National Flood Insurance Program

Risk Rating 2.0 Methodology

and Data Sources

January 18, 2022
TOC 1

TABLE OF CONTENTS

PURPOSE AND ORGANIZATION OF REPORT___________________________________ 1

DATA AND DATA VALIDATION _______________________________________________ 2

GIS DATA _________________________________________________________________________ 2

MARKET BASKETS ________________________________________________________________ 2

Single Family Home Locations ________________________________________________________________ 2

HISTORICAL AND INFORCE EXPOSURES ___________________________________________ 4

CATASTROPHE MODELING________________________________________________________ 4

Inland Flood and Storm Surge Models___________________________________________________________ 4

Great Lakes Flooding________________________________________________________________________ 5

Tsunami __________________________________________________________________________________ 5

Leveed Areas ______________________________________________________________________________ 7

Catastrophe Model Review ___________________________________________________________________ 7

METHODOLOGY _____________________________________________________________ 8

TARGET RATE LEVEL _____________________________________________________________ 8

Introduction _______________________________________________________________________________ 8

Mainland Inland Flood and Storm Surge Losses ___________________________________________________ 8

Non Mainland Inland Flood and Storm Surge Losses _______________________________________________ 9

Tsunami Losses ____________________________________________________________________________ 9

Great Lakes Losses__________________________________________________________________________ 9

Coastal Erosion Losses_______________________________________________________________________ 9

Expenses__________________________________________________________________________________ 9

CONCENTRATION RISK __________________________________________________________ 10

RATING FACTORS________________________________________________________________ 11

Geographic Rating Factors___________________________________________________________________ 12

Property Characteristics Rating Factors _________________________________________________________ 13

Rating Territories __________________________________________________________________________ 14

Leveed Area Analysis ______________________________________________________________________ 16

Non Single-Family Home Analysis ____________________________________________________________ 17

Insurance To Value/Deductible Analysis________________________________________________________ 17

COMPARISON TO HISTORICAL RESULTS__________________________________________ 19

RATE ORDER CALCULATION AND SELECTIONS ___________________________________ 20

Community Rating System Discount ___________________________________________________________ 20

Elevated Machinery and Equipment ___________________________________________________________ 20

First Floor Height__________________________________________________________________________ 20

Foundation Type __________________________________________________________________________ 20

Maximum Rate____________________________________________________________________________ 20

Prior Claims ______________________________________________________________________________ 21

Rating Factor Capping ______________________________________________________________________ 21

BASE RATES _____________________________________________________________________ 21

FINAL RATING FACTORS AND RATING EXAMPLES ________________________________ 21

ACTUARIAL STANDARDS OF PRACTICE _____________________________________ 24

CAS STATEMENT OF PRINCIPLES REGARDING PROPERTY & CASUALTY INSURANCE 

RATEMAKING ___________________________________________________________________ 24

ASOP 12: RISK CLASSIFICATION (FOR ALL PRACTICE AREAS) _____________________ 25

ASOP 23: DATA QUALITY_________________________________________________________ 26

ASOP 25: CREDIBILITY PROCEDURES ____________________________________________ 26
TOC 2

ASOP 29: EXPENSE PROVISIONS IN PROPERTY/CASUALTY INSURANCE 

RATEMAKING ___________________________________________________________________ 26

ASOP 30: TREATMENT OF PROFIT AND CONTINGENCY PROVISIONS AND THE COST OF 

CAPITAL IN PROPERTY/CASUALTY INSURANCERATEMAKING ____________________ 27

ASOP 38: USING MODELS OUTSIDE THE ACTUARY'S AREA OF EXPERTISE (PROPERTY 

AND CASUALTY) _________________________________________________________________ 27

ASOP 39: TREATMENT OF CATASTROPHE LOSSES IN PROPERTY/CASUALTY 

INSURANCE RATEMAKING _______________________________________________________ 28

ASOP 41: ACTUARIAL COMMUNICATIONS ________________________________________ 29

ASOP 53: ESTIMATING FUTURE COSTS FOR PROSPECTIVE PROPERTY/CASUALTY 

RISK TRANSFER AND RISK RETENTION ___________________________________________ 29

ASOP 56: MODELING _____________________________________________________________ 30

LIMITATIONS_______________________________________________________________ 32

GLOSSARY OF TERMS_______________________________________________________ 34

APPENDIX A - GIS Technical Appendix _________________________________________ 35

APPENDIX B - Market Basket Variable Imputation ________________________________ 55

APPENDIX C - Uniform Book Assumptions _______________________________________ 63

APPENDIX D - Rating Factors

APPENDIX E - Rating Examples
1

PURPOSE AND ORGANIZATION OF REPORT

The Federal Emergency Management Agency (“FEMA”) requested Milliman, Inc. (“Milliman”)

assist it with its Risk Rating 2.0 initiative, which consists in developing a new rating plan for the 

National Flood Insurance Program (“NFIP”). Milliman’s work under Risk Rating 2.0 has been fully 

documented in over 40 separate actuarial communications to FEMA provided over the course of the 

engagement, including both actuarial reports and other formats such as spreadsheets and electronic 

data files. The purpose of this report (“the Milliman report”) is to provide a consolidated description

of the methodology and data sources used to develop the Risk Rating 2.0 plan and information about 

how our work complied with relevant actuarial standards of practice. The report also shows the Risk 

Rating 2.0 algorithm, rates, and rating factors, and provides examples to illustrate how premiums are

calculated under the new plan. Milliman provided FEMA with a draft report on September 28, 2020, 

a revised draft report on March 11, 2021, and this final report on March 25, 2021.

This report is divided into several sections. The first main section describes the data sources used 

and the methods of validation employed. Data sources include GIS data, Market Basket data, NFIP 

inforce exposures, NFIP historical loss and exposure data, and catastrophe model output.

The next section, comprising the bulk of this report, discusses the methodology employed in the 

analysis. This includes a discussion of how Milliman calculated the target rate level, the net cost 

of reinsurance and retained risk, the rating factors, the concentration risk loads, and how Milliman 

used FEMA’s historical loss data.

The next section consists of a discussion of the rating algorithm and selections made by FEMA.

The final sections of the report provide information about how Milliman complied with relevant 

actuarial standards of practice, describe the limitations of this report, and provide a glossary of 

terms.

The report also includes the following appendices, which are referenced in relevant sections of the 

report text:

• Appendix A: GIS Technical Appendix

• Appendix B: Market Basket Variable Imputation

• Appendix C: Uniform Book Assumptions

• Appendix D: Rating Factors

• Appendix E: Rating Examples
2

DATA AND DATA VALIDATION

GIS DATA

Documentation of the GIS data and analyses used in Risk Rating 2.0 can be found in the attached 

Appendix A. This appendix describes how geospatial datasets used in Risk Rating 2.0 were 

acquired and/or created, how these datasets were queried to append rating variables to policy 

locations, and additional GIS analysis that was incorporated into the rating plan design.

MARKET BASKETS

Single Family Home Locations

Due to the concentrated nature of FEMA’s inforce policies, Milliman created Market Baskets for 

all states and territories to use in this analysis. The Market Baskets were created to be a 

representative sample of all single-family homes (“SFHs”). These Market Baskets were primarily 

designed to assist in the creation of rating factors.

Three separate books (portfolios of policies) were created from the Market Basket data:

1. Uniform Book. The Uniform Book is a representative sample of locations of SFHs. Each 

location was assigned identical property and coverage characteristics, which are shown in 

Appendix C.

2. Uncorrelated Market Basket. Approximately half of all locations were designated as the 

Uncorrelated Market Basket. These locations were assigned generally uncorrelated 

property and policy characteristics that are not necessarily representative of the state or 

territory’s true distributions, as described in more detail in Appendix B. This book was 

utilized to understand non-geographic (i.e. property and coverage) risk differences. An 

exception to the lack of correlation was the relationship between foundation type and first 

floor height, since certain combinations are inherently not possible. For example, it is not 

possible to have a slab foundation with a 10-foot first floor height.

3. Correlated Market Basket. The remaining locations not designated as part of the 

Uncorrelated Market Basket make up the Correlated Market Basket. In the Correlated 

Market Basket, property and policy characteristics are representative of the distributions 

by state or territory, as described in more detail in AppendixB.

In creating these books, the first major step was to create the location of all SFHs within each state 

or territory. The uniform book was made directly from these locations. The next major step was to

impute unknown property and coverage characteristics. Details on the imputations are included in 

Appendix B. The final step was to separate the Uncorrelated and Correlated Market Basket 

locations, and to re-impute the Uncorrelated Market Basket property and coverage characteristics.

Market Basket locations were derived primarily from CoreLogic ParcelPoint data (“parcel data”) 

provided to Milliman by FEMA. This parcel data contained point and polygon features for the
3

majority of counties in the United States. Additional datasets used to create Market Basket 

locations include 2010 Decennial Census Data (“2010 Census”), American Community Survey 

2016 5-year estimates (“ACS”), and Census TIGER files (“Census geographies”) from the U.S. 

Census Bureau as well as water areas from the National Hydrography Dataset (“NHD”). Census 

geographies used include state, county, census block group (“block-group”), and census block 

(“block”) boundaries in addition to road centerlines.

For counties with parcel data, parcel points were initially selected based on their assigned landuse and property indicator types. The initial count of selected parcels was then compared to the 

expected count based on ACS estimates of SFHs by county. In many cases, the initial selections 

varied significantly from the expected counts. Counties were reviewed manually using satellite 

imagery to diagnose material discrepancies between actual and expected counts. For example, 

many counties contained parcel points where the property type was agricultural or the land-use 

type was forest. These parcels were added to the single-family home dataset where property 

characteristics existed that distinguished them as SFH locations, such as year built, number of 

bedrooms, or assessed value of improvements. In other cases where the initial parcel counts were 

high relative to the expected ACS counts, parcel points that were incorrectly assumed to be SFHs 

were identified either as empty using property characteristics or as duplicates using Assessor’s 

Parcel Number. The final selected parcel points were then overlaid with NHD water areas to ensure 

no location was over water; where necessary, parcel polygons were clipped using water areas so 

that a new parcel centroid outside of the water area could be generated.

For the small number of counties with missing or limited parcel data, a stratified, constrained, 

random point generation method was used to create representative locations of SFHs. Estimated 

counts of SFHs from the ACS by block-group were allocated to the block level using counts of 

housing units by block from the 2010 Census. Randomly generated points were placed in each 

block based on these allocations subject to the following constraints:

1) Points cannot be within water areas

2) Points cannot be within 200 m of a highway or highway ramp centerline

3) Points cannot within 25 m of a main road centerline

4) Point must be between 8 and 100 m of a local road centerline

5) Points cannot be within 15 m of anotherpoint.

All constraints were based on a study of known SFH locations and their respective distances to 

road centerlines and neighboring locations.

Details on the development of the Uncorrelated Market Basket and Correlated Market Basket are 

in Appendix B.
4

HISTORICAL AND INFORCE EXPOSURES

Milliman utilized the FEMA inforce exposures from May 31, 2018 for the catastrophe modeling 

and dislocation analysis. The following fields were appended:

• Leveed Area Indicator

• Levee System ID

• Levee Quality

• Distance to River

• Elevation Relative to River

• Flood Depth Difference (the difference between the 10 year and 100 year flood depth)

• Drainage Area

• Hydrologic Unit Codes (“HUCs”)

• Building Replacement Cost Value (provided to FEMA byCoreLogic)

• Contents Actual Cash Value (estimated from Building Replacement Cost Value)

• Ground Up Modeled Average Annual Loss (“AAL”) by catastrophe model and peril

• Gross Modeled AAL by catastrophe model and peril (after limits and deductibles applied)

• Type of Use and Floors of Interest (Non Single Family Homes only)

Milliman utilized NFIP historical losses and exposures from January 1, 1992 to June 30, 2018. For 

these historical exposures the Building Replacement Cost Value (“RCV”) from FEMA’s database 

was used. Although Milliman compared these values to the values supplied to CoreLogic for the 

inforce exposures to assess the reasonableness of the recorded RCVs in the NFIP database, we 

were not able to validate the recorded RCVs.

CATASTROPHE MODELING

Inland Flood and Storm Surge Models

Milliman prepared the Uniform Book, Uncorrelated Market Basket, Correlated Market Basket, 

and FEMA inforce exposures for catastrophe modeling. The catastrophe models used in the 

analysis were KatRisk SpatialKat and SoloKat (“KatRisk model”), AIR Touchstone version 5 

(“AIR model”), CoreLogic RQE v. 17.0 US Flood Model (“CoreLogic model”), Mapping Data 

Integration (“MDI model”), and PFRA (“PFRA model”). The MDI and PFRA models were based 

on FEMA flood maps and other data sources and were developed by FEMA contractors including 

Atkins, STARR II and Compass. When different settings for the models existed, Milliman 

requested that they be run using the long-term event sets and with demand surge. Milliman 

reviewed the models to assess their suitability for the purpose of making rates for the NFIP.
5

AIR and KatRisk input files were provided to FEMA, CoreLogic input files were provided to 

CoreLogic, and MDI input files were provided to Atkins for modeling. PFRA input files were 

provided to STARR II and Compass for modeling.

The catastrophe model vendors and Atkins provided written documentation regarding their 

models. The documentation of the MDI model is in the draft document “Mapping Data Integration 

(“MDI”) Model Documentation Version 2.0” dated February 13, 2020. KatRisk provided 

“KatRisk Data and Model Technical Documentation” dated February 2018. AIR provided “AIR 

Inland Flood Model for the United States” (June 2016), “AIR Hurricane Model for the United 

States” (June 2017), and “AIR Earthquake Model for the United States” (December 2019). The 

earthquake documentation contains AIR’s documentation regarding its Tsunami model. 

CoreLogic provided “RQE v. 18.0 US Flood Model Principles and Methodology” (July 2018). 

Milliman referred to the documentation of all of these models during the course of the analysis. 

No written documentation was provided for the PFRA model, however there were numerous 

conference calls between the PFRA contractors, FEMA, and Milliman to discuss the methodology 

and assumptions used in the model.

The MDI model was not run on the FEMA inforce exposures because it could not be run inside the 

FEMA firewall and the exposure data included Personally Identifiable Information (“PII”) that 

could not be brought outside the firewall. MDI was the only model available for Great Lakes 

Flooding and Tsunami outside the contiguous United States. Inforce AALs for these cases were 

estimated using the Market Basket AALs and an adjustment for the different exposure distribution.

The PFRA model was run on the inforce exposures but was not used to set territory factors due to 

the small number of locations available.

The AIR Inland Flood Model utilized did not include precipitation from tropical cyclones. This 

was accounted for in the analysis by adding a provision for these losses based on KatRisk.

Great Lakes Flooding

The Great Lakes can experience seiche flooding, which occurs when winds produce a standing 

wave. It is Milliman’s understanding that the commercially available catastrophe models do not 

include explicit modeling of this phenomenon. FEMA wished to explicitly address this source of 

flooding and requested that it be incorporated into the MDI model by Atkins. Atkins supplied 

Milliman with AALs for Market Basket locations for this peril.

Tsunami

MDI results for the Uniform Book, Uncorrelated Market Basket, and Correlated Market Basket

were provided for the states of California, Oregon, Washington, Alaska, and Hawaii and for the 

territories of Guam, American Samoa, and the Northern Mariana Islands. AIR results were 

provided only for the states covered by the AIR Tsunami model: California, Oregon, and 

Washington. Atkins stated that the methods they employed were not suitable for evaluating 

Tsunami risk in the Atlantic basin and therefore did not provide Milliman with AALs for states or 

territories in the Atlantic basin. Since no Tsunami models were available for the Atlantic coastal 

states, Milliman’s analysis does not include a provision for Tsunami for this region.
6

Milliman’sreview of the AIR model resultsfound a significant number of locations with extremely 

high AALs. Examples of these locations were provided to FEMA and AIR. FEMA and Milliman 

discussed this issue with AIR, who agreed that AALs near the ocean (or other water sources) could 

be unreasonably high due to the resolution of the model, which was based on a sub-grid of 

approximately 132 meters. Thus, Milliman developed a method to identify and exclude locations 

defined as “outliers” from the analysis.

Key indicators of outliers included:

• Locations with a Distance to Ocean (“DTO”) greater than 20 miles were almost always 

outliers. These were primarily found in Puget Sound, Lake Washington, and the San Juan 

Islands in Washington.

• Outliers tended to have high AALs. Locations with AALs greater than $5,000 were almost 

always outliers. Locations with AALs less than $1,000 were rarely identified as outliers.

• Locations within 150 meters of the ocean tended to be outliers, as expected based on the 

size of the AIR sub-grid.

The conditions for exclusion were as follows:

1) Locations greater than 20 miles from the ocean were removed. 15% of locations with nonzero ground up AALs were removed in this step. A small number of locations that were 

likely not outliers in the San Juan Islands were dropped in this step. This did not materially 

impact results, as these locations had relatively low modeledAALs.

2) Ground up AALs greater than $5,000 were removed. 3% of locations with non-zero ground 

up AALs were removed in thisstep.

3) A Generalized Linear Model (“GLM”) was fit at this point on the subset that included 

locations with DTO greater than 150 meters and with AAL less than $1,000, in Oregon and 

Washington only. The GLM was used to estimate the Tsunami burn rate1 using elevation 

and distance to ocean, and the Pearson Residual (a measure of the difference between the 

actual burn rate and the estimated burn rate) was calculated for each location. Locations 

with ground up AAL greater than $1,000 and a Pearson Residual greater than 2 were 

dropped. 3% of locations with non-zero ground up AALs were removed in thisstep.

4) Locations in California zip codes 92647, 92648, and 92649 were also removed if they had 

ground up AAL greater than $1,500 and DTO greater than 1,000 meters. 2% of locations 

with non-zero AIR ground up AALs were removed in this step.

The removal of many outliers through the above process significantly reduced the bias in the rating 

factors developed based on the AIR model but may not eliminate all bias.

1 Burn rate is AAL per thousand dollars of value.
7

Leveed Areas

FEMA consulted with the United States Army Corps of Engineers to develop assumptions 

regarding the definition of leveed areas, the probability of overtopping, and the probability of 

failure prior to overtopping that could inform the catastrophe modeling. FEMA reran the KatRisk 

model based upon these assumptions and requested that Atkins rerun the model and provide the 

updated AALs to Milliman. FEMA advised that it was not possible to rerun the other catastrophe 

models using these assumptions, and therefore instructed Milliman to exclude them from the 

leveed analysis.

Catastrophe Model Review

Milliman reviewed AALs from the catastrophe models for reasonableness and anomalies using the 

following procedures:

• AALs were mapped and reviewed to ensure losses varied as expected.

• Univariate summaries were run on all GIS variables and reviewed for expected patterns. 

For example, AALs by elevation group were reviewed to ensure a monotonically 

decreasing trend since Storm Surge AALs should decrease as elevation increases, all else 

being equal.
8

METHODOLOGY

TARGET RATE LEVEL

Introduction

Milliman developed target premiums based on NFIP inforce exposures as of May 31, 2018

supplied by FEMA. This date was chosen because the RCV for these exposures had already been 

obtained and the catastrophe modeling data prep performed as part of the NFIP’s reinsurance 

placement. The target premiums were developed separately across several dimensions:

• Coverage type: Building,Contents

• Occupancy type: SFHs, Non Single-family Homes (NSFHs)

• Levee status: Non-leveed,Leveed

• Peril: Inland Flood, Storm Surge, Tsunami, Great Lakes, CoastalErosion

• State/Territory

The target premiums were developed using catastrophe model output and loss selections from 

FEMA, as well as provisions for underwriting expense, loss adjustment expense (“LAE”), and the 

net cost of reinsurance.

Mainland Inland Flood and Storm Surge Losses

AALs for inforce policies in mainland states2 were modeled using the AIR, CoreLogic, and 

KatRisk models for the perils of Inland Flood and Storm Surge. Guy Carpenter compared the AIR 

and KatRisk results to the NFIP’s historical experience. For Inland Flood this was performed at the 

sub-peril level: non-tropical cyclone flooding, hurricane precipitation, and tropical storm

precipitation. Based on this analysis, Guy Carpenter selected adjustment factors and FEMA 

directed Milliman to use these factors to rescale the AALs from the AIR and KatRisk models.

Milliman calculated the target loss by backing out the LAE and Increased Cost of Compliance (

“ICC”) loads from FEMA’s selection and allocated this target loss by occupancy type, levee status, 

and state. Milliman then adjusted the Inland Flood and Storm Surge rescaled AALs for each 

catastrophe model to match the targets by occupancy type and leveed status. The Inland Flood and 

Storm Surge AALs were adjusted uniformly for each combination of model, occupancy type, and 

levee status.

As a result, the mainland states Inland Flood and Storm Surge adjusted AALs for AIR, Corelogic, 

and KatRisk each totaled the target losses by occupancy type and leveed status. AALs still varied 

by peril, coverage (building and contents), and state.

2 These are all states except Alaska and Hawaii.
9

Milliman selected AALs by state using both adjusted AALs from the three catastrophe models and 

the current NFIP premium. Milliman then allocated these selected AALs to peril and coverage.

Non Mainland Inland Flood and Storm Surge Losses

The AIR and CoreLogic models were not available for non-mainland locations. KatRisk Storm 

Surge was not available for non-mainland locations. Consequently, for Storm Surge the nonmainland analysis relied solely on the MDI model. For Inland Flood, both KatRisk and MDI were 

used for SFHs but only KatRisk was used for NSFHs.

When the MDI model was used to select a target loss provision, Milliman used a Market Basket 

burn rate approach since inforce AALs were unavailable. Milliman calculated burn rates (AAL per 

coverage value) for the Correlated Market Basket using MDI AALs. These burn rates were 

calculated by elevation relative to river for Inland Flood and by elevation for Storm Surge. Burn 

rates were then multiplied by inforce coverage value to develop estimates for MDI inforce AAL.

Tsunami Losses

For California, Oregon, and Washington, Milliman used the AIR and MDI models to estimate 

Tsunami inforce AAL. For other states and territories, Milliman used the MDI model to select 

target Tsunami AAL since the AIR model was not available. In the case of MDI, Milliman used a

burn rate by elevation approach, similar to that described above, to estimate inforce AAL.

Great Lakes Losses

Milliman used the MDI model to select target Great Lakes AAL, using a burn rate by elevation 

relative to lake approach, similar to that described above, to estimate inforce AAL.

Coastal Erosion Losses

Coastal Erosion inforce AAL was selected in a different manner from other perils, as Coastal 

Erosion was not available as a peril in any of the available catastrophe models. FEMA provided 

premium rates based on the current Risk Rating 1.0 methodology with and without Coastal Erosion

by state, occupancy group, and leveed status. By applying these rates to inforce coverage, Milliman 

determined the amount of Coastal Erosion premium implied by the current rates. Estimated current 

expenses were removed from these premiums to determine the implied Coastal ErosionAALs.

Expenses

Loss Cost Multiplier and Expense Constant

FEMA provided all current expense data. Based on current expenses and premiums, Milliman 

calculated a current loss cost multiplier (“LCM”) that includes both variable and fixed expenses, 

as there is no expense constant in the current rates.
10

The Risk Rating 2.0 algorithm includes loss and expense constants applied to every policy. The 

Loss Constant is described further below. The Expense Constant reflects the NFIP’s fixed 

expenses. Milliman developed a proposed LCM to load the NFIP’s variable expenses in proportion 

to expected loss. In order to keep total expenses equivalent, the proposed LCM is lower than the 

current LCM because it excludes the fixed expenses which are now included as a flat load per 

contract.

Net Cost of Reinsurance and Retained Risk

Milliman developed net risk loads which were applied as a load on the AALs to reflect the Net 

Cost of Reinsurance (“NCOR”) and the retained risk of non-attritional losses. These net risk loads 

increase each state’s overall target premium based on the expected NCOR and retained risk for 

that state. The loads were developed by first calculating reinsurance premium multiples, which 

reflect the ratio of cost of reinsurance to the ceded expected loss and LAE per layer estimated from 

the KatRisk model. FEMA provided Milliman with reinsurance premium data and GuyCarpenter 

provided Milliman with expected ceded losses used to develop these multiples. Multiples for 

retained layers were estimated based on a curve fit to the reinsurance multiples. The Guy Carpenter

modeled losses by peril, state, and layer were used to derive the combined net risk load by peril 

and state using the reinsurance and retained layer multiples discussed above.

CONCENTRATION RISK

The risk of large loss events is greater in areas with a higher concentration of policies. In order to 

reflect these differences in risk due to differences in policy concentration, Milliman developed 

concentration risk loads that vary by geographic area. The territories selected for the application 

of these loads were based on Metropolitan Statistical Areas (“MSAs”), Micropolitan Statistical 

Areas (“μSAs”), Combined Statistical Areas (“CSAs”), and Hydrological Unit Codes (“HUCs”). 

MSAs and μSAs are composed of counties and are delineated by the United States Office of 

Management and Budget (“OMB”) based on Census Bureau data. The OMB further defines CSAs 

as combinations of adjacent MSAs and μSAs.

Milliman ranked CSAs, MSAs unincorporated into a CSA, and μSAs unincorporated into a CSA 

by policies in force. The top 50 areas by policy count were chosen to be distinct territories for the 

application of the concentration risk load. Each statistical area outside of the top 50 had 0.2% of 

the total policies in force or less.

The remainder of the country outside of the top 50 statistical areas was generally organized by 

HUC at the regional level (HUC 02). The mainland states, Alaska, Hawaii, and territories are 

organized into 21 HUC 02 areas, each representing the drainage area of a major river or the 

combined drainage of a series of rivers. Two HUC 02 regions were split by HUC 04 (sub-region) 

to limit the policies in force in each selected territory. Additionally, several HUC 02 regions were 

combined due to small policy counts. The mapping of HUC 02 to selected territory is shown below 

in Table 1.
11

Table 1: Selection of HUC-Based Concentration Risk Territories

Selected Territory Name HUC 02 HUC 04

H1 HUC South Atlantic 03 (partial) 0301 through 0309

H2 HUC Lakes 04 All

H3 HUC Mid Atlantic 02 All

H4 HUC Gulf 03 (partial) 0310 through 0318

H5 HUC CA 18 All

H6 HUC Lower MS 08 All

H7 HUC OH East 05 (partial) 0501 through 0507, 0509

H8 HUC Rio-CO-Great Basin 13, 14, 15, 16 All

H9 HUC AR 11 All

H10 HUC MO 10 All

H11 HUC Upper MS 07, 09 All

H12 HUC OH West 05 (partial) 0508, 0510 through 0514

H13 HUC TX Gulf 12 All

H14 HUC Pacific Northwest 17 All

H15 HUC New England 01 All

H16 HUC KY 06 All

H17 HUC PR-VI 21 All

H18 HUC HI-Pacific Territories 20 All

H19 HUC AK 19 All

Each HUC-based selected territory had less than 1.5% of total policies in force.

The 69 selected territories (50 based on statistical area, 19 based on HUC) were appended to the 

inforce data by FEMA. Guy Carpenter ran this data through the KatRisk model and summarized 

the resulting expected ceded losses for each layer and concentration territory using the reinsurance 

structure provided for Inland Flood and Storm Surge separately.

For each territory and peril (Inland Flood and Storm Surge), the net risk load calculated from the 

expected loss and LAE by layer was compared to the unlimited expected AAL and LAE. The ratios 

for each territory and peril were compared to the overall ratio to determine concentration risk 

factors.

RATING FACTORS

Rating factors were developed using several different approaches. Initial indicated rating factors 

for geographic rating variables, such as distance to coast (“DTC”), were developed using GLMs 

built on the Uniform Book. Initial indicated rating factors for non-geographic property 

characteristic rating variables, such as number of stories, were developed through GLMs built on 

Uncorrelated Market Baskets. Rating factors for coverage terms, such as deductible and limit, were 

developed using a loss elimination ratio method. Each of these analyses is described in detail in a 

separate section below.
12

Geographic Rating Factors

Geographic rating factors were developed by fitting GLMs on the Uniform Book for each peril 

except Coastal Erosion. Separate GLMs were fit for each catastrophe model, region of the country 

(“segments”)3

, and for leveed areas versus non-leveed areas. Burn rate (defined as AAL per 

thousand dollars of value) was the target variable. The models were fit using a Tweedie distribution 

and log link function. The Tweedie p parameter was selected separately for each modeler and peril 

by maximizing log-likelihood. Two-thirds of the data was randomly assigned to a training dataset 

and the remaining third was used as a holdout to evaluate themodels.

For Storm Surge, Tsunami, and Great Lakes, areas with essentially no risk (far from the coast, at 

high elevation, or far from a lake shore) were removed from the model training data. For Storm 

Surge, Milliman also removed locations in any HUC where the maximum AAL was zero. These 

represent areas with little to no risk. When rating policies in these locations the Storm Surge, 

Tsunami, and/or Great Lakes premium is set to zero. Table 2, below, shows which locations were 

included in the analyses.

Table 2: Storm Surge and Tsunami filters

Peril Filter

Inland Flood Segment 4 – MDI: Distance to River of less than e6.5 meters 

All Other: No Filter

Storm Surge

Segment 1: DTC < 50 miles and Elevation < 40 ft. 

Segment 2: DTC < 50 miles and Elevation < 20 ft.

Segment 5 - HI,GU,AS, MP: DTC < 50 miles and Elevation < 10 ft. 

Segment 5 - AK, PR, VI: DTC < 50 miles and Elevation < 20 ft.

Great Lake Distance to Lake < 10km and Elevation above Lake < 15 ft.

Tsunami

Segment 4: Distance to Ocean < 10 miles and Elevation < 100 ft. 

Segment 5 - AK: Distance to Coast < 3 miles and Elevation < 60 ft. 

Segment 5 - AS: Distance to Coast < 1 miles and Elevation < 40 ft. 

Segment 5 GU/MP: Distance to Coast < 2 miles and Elevation < 60 ft. 

Segment 5 HI: Distance to Coast < 3 miles and Elevation < 75 ft.

Locations with missing GIS values were also removed. In the initial analysis, Milliman found the 

model did not fit well for barrier islands. To address this issue, Milliman constructed polygons to 

define barrier islands and fit storm surge GLMs separately for barrier islands and the mainland.

Preliminary models were fit with binned predictor variables that were selected based on an intuitive 

relationship with loss. For example, the Storm Surge model included elevation and distance to

3 Segment 1 consists of Texas, Louisiana, Mississippi, Alabama, Florida, Georgia, South Carolina, and North Carolina. Segment 2 consists of 

Virginia, Maryland, Washington D.C., Delaware, Pennsylvania, New Jersey, New York, Connecticut, Rhode Island, Massachusetts, New 

Hampshire, and Maine. Segment 3 consists of Arkansas, Iowa, Illinois, Indiana, Kansas, Kentucky, Michigan, Minnesota, Missouri, North Dakota, 

Nebraska, Ohio, Oklahoma, South Dakota, Tennessee, Vermont, West Virginia, and Wisconsin. Segment 4 consists of Arizona, California, 

Colorado, Idaho, Montana, New Mexico, Nevada, Oregon, Utah, Washington, and Wyoming. Segment 5 consists of Hawaii, Alaska, and the US 

territories.
13

coast. The Inland Flood model included variables such as elevation relative to river and relative 

elevation, which is the elevation of the policy minus the average elevation within approximately

500 meters. In each case, Milliman made a preliminary determination as to whether the variables 

should be included in the model based on the fitted coefficients. For example, in cases where the 

variable seem to have no connection to loss, it was excluded. To select between closely related 

variables, such as the elevation minus the average elevation over different radii, double lift charts 

were used to compare models using differentvariables.

Milliman selected transformations for some predictor variables, such as logarithm transformations 

or minimum/maximum caps, by analyzing the factors produced by the binnedmodels.

The GLMs were reviewed using residual plots by state comparing the average actual and predicted 

burn rate as a function of all predictor variables and also variables not included in the model. This 

was used to validate that the estimated burn rates were tracking the actual burn rates as expected 

for each variable consistently across states. The purpose of this step was to evaluate the quality of 

the fit and check if any important variables were omitted from the models. Residual plots on the 

training and holdout data were compared to check for evidence of overfitting.

Other methods of validation included lift charts and residual maps. To create lift charts, the holdout 

dataset was split into deciles by predicted burn rate. A well-performing model should have 

significant differentiation in the average actual burn rate by decile on data not used to train the 

model, which was found to be the case for all of the models. Residual maps were examined to 

assess the presence of spatial autocorrelation in residuals, which would indicate possible omitted 

variables.

Once all selections were finalized, the Correlated Market Baskets were rerated using the selected 

factors to create estimated AALs. Milliman examined expected loss ratios (“ELRs”), the ratio of 

the actual AAL to estimated AAL, by each catastrophe model, peril, and rating variable as a final 

check on the soundness of the GLMs.

For Coastal Erosion there were no available catastrophe models. Since Coastal Erosion can only 

occur in locations very near the coast, the selected Coastal Erosion rating factor decreases linearly

within 100 meters of the coast. No other geographic rating factors apply to CoastalErosion.

Property Characteristics Rating Factors

Property characteristics rating factors include those rating variables that are based on 

characteristics of the insured property other than its location or policy terms. Examples include 

number of stories and first floor height. Similar to the approach for geographic rating factors, the 

indicated rating factors were developed by using a GLM with burn rate as the target variable. The 

models for property characteristics were fit on the Uncorrelated Market Basket. The Uniform 

Book, which was used for the geographic rating factor analysis, has property characteristics that 

are all the same and so could not be used to set the property characteristics rating factors. The
14

Correlated Market Basket has property characteristics that vary, but some levels are verysparsely 

populated and some property characteristics are highly correlated with geographic risk. Because 

of these data issues, the Correlated Market Basket would produce high standard errors for the 

sparsely populated levels and the models would be unstable. The Uncorrelated Market Basket 

corrects for these issues.

The burn rate from the Uniform Book was used as an offset since it captures the risk explained by 

the geographic location. These burn rates were then adjusted to account for the varying coverage 

amounts in the Uncorrelated Market Basket and the different vulnerability in Coverage A versus 

Coverage C. For the Tsunami peril, locations flagged as outliers for the uniform book were 

dropped from the modeling dataset.

Like the geographic factor models, Milliman used a Tweedie distribution and log link, as well as 

binned models to identify variable transformations. Unlike the geographic rating factor models, 

which were fit separately by segment and by leveed/non-leveed, the property characteristic GLMs 

were fit on countrywide data and leveed/non-leveed combined. Milliman reviewed residual plots, 

lift charts, and ELRs on the Correlated Market Basket to assess the GLMs for each catastrophe 

model and peril.

No rating factors based on property characteristics are applied for Coastal Erosion.

Rating Territories

Rating territories were based on HUC12 and barrier island indicators for non-leveed areas and 

HUC12 and Levee ID for leveed areas. For each territory and peril, a preliminary territory factor 

was developed based on the residuals on the Correlated Market Basket. These factors were then 

adjusted based on the inforce book to reflect differences in exposure distribution within the 

territory for the inforce versus the Market Basket.

Territory was not included in the GLMs because of the high number of levels, most of which are 

sparsely populated. Since territory was not included in the GLMs, the territory factors were created 

after the GLMs were finalized. For each catastrophe model and peril, an estimated AAL for each 

location in the Correlated Market Basket was calculated as the estimated burn rate from the 

combined GLM rating factors multiplied by the coverage value. The estimated AALs and actual 

AALs from the catastrophe models were aggregated by HUC 12, HUC10, HUC8, HUC6, HUC4, 

and HUC2. An implied territory factor, the ratio of the actual AAL to estimated AAL, was 

calculated at each level of aggregation.

The credibility of each aggregation level was also calculated and used to weight the implied 

territory factors. For example, the complement of the credibility at the HUC12 level was assigned
15

to the HUC10 factor, the complement of credibility at the HUC10 level was assigned to the HUC8 

factor, and so on. The credibility for each geographic level was set as the difference between the 

credibility of that geographic level and the next smaller geographic level according to the square 

root rule and variable full credibility standards. The full credibility standard varied by territory due 

to the observation that different rating territories had varying levels of heterogeneity. Some 

territories had very low variation in implied territory adjustments, while some territories displayed 

large variation. Milliman believes the territories with low variation are more homogeneous and 

therefore should have a lower full credibility standard than those with high variation in the territory 

adjustment. Rating territories were grouped into deciles by the level of variation for each peril, 

with higher full credibility standards for territories with higher levels of variability.

To separate territories in this manner, Milliman sorted them into deciles by peril as follows. For 

each location, the ratio of actual to estimated AAL was calculated. KatRisk uniform AAL was 

used for the calculation for Inland Flood and mainland Storm Surge. MDI uniform AAL was used 

for the calculation for Tsunami, Great Lakes, and non-mainland Storm Surge. The ratio was 

normalized by dividing by the mean of the ratio of the corresponding HUC12 for non-leveed areas 

and HUC12/Levee ID for leveed areas. The location level transformed territory adjustment was 

calculated as the natural logarithm of the normalized ratio. The logarithm transformation was used 

to create a symmetry between the expected value and actual value and reduce the impact of outliers. 

At each HUC level, the weighted standard deviation was calculated as the square root of the 

variance of the transformed territory adjustments, weighted by actual AAL. The weighted standard 

deviations were ordered at each HUC level into deciles.

To determine the full credibility standard for each decile, Milliman performed a sampling analysis 

as follows. This sampling process occurred separately for each peril. Milliman took all Market 

Basket locations, subject to geographic filters by peril, bucketed them into the deciles based on the 

HUC12, and calculated the natural logarithm of the normalized ratio of the actual to estimated 

AAL at the location level as detailed above. For each decile, the population mean of this ratio was 

calculated. A sample of locations within each decile was taken and the number of locations with a 

ratio within 5% of the population mean of the ratio was determined. This sampling process was 

repeated many times, allowing Milliman to determine the minimum number of locations needed 

to have 95% of sample mean ratios within 5% of the population mean ratio. This minimum number 

of locations was designated the full credibility standard. In cases where the full credibility standard 

implied by the sampling process for a peril decreased as the variable decile increased, deciles were 

judgmentally grouped to result in increasing credibility standards. For the Great Lakes and nonmainland Storm Surge perils, the full credibility standards implied by the sampling process were 

volatile by decile, so the sampling process was performed for all deciles combined.

Credibilities were assigned to each HUC level for each rating territory based on the assigned decile 

and the policy count. Incremental credibilities were calculated from the smallest (HUC12 or 

HUC12/Levee ID) to the largest (HUC2) HUC region for each rating territory. The implied 

territory factors were weighted with the incremental credibilities to arrive at the final territory 

factor for each rating territory.
16

For HUC12s or HUC12/Levee ID combinations without Market Basket locations, the factor was 

filled using the average factor of the nearest hydrologic neighbors (for Inland Flood) or nearest 

neighbors (for Storm Surge, Tsunami, and Great Lakes), weighted by the number of Market Basket 

locations in each neighboring HUC12 or HUC12/Levee ID. Hydrologic neighbors were defined 

using the USGS NHD HUC12 relationship table. If up to the third order neighbors of a given 

HUC12 had no assigned factor, the HUC12 was given a territory factor of 1.0.

There were often significant exposure distributional differences between the Market Basket and 

inforce book for individual rating territories. For example, a rating territory may contain many X 

Zone policies in the Market Basket, but may be dominated by Special Flood Hazard Area 

(“SFHA”) policies in the inforce book. To adjust for these distributional differences, Milliman 

adjusted the territory factors to account for the inforce book distribution. This procedure was done 

separately for both Inland Flood and Storm Surge.

For each rating territory, the inforce credibility was determined using the inforce policy counts and 

the same decile assignment and full credibility standards from the original territory factor 

derivation. The inforce target premium was determined by rating territory using the average AAL 

from the AIR, CoreLogic, and KatRisk models. The estimated premium was calculated as the 

proposed premium using the original territory factors. The ratio of target premium to estimated 

premium provided an implied adjustment. This was credibility weighted with the original territory 

factor and off-balanced to give a final selected territory factor.

Leveed Area Analysis

In general, the analysis approach for leveed areas was similar to that for non-leveed areas, however 

the leveed area analysis relied solely on KatRisk and MDI because it was not possible to adjust the 

AIR and CoreLogic models based on the levee data inputs describedabove.

As in the non-leveed analysis, GLMs were used to develop geographic rating factors for leveed 

areas. For the non-leveed analysis, separate models were fit by segments that consisted of groups 

of states. For the leveed analysis, there was a smaller volume of data that was more highly 

geographically concentrated. Using the same segments as the non-leveed analysis would have 

produced policy counts that were too low within a segment. Instead, the GLMs were fit on the 

countrywide data. Upon reviewing the residuals, Milliman created an interaction term in the Inland 

Flood model to allow for elevation as a rating variable in Louisiana. Without this rating variable, 

AALs were underpredicted in low elevation areas, especially areas with negative elevation in New 

Orleans. Milliman also found it necessary to create separate GLMs for Louisiana StormSurge.

Additionally, Levee Quality was included as a predictor variable in the KatRisk GLM. This 

variable was derived from levee data including the probability of overtopping and probability of

failure prior to overtopping. Levee Quality was not a significant predictor in the MDI GLM.
17

Non Single-Family Home Analysis

The NSFH analysis was done by primarily leveraging the information from the SFH analysis. 

Rating factors for the GIS and most property characteristic factors were selected to be the final 

SFH values. Adjustments were then made to the rating factors and rates to reflect the unique 

elements of NSFH.

In addition to the rating factors used for SFH, NSFH includes rating factors for Type of Use and 

Floor of Interest. The Type of Use rating factor analysis was done in two steps. An initial set of 

rating factors were selected based on available catastrophe model output. However, the catastrophe 

models do not include all the types of use that are available in the FEMA data and on which FEMA 

wishes to rate. Factors for these additional types of use were developed by performing an analysis 

of historical loss ratios (“HLRs”), rerating the historical experience using the initial type of use 

factors based on the catastrophe models. These factors were then adjusted based on the HLR 

results. This final set of factors allows varies by all desired types of use and aligns the rates with 

the NFIP’s historical experience.

The Floor of Interest factors vary for Residential Unit (condominium unit owners, or other insureds 

in a multi-unit residential building) versus all other NSFH types of use. This split was made to 

reflect the fact that Residential Unit insured only have an insurable interest in their own floor. For

these factors, FEMA selected the assumption of a 12 foot height for each floor of a building. The 

factors were then based on the selected height for each floor and the corresponding SFH first floor 

height factor for open, no obstruction locations.

The NSFH Floor of Interest factors for other types of use were based on a separate analysis. 

Buildings between one and three stories received the same factors as a SFH structure with the same 

number of stories. The indicated rating factors for buildings with four or more stories were 

calculated by weighting the factor for the first three floors with the maximum rate discount from 

the Residential Unit analysis. Final factors were then tempered to only apply half of this indicated 

discount. This tempering was applied because of data limitations, specifically the catastrophe 

models do not currently have separate vulnerability functions for buildings with more than three 

stories and FEMA’s current data does not capture the exact number of stories.

Insurance To Value/Deductible Analysis

Milliman developed geographic and property characteristic rating factors based on data gross of 

deductible and limit. This allowed Milliman to analyze both geographic and property 

characteristics and their effect on flood risk independent from effects of deductible and insurance 

to value (“ITV”). Milliman performed a separate deductible and ITV analysis to develop these 

rating factors.

Milliman defined ITV asthe ratio of coverage limit to coverage replacement cost. Policiesidentical 

in all respects other than deductible and limit will have identical expected ground up pure
18

premiums. They may have different expected gross pure premiums due to the loss elimination 

effects of different limits and deductibles.

All policies in the Uncorrelated Market Basket were grouped based on two ratios:

• (Coverage deductible + coverage limit) / coverage replacement cost, (“deductible and limit 

percentage”)

• Coverage deductible / coverage replacement cost, (“deductible percentage”)

The policies were grouped as such for building and contents coverage separately. Within each 

group, the ratio of total gross AAL / ground up AAL was calculated. This was performed for the 

following catastrophe model-peril combinations:

• AIR: Inland Flood, Storm Surge, Tsunami

• CoreLogic: Inland Flood, Storm Surge

• KatRisk: Inland Flood, StormSurge

• MDI: Tsunami, Great Lakes

The deductible effect on gross pure premium was accounted for first. Curves were fit to each 

cumulative loss elimination ratio, resulting in Deductible to Coverage Value Ratio factors for each 

model, peril, and coverage type combination.

The combined ITV and deductible effect on gross pure premium was accounted for second. The 

ratios on these pages account for the Deductible to Coverage Value Ratio factors selected 

previously, thereby isolating the effects due to increased deductible and limit percentage. Curves 

were fit to these ratios, producing Deductible & Limit to Coverage Value Ratio factors for each 

model, peril, and coverage type combination.

Selections were made for each combination of peril and coverage type. Inland Flood and Storm 

Surge selections were based on the average of the indicated factors from three models (AIR, 

CoreLogic, and KatRisk). Tsunami and Great Lakes factor selections were based on the Storm 

Surge factors because of anomalous results for the Tsunami and Great Lakes analyses.

Within the rate order of calculation, the Deductible to Coverage Value Ratio and Deductible & 

Limit to Coverage Value Ratio factors are combined to produce the Initial Deductible & ITV factor 

as follows:

Initial Deductible & ITV factor = Deductible & Limit to Coverage Value Ratio factor

– Deductible to Coverage Value Ratio factor

The Final Deductible & ITV factor is set to zero when the corresponding coverage limit is zero 

and is otherwise floored at 0.001.
19

COMPARISON TO HISTORICAL RESULTS

Milliman compared the rates derived from the catastrophe models to the NFIP’s historical losses 

from January 1, 1992 to June 30, 2018 by looking at univariate historical loss ratios (“HLRs”) 

using the indicated and selected rates. The indicated HLRs were calculated by rerating the 

historical exposures at proposed rates using preliminary rating factors derived from the catastrophe 

models. Based on review of the indicated HLRs, Milliman made adjustments to some rating factors 

and rerated the historical exposures a second time. Selected rating factors were validated by 

comparing the resulting selected HLRs with the indicatedHLRs.

Milliman made the following changes based on the indicated HLRs:

• Flattened the Coverage Value factors. The catastrophe models all assume that AAL is 

proportional to value, all else being equal, because they are built to predict damage ratios 

(i.e. loss divided by replacement cost value). The HLRs indicated that losses have 

historically increased less than proportionately. Milliman tempered the flattening relative 

to what was indicated because of limitations in the quality of the RCV data on the historical 

exposures.

• Tempered Number of Stories factors. The reduction in historical losses from more than one 

story was less than that indicated from the catastrophemodels.

• Addition of Loss Constant. Historical pure premiums did not approach zero as the rerated 

premium approached zero. This is possibly a result of loss exposure not captured by 

catastrophe models. Milliman estimated a non-modeled loss provision by performing a 

linear regression on pure premium versus rerated premium for small values of rerated 

premium. A loss constant (i.e. a constant loading for loss per contract) was selected based 

on the intercept term of thisregression.

• Flattened Distance to River factors. The indicated HLRs suggested the Distance to River 

(“DTR”) factorsshould be flatter than indicated, so Milliman tempered this ratingfactor.

• Flattened Elevation Relative to River and River Class factors. The impact of elevation 

relative to river and River Class on historical losses was more muted than indicated so 

Milliman tempered these factors.

• Distance to Coast and Elevation. Based on the HLRs, Milliman steepened the DTC factors 

near the coast and flattened the elevation factors.

It is important when reviewing HLRs to keep in mind that a large portion of the NFIP’s losses 

resulted from a small number of events and rates should not be overfit to those historical events. 

In order to address this, Milliman considered HLRs filtered on state groups selected based on 

historical events.
20

It is also important to consider the credibility of the historical experience. Although the NFIP has 

over 800,000 claims that are considered in the analysis, the number of claims can become small 

when the data is subdivided. For example, In New York/New Jersey there are only 23 claims with 

an elevation relative to river greater than 75 feet by an H class river in a non-leveed area.

Milliman did not perform an HLR analysis for leveed areas. In order for an HLR analysis to be 

appropriate, it is important that it not be unduly influenced by the inclusion or exclusion of a small 

number of historical events. Nearly half of policies in leveed areas are in Louisiana and therefore 

were affected by the inclusion of Katrina in the historical data. However, if these policies were 

excluded, the remaining number of policies in leveed areas was too small to have a sufficient 

number of claims to be credible.

RATE ORDER CALCULATION AND SELECTIONS

Community Rating System Discount

Currently the NFIP has separate Community Rating System (“CRS”) discounts for the SFHA and 

non-SFHA. Consistent with FEMA’s plan to remove flood zone from rating, under Risk Rating

2.0 the SFHA CRS discounts will be applied to all policies. These discounts are described in the 

NFIP CRS discount documentation.

Elevated Machinery and Equipment

FEMA selected a factor of 0.95 for Machinery and Equipment that is elevated above the first floor.

First Floor Height

FEMA selected First Floor Height (“FFH”) factors that vary by foundation type and foundation

design.

Foundation Type

The following factors were selected:

• 1.30 for Basements

• 1.25 for Elevated with Enclosure, not post, pile, or pier

• 1.20 for Crawlspaces

• 1.00 for all other foundation types

Maximum Rate

A maximum building rate of $15 per thousand dollars of Building Value times the ITV factor and 

a maximum contents rate of $15 per thousand dollars of Contents Value times the ITV factor were 

selected.
21

Prior Claims

A prior claim is defined as a paid flood loss occurring within 20 years prior to the policy period.

The subsequent experience of policies with a prior claim is much worse than claims-free policies, 

justifying a prior claims surcharge. A prior claimssurcharge was selected of $2 perthousand dollars

of value times the weighted ITV Coverage A factor for each claim after the first claim. However,

FEMA informed us that this surcharge will not be applied on any existing policy until they file a 

new claim after the implementation of Risk Rating 2.0 and therefore we did not apply it when 

rerating policies at proposed rates.

Rating Factor Capping

A ceiling on rating factors corresponding to the factor closest to the 95th percentile of the exposure

distribution and a floor at the factor closest to the 5th percentile of the exposure distribution. This 

was done separately by each unique portion of the rating plan (e.g. peril and segment).

BASE RATES

The base rates were determined by state/territory, coverage, and peril separately for Single-Family 

Non-Leveed, Single-Family Leveed, and NSFHs. Base rates were calculated such that the rerated 

premium was within 0.1% or $1,000 of the target premium, whichever was greater. In a few cases, 

states or territories were grouped because they had a very small number of inforce locations and 

premium. In a few small states, there was no base rate where the rerated premium would equal the 

target premium because of the selected maximum rate. In these cases, Milliman capped the 

maximum base rate. Since this only occurred in states or territories with small target premiums, 

the impact of this capping on the overall rerated premium wasminimal.

FINAL RATING FACTORS AND RATING EXAMPLES

The selected rating factors are shown in Appendix D. There are 115,862 territories for non-leveed 

areas and 14,866 territories for leveed areas. Because of the large number of territories, factors are 

not listed for every individual territory, but are shown in ranges.

To illustrate the calculation of premiums under Risk Rating 2.0, four rating examples are shown 

in Appendix E, as follows:

• South Carolina non-Leveed Area SFH risk (Pages 1-2)

• Michigan non-Leveed Area SFH risk (Pages3-4)

• California Leveed Area SFH risk (Pages 5-6)

• South Carolina non-Leveed Area Residential Unit risk (Pages 7-8)
22

In the first rating example, Page 1 shows all the inputs needed to rate a non-Leveed SFH risk when 

a policy is being quoted, asfollows:

• The geographic inputs (e.g. state, HUC12, Distance to River, Relative Elevation, CRS 

discount, etc.) are based on the location (latitude and longitude) of the risk. The first 

example risk is located in South Carolina, 111 meters from a river and 231 meters from the 

coast.

• The property characteristic inputs (Single Family Home indicator, First Floor Height, 

Coverage A value, etc.) are based on the characteristics of the insured property. The 

example risk is a SFH with Coverage A RCV of $250,000 and First Floor Height of 5.5 

feet.

• The policy characteristic inputs (Coverage A limit, Coverage A deductible, etc.) are based 

on the policy coverage being purchased. The example risk is purchasing $250,000 in 

Coverage A limit, with a deductible of $1,250.

Page 2 shows the calculation of the policy premium for the risk, as follows:

• Row A shows the SFH base rate for the state, by flood sub-peril and policy coverage 

(building vs. contents); the base rates are shown in Appendix D.

• Rows B through K show rating factors associated with the policy’s geographic 

characteristics. The rating factors are shown in Appendix D, although actual values will be 

interpolated (e.g. the Distance to Coast factor at 231 meters is between the Distance to 

Coast factors at 200 and 250meters).

• Row L shows the geographic rates by peril and coverage, calculated as the product of Rows 

A through K.

• Rows M through S4 show the rating factors associated with the property and policy 

characteristics; the rating factors are shown in AppendixD.

• Row T shows the rating factors associated with concentration risk; the rating factors are 

shown in Appendix D.

• Rows U1 and U2 show the application of the CRS Discount.

• Rows V through X show the rates by peril and coverage, reflecting all the priorfactors.

• Rows Y through AA show the calculation of a weighted Deductible and ITV factor by 

coverage.

• Rows AB through AG apply a minimum and maximum rate per $1,000 of building and 

contents to determine a final rate.

• Rows AH through AL show the calculation of initial premium without fees based on the 

final rates multiplied by the value of building and contents coverage in $1,000s.
23

• Rows AM and AN modify the premium to include a prior claimssurcharge.

• Rows AO through AQ modify the premium to include an expense constant and loss 

constant per contract.

• Rows AR through AV modify the premium to include the cost of ICC and the Reserve 

Fund.

• Rows AW through AZ modify the premium to include fees.

The calculations for the other two premium examples are similarly depicted.
24

ACTUARIAL STANDARDS OF PRACTICE

Throughout this project Milliman has been guided by relevant Actuarial Standards of Practice 

(ASOPs), Statement of Principles (SOPs) and other guidance promulgated by the Actuarial 

Standards Board (ASB), the American Academy of Actuaries (AAA) and the Casualty Actuarial 

Society (CAS).

This section of the report illustrates how we complied with the most relevant guidance, including 

the following:

• CAS Statement of Principles Regarding Property & Casualty Insurance Ratemaking

• ASOP 12: Risk Classification (for All Practice Areas)

• ASOP 23: Data Quality

• ASOP 25: Credibility Procedures

• ASOP 29: Expense Provisions in Property/Casualty Insurance Ratemaking

• ASOP 30: Treatment of Profit and Contingency Provisions and the Cost of Capital in 

Property/Casualty Insurance Ratemaking

• ASOP 38: Using Models Outside The Actuary’s Area of Expertise (Property and Casualty)

• ASOP 39: Treatment of Catastrophe Losses in Property/Casualty Insurance Ratemaking

• ASOP 41: Actuarial Communications

• ASOP 53: Estimating Future Costs for Prospective Property/Casualty Risk Transfer and 

Risk Retention

• ASOP 56: Modeling

Full text of the CAS SOPs can be found on the CAS website here: 

https://www.casact.org/professionalism/standards/princip/sppcrate.pdf

Full text of the ASOPs can be found on the ASB website here: 

http://www.actuarialstandardsboard.org/standards-of-practice/

Please note that the discussion in this section is intended to be illustrative rather than exhaustive; 

only the SOPs and ASOPs of primary relevance to this particular type of actuarial work are 

referenced below, but our work was conducted in adherence to additional professional guidance, 

as generally applicable to all actuarial work.

CAS STATEMENT OF PRINCIPLES REGARDING PROPERTY & CASUALTY INSURANCE 

RATEMAKING

The CAS SOPs contains four principles applicable to property and casualty insurance ratemaking. 

These principles are asfollows:
25

• Principle 1: A rate is an estimate of the expected value of future costs.

• Principle 2: A rate provides for all costs associated with the transfer of risk.

• Principle 3: A rate provides for the costs associated with an individual risk transfer.

• Principle 4: A rate is reasonable and not excessive, inadequate, or unfairly discriminatory if 

it is an actuarially sound estimate of the expected value of all future costs associated with an 

individual risk transfer.

Examples of how our analysis adhered to these principles are as follows:

• We attempted to capture the costs associated with all sources of flood risk, both modeled 

and non-modeled, in our estimate oflosses.

• We included provisions for all prospective expenses associated with the management of 

the NFIP, including the net cost of reinsurance and a risk load that reflects the NFIP’s 

retained risk.

• We considered the historical loss experience of similar risks and assessed the credibility of 

such experience using data grouped in many different ways.

• We considered the impact of catastrophes on the experience and developed procedures to 

include an allowance for the catastrophe exposure in the rate.

• The resulting Risk Rating 2.0 structure produces an estimated rate for the cost associated 

with the transfer of risk at the policy level, using the classification structure, risk factors 

and rating algorithm shown in Appendix D and E.

The CAS SOPs are frequently referenced in the ASOPs, so additional detail of how we complied 

with those provisions is discussed in the remainder of this section.

ASOP 12: RISK CLASSIFICATION (FOR ALL PRACTICE AREAS)

In accordance with ASOP 12, as part of the design of our classification system we:

• Selected risk characteristics that are related to expected outcomes;

• Selected risk characteristics that are capable of being objectively determined;

• Reflected practical considerations underlying the data capture needed to determine risk 

characteristics;

• Showed that the variation in actual experience correlates to the risk characteristic;

• Considered the interdependence of risk characteristics and made appropriate adjustments; 

and

• Considered the reasonableness of results, including the consistency of patterns of rates, 

values and factors among risk classes.
26

ASOP 23: DATA QUALITY

In accordance with ASOP 23, in considering the data to use for this analysis we:

• Used available data that, in our professional judgment, allowed us to perform the desired 

analysis;

• Considered whether the available data was internally consistent, with a reasonable effort 

to identify data values that were questionable or produced significantly inconsistent 

relationships;

• Considered whether the available data was reasonable as compared to external information 

available to us;

• Considered known significant limitations of the data and the implications of such 

limitations on our analysis;

• Considered the availability of alternate data, and the practicality versus benefit of collecting 

it;

• Made enhancements and/or judgmental adjustments to available data as appropriate;and

• Disclosed our data sources, reliance on data provided by others, limitations of the data and 

how we enhanced or adjusted it in our reports to FEMA.

ASOP 25: CREDIBILITY PROCEDURES

In accordance with ASOP 25, the procedures we developed in order to evaluate the credibility of 

subject experience considered:

• Whether the procedures were expected to produce reasonable results;

• Whether the procedures were appropriate for the intended use; and

• Whether the procedures were practical to implement.

Where we determined that the subject loss experience was not fully credible, we supplemented it 

with relevant experience that, in our judgment, would be predictive of future losses. We also 

considered whether there were segments that were not representative of the experience set as a 

whole, and considered treating them separately in order to enhance the predictive value of the 

experience set as a whole.

ASOP 29: EXPENSE PROVISIONS IN PROPERTY/CASUALTY INSURANCE 

RATEMAKING

In accordance with ASOP 29, in determining our estimated expense provisions:

• We determined the provisions for loss adjustment expenses; commission and brokerage fees; 

other acquisition expenses; general administrative expenses; and taxes, licenses, and fees that
27

are appropriate for the policies to be written or coverages provided during the time the rates 

are expected to be in effect.

• For expenses other than premium-related expenses, we estimated these expenses on a per 

contract basis.

• We included a provision for the net cost of reinsurance that considers the amount to be paid to 

the reinsurer; ceding commissions or allowances; expected reinsurance recoveries; and other 

relevant information specifically relating to cost.

• We reflected the conditions expected during the time these policies or coverages are expected 

to be in effect and included all expenses expected to be incurred in connection with the transfer 

of risk.

ASOP 30: TREATMENT OF PROFIT AND CONTINGENCY PROVISIONS AND THE COST OF 

CAPITAL IN PROPERTY/CASUALTY INSURANCERATEMAKING

Unlike most providers of property/casualty insurance, the NFIP is a government program and does

not hold capital to support its operations. Therefore, some of the provisions in ASOP 30 such as 

calculating the cost of capital do not apply to the NFIP’srates.

However, ASOP 30 does apply to all property/casualty insurance coverages and risk financing 

systems that provide similar coverages, and states that references in the standard to risk transfer 

should be interpreted to include risk financing systems that provide for risk retention in lieu of risk 

transfer.

In adhering to the ASOP 30 guidance regarding underwriting profit provisions, we incorporated 

net risk loads that reflected the NCOR and retained risk of non-attritional losses.

ASOP 38: USING MODELS OUTSIDE THE ACTUARY'S AREA OF EXPERTISE (PROPERTY 

AND CASUALTY)

As discussed in the “Catastrophe Modeling” section of this report, we used models that incorporate 

specialized knowledge outside our own area of expertise when developing the Risk Rating 2.0 

structure. Examples of ways that our use of these models adhered to ASOP 38 are as follows:

• We determined appropriate reliance on experts, considering the expertise of the experts 

who produced the models and whether the models had previously been tested or validated.

• We obtained a basic understanding of the model components, user input and output.

• We evaluated whether the models were appropriate for the intended application,including 

limitations of the models, modifications to the models, and the assumptions needed in order 

to apply the model output.

• We considered the quality of data availability for user input and examined the model output 

for reasonableness, considering:
28

o The results derived from alternate models or methods, where available and 

appropriate;

o How historical observations compared to results produced by themodel;

o The consistency and reasonableness of relationships among various output results; 

and

o The sensitivity of the model output to variations in the user input and model 

assumptions.

• We used professional judgment to determine whether it was appropriate to use the model 

results, subject to any appropriateadjustments.

• We disclosed such adjustments in our reports to FEMA.

ASOP 39: TREATMENT OF CATASTROPHE LOSSES IN PROPERTY/CASUALTY INSURANCE 

RATEMAKING

The flood peril subject to the NFIP’s rates is generally considered to be a catastrophic peril as 

defined in ASOP 39, in that flooding is caused by relatively infrequent events or phenomenon that 

produce unusually large aggregate losses. Therefore, the entire NFIP rating plan would be 

considered an allowance for catastrophe exposure.

Examples of our adherence to ASOP 39 are as follows:

• We considered the applicability of historical insurance data for the insured coverage, 

including whether catastrophe losses are likely to differ significantly among elements of 

the rate structure; whether such differences should be reflected in the ratemaking 

procedures; how to reflect such differences; and whether there is a sufficient number of 

years of comparable, compatible historical insurance data.

• We made adjustments to the historical insurance data to reflect conditions likely to prevail 

during the period in which the rate will be in effect. Such adjustments take into account the 

impact of changes in the exposure to loss, including coverage differences, the underlying 

portfolio of insured risks, building codes and practices; population shifts; and costs.

• We considered the extent to which the rates would change if the catastrophe ratemaking 

procedure were to be carried out using different historical experience periods, and modified 

the procedure to reduce the sensitivity.

• Because in our judgment the available historical insurance data do not sufficiently 

represent the exposure to catastrophe losses, we used noninsurance data such as GIS data 

(including models based thereon) as input to ratemaking procedures; and we also used 

models based on a combination of historical insurance data and noninsurance data.

The resulting ratemaking procedures were designed to appropriately reflect the expected frequency 

and severity distribution of catastrophes, as well as anticipated class, coverage, geographic, and 

other relevant exposure distributions.
29

ASOP 41: ACTUARIAL COMMUNICATIONS

Milliman’s work under Risk Rating 2.0 has been fully documented in over 40 separate actuarial 

communications to FEMA provided over the course of the engagement, including both actuarial 

reports and other formats such as spreadsheets and electronic data files. Some general examples 

of ways in which our actuarial communications adhered to ASOP 41 are as follows:

• We designed the form, content and language of each actuarial communication to be 

appropriate to the particular circumstances, taking into account the intended users.

• In deciding on the timing of the communications, we considered the needs of the intended 

users.

• We identified the actuaries responsible for the communications.

• With respect to actuarial reports:

o We stated the actuarial findings, and identified the methods, procedures, 

assumptions, and data used by theactuary.

o With respect to each report we considered specific circumstances such as its 

intended timing, use and distribution, and whether that report built upon previous 

reports, in deciding how much detail to include.

o We disclosed sources for data and other information, including reliance on other 

parties.

o We identified the party or parties responsible for each material assumption and 

method.

ASOP 53: ESTIMATING FUTURE COSTS FOR PROSPECTIVE PROPERTY/CASUALTY RISK 

TRANSFER AND RISK RETENTION

ASOP 53 incorporates all of the considerations contained in the CAS SOP and addresses issues 

related to the estimation of future costs for risk transfer and risk retention not addressed in 

previously existing ASOPs. It references many of the other ASOPs previously discussed in this 

section. Examples of ways we adhered to ASOP 53 beyond the previous discussion are as follows:

• We determined the elements that are appropriate to include in the future cost estimate. Such 

elements related to the NFIP’s coverage and include loss and LAE, operational and 

administrative expenses, the cost of reinsurance and a risk load.

• We determined the intended measure of the future cost estimate based on the purpose and 

use of the estimate, considering the desires or needs of the principal, legal requirements, 

and the regulatory environments in which the future cost estimate will be used.
30

• We determined what data were available and appropriate for estimating future costs, how 

to organize the data to develop the elements of the future cost estimate, and how to segment 

the data to improve the cost estimationanalysis.

• We selected appropriate methods and models consistent with the intended measure for each 

element of the future cost, and reasonable assumptions (including parameters) appropriate 

to each method and model that, in our professional judgment, have no known significant 

bias in the aggregate relative to the intendedmeasure.

• We selected exposure bases that bear a strong relationship to the cost of risk transfer and 

are practical, in that they are objectively measurable and easily verifiable.

ASOP 56: MODELING

ASOP 56 becomes effective for work performed on or after October 1, 2020 and thus was not in 

effect during the period in which we conducted our Risk Rating 2.0 analysis. However, it has been 

adopted in final form and provides useful guidance on modeling that would otherwise have applied 

to our work, so we have chosen to include it in our discussion of adherence to ASOPs. We have 

already discussed our use of catastrophe models in the ASOP 38 section above, so in this section 

we will focus on additional comments relevant to our use of GLMs and Loss Elimination Ratios 

in the analysis.

In designing and developing each of the models used in our analysis:

• We confirmed that, in our professional judgment, the capability of the model was consistent 

with the intended purpose.

• We assessed whether the structure of the model (including judgments) was appropriate for 

the intended purpose, considering:

o Which provisions and risks specific to a business segment, were material and 

appropriate to reflect in the model;

o Whether the form of the model is appropriate;

o Whether the use of the model dictates a particular level of detail; and

o Whether there is a material risk of the model overfitting the data.

• We used data appropriate for the model’s intended purpose.

• Where applicable, we used assumptions as input that are appropriate given the model’s 

intended purpose. In this step we often used ranges of assumptions, evaluated assumptions 

within the model for consistency, and considered the reasonability of the model output 

when determining whether the assumptions are reasonable in the aggregate.

• We evaluated model risk and, if appropriate, took reasonable steps to mitigate model risk, 

through steps such as:
31

o Testing to ensure that the model reasonably represents that which is intended to be 

modeled;

o Validating that the model output reasonably representsthat which is being modeled; 

and

o Implementing internal procedures regarding model review and checking to reduce 

the risk that the model output is not reliably calculated or not utilized as intended.
32

LIMITATIONS

Qualifications. The above-referenced professional is currently a member in good standing of the 

American Academy of Actuaries (AAA) and the Casualty Actuarial Society (CAS) and has 

satisfied the current continuing education requirements of the AAA.

Geospatial Uncertainty. All geospatial datasets are simplified representations of reality and 

contain varying levels of abstraction and uncertainty. It is certain that actual, real-world values 

will not conform exactly to estimates provided by our geospatial data products. The degree of 

uncertainty will depend heavily on the quality of the input property location and as well as the 

original data used to develop the final products.

Data Reliance. In performing the services, we relied on data and other information provided to us 

by FEMA and other sources. We did not audit, verify or review the data and other information for 

reasonableness and consistency. Such a review is beyond the scope of our assignment. If the 

underlying data or information is inaccurate or incomplete, the results of our analysis may likewise 

be inaccurate or incomplete. In that event, the results of our analysis may not be suitable for the 

intended purpose.

We performed a limited review of the data used directly in our analysis for reasonableness and 

consistency. We did not find material defects in the data. If there are material defects in the data, 

it is possible that they would be uncovered by a detailed, systematic review and comparison of the 

data to search for data values that are questionable or relationships that are materially inconsistent. 

Such a detailed review was beyond the scope of our assignment.

Variability of Results. Any projection of future loss ratios or loss relativities involves estimates of 

future contingencies. While our analysis is based on sound actuarial principles, it is important to 

note that variation from the projected result is not only possible, but, in fact, probable. While the 

degree of such variation cannot be quantified, it could be in either direction from the projections. 

Such uncertainty is inherent in any set of actuarial projections.

Responsibility for Program. It is not possible to guarantee the financial success of FEMA’s 

programs based upon sound rates alone. Responsibility for the program’s success or failure 

ultimately rests with FEMA.

Actuarial Uncertainty. Differences between our projections and actual amounts depend on the 

extent to which future experience conforms to the assumptions made for the analyses. It is certain 

that actual experience will not conform exactly to the assumptions to be used in these analyses. 

Actual amounts will differ from projected amounts to the extent that actual experience is better or 

worse than expected.

Distribution. This report (including the accompanying appendices) is prepared solely for the 

benefit of FEMA. Milliman does not intend to legally benefit any third party recipient of the report, 

even if Milliman consents to the release of this report to a third party. Except as set forth below, 

this report may not be provided to third parties without Milliman’s prior written consent. FEMA
33

may distribute the final, non-draft version of this report at FEMA’s discretion. FEMA shall not 

edit, modify, summarize, abstract or otherwise change the content of this report and any 

distribution must include the entire report. Press releases mentioning this report may be issued by 

Milliman or FEMA upon mutual agreement of FEMA and Milliman as to their content. Mentions 

of this report by FEMA shall provide citations that will allow the reader to obtain the full report.

Use of Milliman’s Name. Any reader of this report agrees that they shall not use Milliman’s name, 

trademarks or service marks, or refer to Milliman directly or indirectly in any third party 

communication without Milliman’s prior written consent for each such use or release, which 

consent shall be given in Milliman’s sole discretion.
34

GLOSSARY OF TERMS

μSA Micropolitan Statistical Area

AAL Average Annual Loss

ACS American Community Survey

BFE Base Flood Elevation

CSA Combined Statistical Area

CRS Community Rating System

DHS Department of Homeland Security

DTC Distance to Coast

DTO Distance to Ocean

ELR Expected Loss Ratio

FEMA Federal Emergency Management Agency

FFH First Floor Height

FIMA Federal Insurance and Mitigation Administration

GIS Geographic InformationSystems

GLM Generalized Linear Model

HUC Hydrological Unit Code

ICC Increased Cost of Compliance

ITV Insurance to Value

LAE Loss Adjustment Expense 

LCM Loss Cost Multiplier

MDI Mapping Data Integration

MSA Metropolitan Statistical Area

NCOR Net Cost ofReinsurance

NHD National Hydrography Dataset 

NFIP National Flood Insurance Program 

NSFH Non-Single Family Home

OMB Office of Management and Budget 

PII Personally Identifiable Information 

RCV Replacement Cost Value

SFH Single Family Home

SFHA Special Flood Hazard Area

USACE United States Army Corps ofEngineers
APPENDIX A - GIS Technical Appendix
Appendix A

Table of Contents

Glossary of Terms......................................................................................................................................... 1

Scope............................................................................................................................................................. 2

Software Requirements................................................................................................................................. 2

GIS Data Sources.......................................................................................................................................... 2

GIS Layers.................................................................................................................................................... 5

Query Process ............................................................................................................................................. 11

Additional GIS Analysis Used in Rating Plan Development: HUC12 Territory Factor Filling................. 14

TOC 1
Appendix A

GLOSSARY OF TERMS

3DEP USGS 3D Elevation Program

5m AK DEM 5 meter Alaska Digital ElevationModel

CONUS Conterminous United States

DEM Digital Elevation Model

ESRI Supplier of GIS software

Feature Class Vector dataset within an ESRI geodatabase

Geodatabase ESRI proprietary database for storing spatial data

GIS Geographic InformationSystems

HUC12 12-digit hydrologic unit code

HUC4 4-digit hydrologic unit code

NAD83 North American Datum of 1983

NAVD88 North American Vertical Datum of 1988

NCCOS NOAA National Centers for Coastal Ocean Science

NFHL FEMA National Flood Hazard Layer

NHD National Hydrography Dataset

NHDPlus HR Beta National Hydrography Dataset High Resolution PlusBeta

NHDPlus V2 National Hydrography Dataset Plus Version 2

NLD National Levee Database

NOAA National Oceanic and Atmospheric Administration

Raster Spatial dataset that defines space as an array of equally sized cells, 

arranged in rows and columns where each cell represents a particular 

value such as elevation orland-use.

Shapefile Spatial data format for storing vector data

USACE United States Army Corps ofEngineers

USGS United States Geological Survey

Vector Spatial dataset comprised of vertices and paths, usually representing 

points, boundaries, or networks.

WBD USGS Watershed Boundary Dataset

1
Appendix A

SCOPE

Geographic Information Systems (“GIS”) is a framework for gathering, managing, manipulating, 

and analyzing spatial datasets. GIS has been a major component of both the risk rating redesign 

process and finalized rating plan. GIS variables are necessary for the development of rating factors 

and determination of final territory factors. Milliman has provided GIS support for Risk Rating

2.0 (“RR2.0”) for the duration of the project with the assistance of other FEMA contractors and 

Milliman subcontractors.

This appendix describes how the geospatial datasets used in RR2.0 were acquired and/or created, 

how these datasets were queried to append rating variables to policy locations, and explains 

additional GIS analysis that was incorporated into the rating plan design.

SOFTWARE REQUIREMENTS

Milliman used ESRI ArcGIS Pro Version 2 with the Spatial Analyst extension and Python 3 for 

the vast majority of RR2.0 GIS dataset development and analysis. The GIS datasets require 

approximately 1.75 terabytes of storage space.

GIS DATA SOURCES

This section provides an overview of all data sources used in the development of the RR2.0 

geospatial datasets. Links to access and download the source data as of September 2020 (if 

available online) are embedded below.

1/3 ARC-SECOND DIGITAL ELEVATION MODEL

The 1/3 arc-second Digital Elevation Model (“DEM”) is a raster dataset from the United States 

Geological Survey (“USGS”) 3D Elevation Program (“3DEP”). The 1/3 arc-second dataset has an 

approximately 10-meter resolution, is seamless for all states and territories, and is published as 1-

degree raster tiles with elevation in meters relative to the North American Vertical Datum of 1988 

(“NAVD88”) for all areas within the Conterminous United States (“CONUS”). 1-degree tiles are 

available via bulk download from The National Map. Milliman requested a hard drive with all 

available 1/3 arc second DEM rasters for the states and territories from the USGS on August 23, 

2018.

1 ARC-SECOND DIGITAL ELEVATION MODEL

The 1 arc-second DEM is a raster dataset from the USGS 3DEP. The 1 arc-second dataset has an 

approximately 30-meter resolution, is seamless for all states and territories, and is published as 1-

degree raster tiles with elevation in meters relative to NAVD88 for all CONUS areas. 1-degree 

tiles are available via bulk download from The National Map. Milliman downloaded these rasters 

from The National Map in July 2019.

2
Appendix A

5 METER ALASKA DIGITAL ELEVATION MODEL

The 5-meter Alaska DEM (“5m AK DEM”) is a raster dataset from the Alaska Division of 

Geological and Geophysical Surveys which represents ground elevation and is published as 1-

degree raster tiles. This data was obtained and processed by another contractor, Atkins Global 

(“Atkins”), at the request of FEMA in 2019.

NATIONAL HYDROGRAPHY DATASET

The National Hydrography Dataset (“NHD”) represents the nation’s drainage networks and related 

geographic features such as rivers, streams, lakes, and coastlines. The national dataset is available 

for download from The National Map. Over the course of this project, Milliman obtained and used 

two additional versions of the NHD, described in the sectionsbelow.

NATIONAL HYDROGRAPHY DATASET PLUS HIGH RESOLUTION BETA

The National Hydrography Dataset Plus High Resolution Beta (“NHDPlus HR Beta”) is a 

geospatial model of the flow of water across the landscape and through a stream network. The 

NHDPlus HR Beta is built using the NHD High Resolution data at a 1:24,000 scale or better, the 

1/3 arc-second 3DEP data, and the National Watershed Boundary Dataset (“WBD”). This product 

is currently in beta version and not available for all states and territories, although new datasets are 

frequently becoming available. It can be downloaded by 4-digit Hydrologic Unit Code (“HUC4”)

from The National Map. Milliman downloaded the NHDPlus HR Beta on April 23, 2020.

NATIONAL HYDROGRAPHY DATASET PLUS VERSION 2

The National Hydrography Dataset Plus Version 2 (“NHDPlus V2”) is a geospatial model of the 

flow of water across the landscape and through a stream network. The NHDPlus V2 is built using 

the NHD Medium Resolution data at 1:100,000 scale or better, the 1 arc-second 3DEP data, and 

the WBD. It is available to download from the Environmental Protection Agency Link. Milliman 

downloaded the NHDPlus V2 on April 23,2020.

NATIONAL WATERSHED BOUNDARY DATASET

The National Watershed Boundary Dataset (“WBD”) is a seamless hydrologic unit dataset and 

contains eight levels of hierarchical hydrologic units identified by unique 2- to 16-digit codes. The 

dataset is complete for the United States to the 12-digit hydrologic unit. The national dataset is 

available for download from The National Map. Over the course of this project, Milliman obtained 

and used two different versions of the WBD, one for Alaska and one for all other areas. These 

versions are described detail in the GIS Layers section below.

3
Appendix A

NATIONAL OCEANIC AND ATMOSPHERIC ADMINISTRATION NATIONAL SHORELINE

The National Oceanic and Atmospheric Administration (“NOAA”) national shoreline represents 

the land/water interface for coastal areas throughout CONUS, with Washington, Maine, and the 

Great Lakes only partially represented; Hawaii and portions of Alaska, Puerto Rico, and the U.S. 

Virgin Islands are also included. This is available for download from NOAA Link. Milliman 

downloaded the shoreline in June 2019.

NATIONAL CENTERS FOR COASTAL OCEAN SCIENCE SHORELINE SHAPEFILES

The NOAA National Centers for Coastal Ocean Science (“NCCOS”) Shoreline Shapefiles are 

geospatial datasets which represent the land/water interface for select coastal areas, including 

many island territories. This is available for download from NOAA Link. Milliman accessed the 

shapefiles in April 2019.

KATRISK FLOOD DEPTH DATA

The catastrophe modeling company, KatRisk LLC (“KatRisk”), created raster datasets 

representing modeled 10- and 100-year return period flooding depths for CONUS, Hawaii, and 

Alaska. This data is not publicly available. Milliman obtained this dataset directly from KatRisk 

on behalf of FEMA on June 6, 2019. This was used to construct River Classes by subdividing the 

difference between the 10 year and 100 year return period flooding depths in one foot increments. 

River Class A corresponds to a flood depth difference between zero and one foot, River Class B 

corresponds to a flood depth difference between one foot and two feet, etc.

US CENSUS TIGER/LINE FILES

U.S. Census TIGER/Line files include, but are not limited to, polygon boundaries for Census 

statistical areas such as Census Block. TIGER/Line files are available for download from the U.S. 

Census Link. Milliman accessed the TIGER/Line files in Q1 of 2018.

FEMA NATIONAL FLOOD HAZARD LAYER

The FEMA National Flood Hazard Layer (“NFHL”) includes effective, pending, and preliminary 

flood hazard data. Milliman additionally obtained preliminary unpublished flood hazard data for 

the following Louisiana parishes from Dewberry Engineers, Inc.: Lafourche, Plaquemines, St. 

Charles, and Terrebonne. The NFHL is available for download from the FEMA Flood Map Service 

Center Link. Milliman used the NFHL as of January 2018.

UNITED STATES ARMY CORPS OF ENGINEERS NATIONAL LEVEE DATABASE

The United States Army Corps of Engineers (“USACE”) National Levee Database (“NLD”) is a 

congressionally authorized database that contains information on locations, general condition, and 

risks associated with the levees throughout the United States. Milliman obtained the NLD directly 

from USACE on May 29, 2020.

4
Appendix A

UNITED STATES ARMY CORPS OF ENGINEERS GREAT LAKES WATER LEVEL DATA

The USACE publishes short-term and long-term average, maximum, and minimum water levels 

for the Great Lakes, updated on a monthly basis. This data is available from the USACE Detroit 

District’s website Link. Milliman accessed this data in December 2019.

GIS LAYERS

This section provides a list and an explanation of how each GIS dataset used in RR2.0 was 

developed. Table 1 lists the GIS datasets along with their names or naming conventions, and their 

original source datasets.

Table 1: GIS Datasets Used in RR2.0

GIS Dataset Database Name(s) Source Data

Elevation Rasters Named by 1-degree tile 1/3 arc-second DEM 

1 arc-second DEM

Focal 50 Rasters Named by 1-degree tile or by state 1/3 arc-second DEM 

1 arc-second DEM

Barrier Island BarrierIsland NHD

Coast Coast

NHD 

NFHL

NOAA National Shoreline 

NCCOS Shoreline Shapefiles

Ocean Ocean NHD

Great Lakes GreatLakes

NHD

USACE Great Lake Water 

Level Data

HUC12 WBDHU12 WBD

Flowlines Named by HUC4

e.g. Flowlines_<HUC4>

NHDPlus HR Beta 

NHDPlus V2 

WBD

KatRisk

River Polygons Named by HUC4

e.g. RiverPolys_<HUC4>

NHD 

WBD

KatRisk

Alaska HUC12 WBDHU12_AK WBD

Alaska Riverlines Riverlines_AK

5m AK DEM 

WBD

KatRisk

Alaska River Polygons RiverPolys_AK

NHD 

WBD

KatRisk

Leveed Areas LeveedAreas USACE NLD

Census Blocks tl_2017_a_us_block Census TIGER/Line Files

5
Appendix A

ELEVATION RASTERS

Data type: Raster

Description: Elevation in meters

Coverage extent: All states and territories. The lower resolution 1 arc-second tiles are only used 

along the U.S.-Canada and U.S.-Mexico borders as well as Alaska to provide additional coverage 

where the 1/3 arc-second data is not available.

Processing steps: No pre-processing was needed.

FOCAL 50 RASTERS

Data type: Raster

Description: Average elevation within an approximately 500 meter radius

Coverage extent: All states and territories

Processing steps: For CONUS, focal 50 rasters were created on a statewide basis by mosaicking 

all the necessary 1-degree raster tiles; for Alaska, Hawaii, and the Island Territories, focal 50 

rasters were created on a 1-degree raster tile basis. Focal 50 rasters were calculated as the average 

ground elevation of all raster cells with a centroid within an approximately 500 meter radius of 

each input raster cell. If there was NoData coverage in any portion of the radius, the output for that 

cell was NoData. For NoData water areas, we assumed that the input elevation was 0 to allow for 

calculations along shorelines.

BARRIER ISLAND

Data type: Vector

Description: Outlines the extent of all coastal barrier islands, barrier spits, and additional coastal 

areas surrounded by mainland waterways

Coverage extent: TX, MS, AL, FL, GA, SC, NC, VA, MD, DE, NJ, and NY

Processing steps: Barrier islands were manually delineated by drawing polygons around selected 

land features based on NHD water areas. All island areas completely detached from the mainland 

and land areas that extend into open water and attached to the mainland at only one end were 

included, as well as additional similar landforms.

COAST

Data type: Vector

Description: Coast

Coverage extent: All states and territories

Processing steps: The coastal features for all states and territories excluding CA, OR, WA, and 

AK were based on a selection of coastal features and near-coastal waterways from the NHD. This 

dataset was modified to include or exclude water features based on their spatial contiguity to all 

NFHL VE Special Flood Hazard Area Zones and selected AE Special Flood Hazard Zoneswhere 

Static Base Flood Elevation is less than or equal to 30 feet. In areas where there was no effective

6
Appendix A

NFHL, preliminary, or pending data, available digitized zones were used instead. Note that some 

coastal areas did not have NFHL coverage and, as such, feature contiguity with NFHL zones could 

not be considered in these areas.

The coastal features for CA, OR, WA, and AK were based on only a selection of coastal features 

from the NHD (SeaOcean, BayInlet, and Estuary) and do not include near-coastal waterways based 

on contiguity to NFHL zones.

Since the NHD does not have coverage for the Islands of Rota and Tinian in the Northern Mariana 

Islands (MP) and portions of the Pribilof Islands in AK, we supplemented the NHD withNCCOS 

Shoreline Shapefiles for MP and the NOAA National Shoreline for AK.

Coastal features for CONUS were based on a version of the NHD downloaded in Q1 2018; Coast 

features for AK, HI, and the island territories are based on a version of the NHD downloaded on 

August 9, 2018. Extensive manual evaluation and editing of raw source data were necessary when 

creating coastline features.

OCEAN

Data type: Vector

Description: Ocean

Coverage extent: Gulf and Atlantic States, CA, OR, and WA

Processing steps: The Ocean dataset was derived from NHDArea SeaOcean features as a starting 

point and then modified to include only the outer seaward edge of barrier islands or major land 

areas if no barrier island system was present. Ocean features were based on a version of the NHD 

downloaded on August 9, 2018.

GREAT LAKES

Data type: Vector

Description: Great Lakes

Coverage extent: MN, WI, IL, IN, MI, OH, PA, and NY

Processing steps: The Great Lakes dataset was derived by selecting and merging all NHD 

Waterbody polygons identified as “Lake Erie”, “Lake Huron”, “Lake Michigan’, “Lake Ontario”, 

or “Lake Superior” within the Great Lakes region. The average water surface elevation for each 

lake was assigned as the 2018 long-term water level as published by the USACE. The USACE 

water surface elevation values were projected from International Great Lakes Datum of 1985 to 

North American Vertical Datum of 1988 (“NAVD88”) to ensure appropriate comparison to ground 

elevation data. Great Lake features were based a version of the NHD downloaded on August 9, 

2018.

7
Appendix A

HUC12

Data type: Vector

Description: Boundaries of 12-digit Hydrologic Units Codes (“HUC12”) sub-watersheds

Coverage extent: All states and territories excluding AK

Processing steps: None – the WBDHU12 was extracted directly from the WBD published on April 

18, 2020.

FLOWLINES

Data type: Vector

Description: Routes that make up the linear surface water drainage network

Coverage extent: All states and territories excluding AK

Processing steps: There are currently 223 separate flowline feature classes, one for each HUC4 

within the “coverage extent” where features matching selection criteria were present. To prepare 

a flowline dataset for a given HUC4 we:

• Downloaded the most recently available NHDPlus geodatabase for the HUC4. If no 

NHDPlus HR Beta data was available, we downloaded the NHDPlus V2 data for that 

region.

• Joined the NHDPlus Flowline Value-Added Attribute table (NHDPlusFlowlineVAA) table 

to the NHDFlowline feature class by NHDPlusID. The NHDPlusFlowlineVAA table 

provides a number of pre-calculated flow network statistics for the NHD flowline network.

• Selected all NHDFlowline features where divergence-routed drainage area (DivDAsqKm) 

was greater than or equal to 1 sq. km and the feature type (FType) was a StreamRiver or 

ArtificialPath (query: DivDAsqKm ≥ 1 AND FType in (460, 558)). These represent 

drainage channels that have a minimum upstream area of 1 square kilometer.

• Removed artificial path flowlines within the extent of the Great Lakes polygons to ensure 

the Great Lakes did not get selected as a River Polygon (see “River Polygons” below). 

Intersected flowlines with the WBDHU12 dataset.

• Calculated the KatRisk flood depth difference for each feature. Depth difference was 

defined as the average 100-year return period flood depth minus the average 10-year return 

period flood depth along the extent of the feature.

RIVER POLYGONS

Data type: Vector

Description: All NHDArea StreamRiver polygons and select NHDArea LakePond features

Coverage extent: All states and territories excluding AK

Processing steps: There are currently 223 separate river polygon feature classes, one for each 

HUC4 within the coverage extent where features matching selecting criteria were present. To 

prepare a river polygon dataset for a given HUC4 we:

8
Appendix A

• Selected all NHDArea StreamRiver features that intersected the given HUC4.

• Selected all NHDArea LakePond features that intersected a filtered flowline within the 

HUC4.

• Merged the selected StreamRiver and LakePond features into a single RiverPolys feature 

class.

• Intersected the river polygons with the WBDHU12 dataset.

• Converted the river polygonsto single part features and added a new unique identifier field.

• Calculated the KatRisk flood depth difference for each feature. Depth difference was 

defined as the average 100-year return period flood depth minus the average 10-year return 

period flood depth along the extent of the feature.

River Polygons were based a version of the NHD and WBD that was downloaded on April 23, 

2020.

ALASKA DISTANCE TO RIVER FEATURES

NHDPlus HR Beta was in development for Alaska and therefore the datasets for Distance to River 

queries in Alaska were based on a raster-derived flow model instead of the NHDPlus HR Beta 

filtered flowlines. The three datasets listed below are only for use in Alaska.

ALASKA HUC12

Data type: Vector

Description: Boundaries of HUC12 sub-watersheds in Alaska

Coverage extent: AK

Processing steps: We extracted the WBDHU12 directly from the WBD downloaded on July 26, 

2018 and selected only HUC12s within the state of Alaska.

ALASKA RIVERLINES

Data type: Vector

Description: Flow model derived riverlines

Coverage extent: AK

Processing steps: The riverlines in Alaska were developed by Atkins at the request of FEMA and 

supplied to Milliman on October 4, 2019. The process to create these riverlines was as follows:

• Mosaicked together the highest-resolution available DEM rasters.

• Created flow accumulation and flow direction rasters from the mosaicked DEMs.

• Utilized the flow accumulation and flow direction rasters to create stream features with a 

minimum flow accumulation of 1 sq. km.

9
Appendix A

• Calculated the KatRisk flood depth difference for each feature. Depth difference was 

defined as the average 100-year return period flood depth minus the average 10-year return 

period flood depth along the extent of the feature.

ALASKA RIVER POLYGONS

Data type: Vector

Description: All NHDArea StreamRiver polygons and NHDArea Lake/Pond features intersected 

by a riverline

Coverage extent: AK

Processing steps: To prepare the River Polygons for Alaska we:

• Selected all NHDArea StreamRiver features that intersected the state of Alaska.

• Selected all NHDArea Lake/Pond features that intersected an AK riverline.

• Merged the selected StreamRiver and LakePond features into a single RiverPolys feature 

class.

• Intersected the RiverPolys with the WBDHU12_AK dataset.

• Converted the RiverPolys to single part features and added new a unique identifierfield.

• Calculated flood depth difference for each feature. Depth difference was defined as the 

average 100-year return period flood depth minus the average 10-year return period flood 

depth along the extent of the feature.

• Alaska River Polygons are based a version of the NHD that was downloaded on April 23, 2020. 

LEVEED AREAS

Data type: Vector

Description: Polygons representing areas protected by levees

Coverage extent: All U.S. states and territories

Processing steps: None – data was received directly from USACE.

CENSUS BLOCKS

Data type: Vector

Description: Polygons representing Census Block boundaries as of 2017

Coverage extent: All U.S. states and territories

Processing steps: None – data was downloaded directly from the U.S. Census.

10
Appendix A

QUERY PROCESS

This section provides an overview of how each GIS dataset is queried to obtain a rating variable 

for a given policy location. Table 2 lists each GIS rating variable and its corresponding GIS source 

dataset(s).

For all queries, it is assumed that input the latitude and longitude references the North American 

Datum of 1983 (“NAD83”) coordinate system.

Table 2: GIS Rating Variables

Rating Variable Variable Name Source Dataset(s)

Ground Elevation Elevation Elevation rasters

Focal 50 Focal50 Focal 50 rasters

Barrier Island BarrierIsland Barrier Island

Distance to Coast DTC Coast

Distance to Ocean DTO Ocean

Distance to Great Lake DTL Great Lakes

Elevation Above Lake ElevAboveLake Elevation rasters

Hydrologic Unit 12 HUC12 Hydrologic Unit 12

Distance to River DTR

Flowlines 

Riverlines 

River Polygons

Drainage Area DrainageArea Flowlines

Flood Depth Difference DepthDiff Flowlines 

River Polygons

Elevation Above River ElevAboveRiver Elevation rasters

Leveed Area System ID SystemID Leveed Areas

Census Block ID CensusBlock Census Blocks

11
Appendix A

GROUND ELEVATION

Query the appropriate raster tile. If no 1/3 arc-second data is available for a given 

latitude/longitude, use the 1 arc-second data instead.

FOCAL 50

Query the appropriate focal 50 raster. If there is no focal 50 raster based on 1/3 arc-second data 

available for a given latitude/longitude, use the focal 50 raster based on the 1 arc-second data.

BARRIER ISLAND

Query the Barrier Island polygons using a spatial join. If the input location is within a Barrier 

Island polygon, assign BarrierIsland as Yes; otherwise assign as No.

DISTANCE TO COAST

If the input state is a coastal state (AK, AL, AS, CA, CT, DC, DE, FL, GA, GU, HI, LA, MA, MD, 

ME, MP, MS, NC, NH, NJ, NY, OR, PA, PR, RI, SC, TX, VA, VI, WA), calculate the geodesic

distance to the nearest Coast polygon within 50 miles. If the input state is not a coastal state or the 

geodesic distance to the nearest Coast polygon is greater than 50 miles, then Distance to Coast is 

null.

DISTANCE TO OCEAN

If the input state is CA, OR, or WA and Distance to Coast is not null, calculate the geodesic 

distance to the nearest Ocean polygon. If the input state is AL, DE, FL, GA, MD, MS, NC, NJ, 

NY, SC, TX, or VA and BarrierIsland indicator is “Yes”, then calculate the geodesic distance to 

the nearest Ocean polygon. If none of these are conditions are met, then Distance to Ocean is null.

DISTANCE TO GREAT LAKE

If input state is MN, WI, IL, IN, MI, OH, PA, or NY, calculate the geodesic distance to the nearest 

Great Lakes polygon within 10 kilometers. If the input state is not MN, WI, IL, IN, MI, OH, PA, 

or NY, or the geodesic distance to the nearest Great Lakes polygon is greater than 10 kilometers, 

then Distance to Great Lake is null.

ELEVATION ABOVE LAKE

If Distance to Great Lake is not null, select the water surface elevation of the nearest Great Lakes 

polygon within 10 kilometers. Subtract the water surface elevation value from the location’s 

ground elevation value to obtain the elevation of the risk above the lake. If Distance to Great Lake 

is null, then Elevation Above Lake isnull.

12
Appendix A

HYDROLOGIC UNIT 12

Query the appropriate HUC12 polygons using a spatial join.

DISTANCE TO RIVER

Calculate the geodesic distance to the nearest river feature (line or polygon) within a location’s 

corresponding HUC12.

DRAINAGE AREA

This is the Drainage Area of the nearest flowline within a location’s corresponding HUC12. 

Drainage Area is not available for Alaska or the island territories.

FLOOD DEPTH DIFFERENCE

This is the Flood Depth Difference of the nearest river feature (line or polygon) within the 

location’s corresponding HUC12.

ELEVATION ABOVE RIVER

Determine the latitude and longitude coordinate of the nearest point on the nearest river feature 

(nearest river point). Query the appropriate raster tile to determine the ground elevation of the near 

river point. Subtract the elevation of the nearest river point from the ground elevation of the risk 

to determine the risk’s Elevation AboveRiver.

LEVEED AREA SYSTEM ID

Query the Leveed Area polygons using a spatial join. If the location is within a Leveed Area 

polygon, assign it the System ID of the associated Leveed Area polygon.

CENSUS BLOCK

Query the Census Block polygons using a spatial join. Assign Census Block asthe 15-digit GEOID 

of the associated polygon.

13
Appendix A

ADDITIONAL GIS ANALYSIS USED IN RATING PLAN DEVELOPMENT: HUC12 TERRITORY 

FACTOR FILLING

Rating territories were based on HUC12 and Barrier Island indicators (i.e., Yes or No) for nonleveed areas and on HUC12 and Levee Area System ID for leveed areas. A territory factor was 

developed for each combination of HUC12 and Barrier Island indicator (HUC12/BarrierIsland) 

and each HUC12 and Leveed Area (HUC12/LeveedArea) combination, based on the Correlated 

Market Basket.

The Correlated Market Basket is a representative sample of single-family homes with property and 

coverage characteristics that generally reflect the distributions of those characteristics in each 

state/territory. Since there were not Market Basket locations in every HUC12/BarrierIsland and 

HUC12/Leveed Area combination, a process called “HUC12 factor filling” was used to assign 

factors to combinations where no territory factors could be directly developed due to a lack of 

market basket locations. Table 3 describes the filling process for each peril in cases where a 

territory factor was missing. In these cases, the best available value was assigned using the territory 

factor fill options listed in Table 3 by order of preference. The remainder of this section describes 

the feature and neighborhood relationship table creation process.

14
Appendix A

Table 3: Process to Fill Missing Territory Factors by Area and Peril

Area Peril Features to Fill Neighborhood Definition Territory Factor Fill Value

Non-Leveed Inland Flood All HUC12 Hydrologic

1) Policy count weighted average factor of 1st order neighbors

2) Policy count weighted average factor of 2nd order neighbors

3) Policy count weighted average factor of 3rd order neighbors

4) Default value: 1

Non-Leveed Non-Mainland 

Storm Surge HUC12 on barrier islands Polygon

1) Policy count weighted average factor of 1st order neighbors

2) Policy count weighted average factor of 2nd order neighbors

3) Policy count weighted average factor of 3rd order neighbors

4) Factor of same HUC12 for non-BarrierIsland

5) Default value: 1

Non-Leveed Mainland 

Storm Surge HUC12 within 50 miles of coast Polygon

1) Policy count weighted average factor of 1st order neighbors

2) Policy count weighted average factor of 2nd order neighbors

3) Policy count weighted average factor of 3rd order neighbors

4) Factor of same HUC12 for BarrierIsland

5) Default value: 1

Non-Leveed 

and Leveed Tsunami

CA/OR/WA: HUC12 within 10 miles of ocean 

AS: HUC12 within 1 mile of coast

GU/MP: HUC12 within 2 miles of coast 

HI: HUC12 within 3 miles of coast

AK: HUC12 within 3 miles of coast

Polygon

1) Policy count weighted average factor of 1st order neighbors

2) Policy count weighted average factor of 2nd order neighbors

3) Policy count weighted average factor of 3rd order neighbors

4) Default value: 1

Non-Leveed 

and Leveed Great Lakes HUC12 within 10 kilometers of Great Lakes Polygon

1) Policy count weighted average factor of 1st order neighbors

2) Policy count weighted average factor of 2nd order neighbors

3) Policy count weighted average factor of 3rd order neighbors

4) Default value: 1

Leveed Inland Flood All HUC12 Hydrologic

1) Policy count weighted average factor of 1st order neighbors

2) Policy count weighted average factor of 2nd order neighbors

3) Policy count weighted average factor of 3rd order neighbors

4) Default value: 1

Leveed Inland Flood All LeveedAreas split by HUC12 

(HUC12/LeveedArea) n/a

1) Policy count weighted average factor for LeveeArea System ID

2) Factor for same HUC12

2) Filled factor for same HUC12

Leveed Mainland 

Storm Surge HUC12 within 50 miles of coast Polygon

1) Policy count weighted average factor of 1st order neighbors

2) Policy count weighted average factor of 2nd order neighbors

3) Policy count weighted average factor of 3rd order neighbors

4) Default value: 1

Leveed Mainland 

Storm Surge

All LeveedAreas split by HUC12 

(HUC12/LeveedArea) within 50 miles of coast n/a

1) Policy count weighted average factor for LeveeArea System ID

2) Factor for same HUC12

3) Filled factor for same HUC12

15
Appendix A

SELECTING FEATURES TO FILL

Table 3 lists the appropriate spatial filters by area (leveed or non-leveed) and peril used when 

determining which HUC12/BarrierIslands and HUC12/LeveedAreas feature combinations require 

filled factors. To determine the HUC12/BarrierIsland features, we spatially intersected the HUC12 

polygons with Barrier Island polygons; to determine the HUC12/LeveedArea features, we spatially 

intersected the HUC12 polygons with the Leveed Areas polygons (see Figure 1 for example). Note 

that for filling features in Alaska, we used the WBDHUC12_AK dataset; for filling features 

everywhere else, we used the WBDHU12 dataset.

Figure 1 Example of HUC12/Leveed area combinations. LeveedArea 001605882344 (in green) is split by 3 HUC12s (black 

outlines), and therefore becomes 3 separate features to fill.

16
Appendix A

NEIGHBORHOOD RELATIONSHIP TABLES

Hydrologic Neighbors: A HUC12’s hydrologic neighbors include the HUC12s to which a given 

HUC12’s water flows to and from (see Figure 2 for example). To determine the 1st order hydrologic 

neighbors for all HUC12s, we exported the “HUC12” and “ToHUC” fields from the WBDHU12 

attribute table in the WBD. Then we assigned each HUC12 its ToHUC(s) and determined which 

HUC12(s) its flow comes from (defined as “FromHUC”). 1st order hydrologic neighbors for a 

given HUC12 include all of its ToHUCs and FromHUCs; 2nd order neighbors are its neighbors’ 

neighbors; 3rd order neighbors are its neighbors’ neighbors’ neighbors. Note that some HUC12s 

may not have any hydrologic neighbors as they have no inflow or outflow. Additionally, some 

HUC12s flow to Mexico, Canada, and the ocean. Any ToHUC listed as “Mexico”, “Canada”, 

“Ocean”, or “Closed Basin” was excluded when building the neighborhood tables.

Figure 2 Representation of hydrologic neighbors for HUC12 100301030901.

17
Appendix A

Polygon neighbors: HUC12 polygon neighbors are those HUC12s that are contiguous to the 

selected HUC12 (see Figure 3 for example). Note that some HUC12s may have no polygon 

neighbors. 1st order polygon neighbors for a given HUC12 include all HUC12s with which it shares 

a boundary; 2nd order neighbors are its neighbors’ neighbors; 3rd order neighbors are its neighbors’ 

neighbors’ neighbors.

Figure 3 Representation of polygon neighbors for HUC12 100301030901.

18
APPENDIX B - Market Basket Variable Imputation
Appendix B

Table of Contents

Table A. Correlated Market Basket Fields.................................................................................................... 1

Table B. Uncorrelated Market Basket Fields................................................................................................ 3

TOC 1
Appendix B

1

TABLE A. CORRELATED MARKET BASKET FIELDS

Characteristic Data Source Filling Method

Year Built Parcel data1

Missing values simulated using distribution by 

county. For counties with insufficient data, 

missing values simulated using distribution by 

state.

Number of Stories Parcel data Missing values simulated by distribution by state 

and year built.

Construction Parcel data

Missing values simulated by distribution by state 

and year built. For states with insufficient data, 

missing values simulated from neighboring state’s 

distribution by year built.

Foundation Type

Hazus2,

2009 Residential 

Energy 

Consumption 

Survey3

Missing values simulated from distribution by 

state, Census Block type (coastal, riverine, or lake, 

as defined by Hazus), flood zone, year built, 

elevation, and base flood elevation.

First Floor Height Hazus Imputed based on elevation, base flood elevation, 

foundation type, and year built.

Dwelling Value

FEMA

NAIC report4

Modeled based on ZIP Code, year built, number of 

stories, and flood zone, and adjusted based on 

distributions of amount of insurance by state from 

NAIC report. Dwelling Value was further 

restricted to be greater than Dwelling Limit.

Dwelling Limit FEMA

Modeled using state and Dwelling Value. 

Randomly set 5% of policies to $0 Dwelling Limit 

to represent Contents-only policies, based on 

FEMA data.

Dwelling Limit was capped at $250,000 and 

restricted to be at least $1,000 greater than 

Dwelling Deductible.

Contents Value Calculated Contents Value based on Dwelling Insurance to 

Value (“ITV”) ratio and Contents Limit.

Contents Limit FEMA

Contents Limit as a percentage of Dwelling Limit 

(Contents Limit %) was simulated using 

distribution by state in 5% increments. Randomly 

set 20% of policies to $0 Contents Limit, based on 

FEMA data, to represent Dwelling-only policies. 

Contents Limit set as Dwelling Limit times
Appendix B

2

Contents Limit %, capped at a maximum of

$100,000. Contents Limit was further restricted to 

be at least $1,000 greater than Contents 

Deductible, and no policies were allowed both $0 

Dwelling Limit and $0 Contents Limit.

Dwelling 

Deductible FEMA

Randomly assigned 25% of the policies a $1,250 

Dwelling Deductible and $1,250 Contents 

deductible. All other combinations of 

Dwelling/Contents Deductibles were assigned 

randomly with equal weight. Finally, the 

Deductibles were capped according to NFIP’s 

minimum deductible rule.

Contents 

Deductible FEMA

Randomly assigned 25% of the policies a $1,250 

Dwelling Deductible and $1,250 Contents 

deductible. All other combinations of 

Dwelling/Contents Deductibles were assigned 

randomly with equal weight. Finally, the 

Deductibles were capped according to NFIP’s 

minimum deductible rule.

Equipment in 

Basement None Set to No.

Finished Basement None Set to No. All basements simulated in the market 

basket are considered unfinished.

Detached 

Structures Limit None Set to 0.

Loss of Use Limit None Set to 0.

Notes

1. Parcel data refers to CoreLogic ParcelPoint data provided to Milliman by FEMA.

2. Hazus refers to “Hazus – MH, Multi-hazard Loss Estimation Methodology, Flood Model 

Technical Manual”, developed byFEMA.

3. “2009 Residential Energy Consumption Survey” from U.S. Energy Information 

Administration.

4. NAIC report refers to “Dwelling Fire, Homeowners Owner-Occupied, and Homeowners 

Tenant and Condominium/Cooperative Unit Owner’s Insurance Report: Data for 2015” 

issued by National Association of Insurance Commissioners.
Appendix B

TABLE B. UNCORRELATED MARKET BASKET FIELDS

Characteristic Filling Method

Year Built Simulated from uniform distribution from 1900 to 2017.

Number of Stories Simulated from uniform distribution of 1, 2, and 3.

Construction Simulated from uniform distribution of Frame and Masonry.

Foundation Type Simulated from uniform distribution of Pile, Wall, Pier, Basement, 

Crawlspace, Fill, and Slab.

First Floor Height

Based on Foundation Type. Foundation type and first floor height have 

a necessary relationship and are therefore not decoupled in the 

uncorrelated book.

Dwelling Value Simulated on uniform distribution from min to max Correlated Market 

Basket Dwelling Value by state.

Dwelling Limit

Simulated on uniform distribution from $0 to $250,000, with 5% set to

$0. Dwelling Limit was further restricted to be at least $1,000 greater 

than Dwelling Deductible.

Contents Value Simulated on uniform distribution from min to max Correlated Market 

Basket Contents Value by state.

Contents Limit

Simulated on uniform distribution from $0 to $100,000, with 20% set 

to 0. Contents Limit was further restricted to be at least $1,000 greater 

than Contents Deductible. No policies were allowed $0 Dwelling and 

Contents Limits.

Dwelling Deductible

Simulated on uniform distribution of all combinations of 

Dwelling/Contents Deductibles. Deductibles are capped according to 

NFIP’s minimum deductible rule.

Contents Deductible

Simulated on uniform distribution of all combinations of 

Dwelling/Contents Deductibles. Deductibles are capped according to 

NFIP’s minimum deductible rule.

Equipment in 

Basement Set to No.

Finished Basement Set to No. All basements simulated in the uncorrelated book are 

considered unfinished.

Detached Structures 

Limit Set to 0.

Loss of Use Limit Set to 0.

3
APPENDIX C - Uniform Book Assumptions
Appendix C

TABLE 1. UNIFORM BOOK ASSUMPTIONS

Characteristic Value

Dwelling Value $200,000

Dwelling Limit $200,000

Dwelling Deductible $1,500

Contents Value $50,000

Contents Limit $50,000

Contents Deductible $1,500

Construction Type Frame

Basement No

Year Built 1990

Number of Stories 1

First Floor Height 1 foot

1