A Comparison of Hurricane Loss Models (Preliminary Results)

Transcription

1 A Comparison of Hurricane Loss Models (Preliminary Results) presentation to the Florida House of Representatives Committee on Insurance April 16,

2 Today s Briefing Overview of the Commission How do hurricane loss models work? Why are models different? What is the reasonable range of variation, and where do current models fit within that range? How have the models submitted to the Commission varied over time? 2

3 Background Traditional econometric methods to project hurricane loss cost have been considered inappropriate after Hurricane Andrew. Hurricane Modeling offered a more scientific approach, but has been considered controversial due to the proprietary nature of the models. The Legislature recognized the need for expert evaluation of computer models to resolve conflicts among actuarial professionals and created a Commission. 3

4 Creation of Commission In 1995, the Florida Legislature created the 11 member Florida Commission on Hurricane Loss Projection Methodology (see s , F.S.) Panel of Independent Experts formed to provide the most actuarially sophisticated guidelines and standards for projection of hurricane losses possible. 4

5 Composition of the Commission Three actuaries: OIR (appointed by Director of OIR) Insurance Industry (appointed by CFO) Actuary Member of the FHCF Advisory Council Experts from the State University System (appointed by the CFO): Insurance Finance (Actuarial Science) Statistics (Insurance) Computer System Design Meteorology (Hurricanes) Insurance Consumer Advocate Executive Director of Citizens Senior FHCF Officer Director, Division of Emergency Management 5

6 The Professional Team Inputs Statistics Expert Evaluation Requires: Statistician Meteorologist Structural Engineer Actuary Computer Scientist 46 On-Site Reviews to date Meteorology Engineering Actuarial Outputs Computer Programming Hurricane Computer Models 6

7 The Acceptability Process Revising & Developing Standards Reviewing Models Jun Jul Aug Sept Oct Nov Dec Jan Feb Mar Apr May Jun Commission Reviews Models On-Site Reviews by Pro-team Commission Reviews Submissions Modelers Submissions Due Planning Workshops Report of Activities November 1 This is where we are today. February 28 Commission Meeting to Adopt Standards Committee Meetings to Revise Standards Professional Team & Staff Conference Modelers have 4 months to revise models 7

8 Overview of Hurricane Loss Models Input data bases Wind Models Surface Friction and Topography Adjustments Damage Functions Frequency of occurrence of events Supporting decisions. For example: What constitutes an event? Spatial aggregation of numerical results 8

9 Traditional Loss Models Historical Storm Data Land Cover Topography Data Exposure Data Storm Set Wind Model Friction Model Damage Function Actuarial Module Frequency Model Historical data can be used directly, statistically smoothed, or otherwise analyzed to create a data base of storm characteristics used to create the storm set for simulations. Loss Costs 9

10 Research/Comparison Approach Nine wind fields Four surface friction methods Nine damage (vulnerability) functions Three frequency methods 9 x 4 x 9 x 3 = 972 models Other options include changing historical storm data bases, exposures, and other storm assumptions. Result is thousands of possible outcomes. 10

11 Input Data Bases Digital Elevation Model (topography) Not all models use topography Ridge and valley effects important in upland areas Land Cover/Land Use Friction effects to adjust wind impacts on structures at surface Historical Storm Track and intensity data Required to simulate individual storms for comparison with observed losses. Used as a basis for the determination of frequency of occurrence and other storm characteristics Exposure data set Location, characteristics, and value of properties at risk 11

12 Range of Results from Public Domain Models Models Range of results: maximum, median, and minimum MONROE COLLIER MIAMI-DADE LEE PALM BEACH GULF MARTIN BROWARD BAY CHARLOTTE SAINT LUCIE FRANKLIN SARASOTA INDIAN RIVER HENDRY OKALOOSA CALHOUN MANATEE WALTON PINELLAS ESCAMBIA GLADES LIBERTY SANTA ROSA DESOTO OKEECHOBEE PASCO WASHINGTON HERNANDO BREVARD CITRUS OSCEOLA HIGHLANDS HARDEE HILLSBOROUGH HOLMES POLK ORANGE SAINT JOHNS GADSDEN FLAGLER SUMTER NASSAU JACKSON LEON WAKULLA LAKE SEMINOLE DUVAL LEVY VOLUSIA PUTNAM MARION DIXIE CLAY TAYLOR UNION ALACHUA BAKER JEFFERSON GILCHRIST BRADFORD LAFAYETTE COLUMBIA MADISON HAMILTON SUWANNEE Median Min max

13 Why Are Models Different? We examined many additional aspects of why models differ besides the obvious one of different equations: Meteorological input variables Historical Data Land Cover Exposure data bases and aggregation 13

14 Impact of Meteorological Assumptions 14

15 The Explosion in Hurricane Data Data per day on landfalling Hurricane 1.00E E E E+08 Active low earth orbiting (POES) sensors and scatterometers, GPS dropsondes Microwave Satellite Data (sounders) and improved GEOS satellite sensors Data (bytes) 1.00E E E+05 Coastal Radar Networks in place Geosynchronous satellites 1.00E+04 Airports begin systematic weather reporting 1.00E E+02 Aircraft begin penetrating hurricanes to collect data 1.00E+01 GTS (telegraph), Lloyds ship reports 1.00E Year Note Each vertical division represents 10 times the amount of data! 15

16 Short Term Trends in Hurricane Winds vmaxkts Peak Hurricane Winds kts 80.8 kts kts Data for DeFuniak Springs, Walton County kts vmaxkts Probability of Hurricane Force Winds: Overall: 1 in 7.8 El Nino Year: 1 in 6.9 La Nina Year: 1 in

17 Frequencies and Data Base Comparison Does one or two years of additional history make a difference? Can climate models be used instead of parametric models? 17

18 Variability Chart for Miami-Dade 18

19 Summary: Why Models Vary Model component selections, especially wind field. Meteorological input variables very sensitive to assumptions, more sensitive than our ability to measure. Can drive wind model selections. Land Cover and other support data bases out of date can make significant difference. Spatial Aggregation and representation level of aggregation can bias results; ZIP Codes, especially in rural areas can introduce significant errors. 19

20 Where do we stand? While we can t expect individual models to agree, we can understand the variation we should expect from models. With the results of the above studies, especially the results of nearly one thousand public domain hurricane loss models, the Commission now has a baseline against which to evaluate individual model submissions. 20

21 Analysis of Submitted Models Caution: Some of the model submissions under the 2006 Standards are being revised based on findings of the initial reviews by the Commission. 21

22 All 5 Models + Public Domain Loss Costs MONROE COLLIER MIAMI-DADE LEE PALM BEACH GULF MARTIN BROWARD BAY CHARLOTTE SAINT LUCIE FRANKLIN SARASOTA INDIAN RIVER HENDRY OKALOOSA CALHOUN MANATEE WALTON PINELLAS ESCAMBIA GLADES LIBERTY SANTA ROSA DESOTO OKEECHOBEE PASCO WASHINGTON HERNANDO BREVARD CITRUS OSCEOLA HIGHLANDS HARDEE HILLSBOROUGH HOLMES POLK ORANGE SAINT JOHNS GADSDEN FLAGLER SUMTER NASSAU JACKSON LEON WAKULLA LAKE SEMINOLE DUVAL LEVY VOLUSIA PUTNAM MARION DIXIE CLAY TAYLOR UNION ALACHUA BAKER JEFFERSON GILCHRIST BRADFORD LAFAYETTE COLUMBIA MADISON HAMILTON SUWANNEE Median Min max FPM 06 AIR06 ARA06 EQE06 RMS

23 AIR Loss Cost MONROE COLLIER MIAMI-DADE LEE PALM BEACH GULF MARTIN BROWARD BAY CHARLOTTE SAINT LUCIE FRANKLIN SARASOTA INDIAN RIVER HENDRY OKALOOSA CALHOUN MANATEE WALTON PINELLAS ESCAMBIA GLADES LIBERTY SANTA ROSA DESOTO OKEECHOBEE PASCO WASHINGTON HERNANDO BREVARD CITRUS OSCEOLA HIGHLANDS HARDEE HILLSBOROUGH HOLMES POLK ORANGE SAINT JOHNS GADSDEN FLAGLER SUMTER NASSAU JACKSON LEON WAKULLA LAKE SEMINOLE DUVAL LEVY VOLUSIA PUTNAM MARION DIXIE CLAY TAYLOR UNION ALACHUA BAKER JEFFERSON GILCHRIST BRADFORD LAFAYETTE COLUMBIA MADISON HAMILTON SUWANNEE Median Min max AIR 06

24 ARA Loss Cost MONROE COLLIER MIAMI-DADE LEE PALM BEACH GULF MARTIN BROWARD BAY CHARLOTTE SAINT LUCIE FRANKLIN SARASOTA INDIAN RIVER HENDRY OKALOOSA CALHOUN MANATEE WALTON PINELLAS ESCAMBIA GLADES LIBERTY SANTA ROSA DESOTO OKEECHOBEE PASCO WASHINGTON HERNANDO BREVARD CITRUS OSCEOLA HIGHLANDS HARDEE HILLSBOROUGH HOLMES POLK ORANGE SAINT JOHNS GADSDEN FLAGLER SUMTER NASSAU JACKSON LEON WAKULLA LAKE SEMINOLE DUVAL LEVY VOLUSIA PUTNAM MARION DIXIE CLAY TAYLOR UNION ALACHUA BAKER JEFFERSON GILCHRIST BRADFORD LAFAYETTE COLUMBIA MADISON HAMILTON SUWANNEE Median Min max ARA

25 18 EQE Loss Costs MONROE COLLIER MIAMI-DADE LEE PALM BEACH GULF MARTIN BROWARD BAY CHARLOTTE SAINT LUCIE FRANKLIN SARASOTA INDIAN RIVER HENDRY OKALOOSA CALHOUN MANATEE WALTON PINELLAS ESCAMBIA GLADES LIBERTY SANTA ROSA DESOTO OKEECHOBEE PASCO WASHINGTON HERNANDO BREVARD CITRUS OSCEOLA HIGHLANDS HARDEE HILLSBOROUGH HOLMES POLK ORANGE SAINT JOHNS GADSDEN FLAGLER SUMTER NASSAU JACKSON LEON WAKULLA LAKE SEMINOLE DUVAL LEVY VOLUSIA PUTNAM MARION DIXIE CLAY TAYLOR Median Min max EQE 06 UNION ALACHUA BAKER JEFFERSON GILCHRIST BRADFORD LAFAYETTE COLUMBIA MADISON HAMILTON SUWANNEE

26 RMS Loss Costs MONROE COLLIER MIAMI-DADE LEE PALM BEACH GULF MARTIN BROWARD BAY CHARLOTTE SAINT LUCIE FRANKLIN SARASOTA INDIAN RIVER HENDRY OKALOOSA CALHOUN MANATEE WALTON PINELLAS ESCAMBIA GLADES LIBERTY SANTA ROSA DESOTO OKEECHOBEE PASCO WASHINGTON HERNANDO BREVARD CITRUS OSCEOLA HIGHLANDS HARDEE HILLSBOROUGH HOLMES POLK ORANGE SAINT JOHNS GADSDEN FLAGLER SUMTER NASSAU JACKSON LEON WAKULLA LAKE SEMINOLE DUVAL LEVY VOLUSIA PUTNAM MARION DIXIE CLAY TAYLOR UNION ALACHUA BAKER JEFFERSON GILCHRIST BRADFORD LAFAYETTE COLUMBIA MADISON HAMILTON SUWANNEE Median Min max RMS 06

27 18 FPM Loss Costs MONROE COLLIER MIAMI-DADE LEE PALM BEACH GULF MARTIN BROWARD BAY CHARLOTTE SAINT LUCIE FRANKLIN SARASOTA INDIAN RIVER HENDRY OKALOOSA CALHOUN MANATEE WALTON PINELLAS ESCAMBIA GLADES LIBERTY SANTA ROSA DESOTO OKEECHOBEE PASCO WASHINGTON HERNANDO BREVARD CITRUS OSCEOLA HIGHLANDS HARDEE HILLSBOROUGH HOLMES POLK ORANGE SAINT JOHNS GADSDEN FLAGLER SUMTER NASSAU JACKSON LEON WAKULLA LAKE SEMINOLE DUVAL LEVY VOLUSIA PUTNAM MARION DIXIE CLAY TAYLOR Median Min max FPM 06 UNION ALACHUA BAKER JEFFERSON GILCHRIST BRADFORD LAFAYETTE COLUMBIA MADISON HAMILTON SUWANNEE

28 Loss Costs AIR ARA EQE FPM RMS Loss Cost per $1000 Of Exposure for Wood Frame 28

29 Rank Comparison AIR ARA EQE FPM RMS Rank with respect to the 972 Public Domain Models 29

30 Rank Comparison Table This table shows the number of counties (out of 67) in each quartile of the 972 Public Domain model outputs, as well as those exceeding the maximum or falling below the minimum. Model below min max above 2005 AIR ARA EQE RMS FPM AIR ARA EQE RMS FPM Caution: Being revised due to issues detected during review. 30

31 Probable Maximum Loss Analysis 2006 FHCF Exposure Data Return Probability Time AIR ARA EQE FPM RMS 0.4% % % AAL AAL: Average Annual Loss Return time in years All numbers for the models in $ billions All probable maximum loss (PML) data is based on the 2006 Florida Hurricane Catastrophe Fund Exposure data using models found acceptable under the 2005 standards (with the exception of the Florida Public Model which was not submitted in Version 1.5 was used for the Public Model) Note: All numbers subject to change as part of the Commission s review process. 31

32 PML Analysis 2006 FHCF Exposure Data $ Billions AIR ARA EQE FPM RMS Return Times 32

33 PML Analysis 2006 FHCF Exposure Data Average Annual Loss $ Billions AIR ARA EQE FPM RMS 0 AAL 33

34 Probable Maximum Loss Analysis FHCF Hypothetical Exposure Data Form S-2S Return Probability Time AIR ARA EQE FPM RMS 0.4% % % AAL AAL: Average Annual Loss Return time in years All numbers for the models in $ millions All probable maximum loss (PML) data is based on the Commission s Form S-2 Hypothetical Exposure data using models under current review for compliance with the 2006 standards. Note: All numbers subject to change as part of the Commission s review process. 34

35 PML Analysis FHCF Hypothetical Exposure Data Form S-2S $ Millions AIR ARA EQE FPM RMS Return Times 35

36 PML Analysis FHCF Hypothetical Exposure Data Form S-2, S Average Annual Loss $ Millions AAL AIR ARA EQE FPM RMS 36

37 Statewide Dynamic Range of Loss Costs Ratio of top 5 to bottom 5 loss costs Ratio Min 25th pct Median 75th pct Max AIR ARA EQE FPM RMS 37

38 Changes in Models (incl. revisions) 400% % 200% AIR ARA EQE 100% FPM RMS Percent Change 0% -100% -200% MONROE COLLIER MIAMI-DADE LEE PALM BEACH GULF MARTIN BROWARD BAY CHARLOTTE SAINT LUCIE FRANKLIN SARASOTA INDIAN RIVER HENDRY OKALOOSA CALHOUN MANATEE WALTON PINELLAS ESCAMBIA GLADES LIBERTY SANTA ROSA DESOTO OKEECHOBEE PASCO WASHINGTON HERNANDO BREVARD CITRUS OSCEOLA HIGHLANDS HARDEE HILLSBOROUGH HOLMES POLK ORANGE SAINT JOHNS GADSDEN FLAGLER SUMTER NASSAU JACKSON LEON WAKULLA LAKE SEMINOLE DUVAL LEVY VOLUSIA PUTNAM MARION DIXIE CLAY TAYLOR UNION ALACHUA BAKER JEFFERSON GILCHRIST BRADFORD LAFAYETTE COLUMBIA MADISON HAMILTON SUWANNEE County

39 Changes from 2005 to 2006 (All 5 Models) AIR ARA EQE FPM RMS 39

40 AIR Note: On-site audit completed. 40

41 AIR Loss Cost 41

42 AIR Loss Cost MONROE COLLIER MIAMI-DADE LEE PALM BEACH GULF MARTIN BROWARD BAY CHARLOTTE SAINT LUCIE FRANKLIN SARASOTA INDIAN RIVER HENDRY OKALOOSA CALHOUN MANATEE WALTON PINELLAS ESCAMBIA GLADES LIBERTY SANTA ROSA DESOTO OKEECHOBEE PASCO WASHINGTON HERNANDO BREVARD CITRUS OSCEOLA HIGHLANDS HARDEE HILLSBOROUGH HOLMES POLK ORANGE SAINT JOHNS GADSDEN FLAGLER SUMTER NASSAU JACKSON LEON WAKULLA LAKE SEMINOLE DUVAL LEVY VOLUSIA PUTNAM MARION DIXIE CLAY TAYLOR UNION ALACHUA BAKER JEFFERSON GILCHRIST BRADFORD LAFAYETTE COLUMBIA MADISON HAMILTON SUWANNEE Median Min max AIR 06

43 AIR Rank 43

44 AIR Change Wood Frame

45 ARA Note: Undergoing revision following on-site audit. 45

46 ARA Loss Costs 46

47 ARA Loss Cost MONROE COLLIER MIAMI-DADE LEE PALM BEACH GULF MARTIN BROWARD BAY CHARLOTTE SAINT LUCIE FRANKLIN SARASOTA INDIAN RIVER HENDRY OKALOOSA CALHOUN MANATEE WALTON PINELLAS ESCAMBIA GLADES LIBERTY SANTA ROSA DESOTO OKEECHOBEE PASCO WASHINGTON HERNANDO BREVARD CITRUS OSCEOLA HIGHLANDS HARDEE HILLSBOROUGH HOLMES POLK ORANGE SAINT JOHNS GADSDEN FLAGLER SUMTER NASSAU JACKSON LEON WAKULLA LAKE SEMINOLE DUVAL LEVY VOLUSIA PUTNAM MARION DIXIE CLAY TAYLOR UNION ALACHUA BAKER JEFFERSON GILCHRIST BRADFORD LAFAYETTE COLUMBIA MADISON HAMILTON SUWANNEE Median Min max ARA

48 ARA Rank 48

49 ARA Change Wood Frame

50 EQE Note: Audit scheduled. 50

51 EQE Loss Costs 51

52 18 EQE Loss Costs MONROE COLLIER MIAMI-DADE LEE PALM BEACH GULF MARTIN BROWARD BAY CHARLOTTE SAINT LUCIE FRANKLIN SARASOTA INDIAN RIVER HENDRY OKALOOSA CALHOUN MANATEE WALTON PINELLAS ESCAMBIA GLADES LIBERTY SANTA ROSA DESOTO OKEECHOBEE PASCO WASHINGTON HERNANDO BREVARD CITRUS OSCEOLA HIGHLANDS HARDEE HILLSBOROUGH HOLMES POLK ORANGE SAINT JOHNS GADSDEN FLAGLER SUMTER NASSAU JACKSON LEON WAKULLA LAKE SEMINOLE DUVAL LEVY VOLUSIA PUTNAM MARION DIXIE CLAY TAYLOR Median Min max EQE 06 UNION ALACHUA BAKER JEFFERSON GILCHRIST BRADFORD LAFAYETTE COLUMBIA MADISON HAMILTON SUWANNEE

53 EQE Rank 53

54 EQE Change Wood Frame

55 FPM Note: Undergoing revision following on-site audit. 55

56 FPM Loss Costs 56

57 18 FPM Loss Costs MONROE COLLIER MIAMI-DADE LEE PALM BEACH GULF MARTIN BROWARD BAY CHARLOTTE SAINT LUCIE FRANKLIN SARASOTA INDIAN RIVER HENDRY OKALOOSA CALHOUN MANATEE WALTON PINELLAS ESCAMBIA GLADES LIBERTY SANTA ROSA DESOTO OKEECHOBEE PASCO WASHINGTON HERNANDO BREVARD CITRUS OSCEOLA HIGHLANDS HARDEE HILLSBOROUGH HOLMES POLK ORANGE SAINT JOHNS GADSDEN FLAGLER SUMTER NASSAU JACKSON LEON WAKULLA LAKE SEMINOLE DUVAL LEVY VOLUSIA PUTNAM MARION DIXIE CLAY TAYLOR Median Min max FPM 06 UNION ALACHUA BAKER JEFFERSON GILCHRIST BRADFORD LAFAYETTE COLUMBIA MADISON HAMILTON SUWANNEE

58 FPM Rank 58

59 FPM Change Wood Frame

60 RMS Note: Audit scheduled. 60

61 RMS Loss Costs 61

62 RMS Loss Costs MONROE COLLIER MIAMI-DADE LEE PALM BEACH GULF MARTIN BROWARD BAY CHARLOTTE SAINT LUCIE FRANKLIN SARASOTA INDIAN RIVER HENDRY OKALOOSA CALHOUN MANATEE WALTON PINELLAS ESCAMBIA GLADES LIBERTY SANTA ROSA DESOTO OKEECHOBEE PASCO WASHINGTON HERNANDO BREVARD CITRUS OSCEOLA HIGHLANDS HARDEE HILLSBOROUGH HOLMES POLK ORANGE SAINT JOHNS GADSDEN FLAGLER SUMTER NASSAU JACKSON LEON WAKULLA LAKE SEMINOLE DUVAL LEVY VOLUSIA PUTNAM MARION DIXIE CLAY TAYLOR UNION ALACHUA BAKER JEFFERSON GILCHRIST BRADFORD LAFAYETTE COLUMBIA MADISON HAMILTON SUWANNEE Median Min max RMS 06

63 RMS Rank 63

64 RMS Change Wood Frame

65 Overall Summary Fundamentally, models vary because their developers select different, on the surface equally valid, methods to solve the four basic components (frequency, wind, friction, and vulnerability) of loss modeling. Even if we could decide on a perfect solution for the four components, the uncertainty in meteorological parameters and other input data would cause significant uncertainty in loss costs. The five models submitted to the Commission are, with some anomalies, within the range of values we would expect given the universe of possible scientifically valid approaches. Although individual models have varied, over time the range of loss costs produced by the submitted models have not varied all that much. 65