## Howard C. Kunreuther and Erwann O. Michel-Kerjan

Print publication date: 2009

Print ISBN-13: 9780262012829

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262012829.001.0001

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# Policy Analysis of Alternative Programs: Comparing the Status Quo with a True Competitive Insurance Market

Chapter:
(p.269) 13 Policy Analysis of Alternative Programs: Comparing the Status Quo with a True Competitive Insurance Market
Source:
At War with the Weather
Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262012829.003.0013

# Abstract and Keywords

This chapter provides empirical evidence showing that residential losses to homeowners caused by hurricanes will be shared among the affected stakeholders under different market conditions. It looks at various stakeholders, including insured and uninsured homeowners, private insurers and reinsurers, state insurers, insurance policyholders, and general taxpayers. It also examines the role of mitigation in reducing the homeowners’ losses as well as the economic impact of a series of major hurricanes on the key stakeholders under the current disaster insurance programs in Texas, Florida, New York, and South Carolina. The four states varied in terms of the proportion of hurricane losses covered by private insurers for the 100-year return period under existing insurance programs. Coastal communities have the highest risk of wind damage from hurricanes in each of the four states and are therefore expected to pay much more for insurance than would other regions in these states.

# 13.1 Introduction

Who will pay for the losses from future catastrophes? This chapter provides a series of empirical analyses on how residential losses to homeowners due to hurricanes will be shared among the affected stakeholders under different market environments. The stakeholders we consider are uninsured homeowners, insured homeowners, all insurance policyholders, private insurers, private reinsurers, state insurers, and reinsurers, and general taxpayers. (p.271) We also examine the significant role that mitigation can play in reducing such losses. There are four principal objectives in undertaking these analyses:

• Determine the economic impact of a series of major hurricanes on the key stakeholders under the current disaster insurance programs in Florida, New York, South Carolina, and Texas. We denote this as the status quo analysis.

• Examine the ability of the insurance industry to provide coverage against hurricanes in each of these four states if they were able to charge risk-based premiums and adopt a maximum-exposure strategy. We denote this as the competitive market analysis.

• Analyze the impact of mitigation measures on the reduction of aggregate damage to homes and contents (ground-up losses) and insured losses from severe hurricanes in each of the four states.

• Characterize the relative magnitude of damage from hurricanes to the four metropolitan areas in relation to the state in which they are located: Miami, Florida, area (Miami-Dade County); New York City area (Bronx, New York, Queens, Kings, and Richmond counties); Charleston, South Carolina, area (Charleston, Berkeley, Colleton, and Dorchester counties); and Houston, Texas, area (Galveston, Harris, Fort Bend, Montgomery, Brazoria, Liberty, Waller, Chambers, Austin, and San Jacinto counties). The insured exposure at risk was provided by Risk Management Solutions (RMS) based on their latest research.

The maps in figures 13.1 depict the four states and the metropolitan areas (circled) on which these analyses are based.

# 13.2 Data Sources

In order to undertake these analyses, we obtained data from several complementary sources: the catastrophe modeling firm Risk Management Solutions (RMS), the rating agency A.M. Best, and the state insurance regulatory offices and state funds in Florida (Florida Hurricane Catastrophe Fund and Citizens Property Insurance Corporation) and Texas (Texas Windstorm Insurance Association).

## State Exposure Data: Residential

Data on states’ residential-only insured exposure to hurricane risk were provided by RMS. RMS used its proprietary U.S. Hurricane Industry Exposure Database, which contains residential exposures at the postal code level of resolution using a standard occupancy (e.g., single-family dwelling or multifamily dwelling) and construction (e.g., wood-frame or masonry) class for the standard coverages for building, contents, and time element costs (e.g., additional living expenses). These data provide information about the total insured values of residential structures in the four target states.

(p.272)

Figure 13.1 Counties and main cities in Florida, New York, South Carolina, and Texas. source: Geology.com. Note: Metropolitan areas studied are circled.

(p.273) (p.274)

Table 13.1 Total insured value of all dwelling types and residential/single-family homes and damage multipliers

State

TIV: All dwelling types (in $billion) TIV: Residential and single-family homes (in$ billion)

Damage multiplier

Florida

$1,769$1,504

0.850

New York

$2,063$1,749

0.847

South Carolina

$362$321

0.888

Texas

$2,053$1,857

0.904

Note: TIV = total insured value.

Our analyses in New York, South Carolina, and Texas were performed looking at both the wind and storm surge peril using the RMS U.S. Hurricane Model. While flooding losses are primarily covered by the National Flood Insurance Program (NFIP), it is assumed that a portion of the flooding from storm surge would be covered by private insurance (e.g., large commercial coverage). In Florida the residential exposure data came from the publicly available Florida Hurricane Catastrophe Fund (FHCF) through Paragon Strategic Solutions, the FHCF administrator. The RMS analyses in Florida did not include storm surge damage, because the FHCF insures only damage caused by the wind peril. The RMS model can analyze losses to multiple types of residential exposure, including, for example, single-family dwellings, mobile homes, condos, and rental properties. We used damage multipliers for each of the four states to isolate this single-family dwelling component of the total exposure at risk. Comparing the total insured value (TIV) of residential single-family homes to the TIV of all residential exposure yields the damage multiplier (TIVResidential/TIVTotal).

Table 13.1 details the TIV for all residential structures and the TIV for residential single-family homes in each of the four states, along with the relevant damage multiplier.

The RMS U.S. Hurricane Model allows us to measure the following elements at a postal code level for each of the four states:

• Average annual loss (AAL) due to wind and storm surge peril, as applicable1

• Standard deviation of AAL

## Data on Insurance Companies

The rating agency A.M. Best provided us with data on the 2006 statutory annual statements of individual insurance and reinsurance groups that included their total surplus and direct insurance premiums written, as well as the amount of homeowners’ multiperil reinsurance coverage they assumed from both affiliates and nonaffiliates. A.M. Best also provided aggregate data on the gross and net probable maximum losses (PMLs) for hurricane (p.275) events with a 100-, 250-, and 500-year return period for each of these groups. Probable maximum loss (PML) data came from the 2005 Supplemental Rating Questionnaires completed by all the insurers rated by A.M. Best. We used the PML data to estimate the amount of reinsurance and other risk transfer instruments available under the status quo program.

Note that A.M. Best provides group data and data from unaffiliated single companies. An unaffiliated single company is a stand-alone company that is not part of a group. A.M. Best provided us with the total surplus for each group of companies. This means that companies and their affiliates’ surpluses were aggregated under one umbrella insurance group. One assumption inherent in this analysis is that the umbrella insurance group relies on its total surplus to determine how much insurance and reinsurance it will want to provide should it have the freedom to charge risk-based rates as assumed under the competitive market program.

## Florida Office of Insurance Regulation Data

Insurers doing business in Florida are required by law to report statistical information to the Florida Office of Insurance Regulation (FLOIR). We used these data from its Quarterly Supplement Report (QUASR) to determine the insurer exposure in Florida. Because our analysis is undertaken using 2005 data from RMS, we also used the 2005 data from QUASR. Because we restricted our analysis of losses to single-family dwellings, we focused on the homeowners’ exposure (including tenants and condo owners) from the QUASR data set. We used this exposure to develop individual insurer market shares to calculate the FHCF payouts on an individualized basis.

## Data on State Funds

Given recent changes in the operation of state funds in Florida and Texas, we used the most recent information on their operation. The Florida Hurricane Catastrophe Fund (FHCF) provided us with 2007 information about its operations and surplus estimates. The data were compiled by Paragon, which also develops the reimbursement premium formula and the rates used in determining the FHCF’s annual reimbursement premium. The FHCF annual reimbursement premiums are those paid by an insurer for its mandatory coverage. It does not include premiums of optional coverage above and below the standard coverage limit, Temporary Emergency Additional Coverage Options (TEACO) and Temporary Increase in Coverage Limit (TICL).

Data on Citizens Property Insurance Corporation, the state-run insurance company in Florida, were obtained from the same data sources used for other private insurers. We also obtained market share estimates for Citizens from news reports. A May 13, 2007, article in the South Florida Sun-Sentinel reported that Citizens’ board chairman estimated its market share to be about 30 percent.2 We recognize that this market share may (p.276) increase substantially in the near future but use this figure for this analysis. Data on the Texas Windstorm Insurance Association (TWIA), the state-run insurance company in Texas, came from publicly available 2008 data, as well as the Insurance Information Institute.

## American Housing Survey

Other housing statistical information came from the U.S. Census American Housing Survey (AHS), the largest, regular national housing sample survey in the United States. The U.S. Census Bureau conducts the AHS to obtain up-to-date housing statistics for HUD. The national sample covers an average 55,000 housing units. Each metropolitan area sample covers 4,100 or more housing units. National data are collected in odd-numbered years, and data for each of forty-seven selected metropolitan areas are collected every six years (not all metropolitan areas are collected at the same time). We used data from the following five metropolitan areas to project losses to uninsured homes: Tampa, Florida (1998); Miami/Fort Lauderdale, Florida (2002); Houston, Texas (1998); Dallas, Texas (2002); and Fort Worth/Arlington, Texas (2002).

# 13.3 Assumptions for the Status Quo Analysis

Our analysis of the status quo estimates how much insurance and reinsurance is provided by the public and private sectors in each of the four states we studied. We then examine the effects of hurricanes of different magnitudes and intensities in each of these states and four metropolitan areas based on the insurance programs currently in place. Should a hurricane occur tomorrow, and given the assumptions we have made, this analysis would specify who pays for the damage. We also discuss the nature and length of the ultimate loss sharing in Florida and Texas should their state-run funds have insufficient reserves to cover all homeowners’ losses from the disaster.

## Losses, Insurance Coverage, Take-Up Rate, Uninsured Projections, Reinsurance, and Mitigation Assumptions

To undertake a systematic analysis of insurance programs currently in place using data from the identified sources, we made a set of assumptions that we detail next.

### Losses

Insurers’ losses from hurricanes that occur in a particular state are determined by multiplying their market shares in that state by the total industry loss. This assumption implies that each insurer’s portfolio of risks is distributed in the same manner across the entire state. While we recognize that this is a simplifying assumption, there is no systematic data collection publicly available on market share at a finer grain (e.g., postal code level). (p.277)

Table 13.2 Take-up rates for selected American Housing Survey cities

City

Year

Take-up rate

Tampa, Florida

1998

93%

Miami/Fort Lauderdale, Florida

2002

87

Houston, Texas

1998

89

Dallas, Texas

2002

95

Fort Worth/Arlington, Texas

2002

92

Average

91

### Take-Up Rate

Take-up rate (TUR), or market penetration, refers to the percentage of homeowners in a given state or metropolitan area who have purchased insurance against wind damage in Florida and wind and storm surge damage in the other states. The TUR enables one to derive the amount of losses to uninsured structures. We used AHS data to estimate the take-up rate for Tampa, Miami/Fort Lauderdale, Houston, Dallas, and Fort Worth/Arlington, as shown in table 13.2. Based on this analysis, we estimated the take-up rate for all four states to be 90 percent.

### Mitigation

From the RMS U.S. Hurricane Model, losses were calculated on a ground up and gross basis, assuming an appropriate mitigation measure across the insured portfolio. The mitigation measures were based on various assumptions for the different regions. For example, in Florida, the requirements as defined by the Institute for Business and Home Safety’s (IBHS) Fortified … for Safer Living program were used to incorporate mitigation. Of course, this program is only for new construction. So, when we describe an analysis using these recommendations, it is the retrofit techniques that are aligned with the features of the Fortified program.3 In New York, South Carolina, and Texas mitigation means the application of the latest building codes to the residential structures.

### Uninsured Losses Projection

The TUR enables us to obtain an approximation of the residential uninsured losses. Loss calculations include the ground-up losses for insured structures only, as well as the gross losses, the portion of the ground-up losses covered by insurers. In order to determine the ground-up losses to uninsured structures, we had to extrapolate using the TUR.

We divided the ground-up losses by the TUR to determine the total ground-up losses assuming all structures were valued the same. Next, we subtracted the original value of ground-up losses to remove the insured structures from our new total figure. We used the ratio of the average uninsured home value (UHV) over the average insured home value (IHV) to estimate the value of the uninsured homes using the following formula: (p.278)

Table 13.3 Ratio of insured to uninsured home values

Metropolitan area

Uninsured home value/insured home value

Tampa, Florida

62%

Miami/Fort Lauderdale, Florida

73

Houston, Texas

53

Dallas, Texas

60

Fort Worth/Arlington, Texas

46

Average

59

source: American Housing Survey

(Insured ground-up losses/take-up rate – Insured ground-up losses) · UHV/IHV.

We assumed that UHV/IHV = 60 percent based on the mean home values for insured and uninsured people from the AHS data on the five metropolitan areas in Florida and Texas, as shown in table 13.3.

The following example shows how we incorporate uninsured losses to obtain total losses from the wind peril. If the ground-up losses are $9 billion and the take-up rate is 90 percent, then we divide$9 billion by 90 percent to get total losses of $10 billion. But the additional$1 billion must be diminished because it represents uninsured structures, which are worth less than the insured structures we used to estimate the loss. We multiply this figure by 60 percent since we assume these homes are valued at 60 percent of the insured homes. This yields an uninsured value of $0.6 billion. This adjustment in value yields total losses of$9.6 billion. Nine billion dollars is the original value of ground-up loss, and $0.6 billion is our loss projection for the uninsured. ### Reinsurance Assumptions and Calculations Gross losses used in this analysis are calculated based on complete retention (i.e., there is no reinsurance and other risk transfer instruments in place). Note that hereafter when we refer to reinsurance, we mean all types of alternative risk transfer (ART) instruments such as industry loss warranties, catastrophe bonds, and sidecars.4 To estimate the aggregate amount of reinsurance, we rely on PML data from A.M. Best. We use the national pretax per-occurrence hurricane gross and net PMLs for the 100-, 250-, and 500-year return periods for ninety groups categorized within the personal lines and homeowners’ segments using 2005 data. The ninety groups in the analysis represent companies that submitted a supplemental rating questionnaire to A.M. Best. Groups that provided the PML information verbally or in a group presentation are not included in the study. (p.279) Table 13.4 Estimating reinsurance percentages using PML data on homeowners’ losses from hurricanes Return period Gross PML Net PML Net/gross Reinsurance (including alternative risk transfer) 100 21.3 8.5 39.7% 60.3% 250 33.3 16.1 48.3 51.7 500 44.7 25.4 56.9 43.1 source: Data from A.M. Best; authors’ calculations Although some groups were omitted from the analysis, we believe this is an accurate portrayal of the industry. Furthermore, we used PML ratios, not the absolute value. These ratios from our sample should be generally applicable to the entire industry. The gross PML is the total projected loss from a catastrophic event for an insurer, while the net PML is the total projected loss from this event after subtracting reinsurance and other alternative risk transfer payments. The percentage of losses paid by reinsurance is derived using the following formula: $Display mathematics$ To illustrate, suppose that for a 100-year return period, an insurer had a gross PML from hurricanes in Florida of$500 million and a net PML of $300 million. Then equation 13.1 implies that the percentage Reinsurance = 1 – ($300/$500) = 40 percent. A.M. Best estimated gross and net PMLs for the insurance industry from hurricanes using data at the group level for the 100-, 250-, and 500-year return periods. This enabled us to estimate the percentage of reinsurance that insurers had purchased for these catastrophic losses, as shown in table 13.4. Because these reinsurance percentages are based on aggregate group PMLs, we feel they are accurate only for Florida hurricane coverage with no mitigation in place, since the losses comprise most of the nation’s PML. Furthermore, reinsurance is linked to damage amounts instead of return periods. Considering the case of no mitigation in Florida, we find that gross (insured) losses are$76 billion at the 100-year return period level, $114 billion at the 250-year return period level, and$145 billion at the 500-year return period level. The gross losses for the other three states are somewhat lower. Based on PML analysis and discussions with several reinsurers, we developed the following assumptions for reinsurance percentages for the spectrum of gross loss amounts, as shown in table 13.5.

The private reinsurance in place to cover wind and storm surge losses in New York, South Carolina, and Texas is estimated by multiplying the gross losses by the relevant percentage in table 13.5. In Florida, the FHCF provides significant amounts of reinsurance to many insurers at rates more generous than those we assumed existed for private reinsurance in table 13.5. In the cases where reinsurance by the FHCF exceeds our estimates of (p.280)

Table 13.5 Percentages of reinsurance as a function of catastrophic loss

Gross loss

Reinsurance

$0–10 billion 10%$10–20 billion

20

$20–30 billion 30$30–50 billion

40

$50–90 billion 60.3$90–120 billion

51.7

### Post-Disaster AssessmentFund

Since the FHCF has far less reserves than potential liabilities, the additional capacity that might be needed to meet all its claims following a severe hurricane would be funded by bonds supported by the FHCF’s emergency assessment authority, which will assess all property and casualty lines of business, including surplus lines but excluding workers’ compensation, accident and health, medical malpractice, and federal flood. Here again, all Floridians who purchased these types of insurance coverage will have to pay over the years when the FHCF encounters a shortfall in resources provided from reimbursement premiums and investment earnings. The assessment is limited to 6 percent annually for losses realized in any one year and 10 percent annually in the aggregate for all assessments for losses in all years.

### FHCF Payout CalculationFund

We obtained 2005 data on the exposure of different insurers at the county level from FLOIR. These data are reported by insurers to the FLOIR under the QUASR system. Of these companies, 138 had homeowners’ exposure in Florida, and we estimated the market share of each insurer i to be

$Display mathematics$

We then use the gross losses to estimate the gross loss by insurer i as follows:

$Display mathematics$

Under the current arrangement between the FHCF and insurers in Florida, the FHCF will reimburse a fixed percentage of a participating insurer’s losses from each covered event in excess of a per event retention and subject to a maximum aggregate limit for all events. The percentage of proportional reinsurance covered by the FHCF can be 45 percent, 75 percent, or 90 percent at the option of the insurer. The event retentions and limits vary by insurer according to a formula based on FHCF premiums. In order to calculate the payout from the FHCF to each insurer, we accessed data on the FHCF. With knowledge of the retention cap, the choice of proportional reinsurance rate (45 percent, 75 percent, or 90 percent) and the FHCF’s obligation to pay an additional 5 percent of the calculated reimbursement figure for loss adjustment expenses, the FHCF payout to insurer i is determined by the following formula:

$Display mathematics$

(p.282) To illustrate the FHCF payout, consider an insurer with a $50 million retention that selected the 90 percent proportional reinsurance rate. If that insurer suffers$500 million losses, the FHCF will be responsible for $425.25 million [$405 million (i.e., 90 percent above the $50 million retention) × 1.05]. We sum all the individual payouts to insurers to obtain the total FHCF payout: $Display mathematics$ If the total payout is greater than$27.8 billion, the FHCF payout is capped at $27.8 billion because that is its defined maximum coverage. Then the$27.8 billion is multiplied by the damage multiplier (TIVResidential/TIVTotal) to determine the portion of the payout going toward residential structures.6 Note that the $27.8 billion includes the 5 percent loss adjustment expenses. The total losses would be$26.5 billion ($27.8/1.05), and the loss adjustment expense would be$1.3 billion ($26.5 billion × 5 percent). Note that for any given event, individual insurers may be capped out due to exhausting their coverage limit or FHCF payout. For example; in 2004, there were fifty-nine insurers that had FHCF losses in excess of their coverage limits, and in 2005 there were twelve such insurers. ### Funding Arrangements The 2007–2008 FHCF funding structure is displayed in figure 13.2. The$6.1 billion industry retention is the sum of the retentions (deductibles) of the individual insurers covered by the FHCF. Each insurer’s retention is a per occurrence retention that is applied to each hurricane event during the FHCF’s contract year. Insurers are assumed to have a 90 percent proportional reinsurance rate so that they are responsible for only 10 percent of the insurance wind losses from a hurricane above their share of the overall $6.1 billion retention. The FHCF structure uses a buy-down option below the aggregate industry overall retention level, temporary emergency additional coverage options (TEACO), which lowers the retention (deductible) of an insurer.7 The FHCF offers an additional buy-up option above the FHCF limit, temporary increase in coverage limit (TICL), which can be chosen by the insurer to increase its coverage against catastrophic wind losses from hurricanes. TICL coverage applies when hurricane losses exceed an insurer’s mandatory FHCF coverage. It acts to expand an insurer’s limit of FHCF coverage. The FHCF claims-paying capacity is the fund’s balance as of December 31 of a contract year plus any reinsurance purchased by the FHCF plus the amount that can be raised through the issuance of revenue bonds. The 2007–2008 FHCF funding structure is used to analyze the FHCF shortfall should a severe hurricane occur. Table 13.6 compares the potential coverage as stated in the legislation to the actual coverage based on availability and insurer selections. We have estimated actual coverage using 2006 premiums. The State Board of Administration (SBA) may make available an additional$4 billion of capacity, but this optional coverage is not currently offered to insurers. Mandatory layer (p.283)

Figure 13.2 2007–2008 FHCF event funding structure

Table 13.6 FHCF funding structure for paying hurricane claims

Potential coverage

Actual coverage based on selections

TICL layer

$12.000 billion$11.428 billion

SBA optional limit

$4.000 billion$0

Mandatory layer bonds

$13.767 billion$13.767 billion

$10 million coverage option$0.600 billion

$0.557 billion TEACO$6.000 billion

$0 Cash balance$2.078 billion

$2.078 billion Totals$38.445 billion

$27.830 billion (p.284) bonds are the bonds taken out by the FHCF to pay for the regular (mandatory) coverage. The$10 million coverage option is available to certain limited apportionment companies and companies that participated in the Insurance Capital Build-Up Incentive Program. No insurer actually purchased TEACO since it is designed to be a last-resort coverage; private reinsurance was used as a substitute for TEACO. As a result, the total actual coverage of $27.8 billion is about$10.6 billion less than its potential coverage.8 For our analyses of hurricane damage to homeowners, FHCF coverage is $23.6 billion the portion of the state fund that would be available for covering insurers’ hurricane wind claims to residences. That represents the maximum level of claims the fund will be responsible for in our analysis. We discuss how the FHCF can finance such a level of claims (using future reinsurance premiums collected against insurers or ex post assessment against all policyholders in the state). ## Operation of the Texas Windstorm Insurance Association In 1970, Hurricane Celia caused an estimated$310 million in insured losses in Texas ($1.55 billion in 2005 dollars).9 Many insurers sustained significant losses and discontinued coverage in the state’s exposed coastal communities. As a result, in 1971, the state created the Texas Catastrophe Property Insurance Association, which later became the Texas Windstorm Insurance Association (TWIA). The TWIA provides wind and hail coverage for Texas Gulf Coast property owners as an insurer of last resort. As such, the TWIA writes higher-risk policies than other carriers. The TWIA writes policies in only the following fifteen counties, all of which are on the coast, as shown in figure 13.3.  Aransas Galveston Matagorda Brazoria Harris County (partial)* Nueces Calhoun Jefferson Refugio Cameron Kenedy San Patricio Chambers Kleberg Willacy (*) Although not on the map in figure 13.3, the following portions of Harris County, located inside the city limits and east of Highway 146 are also included: LaPort, Morgan’s Point, Pasadena, Seabrook, and Sore Acres. ### Insured Property The number of structures insured by TWIA has been growing dramatically. In 2001, it had 68,756 policyholders; as of August 31, 2008, 224,468 policies were in place. In 1992, TWIA had about$5 billion exposure in these counties. As of December 31, 2008, TWIA had approximately $58.5 billion in direct exposure (building and contents coverage) on issued policies, not including loss of business coverage and additional living expense coverage. Due to underwriting procedures implemented after Hurricane Ike, there are an unusually large number of policies that have been bound but not issued that are not reflected in this (p.285) Figure 13.3 Coastal counties where coverage is provided by TWIA total. When business interruption and additional living expense coverage are included, the total TWIA exposure rises to over$68.4 billion.

Residential and commercial policyholders can purchase TWIA coverage up to the following statutory limits:

• Residential: Dwelling building and contents: $1.7 million • Apartment, condo, townhouse: Contents only:$350,000

• Mobile home: Building and contents: $84,000 • Commercial: Commercial building and contents:$4 million

### How the TWIA Operates

All companies licensed to write property insurance in Texas are required to contribute to the TWIA as pool members. Any excess and surplus lines carriers that are affiliated with a member company are also included. TWIA is governed by a nine-member board of (p.286) directors, comprising five insurance company representatives, two agent representatives, and two consumer representatives. While our analysis focuses on residential property only, the TWIA covers residential and commercial structures as well as miscellaneous items such as signs, fences, flagpoles, and swimming pools.

The TWIA operates similarly to a standard insurance company. It issues policies, collects premiums, and pays claims. Any profits accrued are deposited annually into the Catastrophe Reserve Trust Fund, which provides one source of funding in the event of a catastrophic loss or series of losses, as detailed in the funding arrangement section below.

The TWIA estimates that its 2008 share of the coastal residential market where it is authorized to do business is 55.3 percent. The average annual losses (AAL) of the coastal areas where TWIA does business relative to the total AAL in Texas was calculated. It ranges from 53.6 percent if there is no mitigation in place to 46.7 percent if there is mitigation in place. Given the 55.3 percent market share for TWIA in these coastal areas, this translates to a 26 to 30 percent overall market share (with mitigation and without mitigation, respectively) in Texas. As of December 31, 2008, TWIA had approximately $58.5 billion in direct exposure (building and contents coverage) on issued policies. ### Funding Arrangements The 2008–09 funding structure for TWIA is shown in figure 13.4. Texas statute requires that losses to the TWIA be paid from the following sources in this order: 1. 1.$100 million assessed against members of TWIA

2. 2. The current reserve in the Catastrophe Reserve Trust Fund, which acts as the TWIA surplus

3. 3. Any reinsurance

4. 4. $200 million assessed against members of the association, nonreimbursable 5. 5. Unlimited additional assessments against members of the association, reimbursable through premium tax credits over five or more years The amount of reinsurance recoverable by the TWIA is contractual. For the 2008 storm season,$1.5 billion in reinsurance coverage was purchased by the TWIA. The amount paid from the Catastrophe Reserve Trust Fund (CRTF) is based on the actual fund balance, which was approximately $500 million at the beginning of the 2008 storm season. Given current premiums and reinsurance expenses, average noncatastrophic losses, and no catastrophic losses, the TWIA expects to be able to contribute an average of$50 million annually to the CRTF. Unexpected growth, changes in reinsurance costs, non-catastrophic losses significantly above or below historic averages, or any catastrophes could materially affect the amount of any contribution to the CRTF.

(p.287)

Figure 13.4 Funding structure of the TWIA (2008–09). source: Texas Windstorm Insurance Association (TWIA)

The premium tax credit provision in the TWIA statute has never been executed because losses have never exceeded the relevant threshold. In the event of an assessment in this layer, TWIA member companies would claim up to 20 percent of their assessment annually as a credit against their Texas premium taxes for five or more years.10 TWIA representatives indicated to us that the Texas Windstorm Insurance Association was seeking solutions to pay losses and avoid compromising the Texas General Revenue Fund should a major hurricane make landfall in a coastal area of the state.

As a result of the storms that hit Texas in 2008, the funding structure for TWIA has changed significantly. The CRTF was used in its entirety to pay claims from Hurricanes Dolly and Ike. As of December 31, 2008, $921.5 million had been paid by TWIA for Hurricanes Dolly and Ike. The TWIA assessed the entire$300 million nonreimbursable available (p.288) to them from member companies and has had to use the unlimited, reimbursable layer. The Texas legislature will most likely address TWIA funding with potentially significant changes during 2009.

## Mitigation

In undertaking our series of analyses, we assume two extreme cases regarding mitigation in place: no mitigation or full mitigation on all residential homes. As our analysis reveals, mitigation plays a critical role in reducing losses associated with the different major hurricanes we analyze.

# 13.4 Status Quo Analysis

## Comparisons across the Four States

We use the following base case assumptions for our analyses of current insurance programs in place. Table 13.7 provides a dollar comparison of the impact of a 1-in-100-year return period loss event on the different stakeholders for each of the four states. An explanation of each column of this table is described below:

• Losses to uninsured homes: Losses to homeowners who do not have insurance.

• Losses to insured homes: Losses to homeowners who have insurance after claims reimbursements. These residual losses include deductibles and payments above the coverage limit.

• State insurer: Losses to Citizens in Florida (this amount is the level of claims that Citizens will be responsible for under different scenarios) and the TWIA in Texas after reinsurance reimbursements.

• State assessment to insurers: Losses paid by TWIA pool members (private insurers in Texas).

• Private insurers: Losses to private insurers after reinsurance reimbursement.

• Private insurers reimbursable tax credits: Losses paid by TWIA pool members (private insurers in Texas), that can be recouped through future tax credits.

• State reinsurer: Losses to the FHCF in Florida only (that amount is the level of claims the FHCF will be responsible for under different scenarios; later we discuss how the fund will meet its claims if they are higher than its current reserves).

• Private reinsurance: Losses to private reinsurers.

Table 13.8 provides a percentage comparison of the impact of a 1-in-100-year return period loss event on the different stakeholders for each of the four states. Several observations are relevant regarding the distribution of losses between the affected parties. (p.289)

Table 13.7 State comparison $losses: 100-year return period: Hurricane without mitigation ($ billion)

Homeowner losses

Insurer losses

Reinsurer losses

State

Details

Total loss

Losses to uninsured homes

Losses to insured homes (below deductible and above limit)

State insurer (FL = Citizens, TX = TWIA)

State assessment to insurers (TWIA only)

Private insurers

Private insurers’ reimbursable tax credits portion (TWIA only)

State reinsurer (FHCF only)

Private reinsurance

Florida

Return period: 100

$75.73$4.73

$6.18$7.72

$18.01$23.59

$15.49 Take-up rate: 90% Mitigation: No Reinsurance: 60.3% Citizens market share: 30% Citizens reserve:$4.18B

FHCF reserve: $2.78B Texas Return period: 100 16.6 1.04 1.08 0.4$0.3

8.59

$2.29 2.90 Take-up rate: 90% Mitigation: No Reinsurance: 20% New York Return period: 100 5.41 0.34 0.82 3.82 0.42 Take-up rate: 90% Mitigation: No Reinsurance: 10% South Carolina Return period: 100 4.13 0.26 0.21 3.29 0.37 Take-up rate: 90% Mitigation: No Reinsurance: 10% (p.290) Table 13.8 State comparison (percentage): 100-year return period: Hurricane without mitigation ($billion)

Homeowner losses

Insurer losses

Reinsurer losses

State

Details

Total loss

Losses to uninsured homes

Losses to insured homes

State insurer (FL = Citizens, TX = TWIA)

State assessment to insurers (TWIA only)

Private insurers

Private insurers’ reimbursable tax credits portion (TWIA only)

State reinsurer (FHCF only)

Private reinsurance

Florida

Return period: 100

$75.73 6% 8% 10% 24% 31% 20% Take-up rate: 90% Mitigation: No Reinsurance: 60.3% Citizens market share: 30% Citizens reserve:$4.18B

FHCF reserve: $2.78B Texas Return period: 100 16.6 6 7 2 2% 52 14% 17 Take-up rate: 90% Mitigation: No Reinsurance: 20% New York Return period: 100 5.41 6 15 71 8 Take-up rate: 90% Mitigation: No Reinsurance: 10% South Carolina Return period: 100 4.13 6 5 80 9 Take-up rate: 90% Mitigation: No Reinsurance: 10% ### (p.291) Differences in State Losses Florida is by far the most exposed of the four states we studied. A 1-in-100-year return period loss event is estimated to produce total losses of$75.7 billion in Florida, $16.6 billion in Texas,$5.4 billion in New York, and $4.1 billion in South Carolina. As shown in appendix 13A, total losses at the 250-year return period level are$114.2 billion in Florida, $26.1 billion in Texas,$11.4 in New York, and $6.5 billion in South Carolina. Total losses at the 500-year level are$144.9 billion in Florida, $35.5 billion in Texas,$17.4 billion in New York, and $8.7 billion in South Carolina. (See appendix 13A.) ### Homeowners’ Losses after a 500-year Event Due to the nature of our analysis, the losses to homeowners are derived from the take-up rate and estimates of ground-up loss, based on the assumption that uninsured home values are 60 percent of the insured homes in any given state. These assumptions result in uninsured homeowners in all states paying for 6 percent of the total 500-year return period loss. Insured homeowners are relatively well protected against large catastrophes, generally suffering less than 4 to 11 percent of the total losses. ### State Fund Losses after a 500-year Event In Florida, Citizens will incur only 14 percent of total losses even though it is assumed to have a market share of 30 percent. This is because Citizens is heavily reinsured by the FHCF for the 500-year return period loss ($9.45 billion in reinsurance coverage in 2009). TWIA would be responsible for only 1 percent of the losses in Texas, so that private insurers in that state would incur a higher percentage of the losses from the 500-year return period hurricane loss than those marketing policies in Florida.

Figure 13.5 details the losses sustained by homeowners, insurers, and reinsurers in each of the four states for events at the 50-, 100-, 250-, and 500-year return period levels when there are no mitigation measures in place. Private insurers in New York and South Carolina pay for a significant portion of the total losses, but the actual dollar amounts are much lower than in Florida and Texas.

We now analyze the residential losses that would be sustained by Citizens and the FHCF from the 500-year return period loss in 2009 and determine who ultimately will pay for these losses. Citizens will be responsible for $20.6 billion in claims from its policy-holders, and the FHCF will be responsible for$23.6 billion in claims from the insurers it covers with respect to residential damage.11 As of December 31, 2008, Citizens, which has become the largest provider of homeowners’ insurance in the state, had $4.18 billion in reserves (claims available from surplus), another$4.17 billion of pre-event liquidity, and had purchased nearly $10 billion of reinsurance coverage ($9.45 billion from the FHCF and $.44 billion from private reinsurers). In other words, at the end of 2008, Citizens had$18.24 billion in claims-paying capacity to cover its losses from a future hurricane. Given that Citizens expects to collect about $2.8 billion in premiums during 2009, our analysis reveals that the state-run insurer would be able to handle its$20.6 billion insured losses (p.292)

Figure 13.5 Homeowner, insurer, and reinsurer losses (initial claims) from hurricanes with 50- to 500-year return periods for Florida, Texas, New York, and South Carolina

(p.293) from a 500-year hurricane during the coming season. The major challenge is that as of 2009, the FHCF had reserves of $2.79 billion, and expected premiums for 2009 of$1.3 billion to cover reinsurance claims should a severe hurricane occur in the coming years. There are at least two possible ways the resulting deficit can be absorbed: ex post assessment (recoupment process) or use of future premiums.

## Impact of Mitigation

The above analyses were undertaken with the assumption that no mitigation measures were in place to protect homes. Now we assume that all of the residential structures are fully mitigated.13 Table 13.10 indicates the differences in losses for hurricanes with return periods of 100, 250, and 500 years for each of the four states we are studying when these loss-reduction measures are in place.

The analyses reveal that mitigation has the potential to significantly reduce losses from future hurricanes, ranging from 61 percent in Florida for a 100-year return period loss to 31 percent in New York for a 500-year return period loss. Appendix 13B depicts these differences graphically. (p.294)

Table 13.9 Original claim distribution and ultimate loss sharing after 100% postdisaster assessment: 500-year return period without mitigation in Florida ($billion) Homeowner losses Insurer losses Reinsurer losses Details Total loss Losses to uninsured homes Losses to insured homes (below deductible and above limit) Citizens Private insurers FHCF Private reinsurance Return period: 100 Take-up rate: 90% Mitigation: No Reinsurance: 60.3% Citizens market share: 30% Citizens reserve:$4.18B

FHCF reserve: $2.78B Original claims$144.9

$9.1$15.3

$20.6$48.0

$23.6$28.3

Ultimate loss distribution

$144.9$9.1

$34.81$20.6

$48.0$4.08

$28.3 a The increase here assumes that the entire deficit of the FHCF is recouped only against homeowners who have wind coverage. In reality, the deficit would be recouped against all lines of insurance, with the exception of medical malpractice and workers’ compensation. (p.295) Figure 13.6 Number of years for the FHCF to recoup claims from a 500-year hurricane above its current reserve using future premiums. Note: The paying capacity assumes no event during the recoupment period. ## Focus on Metropolitan Areas in the Four States The total insured value (TIV) for each state is assumed to be equivalent to the total amount at risk in the state. Miami-Dade County represents 9 percent of Florida’s$1.8 trillion TIV. The New York City area counties comprise 3 percent of New York’s $2.1 trillion TIV. The Charleston area counties represent 15 percent of South Carolina’s$0.36 trillion TIV, and the Houston area counties comprise 26 percent of Texas’s $2.1 trillion TIV. Table 13.11 shows that the four metropolitan areas we selected comprise a much larger proportion of the ground-up losses than would be implied by looking at their percentage of the TIV in each of their respective states (without mitigation). These regions are subject to a disproportionate hurricane risk than other parts of their states. This was not a surprise, as we intentionally chose the riskiest counties for this segment of our analysis. # 13.5 Assumptions and Methodology for the Competitive Market Analysis ## Databases The competitive market analysis relied on two data sets. Projections of insurance industry losses (with and without mitigation) for different locations and return periods were used, as previously discussed for the status quo analysis. A.M. Best provided us with publicly available data on 1,379 insurance and reinsurance groups. From this data set, we had the direct premiums written (DPW) for homeowners’ (p.296) Table 13.10 Money saved (reduced losses) from full mitigation for di¤erent return periods 100-year event 250-year event 500-year event State Unmitigated losses Savings (reduced losses) from mitigation Unmitigated losses Savings (reduced losses) from mitigation Unmitigated losses Savings (reduced losses) from mitigation Florida$84 billion

$51 billion 61%$126 billion

$69 billion 55%$160 billion

$83 billion 52% New York 6 billion 2 billion 39 13 billion 5 billion 37 19 billion 7 billion 35 South Carolina 4 billion 2 billion 44 7 billion 3 billion 41 9 billion 4 billion 39 Texas 17 billion 6 billion 34 27 billion 9 billion 32 37 billion 12 billion 31 Table 13.11 Metropolitan area regions’ share of state ground-up losses for different return periods without mitigation 100-year event 250-year event 500-year event Region State loss Region loss Percentage of state loss State loss Region loss Percentage of state loss State loss Region loss Percentage of state loss Miami-Dade$84 billion

$18 billion 21%$126 billion

$37 billion 29%$160 billion

$58 billion 36% New York City area 6 billion 1.4 billion 24 13 billion 4 billion 28 19 billion 6 billion 30 Charleston area 4 billion 2.4 billion 56 7 billion 4 billion 60 9 billion 6 billion 60 Houston area 17 billion 15 billion 89 27 billion 25 billion 92 37 billion 34 billion 93 (p.297) multiperil insurance in each of the four states. We were then able to derive a market share for each group i in each state using the following formula: $Display mathematics$ There are seventy-five groups with DPW in Florida, one hundred groups with DPW in New York, sixty-two groups with DPW in South Carolina, and sixty-eight groups with DPW in Texas. The A.M. Best data also included the total surpluses of each of the groups, which we used to determine the amount of coverage they would offer in the competitive market. For Florida, we use the DPW to estimate market share in the competitive market analysis, and exposure to estimate market share in calculating the FHCF payout in the status quo analysis. This is because we had exact exposure data from the FLOIR, which allowed us to calculate a more precise estimate of the FHCF payout. These data did not extend to the other three states, and this information was not relevant to our competitive market analysis since it assumes there are no state funds in existence. Insurance is provided only by private insurers, and reinsurance is provided only by reinsurers or other alternative risk transfer instruments. ## General Assumptions In undertaking the analyses of a competitive insurance market, we made the following general assumptions: • Total insurance is based on the amount of surplus each group is willing to put at risk in the particular state for the relevant hurricane return period. • Insurers allocate their surplus to each state independently. • Total surplus of insurance companies in a group is equal to total surplus for the entire group.14 • Reinsurance and other risk transfer amounts are identical to the status quo. This assumption can be justified given that reinsurers are not restricted by state regulators under the status quo. ## Insurance Calculation To determine the amount of insurance coverage offered in the competitive market, we focused on the surpluses of the top twenty-five insurers by market share in each state. The data for these twenty-five insurers in each of the four states are in appendix 13C. These companies typically represent more than 90 percent of the private market for homeowners’ coverage. This surplus data came from A.M. Best for every company except for State Farm,15 which is unique in that it has separate entities for different lines of insurance. We took the sum of the surpluses from its five property companies instead of using the A.M. (p.298) Best figure. To determine how much capacity insurers are willing to provide to cover hurricane risk under a competitive market, we assumed that each insurance group would risk 10 percent of its surplus to provide coverage for a 100-year, 250-year, or 500-year return period loss. This 10 percent figure was confirmed by the insurers and rating agencies with whom we spoke as a reasonable assumption for these analyses. In reality, of course, the determination by each insurer as to how much surplus it is willing to assign to a specific risk (e.g., wind damage) in a given state depends on its financial characteristics and the distribution of its portfolio in other states and countries. The capacity that insurers are willing to offer is also likely to vary with the return period of the catastrophic event under consideration and the price they can charge for providing coverage. More specifically, we have: $Display mathematics$ By aggregating the amount of coverage provided by each insurer in the state, we obtained the amount of coverage available to cover losses from hurricanes. For example, there was about$150 billion of surplus available amongst the top twenty-five private insurers operating in Florida. We thus assumed that $15 billion would be available for coverage against wind damage in the competitive market in Florida. We derived the percentage of the residential market covered in each of the states when no reinsurance was in place by using the following formula: $Display mathematics$ In a complementary series of analyses, we also calculated the percentage of total insurers’ surplus that would have to be allocated against insured losses from hurricanes in each state so that all residential structures would be provided with full coverage. These figures were determined by the following formula: $Display mathematics$ We conducted this analysis of competitive market insurance for the 100-year, 250-year, and 500-year return period losses both with and without mitigation in place. A separate analysis was undertaken to account for the impact of reinsurance and other risk transfer instruments on insurers’ ability to provide coverage. ## Insurer Surplus Assumption We used the surplus from the top twenty-five insurers within each state for undertaking our base case analyses. We also examined other amounts of total surplus in 2006, including the total surplus of the top insurers writing coverage that comprise 95 percent of the market and the total surplus of all insurers writing coverage in the state (100 percent of (p.299) the market). The increase in coverage as we expanded the pool of insurers is not large, as shown in table 13.12. ## Note on Competitive Market Insurer Surpluses The competitive market insurer surplus allocations may differ from how these companies actually allocate their surpluses. This is a theoretical analysis of what these companies are capable of doing based on their total conglomerate surplus reported in the A.M. Best data set. However, in practice they may make their capital allocation decisions differently. # 13.6 Competitive Market Analysis Our specific interest in undertaking this analysis is to determine how much coverage insurers could provide in each of the four states. We focus on losses at the 100-, 250-, and 500-year return periods and vary assumptions regarding mitigation in place and availability of reinsurance (whether it is provided by traditional reinsurance or alternative risk transfer instruments). We study two cases: first, insurers do not have any way to reinsure part of their exposure; second, we introduce additional capacity to insurers provided by reinsurance. ## Case 1: No Reinsurance We first examine the case where none of the homes had adopted mitigation measures and insurers could not rely on reinsurance to protect themselves against future damage. Even when insurers are entirely on their own, our analysis shows that using the strategy of allocating 10 percent of surplus, they will have enough capacity to be able to provide protection to all homeowners in New York and South Carolina for the 100-, 250-, and 500-year loss event, as shown in table 13.13. The hurricane damage even for a 500-year loss in New York and South Carolina will be sufficiently small that insurers would not have to commit a large percentage of their surplus to be able to provide full protection against all residential structures in the area. For example, insurers providing wind coverage in New York State would have to commit only 2.5 percent of their surplus to cover total losses to homes (except for the deductible portions of the policy) from a 100-year loss. This percentage would increase to 8.9 percent for a hurricane with a 500-year return period loss. The percentage of surplus required in South Carolina to cover hurricanes with these return periods would be even smaller than in New York. Private insurers in Florida and Texas would need financial backup to cover all the wind losses from a severe hurricane if homeowners have not undertaken mitigation measures. Even for a 100-year return period loss in Florida, the total amount of insurance in place would cover only 23.8 percent of the loss. Insurers providing wind coverage in Florida would have to allocate 42 percent of their surplus to provide full coverage to homeowners (p.300) Table 13.12 Industry surplus analysis for top 25 insurers, and insurers comprising 95 to 100 percent of the market Top 25 companies 95% of the market 100% of the market State Number of companies Market share Surplus ($ billions)

Number of companies

Market share

Surplus ($billions) Number of companies Market share Surplus ($ billions)

Florida

25

91.7%

$154.4 32 94.9%$164.3

75

100.0%

$205.2 New York 25 93.2 167.8 29 95.0 168.7 100 100.0 225.6 South Carolina 25 95.2 175.0 25 95.2 175.0 62 100.0 203.7 Texas 25 95.3 139.2 25 95.3 139.2 68 100.0 195.7 (p.301) Table 13.13 Percentage of loss covered and required surplus for full coverage by insurers with no reinsurance ($ billion)

State

Surplus

Return period (years)

Gross losses

Amount of coverage (10% of surplus)

Market covered

Surplus necessary for full coverage

No mitigation of current homes

Florida

$154.4 100$64.8

15.4

23.8%

42.0%

250

96.4

15.4

16.0

62.5

500

120.5

15.4

12.8

78.1

New York

167.8

100

4.2

16.8

100.0

2.5

250

9.6

16.8

100.0

5.7

500

14.9

16.8

100.0

8.9

South Carolina

175.0

100

3.7

17.5

100.0

2.1

250

5.8

17.5

100.0

3.3

500

7.7

17.5

100.0

4.4

Texas

139.2

100

14.5

13.9

96.2

10.4

250

22.9

13.9

60.7

16.5

500

31.4

13.9

44.4

22.5

Full mitigation of current homes

Florida

154.4

100

23.7

15.4

65.1

15.4

250

42.9

15.4

36.0

27.8

500

58.9

15.4

26.2

38.2

New York

167.8

100

2.4

16.8

100.0

1.4

250

5.8

16.8

100.0

3.5

500

9.4

16.8

100.0

5.6

South Carolina

175.0

100

2.0

17.5

100.0

1.1

250

3.3

17.5

100.0

1.9

500

4.7

17.5

100.0

2.7

Texas

139.2

100

9.4

13.9

100.0

6.7

250

15.2

13.9

91.4

10.9

500

21.1

13.9

65.9

15.2

against this event. In Texas, the loss would be less severe than in Florida, so that insurers would be able to offer close to full protection (96.2 percent) against a 100-year loss but not against the 250- or 500-year return period loss (table 13.13).

The situation improves considerably in these two states if all homes are required to adopt cost-effective mitigation measures, as shown in the bottom portion of table 13.13. In Florida, insurers would now be able to cover 65.1 percent of the losses from a 1-in-100-year return period loss event when mitigation was in place, without having to rely on reinsurance (see chapter 7) or alternative risk transfer (ART) instruments (see chapter 8). A similar story emerges for homes in Texas, where full coverage will be provided for a 1-in-100-year return period loss event if all residential structures had adopted mitigation measures specified in current building codes. (p.302)

Table 13.14 Percentage of losses covered and required surplus for full coverage by insurers with reinsurance in place ($billion) State Surplus Return period (years) Gross losses Reinsurance coverage Unreinsured losses Amount of coverage (10% of surplus) Market covered Surplus necessary for full coverage No mitigation of current homes Florida$154.4

100

$64.8 39.1$25.7

15.4

60.0%

16.7%

250

96.4

49.8

46.6

15.4

33.1

30.2

500

120.5

51.9

68.6

15.4

22.5

44.4

New York

167.8

100

4.2

0.4

3.8

16.8

100.0

2.3

250

9.6

1.9

7.7

16.8

100.0

4.6

500

14.9

3.0

11.9

16.8

100.0

7.1

South Carolina

175.0

100

3.7

0.4

3.3

17.5

100.0

1.9

250

5.8

0.6

5.2

17.5

100.0

3.0

500

7.7

0.8

7.0

17.5

100.0

4.0

Texas

139.2

100

14.5

2.9

11.6

13.9

100.0

8.3

250

22.9

6.9

16.1

13.9

86.7

11.5

500

31.4

12.5

18.8

13.9

74.0

13.5

Full mitigation of current homes

Florida

154.4

100

23.7

20.8

2.9

15.4

100.0

1.9

250

42.9

23.6

19.3

15.4

80.1

12.5

500

58.9

35.5

23.4

15.4

66.0

15.1

New York

167.8

100

2.4

0.2

2.1

16.8

100.0

1.3

250

5.8

0.6

5.2

16.8

100.0

3.1

500

9.4

1.9

7.5

16.8

100.0

4.5

South Carolina

175.0

100

2.0

0.2

1.8

17.5

100.0

1.0

250

3.3

0.3

3.0

17.5

100.0

1.7

500

4.7

0.5

4.2

17.5

100.0

2.4

Texas

139.2

100

9.4

1.9

7.5

13.9

100.0

5.4

250

15.2

3.0

12.2

13.9

100.0

8.8

500

21.1

6.3

14.8

13.9

94.2

10.6

## Case 2: Reinsurance in Place

With reinsurance in place, we made the same assumption as in case 1 regarding allocation of insurer surplus to homeowners’ insurance, but used different estimates of insured losses. We subtracted the total reinsurance obtained from the status quo analysis so insurers had to cover only their unreinsured losses instead of their total gross losses.

As shown in table 13.14, when reinsurance is available, the private insurance market is able to cover all the losses in Texas from a 100-year return period loss, 86.7 percent of the 250-year losses, and 74 percent of the 500-year losses for a 1-in-500-year return period loss event even if homes had not undertaken mitigation measures. In Florida, 60 percent of the 100-year return period losses will be covered from a 1-in-100-year return period loss event, (p.303) but the majority of the 250-year and 500-year losses will still be uninsured for 1-in-250-year and 1-in-500-year return period loss events if mitigation measures had not been adopted by homeowners. Should all homes be mitigated, there will be less than full coverage in Texas only for the 1-in 500-year loss event and in Florida for the 250-year and 500-year loss events.

# 13.7 Impact of Insurance Premiums Reflecting Risk

We conclude this series of analyses by determining the loss cost for Florida, New York, South Carolina, and Texas and counties in these states as a basis for determining premiums that reflect risk for covering wind damage. To determine these premiums one needs to augment the expected average annual loss by using a loading factor.

## Assumptions for the Analysis

### Loss Cost per $1,000 In order to do our analysis at the county level, we summed the average annual loss (AAL) and total insured value (TIV) from the relevant postal codes to obtain the appropriate figures for each county. The loss cost per$1,000 of coverage for each county i was determined by the following formula:

$Display mathematics$

To illustrate, suppose a particular county has an AAL = $1 million and TIV =$20 million. This implies that a loss cost for homes in this county would be $50 per$1,000 of coverage.

In addition to the loss cost, we assume insurers incorporate a loading factor (λ) into their pricing to cover administrative, marketing and claims processing costs and the cost of capital. As discussed in chapter 6, each policy the insurer sells imposes its own capital burden. If an additional policy were sold without adding to the insurer’s overall capital, there would normally be a small increase in the likelihood that the insurer could default. Just how much of an increase would depend on the riskiness of the policy and its covariance with other policies and assets held by the insurer. The appropriate allocation of capital to a policy would be that required to maintain the insurer’s credit status; the addition of the policy and the accompanying capital would leave the insurer with the same credit status as before. We thus define a fair price for insurance as a premium that provides a fair rate of return on invested equity.

Let k be the ratio of capital to expected losses for the insurer to maintain its credit rating. Here we will use k = 1, a value that many property liability insurers use for their combined book of business.16

(p.304) In addition to paying claims, the insurer is assumed to set aside capital for covering additional expenses (X) in the form of commissions to agents and brokers, and underwriting and claims assessment expenses. For this example suppose k =1 and X = $200. Given the risk characteristics of the portfolio, we assume that investors in insurance companies require a return on equity (ROE) of 15 percent to compensate for risk. The insurer invests its funds in lower-risk vehicles that yield an expected return r of 5 percent. What premium π would the insurer have to charge its policyholders to cover them against hurricanes and to secure a return of 15 percent for its investors? Using the hypothetical example in chapter 6 (section 6.4) the premium is: π =$1274, so that the loading factor is λ = .274.

In the analysis that follows, we will use a value of λ = .5 (a 50 percent loading factor). This is still a conservative estimate, as it does not take into account reinsurance costs associated with catastrophic risks and other expenses that the insurer incurs in modeling catastrophic risk by either purchasing commercial models or using their own in-house modeling capability, nor does it include state and federal taxes which the insurer has to pay as any other business.17

### Statewide Prices

Prices for the entire state were determined using the same procedure as for the individual counties, except the postal code data were aggregated for the entire state.

## Determination of Loss Cost and Insurance Premiums

Table 13.15 specify the loss costs and premiums with a 50 percent loading factor for hurricane wind damage for the ten counties with the highest risk within each state, as well as any counties from the metropolitan areas we studied that were not among the top ten. The loss costs and premiums for the entire state are specified at the bottom of the table for each state. We also specify ratios of the county/state premium so one can appreciate how much more (or less) insurers would have to charge in areas that have a significant hurricane risk relative to other parts of the state in order to cover their expected claims payments. The counties from our designated metropolitan areas in each of the four states are in bold type in table 13.15. Appendix 13D presents tables that include all the counties in the four states that we studied.

The data reveal that coastal counties subject to hurricane risk will pay significantly higher premiums than other portions of the state. This is particularly true in Texas, where Calhoun, Aransas, and Galveston counties are charged over nine times the average for the entire state. For the other three states, the differences are less extreme. In the case of Florida, the risk-based premiums do not include any losses from storm surge, so they are likely to be somewhat higher when this cause of damage is incorporated. It is important to remember that premiums based on loss costs are likely to be somewhat lower than homeowners’ insurance premiums, since a standard policy covers losses from other causes, such as fire and theft. (p.305)

Table 13.15 Loss costs and insurance premiums for hurricane wind-insured damage in counties with highest risk

Price rank

County

Loss cost per $1,000 Premium with 50% loading County/state premium ratio Florida 1 Monroe$16.22

$24.32 4.62 2 Franklin 6.84 10.26 1.95 3 Martin 6.69 10.03 1.90 4 Miami-Dade 6.48 9.73 1.85 5 Palm Beach 6.47 9.71 1.84 6 Indian River 5.67 8.51 1.62 7 Broward 5.64 8.46 1.61 8 Collier 5.04 7.56 1.44 9 Saint Lucie 4.68 7.02 1.33 10 Santa Rosa 4.46 6.69 1.27 Entire state 3.50 5.25 New York 1 Suffolk 0.38 0.56 4.10 2 Nassau 0.17 0.25 1.85 3 Richmond 0.12 0.17 1.26 4 Queens 0.10 0.15 1.10 5 Kings 0.08 0.12 .89 6 Westchester 0.08 0.12 .88 7 Rockland 0.07 0.11 .78 8 Bronx 0.07 0.10 .71 9 Putnam 0.06 0.09 .69 10 Dutchess 0.04 0.06 .43 14 New York (Manhattan) 0.02 0.04 .27 Entire state 0.09 0.14 South Carolina 1 Georgetown 2.69 4.04 4.74 2 Charleston 2.62 3.93 4.61 3 Horry 2.11 3.17 3.72 4 Beaufort 2.10 3.15 3.70 5 Colleton 1.37 2.05 2.41 6 Berkeley 1.20 1.80 2.12 7 Jasper 0.92 1.38 1.62 8 Dorchester 0.85 1.27 1.49 9 Williamsburg 0.81 1.21 1.42 10 Marion 0.65 0.97 1.14 Entire state 0.57 0.85 Texas 1 Calhoun 4.60 6.89 10.53 2 Aransas 4.56 6.84 10.45 3 Galveston 3.95 5.92 9.05 4 Matagorda 3.19 4.79 7.32 5 Chambers 2.59 3.88 5.93 6 Brazoria 2.44 3.65 5.59 7 Nueces 2.32 3.48 5.32 8 San Patricio 2.27 3.40 5.19 9 Cameron 2.08 3.12 4.77 10 Jackson 2.04 3.07 4.69 17 Fort Bend 1.07 1.61 2.46 19 Liberty 1.02 1.53 2.34 22 Harris 0.81 1.22 1.86 28 Waller 0.57 0.85 1.39 29 Austin 0.50 0.75 1.14 34 San Jacinto 0.38 0.56 .86 36 Montgomery 0.32 0.48 .74 Entire state 0.44 0.65 Note: Counties in bold are in our designated metropolitan areas. ## (p.306) Summary This chapter provides a series of analyses to evaluate how catastrophe losses would be shared among different stakeholders under the status quo using data provided by Risk Management Solutions on the exposure at risk and expected losses, A.M. Best data on insurance groups, and data from the state funds in Florida and Texas. We estimate the distribution of losses to homeowners, insurers, and reinsurers for return period loss events of 1-in-100 years, 1-in-250-years, and 1-in-500 years for Florida, New York, South Carolina, and Texas and metropolitan areas in each of these states with and without mitigation in place (status quo analysis). We also determine the ability of the private market to offer insurance protection if companies are permitted to charge rates that reflect risk (competitive market analysis). The analysis reveals that insurers would not need reinsurance in order to be able to provide protection to most homeowners in New York, South Carolina, and Texas, but would have to rely on private reinsurance or other alternative risk transfer instruments to provide coverage to the majority of homes in Florida. If all single-family residences were required to adopt mitigation measures, the private sector should be able to provide full insurance protection in all four states without having to allocate a significant portion of its surplus to the hurricane risk. The chapter concludes with an analysis of loss costs and premiums reflecting risks and the cost of capital in the counties most subject to hurricane damage in each of the four states. These premiums are compared with the state average to show the degree of variation in the hurricane risk. Table 13A.1 250- and 500-year return state comparisons. Event: 250-year return period without mitigation ($ billion)

Homeowner losses

Insurer losses

Reinsurer losses

State

Details

Total loss

Losses to uninsured homes

Losses to insured homes

State insurer (FL = Citizens, TX = TWIA)

State assessment to insurers (TWIA only)

Private insurers

Private insurers’ reimbursable tax credits portion (TWIA only)

State reinsurer (FHCF only)

Private reinsurance

Florida

Return period: 250

$114.2$7.1

$10.6$14.0

$32.6$23.6

$26.2 Take-up rate: 90% Mitigation: No Reinsurance: 60.3% Citizens market share: 30% Citizens reserve:$4.18B

FHCF reserve: $2.78B Texas Return period: 250 26.1 1.6 1.5 0.4$0.3

11.9

$4.6 5.7 Take-up rate: 90% Mitigation: No Reinsurance: 20% New York Return period: 250 11.4 0.7 1.1 7.7 1.9 Take-up rate: 90% Mitigation: No Reinsurance: 10% South Carolina Return period: 250 6.5 0.4 0.3 5.2 0.6 Take-up rate: 90% Mitigation: No Reinsurance: 10% (p.308) Table 13A.2 State comparisons. Event: 250-year return period without mitigation ($ billion)

Homeowner losses

Insurer losses

Reinsurer losses

State

Details

Total loss

Losses to uninsured homes

Losses to insured homes

State insurer (FL = Citizens, TX = TWIA)

State assessment to insurers (TWIA only)

Private insurers

Private insurers’ reimbursable tax credits portion (TWIA only)

State reinsurer (FHCF only)

Private reinsurance

Florida

Return period: 250

$114.2 6% 9% 12% 29% 21% 23% Take-up rate: 90% Mitigation: No Reinsurance: 60.3% Citizens market share: 30% Citizens reserve:$4.18B

FHCF reserve: $2.78B Texas Return period: 250 26.08 6 6 2 1% 46 18% 22 Take-up rate: 90% Mitigation: No Reinsurance: 20% New York Return period: 250 11.44 6 10 67 17 Take-up rate: 90% Mitigation: No Reinsurance: 10% South Carolina Return period: 250 6.46 6 4 80 9 Take-up rate: 90% Mitigation: No Reinsurance: 10% (p.309) Table 13A.3 State comparisons. Event: 500-year return period without mitigation ($ billion)

Homeowner losses

Insurer losses

Reinsurer losses

State

Details

Total loss

Losses to uninsured homes

Losses to insured homes

State insurer (FL = Citizens, TX = TWIA)

State assessment to insurers (TWIA only)

Private insurers

Private insurers’ reimbursable tax credits portion (TWIA only)

State reinsurer (FHCF only)

Private reinsurance

Florida

Return period: 500

$144.9$9.1

$15.3$20.6

$48.0$23.6

$28.3 Take-up rate: 90% Mitigation: No Reinsurance: 60.3% Citizens market share: 30% Citizens reserve:$4.18B

FHCF reserve: $2.78B Texas Return period: 500 35.5 2.2 1.9 0.4$0.3

14.0

$6.9 9.7 Take-up rate: 90% Mitigation: No Reinsurance: 20% New York Return period: 500 17.4 1.1 1.3 11.9 3.0 Take-up rate: 90% Mitigation: No Reinsurance: 10% South Carolina Return period: 500 8.7 0.5 0.4 7.0 0.8 Take-up rate: 90% Mitigation: No Reinsurance: 10% (p.310) Table 13A.4 State comparisons. Event: 500-year return period without mitigation ($ billion)

Homeowner losses

Insurer losses

Reinsurer losses

State

Details

Total loss

Losses to uninsured homes

Losses to insured homes

State insurer (FL = Citizens, TX = TWIA)

State assessment to insurers (TWIA only)

Private insurers

Private insurers’ reimbursable tax credits portion (TWIA only)

State reinsurer (FHCF only)

Private reinsurance

Florida

Return period: 500

$144.9 6% 11% 14% 33% 16% 20% Take-up rate: 90% Mitigation: No Reinsurance: 60.3% Citizens market share: 30% Citizens reserve:$4.18B

FHCF reserve: $2.78B Texas Return period: 500 35.46 6 5 1 1% 40 20% 27 Take-up rate: 90% Mitigation: No Reinsurance: 20% New York Return period: 500 17.36 6 8 69 17 Take-up rate: 90% Mitigation: No Reinsurance: 10% South Carolina Return period: 500 8.66 6 4 81 9 Take-up rate: 90% Mitigation: No Reinsurance: 10% Figure 13B.1 Money saved (reduced losses) from full mitigation for a 100-year event (p.312) Figure 13B.2 Money saved (reduced losses) from full mitigation for a 250-year event Figure 13B.3 Money saved (reduced losses) from full mitigation for a 500-year event Table 13C.1 Market share analysis for the top twenty-five insurers in Florida Rank Company name Company surplus (billions) DPW market share Surplus market share DPW market share (excluding Citizens) 1 State Farm Group$12.8

19.7%

8.3%

23.9%

2

Citizens Property Insurance Corporation

0.0

17.7

0.0

0.0

3

Allstate Insurance Group

19.2

7.2

12.5

8.7

4

Tower Hill Group

0.1

4.7

0.0

5.7

5

Universal P&C Insurance Co.

0.1

4.6

0.0

5.6

6

USAA Group

11.7

4.3

7.6

5.2

7

Nationwide Group

12.8

4.1

8.3

4.9

8

Liberty Mutual Insurance Companies

12.1

3.0

7.9

3.7

9

American Strategic Insurance Companies

0.2

3.0

0.1

3.6

10

Universal Insurance Group of Puerto Rico

0.2

2.5

0.1

3.1

11

American International Group

27.0

2.2

17.5

2.7

12

Chubb Group of Insurance Companies

11.3

2.1

7.3

2.6

13

United Property and Casualty Insurance Co.

0.1

1.9

0.0

2.3

14

Hartford Insurance Group

14.5

1.9

9.4

2.3

15

Travelers Insurance Companies

20.2

1.8

13.1

2.2

16

Gulfstream Property and Casualty Insurance Co.

0.0

1.6

0.0

2.0

17

GeoVera Insurance Group

0.1

1.5

0.1

1.8

18

Southern Farm Bureau Group

1.8

1.2

1.2

1.5

19

Sunshine State Insurance Company

0.0

1.2

0.0

1.5

20

Cypress Holdings Group

0.0

1.2

0.0

1.5

21

Farmers Insurance Group

5.3

1.1

3.4

1.3

22

Florida Family Insurance Company

0.0

1.0

0.0

1.2

23

Allianz of America

3.8

0.9

2.5

1.1

24

1.0

0.8

0.7

1.0

25

Coral Insurance Company

0.0

0.7

0.0

0.8

(p.314)

Table 13C.2 Market share analysis for the top twenty-five insurers in New York

Rank

Company name

Company surplus (billions)

DPW market share

Surplus market share

1

Allstate Insurance Group

$19.2 19.1% 11.5% 2 State Farm Group 12.8 14.6 7.6 3 Travelers Insurance Companies 20.2 11.3 12.0 4 Chubb Group of Insurance Companies 11.3 10.0 6.7 5 Liberty Mutual Insurance Companies 12.1 6.3 7.2 6 Central Services Group 0.3 4.1 0.2 7 Nationwide Group 12.8 4.0 7.7 8 MetLife Auto and Home Group 1.9 3.4 1.1 9 Tower Group Companies 0.2 2.2 0.1 10 USAA Group 11.7 2.0 7.0 11 Hartford Insurance Group 14.5 2.0 8.7 12 Allianz of America 3.8 1.9 2.3 13 American International Group 27.0 1.5 16.1 14 White Mountains Insurance Group 3.3 1.4 2.0 15 Unitrin 1.2 1.2 0.7 16 Hanover Insurance Group Property and Casualty Co. 1.5 1.1 0.9 17 Preferred Mutual Insurance Company 0.1 1.1 0.1 18 Amica Mutual Group 2.1 1.0 1.2 19 Farmers Insurance Group 5.3 0.9 3.1 20 Andover Companies 0.7 0.8 0.4 21 Sterling Insurance Company 0.0 0.8 0.0 22 Utica National Insurance Group 0.7 0.7 0.4 23 Security Mutual Insurance Company 0.0 0.7 0.0 24 Main Street America Group 0.6 0.6 0.3 25 Erie Insurance Group 4.3 0.6 2.6 (p.315) Table 13C.3 Market share analysis for the top twenty-five insurers in South Carolina Rank Company name Company surplus (billions) DPW market share Surplus market share 1 State Farm Group$12.8

26.0%

7.3%

2

Allstate Insurance Group

19.2

14.9

11.0

3

Nationwide Group

12.8

8.9

7.3

4

Southern Farm Bureau Group

1.8

6.8

1.1

5

USAA Group

11.7

6.8

6.7

6

Travelers Insurance Companies

20.2

6.6

11.5

7

American International Group

27.0

3.9

15.4

8

Farmers Insurance Group

5.3

3.9

3.0

9

Auto-Owners Insurance Group

4.8

2.9

2.8

10

Assurant Solutions

0.9

2.1

0.5

11

GeoVera Insurance Group

0.1

1.7

0.1

12

Liberty Mutual Insurance Companies

12.1

1.7

6.9

13

Safeco Insurance Companies

3.8

1.0

2.2

14

Chubb Group of Insurance Companies

11.3

1.0

6.4

15

Hartford Insurance Group

14.5

0.9

8.3

16

State Auto Insurance Companies

1.6

0.9

0.9

17

Companion Property and Casualty Group

0.1

0.7

0.1

18

Allianz of America

3.8

0.7

2.2

19

American National P&C Group

0.7

0.7

0.4

20

Horace Mann Insurance Group

0.3

0.7

0.2

21

MetLife Auto and Home Group

1.9

0.6

1.1

22

Balboa Insurance Group

0.7

0.6

0.4

23

Selective Insurance Group

1.0

0.5

0.6

24

Zurich Financial Services NA Group

6.2

0.5

3.6

25

Homesite Group

0.2

0.4

0.1

(p.316)

Table 13C.4 Market share analysis for the top twenty-five insurers in Texas

Rank

Company name

Company surplus (billions)

DPW market share

Surplus market share

1

State Farm Group

$12.8 29.3% 9.2% 2 Allstate Insurance Group 19.2 15.7 13.8 3 Farmers Insurance Group 5.3 12.6 3.8 4 USAA Group 11.7 7.4 8.4 5 Travelers Insurance Companies 20.2 5.7 14.5 6 Nationwide Group 12.8 4.0 9.2 7 Southern Farm Bureau Group 1.8 2.6 1.3 8 Chubb Group of Insurance Companies 11.3 2.4 8.1 9 Liberty Mutual Insurance Companies 12.1 1.9 8.7 10 Hartford Insurance Group 14.5 1.6 10.4 11 Republic Companies Group 0.2 1.5 0.1 12 Safeco Insurance Companies 3.8 1.3 2.7 13 NLASCO Group 0.1 1.0 0.1 14 Texas FAIR Plan Association 0.0 1.0 0.0 15 American Strategic Insurance Companies 0.2 1.0 0.1 16 Amica Mutual Group 2.1 0.9 1.5 17 Auto Club Enterprises Insurance Group 3.2 0.7 2.3 18 Cypress Holdings Group 0.0 0.7 0.0 19 Unitrin, Inc. 1.2 0.7 0.9 20 State National Companies 0.1 0.6 0.1 21 American National P&C Group 0.7 0.6 0.5 22 GeoVera Insurance Group 0.1 0.5 0.1 23 Allianz of America 3.8 0.5 2.7 24 MetLife Auto & Home Group 1.9 0.5 1.3 25 Colonial Insurance Group 0.0 0.4 0.0 Table 13D.1 Loss costs and insurance premiums for hurricane wind insured damage in all counties in Florida Rank County Loss costs per$1,000

Ratio to state

1

Monroe

$16.22$24.32

4.62

2

Franklin

6.84

10.26

1.95

3

Martin

6.69

10.03

1.90

4

6.48

9.73

1.85

5

Palm Beach

6.47

9.71

1.84

6

Indian River

5.67

8.51

1.62

7

Broward

5.64

8.46

1.61

8

Collier

5.04

7.56

1.44

9

Saint Lucie

4.68

7.02

1.33

10

Santa Rosa

4.46

6.69

1.27

11

Manatee

4.20

6.30

1.20

12

Lee

4.19

6.28

1.19

13

Walton

4.15

6.22

1.18

14

Sarasota

4.14

6.21

1.18

15

Escambia

4.04

6.06

1.15

16

Okaloosa

4.00

6.00

1.14

17

Gulf

3.98

5.96

1.13

18

Pinellas

3.87

5.81

1.10

19

Charlotte

3.29

4.94

0.94

20

Brevard

3.09

4.63

0.88

21

Okeechobee

3.07

4.61

0.87

22

2.91

4.37

0.83

23

Hendry

2.88

4.32

0.82

24

Bay

2.80

4.20

0.80

25

De Soto

2.23

3.34

0.64

26

Wakulla

1.89

2.83

0.54

27

Pasco

1.84

2.76

0.52

28

Hillsborough

1.70

2.55

0.48

29

Dixie

1.54

2.30

0.44

30

Levy

1.47

2.20

0.42

31

Hardee

1.45

2.18

0.41

32

Volusia

1.44

2.15

0.41

33

Hernando

1.41

2.12

0.40

34

Washington

1.40

2.09

0.40

35

Flagler

1.32

1.99

0.38

36

Saint Johns

1.28

1.92

0.36

37

Taylor

1.26

1.89

0.36

38

Highlands

1.23

1.84

0.35

39

Liberty

1.09

1.63

0.31

40

Citrus

1.07

1.61

0.31

41

Gilchrist

1.05

1.58

0.30

42

Nassau

1.04

1.55

0.30

43

Calhoun

1.02

1.52

0.29

44

Polk

0.97

1.45

0.28

45

Holmes

0.85

1.27

0.24

46

Osceola

0.82

1.23

0.23

47

Lake

0.78

1.17

0.22

48

Sumter

0.75

1.12

0.21

49

Duval

0.74

1.11

0.21

50

Lafayette

0.70

1.05

0.20

51

Marion

0.65

0.98

0.19

52

Putnam

0.64

0.97

0.18

53

Jackson

0.64

0.95

0.18

54

Seminole

0.62

0.93

0.18

55

Jefferson

0.60

0.90

0.17

56

Orange

0.60

0.90

0.17

57

0.60

0.90

0.17

58

Leon

0.59

0.89

0.17

59

Suwannee

0.54

0.82

0.16

60

0.52

0.78

0.15

61

Clay

0.48

0.72

0.14

62

Alachua

0.46

0.69

0.13

63

Columbia

0.39

0.58

0.11

64

Union

0.37

0.55

0.10

65

0.32

0.48

0.09

66

Hamilton

0.31

0.46

0.09

67

Baker

0.23

0.34

0.07

Total

All counties

3.51

5.27

1.00

(p.319) (p.320)

Table 13D.2 Loss costs and insurance premiums for hurricane wind insured damage in all counties in New York

Rank

County

Loss cost per $1,000 50% loading Ratio to state 1 Suffolk$0.38

$0.56 4.10 2 Nassau 0.17 0.25 1.85 3 Richmond 0.12 0.17 1.26 4 Queens 0.10 0.15 1.11 5 Kings 0.08 0.12 0.89 6 Westchester 0.08 0.12 0.88 7 Rockland 0.07 0.11 0.78 8 Bronx 0.07 0.10 0.71 9 Putnam 0.06 0.09 0.69 10 Dutchess 0.04 0.06 0.43 11 Columbia 0.04 0.05 0.40 12 Ulster 0.03 0.05 0.35 13 Orange 0.03 0.05 0.35 14 New York 0.02 0.04 0.27 15 Rensselaer 0.02 0.03 0.22 16 Greene 0.02 0.03 0.21 17 Washington 0.02 0.03 0.19 18 Albany 0.01 0.02 0.15 19 Saratoga 0.01 0.02 0.15 20 Sullivan 0.01 0.02 0.14 21 Warren 0.01 0.02 0.11 22 Schenectady 0.01 0.01 0.10 23 Montgomery 0.01 0.01 0.09 24 Clinton 0.01 0.01 0.08 25 Schoharie 0.01 0.01 0.08 26 Essex 0.01 0.01 0.07 27 Fulton 0.01 0.01 0.06 28 Delaware 0.01 0.01 0.06 29 Herkimer 0.00 0.01 0.04 30 Otsego 0.00 0.01 0.04 31 Hamilton 0.00 0.00 0.03 32 Chenango 0.00 0.00 0.03 33 Oneida 0.00 0.00 0.03 34 Broome 0.00 0.00 0.02 35 Madison 0.00 0.00 0.02 36 Cortland 0.00 0.00 0.02 37 Tioga 0.00 0.00 0.02 38 Lewis 0.00 0.00 0.02 39 Seneca 0.00 0.00 0.01 40 Cayuga 0.00 0.00 0.01 41 Tompkins 0.00 0.00 0.01 42 Franklin 0.00 0.00 0.01 43 Saint Lawrence 0.00 0.00 0.01 44 Jefferson 0.00 0.00 0.01 45 Oswego 0.00 0.00 0.01 46 Yates 0.00 0.00 0.01 47 Onondaga 0.00 0.00 0.01 48 Wayne 0.00 0.00 0.01 49 Ontario 0.00 0.00 0.01 50 Schuyler 0.00 0.00 0.01 51 Chemung 0.00 0.00 0.00 52 Steuben 0.00 0.00 0.00 53 Monroe 0.00 0.00 0.00 54 Livingston 0.00 0.00 0.00 55 Genesee 0.00 0.00 0.00 56 Orleans 0.00 0.00 0.00 57 Wyoming 0.00 0.00 0.00 58 Chautauqua 0.00 0.00 0.00 59 Cattaraugus 0.00 0.00 0.00 60 Niagara 0.00 0.00 0.00 61 Allegany 0.00 0.00 0.00 62 Erie 0.00 0.00 0.00 Total All counties 0.09 0.14 1.00 (p.321) (p.322) Table 13D.3 Loss costs and insurance premiums for hurricane wind insured damage in all counties in South Carolina Rank County Loss costs per$1,000

Ratio to state

1

Georgetown

$2.69$4.04

4.74

2

Charleston

2.62

3.93

4.61

3

Horry

2.11

3.17

3.72

4

Beaufort

2.10

3.15

3.70

5

Colleton

1.37

2.05

2.41

6

Berkeley

1.20

1.80

2.12

7

Jasper

0.92

1.38

1.62

8

Dorchester

0.85

1.27

1.49

9

Williamsburg

0.81

1.21

1.42

10

Marion

0.65

0.97

1.14

11

Hampton

0.55

0.83

0.97

12

Clarendon

0.50

0.75

0.88

13

Dillon

0.38

0.57

0.67

14

Florence

0.36

0.54

0.63

15

Orangeburg

0.36

0.53

0.63

16

Allendale

0.31

0.46

0.54

17

Bamberg

0.27

0.40

0.47

18

Calhoun

0.25

0.37

0.43

19

Darlington

0.20

0.30

0.35

20

Lee

0.20

0.30

0.35

21

Sumter

0.19

0.28

0.33

22

Marlboro

0.18

0.28

0.32

23

Barnwell

0.15

0.23

0.27

24

Chesterfield

0.06

0.09

0.11

25

Kershaw

0.06

0.09

0.10

26

Aiken

0.04

0.06

0.07

27

Lexington

0.04

0.06

0.07

28

Richland

0.04

0.06

0.07

29

Fairfield

0.02

0.02

0.03

30

Lancaster

0.02

0.02

0.03

31

Edgefield

0.02

0.02

0.03

32

Saluda

0.01

0.02

0.02

33

Newberry

0.01

0.01

0.01

34

Mccormick

0.01

0.01

0.01

35

Chester

0.01

0.01

0.01

36

Greenwood

0.00

0.00

0.00

37

Abbeville

0.00

0.00

0.00

38

Laurens

0.00

0.00

0.00

39

York

0.00

0.00

0.00

40

Union

0.00

0.00

0.00

41

Cherokee

0.00

0.00

0.00

42

Anderson

0.00

0.00

0.00

43

Spartanburg

0.00

0.00

0.00

44

Greenville

0.00

0.00

0.00

45

Pickens

0.00

0.00

0.00

46

Oconee

0.00

0.00

0.00

Total

All counties

0.57

0.85

1.00

(p.323) (p.324) (p.325) (p.326) (p.327) (p.328) (p.329)

Table 13D.4 Loss costs and insurance premiums for hurricane wind insured damage in all counties in Texas

Rank

County

Price

Ratio to state

1

Calhoun

$4.60$6.89

10.53

2

Aransas

4.56

6.84

10.44

3

Galveston

3.95

5.92

9.05

4

Matagorda

3.19

4.79

7.32

5

Chambers

2.59

3.88

5.93

6

Brazoria

2.44

3.65

5.58

7

Nueces

2.32

3.48

5.32

8

San Patricio

2.27

3.40

5.19

9

Cameron

2.08

3.12

4.77

10

Jackson

2.04

3.07

4.69

11

Refugio

2.01

3.01

4.60

12

Jefferson

1.72

2.58

3.95

13

Kenedy

1.54

2.30

3.52

14

Willacy

1.53

2.30

3.52

15

Orange

1.41

2.12

3.24

16

Wharton

1.39

2.08

3.18

17

Fort Bend

1.07

1.61

2.46

18

Victoria

1.06

1.58

2.42

19

Liberty

1.02

1.53

2.34

20

0.91

1.37

2.09

21

Kleberg

0.90

1.34

2.05

22

Harris

0.81

1.22

1.86

23

Brooks

0.80

1.20

1.83

24

Hardin

0.76

1.14

1.74

25

Jim Wells

0.75

1.12

1.71

26

Hidalgo

0.72

1.09

1.66

27

Bee

0.65

0.98

1.50

28

Waller

0.57

0.85

1.30

29

Austin

0.50

0.75

1.14

30

0.48

0.72

1.11

31

De Witt

0.47

0.70

1.08

32

Newton

0.46

0.69

1.05

33

Lavaca

0.38

0.57

0.87

34

San Jacinto

0.38

0.56

0.86

35

Jasper

0.35

0.52

0.80

36

Montgomery

0.32

0.48

0.74

37

Duval

0.29

0.44

0.67

38

Live Oak

0.29

0.44

0.67

39

Karnes

0.29

0.43

0.66

40

Washington

0.24

0.35

0.54

41

Tyler

0.22

0.34

0.51

42

Jim Hogg

0.20

0.30

0.45

43

Fayette

0.19

0.29

0.44

44

Starr

0.19

0.29

0.44

45

Grimes

0.19

0.28

0.43

46

Mcmullen

0.18

0.27

0.41

47

Gonzales

0.18

0.26

0.40

48

Polk

0.16

0.25

0.38

49

Walker

0.13

0.20

0.31

50

Wilson

0.12

0.18

0.28

51

Sabine

0.10

0.16

0.24

52

Burleson

0.10

0.16

0.24

53

Trinity

0.10

0.16

0.24

54

Lee

0.08

0.13

0.19

55

Atascosa

0.07

0.11

0.17

56

0.07

0.11

0.16

57

Caldwell

0.06

0.09

0.14

58

San Augustine

0.06

0.09

0.13

59

0.06

0.09

0.13

60

Zapata

0.05

0.08

0.13

61

Bastrop

0.05

0.07

0.11

62

Brazos

0.04

0.06

0.09

63

Angelina

0.04

0.06

0.09

64

La Salle

0.04

0.06

0.09

65

Webb

0.03

0.05

0.08

66

Houston

0.03

0.05

0.08

67

Frio

0.03

0.04

0.07

68

Comal

0.02

0.03

0.05

69

Robertson

0.02

0.03

0.05

70

Milam

0.02

0.03

0.05

71

Hays

0.02

0.03

0.05

72

Nacogdoches

0.02

0.03

0.04

73

Leon

0.02

0.03

0.04

74

Bexar

0.02

0.02

0.04

75

Medina

0.01

0.02

0.03

76

Shelby

0.01

0.02

0.03

77

Dimmit

0.01

0.01

0.02

78

Williamson

0.01

0.01

0.02

79

Cherokee

0.01

0.01

0.02

80

Falls

0.01

0.01

0.01

81

Bell

0.01

0.01

0.01

82

Travis

0.01

0.01

0.01

83

Freestone

0.01

0.01

0.01

84

Kendall

0.01

0.01

0.01

85

Anderson

0.00

0.01

0.01

86

Blanco

0.00

0.01

0.01

87

Limestone

0.00

0.01

0.01

88

Zavala

0.00

0.01

0.01

89

Panola

0.00

0.01

0.01

90

Rusk

0.00

0.00

0.01

91

Coryell

0.00

0.00

0.01

92

Navarro

0.00

0.00

0.01

93

Uvalde

0.00

0.00

0.00

94

Mclennan

0.00

0.00

0.00

95

Bandera

0.00

0.00

0.00

96

Burnet

0.00

0.00

0.00

97

Henderson

0.00

0.00

0.00

98

Gregg

0.00

0.00

0.00

99

Lampasas

0.00

0.00

0.00

100

Harrison

0.00

0.00

0.00

101

Bosque

0.00

0.00

0.00

102

Gillespie

0.00

0.00

0.00

103

Llano

0.00

0.00

0.00

104

Smith

0.00

0.00

0.00

105

Hill

0.00

0.00

0.00

106

Upshur

0.00

0.00

0.00

107

Hamilton

0.00

0.00

0.00

108

Maverick

0.00

0.00

0.00

109

Marion

0.00

0.00

0.00

110

Kerr

0.00

0.00

0.00

111

Van Zandt

0.00

0.00

0.00

112

Rains

0.00

0.00

0.00

113

Kaufman

0.00

0.00

0.00

114

Ellis

0.00

0.00

0.00

115

Johnson

0.00

0.00

0.00

116

Wood

0.00

0.00

0.00

117

Camp

0.00

0.00

0.00

118

Franklin

0.00

0.00

0.00

119

Mills

0.00

0.00

0.00

120

Morris

0.00

0.00

0.00

121

Cass

0.00

0.00

0.00

122

Hunt

0.00

0.00

0.00

123

Titus

0.00

0.00

0.00

124

Somervell

0.00

0.00

0.00

125

Mason

0.00

0.00

0.00

126

Hood

0.00

0.00

0.00

127

San Saba

0.00

0.00

0.00

128

Hopkins

0.00

0.00

0.00

129

Rockwall

0.00

0.00

0.00

130

Bowie

0.00

0.00

0.00

131

Real

0.00

0.00

0.00

132

Erath

0.00

0.00

0.00

133

Red River

0.00

0.00

0.00

134

Delta

0.00

0.00

0.00

135

Brown

0.00

0.00

0.00

136

Comanche

0.00

0.00

0.00

137

Parker

0.00

0.00

0.00

138

Tarrant

0.00

0.00

0.00

139

Dallas

0.00

0.00

0.00

140

Collin

0.00

0.00

0.00

141

Lamar

0.00

0.00

0.00

142

Palo Pinto

0.00

0.00

0.00

143

Eastland

0.00

0.00

0.00

144

Edwards

0.00

0.00

0.00

145

Fannin

0.00

0.00

0.00

146

Mcculloch

0.00

0.00

0.00

147

Denton

0.00

0.00

0.00

148

Val Verde

0.00

0.00

0.00

149

Kinney

0.00

0.00

0.00

150

Jack

0.00

0.00

0.00

151

Wise

0.00

0.00

0.00

152

King

0.00

0.00

0.00

153

Andrews

0.00

0.00

0.00

154

Archer

0.00

0.00

0.00

155

Armstrong

0.00

0.00

0.00

156

Bailey

0.00

0.00

0.00

157

Baylor

0.00

0.00

0.00

158

Borden

0.00

0.00

0.00

159

Brewster

0.00

0.00

0.00

160

Briscoe

0.00

0.00

0.00

161

Callahan

0.00

0.00

0.00

162

Carson

0.00

0.00

0.00

163

Castro

0.00

0.00

0.00

164

Childress

0.00

0.00

0.00

165

Clay

0.00

0.00

0.00

166

Cochran

0.00

0.00

0.00

167

Coke

0.00

0.00

0.00

168

Coleman

0.00

0.00

0.00

169

Collingsworth

0.00

0.00

0.00

170

Concho

0.00

0.00

0.00

171

Cooke

0.00

0.00

0.00

172

Cottle

0.00

0.00

0.00

173

Crane

0.00

0.00

0.00

174

Crockett

0.00

0.00

0.00

175

Crosby

0.00

0.00

0.00

176

Culberson

0.00

0.00

0.00

177

Dallam

0.00

0.00

0.00

178

Dawson

0.00

0.00

0.00

179

Deaf Smith

0.00

0.00

0.00

180

Dickens

0.00

0.00

0.00

181

Donley

0.00

0.00

0.00

182

Ector

0.00

0.00

0.00

183

El Paso

0.00

0.00

0.00

184

Fisher

0.00

0.00

0.00

185

Floyd

0.00

0.00

0.00

186

Foard

0.00

0.00

0.00

187

Gaines

0.00

0.00

0.00

188

Garza

0.00

0.00

0.00

189

Glasscock

0.00

0.00

0.00

190

Gray

0.00

0.00

0.00

191

Grayson

0.00

0.00

0.00

192

Hale

0.00

0.00

0.00

193

Hall

0.00

0.00

0.00

194

Hansford

0.00

0.00

0.00

195

Hardeman

0.00

0.00

0.00

196

Hartley

0.00

0.00

0.00

197

0.00

0.00

0.00

198

Hemphill

0.00

0.00

0.00

199

Hockley

0.00

0.00

0.00

200

Howard

0.00

0.00

0.00

201

Hudspeth

0.00

0.00

0.00

202

Hutchinson

0.00

0.00

0.00

203

Irion

0.00

0.00

0.00

204

Jeff Davis

0.00

0.00

0.00

205

Jones

0.00

0.00

0.00

206

Kent

0.00

0.00

0.00

207

Kimble

0.00

0.00

0.00

208

Knox

0.00

0.00

0.00

209

Lamb

0.00

0.00

0.00

210

Lipscomb

0.00

0.00

0.00

211

Loving

0.00

0.00

0.00

212

Lubbock

0.00

0.00

0.00

213

Lynn

0.00

0.00

0.00

214

Martin

0.00

0.00

0.00

215

Menard

0.00

0.00

0.00

216

Midland

0.00

0.00

0.00

217

Mitchell

0.00

0.00

0.00

218

Montague

0.00

0.00

0.00

219

Moore

0.00

0.00

0.00

220

Motley

0.00

0.00

0.00

221

Nolan

0.00

0.00

0.00

222

Ochiltree

0.00

0.00

0.00

223

Oldham

0.00

0.00

0.00

224

Parmer

0.00

0.00

0.00

225

Pecos

0.00

0.00

0.00

226

Potter

0.00

0.00

0.00

227

Presidio

0.00

0.00

0.00

228

Randall

0.00

0.00

0.00

229

Reagan

0.00

0.00

0.00

230

Reeves

0.00

0.00

0.00

231

Roberts

0.00

0.00

0.00

232

Runnels

0.00

0.00

0.00

233

Schleicher

0.00

0.00

0.00

234

Scurry

0.00

0.00

0.00

235

Shackelford

0.00

0.00

0.00

236

Sherman

0.00

0.00

0.00

237

Stephens

0.00

0.00

0.00

238

Sterling

0.00

0.00

0.00

239

Stonewall

0.00

0.00

0.00

240

Sutton

0.00

0.00

0.00

241

Swisher

0.00

0.00

0.00

242

Taylor

0.00

0.00

0.00

243

Terrell

0.00

0.00

0.00

244

Terry

0.00

0.00

0.00

245

Throckmorton

0.00

0.00

0.00

246

Tom Green

0.00

0.00

0.00

247

Upton

0.00

0.00

0.00

248

Ward

0.00

0.00

0.00

249

Wheeler

0.00

0.00

0.00

250

Wichita

0.00

0.00

0.00

251

Wilbarger

0.00

0.00

0.00

252

Winkler

0.00

0.00

0.00

253

Yoakum

0.00

0.00

0.00

254

Young

0.00

0.00

0.00

## Notes:

(1.) By definition, the average annual loss (AAL) is the sum over all possible events of the expected losses associated with each of these individual events in a given state or postal zone and for a given year.

(2.) Bushouse (2007). This figure is consistent with other estimates; see, for instance, Holborn (2007).

(4.) See chapter 8 for a more detailed discussion of these insurance-linked securities.

(5.) Insurance Information Institute (2007).

(6.) We thank Jack Nicholson, the Chief Operating Officer of the Florida Hurricane Catastrophe Fund, for many insightful discussions on the operation of the fund.

(7.) In 2007, no insurers selected this coverage.

(8.) More details can be found at http://www.sbafla.com/fhcf/ as of December 2007.

(9.) We thank Jim Oliver and Jim Murphy of the Texas Windstorm Insurance Association for discussions we had on the nature of the association’s operations.

(10.) If a company’s potential tax credit were greater than its tax liability, it could carry over the additional credit to future years. In this case, a company with a small tax liability or a large assessment (or both) might require significantly more than five years to fully recoup its assessment. Under no circumstance would a company be able to recoup its assessment in less than five years, as the maximum annual credit is 20 percent of the assessment.

(11.) The actual figure, including commercial coverage, would be higher.

(12.) Personal correspondence with Jack Nicholson, January 12, 2009.

(13.) We are assuming that because these measures are incorporated in building codes they are cost-effective. In other words, the discounted long-term expected benefits from the mitigation measure over the projected life of the house is greater than its upfront costs. By obtaining detailed cost estimates for specific mitigation measures incorporated in building codes or Florida’s Fortified … for Safer Living program, one could rank their relative cost-effectiveness.

(14.) State Farm, which groups its companies differently, is the exception. See note 15 for a full explanation of how we handled this special case.

(15.) State Farm is a parent company that has an atypical structure in that it divides its subunits by line of business. Other companies do not separate their surpluses at all or they do it based on state-level companies, which still handle all of the different types of insurance. The A.M. Best data gave the total surpluses for entire conglomerate companies. Typically this entire surplus is available for property insurance since the companies are not separate entities. However, State Farm separates property insurance so its A.M. Best figure is too high. In order to rectify (p.382) this, we used the sum of the surpluses for State Farm’s property insurance subsidiaries: State Farm Fire and Casualty: $8.95 billion; State Farm General (California):$1.85 billion; State Farm Florida: $0.72 billion; Texas Lloyds:$1.29 billion; Total: \$12.81 billion.

(16.) Doherty (2000).

(17.) For more details on this point, see chapter 6 (section 6.4).