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Who Bears Climate-Related Physical Risk?*†

David Wylie‡

Federal Reserve Bank of Philadelphia 

Natee Amornsiripanitch

Federal Reserve Bank of Philadelphia 

John Heilbron

Office of Financial Research

Kevin Zhao

Office of Financial Research

Abstract

This paper combines data on current and future property-level physical risk from major climate-related 

perils (severe convective storm, inland floods, hurricane storm surge, hurricane wind, winter storms, and 

wildfires) that single-family residences (SFRs) face with data on local economic characteristics to study 

the geographic and demographic distribution of such risks in the contiguous United States. Current 

expected damage to SFRs from climate-related perils is approximately $39 billion per year and will rise to 

$48 billion by 2050 under a “middle-of-the-road” emissions scenario. Severe convective storms are 

the leading contributor to expected damage, however the riskiest areas are predominantly areas 

facing hurricane and/or inland flood risk on the Gulf and South Atlantic coasts. Higher current physical 

risk is associated with lower household incomes, lower labor market participation rates, lower 

education atainment, higher in-migration, higher increase in expected physical risk by 2050, lower belief 

in climate change, and greater Republican vote shares. Overall, the results suggest that climate risk 

mitigation policies are likely to be progressive now and into the future. 

JEL Codes: G5, Q54, D63

Keywords: Climate Risk, Physical Risk, Inequality, Housing

* The views expressed in this paper are solely those of the authors and do not necessarily reflect the views of the

Federal Reserve Bank of Philadelphia, the Federal Reserve Board, the Federal Reserve System, the Office of Financial

Research, or the U.S. Department of the Treasury. Any errors or omissions are the responsibility of the authors. No

statements here should be treated as legal or investment advice.

† This paper subsumes Section 2 of the OFR Brief "The Uneven Distribution of Climate Risk and Discounts", which

also documented cross-sectional heterogeneity in climate risk factors using CoreLogic data.

‡ Corresponding author: david.wylie@phil.frb.org.
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For the majority of American homeowners, housing is, by far, the largest component of their net worth.

1

An open question in the climate risk literature is who bears the current and future climate-related 

physical risk to residential properties? Since floods are among the most damaging natural disasters 

related to climate change, the literature has mostly focused on flood risk.2, 3, 4 However, flooding is just 

one of several disasters that are influenced by expected changes in climate paterns.

5

Therefore, 

studies that focus exclusively on floods cannot yield a comprehensive picture of the magnitude and 

distribution of climate-related physical risk. 

We fill this gap by using novel data on current and future property-level physical risk from six major 

climate-related perils (severe convective storms, winter storms, inland floods, wildfires, hurricane wind, 

and hurricane storm surge) that single-family residences (SFRs) face in the contiguous U.S. The physical 

risk data are produced by CoreLogic, a major commercial catastrophic risk modeler and property data 

vendor used by private industry and government. CoreLogic’s proprietary models incorporate natural 

hazard information with detailed structure and property characteristics to generate structure-level 

estimates of loss for several different perils. 

Our analysis is based on property-level average annual loss (AAL), provided under 2021 conditions and 

2050 conditions. AAL is the expected annual loss to structure and contents generated by simulating many

possible iterations of a given year and then calculating the average loss across all iterations. Thus, AALs 

account for the magnitude of damage resulting from events of different severity as well as the likelihood 

of events of different severities occurring. CoreLogic reports AALs as share of total insurable value (TIV), 

which can be understood as the replacement cost of the structure. 

Combining the property-level physical risk data with property-specific transactions data, local economic 

conditions data from the American Community Survey (ACS), and county-level data on climate change 

beliefs and voting behavior allows us to paint a more comprehensive picture of the magnitude and the 

(geographic and demographic) distribution of climate-related physical risk in the U.S. In addition, we can 

identify the specific climate-related perils that are likely to be the costliest now and in the future. The 

knowledge of the distribution of climate-related physical risk along different dimensions informs 

policymakers on the segments of the U.S. population that are likely to benefit from policies that aim to 

mitigate such risks and whether such policies are likely to be regressive or progressive with respect to 

key socioeconomic characteristics such as income and education atainment, which are proxies for 

earning potential.

6

Results

Who geographically bears the current physical risk? 

Many of the climate-related perils examined in our analysis are geographically concentrated. As shown in 

Table 1, the most concentrated peril is hurricane storm surge, which only affects five percent of SFRs, all 

of which are located near the Gulf and Atlantic coastal waters. Hurricane wind is also concentrated in 

coastal states, but the extent of potential damage extends further inland than it does for hurricane storm 

surge. About half of SFRs in the contiguous U.S. are exposed to some hurricane wind. That is also true to 

for inland flood risk, which is concentrated along rivers, lakes, and streams, as well as in regions that are 

susceptible to flash flooding. Inland flood risk is also present in coastal areas since ground flooding from 

hurricane precipitation and non-hurricane coastal flooding are classified as inland flooding. Wildfires are 

mostly limited to the western half of the country, with the notable exception of Florida.
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[TABLE 1 HERE] 

In contrast, severe convective storms and winter storms reach a substantially larger share of SFRs in the 

contiguous U.S. Severe convective storms, which include thunderstorms, hailstorms, and tornadoes, are 

the only true nationwide peril — nearly every SFR in the contiguous U.S. has some exposure. Winter 

storm risk is close to nationwide, with 86 percent of SFRs having non-zero expected losses from this 

climate-related peril. However, severe convective storms are the leading risk across much of the 

U.S.; with a few exceptions, winter storms are the leading risk only in parts of New England (see 

Appendix Figure 1).

The wide geographic reach of severe convective storms plays a key role in them having the highest 

expected loss averaged over all SFRs, which amounts to 0.06 percent of TIV, compared with 0.01 percent 

of TIV for both hurricane storm surge and wildfire. However, when we look at expected losses 

conditional on having some risk of damage, we see that flood- and hurricane-related perils are the most 

damaging in the areas that they can potentially impact. Hurricane storm surge has the largest 

average AAL (0.18 percent) among SFRs with some risk of damage, followed by inland floods (0.10 

percent). The magnitude of the expected damage is driven by the long right tail of the distribution. As 

shown in Table 1, SFRs at the 99th percentile of non-zero AALs for hurricane storm surge and inland 

floods both face expected losses of over 2 percent of TIV. Hurricane wind and wildfire are the next 

closest at 0.60 percent and 0.45 percent, respectively.

We look at the regional breakdown of expected losses in Table 2. We find that severe convective storms 

are the greatest contributor to overall expected losses in the U.S. and the leading component in four of 

the nine census regions. Inland floods are the second-largest contributor to overall expected losses and 

the leading component in three of the nine regions. Only in the South Atlantic (hurricane wind) and New 

England (winter storm) do different perils play the largest role in expected losses. 

[TABLE 2 HERE] 

The greatest expected losses are in the West South Central (0.32 percent), East South Central (0.21 

percent), West North Central (0.21 percent), and South Atlantic (0.19 percent). Collectively, these regions 

encompass the “tornado alley” in the central U.S., the hurricane-prone Gulf and southern Atlantic coasts, 

and flood-prone Appalachia. The least risky regions, on average, are the Pacific (0.07 percent) and 

Mountain (0.11 percent) regions, where wildfires and inland floods are the main contributors to 

expected losses and severe convective storm risk is relatively low. On average, the West South Central, 

the riskiest region in terms of AAL as a share of TIV, is more than four times riskier than the least risky 

region, the Pacific. 

In Figure 1(a), we take a more granular look at the geographic distribution of risk by sorting Census Tracts 

into deciles of average AAL. Most of the safest tracts are in the Pacific and Mountain regions. In 

fact, 59 percent of Pacific tracts are in the lowest decile of risk, and the Pacific region accounts for 87 

percent of first-decile tracts. Most of the remaining safest tracts are in the Mountain region. The highestdecile tracts are mostly spread among the West South Central (35 percent), South Atlantic (30 percent), 

and East South Central (9 percent) regions. 

[FIGURE 1(a)(b) HERE]

The riskiest tracts are distinctive both in terms of their magnitude of expected damage and their peril 

composition. As shown in Figure 1(b), the distribution of tract-level average AALs is highly right-skewed; 
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tracts in the highest deciles of expected losses are over five times as risky as the median tract and more 

than twice as risky as tracts in the ninth decile, on average. 

Tracts in the top decile face a substantially higher level of risk compared with other tracts, largely 

because of hurricane- and inland flood-related damage. Hurricane storm surge and hurricane wind make 

up nearly half of expected losses, on average, in the top decile tracts. These two perils comprise less 

than one-fifth of expected losses, on average, in all other deciles. Together with inland floods, the three 

perils comprise over 80 percent of expected losses in the top decile tracts. Despite severe convective 

storm accounting for the greatest share of expected damage across the country, expected damage in the 

riskiest areas of the U.S. is driven by hurricane and flood risk. 

In fact, if we examine the very top of the risk distribution, the differences in magnitude and composition 

of risk become even starker. Tracts in the top one percent of the distribution have an average AAL of 

1.65 percent of TIV, over 14 times as large as the median tract AAL. Moreover, hurricane storm surge 

makes up 42 percent of expected losses, on average, in these tracts (see Appendix Table 1). Together 

with hurricane wind and inland flood, they comprise 96 percent of expected losses in these very risky 

tracts. The locations of these top one percent tracts are concentrated along the Gulf and South Atlantic 

coasts. Among the top one percent tracts, 30% are in Louisiana, 26% are in Florida, 12% are in Texas, and 

8% are in Mississippi. Nearly half of them are in either New Orleans-Metairie (28%), Miami-Fort 

Lauderdale (12%), or Gulfport-Biloxi (7%). Outside of the Gulf and South Atlantic coasts, areas of 

Appalachia (inland flood) and the northeast Atlantic coast (hurricane and inland flood) have the largest 

presence of very risky tracts. 

We also provide estimates of AAL in 2021 dollars in Table 2. In aggregate, annual expected losses for 

SFRs due to all climate-related perils are $39 billion, based on 2021 conditions (see Methods section

for full description of methodology). Roughly $12 billion (30 percent) is atributable to severe convective 

storms, $10 billion (25 percent) to inland floods, and $8 billion (20 percent) to hurricane wind. The 

West South Central had the largest average AAL of $648, over 60 percent greater than the U.S. average 

AAL ($410). The full regional and peril breakdown of AALs in terms of dollars is provided in Appendix 

Table 2. 

In Appendix Figure 2, we plot the tract-level average AALs in terms of dollars, by the percentile of 

tract average AAL. We again see the right-skew nature of the distribution as well as the 

dominance of hurricane-related damage in the riskiest tracts. We estimate that the top 1% of 

tracts in terms of expected losses in dollars account for 10% of total expected dollar losses in the US; 

the top 10% of tracts account for 40% of total expected dollar losses. 

Who demographically bears the current physical risk?

Table 3 shows select tract characteristics by AAL (as percent of TIV) decile based on the 2019 five-year 

ACS. Tracts in the highest-risk decile have, on average, lower education atainments, lower household 

incomes, lower prime age (16 to 54) labor force participation rates, and higher non-seasonal vacancy 

rates, a proxy of neighborhood quality.

7 The differences between the highest-risk decile and the fifth 

decile are significant: 18 percent lower higher education atainment rate, 16 percent lower 

household income, 5 percent lower prime age labor participation rate, and 74 percent greater nonseasonal vacancy rate. The differences are even greater between the highest-risk decile and the lowestrisk decile. Notably, we do not see a clear patern in White population share across the deciles of 

physical risk.

[TABLE 3 HERE] 
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Urban status is a contributor to the pattern shown in Table 3. Urban tracts face less physical risk, on 

average, than rural tracts as illustrated by the fact that the share of tracts that are urban core tracts 

decreases across tract AAL deciles, while the share of rural tracts increases. The share of tracts that are 

suburban (not shown in Table 3) also tends to be larger in higher risk tracts. However,the differences in 

tract characteristics remain qualitatively and quantitatively similar when we limit the analysis to only 

urban core tracts (see Appendix Table 3), so the empirical patern that we observe in Table 3 is not purely 

driven by the degree of urbanization. Overall, current climate-related physical risks appear 

to be disproportionately borne by homeowners who live and/or own SFRs in less economically viable 

areas. 

To further investigate the drivers of regressive impact of physical risk, we look at tract average AALs 

broken down by peril for each decile of median household income. Appendix Figure 3(a) shows a 

near-linear decrease in expected losses from the lowest to highest tract income deciles. The highestincome decile faces only about two-thirds of the risk that the lowest income decile faces, on average. 

Notably, the share of AAL that comes from inland floods increases substantially as we move down the 

income deciles. In fact, in the bottom quintile income tracts, inland floods are the primary contributor to 

average expected losses. We see similar patterns when examining educational attainment, labor 

force participation rate, and vacancy rate (see Appendix Figure 3(b)(c)(d)). 

Additionally, the pattern in local economic characteristics we observe appears to be driven more by crossmetro than intra-metro differences. The income difference between high- and low-risk tracts attenuates

when we sort tracts within metropolitan statistical area (MSA) average AAL deciles (see Appendix 

Table 4). The prime age labor force participation rate, vacancy rate, and educational atainment 

patterns show similar attenuation. We do see larger White population share in riskier areas using the 

within-MSA sort. 

Next, we investigate whether climate risk is related to changes in tract characteristics from 2010 to 2019 

across 2021 average tract AAL deciles. Appendix Table 5 shows larger increases in the vacancy rate, 

larger decreases in prime age labor force participation, and smaller increases in education atainment in 

tracts in the upper AAL deciles. We again see similar paterns if we limit to only urban core tracts. The 

findings suggest that currently high-risk tracts are not only just currently less economically vibrant when 

compared with currently low-risk tracts but also that the gap in economic performance between the two 

groups has been increasing over time. The empirical paterns might suggest that climaterelated physical risk itself and/or the realization of such risk may be important determinants of 

economic performance and outcomes, although it is not the focus of the current paper to 

evaluate these mechanisms.

8

 

While we do see smaller average 2010–2019 total population change in riskier areas (see Appendix Table 

5), we do not find evidence that, in aggregate, people are migrating away from higher-risk areas during 

that period. The results in Appendix Table 6 suggest net movement into higher risk areas based on the 

tract composite AAL (see Methods section for a complete description of methodology). When broken 

down by peril, we see greater average in-migration in the riskiest decile than the safest decile for all 

perils except winter storm. In aggregate, climate-related physical risk does not appear to be a 

significant deterrent of in-migration to high-risk places.9

Finally, we examine how voting behavior and beliefs about climate change are related to physical risk 

faced across the contiguous U.S. Due to data limitations, we perform this analysis using 

county-level observations. We find that counties with the greatest physical risk were least likely to 

believe global warming is happening (see Appendix Table 7). We also find that a more muted but 

directionally similar patern using the share of respondents in the county who say they have been 

personally affected by global 
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warming. Together, the results suggest that the personal experience of climate-related events as well as 

the real physical risk faced are not deterministic in forming beliefs about whether climate change is 

happening. In addition, local differences in beliefs may contribute to the net migration patern that we 

document. One potential driver to these observed paterns is political beliefs. In Appendix Table 7, we 

find a strong relationship between physical risk and Republican vote share in 2020 – counties with the 

larger AALs, on average, have larger Republican vote share.10 

What does future physical risk look like? 

Physical risk is expected to increase in the future because of climate change. 11 Future climate scenarios 

are characterized by Representative Concentration Pathways (RCPs), which depict different trajectories 

of greenhouse gas emissions that then affect different climate-related outcomes. Under the middle-ofthe-road scenario, RCP 4.5, we estimate that expected losses for SFRs in the contiguous U.S. will 

increase by 3.8 basis points of TIV by 2050 (See Table 4). This represents a 22 percent increase, 

on average. Under a more severe emissions scenario, RCP 8.5, we estimate an average increase in 

expected losses of 5.6 basis points of TIV, a 33 percent increase. 

In dollar terms, we estimate expected damages will increase by 24 percent to $48 billion under RCP 4.5 

and by 36 percent to $53 billion under RCP 8.5. These estimates are based on the 2021 inventory of SFRs 

and therefore do not consider future changes in development and home values. In other words, these 

are estimates of the change in expected dollar losses that are atributable exclusively to the change in 

risk from the six climate-related perils. 

As shown in Table 4, the majority of the increase in physical risk will be due to severe convective storms. 

Severe convective storms will be the largest contributor to the increase in expected losses in all regions 

except the Pacific and Mountain regions, where wildfires are the chief contributor, as well as in the South 

Atlantic, where hurricane wind are the largest component. Overall, hurricane wind will make up 17 

percent of the national increase. Despite its limited geographic scope, hurricane storm surge will 

make also up 19 percent of the national average increase in expected losses. 

[TABLE 4 HERE]

The largest increases in AAL will occur in the regions where current AALs are the greatest. Figure 2 plots 

the tract-average change in AAL by the tract-average 2021 AAL. There is a clear positive 

relationship — higher-risk tracts today will, on average, experience greater increases in physical risk. 

There is also a right-skew to the distribution of changes in tract-average AALs — tracts in the highest 

2021 AAL decile have an average increase in AAL that is about three times the average increase of 

those in the ninth decile. The changes in AAL in the highest decile are driven more by hurricane wind 

and hurricane storm surge than they are in the other deciles, where severe convective storm dominates. 

[FIGURE 2 HERE]

Discussion 

In this analysis, we provide a comprehensive accounting of the climate-related physical risk to SFRs in the 

contiguous U.S. However, our study has several notable limitations. Because of data limitations, 

we cannot make claims about the expected damage to other property types (e.g., multifamily 

residential properties, commercial properties, public infrastructure, etc.). Additionally, we are 

not capturing non-property or 
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indirect losses, such as loss of business, hardship costs, mortality costs, or the cost of potential lost future 

growth.

12 Last, we do not differentiate between the proportion of the cost that will be borne by insurance 

companies and the proportion of the cost that will be borne by homeowners.13 

The 2021 damage estimate of $39 billion is relatively small when compared with the size of the national 

economy — it represents about 0.2 percent of the U.S. gross domestic product (GDP) in 2021. However, 

expected losses should not be confused with realized losses. Realized losses from natural disasters tend 

to be temporally lumpy (see Appendix Figure A1), and they are not evenly distributed across the entire 

U.S. For example, in 2005, the U.S. experienced close to $150 billion in property losses, with most of that 

occurring in a few Gulf Coast states, like Louisiana. 

Our distributional analysis provides insights into where and who bear the physical risk. Geographically, 

the safest areas tend to be in the Pacific and Mountain regions while the riskier areas tend to be in 

the southeastern and central parts of the country. Within those riskier areas, it is the hurricane- and 

flood- exposed areas on the Gulf and South Atlantic coasts that face the greatest risk, along with some 

inland flood exposed areas, like parts of Appalachia. Severe convective storms are estimated to be the 

largest contributor to expected losses across the U.S., but the areas with the largest expected losses are 

largely hurricane- and flood-exposed areas. Demographically, we find that low-income, less educated, 

low labor market participation, and high vacancy rate areas face the greatest physical risk. Inter-metro 

differences in local economic conditions across the distribution of physical risk appear larger than 

intra-metro differences. 

Altogether the results suggest that areas that are already economically struggling face the greatest risk of 

disruption from climate-related perils. Thus, policies that aim to decrease the underlying physical risk (e.g., 

slowing global temperature increases) and/or mitigate the impacts of these climate-related perils will 

likely be progressive now and into the future. With insurance markets in place, realized damages are 

likely to manifest in the form of higher insurance premiums and disruption to general economic activities 

in the affected areas. However, as insurance companies pull out of risky markets, realized damages are 

likely to fall fully on homeowners, or, in the case of rental SFRs, on a combination of homeowners and 

renters.

14

While a full investigation of the mechanisms driving the regressivity of climate-related physical risk is 

beyond the scope of this paper, we do offer a few possibilities for further exploration. First, the income 

pattern we observe is consistent with climate-related physical risk being viewed as a “bad amenity” by 

households.15,16 Second, the strength of the inter-metro differences we observe is consistent with lower 

income households being priced out of more expensive metro areas that tend to face less physical risk. 

Barriers to new housing development (e.g., regulatory and physical constraints) may be of particular 

importance.

17,18 Lastly, the relationship between beliefs about climate change and physical risk suggests 

that people may have beliefs about climate change that differ from the objective measure of risk we 

employ. Therefore, the regressivity of climate-related physical risk may be partly driven by the correlation 

between income, education atainment, and access to accurate information. 

Looking to the future, the riskiest areas today will, by and large, still be the riskiest areas in 2050. In fact, 

the changes over the next 30 years will be most dramatic for the areas already facing the highest risk. The 

largest contributors to the change in physical risk are severe convective storms, hurricane storm surge, 

and hurricane wind. Given that the gap in economic performance between high- and low-risk tracts has 

been increasing over time and that aggregate migration paterns do not appear to be sensitive to the 

geographic distribution of climate-related physical risk, it is plausible that policies that aim to mitigate 

such 
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risk will continue to be progressive with respect to local economic conditions and will benefit a larger 

proportion of the U.S. population.19

In terms of targeting specific perils, we find that inland floods are the largest contributor to expected 

losses in the areas that appear worst off in terms of income, college atainment, vacancy, and labor 

force participation. This finding is relevant to both mitigation investment decisions and insurance 

take-up efforts. Flood insurance take-up through the government-operated National Flood Insurance 

Program (NFIP) is low, particularly in many inland areas that may still face risk of flooding despite being

outside of the FEMA-designated floodplain.

20,21 Moreover, within the FEMA floodplain, NFIP-insured 

homeowners have about 60 percent higher incomes than uninsured homeowners, suggesting that the 

insurance gap is more pronounced among low-income households.22 Consequently, efforts to increase 

flood insurance take-up in areas at risk of flooding will likely be disproportionately beneficial to lowincome households.

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Methods

Estimates of Average Annual Loss (AAL)

The AALs used in this analysis are sourced from CoreLogic, which is a major commercial catastrophic risk 

modeler and property data vendor. CoreLogic’s estimates are used in private industry as well as by 

various government agencies.23 CoreLogic’s inland flood and storm surge models were recently used in

the Federal Emergency Management Agency’s (FEMA’s) “Risk Rating 2.0”, which revamped the 

National Flood Insurance Program’s (NFIP’s) rate-setting procedures to beter align charged insurance 

premiums with property-specific risk.24

CoreLogic provides AAL estimates for approximately 190 million structures in the contiguous 

United States. AALs are expected losses, meaning they represent the loss per year averaged over many 

possible iterations of that year. Mathematically, the AAL is the area under the exceedance 

probability curve, which, for every possible loss amount, provides a likelihood that the loss 

amount will be met or exceeded for a given structure. Importantly for our multiperil analysis, AALs 

are additive because they are expectations.

CoreLogic’s AALs are ground-up losses, meaning they represent gross losses to the structure and 

contents prior to applying any insurance policy terms (e.g., deductible). The AALs are expressed as 

a share of total insurable value (TIV), and therefore range from 0 to 1 for any single peril. CoreLogic 

uses replacement cost as its measure of TIV. 

This paper uses AALs based on current conditions (circa 2021) and on conditions in 2050 under two 

different greenhouse gas emissions pathways: Representative Concentration Pathways (RCPs) 4.5 and 

8.5, as specified in the Intergovernmental Panel on Climate Change’s Fifth Assessment Report (IPCC 

AR5).25 Our main 2050 risk estimates are based on RCP 4.5, which depicts a “middle-of-the-road” climate 

scenario. RCP 4.5 is associated with a global mean surface temperature increase of 0.9–2.0 

degrees Celsius by the mid-21st century (relative to 1986–2005), while the more severe 

business-as-usual scenario, RCP 8.5, is associated with an increase of 1.4–2.6 degrees Celsius. In terms of 

global mean sea level rise, the increase by the mid-21st century under RCP 8.5 is expected to be about 

15 percent larger than it would be under RCP 4.5.26

The AALs are generated by CoreLogic’s proprietary climate, hazard, and vulnerability models. Inputs 

to CoreLogic’s modeling consist of natural hazard information and “industry-leading property 

data.”

27 These models account for future changes to environmental conditions, but they do not 

incorporate any changes to development. All 2050 AAL estimates are based on the current stock of 

structures. 

It is important to note that the AAL estimates are model-generated and, hence, they inherently contain 

a degree of uncertainty around the point estimates that we use in our analyses. The 

sources of uncertainty include CoreLogic’s modeling choices and the choice of historical data sets used 

to feed the models. We cannot assess the degree of uncertainty because CoreLogic does not 

provide the necessary data. 

Validating AAL estimates is difficult because the ground truth is unknown. CoreLogic performs validation 

exercises on its model output to test reasonableness. While specific validation analyses vary by peril, they 

typically involve comparison with data on historical events and, if available, damages from those events. 

In our own validation exercise, we find our national expected loss total to be reasonable when compared 

with long-run historical losses recorded in the Spatial Hazard Events and Losses Database for the United 

States (SHELDUS). The SHELDUS comparison and full methodology is discussed in Appendix A. 
PERSONAL/NONWORK // EXTERNAL

Additionally, as a robustness check on our distributional analyses, we show that tract-level averages based 

on CoreLogic’s AALs correlate moderately well with tract-level averages based on AAL estimates from a 

different risk modeler for a subset of perils (see Appendix B). Agreement between the two sets of tractlevel estimates is strongest at the top end of the risk distribution. 

Perils

We focus on AALs for six climate-related perils provided by CoreLogic: inland floods, hurricane wind, 

hurricane storm surge, severe convective storms, winter storms, and wildfires. We selected these perils 

because they are likely to be affected by future environmental changes brought on by climate change. By 

this logic, we ignore data on earthquake-related perils. The perils are defined to be mutually exclusive 

and consistent with insurance industry practices. For example, water damage resulting from hurricane 

wind tearing off part of a roof and allowing for rainfall to enter the home would be considered hurricane 

wind damage and would be covered under a standard homeowner’s insurance policy. However, water 

damage resulting from a stream overflowing because of hurricane precipitation would be considered 

inland flooding damage and would not be covered under a standard homeowner’s insurance policy. 

The peril descriptions from CoreLogic are as follows: 

1. Inland Flooding – Inundation caused by (1) water in an existing waterway (river, stream, or pond)

rising overtop the normal banks and spreading onto adjacent land, (2) ponding of rainwater in

low-lying areas, and/or (3) coastal flooding from unusually high tides, strong onshore winds, and

storm surge associated with a landfalling strong storm (other than a hurricane). Water depth, flow

velocity, building age, first floor height, construction type, occupancy type, number of stories, and

presence of a basement are considered in determining damage.

2. Hurricane Wind – Damage caused by sheer force of hurricane wind (>74 mph one-minute

sustained wind speed at landfall) and any resulting water damage from precipitation entering the

structure. The peak gust, flood depth and velocity, structure type, occupancy type, and total 

value of the exposure are considered in determining damage.

3. Hurricane Storm Surge – Inundation caused by hurricane-force winds (>74 mph) pushing shallow

coastal waters in such a way that the sea level rises. Powerful storms can cause up to 30 feet of

storm surge. Storm surge flood depth and velocity can depend on factors like variations in

astronomical tides, flood defense systems, and first floor elevation of building. Storm surge flood

depth and velocity, structure type, occupancy type, and total value of the exposure are considered 

in determining damage.

4. Severe Convective Storm – Damage caused by one of three different types of storms: tornadoes,

hailstorms, or straight-line winds (e.g., squall or derecho). The hazard intensity, structure type,

occupancy, building material, cladding, and height of structure are considered in determining

damage.

5. Winter Storm – Damage caused by winter storm precipitation and prolonged cold temperatures.

Types of damage include roof damage due to snow accumulation, frozen and ruptured pipes, and

ice dams on roofs and guters causing flooding from melting snow. Snow depth, snow and ice

thickness, wind speed, as well as structure and occupancy types are considered in determining

damage.

6. Wildfire – Damage caused by fire and smoke from combustion of vegetative fuel. Fire behavior is

modeled considering available fuel load, topography of area, prevailing weather conditions, and
PERSONAL/NONWORK // EXTERNAL

fire suppression factors, including firefighting resources. Structure type, occupancy type, age of 

structure, number of stories, vegetation clearance, roofing fire class, and the presence of fire 

resistive windows or siding are considered in determining damage. 

Some of the perils are not modeled for the entire contiguous U.S. Hurricane wind and hurricane storm 

surge are only modeled for states on the Gulf and Atlantic coasts. Wildfires are only modeled for the 

western U.S. and Florida. Nonmodeled areas are considered to have negligible risk, according to 

CoreLogic. Consequently, when aggregating the AALs over the different perils, we consider structures in 

nonmodeled areas to have an AAL of zero for the geographically limited perils. 

Sample Construction

The analyses in this paper are based on property-level estimates of AAL. Thus, we do not aggregate over 

all structures in the CoreLogic data. Instead, we select one structure per property. If a primary structure 

is identifiable, we use that structure. If the primary structure is unknown, we take the structure with the 

largest AAL. 

We then use CoreLogic’s property tax assessment data to identify properties that are singlefamily residences (SFRs). To determine SFR status, we use the CoreLogic-standardized land use code. For 

tax year 2021, we identify about 96.7 million SFRs in the contiguous US. 

To generate tract-level average AALs, we identify each property’s census tract by performing a spatial 

join of the chosen properties (using structure-specific coordinates) and 2010-vintage census tracts. We 

find that 70,558 out of the total 72,247 land area tracts in the contiguous US have at least one SFR. 

Then, for each property, we sum the AALs over all perils and calculate the average all-peril AAL for 

properties in the tract. Among the 70,558 tracts, we exclude about 4 percent of the tracts that had 

fewer than 30 non-missing AAL values for SFRs. These are either tracts with a very small number of SFRs 

or tracts where data limitations prevented CoreLogic from providing an AAL estimate for most 

properties. Together, these excluded tracts contain about 0.2 million SFRs. We are left with 67,487 

tracts in the final sample covering 96.5 million SFRs. 

Census Tract Characteristics

Census tract characteristics are produced using 2019 five-year American Community Survey (ACS) 

estimates. The 2019 ACS was chosen because of the economic activities and migration paterns that were 

driven by the COVID-19 pandemic.

28 However, migration and economic shocks related to COVID-19 do not 

materially affect our conclusions. All the results shown in Table 3 are quantitatively and qualitatively 

similar when we use the 2021 five-year ACS estimates, as opposed to the 2019 five-year ACS estimates.29 

The 2019 ACS fields used were:

1. Median Household Income (B19013_001)

2. Total Households (B17017_001)

3. Total Households – Income in the Past 12 Months Below Poverty Level (B17017_002)

4. Population 25 Years and Over (B15003_001)

5. Population 25 Years and Over – Bachelor’s Degree (B15003_022)

6. Population 25 Years and Over – Master’s Degree (B15003_023)

7. Population 25 Years and Over – Professional Degree (B15003_024)
PERSONAL/NONWORK // EXTERNAL

8. Population 25 Years and Over – Doctorate Degree (B15003_025)

9. Population 16 Years and Over (B23025_001)

10. Population 16 Years and Over – In Labor Force (B23025_002)

11. Male: 25 to 29 Years: In Labor Force (B23001_025)

12. Male: 30 to 34 Years: In Labor Force (B23001_032)

13. Male: 35 to 44 Years: In Labor Force (B23001_039)

14. Male: 45 to 54 Years: In Labor Force (B23001_046)

15. Female: 25 to 29 Years: In Labor Force (B23001_111)

16. Female: 30 to 34 Years: In Labor Force (B23001_118)

17. Female: 35 to 44 Years: In Labor Force (B23001_125)

18. Female: 45 to 54 Years: In Labor Force (B23001_132)

19. Male: 25 to 29 years (B01001_011)

20. Male: 30 to 34 years (B01001_012)

21. Male: 35 to 39 years (B01001_013)

22. Male: 40 to 44 years (B01001_014)

23. Male: 45 to 49 years (B01001_015)

24. Male: 50 to 54 years (B01001_016)

25. Female: 25 to 29 years (B01001_035)

26. Female: 30 to 34 years (B01001_036)

27. Female: 35 to 39 years (B01001_037)

28. Female: 40 to 44 years (B01001_038)

29. Female: 45 to 49 years (B01001_039)

30. Female: 50 to 54 years (B01001_040)

31. Housing Units (B25002_001)

32. Housing Units — Vacant (B25002_003)

33. Vacant Housing Units – For Seasonal, Recreational, or Occasional Use (B25004_006)

34. Total Population (B03002_001)

35. Total Population – Not Hispanic or Latino: White Alone (B03002_003)

36. Total Population – Not Hispanic or Latino: Black or African American Alone (B03002_004)

37. Total Population – Hispanic or Latino (B03002_012)

38. Total Population – Hispanic or Latino: White Alone (B03002_013)

Urban/Rural Tract Classification 

Tracts are designated as urban core, suburban, or rural based on 2010 Census Urban Areas and CoreBased Statistical Area (CBSA) definitions. A tract is classified as “urban core” if the tract centroid 

intersects with a Census “Urbanized Area” (not “Urban Cluster”). A tract is defined as “suburban” if it is 

located within a Census CBSA or Census Urban Cluster but not within a Census Urbanized Area. All tracts 

outside CBSAs are considered “rural”. 

Generating Tract Characteristics by Average AAL Decile

In Table 3, we examine tract characteristics by tract average AAL deciles. We do this as follows. First, we 

sort our sample of 67,487 tracts into deciles based on the tract average AAL. Second, we winsorize the 

distribution of the tract characteristics at 0.01 and 0.99 within each AAL decile to mitigate the influence 

of extreme values. Last, we take averages of the tract characteristics within each decile. 
PERSONAL/NONWORK // EXTERNAL

Generating AALs in Dollars

We use the following procedure to convert the property-level CoreLogic AALs, which are normalized by 

TIV, to dollar values. 

We employ several different data sources to estimate AAL in dollars. We first use a combination of 

CoreLogic public records and the Zillow Home Value Index (ZHVI) to calculate home values for each SFR 

in the contiguous US.30 For SFRs where we observe an arm’s length transaction in the CoreLogic 

public records data, we use the latest observed transaction and impute the 2021 home value using 

the ZHVI, which we transform from ZIP code- to tract-level using the Housing and Urban 

Development (HUD) crosswalk.31 We are able to impute the 2021 home value from transaction data 

for just over half of the SFRs in our data. We winsorize the imputed values at the 1st and 99th percentile 

and we top code the values at $10 million to address extreme outliers. For the vast majority of the 

remaining SFRs, we use the December 2021 tract-level ZHVI value as the home value. For the small set 

of remaining SFRs, we use the county-level average home value. The national average and median home 

value from our estimates are $385,000 and $276,000, respectively. 

Next, we merge in land value share of home value estimates from Davis et al. (2021), which provides, to 

our knowledge, the most granular estimates of land value shares. 32 Davis et al. provides tract-level 

measures for over three-quarters of tracts in the U.S. When a tract measure is not available, we use the 

most granular option available among ZIP code-, county-, or state-level measure. 

We then calculate the AAL in dollars for each SFR as AAL*Home Value*(1 – Land Share of Home Value). 

Net Migration by Tract AAL Decile

We examine net migration between 2010 and 2019 for areas of different climate risk levels using the 

anonymized FRBNY Consumer Credit Panel /Equifax data (CCP) (see Appendix Table 6). The CCP is a 5 

percent random sample that is representative of all U.S. individuals who have a credit history. It is widely 

used in consumer finance research, but it has also been used in several studies of mobility and 

migration.33 We use the CCP because its size (about 10 million borrowers per year) enables us to 

generate more granular migration estimates than some other sources of migration data allow. For 

example, ACS data only provide county-level flows based on five years of pooled data. We provide a 

county-level comparison of those sources below to help validate our CCP-derived migration estimates in 

Appendix C. 

Using the CCP, we generate census tract-level net migration estimates and then group tracts by the same 

AAL deciles that are used in the main analyses. The migration estimation proceeds as follows.

1. Identify individuals in the CCP who have a different reported street address and zip code in year t

compared with year t-1 for t = 2010 to t = 2019 and generate counts by 2000 census block. The

CCP provides a scrambled address where the trailing characters are affected by small variations

such as whether “Unit” or “Apt” is used. To deal with this issue, we only use the first five

characters of the scrambled address in conjunction with the zip code to avoid falsely identifying

movers that result from small variations in address syntax.

2. Merge counts with the National Historical Geographic Information System (NHGIS) 2000–2010

block crosswalk to convert to 2010 census geography definitions.34 Using the weights provided in

the crosswalk, we allocate each 2000 block migration count to a 2000 block to a 2010 block

pair. Then we aggregate to the 2010 block level.
PERSONAL/NONWORK // EXTERNAL

3. Aggregate counts to the 2010 tract level.

4. Multiply counts by 22 because the CCP is a 5 percent nationally representative sample among

those with credit histories. We multiply by 22 instead of 20 to account for individuals without

credit histories. We implicitly assume that the migration patern of individuals who lack credit

histories is similar to that of individuals who have credit histories.

5. Scale migration counts by 2010 tract population.

6. Group tracts by AAL decile and winsorize the distribution of net migration at 1st and 99

th

percentiles within each decile.

7. Calculate average net migration as share of 2010 population within AAL tract deciles.

We also consider migration within MSAs to investigate if people are moving to or away from riskier areas 

within their original MSA. In this case, we generate AAL deciles within the MSAs instead of across all 

tracts. We also calculate the average net migration among movers who moved within the same MSA, 

excluding the relatively small percentage of movers who move across different MSAs.

Climate Change Beliefs and Voting Data

Climate change beliefs survey data is sourced from the Yale Climate Opinion Map 2021, which is based on 

the methodology developed in Howe et al. (2015).

35 We draw on county-level results for two survey 

questions: 

1. “Recently, you may have noticed that global warming has been getting some atention in the news.

Global warming refers to the idea that the world’s average temperature has been increasing over

the past 150 years, may be increasing more in the future, and that the world’s climate may change

as a result. What do you think: Do you think that global warming is happening?” RESPONSES: Yes,

No, Don’t Know.

2. “How much do you agree or disagree with the following statement: “I have personally 

experienced the effects of global warming”?” RESPONSES: Strongly agree, Somewhat agree, 

Somewhat disagree, Strongly disagree.

In the first question we use the share of respondents who responded “Yes”. In the second question, we 

use the share of respondents who responded, “Strongly agree” or “Somewhat agree”. 

The county-level 2020 voting data is sourced from the MIT Election and Science Lab.36

References (Methods)

23 CoreLogic, Modeling a Secure Future for Real Estate Portfolios With Climate Risk Analytics (Accessed December 

2023). htps://www.corelogic.com/intelligence/case-studies/climate-risk-modeling-future-for-real-estateportfolios/. 

24 FEMA. “National Flood Insurance Program Risk Rating 2.0 Methodology and Data Sources”. March 2021. 

htps://www.fema.gov/sites/default/files/documents/fema_risk-rating-2.0-methodology-datasources_3-2021.pdf. 25 IPCC. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to 

the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Geneva, Switzerland (2014).

26 Hayhoe, K. et al. “Our changing climate.” In Impacts, Risks, and Adaptation in the United States: Fourth National

Climate Assessment, Volume II. Washington, D.C.: U.S. Global Change Research Program, pp. 72–144 (2018). 
PERSONAL/NONWORK // EXTERNAL

27 CoreLogic, Climate Risk Analytics (Accessed July 2023). www.corelogic.com/data-solutions/property-datasolutions/climate-risk-analytics. 

28 Bloom, N., Davis, S. J., & Zhestkova, Y. “COVID-19 shifted patent applications toward technologies that support 

working from home.” In AEA Papers and Proceedings, vol. 111, pp. 263–6 (2021).

29 Coven, J. , Gupta, A. & Yao, I. “JUE Insight: Urban flight seeded the COVID-19 pandemic across the United States.” 

Journal of Urban Economics 133, 103489 (2023).

30 Zillow, Zillow Home Value Index, htps://www.zillow.com/research/data/. 

31 Department of Housing and Urban Development, HUD USPS Zip Code Crosswalk Files,

htps://www.huduser.gov/portal/datasets/usps_crosswalk.html. 

32 Davis, M. A., Larson, W. D., Oliner, S. D., & Shui, J. “The price of residential land for counties, ZIP codes, and 

census tracts in the United States.” Journal of Monetary Economics 118, 413–31 (2021).

33 DeWaard J., Johnson J., & Whitaker S. “Internal migration in the United States: A comprehensive comparative 

assessment of the Consumer Credit Panel.” Demographic Research, 41, 953–1006 (2019). 

34 National Historical Geographic Information System. Geographic Crosswalks (Accessed August 2023).

www.nhgis.org/geographic-crosswalks. 

35 Howe, P., Mildenberger, M., Marlon, J., & Leiserowitz, A. (2015) “Geographic variation in opinions on climate 

change at state and local scales in the USA,” Nature Climate Change, 5, 596-603 (2015).

36 MIT Election and Science Lab, “County Presidential Election Returns 2000-2020”. June 2021. 

htps://doi.org/10.7910/DVN/VOQCHQ. 
PERSONAL/NONWORK // EXTERNAL

Tables

Table 1. Average Annual Loss (AAL) for Single-Family Residences (SFRs) in 2021, by Peril

AAL Among SFRs with >0 AAL

Peril

Average AAL 

Among All SFRs

Percent of 

SFRs with >0 

AAL

Average AAL 

Among SFRs with 

>0 AAL p10 p25 Median p75 p90 p99

Severe Convective Storm 0.06% 99.8% 0.06% * 0.01% 0.04% 0.08% 0.12% 0.26%

Inland Flood 0.05% 47.6% 0.10% * * 0.01% 0.03% 0.13% 2.46%

Hurricane Wind 0.03% 48.0% 0.07% * * 0.02% 0.07% 0.19% 0.60%

Winter Storm 0.02% 86.4% 0.02% * 0.01% 0.02% 0.03% 0.04% 0.09%

Hurricane Storm Surge 0.01% 5.2% 0.18% * * 0.01% 0.11% 0.50% 2.16%

Wildfire 0.01% 27.2% 0.03% * * * 0.01% 0.05% 0.45%

Note: The table shows summary statistics of AAL by peril. AAL is presented as percentage of total insurable value. Asterisks indicate values greater than 0% but 

less than 0.005%.
PERSONAL/NONWORK // EXTERNAL

Table 2. Average Annual Loss (AAL) for SFRs in 2021, by Census Region Division and Peril

Percent of Census Region Division's Average AAL 

East 

North 

Central

East 

South 

Central

Middle 

Atlantic Mountain

New 

England Pacific

South 

Atlantic

West 

North 

Central

West 

South 

Central U.S.

Severe Convective Storm 53% 36% 19% 24% 13% 2% 23% 56% 40% 33%

Inland Flood 26% 40% 35% 39% 19% 52% 19% 33% 25% 28%

Hurricane Wind 0% 16% 10% 0% 17% 0% 39% 0% 24% 19%

Winter Storm 21% 5% 28% 7% 46% 6% 7% 11% 3% 11%

Hurricane Storm Surge 0% 3% 8% 0% 5% 0% 11% 0% 7% 6%

Wildfire 0% 0% 0% 30% 0% 40% 1% 0% 2% 4%

Avg AAL (% of TIV) 0.12% 0.21% 0.14% 0.11% 0.15% 0.07% 0.19% 0.21% 0.32% 0.17%

Property Exposure 

Count SFRs (millions) 14.2 7.3 10.0 7.6 3.7 11.8 21.9 7.1 12.9 96.5

Avg Structure Value ($) 172,861 188,445 252,229 365,063 269,502 413,085 257,413 193,591 211,948 256,423

Expected Loss in Dollars 

Avg AAL ($) 192 342 350 385 411 267 513 392 649 401

Total AAL ($ Billions) 2.7 2.5 3.5 2.9 1.5 3.2 11.2 2.8 8.4 39

Note: By-peril contribution to average AAL in 2021, measured as share of total insurable value (TIV). Results shown by Census Region Division. Structure value 

does not include land value. Dollar values are shown in 2021 dollars.
PERSONAL/NONWORK // EXTERNAL

Table 3. 2019 Census Tract Characteristics by Tract AAL Decile

Mean (Standard Error)

Decile of Tract Average AAL (measured as % of TIV)

Description

Percent White

1 2 3 4 5 6 7 8 9 10

45.2 56.7 65.4 64.0 66.1 68.6 68.3 66.5 63.4 62.3

Percent with Bachelor's 

(0.3) (0.4) (0.4) (0.4) (0.3) (0.3) (0.3) (0.3) (0.4) (0.4)

34.4 33.3 31.4 31.9 31.8 31.0 29.8 27.2 26.1 26.1

Degree or Higher

Median Household Income

(0.3) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2)

84,244 78,522 72,682 73,289 72,185 70,578 68,947 65,684 63,161 60,906

Home Value 

(477) (481) (433) (418) (401) (386) (384) (362) (344) (331)

838,602 491,514 353,840 388,196 353,773 333,720 343,697 317,659 282,167 343,244

Percent Prime Age Labor 

Force Participation

Percent Vacant (Excluding 

Seasonal Units)

Percent of Tracts Rural

Percent of Tracts Urban Core

(6,983) (4,767) (3,005) (3,267) (2,934) (2,658) (3,296) (2,950) (2,436) (3,869)

78.9

(0.1)

11.3

(0.08)

12.5

50.9

82.7

(0.1)

82.4

(0.1)

82.5

(0.1)

82.6

(0.1)

83.0

(0.1)

82.8

(0.1)

82.3

(0.1)

81.3

(0.1)

80.4

(0.1)

5.0

(0.04)

7.6

(0.09)

8.2

(0.08)

7.9

(0.07)

8.3

(0.07)

8.2

(0.07)

8.7

(0.07)

9.4

(0.07)

10.1

(0.07)

0.5 2.3 3.9 4.3 4.8 7.0 9.1 11.7 12.7

90.1 76.5 69.5 68.6 64.5 59.5 54.2 49.3 50.1

Note: Census tract average characteristics by tract-level average AAL decile. Dollar values are in 2021 dollars.
PERSONAL/NONWORK // EXTERNAL

Table 4. Change in Average Annual Loss (AAL) 2021–2050 Under RCP 4.5 and RCP 8.5, by Census Region Division

Panel A: Change in AAL 2021-2050 Under RCP 4.5 

Percent of Change in AAL by Census Region Division

East 

North 

Central

East 

South 

Central

Middle 

Atlantic Mountain

New 

England Pacific

South 

Atlantic

West 

North 

Central

West 

South 

Central U.S.

Severe Convective Storm 83% 68% 40% 37% 36% 2% 33% 90% 56% 51%

Inland Flood 15% 8% 8% 3% 6% 32% 3% 8% -2% 6%

Hurricane Wind 0% 15% 10% 0% 23% 0% 35% 0% 19% 17%

Winter Storm 3% -1% 5% -1% 14% -2% -1% 1% -1% 1%

Hurricane Storm Surge 0% 10% 36% 0% 21% 0% 29% 0% 26% 19%

Wildfire 0% 0% 0% 60% 0% 68% 1% 0% 2% 6%

Avg Change in AAL (bps of TIV) 2.7 3.9 2.8 2.2 2.4 1.4 4.9 4.8 7.0 3.8

Panel B: Change in AAL 2021-2050 Under RCP 8.5 

Percent of Change in AAL by Census Region Division

East 

North 

Central

East 

South 

Central

Middle 

Atlantic Mountain

New 

England Pacific

South 

Atlantic

West 

North 

Central

West 

South 

Central U.S.

Severe Convective Storm 77% 65% 33% 40% 28% 3% 26% 87% 50% 45%

Inland Flood 19% 2% 9% 0% 4% 17% 1% 11% -2% 4%

Hurricane Wind 0% 22% 13% 0% 28% 0% 44% 0% 25% 23%

Winter Storm 4% 0% 8% 0% 21% -1% 0% 2% 0% 2%

Hurricane Storm Surge 0% 11% 36% 0% 19% 0% 29% 0% 26% 20%

Wildfire 0% 0% 0% 60% 0% 82% 1% 0% 2% 6%

Avg Change in AAL (bps of TIV) 3.8 5.2 4.2 2.9 3.8 1.5 7.9 6.7 10.4 5.6

Count SFRs (millions) 14.2 7.3 10.0 7.6 3.7 11.8 21.9 7.1 12.9 96.5

Note: By-peril contribution to change in average AAL, measured as share of total insurable value (TIV), from 2021 and 2050 under RCP 4.5 and RCP 8.5. Results

shown by Census Region Division. Negative value indicates a decrease in average AAL. RCP = Representative Concentration Pathway.
PERSONAL/NONWORK // EXTERNAL

Figures 

Figure 1(a)(b). Deciles of Tract Average AAL for SFRs in 2021 and By-Peril Contribution Within Each 

Decile 

(a) Tract-level average composite AAL. Tracts without sufficient data to calculate an average AAL are shown in dark gray. (b) By-peril contribution of average

AAL in each decile of tract average AAL. Ninety-five percent confidence intervals for decile average AALs appear in gray. The confidence intervals characterize

the cross-sectional variation in tract-level average AALs and do not account for model uncertainty because CoreLogic does not provide sufficient information for

us to account for such uncertainty; TIV = total insurable value.
PERSONAL/NONWORK // EXTERNAL

Figure 2. Average Change in AAL 2021–2050, by 2021 Tract AAL Decile 

Average change in AAL between 2021 and 2050 sorted by 2021 average tract AAL decile. Ninety-five percent confidence intervals for decile average AALs appear 

in gray. The confidence intervals characterize the cross-sectional variation in tract-level average AALs and do not account for model uncertainty because 

CoreLogic does not provide sufficient information for us to account for such uncertainty.
PERSONAL/NONWORK // EXTERNAL

Appendix Tables

Appendix Table 1. By-Peril, By-State, and By-MSA Summary of Tracts in Top 1% of Tract AAL 

By-Peril By-State By-MSA

Peril Percent of AAL State Percent of Tracts MSA Percent of Tracts

Hurricane Storm Surge 42% LA 30% New Orleans-Metairie, LA 28%

Inland Flood 28% FL 26% Miami-Fort Lauderdale-Pompano Beach, FL 12%

Hurricane Wind 25% TX 12% Gulfport-Biloxi, MS 7%

Severe Convective Storm 3% MS 8% Houston-The Woodlands-Sugar Land, TX 6%

Wildfire 1% WV 5% Tampa-St. Petersburg-Clearwater, FL 5%

Winter Storm 0.4% NY 5% New York-Newark-Jersey City, NY-NJ-PA 4%

KY 2% Key West, FL 3%

CA 2% Beaumont-Port Arthur, TX 3%

AL 1% Charleston, WV 2%

NJ 1% Sebastian-Vero Beach, FL 2%

Note: By-peril contribution and top geographic locations of the top one percent of riskiest tracts in the U.S. based on AALs measured as share of total insurable 

value.

Appendix Table 2. AAL in Dollars for SFRs in 2021, by Peril

Percent of Census Region’s Expected Losses (in terms of dollars)

East 

North 

Central

East 

South 

Central

Middle 

Atlantic Mountain

New 

England Pacific

South 

Atlantic

West 

North 

Central

West 

South 

Central U.S.

55% 42% 18% 24% 13% 1% 21% 58% 43% 30%

23% 32% 28% 36% 17% 52% 14% 32% 23% 25%

0% 15% 14% 0% 18% 0% 43% 0% 23% 20%

22% 6% 28% 8% 46% 6% 6% 11% 3% 10%

0% 4% 13% 0% 5% 0% 15% 0% 7% 8%

Severe Convective 

Storm Inland Flood 

Hurricane Wind

Winter Storm

Hurricane Storm Surge

Wildfire 0% 0% 0% 31% 0% 41% 1% 0% 1% 6%

Avg AAL ($) 192 342 350 385 411 267 513 392 649 401

Total AAL ($ Billions) 2.7 2.5 3.5 2.9 1.5 3.2 11.2 2.8 8.4 39

Note: By-peril contribution to average dollar-value AAL. Dollar values are shown in 2021 dollars.
PERSONAL/NONWORK // EXTERNAL

Appendix Table 3. Urban Core 2019 Census Tract Characteristics by Tract AAL Decile

Mean (Standard Error)

Decile of Tract Average AAL (% of TIV)

Description 1 2 3 4 5 6 7 8 9 10

Percent White 43.7 51.1 57.4 55.7 57.4 59.8 58.2 55.4 51.4 49.7

(0.4) (0.4) (0.4) (0.4) (0.4) (0.4) (0.5) (0.5) (0.5) (0.5)

Percent with Bachelor's 

Degree or Higher

35.1 34.5 33.4 34.8 35.6 35.9 35.4 32.3 30.6 31.0

(0.3) (0.3) (0.3) (0.3) (0.3) (0.3) (0.3) (0.3) (0.3) (0.3)

Median Household Income 85,026 78,720 73,430 75,757 75,287 74,487 73,645 70,334 66,651 63,905

(505) (573) (556) (543) (547) (552) (591) (593) (571) (523)

Home Value 866,904 506,944 371,729 431,469 399,194 386,362 419,675 384,883 330,175 424,745

(7,457) (5,661) (3,813) (4,193) (3,985) (3,669) (4,948) (4,612) (3,750) (6,042)

Percent Prime Age Labor 

Force Participation 

83.0 82.7 82.9 83.3 83.8 83.9 83.6 82.9 82.0 81.5

(0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1)

Percent Vacant (Excluding 

Seasonal Units)

4.9 7.8 8.4 7.7 8.1 7.7 8.0 8.2 9.0 10.0

(0.05) (0.11) (0.10) (0.09) (0.10) (0.10) (0.10) (0.10) (0.10) (0.11)

Number of Urban Core Tracts 6,084 5,166 4,689 4,628 4,352 4,016 3,655 3,325 3,378 3,437

Note: Urban core census tract average characteristics by tract-level average AAL decile. Dollar values are in 2021 dollars.
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Appendix Table 4. 2019 Census Tract Characteristics by Tract AAL Decile Sorted Within MSA 

Mean (Standard Error)

Decile of Tract Average AAL (% of TIV) within MSA

Description 1 2 3 4 5 6 7 8 9 10

Percent White 59.2 56.8 57.1 56.9 56.4 57.7 59.0 61.2 63.2 64.6

(0.4) (0.4) (0.4) (0.4) (0.4) (0.4) (0.4) (0.4) (0.4) (0.4)

Percent with Bachelor's 

Degree or Higher

33.8 33.1 33.2 32.7 32.7 32.2 32.2 32.3 31.8 30.7

(0.3) (0.3) (0.3) (0.3) (0.3) (0.3) (0.3) (0.3) (0.3) (0.3)

Median Household Income 75,302 74,338 74,052 74,013 73,898 74,558 75,300 76,488 76,259 73,852

(474) (468) (466) (465) (475) (475) (480) (486) (488) (482)

Home Value 439,936 435,164 443,989 436,560 440,513 436,495 448,987 448,828 454,085 473,207

(4,450) (4,336) (4,605) (4,567) (4,716) (4,575) (5,032) (4,853) (5,120) (5,823)

Percent Prime Age Labor 

Force Participation 

83.1 82.9 82.8 82.8 83 82.8 82.6 82.6 82.5 81.4

(0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1)

Percent Vacant (Excluding 

Seasonal)

7.4 7.8 7.7 7.8 7.8 7.7 7.7 7.6 7.9 8.6

(0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1)

Note: Census tract average characteristics by within-MSA tract-level average AAL decile. Dollar values are in 2021 dollars.
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Appendix Table 5. Change in Census Tract Characteristics (2010–2019) by Tract AAL Decile

Mean (Standard Error)

Decile of Tract Average AAL

Description 1 2 3 4 5 6 7 8 9 10

Change in Percent with Bachelor's 

Degree or Higher

4.2 3.7 3.8 3.9 3.9 3.8 3.5 3.3 2.8 3.3

(0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1)

Percent Change Median Household 

Income 

9.5 4.3 4.1 4.3 4.0 4.3 4.9 5.4 4.3 4.3

(0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.3)

Change in Percent Prime Age Labor 

Force Participation 

0.7 0.1 0.2 0.1 0.0 -0.2 -0.4 -0.4 -0.7 -0.5

(0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1)

Change in Percent Vacant (Excluding 

Seasonal Units) -0.9 -0.6 -0.4 -0.1 -0.2 -0.2 -0.1 0.2 0.4 0.2

(0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1)

Percent Change in Total Population 8.4 7.0 4.6 4.7 5.5 6.3 6.9 6.6 6.9 5.5

(0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.3) (0.2)

Note: Average change in select census tract characteristics by 2021 tract average AAL decile.
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Appendix Table 6. Net Migration (2010–2019) by Tract Average AAL Decile and Tract Average AAL Decile Sorted Within MSA 

Panel A: Mean Tract Net Migration (2010-2019) as % of 2010 Population

Decile of Tract Average AAL

1 2 3 4 5 6 7 8 9 10

All Perils -1.7 -1.0 -1.3 -1.8 -0.9 0.1 0.3 0.8 1.0 0.6

(0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2)

Severe Convective Storm -1.5 2.7 1.8 -2.2 -3.2 -1.7 0.5 0.7 -1.2 0.4

(0.2) (0.3) (0.3) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.3)

Inland Flood -2.7 -1.3 -0.2 -0.1 0.4 0.2 0.1 0.9 0.3 -1.4

(0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2)

Hurricane Wind -2.7 -0.7 0.3 0.4 0.1 -3.2 -2.5 0.2 6.3 2.9

(0.2) (0.3) (0.4) (0.4) (0.3) (0.3) (0.3) (0.4) (0.5) (0.4)

Winter Storm -3.4 7.1 3.6 2.8 -0.4 0.0 -1.7 -3.3 -3.7 -4.1

(0.2) (0.3) (0.3) (0.3) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2)

Hurricane Storm Surge 2.4 1.2 -4.4 -6.9 0.5 -2.0 1.8 1.9 1.1 5.9

(0.4) (0.3) (0.2) (0.2) (0.3) (0.2) (0.5) (0.4) (0.4) (0.4)

Wildfire 3.4 -0.8 1.8 4.8 0.2 -0.5 2.0 5.7 6.3 4.6

(0.5) (0.5) (0.5) (0.6) (0.5) (0.5) (0.5) (0.5) (0.5) (0.4)

Panel B: Mean Tract Net Migration (2010-2019) as % of 2010 Population Within MSA

Decile of Tract Average AAL Within MSA

1 2 3 4 5 6 7 8 9 10

All Perils -0.8 -1.3 -0.4 -0.4 -0.7 -0.7 -0.2 0.6 0.4 -0.3

(0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2)

Standard errors in parentheses. Panel A: Average tract net in-migration during 2010–2019 by decile of tract composite peril average AAL and decile of tract subperil average AAL. Perils with non-modeled areas (Wildfire, Hurricane Storm Surge, and Hurricane Wind) exclude tracts outside the modeled area. For example, 

Hurricane Wind only includes tracts in states on the Gulf and Atlantic coasts. Panel B: Average tract net in-migration during 2010–2019 among the set of movers 

who moved within the same MSA by decile of tract composite peril average AAL sorted within MSA.
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Appendix Table 7. Climate Change Beliefs and 2020 Voting Behavior by County AAL Decile

Mean (Standard Error)

Decile of County Average AAL 

Description 1 2 3 4 5 6 7 8 9 10

Percent Believe Global 

Warming is Happening

69.9 66.6 66.4 65.2 64.8 64.9 63.9 64.3 62.7 63.1

(0.4) (0.4) (0.4) (0.4) (0.3) (0.3) (0.3) (0.3) (0.3) (0.3)

Percent Said Personally 

Affected by Global Warming

44.4 42.0 41.5 40.5 40.7 41.0 40.4 40.8 39.8 40.5

(0.3) (0.3) (0.3) (0.3) (0.3) (0.3) (0.2) (0.3) (0.3) (0.3)

Percent Voted Republican in 

2020

54.7 61.2 61.4 64.4 65.7 67.2 70.0 70.9 73.8 72.7

(1.0) (1.0) (0.9) (0.8) (0.9) (0.9) (0.7) (0.8) (0.8) (0.8)

Note: Average county climate change beliefs survey response and 2020 voting behavior by 2021 tract average AAL decile. AALs measured as share of total insurable 

value. 
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Appendix Figure 1. Peril with Largest Proportion of All-Peril Tract AAL, 2021 

The peril or set of perils that make largest contribution to tract average all-peril AAL in 2021. AAL measured as share 

of total insurable value. 
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Appendix Figure 2. By-Peril Contribution to AAL in Dollars by Percentile of Tract AAL in Dollars 

By-peril contribution of average AAL in dollars in each percentile of tract average AAL in dollars.
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Appendix Figure 3(a)(b)(c)(d). By-Peril AAL by Deciles of Median Household Income, Bachelor’s Degree 

or Higher Share, Vacancy Rate, and Prime Age Labor Force Participation Rate 

Note: (a) By-peril average contribution to tract-level average AAL sorted by 2019 median household income. (b) Byperil average contribution to tract-level average AAL sorted by average 2019 share of adults with bachelor’s degree 

or higher decile. (c) By-peril average contribution to tract-level average AAL sorted by average 2019 percent vacant 

home (excluding seasonal units) decile. (d) By-peril average contribution to tract-level average AAL sorted by average 

2019 prime age labor force participation decile. Ninety-five percent confidence intervals for decile average AALs 

appear in gray. The intervals characterize the cross-sectional variation in tract-level average AALs and do not account 

for model uncertainty because CoreLogic does not provide sufficient information for us to account for such 

uncertainty. 
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Appendix A: Comparing Expected Dollar Losses with Realized Historical Losses in SHELDUS 

One way to assess the reasonableness of our estimates of expected losses is to compare them 

with historical losses. SHELDUS is a public database of historical direct losses caused by natural disasters 

in the U.S. The database contains county-level information on property and crop losses as well 

as injuries and fatalities from 1960 to the present for a wide selection of hazards. The data are 

primarily sourced from the National Centers for Environmental Information (NCEI) Storm Data

publication, which catalogs information on “storm paths, deaths, injuries, and property 

damage.”

37 SHELDUS is updated regularly with data additions and corrections. The subsequent 

analysis is based on SHELDUS 21.

We expect SHELDUS to systemically underreport losses because, when the original data source reports a 

loss estimate as a range, SHELDUS uses the lower bound of that estimate. In fact, prior to 1994, all NCEI 

Storm Data estimates were reported as logarithmic ranges ($<50, $50-500, 

$500-5,000, $5,000-50,000, $50,000-500,000, $500,000-$5 million, $5 million-50 million, $50 million 

to 500 million, $500 million to 5 billion).38 

There are several additional considerations when trying to compare our expected losses for 2021 with 

historical losses in SHELDUS. The main ones are hazard types, property types, and time frame. Regarding 

hazard types, SHELDUS classifications allow us to construct a collection of hazards that are comparable 

in the aggregate with the set of hazards included in our expected loss estimates. We exclude losses from 

the following SHELDUS hazard types: heat, tsunamis/seiches, earthquakes, volcanoes, avalanches, fog, 

droughts, and landslides. 

Regarding property types, our estimate of expected losses is for SFRs only. SHELDUS 

property losses include damages to all property types as well as damage to vehicles and 

infrastructure like roads and power lines.39 We cannot isolate types of property losses in 

SHELDUS and so this feature will be a countervailing force against the underestimation 

stemming from SHELDUS using lower bound estimates. 

The trickiest consideration is time frame. CoreLogic AALs are based on many simulations of a given year 

and therefore represent a large-sample average for a given point in time. The same quantitative exercise 

cannot be applied to the SHELDUS data. If conditions were held constant, we could “observe” annual 

expected loss by averaging across a very long period of historical losses. However, conditions are not held 

constant, which leaves a fundamental tradeoff between getting a historical average that is “longer-run” 

and one that better reflects current conditions. Thus, on one hand, we would like to go as far back in time 

as possible to avoid being misled by “lucky” or “unlucky” periods. On the other hand, environmental 

conditions and property exposure are not held constant over time, so the underlying risk may be less 

reflective of current conditions as one looks further into the past. 

To address this issue, we examine multiple time frames and explicitly adjust for changes in exposure. The 

exposure adjustment accounts for the fact that the number of properties that is exposed to losses from 

hazards has increased over time. If we are trying to make a comparison between SHELDUS losses and 

expected losses in 2021, we want SHELDUS losses to be scaled to 2021 exposure. Consequently, we 

perform an exposure adjustment similar to the one performed in Wiese (2020).40 In short, we construct 

county-year-level aggregate housing values using county-level data on total housing units for census years 

1970, 1980, 1990, 2000, 2010, and 2020; county-level housing values for 2021 derived from our estimates 

described in methods section; the state-level Federal Housing Finance Agency (FHFA) All-Transactions 

House Price Index for the period from 1970 to 2022; and the GDP Price Deflator for the period from 1960 
PERSONAL/NONWORK // EXTERNAL

to 2021.41, 42 We then use the ratio of the real housing exposure values with the 2021 housing exposure 

value to inflate the SHELDUS county-year damages, which are adjusted to 2021 dollars using the GDP price 

deflator.

Appendix Figure A1. SHELDUS Property Losses (1960–2021)

Note: SHELDUS property losses (1960–2021): exposure-adjusted and not exposure-adjusted. 

The exposure-adjusted property damages for the entire history of SHELDUS (1960–2021) and for the 

post-2000 period are shown in Appendix Table A1. The average annual damage recorded in SHELDUS 

for 1960–2021 and for 2000–2021 are $23 billion and $33 billion, respectively, when adjusted for 

exposure. Given worsening environmental conditions and the underestimation built into 

SHELDUS estimates, it is not surprising our 2021 expected damage estimate of $39 billion for SFRs is 

larger than the SHLEDUS annual averages but is closer to the 2000-2021 annual average than the 

1960-2021 annual average. Overall, we assess our current expected damage estimate of $39 billion 

to be in a reasonable range of exposure-adjusted average historical losses. 

Appendix Table A1. Exposure-Adjusted SHELDUS Damages (1960–2021 and 2000–2021) 

SHELDUS Hazard

Exposure-Adjusted 

Damage 1960-2021 

(Billions of 2021 $)

% of Damage 

1960-2021

Exposure-Adjusted 

Damage 2000-2021 

(Billions of 2021 $)

% of Damage 

2000-2021

Hurricane/Tropical Storm 495 34.5% 265 36.0%

Flooding 424 29.5% 266 36.2%

Tornado 111 7.7% 51 6.9%

Severe Storm/Thunder Storm 94 6.5% 7 1.0%

Wildfire 84 5.9% 48 6.5%

Hail 83 5.8% 48 6.6%

Wind 81 5.6% 32 4.3%

Winter Weather 55 3.8% 17 2.3%

Lightning 7 0.5% 1 0.2%

Coastal 4 0.3% 0.5 0.1%
PERSONAL/NONWORK // EXTERNAL

Total 1,437 100% 736 100%

Annual Average 23 N/A 33 N/A

Note: By-hazard exposure-adjusted SHELDUS property losses for 1960-2021 and 2000-2021. “Hurricane/Tropical 

Storm” category includes some flooding from hurricanes and tropical storms. 

In addition to assessing the aggregate damage estimate, we can also compare the breakdown of historical 

losses among hazards with the breakdown of our estimated expected losses by hazard. In this case, the 

best comparison is with the peril shares provided in Appendix Table 2 because those shares were 

determined using dollar-value AALs instead of AALs as a share of TIV. The crosswalk between SHELDUS 

hazards and CoreLogic perils is provided in Appendix Table A2. In the case of “hurricane/tropical storm”, 

there is no clear 1:1 match to a CoreLogic peril. For example, some non-surge hurricane-related ground 

flooding is counted as “hurricane/tropical storm” in SHELDUS, while some is counted as “flooding”.27 Nonsurge hurricane-related ground flooding (as well as non-hurricane coastal flooding) is classified as inland 

flooding in the CoreLogic data. Thus, the hurricane/tropical storm category is somewhat inflated relative 

to what it would be under CoreLogic classifications. For the same reason, flooding is smaller than what it 

would be under CoreLogic classifications. 

Appendix Table A2. Crosswalk of SHELDUS Hazards and CoreLogic Perils

SHELDUS Hazard CoreLogic Peril

Hurricane/tropical storm Hurricane wind/inland flooding/hurricane storm surge

Flooding Inland flooding

Tornado Severe convective storm

Severe storm/Thunderstorm Severe convective storm

Hail Severe convective storm

Wind Severe convective storm

Wildfire Wildfire

Winter weather Winter storm

Lightning Severe convective storm

Coastal Inland flooding

Note: Crosswalk between SHELDUS hazard names and CoreLogic perils. 

One way to deal with this problem is to combine hurricane and flooding categories. For SHELDUS, this

would include hurricane/tropical storm, flooding, and coastal (non-hurricane coastal flooding); for 

CoreLogic, it would include hurricane wind, hurricane storm surge, and inland flooding. Over the entire 

SHELDUS history, hurricane and flooding damages have been 65 percent of damages, while they are 53

percent of our estimate of 2021 expected losses (see Appendix Table A3). The difference suggests that we 

may be understating the influence of hurricanes and flooding. Conversely, the SHELDUS data suggest that 

we overstate the role of severe convective storms as well as winter storm. The damage share for wildfires 

lines up exactly. Overall, the by-peril share of expected damage from CoreLogic appears to be qualitatively 

similar to the realized damage share from SHELDUS. 

Appendix Table A3. Hazard Shares of SHELDUS Damages and Our Estimate of Expected Losses 

Hazard Category SHELDUS Damage Share (1960–

2021)

Estimated 2021 Expected Loss Share 

Based on CoreLogic AALs

Hurricane and flooding 65% 53%

Severe convective storm 25% 30%

Winter storm 4% 10%
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Wildfire 6% 6%

Note: Comparison of peril share of property losses recorded in SHELDUS (1960–2021) and peril share of estimated 

2021 expected losses based on CoreLogic AALs translated to dollar values.

We posit that there are several factors contributing to the observed differences. First, to the extent that 

environmental conditions have changed from 1960 to 2021, they may differentially impact the 

contribution of individual hazards. (Of course, if one thinks that hurricanes and flooding have been most 

acutely affected by environmental changes over the 1960–2021 period, then this explanation only 

exacerbates the difference). Similarly, it’s possible that hazards have differential impacts on property 

types that may drive the differences because of the difference in property types included in our expected 

loss estimate compared with SHELDUS losses. For example, if hurricanes and flooding have a

disproportionate impact on public infrastructure, then that would drive up the SHELDUS share compared 

with the share we estimate based on SFRs. Third, there is very likely overreporting of flood events in 

SHELDUS data relative to other hazards due to the collection procedure requirement that a monetary loss 

amount be provided for all flood events, even if it is a “guesstimate.” For other events, the reporting entity 

is allowed to provide an unknown amount if they cannot provide a monetary loss estimate based on 

authoritative data.43 Fourth, hurricane wind is only modeled by CoreLogic for states on the Gulf and 

Atlantic coasts. Elsewhere, we assume hurricane wind damage is zero. To the extent that hurricane winds 

have the potential to reach areas further inland, this assumption would cause us to underestimate the 

expected losses from hurricane wind.
PERSONAL/NONWORK // EXTERNAL

Appendix B: Analyzing CoreLogic Risk Estimates Using Alternative Risk Estimates

Validating the average annual loss (AAL) values from CoreLogic, in the context of using these values for 

our research, is inherently difficult because the ground truth is unknown. One check of reasonableness is 

to compare to historical loss data, which we do in Appendix A. In that check we focus on the level of 

aggregate damage and the by-peril contribution to aggregate damage. However, our distributional 

analysis relies on the risk estimates from CoreLogic being ordinally accurate across geographies. One 

way to assess that is to examine how well CoreLogic’s AAL estimates correlate with estimates from 

another catastrophic risk modeler. While we take no position on whether estimates from a different 

modeler are more or less accurate, we think rank-order agreement among the different sets of estimates 

provides more confidence in our distributional analyses. 

We are able to perform this exercise for a subset of perils in our analysis using property-level AAL 

estimates from First Street Foundation (FSF). FSF is a non-profit organization that models flood, 

hurricane wind, wildfire, and extreme heat risk for the contiguous U.S. FSF’s “flood” peril is equivalent to 

the combination of CoreLogic’s Inland Flooding and Hurricane Storm Surge perils. Detailed 

methodology for all of First Street Foundation’s models can be found on their website.44

We compare the rank-order of AAL estimates between CoreLogic and FSF for Inland Flooding and 

Hurricane Storm Surge (together referred to as “Flood”), Hurricane Wind, and Wildfire. We compare 

tract-level average AALs among SFRs. In order to conduct the comparison, we match the SFRs identified 

in the CoreLogic data to properties in the FSF data using a nearest-neighbor geospatial match. Like in the 

main analysis, we limit the comparison to tracts where at least 30 SFRs have a non-missing AAL value 

reported in the CoreLogic and First Street Foundation data. We normalize the nominal AALs provided by 

FSF by their estimate of rebuilding cost in order to be consistent with how CoreLogic expresses AAL 

values. Because nominal AAL values can infrequently exceed FSF’s rebuilding cost measure, we top-code 

AAL values at 1. Note that because we have to normalize FSF’s AALs, differences between each entity’s 

AAL estimates could be driven by the underlying the hazard risk estimates, FSF’srebuilding cost estimate, 

or a combination of both. 

There are a few notable differences between the two data sources beyond the inherent differences in their 

risk modeling approaches. First, the climate modeling data and emissions pathways underlying the risk 

modeling are of slightly different vintages. The CoreLogic estimates used in this analysis are based on the 

climate modeling data featured in the Intergovernmental Panel on Climate Change’s (IPCC) 

Fifth Assessment Report (AR5) completed in 2014. Consequently, their baseline estimate is 

based on Representative Concentration Pathway (RCP) 4.5, which is the “middle-of-the-road” 

emission pathways featured in AR5. The FSF estimates used in this analysis rely on the latest climate 

modeling data from the IPCC’s Sixth Assessment Report (AR6), which was completed in 2022. Therefore, 

their baseline estimates are based on Shared Socioeconomic Pathway (SSP) 2-4.5, which is 

the “middle-of-the-road” emission pathways featured in AR6. 

Second, FSF and CoreLogic have different sources of property data that feed into their risk modeling. FSF

licenses property data from the company LightBox. CoreLogic uses its own proprietary property 

data collection. 

Third, the modeled areas for some perils differ between CoreLogic and FSF. As noted in the Methods 

section, CoreLogic only models Wildfire for the western half of the country plus Florida and only models 
PERSONAL/NONWORK // EXTERNAL

Hurricane Wind risk for states on the Gulf and Atlantic coasts. FSF estimates non-zero Hurricane Wind risk 

further inland, including properties in states like Tennessee, Arkansas, and Oklahoma. Similarly, FSF 

models wildfire risk for the entire contiguous U.S. and finds non-zero wildfire risk for properties 

outside of the CoreLogic modeled area. According to First Street Foundation’s estimates, the areas 

with the greatest wildfire risk outside of CoreLogic’s modeled area are Oklahoma, South Dakota, and 

parts of West Virginia, Kentucky, North Carolina, and Georgia. 

Lastly, FSF does not provide an AAL estimate for wildfire. However, they provide a “destruction 

probability”, which represents the annual likelihood of a structure being destroyed. Given that 

conditional on a structure combusting, it is very likely to be completely destroyed, the 

destruction probability can substitute well as an approximation of the AAL (as share of rebuilding cost).45

With those caveats in mind, Appendix Table B1 provides the correlation between the average tract AALs 

and average tract AAL deciles from CoreLogic and FSF. In constructing the composite AAL (Flood + 

Hurricane Wind + Wildfire), we assume the expected loss is zero if it is outside the CoreLogic modeled area 

for Hurricane Wind and Wildfire. The composite AAL deciles correlate moderately well at r = 0.58. The 

tract average AAL values correlate similarly at r = 0.49. The by-peril correlations suggest that there is 

greater rank-order agreement for Hurricane Wind damage than for Flood and Wildfire damage. 

Appendix Table B1. Correlation Between CoreLogic and First Street Foundation Tract Average AALs

Peril

Correlation of Tract 

Average AAL Decile

Correlation of Tract 

Average AAL

Flood 0.41 0.37

Hurricane Wind 0.88 0.82

Wildfire 0.56 0.34

Composite (Flood + Hurricane Wind + Wildfire) 0.58 0.49

Note: In the case of Hurricane Wind and Wildfire, analysis limited to areas modeled by both CoreLogic and First Street 

Foundation. Tracts outside of peril’s modeled area are considered to have no risk for that peril when generating 

composite AAL.

Notably, agreement between the two sets of composite AAL estimates is strongest among the riskiest 

tracts – 54 percent of tracts in the 10th decile of AAL according to CoreLogic were also in the 10th decile of 

AAL according to FSF. Another 21 percent of CoreLogic’s 10th decile tracts were in FSF’s 9

th decile. This is 

evident in Appendix Figure B1, which maps the difference in tract AAL decile, and shows strong 

agreement along the Gulf and southern Atlantic coasts, where many of the riskiest tracts are located. 
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Appendix Figure B1. Difference between CoreLogic and First Street Foundation Tract Average 

AAL Decile for Flood, Hurricane Wind, and Wildfire 

Note: Difference between tract average AAL decile using CoreLogic and First Street Foundation data. Positive number 

(red) indicates the CoreLogic decile is greater than the First Street Foundation decile. The maximum difference in 

decile placement is nine. Tracts in grey have fewer than 30 single-family residences with AAL estimates in CoreLogic 

and/or First Street Foundation data. 
PERSONAL/NONWORK // EXTERNAL 

ACS County Net Migration Deciles CCP County Net Migration Deciles

Appendix C: Validating the CCP Migration Measure Against ACS County-to-County Flows Data 

We examine net migration between 2010 and 2019 for areas of different climate risk using the FRB 

NY/Equifax CCP data (see Appendix Table 6). One concern with using the CCP data set is its selection 

criteria of individuals with credit histories, which skews the sample towards older and more financially 

sophisticated individuals. Thus, CCP-based migration estimates would be biased to the extent that 

migration patterns systematically differ between those with and without credit histories. 

We test the reasonableness of the CCP-based migration measure by comparing its county-level migration 

estimates with the ACS county-to-county migration flows for 2010–2019. For this comparison, we perform 

steps (1)-(4) from above using the CCP, except that we aggregate to the county level, which precludes the 

need to convert to 2010 census tract definitions. For the ACS-based measure, we use the 2010–2014 

and 2015–2019 county-to-county flows data and sum the values to generate a total 2010–2019 net 

migration estimate for each county.

The ACS and the CCP net migration counts correlate relatively strongly (r = 0.84), which lends support to 

use of the CCP-based net migration estimates at the tract level. This result is consistent with more 

comprehensive assessments of migration estimates from the CCP using different sources of data, 

including the ACS data.46 

The deciles of ACS and CCP county net migration counts are shown in Appendix Figure C1. The highest 

decile represents the counties with the most net in-migration. The CCP-based deciles suggest more 

severe out-migration in the Northeast than the ACS-based deciles, but overall, the deciles are consistent 

with one another. 

Appendix Figure C1. Comparison of ACS and CCP County-Level Net Migration Estimates, 2010–2019 

Note: Deciles of county-level net in-migration during 2010–2019 using ACS (left) and CCP (right) data. 
PERSONAL/NONWORK // EXTERNAL

References (Appendices) 

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45 Discussion with representatives from First Street Foundation.

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