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Could Flood Insurance be Privatised in the United States? A Primer

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Abstract

Since 1968, homeowners’ flood insurance in the United States has been mainly provided through the federally-run National Flood Insurance Program (NFIP). The Flood Insurance Reform Act of 2012 raises the possibility of moving coverage to the private sector, assuming the market can price this risk effectively and that premiums reflect risk. This paper provides the first large-scale quantification of risk-based premiums for over 300,000 residences prone to either storm surge or inland flooding using commercially developed probabilistic catastrophe models, and compares these premiums with those currently charged by the NFIP. Our findings reveal significant differences between the two. In some areas, the NFIP charges prices that are more than 15 times the pure premium, while other areas are charged up to three times less than the pure premium. The paper discusses the market and policy implications of these findings.

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Notes

  1. City of New York (2013).

  2. Michel-Kerjan (2010).

  3. The four options FEMA discusses in its 2011 report are: keeping the programme as is (status quo), modernising the programme in its current form, privatising flood insurance, and transferring the responsibility of flood insurance from the federal government to the communities. See FEMA (2011) for more details. The Flood Insurance Reform Act of 2012 (also known as Biggert–Waters 2012, or BW12), modified in 2014, phased in risk-based premiums over time for policies currently subsidised by the programme.

  4. The NFIP covers both residential and small business lines but we focus here on residential insurance only, specifically single-family residential. NFIP commercial coverage in Texas represents less than 5 per cent of the total NFIP policies in force.

  5. Clark (1998); von Ungern-Sternberg (2004); Thieken et al. (2006); Botzen and van den Bergh (2008).

  6. GAO (2008).

  7. Overman (1957); Gerdes (1963).

  8. Considerable progress has been made in catastrophe modelling, GIS and risk map digitalisation in the past 20 years and has improved the risk assessment process considerably as discussed in the section ‘Determination of flood insurance pure premiums using catastrophe modelling’.

  9. Kunreuther et al. (1978); Burby (2001).

  10. See Michel-Kerjan (2010) for an analysis of the operation of the programme between 1968 and 2009.

  11. Michel-Kerjan and Kousky (2010).

  12. Dixon et al. (2007).

  13. For instance, according to the U.S. Census data, the population in Texas increased from 9.5 million in 1960 to nearly 26 million in 2011 and the population of Florida from 2.5 million in 1950 to 19 million in 2011, a large portion of those people being exposed to flood hazard.

  14. Inflation-corrected data shows that the average quantity of insurance per policy almost doubled over 30 years, from US$114,000 in 1978 to US$217,000 in 2009 (Michel-Kerjan, 2010).

  15. All states have at least some NFIP policies in force. The states with the lowest number of policies in force — all less than 5,000 policies are: Alaska, the District of Columbia, Montana, North Dakota, South Dakota, Utah, Vermont and Wyoming.

  16. We thank the NFIP team of actuaries at FEMA for their feedback on an earlier version of this section.

  17. Hayes et al. (2007).

  18. Pasterick (1998); Wetmore et al. (2006); CBO (2007); Kousky (2011).

  19. See Bin and Polasky (2004) and Bin et al. (2008) for an analysis of how flood hazard negatively impacts housing market price.

  20. Hayes and Spafford (2008).

  21. Hall and Sobel (2013).

  22. Aerts et al. (2014).

  23. King (2012).

  24. Wetmore et al. (2006).

  25. Note that we have focused here on the insurance pillar of the NFIP; the programme also integrates several other elements (e.g. risk mapping; Community Rating System; risk awareness campaigns).

  26. U.S. Bureau of the Census (2011).

  27. Texas State Data Center (2011).

  28. SHELDUS (2011).

  29. The flood model originally built by Swiss Re is executed by CoreLogic with hazard, exposure and vulnerability components jointly enhanced from each organisation building on each of their respective data strengths.

  30. The CoreLogic database consists of over 3.3 billion property and financial records spanning 40 years, and contains more than 99 per cent of U.S. property records. Nineteen federal agencies rely on CoreLogic data for their analyses.

  31. www.media.swissre.com/documents/pitchbook_global_fl_zones_final.pdf (accessed 1 September 2014)

  32. In Europe, a group of insurance companies has been working with the European Space Agency on a project to improve flood risk quantification. The joint effort between satellite operators, Earth observation service providers and the insurance industry will provide detailed flood footprints based on satellite data.

  33. For more information on catastrophe modelling, see Grossi and Kunreuther (2005) and Born and Martin (2006).

  34. For more information about the flood catastrophe model used here, see Czajkowski et al. (2013).

  35. The total insured value of these single-family residences input into the model was the collected building value with a conservatively assigned content assumption of 40 per cent of the building value which is aligned with Swiss Re client data content percentages. Building value was provided by CoreLogic as the market improvement value, where market improvement value equals the residence’s total market value net of the market land value with all market values as provided by the county or local taxing/assessment authority.

  36. In Travis County, total AAL is comprised of only riverine flood loss as Travis County is an inland county not subject to storm surge losses. Total AAL in Galveston County is comprised of both riverine and storm surge flood losses.

  37. Note that these values are the average across each individual single-family residence’s AAL exposure per US$1,000 determined result. Consequently, taking the (Total AAL/Total Exposure) times 1,000 at the county or flood zone levels shown in Table 2 will not provide the same result. We find that the median is US$3.44, US$0.34 and US$0.08 for single-family residences in zones A, X500 and X/C, respectively (the skewness is 0.55, 2.24 and 8.35, respectively).

  38. The NFIP data set we accessed from FEMA provides us only with ZIP codes which we used to extract associated NFIP single-family policy data for Travis County and Galveston County. We used 60 ZIP codes for Travis County and 17 ZIP codes for Galveston County. We also conducted a series of robustness checks on this matched ZIP code data with very similar results as to what is presented here.

  39. We also conducted a similar analysis for the 188,496 single-family residences in Travis County with a building value less than or equal to the NFIP building value coverage limit of US$250,000 (83 per cent of total single-family residences analyzed). Average loss cost results per US$1,000 were very similar.

  40. This X zone result also holds for the 49,069 single-family residences in the X zone that have some flood exposure: the average probabilistic pure premium in this case is US$0.31 as shown in Table 2.

  41. Travis County’s location is such that there is no storm surge risk there; so no V zone.

  42. Note here that this is based on only 48 policies; so results may not be representative.

  43. King (2012); PCIAA (2011).

  44. Dixon et al. (2007) found a similar result for the lender-place market only—see Table 4.2, p. 32.

  45. Storm surge flood-related losses are the main driver behind the Table 4 probabilistic model unloaded premium results comprising at least 89 per cent of the average AAL loss costs per US$1,000 values across all flood zones even for the areas outside the high-risk V and coastal A zones, or the areas not subject to storm-surge flood risk according to FEMA flood zone classifications.

  46. This loading will depend on the characteristics of each firm, its portfolio diversification across geographies and types of risks (e.g. an insurer covering hurricane risks in coastal states might diversify its portfolio by selling flood risk insurance in Minnesota), correlation of risks regions, and hazards it covers (e.g. flood vs surge), whether there is some possible exposure to extreme events, whether it purchases some reinsurance and if so at what price, taxes it has to pay, administrative costs, etc. Note also that insurance regulators might not allow insurers to charge this premium because of pressure to keep premiums artificially low to please consumers.

  47. PCIAA (2011).

  48. Michel-Kerjan (2010); Michel-Kerjan and Kunreuther (2011); GAO (2014).

  49. More details on the bond structure are available on the FHCF’s website at: www.sbafla.com/fhcf/LinkClick.aspx?fileticket=PpfxTWgmo9w%3d&tabid=316&mid=998

  50. www.earthquakeauthority.com/UserFiles/File/Release/CEA%20Second%20Transformer%20Deal%20Release%20FINAL.pdf

  51. GAO (2014).

  52. Bayot (2005).

  53. The numbers are believed to have been even lower for business; approximately 26,400 businesses with fewer than 50 employees were in the Sandy inundation zone in New York, but only 1,400 commercial NFIP policies were in effect when Sandy hit—95 per cent of them had no flood insurance.

  54. Landry and Jahan-Parvar (2011).

  55. Browne and Hoyt (2000).

  56. Landry and Jahan-Parvar (2011).

  57. Browne and Hoyt (2000); Kriesel and Landry (2004); Michel-Kerjan and Kousky (2010); Kousky (2011); Landry and Jahan-Parvar (2011).

  58. Botzen and van den Bergh (2012).

  59. Michel-Kerjan et al. (2012).

  60. Jaffee et al. (2010).

  61. Kunreuther and Michel-Kerjan (2009); Kousky and Kunreuther (2014).

  62. We thank two referees for pointing out the possible barrier that large modelling cost would constitute. Catastrophe models for earthquakes and hurricanes are affordable and used by many organisations; flood catastrophe models will certainly become so as well.

  63. Botzen and van den Bergh (2008).

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Acknowledgements

The authors acknowledge partial funding by the National Science Foundation (grants # SES-1062039 and SES-1061882), Swiss Re, the Travelers–Wharton Partnership for Risk Management and Leadership Fund, the U.S. Department of Homeland Security Center of Excellence (the Center for Risk and Economic Analysis of Terrorism Events – CREATE) at USC, CRED at Columbia University, the Willis Research Network, the Zurich Insurance Foundation on community flood resilience and the Wharton Risk Center’s Managing and Financing Extreme Events project.

We appreciate helpful comments on an earlier version of this paper from participants at the following conferences: Overby-Seawell Company Annual Conference (Kennesaw, GA); Intermediaries and Reinsurance Underwriters Association Fall Conference (Florham Park, NJ); Center for Insurance Policy and Research Summit (Atlanta, GA); Torrent Technologies 5th Annual Client Appreciation & Education Seminar (Kalispell, MT); National Flood Conference, 29th Annual Meeting (Austin, TX); Reinsurance Association of America Current Issues Forum (Philadelphia, PA); National Flood Determination Association (Scottsdale, AZ); Southern Economic Association 81st Annual Meeting (Washington, DC); Managing and Financing Extreme Events Project 2011 Annual Meeting, Wharton Risk Management and Decision Processes Center (Philadelphia, PA); Hazards and Disasters Researchers Meeting (Broomfield, CO); 2011 Natural Hazards Workshop (Broomfield, CO).

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A correction has been made to the author biography for Erwann Michel-Kerjan.

Appendices

Appendix A

Definitions of FEMA flood zone designations

Flood zones are geographic areas that the FEMA has defined according to varying levels of flood risk. These zones are depicted on a community's Flood Insurance Rate Map (FIRM) or Flood Hazard Boundary Map. Each zone reflects the severity or type of flooding in the area.

Moderate- to low-risk areas

In communities that participate in the NFIP, flood insurance is available to all property owners and renters in these zones:

illustration

figure c

High-risk areas

In communities that participate in the NFIP, mandatory flood insurance purchase requirements apply to all these zones:

illustration

figure b

High-risk—coastal areas

In communities that participate in the NFIP, mandatory flood insurance purchase requirements apply to all these zones:

illustration

figure a

Appendix B

Additional information on the catastrophe model

Riverine flood hazard determination

The flood frequency map quantifies the probability of any given location being flooded, and is constructed via three inputs—FEMA national flood risk zone maps, United States Geological Survey (USGS) National Hydro data set, and the USGS National Elevation data set. Flooding probabilities are defined for each 90 × 90 meter (m) area over the entire United States. Therefore, for any given property’s latitude and longitude, the model will locate the associated 90 × 90 m area and retrieve the assigned probability value.

The event return period is based upon 43 years’ worth of monthly maximum discharge measurements from over 4,100 gauging stations located throughout the United States. To get the best possible capture of historical discharges, this data set was extended to outlets of each of the 24,000 drainage basins the U.S. counts, using a routing methodology that uses river networks, drainage area and precipitation as input parameters.

Then, Monte Carlo simulations were implemented to create an expanded probabilistic event return period set to extend the 43-year historical event return period set. These return period events have the same spatial and temporal correlations as the original ones, but unlike the original data, cover a time span of 10,000 years. Return periods of events are defined at a ZIP code resolution. Figures B1 and B2 illustrate the event return period distribution for Galveston and Travis Counties used in our model.

Figure B1
figure 3

Galveston County riverine event distribution.

Figure B2
figure 4

Travis County riverine event distribution.

At a given property’s latitude and longitude, the riverine flood inundation water depths from a collection of flood events are computed through an empirical relationship determined by the probability of flood occurrence combined with the flood intensity (event return period). Thus, the impact of flood events on a targeted geographical area (such as a county) can be quantitatively measured by the set of varying water depths across all flooded locations. For this study, the South Central USA geographical entity was used with 100,000 probabilistic events, with each event assigned an occurrence probability of 0.0001. With the probability of flood occurrence in the ZIP code area and flood intensities (event return period) from the flood events that would have impact on the area, the flood depths can be determined through the empirical relationship.

Storm surge module

Given this generated storm surge intensity from the stochastic event set, the storm surge height at a specific geographic location can be determined. Table B1 illustrates the distribution of the various hurricane categories for Galveston County generated in the model.

Table B1 Hurricane event set: Saffir-Simpson category summary for Galveston County (storm surge)
Inventory module

Table B2 summarises the type and property account we consider in the catastrophe model for Galveston County.

Table B2 Summary of Galveston County total parcels by property types
Vulnerability module

Vulnerability for flood hazards in the CoreLogic and Swiss Re models represents the relationship of water depth and mean damage ratio (MDR) on standardised categories of residential properties. Figure B3 illustrates normalised mean damage degrees per various water depths. Multiple sources of vulnerability data were used to generate the vulnerability curves in the model. The main source of data for residential risks was the detailed NFIP loss statistics compiled between 1978 and 2002, with over 850,000 single losses. To complete the vulnerability set, engineering methods of damage assessment and expert opinion were used as well.

Figure B3
figure 5

Indicative riverine flood vulnerability curve for mixed residential building.

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Michel-Kerjan, E., Czajkowski, J. & Kunreuther, H. Could Flood Insurance be Privatised in the United States? A Primer. Geneva Pap Risk Insur Issues Pract 40, 179–208 (2015). https://doi.org/10.1057/gpp.2014.27

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