount
of property damage incurred due to 1 foot (3 feet) of flooding in a home is $72,162 ($94,538), according to
National Flood Services cost tables. In addition, SBA approved over $2.9 billion individual home loans for
Harvey victims, with an average approved loan amount of $79,183, and FEMA provided $1.6 billion grants,
with an average of $7,446 (Billings, Gallagher, and Ricketts, 2022).
10hurricane-damaged homes (e.g., Freddie Mac, 2017; Fannie Mae, 2017). For affected borrow-
ers who can no longer afford to resume payments, banks can also restructure loan repayment
terms and schedules. More generally, newly issued bank loans in disaster areas help revitalize
or stabilize the local economy, indirectly improving the financial conditions of local borrow-
ers. Meanwhile, bank regulators often give banks more flexibility in financial reporting after
disasters to encourage them to “work constructively” with affected borrowers. As long as
banks make reasonable efforts to comply with the regulatory requirements in reporting items
such as nonaccrual loans, allowance, and charge-offs, regulators will not punish banks if their
financials cannot fully satisfy the reporting requirements given the impact of hurricanes (e.g.,
Federal Reserve, 2017; FFIEC, 2005). Banks are expected to gradually improve the accuracy
of their reporting only “as information becomes available.”
These financial supports indirectly reduce the short-term impact of hurricanes on bank
loan losses via several channels. First, multiple sources of cash inflows from disaster assis-
tance help affected borrowers recover their financial losses and continue their debt repay-
ment. The assistance can even improve the financial conditions for borrowers who would
have defaulted without the hurricane, and help them keep debt repayment. Second, hurri-
cane victims can, and many choose to, use the financial assistance to pay down debt rather
than repair houses, especially when rebuilding cost is greater than home value. Moreover,
because flood insurance payment is typically held in escrow by the lender for homes used
as collateral of mortgages, banks have the incentive and ability to press borrowers to use
these funds to repay mortgages (Gallagher and Hartley, 2017). Finally, the suspension of
repayment and foreclosure, facilitated by increased reporting flexibility, “freezes” affected
borrowers’ past-due status, which mechanically reduces reported charge-offs.
Consistent with these arguments, prior research finds that household debt, defaults, and
foreclosures decline after hurricanes and other natural disasters, and that the decline is more
pronounced after more severe disasters (Issler, Stanton, Vergara-Alert, and Wallace, 2020;
Billings, Gallagher, and Ricketts, 2019; Gallagher and Hartley, 2017; Overby, 2007). These
11papers attribute the decline to disaster assistance and increased debt repayment. In addition,
Blickle, Hamerling, and Morgan (2021) finds that bank loan losses do not increase until at
least three years after weather disasters. Their findings also suggest that disaster assistance
helps reduce bank loan losses.
While bank losses are suppressed in the short term, they are likely to increase in the long
term due to the prolonged damages hurricanes inflict on the local economy. Jerch, Kahn,
and Lin (2020) find that hurricanes lead to significant decline in local government revenues,
expenditures, and debt in the following decade. These declines are mitigated by disaster
assistance in the short term, but ramp up quickly six to ten years after a hurricane. Moreover,
local governments experience increased cost of debt and reduced investment, which further
strengthen their fiscal declines and weaken their ability to provide public services. Boustan,
Kahn, Rhode, and Yanguas (2020) find that severe natural disasters such as hurricanes
reduce firm productivity, which leads to lower wages and family income, heightened out-
migration rates, and reduced housing prices in affected counties over the decade of disaster
events. These long-term effects of hurricanes can increase bank losses several years after a
hurricane shock. Related to this conjecture, Blickle, Hamerling, and Morgan (2021) find
that loan losses significantly increase three to five years after a weather disaster shock.
Overall, disaster assistance efforts by the government and banks not only provide crucial
support to hurricane victims, but also push back the increase in loan losses by a few years
after a hurricane shock. This creates a precious “grace period” for banks to gradually build
up reserves for the long-term loan losses that stem from hurricanes’ prolonged damage to
the economy. This grace period is especially valuable considering the unpredictability of
hurricanes. That is, since hurricanes are very difficult to predict, provisioning for loan losses
of future hurricanes in advance is much more costly than preparing for the delayed realization
of loan losses after a hurricane has made landfall.
123 Measurement and empirical design
Key measures in my study are banks’ exposure to hurricanes and the timeliness in recog-
nizing post-hurricane loan losses. Using these measures, I conduct my empirical analysis in
three steps. First, I examine the effect of hurricane exposure on banks’ timeliness in loan loss
recognition. Then, I investigate the influence of the timeliness on the relationship between
hurricane exposure and risk-taking. Finally, I examine how banks’ post-hurricane provision-
ing timeliness and its effect on risk-taking change when banks become more experienced with
hurricanes and when banks are hit by unexpected hurricanes.
3.1 Measurement
I measure a bank’s exposure to hurricane impact in a given quarter based on its mortgage
market share in affected counties and hurricane-related property damage in each of these
counties in that quarter. I first calculate a raw measure of bank i’s exposure to hurricane
damages in quarter t, HurrExposureRawi,t , by summing property damages from hurricanes
across all counties weighted by bank i’s mortgage market share in each county c in quarter t,
before scaling the weighted total by the bank’s lagged total loan. This measure is essentially
the amount of hurricane-related property losses that are “allocated” to a bank based on its
mortgage market share in affected regions, scaled by the bank’s total lending. Formally:
P
c HurrLossc,t ∗ M ortgageSharei,c,t
HurrExposureRawi,t = (1)
Loani,t−1
HurrLossc,t is the dollar amount of property losses caused by hurricanes in county c
OwnedM ortgage3yri,c,t
in quarter t; M ortgageShare = OwnedM ortgage3yrc,t
, where OwnedM ortgage3yri,c,t is the
amount of mortgages issued and held by bank i in county c in the three years ending in the
year of quarter t, and OwnedM ortgage3yrc,t is the amount of mortgages issued and held by
all banks in the same county and the same period. Loani,t−1 is bank i’s loans outstanding
at the end of the previous year. Because HurrExposureRawi,t has a long-tail distribution,
13I use a natural log transformation of this variable in the analyses:
log(HurrExposureRawi,t ∗ 1014 + 1)
HurrExposurei,t = (2)
105
Because HurrExposureRawi,t is much smaller than 1, and the smallest cases are of the
order 10−14 , I need to multiply it by a sufficiently large number, i.e., 1014 , such that the
log transformation does not have a long-tail distribution (Figure 3). I then scale the log
transformation by 105 such that the regression coefficients are easier to read.11
Similarly, I calculate banks’ exposure to post-hurricane disaster assistance by replacing
HurrLoss with Assistance in Equation 1, where Assistance is either the dollar amount
of flood insurance claim (F loodInsuranceClaim), of SBA disaster loans (SBALoan), of
FEMA assistance (F EM AAssistance), or the sum of the three (T otalAssistance).
Another important measure in this paper is banks’ timeliness in recognizing post-hurricane
loan losses. I evaluate the timeliness in recognizing realized losses by comparing the post-
hurricane trend of allowance with that of loan losses. If both allowance and loan losses
decline in the short term before reversing later, then the short-term provisioning is likely
insufficient in capturing the long-term increases in loan losses.12 While this comparison eval-
uates provision timeliness from an ex-post point of view, it does not provide insight as to
what extent this timeliness is attributable to bank choice rather than the predictability of
future loan losses. To study banks’ provisioning choice, I need a measure without look-ahead
bias, which allows me to evaluate the timeliness in recognizing expected loan losses that can
11
HurrExposurei,t is calculated following Cortés and Strahan (2017), with two modifications. First,
I measure a bank’s exposure to a county based on its share of mortgage amount instead of its share of
branches in that county, because mortgage market share is more likely to capture a bank’s actual exposure
to a county’s total property lending (and thus the county’s property losses from hurricanes). Second, I take
the log transformation on the ratio of the weighted sum of hurricane losses to total lending, instead of taking
the log transformation only on the weighted sum before scaling it by total lending. Under this treatment,
HurrExposurei,t can be more easily interpreted as a log transformation of “hurricane-related property loss
ratio,” the ratio of hurricane-related property losses to total lending.
12
The idea of evaluating the timeliness of realized loss recognition by comparing current provisioning
with future losses is similar to the idea underlying provisioning timeliness measures in prior research such
as Akins, Dou, and Ng (2017), Balakrishnan and Ertan (2021), and Yang (2021). One important difference,
though, is that these papers focus on banks’ provision for short-term losses while I look at provision’s ability
to capture long-term losses.
14be reasonably predicted at the time of provisioning.
I estimate expected losses and calculate the timeliness of expected loss recognition using
the model developed by Lu and Nikolaev (2022). This model provides reasonable and sup-
portable prediction of long-term loan losses out-of-sample with much greater accuracy than
typical cross-sectional models. Lu and Nikolaev (2022) show that the long-term forecasts of
loan losses from the model achieve positive out-of-sample R2 up to five years in the future,
considerably outperforming long-term forecasts from other models (e.g., Harris, Khan, and
Nissim, 2018). The loan loss forecasts are also superior in detecting long-term bank failures.
Key to the model’s out-performance is its accurate prediction of the aggregate component of
loan losses over the business cycle, thanks to a high-dimensional dynamic factor model and
a broad set of bank-level and macroeconomic predictors. This aggregate component is then
combined with cross-sectional predictions of individual banks’ deviation from the aggregate
trend to form a bank-quarter level prediction of future losses.
Using the present value of predicted losses in the next five years, Lu and Nikolaev (2022)
calculate cumulative expected loan losses, ExpLoss (which is ALLE in their paper). Then,
they subtract reported allowance from ExpLoss to calculate expected loss overhang, OV H,
which is a proxy for the extent of delay in expected loss recognition. Higher OV H indicates
greater delay. They find that ExpLoss is an order-of-magnitude more effective in explaining
long-term loan losses than allowance, and that ExpLoss subsumes information in allowances
and in fair value disclosures about long-term losses. They also show that OV H is associated
with increased risk-taking.
OV H is a useful tool to evaluate banks’ timeliness in recognizing loan losses caused
by hurricanes. Specifically, since the prediction model does not use any hurricane-related
information in estimating ExpLoss, I use ExpLoss as a benchmark measure for expected
loan losses that are unrelated to hurricanes. After controlling for ExpLoss, a decrease in
OV H after hurricanes reflects a more positive change (i.e., a greater increase or a smaller
decrease) in allowance than in the benchmark expected losses (that do not capture losses
15from hurricanes), which indicates that the post-hurricane allowance captures at least some
hurricane-induced losses. On the other hand, an unchanged or even increased OV H after
hurricanes, after controlling for ExpLoss, indicates that the post-hurricane allowance does
not capture loan losses from hurricanes.
3.2 Empirical design
In the first step of my empirical analysis, I examine the long-term impact of hurricanes on
banks’ loan losses and banks’ timeliness in recognizing losses caused by hurricanes. Formally,
I run the following regressions:
h=t−1
X
DepV ari,t = αi + αt + βh HurrExposurei,h + γXi,t + ϵi,t (3)
h=t−8
DepV ar represents N CO or OV H. N CO is net charge-offs scaled by average loan
balance. OV H is expected loss overhang as defined in Lu and Nikolaev (2022). αi and
αt are bank fixed effects and quarter fixed effects. HurrExposure is a bank’s hurricane
exposure, as defined in Section 3.1. X controls for bank characteristics that can explain
changes in loan losses, including size, capital, loan type, loan maturity, and loan yields. As
discussed in Section 3.1, I also control for ExpLoss when OV H is the dependent variable.
t − 1 through t − 8 indicate the quarters that are one to eight years before quarter t.
In the second step of my analysis, I study the influence of expected loss recognition on
the relationship between hurricane exposure and banks’ risk-taking. My main specifications
regress recovery lending, total lending, mortgage price, and long-term loan losses and failures
on the interaction of hurricane exposure in a quarter and OV H in the year following that
quarter:
DepV ari,t = αi + αt + β1 HurrExposurei,t−h ∗ OV Hi,t−h+1
+ β2 HurrExposurei,t−h + β3 OV Hi,t−h+1 (4)
+ γ1 Xi,t + γ2 HurrExposurei,t−h ∗ Xi,t + ϵi,t
16DepV ar represents RecoveryLending, ∆Loan, Share, Spread, N CO, ExpLoss, and
F ailure. RecoveryLending is the amount of mortgages issued to counties that experienced
hurricanes in the previous year, scaled by total loans outstanding at the end of previous
year. ∆Loan is the percentage change in total loans outstanding. Share is the proportion
of mortgages issued with a positive spread over “3% + the yield on Treasury securities of
comparable maturity”, and Spread is the average spread of all mortgages issued. ExpLoss
is the estimate of expected lifetime losses scaled by the average loan balance following Lu
and Nikolaev (2022). X includes all control variables in Equation 3 and ExpLoss. After
controlling for ExpLoss, any effect of OV H will be due to delayed recognition of expected
losses instead of to high expected losses. h is equal to one year when analyzing short-term
risk-taking behaviors (i.e., when DepV ar represents RecoveryLending, ∆Loan, Share, or
Spread) and is equal to four years when analyzing long-term vulnerability (i.e., when DepV ar
represents N CO, ExpLoss, or F ailure).
In the third step of my analysis, I first explore how hurricane experience and unexpected
hurricanes affect banks’ timeliness in loss recognition:
OV Hi,t = αi + αt + β1 HurrExposurei,t−1 ∗ IndepV ari,t−1
+ β2 HurrExposurei,t−1 + β3 IndepV ari,t−1 (5)
+ γ1 Xi,t + γ2 HurrExposurei,t−1 ∗ Xi,t + ϵi,t
IndepV ar represents P astHurr or U nexpected. P astHurr is the measure of a bank’s
hurricane experience, which is the number of previous quarters a bank has had positive
HurrExposure. U nexpected is an indicator for unexpected hurricanes, which is equal to one
if over half of a bank’s hurricane exposure in a quarter comes from counties that experienced
no more than two hurricanes in my sample period. X is the same as the X in Equation 4.
Next, I examine the roles of hurricane experience and unexpected hurricanes in banks’
risk-taking behavior in response to a hurricane shock. I focus on the triple-interaction in the
following regressions:
17DepV ari,t = αi + αt + β1 HurrExposurei,t−h ∗ IndepV ari,t−h ∗ OV Hi,t−h+1
(6)
+ IN T + γXi,t + ϵi,t
DepV ar represents ∆Loan, RecoveryLending, or N CO. IndepV ar represents P astHurr
or U nexpected. IN T represents all two-way interaction terms between HurrExposure,
IndepV ar, and OV H, as well as each of these three terms. X is the same as the X in
Equation 4.
4 Data and sample
I construct the sample using data from several sources. Data on hurricane losses is acquired
from the Spatial Hazard Events and Losses Database for the United States (SHELDUS).
SHELDUS provides county-level data on direct losses caused by natural disasters. This data
includes information on the date of a hazard event, disaster type, affected location (county
and state), and the dollar amount of property losses caused by the event in each county.
The main data source of SHELDUS is the Storm Data and Unusual Weather Phenomena
provided by the National Centers for Environmental Information (NCEI) of the National
Oceanic and Atmospheric Administration (NOAA). My sample includes property losses from
all hurricanes recorded in SHELDUS. Since some events coded as “floods” are actually the
result of hurricanes, I treat flood losses as hurricane losses if a flood event happens in a
county-quarter where other counties in the same state have a “hurricane” record in that
quarter.
Mortgage data is from the Home Mortgage Disclosure Act (HMDA) dataset. HMDA
includes mortgage-level information on the lender, dollar amount, property location (down
to the census-tract level), and type of purchaser (i.e., mortgage not sold or the type of
purchaser if sold) of a mortgage. I use the proportion of past three years’ mortgages in a
county that are originated and not sold by a bank to measure the bank’s mortgage market
18share in that county. I use a bank’s mortgages in counties affected by a hurricane to measure
recovery lending.13
Other bank accounting data is acquired from FR Y-9C reports. I measure balance sheet
variables at the end of the fiscal period, and I annualize income statement items by summing
the numbers in the current and preceding three quarters. Bank failure data is obtained from
FDIC Failed Bank List. Flood insurance and FEMA assistant data is from the OpenFEMA
dataset. SBA disaster loan data is from the SBA dataset. Variables related to expected
losses and the timeliness of expected loss recognition are from Lu and Nikolaev (2022). To
reduce the impact of extreme observations, I take log transformation or truncate top and
bottom 1% observations for continuous variables. The final sample covers quarterly US bank
holding companies’ observations between 1994 and 2019.
Table 1 reports the descriptive statistics of variables used in my analysis. The mean and
median of positive values of HurrExposure is 0.00019. This means that a bank’s exposure
to property losses from hurricanes (based on its mortgage market share in affected counties)
is about 0.0002% of its total loans outstanding on average. This number grows to 0.0036%
for the first quartile HurrExposure (0.00022), and 1586% for the largest observation of
HurrExposure (0.00035). These statistics suggest that while banks’ exposure to hurricane-
caused damages is small on average, the exposure increases exponentially for more extreme
cases. The log transformation in calculating HurrExposure effectively converts an extremely
right-skewed distribution to a distribution much closer to normal (Figure 3).
The mean and median of positive values of F loodInsuranceClaim, SBALoan, and
F EM AAssistance are around 0.00018, which translates into 37% of the average (or me-
dian) level of banks’ hurricane exposure. This suggests that these three sources of disas-
13
Since HMDA only provides annual mortgage data while my analysis requires annualized quarterly data,
I use the annual number as the annualized quarterly number. This approximation is reasonable for this paper
as the quarter-on-quarter changes of mortgage are not the focus of my analysis. Specifically, I use either the
mortgage amount over one year after a hurricane or the cumulative mortgage amount over the past three
years in my analyses. In addition, because hurricanes typically happen in the third or fourth quarter of a
year, the annualized quarterly number is quite close to the annual number around the hurricane shock that
I focus on.
19ter assistance provide sizable coverage for damages caused by an average hurricane. The
coverage drops, but is still decent, for more extreme exposures: 14% for the top quartile
HurrExposure. These results are consistent with the argument that disaster assistance is
sizable enough to meaningfully push back the increase in loan losses after hurricanes.
The average (median) loan losses is 0.34% (0.19%) of the average loan balance, and the
average (median) allowance is around 4 (7) times of loan losses. The mean and median of
model-estimated expected losses are around 70% greater than those of allowance, resulting
in a positive average (median) OVH of around 1%. These summary statistics are similar to
those documented in Lu and Nikolaev (2022) and suggest that on average, banks are not
timely in recognizing expected loan losses.
5 Results
5.1 The timeliness of loan loss recognition after hurricanes
To investigate banks’ timeliness in recognizing post-hurricane loan losses, I first document
the long-term pattern of loan losses after hurricanes. This pattern provides a useful ex-post
benchmark for evaluating how quickly banks recognize future loan losses after hurricanes.
How hurricanes affect loan losses is unclear ex ante. In the short term, hurricanes can
cause significant physical damages to properties and businesses, leading to increased loan
losses. However, multiple sources of disaster assistance can mitigate the realization of these
losses. In the long term, cash inflows from disaster assistance and increased credit demand
for rebuilding in affected regions can mitigate hurricanes’ impact on bank losses. On the
other hand, hurricanes lead to decade-long damages on the affected local economy and gov-
ernments, which can cause increased long-term losses for banks. Potential short-term and
long-term impacts of hurricanes on bank losses are discussed in more detail in Section 2.3.
Table 2, Panel A reports the long-term impact of hurricanes on bank losses and failures.
Hurricane exposure significantly reduces a bank’s loan losses in the first two years following
20a hurricane shock. However, loan losses return to pre-hurricane level by the third year,
then surge above the pre-hurricane level four to seven years after the shock. In terms of
economic magnitude, a median-level hurricane exposure leads to 0.017 to 0.024 percentage
point increase in net charge-offs four to seven years after a hurricane, which accounts for
9% to 13% (5% to 7%) of median (average) net charge-offs. These results are in line with
Blickle, Hamerling, and Morgan (2021)’s finding that on average, loan losses increase by
9% five years after weather disasters (yet with no increase during the first three years). In
addition, the probability of bank failures significantly increases four to five years after a
hurricane shock.
In Table 2, Panel B, I examine the role of disaster assistance in mitigating loan losses
in the short term after hurricanes. Consistent with the prediction that disaster assistance
indirectly helps banks reduce loan losses, I find that the interactions of hurricane exposure
with each type of the three primary disaster assistance (i.e., flood insurance, SBA loans, and
FEMA assistance), as well as with their sum, are significantly negatively associated with
next year’s net charge-offs.14
The “decline-before-increase” pattern of post-hurricane loan losses is an important re-
sult. This pattern suggests that the negative impact of hurricanes on banks is of a long-term
nature, and that banks enjoy a “grace period” in the short term, during which they have
the opportunity to provision for long-term losses without the pressure of heightened losses.
However, the short-term decline in loan losses also allows banks to delay provisioning, which
can be justified by the need to release lending capacity and the uncertainty of post-hurricane
losses. Insufficient short-term provisioning would make banks more vulnerable to long-term
loan losses, leading to higher default probability. This unique ”grace period” calls to atten-
14
For this analysis, I use the subsample of observations with positive hurricane exposure and positive
assistance values. Only when these variables are non-zero can I reasonably evaluate how the sensitivity of loan
losses to hurricane exposure varies with the extent of disaster assistance. In the full sample, observations with
zero (positive) disaster assistance are typically also ones with zero (positive) hurricane exposure. Therefore,
the variation of disaster assistance among zero observations is not useful, and it is also not meaningful to
test the effect of disaster assistance changing from zero to positive, when hurricane exposure also typically
changes from zero to positive.
21tion the importance of studying banks’ short-term responses to climate hazards.
After documenting the pattern of post-hurricane loan losses, I examine how well banks’
short-term provisioning covers their long-term losses after hurricanes.15 The results are
reported in Table 3. The first two columns show that, similar to loan losses, both the
model-estimated expected loan losses, ExpLoss, and the reported allowance decline in the
first two years after a hurricane shock before increasing upon the fourth or fifth year. This
pattern suggests that neither ExpLoss nor allowance fully captures long-term losses caused
by hurricanes. ExpLoss’s post-hurricane pattern is expected, as the prediction model that
generates this measure does not incorporate hurricane-related predictors. The same pattern
for allowance, however, suggests that banks behave similarly to a model that does not use
hurricane-related information. Column 3 reports my primary tests on banks’ recognition of
expected losses after hurricanes. I find that, after controlling for ExpLoss, OV H significantly
increases in the year following a hurricane shock. As discussed in Section 3.1, this result
suggests that banks hit by hurricanes not only fail to recognize hurricane-induced losses, but
also delay more in recognizing expected losses unrelated to hurricanes.
5.2 Expected loss recognition and risk-taking after hurricanes
In the second step of my analysis, I investigate the influence of the delayed loss recognition
on banks’ post-hurricane risk-taking behavior. Delays in expected loss recognition inflates
capital and reduces bank transparency, which give banks the opportunities and incentives
to take on more risks.
In assessing banks’ risk-taking behavior, I focus on their “recovery lending,” i.e., mortgage
lending to hurricane-affected counties. Lending behaviors are an efficient measure of post-
hurricane risk-taking. Increasing the amount of recovery lending relative to total lending
15
Note that while banks follow the incurred-loss model during the sample period, it is still meaningful
to examine their recognition of long-term losses. This is because banks have considerable discretion in
recognizing incurred losses (Ryan, 2012, 2017), and because their allowance on average covers multiple years
of charge-offs, which suggests that banks reserve significant buffer against long-term losses (Lu and Nikolaev,
2022).
22increases banks’ exposure to hurricane-related climate risks, because the economic recovery
in hurricane-affected regions is highly uncertain. In addition, the heightened credit demand in
affected regions makes recovery lending a convenient method (thus an appropriate measure)
of risk-shifting.
I measure recovery lending using the total amount of mortgage originated in hurricane-
affected counties scaled by lagged total loans outstanding, and I measure total lending using
the growth of total loans outstanding. Results in Table 4, Panel A suggest that hurricane
exposure increases recovery lending and has no significant effect on total lending in the
year following a hurricane shock. Moreover, OV H in the year following a hurricane shock
significantly strengthens the positive relationship between recovery lending and hurricane
exposure. OV H has an insignificant effect on the relationship between total lending and
hurricane exposure. In terms of economic magnitude, for a bank exposed to an average level
of hurricane damage, one standard deviation higher OV H in the year following a hurricane
shock increases the bank’s recovery lending by 0.26 percentage point (0.00019 × 0.00947 ×
1447), which is 1.8 times the average level of recovery lending, or 17% of the average level
of positive recovery lending. The finding that OV H does not increase total lending suggests
that the delayed loss recognition is not used to increase lending capacity after hurricanes.
Instead, the fact that OV H increases the concentration of lending to disaster-affected regions
suggests that the delay facilitates post-hurricane risk-taking.
To provide further evidence on the effect of delaying loss recognition on risk-taking, I
examine whether banks charge higher mortgage rates after shifting lending to hurricane-
affected regions. Following Yang (2021), I measure mortgage price using the proportion
of mortgages issued with a positive spread over “3% + the yield on Treasury securities of
comparable maturity”, Share, and the average spread of all mortgages issued, Spread. In
Table 4, Panel B, I find that on average, hurricanes do not increase Share or Spread (columns
1 and 2), but hurricanes significantly increase the Share and Spread in hurricane-affected
regions (columns 3 and 4). These results suggest that mortgage prices are higher in hurricane
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