1

Assessing the impact of hurricane frequency and intensity on 

mortgage default risk

Clifford V. Rossi1 

Abstract

Considerable meteorological research suggests that the frequency and intensity of North Atlantic 

hurricanes is rising. This analysis focuses on estimating the impacts of hurricane intensity and 

frequency on mortgage default. Based upon a large loan level dataset of mortgages purchased by 

Freddie Mac between 2000-2013, loans where a Category 3, 4 or 5 hurricane occurred during a 

loan’s life were found to be 13-18 percent more likely to become delinquent than other loans in 

the same locations, controlling for all other risk factors. In addition, loans that experienced more 

than 12 hurricanes during a loan’s life were two times more likely to default than loans 

experiencing 1 or fewer hurricanes on average over time. These results have major implications 

for mortgage and insurance markets and homeowners.

First, if long-term hurricane trends bear out, mortgage default risk in areas with a higher incidence 

of major hurricanes will likely rise significantly over time. Second, investors in mortgage credit 

risk from these locations will face higher default losses in the future. Third, private investors in 

mortgage credit-risk transfer (CRT) securities could experience higher credit losses of loans from 

hurricane-prone areas. Investors in lower-rated tranches would be particularly impacted given the 

nature of their exposure to losses earlier than more highly-rated tranches. Catastrophe bonds could 

be used to diversify hurricane risks to investors that may be in a better position to assess and hold 

this risk.

Keywords Hurricane Risk, Mortgage Default, Risk Management, Reinsurance

JEL Classification G21 G28 Q51 Q54 R31

1 Professor-of-the Practice and Executive-in-Residence, Robert H. Smith School of Business, University of Maryland, 

College Park, Maryland. The author thanks Michael Fratatoni, Daniel Wu, Russ Wermers, Greg Young, and Joe 

Mattey for their review of and insightful comments on this paper. Any errors or omissions remaining are those solely 

of the author. I would also like to acknowledge the excellent technical support by Shivam Choudhary for this work. 

Comments and perspectives provided in this paper those of the author and do not reflect the views of the University 

of Maryland or the Robert H. Smith School of Business.
2

1 Introduction

Considerable meteorological research suggests that the frequency and intensity of North Atlantic 

hurricanes is on the rise. The destructive power of these storms is well documented, that in addition 

to deaths and injuries causes significant economic losses, business disruption and life- changing

dislocation for individuals and families. The Congressional Budget Office estimates that annual 

economic losses from hurricanes in the United States are $54 billion, with losses from the 

residential sector accounting for $34 billion of that amount.2 Moreover, according to Pielke et al., 

85% of damage caused by hurricanes is associated with hurricanes rated 3-5 on the Saffir-Simpson 

Hurricane Wind Speed Scale.3

Extensive modeling of hurricanes suggests that while the overall frequency of hurricanes in the 

future may actually decline over the next century, the frequency and intensity of the strongest 

hurricanes i.e., those rated Category 4 and 5 is likely to significantly increase over this period. At 

the upper end, one study suggests that the aggregate strength of North Atlantic hurricanes could 

rise 300% above where they have been in the recent past.4 Other studies suggest a more moderate 

path for future hurricane intensity and frequency despite the fact that the aggregate power of 

hurricanes as measured by the Power Dissipation Index (PDI) in 2007 was approximately 6 times 

the PDI level of the early 1980s, signaling that this recent period was not only marked by more 

hurricanes, but ones with greater intensity.5

The linkages between hurricanes and mortgage default are emerging from a variety of empirical 

studies on this topic. Fannie Mae, for example found mixed results when reviewing the impacts 

from two major hurricane events; Hurricanes Katrina (August 2005) and Sandy (October 2012).6

In the months immediately following Hurricane Katrina for instance, loans delinquent 180 days or 

more (D180+) peaked at a rate approximately 5 times the D180+ rate in the months leading up to 

the storm. By contrast, Fannie Mae found that D180+ rates following Hurricane Sandy remained

2 Congress of the United States, Congressional Budget Office, Expected Costs of Damage From Hurricane Winds and 

Storm-Related Flooding, April, 2019, pg. 1-2.

3 R.A. Pielke, Jr. et al., “Normalized Hurricane Damage in the United States: 1900-2005”, Natural Hazard Review 

(2008).

4 Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration, Global Warming and 

Hurricanes, An Overview of Current Research Results, June 12, 2020.

5 Emanuel, K.A. 2016 update to data originally published in: Emanuel, K.A. 2007. Environmental factors affecting 

tropical cyclone power dissipation. J. Climate 20(22):5497–5509

6 Fannie Mae, Historical Data Provides Insights Into Past Hurricane Experience, November 6, 2017.
3

relatively steady. Underscoring the magnitude of potential risk for the mortgage sector, CoreLogic 

estimates that 7.4 million residential and multifamily properties, accounting for approximately

$1.8 trillion in replacement costs would be affected by storm surge from hurricanes in the U.S.7

The focus of this analysis is to understand the specific impacts of hurricane intensity and frequency 

on mortgage default. Using a sample of 100,000 mortgage loans purchased by Freddie Mac that 

were originated between 2000-2013, models describing a borrower’s probability to default as 

defined by either loans that become delinquent 90 days or more (D90+) or D180+, and augmented 

with data on hurricanes from the FEMA Disaster Declarations Summaries, were estimated. In 

addition to standard risk attributes associated with the borrower, loan product, and property, factors 

describing the frequency and intensity of hurricanes experienced in counties where these properties 

are located were found to be statistically significant.

Loans where a Category 3, 4 or 5 hurricane was experienced during the loan’s life were found to 

be 13 - 18 percent more likely to become 180 days or 90 days delinquent or more, respectively 

than other loans in the same locations, controlling for all other risk factors. In addition, loans that 

experienced more than 12 hurricanes during the loan’s life were two times more likely to default 

than loans experiencing 1 or fewer hurricanes on average over time. These results were comparable 

for the D180+ model and both models’ results have major implications for mortgage and insurance 

markets and homeowners.

First, if long-term hurricane trends bear out, mortgage default risk in areas with a higher incidence 

of major hurricanes will likely rise significantly over time. Second, investors in mortgage credit 

risk from these locations will face higher default losses in the future. At the same time, unless the 

government sponsored enterprises (GSEs) Fannie Mae, Freddie Mac and the FHA factor such risk 

into their pricing of credit risk, these agencies would be underpricing hurricane risk effects on 

default. This could also affect risk-based capital requirements and loan loss reserve estimates for 

Fannie Mae and Freddie Mac over time. Third, private investors of GSE credit-risk transfer (CRT) 

securities could experience higher credit losses associated with pools of loans from hurricaneprone areas. Those investors in lower-rated tranches would be particularly impacted given the 

nature of their exposure to losses earlier than more highly rated tranches. Further use of

7 CoreLogic, 2020 CoreLogic Storm Surge Report, May 28, 2020.
4

catastrophe bonds (cat bonds) could be one vehicle to diversify hurricane risks away from specific 

investors that may be in a better position to assess and hold this risk. A cat bond could be set up 

as part of a CRT transaction that would transfer hurricane default risk to another investor such as 

a reinsurer.

2 Theoretical Linkages between Mortgage Default and Hurricane Risk

This study builds upon a large academic literature treating mortgage default in an option-theoretic 

framework. The contingent-claims literature starting with Black and Scholes (1973) and Merton 

(1973) serves as the foundation for describing mortgage cash flows.8 Examples of early work to 

describe mortgage valuation in an option-theoretic framework included Cunningham and 

Hendershott (1984) and Epperson, Kau, Keenan, and Muller (1985).

9 Completing the contingentclaim mortgage valuation framework requires consideration of the competing risk nature of the 

default (put option) and prepayment (call option) options as described in Kau, Keenan and Muller 

and Epperson (1992).10 Fundamentally, mortgage value (VM) can be viewed as comprising three 

components as described in Equation 1; the value of a risk-free bond (VRF) less the value of two 

embedded borrower options; the option to default on the mortgage (VD) and the option to prepay 

the mortgage (VPP); where H represents changes in home prices which affects property value and 

hence the borrower’s incentive to exercise the default option where unpaid principal balance 

(UPB) of the loan at time of default represents the default option strike “price”, r represents 

changes in mortgage rates which affects the borrower’s incentive to exercise their prepayment 

option and t reflects changes in time over which the value of all three components change.

(1) VM (H,r,t) = VRF (H,r,t) – VD (H,r,t) – VPP (H,r,t)

The focus of the present analysis is to empirically analyze the effect of hurricane intensity and 

frequency on the mortgage default component VD of equation 1. The classic depiction of a 

mortgage default option is of a borrower ruthlessly exercising that put option whenever the value

8 Black, F. and M.S. Scholes(1973), “ The pricing of Options and Corporate Liabilities,” Journal of Political Economy, 

81: 637-654., and Merton, R.C., (1973), “Theory of Rational Option Pricing,” Bell Journal of Economics and 

Management Science, 4, 141-183.

9 Cunningham, D.F., and P.H. Hendershott, (1984), “Pricing of FHA Mortgage Default Insurance,” Housing Finance 

Review, 13, 373-392 and Epperson, J.F., J.B. Kau, D.C. Keenan, and W.J. Muller III (1985), “Pricing Default Risk in 

Mortgages,” Journal of the American Real Estate and Urban Economics Association, 13, 261-272.

10 Kau, J.B., D.C. Keenan, W.J. Muller, and J.F. Epperson (1992), “A Generalized Valuation Model for Fixed-Rate 

Residential Mortgages,” Journal of Money, Credit and Banking, 24, 279-299.
5

of the property falls below the UPB at time of default. In reality, borrowers are not perfectly 

efficient in exercising their default option due to a variety of friction costs and other contributing 

factors or default triggers that can induce a default event. Friction costs include the impact of 

default on borrower credit and future foregone access and cost of credit opportunities following 

default. Default trigger events include job loss, reduction in income, divorce, serious medical or 

other catastrophic life event. Empirically, we are typically unable to observe the actual trigger 

event that is the catalyst for mortgage default, however, we can characterize the risk factors into 

several categories; borrower-specific, product- or loan-specific, property-specific, 

macroeconomic-specific, and external-specific.

In terms of borrower-specific risk factors, mortgage underwriting has been influenced for decades 

by the 3C’s of underwriting, representing credit, capacity and collateral. The credit factor 

represents the borrower’s willingness-to-repay the mortgage obligation. Typical proxies for 

borrower creditworthiness include credit score and/or detailed credit attributesfrom the borrower’s 

credit report. Capacity represents the borrower’s ability-to-repay the obligation and typical proxies 

include borrower income or relative measures such as borrower debt-to-income ratios (DTI). 

Multiple borrowers on the mortgage note tend to reduce default propensity due to income 

diversification. Finally, collateral measures the borrower’s leverage in the property, or 

alternatively their equity stake. This factor may be captured in various ways including the loanto-value (LTV) ratio and with house price volatility, among others. Borrower underwriting takes 

into account the LTV at origination, however, since both the loan amount and property value vary 

over time and especially at the time of default, current LTV is more representative of default over 

time. Collateral risk factors lie at the heart of the borrower’s default option decision as the 

underlying property value changes relative to the borrower’s remaining loan balance. A good 

example of this interaction was during the financial crisis of 2008 when many residential properties 

declined significantly below the value of the mortgage balance due to plummeting home values 

during this time. The crash in home prices drove many borrower current LTVs above 100%, 

leaving them effectively “underwater” on their mortgages and thus incented to default, 

notwithstanding the friction costs mentioned earlier.

Loan product risk factors may also influence the borrower’s incentive to default. Factors such as 

product type; i.e., whether the loan is a fixed-rate amortizing mortgage (FRM) or adjustable-rate
6

mortgage (ARM), or has a 30-year or 15- or 20-year term, for example can affect mortgage default. 

The variable nature of the ARM product along with potential borrower selection issues can elevate 

default risk relative to fixed-rate products. Likewise, loans with shorter amortization periods, 

despite their higher monthly payments may reflect borrower preferences, financial wherewithal 

and intentions to pay off the mortgage more quickly. The borrower’s note rate, relative to 

prevailing mortgage rates may provide market signals regarding the borrower’s credit risk. For 

example, if the spread between the prevailing fixed-rate 30-year amortizing mortgage rate at the 

time of origination and the actual 30-year note rate obtained by the borrower is positive, this could 

be an indication that the borrower carries incrementally higher credit risk that is priced into the 

mortgage rate. Subprime borrowing rates are an example of how credit risk can be priced into a 

mortgage rate. Another important risk factor affecting default is loan purpose. There are several 

reasons why a mortgage is taken out. One reason is the borrower is purchasing a home, another is 

they are taking advantage of lower mortgage rates on an existing property (hence exercising their 

prepayment option to refinance the home), or they could be extracting equity from the property for 

other uses such as a remodeling project or nonresidential purpose (e.g., taking a vacation). The 

latter purpose tends to be a riskier proposition than the other two alternatives. Lastly, the channel 

through which the loan was originated can contribute to credit risk. Loans that are originated 

through retail branches of the lender tend to have lower default risk than broker- and correspondent 

loan channels. These non-retail outlets may reflect issues associated with less robust loan 

manufacturing processes.

Property attributes can also affect mortgage default. Dwellings other than single-family homes 

such as condominiums, manufactured housing or mobile homes and coop units may influence the 

default outcome. Likewise, whether the home is a 1- unit or 2-4 unit property can affect default. 

The occupancy status of the property can affect default. This factor embodies the borrower’s 

psychic attachment to the property as well as an indication of the potential leverage in housing 

assets a borrower has and the stability of income flows on investment properties. Occupancy status 

is usually reflected by three categories; primary residence; 2nd home, and investor-owned. The 

latter typically is a riskier outcome followed by 2nd home properties.

As described earlier several macroeconomic factors can affect default including changes in home 

prices, unemployment rates and mortgage rates. Underwriting models do not include these factors
7

in arriving at loan decisions, but will be found in loan pricing and loss measurement modeling 

where intertemporal changes in default and prepayment are captured in computing discounted 

mortgage payment cash flows, defaults and prepayments over the life of the loan.

External events such as natural disasters form the last category of risk factors in mortgage default 

analysis. Specifically, for this analysis, the impact of hurricane events is of primary interest. Over 

the years, a number of studies have examined the impact of floods and hurricane events on 

mortgage default, but until now, none have directly measured the impact of hurricane intensity and 

frequency on mortgage default propensity. The linkage between hurricanes and mortgage default 

is posited to come about in several ways through key economic relationships that affect the 

borrower’s default option. When a hurricane event occurs, depending on its severity it can have 

economic impacts on an affected area for some time. A direct effect from a hurricane is damage it 

inflicts on the borrower’s property from high winds and extensive flooding events. 

Notwithstanding the existence of national flood insurance and homeowner hazard insurance 

policies, high deductibles (e.g., hurricane events often require higher deductibles) and in some 

cases undervalued policies may leave homeowners with few options than to default on their 

mortgage if the costs to rebuild exceed insurance payouts plus any additional resources the 

borrower may have to put toward rebuilding. Further, potential job or income loss due to a 

hurricane can put further pressure on borrowers after the hurricane event.

Research findings on the effect of hurricanes on mortgage default, not surprising vary given the 

specific focus of hurricane research. At the individual storm level, for instance, Fannie Mae’s 

assessment of two major hurricane events; Hurricanes Katrina and Sandy show divergent results. 

Hurricane Katrina exhibited a clear spike in D180+ delinquency rates in the 6 months afterward 

that were approximately 5 times greater than D180+ rates preceding the storm. Fannie Mae 

reported that weighted average delinquency rates (30 days and greater) were 4.24% in Katrinaaffected areas versus 1.99% elsewhere. However, after Hurricane Sandy, Fannie Mae found no 

such spike in D180+ delinquency rates, although weighted average 30+delinquency rates were 

higher for affected areas (8.4%) versus those not affected by the hurricane (5.31%). Analysis on
8

Hurricane Florence in 2018 by CoreLogic found mortgage default rates doubled 3 months 

following the storm.11

Hurricane Katrina, the costliest hurricane affecting the United States started out as a Category 5 

hurricane before weakening to become a Category 3 by the time it made landfall in Louisiana and 

Mississippi and eventually causing $125 billion in damage.12 Hurricane Sandy was the second 

costliest hurricane in U.S. history at $70 billion.13 It started out as a Category 3 hurricane before 

weakening to a Category 1 when it made landfall along the northeast coast of the U.S. Looking at 

hurricane risk at a macro level, Kahn and Ouazad examined the impact of hurricane events over a 

180 year period in the U.S and found that a natural disaster would increase the probability of 

foreclosure by 1.6% taking into account a variety of the risk factors described earlier.14

This analysis is unique in that it is the first to examine the intensity and frequency of hurricanes 

on mortgage default. The reason why this aspect of hurricane event dynamics is critical to 

understand is that a number of meteorological studies are finding that the strength and frequency 

of these natural disasters could be on the rise over this century. The Saffir-Simpson Hurricane 

Wind Scale is a familiar metric for relating wind intensity to damage on a logarithmic scale. Figure 

1 provides insight into the relationship between wind speed and potential damage. For instance, 

when Hurricane Katrina first came ashore in Louisiana as a Category 3 hurricane with sustained 

winds of 125 mph, the potential damage from those winds were 60 times worse than a Category 1 

hurricane with 75 mph winds. The Saffir-Simpson scale thus illustrates the wide divergence in 

potential hurricane impacts. The scale also does not account for other storm damage such as surge,

11 CoreLogic, Press Release, Corelogic Estimates Nearly 7.4 Million Homes are at Risk of Storm Surge Ahead of 

Hurricane Season and an Uncertain Economy, May 28, 2020.

12 National Oceanic and Atmospheric Administration. "Costliest U.S. Tropical Cyclones Tables Updated," Page 2. 

Accessed Jan. 28, 2020.

13 FEMA Fact Sheet: Mitigation Assessment Team Results – Hurricane Sandy

14 Ouazad A. and M.E. Kahn, Mortgage Finance in the Face of Rising Climate Risk, NBER Working Paper 26322, 

September 2019.
9

Max

Fig. 1: Saffir-Simpson Hurricane Wind Speed and Potential Damage Relationship15

rainfall and tornadic events which if accounted for would drive the potential damage multipliers 

higher.

To measure the combined effects of hurricane frequency, intensity and duration, Emanuel (2005) 

developed the Power Dissipation Index (PDI). PDI is defined according to equation 2 where V3is 

the cubed maximum sustained wind speed at an altitude of 10 meters.16 Historical trends of 

hurricanes along two important dimensions of frequency and intensity provides some insight into

τ

(2) PDI = ∫ V3 dt

0

hurricane dynamics. Figure 2, summarizes the annual frequency of hurricanes in the U.S. between 

1851 and 2006.17 The number of hurricanes has risen over time overall and for major hurricanes.

15 Adapted from data provided in National Weather Service, National Oceanic and Atmospheric Administration, 

Hurricane Damage Potential.

16 Emanuel K. (2005), Increasing Destructiveness of Tropical Cyclones Over the Past 30 Years, Nature, 436, 686-688. 

17 Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration, Global Warming and 

Hurricanes, An Overview of Current Research Results, June 12, 2020.
10

Major Hurricanes

8

7

6

5

4

3

2

1

0

1851 1901 1951 2001

Year

Tropical Storms

30

25

20

15

10

5

0

1851 1901 1951 2001

Year

Hurricanes

16

14

12

10

8

6

4

2

0

1851 1901 1951 2001

Year

Fig. 2 – Atlantic Basin Hurricane Counts (1851-2013) Number Number Number
11

Figure 3 plots the PDI and sea surface temperatures (SST) over time. Consistent with Figure 2, 

between 1980 and the mid-2000’s, the PDI of hurricanes rose sharply. Considerable research has 

been conducted to understand the degree to which anthropogenic causes such as man-made fossil 

fuel emissions and other sources have resulted in higher sea surface temperatures and their impact 

on hurricanes. Debate continues among meteorological researchers as to the extent to which 

climate change from whatever source poses a long-term increase in the frequencyand intensity of 

North Atlantic hurricanes.

Fig. 3 – North Atlantic Tropical Cyclone Activity According to the Power Dissipation Index 1951-

2013

Changes in SST over time as a result of man-made activities have been a central focus of much of 

the research to understand the trajectory of future hurricane risk. Research examining historical 

correlations between SST and hurricane PDI results in a wide range of potential outcomes over 

this century in terms of PDI. On an absolute basis, the relationship between SST and PDI for

7 83.2

83

6

82.8

5 82.6

4

82.4

82.2

3

82

2

81.8

81.6

1

81.4

0

1940 1950 1960 1970 1980 1990 2000 2010

81.2

2020

Power Dissipation Index Sea Surface Temperature

Power Dissipation Index

Sea Surface Temperature (

oF)
12

tropical Atlantic hurricanes applied to 24 different hurricane models suggest a 300% increase in 

hurricane PDI by the year 2100.18 Alternatively, if SST is measured relative to mean tropical SST

Fig. 4 Simulated Category 4 and 5 Hurricane Tracks19

18 Vecchi, G.A., K.L. Swanson, B.J. Soden, (October 31, 2008), “Whither Hurricane Activity,” Science, 322, 688-

689.

19 Knutson, T.R., J.J. Sirutis, G.A. Vecchi, S. Garner, M. Zhao, H.S. Kim, M. Bender, R.E. Tuleya, I.M. Held, G. 

Villarini, (2013), “Dynamical Downscaling Projections of Twenty-First Century Atlantic Hurricane Activity: 

CMIP3 and CMIP5 Model-Based Scenarios,” Journal of Climate, 26(17):6591-6617. The authors compared two 

different hurricane models (GFDL and GFDN) with two versions of climate change models (CMIP3 and CMIP5) in 

producing the simulated tracks shown in Figure 4.
13

rather than to tropical Atlantic SST, the impact on PDI is slight. Other studies tend to support the 

results on Vecchi et. al in Figure 4b that a long-term increase in hurricane PDI would be small.20

However, Bender et. al found that the number of Category 4 and 5 Atlantic hurricanes could 

increase 90% over time. Corroborating this result, Knutson et. al reported large percentage 

increases in Category 4 and 5 hurricanes in the early (45%) and late (39%) part of the 21st century.

3 Mortgage and Hurricane Data Structure and Summary

The statistical analysis of mortgage default and hurricane intensity and frequency is based on two 

datasets. Data on individual mortgage performance is sampled from the Freddie Mac SingleFamily Loan-Level Dataset. The data includes details on 27.8 million fixed-rate mortgages 

purchased by Freddie Mac originated between 1999 and 2018. Monthly performance updates on 

each loan are available through June 2019. Key risk factors described earlier are included in the 

data files such as FICO score, original and combined LTV, debt-to-income ratio, loan purpose, 

amortization, owner-occupancy, first-time homebuyer indicator, number of units, number of 

borrowers, property type, loan amount, and origination channel.21 A random sample of 100,000 

loans originated between 2000 and 2013 was taken from the full dataset for properties in Gulf and 

east coast states impacted by hurricane events during this period according to FEMA records.22

Two definitions of default were applied in the analysis, loans that were 90 days past due or more 

(D90+) and 180 days past due or more (D180+) to gain a sense of the impact on different 

definitions of late-stage mortgage delinquency. For the final sample, the mean D90+ and D180+ 

rates were 6.1% and 5.6%, respectively. A summary of key attributes of the Freddie Mac data is 

found in Tables 1-11.

20 Bender, M.A., T.R. Knutson, R.E. Tuleya, J.J. Sirutis, G.A. Vecchi, S.T. Garner, I.M. Held, (January 2010), 

“Modeled Impact of Anthropogenic Warming on the Frequency of Intense Atlantic Hurricanes,” Science , 327, Issue 

5964, 454-458.

21 UPB was transformed into a relative median UPB measure. That is, the median UPB of the MSA or state (if 

identified as a rural property) was used to divide each loan’s UPB. Relative median UPB is a more accurate reflection 

of the relative size of each loan in its MSA or state in terms of its relationship with default.

22 States included in the analysis were Alabama, Connecticut, Florida, Georgia, Louisiana, Massachusetts,Maryland, 

Maine, Mississippi, North Carolina, New Hampshire, New Jersey, New York, Rhode Island, South Carolina, Texas, 

Virginia.
14

Table 1 – Key Risk Factor Statistics

Attribute Mean Standard Deviation Minimum Maximum

FICO 730 56.2 356 840

Original LTV 73.4 15.8 6.0 100

DTI 35.2 12.0 1.0 65.0

Relative Median

UPB

108.0 49.3 6.8 496.5

Table 2 – Occupancy Status

Attribute Number Percent of Total D90+ Rate (%) D180+ Rate (%)

Investor-owned 5,136 5.14 8.45 7.36

Primary Residence 89,533 89.53 6.06 5.60

Second Home 5,331 5.33 4.60 4.18

Table 3 – Property Type

Attribute Number Percent of Total D90+ Rate (%) D180+ Rate (%)

Condominium 8,818 8.82 6.91 6.43

Planned Unit 20,184 20.18 5.08 4.69

Single-Family 70,998 71.00 6.30 5.77

Table 4 – Loan Purpose

Attribute Number Percent of Total D90+ Rate D180+ Rate

Cash-out Refinance 26,276 26.28 9.51 8.88

Rate & Term Refinance 23,468 23.47 4.84 4.41

Purchase 50,256 50.26 4.92 4.47
15

Table 5 – First-time Homebuyer

Attribute Number Percent of Total D90+ Rate D180+ Rate

No 86,557 86.56 6.20 5.70

Yes 13,443 13.44 5.53 5.04

Table 6 – Number of Units

Attribute Number Percent of Total D90+ Rate D180+ Rate

1 97,217 97.22 6.01 5.52

2 2,200 2.20 9.77 9.00

3 399 .40 10.28 9.27

4 184 .18 7.61 5.98

Table 7 – Origination Channel

Attribute Number Percent of Total D90+ Rate D180+ Rate

Broker 30,394 30.39 8.24 7.81

Correspondent 26,266 26.27 5.37 4.69

Retail 43,340 43.34 5.06 4.64

Table 8 – Number of Borrowers

Attribute Number Percent of Total D90+ Rate D180+ Rate

1 45,570 45.57 8.36 7.70

2 54,430 54.43 4.22 3.86
16

Table 9

Attribute Number Percent of Total D90+ Rate D180+ Rate

<=620 3,799 3.80 19.03 17.72

620-660 8.996 9.00 16.08 15.03

660-700 15,916 15.92 9.88 9.09

700-750 26,808 26.81 5.57 5.07

>750 44,481 44.48 1.96 1.76

Table 10 = LTV

Attribute (%) Number Percent of Total D90+ Rate D180+ Rate

<= 50 9,676 9.68 2.19 1.96

50 - 80 62,571 62.57 5.59 5.18

80 - 90 14,035 14.04 8.56 7.78

>90 13,418 13.42 8.71 7.94

Table 11 – DTI

Attribute (%) Number Percent of Total D90+ Rate D180+ Rate

< 30 33,149 33.15 3.25 2.88

30 - 40 30,038 30.04 5.26 4.81

 40 36,813 36.81 9.37 8.73

The bivariate results of individual risk factors by the two default definitions in Tables 2-11 

generally conform to the earlier discussion of how borrower, loan, and property factors relate to 

mortgage default risk. Noticeably, key variables such as LTV, FICO and DTI exhibit a nonlinear 

relationship to default. For example, D90+ and D180+ rates for borrowers with FICOs at or below 

620 are approximately 10 times greater than those with FICO scores over 750. Likewise, borrowers 

with LTVs greater than 90% experience D90+ and D180+ rates that are about 4 times higherthan 

borrowers with LTVs less than or equal to 50%. Of course, these summary results are uncontrolled 

for other factors which will be examined in more detail in the next section.
17

The other dataset used in the analysis is the FEMA OpenFEMA Dataset: Declarations Summaries. 

The data consists of information on each federally declared disaster since 1953. The data include 

information on the type of disaster such as a hurricane, the hurricane name, beginning and end date

of the event, state, and county. In order to merge this data with the Freddie Mac loan level data, 

several additional steps were taken. First, only hurricane and tropical storm events occurring 

during the loan origination periods of the Freddie Mac data; i.e., 2000-2013 were included. The 

Saffir-Simpson Hurricane Category for each named hurricane for each storm was obtained from 

National Hurricane Center Tropical Cyclone Reports and the category at the time of first landfall 

in the U.S was used to designate the initial hurricane strength in the modeling. It is recognized that 

strength of each storm could change as it moved inland or over water, however, the initial rating 

used provides a reasonable benchmark for gauging overall impact relative to other storms and 

categories during the 2000-2013 period of interest. According to the FEMA data, there were 41 

named hurricanes in the Atlantic region that resulted in a disaster declaration. A distribution of 

storms by category is shown in Table 12. Figures 5 and 6 display the average number of hurricanes 

experienced for each property by county and the average hurricane rating by property. The D90+ 

and D180+ rates for each category of hurricane are displayed in Table 13. On an uncontrolled basis 

there appears to be some association between hurricane rating and default rates, although that 

relationship is not monotonic for category 4 or 5 storms. Further analysis is required on a 

multivariate basis to determine the nature of this relationship in a more robust fashion. Table 14

Table 12 – Tropical Storm and Hurricane Summary for Sample

Storm Category Number in

Sample Data

Tropical Storm 9

1 11

2 7

3 9

4 2

5 3
18

Fig. 5 – Number of Hurricanes of Freddie Mac Sample Loans Originated 2000-2013

Fig. 6 –Average Hurricane Ratings of Freddie Mac Sample Loans Originated 2000-2012

1-5

6-10

11-15

>15
19

depicts the relationship between the average number of hurricanes experienced by each property 

in the sample and D90+ and D180+ rates. The uncontrolled bivariate results show a monotonic 

increase in both D90+ and D180+ rates.

Table 13 – Hurricane Rating by D90+ and D180+ Rates

Category D90+ D180+

Tropical Storm or Category 1 5.34 5.02

2 5.30 4.92

3 7.47 6.86

4 6.63 5.71

5 3.45 3.45

Table 14 – Average Number of Hurricanes and Default Rates

Average Number of

Hurricanes

Number D90+ Rate D180+ Rate

0-1 2,227 4.31 3.95

2-3 7,588 5.40 4.89

>3 90,185 6.21 5.72

4 Methodology and Empirical Approach

To analyze the impact of hurricane intensity and frequency on mortgage default, a standard logistic 

regression model applied in underwriting borrowers is used. Two logistic regression models are 

specified reflecting two different definitions of the binary choice dependent variable; D90+ and 

D180+ with the default event taking on a value of 1 and nondefault events are treated as 0 

otherwise. This ensures the estimated probabilities are confined to the 0-1 domain. Following the 

theoretical model of mortgage default presented earlier, mortgage default in both models is a 

function of borrower, product, property and hurricane risk factors. The general form of the 

regression models is presented in equation 3 and 4 below:
20

(3) PDefault

=

1

1+e−Z

(4) Z = f(FICO, CLTV, LTV, DTI, NUMUNIT, OCC, CHANNEL, < PROPTYPE, PURPOSE, 

NUMBORR, FTHB, HURFREQ, HURINT, AGE)

A description of the candidate variables for analysis are found in Tables 15 and 16. Several 

transformations of key variables were made prior to modeling. Due to inherent nonlinearities in 

FICO and credit score, a set of splined variables were created and tested with different knot points. 

The general form of each spline is shown in equation 5.

M

(5) βiVARi + ∑ βm(Max(VARi − KPm,0))

m=1

where VARi is variable i to be splined, and KPm is the mth knot point chosen. For FICO, a set of 

knot points consistent with industry practice were tested at 620, 660, 700, 720 and 750. For CLTV, 

candidate knot points tested included 50%, 80%, 85%, 90%, and 95%. Final estimates for the 

number of splines and knot point settings were based on statistical significance of each spline and 

contribution to model performance. The variable AGE, was included to control for the age of each 

loan from its origination.

Measurement of hurricane frequency and intensity for the models were based on the FEMA 

hurricane data merged with the Freddie Mac loan level data. The average of the Saffir-Simpson 

hurricane category of all hurricanes generating a FEMA disaster declaration experienced during 

the life of each loan in the county where a loan’s property was located was used to measure the 

impact of hurricane intensity on default. Hurricane frequency was defined as the number of 

hurricanes or tropical storms generating a FEMA disaster declaration experienced in the county 

where the property is located over the loan’s life. The default models with the best model 

performance as discussed below were when the hurricane frequency variable was set delineated 

by 12 or more hurricanes or tropical storms in a loan’s life. With a weighted average life in the 

sample of about 4.5 years, that roughly translates into an average of 2.7 storms per year for the 

highest frequency HURFREQ category.
21

Table 15 – Candidate Variable Description

Risk Factor

Category

Variable Name Definition Variable Type

Borrower FICO Credit score Continuous

CLTV Original combined LTV Continuous

DTI Debt-to-income ratio Continuous

OCC Occupancy type Categorical

NUMBORR Number of borrowers Categorical

FTHB First-time homebuyer Categorical

Property

NUMUNIT Number of property units Categorical

PROPTYPE Property type Categorical

Product/Channel

PURPOSE Loan purpose Categorical

RUPB Relative median UPB Continuous

CHANNEL Origination channel Categorical

Hurricane

HURFREQ Average number of

hurricanes experienced

Categorical

HURINT Average hurricane rating Categorical

5 Results

The results from the final set of estimations for both the D90+ and D180+ models are presented in 

Table 17.23 To compare alternative specifications when determining the “best” models for D90+ 

and D180+, the Kolmogorov-Smirnov (KS) test and the area under the curve (AOC) were used as 

model performance criteria. Specific attention is paid to these measures used to assess the model’s 

discriminatory power between default and non-default loans. On this basis, the splined effects for

23 The control variate, AGE is not reported for ease of exposition but are available from the author upon request.
22

FICO and CLTV resulted in a single knot point for FICO at 660 and two knot points; 80% and 

95% for CLTV. Some candidate variables such as relative median UPB and first-time homebuyer 

were not statistically significant and thus were removed from the models. In the final 

specifications, all estimated coefficients carry the expected signs and are all are statistically

Table 16- Categorical Variable Description

Variable Name Category

Name

Description

OCC Primary Primary residence

Investor Investor-owned

2

nd Home 2nd or vacation home

NUMBORR 1 1 borrower

2+ 2 or more borrowers

FTHB 1 First-time homebuyer

0 Non-FTHB

NUMUNIT 1 1 unit property

2-4 2-4 unit property

PROPTYPE SF Single-family

PUD Planned Unit Development

Condo/Coop Condominium or Coop

PURPOSE Purchase Purchase-only mortgage

Cash-out Cash-out refinance mortgage

R&T Rate & Term refinance

CHANNEL R Retail originated

B Broker originated

C or TPO Correspondent or third-party originated

HURFREQ 1 12 or more hurricanes over the life of the loan

0 Less than 12 hurricanes over the loan’s life

HURINT 1 Loan’s average hurricane rating <=2

0 Loan’s average hurricane rating >2 - 5
23

significant at the 10% level or lower. The majority of parameters were significant at the 1% level. 

The model performance statistics are robust as shown in Table 17.

Table 17 – Logistic Regression Results

Parameter Estimates Standard Errors

D90+ D180+ D90+ D180+

Intercept** -1.1335 -1.4160 .4716 .4858

FICO* -.0078 -.0077 .0007 .0007

FICO660* -.0065 -.0068 .0009 .0010

CLTV* .0291 .0298 .0016 .0017

CLTV80** .0099 .0066 .0037 .0038

CLTV95* .0594 .0654 .0189 .0195

DTI* .0293 .0304 .0012 .0013

NUMUNIT* .5183 .5146 .0720 .0749

Investor* .5034 .4301 .0570 .0603

B* .2657 .3040 .0318 .0329

C* .1090 - .0367 -

Condo .3000 .3191 .0483 .0499

Cash-out* .5990 .6147 .0398 .0413

Purchase* -.4045 -.4029 .0406 .0423

NUMBORR* .5965 .5972 .0286 .0297

HURINT* .1627 .1238 .0297 .0309

HURFREQ1* .7733 .7970 .1129 .1173

HURFREQ2** .2071` .2127 .1101 .1145

KS 46.4 47.0

AUC 80.1 80.4

Note: Parameters designated * or ** are statistically significant at the 1% and >1% - 10% levels, 

respectively
24

To understand the relative impact of the categorical variables, most notably the hurricane 

frequency and intensity effects, odds ratios were computed and shown in Table 18 along with the 

reference category for each variable. The odds ratios are consistently stable across default 

definitions. Holding all else constant, the odds ratio for 2-4 unit properties indicates that the 

incidence of default is 1.67 times that of a 1-unit property. The other categorical variables have 

comparable interpretations relative to the reference category indicated in Table 18. Of interest 

among these effects are the hurricane frequency and intensity variables. The results indicate that 

controlling for all other factors, default risk is 1.13 to 1.18 times higher for loans experiencing an 

average hurricane rating over 2. This result is consistent with the historical meteorological data 

showing that hurricanes rated 3-5 are associated with greater wind and flooding damage. In 

addition to this incremental hurricane risk, the results indicate that borrowers that have

Table 18 – Categorical Variable Odds Ratios

Variable Odds Ratio

D90+ Model D180+ Model

2-4 Units (relative to 1 unit) 1.67 1.67

Investor (relative to owner-occupied) 1.65 1.54

Broker (relative to retail_ 1.30 1.35

Correspondent (relative to retail) 1.12 1.06

Condo (relative to single-family) 1.35 1.38

Cash-out refinance (relative to R&T refinance) 1.82 1.85

Purchase (relative to R&T refinance) .67 .67

1 Borrower (relative to 2 or more) 1.82 1.82

Average hurricane rating >2 - 5 1.18 1.13

>1 – 12 hurricanes (relative to 1 or less) 1.23 1.24

12 or more hurricanes (relative to 1 or less) 2.17 2.22

Note: Odds Ratio = e

β

experienced 1-12 storms over the loan’s life are 1.23 times more likely to default than a borrower 

that has experienced an average of 1 or fewer such storms. For borrowers experiencing 12 or more
25

storms over the loan’s life, default incidence more than doubles. These findings have important 

implications for mortgage investors now and in the future. If the meteorological research cited 

earlier bears out that the frequency of major storms rated 3-5 would increase over the next century, 

the analysis just presented suggests that mortgage delinquency rates in hurricane affected areas of 

the country would rise considerably from where they are today. To gain a sense of the sensitivity 

of mortgage default rates under various assumptions on hurricane and intensity, the estimated 

models were run to generate predicted default rates for each loan in the sample. The loans were 

reweighted in the sample reflecting different proportions of borrowers with average hurricane 

ratings above 2 and an increase in the proportion of borrowers experiencing more than 12 

hurricanes over the life of the loan. This analysis was conducted for both default definitions and 

the results are presented in Tables 19 and 20. In one test, the proportion of borrowers in the sample 

experiencing an average hurricane rating greater than 2 were raised 10-100% in the increments 

shown in Tables 19 and 20 while reducing the proportion of other borrowers accordingly. The 

increments provide a reasonable range of long-term hurricane intensity outcomes that are 

consistent with those reported by Bender et. al and Knutson et.al.24 In addition to this set of 

scenarios, a set of tests were included that raised the proportion of borrowers experiencing more 

than 12 hurricanes in the same percentages (i.e., 10-100%) as before.

The results suggest that increasing the proportion of borrowers experiencing major hurricanes and 

more hurricanes overall has a moderate effect on raising default rates. For example, if the 

proportion of borrowers experiencing major hurricanes and more hurricanes in general doubled, 

that would raise D90+ and D180+ rates 14-15% above baseline rates. These results reflect the fact 

that the size of the borrower cohorts experiencing major hurricanes and more storms generally is 

itself relatively small (5.12% of the sample). In other words, the incremental effects of more 

hurricanes as well as those rated 3-5 may be substantial as shown in Table 18, but are muted by 

the proportion of borrowers affected by these hurricane attributes.

24 Bender et. al (2010) and Knutson et. al (2013).
26

Table 19 - Sensitivity of D90+ Rates to Increased Hurricane Intensity and Frequency

% Increase in 

Intensity and 

Frequency

D90+ (%)

Rates 3-5 

Rated 

Hurricanes

% Change 

from 

Baseline 

D90+

Rates

Change in 

D90+

Rates 

(bps)

D90+ (%)

Rates 3-5 

Rated and 

12+

Hurricanes

% Change 

from 

Baseline 

D90+

Rates

Change in 

D90+

Rates 

(bps)

Baseline 6.11 6.11

10 6.14 .57 3.49 6.20 1.55 9.49

25 6.20 1.55 9.49 6.35 4.01 24.49

50 6.30 3.19 19.49 6.56 7.45 45.49

75 6.40 4.83 29.49 6.79 11.22 68.49

100 6.50 6.47 39.49 7.02 14.99 91.49

Table 20 - Sensitivity of D180+ Rates to Increased Hurricane Intensity and Frequency

% Increase in 

Intensity and 

Frequency

D180+ (%)

Rates 3-5 

Rated 

Hurricanes

% Change 

from 

Baseline 

D180+

Rates

Change in 

D180+

Rates 

(bps)

D180+ (%)

Rates 3-5 

Rated and 

12+

Hurricanes

% Change 

from 

Baseline 

D180+

Rates

Change in 

D180+

Rates 

(bps)

Baseline 5.61 5.61

10 5.64 .52 2.93 5.69 1.41 7.93

25 5.68 1.23 6.93 5.81 3.55 19.93

50 5.76 2.66 14.93 6.01 7.12 39.93

75 5.83 3.91 21.93 6.21 10.68 59.93

100 5.90 5.16 28.93 6.41 14.25 79.93
27

6 Conclusions and Implications

The results from this analysis have several implications for borrowers and investors in mortgage 

credit risk. First, if hurricane frequency and intensity for major Atlantic hurricanes rises over the 

next decades as some meteorological research suggests, more borrowers will be affected and the 

resulting wind and flood damage on businesses and residential properties appears likely to lead to 

much higher default rates in the future. This potential increase in default rates from hurricane 

events could leave investors in mortgage credit risk exposed unless that risk is appropriately priced 

into guarantee fees in the case of the GSEs or tranche pricing of credit risk transfer (CRT) 

transactions.

More intense and frequent hurricanes could reduce market liquidity in CRT transactions if private 

investors are not able to assess the impact of hurricane risk in these transactions. There is some 

evidence that hurricane events in recent years have exposed the CRT market to some volatility. 

The CRT market was temporary roiled starting after Hurricane Harvey in August 2017 and 

Hurricane Irma in September 2017 as yields on subordinate (B1) CRT tranches widened by 125bps 

over a 5-week period. These back-to-back major hurricanes caught investors off-guard and 

eventually led the Association of Mortgage Investors (AMI) to request that Fannie Mae and Fannie 

Mae exclude such loans from CRT pools as they contended that catastrophic risk from natural 

disasters is a risk that investors do not know how to effectively analyze or price.25 Freddie Mac 

has taken steps since then to remove loans in CRT transactions that are located in FEMAdesignated disaster areas, however, an alternative strategy might be to create a separate “clean-up” 

tranche in individual or multiple CRT transactions that provides cat risk protection to CRT tranche 

investors for hurricane risk. While removing loans from CRT deals by the GSEs is a viable 

alternative to addressing investors’ concerns about absorbing cat risk in CRT transactions, it may 

not be a satisfactory outcome unless the GSEs are obtaining some form of reinsurance of the cat 

risk they hold. The GSEs are not a natural entity to price or take on natural disaster risk and so 

should hurricane risk rise in the future, finding alternatives to transfer cat risk from the GSEs to 

other investors could remedy this exposure the GSEs have retained. Since CRT transactions have

25 Yoon, A., “DoubleLine, Like-minded Investors, Want Cat Risk Out of CRT,” Debtwire, October 2017.
28

attracted reinsurance companies as investors over the years, it is possible that a cat risk carve-out 

structure in CRT deals involving reinsurers could be possible.

The future risk to the mortgage market from hurricane risk appears to be on the upswing according 

to the consensus of scientific research on hurricane intensity and frequency. Prospective 

homeowners when shopping for a new home should become more informed on where their 

property is located in terms of flood and hurricane risk before deciding where to buy. Traditional 

investors in mortgage credit such as the GSEs and private mortgage insurance companies are not 

well-equipped to assess and price for cat risk, particularly if that risk is rising over time. Instead, 

alternative financial structures such as cat risk tranches of CRT deals may be a more appropriate 

way of distributing this risk in the future.
29

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