Flood Hazard Demographics and NFIP Policy/Claims Analysis

ASFPM - Hartford, CT

June 2013

Mohan Rajasekar

Mark Crowell

Andy Neal

NFIP – A means of discouraging unwise occupancy of flood prone areas, yet occupancy of these areas has expanded since 1968.

Risks continue to increase - 40 years after the program’s inception, only 20% to 30% of individuals exposed to the flood hazard actually purchase insurance.

The primary challenge: Optimize the NFIP to balance fiscal soundness, affordability of

insurance, adequate coverage for those at risk, floodplain management (reduction of flood hazard vulnerability), economic development, individual freedoms, and environmental concerns.

Problem Statement

2010 Census

Population and Housing Units

Roughly two-thirds of the 27.3 million new U.S. residents between

2000 and 2010 lived in the southern and western states of Texas,

California, Florida, Georgia, North Carolina, Virginia, Nevada, and

Colorado - the “demographics of devastation.”

Map Modernization

Expansion of the digital quilt

Increased Spatial Resolution of Risk Exposure

LandScan

Opportunity Summer 2011

Risk Identification Project Prioritization

Impacted Population & Housing Units

Augmented by Needs Data (CNMS)

Risk Assessment Tangible Improvements in Resolution

Risk Communication Targeted outreach based on Census 2010 Demographic Data

Risk Mitigation, Areas with Large Mitigation Potential

Cost-effectiveness of the Hazard Mitigation Assistance (HMA) grant programs

Evaluate Communities’ Commitment and Adherence to Floodplain Management Best Practices

This study delivered crucial quantitative and geospatial data needed to

analyze the current impact, reach, and future needs of the NFIP

Benefits

How many people currently reside within effective Special Flood Hazard Area (SFHA) ? By CBG using LandScan

How many housing units currently fall within effective SFHA? By CBG

How did the Map Mod program affect the SFHA (increases/decreases)?

How did Map Mod affect the identification of the number of people and structures in the SFHA?

Questions the Study (Part 1) Answered

Where, relative to the SFHA, are NFIP

policies and claims located?

What is the extent of Zone Grandfathering

within the NFIP policy base?

Questions the Study (Part 2) Answered

2009-2010 SFHA DEMOGRAPHICS STUDY

2000 Census Block Groups

Even Population Distribution Assumption

TransAmerica / Core Logic 2005 Pre Map Mod Snap Shot

798 Counties with Sufficient NFHL Coverage for

Comparison with Pre Map Mod

Comparison with 2010 Study

2011 LandScan as Population Source

In Most Areas Provides Sub-Census Block

Level Re-Distribution of Census 2010

Population Data

February 2012 NFHL

1,750 Counties with Sufficient NFHL Coverage

for Comparison with Pre Map Mod

RAMPP 2010 Study

Census 2010 Population Distribution

Region I, 4.6% Region II, 10.2%

Region III, 9.5%

Region IV, 19.5%

Region V, 16.6%

Region VI, 12.3%

Region VII, 4.4%

Region VIII, 3.5% Region IX, 15.3%

Region X, 4.1%

Census 2010 Population Distribution

Distribution of Population within SFHA

Region I, 3.0%

Region II, 9.3%

Region III, 6.2%

Region IV, 37.0%

Region V, 7.6%

Region VI, 19.3%

Region VII, 3.9%

Region VIII, 1.6%

Region IX, 9.7% Region X, 2.3%

Distribution of Population within SFHA

Part 1 Results Tables

Part 1 Results Tables

Effects of Map Mod

Region I, 0.4%

Region II, 14.0%

Region III, 3.1%

Region IV, 29.7%

Region IX, 2.2% Region V, 1.9%

Region VI, 38.2%

Region VII, 5.7%

Region VIII, 2.5%

Region X, 2.3%

Distribution of Population Mapped in to SFHA

Effects of Map Mod

Region I, 0.9%

Region II, 7.7%

Region III, 1.5%

Region IV, 51.8%

Region IX, 12.0%

Region V, 15.6%

Region VI, 3.4%

Region VII, 3.8% Region VIII, 2.5%

Region X, 0.7%

Distribution of Population Mapped out of SFHA

Bottom Up Data Model Design

Automation of Processes to Greatest Extent Practicable

Zonal Statistic Based on Re-Sampled LandScan

Population Totals per Reporting Area Based on Aggregates Zonal

Results

Outputs Include Population, Housing Unit, and Area

Statistics

Part 1 Detail

Discrete, Topologically Flat

Initial Requirements Political Boundaries

Coastal vs. Riverine

Anticipating Congressional Requests and Potential for Further Analysis Census Block Groups

Congressional Districts

Results from Parts 1 and 2 Can be Aggregated or Isolated to Any Reporting Area Resolution, or Combination Thereof

Reporting Areas

Geospatial and geocoding analyses that can

support the assessment of the NFIP’s actuarial

soundness

Geolocation of Nearly 16 Million Points

Policies, Claims, Historic PRPs

Proximity Determinations

Nearest SFHA

Nearest SFHA Type and Coastal vs. Riverine

Part 2 Detail

Part 2 Results Summary Tables

Policy / Effective Zone Match

31 | April 30, 2013

Handling of PII

Geocoding

NFHL

LandScan

Challenges

Personally Identifiable Data

Client Requirements

RAMPP Strategy

Firewall

Data Handling Role Assignments

Disassociation of Location Data from Personal

Information to Allow for More Flexibility During

Certain Phases

Handling of PII

Assumed Levels of Accuracy Limited Clean Up at this Scale

ROOFTOP vs. INTERPOLATED

Standardizing Input Address String from Policy

and Claims Records Highlights Shortcoming in Policy Data Collection Systems

Geocoding

Geocoding Challenges

Clustered or Poorly

Interpolated Location

Returns

Geocoding Summary

Policy Data Geocoding Summary

NFHL Challenges

Tracking Lack of Accurate Coverage Definition

Topology Overlapping Polygons

Slivers

Presumably from LOMR Stitching

Attribution Consistency

Oak Ridge National Laboratory Product

Represents ‘Ambient’ Population Distribution

Population Location by Probability

Multi-Variable Dasymetric Model

Day/Night Data Available

Sub-Block Resolution in Most Areas

Found that Population Totals within Certain Administrative

Boundaries were Inaccurate

2011 LandScan

LandScan

LandScan

Vs Census Block

Vs Census Block Group

BW12 CONSIDERATIONS

Utilizing Study Results to Hotspot and

Understand BW12 Impacts

1) Stakeholder Education

2) Stakeholder Needs

3) Variability of Localized Data Sets

Necessary to consider a ‘standardized’ BW12

approach as an organized analytical framework

comprised of Conceptual, Logical and a Physical

Model rather than a specific turn key application

or a series of individual determinations/concepts.

Defining the Need

A Data Model is an

abstract

representation of

how data and

information are

represented and

accessed

Conceptual Model

Process & Data

Logical Model

Physical Model

Timing of BW12 and Sandy

Completion of the NFHL/NFIP Policy and

Claims Demographic Study

Community Interest in Understanding the

Implications of BW12

The Rebuild App

Current Opportunity

Integrated Adaption Assessment (Conceptual Models)

Questions answered by this task Outcomes

What goals and process will be used to guide the assessment?

What county and community entities will be included in the assessment?

What planning groups will be formed to guide the assessment?

What neighboring county and community entities will we partner with in the assessment?

What will be our stakeholder engagement strategy?

Form adaptation advisory group

Form neighboring partner group

Develop Stakeholder Engagement Plan and

execute throughout

Create transparency website

Questions answered by this task Outcomes

What areas could be better aligned with federal programs to position for federal credits or grants?

What regulatory strategies can be offered to influence the areas of priority?

Develop policy or other strategy

recommendations

Questions answered by this task Outcomes

What is the baseline impact of the BW12 changes? Where are the locations of current policies and historical

NFIP claims? What actions can be taken to lower risk and insurance rates? What actions are likely to be taken based on cost vs. value? Where are the recovery vulnerable areas within the

assessment area?

Create Adaptation GIS geodatabase populated

with source data layers

Add geo-located Policies and Claims data

Add BW12 assessment layers

Evaluate recovery cycle activities

Score value and vulnerability

Rollup scores at the block level

Questions answered by this task Outcomes

What is the baseline of current CRS program activities? What can be done to lower CRS rating and increase NFIP

insurance reductions to the public? What recommendations can be made to decrease flood risk

by increasing insurance policy penetration?

Develop Flood Insurance Coverage Assessment

Report to credit for CRS 372

Develop and prioritize recommendations

Evaluate recommendations and per capita

costs/savings

Questions answered by this task Outcomes

What are the current planned activities within the county? How will these activities affect the value and vulnerability

scores from the BW12 assessment? What additional activities can be undertaken from the BW12

and CRS assessments?

Develop investment activities database

Score and prioritize activities with wide

improvement potential

Perform scenario analysis on larger mitigation

projects in relation to BW12 and CRS affects

Use database results to develop rapid localized

plans/views

Questions answered by this task Outcomes

How can we more holistically approach the allocation of our ongoing activities?

How can we better position for ongoing programmed funding activities?

How can we incentivize private activities to align with our vision?

Develop funding strategy recommendations for

ongoing activities, programmed funding

initiatives, and private activities

Questions answered by this task Outcomes

How will BW12 analysis help individuals? How will BW12 analysis help county planning and

implementation? How can we help educate and inform the public? How can we partner better with our county neighbors?

Develop and present final report

Develop media as designed within the outreach

strategy

Conduct BW12 101 workshop - basics

Conduct BW12 201 workshop – options and

economics

Develop dissemination website

Develop property assessment web apps

ASFPM Showcase (Conceptual Model)

Existing Study Data must be Analyzed for

providing Insights into:

Relative Magnitude of Issues

Hotspots of Interest

Data Collection Efforts Needed

Breadth of Knowledge

Caveat Emptor

Building the Logical Model

Objectives for NC

Provide an assessment of potential rate increases compared to current

policy values in a geospatial format,

Identify target areas or neighborhoods of concern where there may be

patterns of significance or clusters of policies with the potential for very

high actuarial rates.

Identify strategies for reducing the impact of BW12 on current and

future development

Statewide Plan Objectives – Case Example

Framework Organization

Framework intends to represent a flexible model through which a

certain number of required attributes are garnered.

Varying resolutions provide different reporting opportunities

High quality, structure level data is a rarity

Sampling techniques should be developed to model the BW12

Impact trends and used to extrapolate from or recalibrate statewide

results

Step 1: Geocoding and

Conflation

Geocode Policy Data

Match Policy Data to Building Footprints

Integrate Tax Assessor Database

Information

Step 2: Analyze Policy Data

Identify Policy Attributes Relevant to BW12 Actuarial

Rate Triggers

Determine Time Frame for Actuarial

Rate Phase In

Estimate Localized BW12 Financial

Impact

Determine Effects of Revised Map

Maintenance SFHAs

Step 3: Aggregate Results to Political

Boundaries

Census Block Group

Community Sub Basins Custom Boundaries

Provided by NC GTM

Step 4: Review Hotspots for Mitigating the

Impacts of BW12

Map BW12 Financial Impact

Hotspots

Present Mitigation Strategies

• CRS 370

• Property Elevation Collection

• Grants

Provide Recommendations on Limiting Impacts

Recommend Actions for

Potential CRS Rating Gains

Conceptual Model, and Work Breakdown Structure

1. Best case scenario, individual building footprints serve as the highest

resolution units which can be flagged as impacted. Conflation of

actual policy data

a) This includes the ability to perform financial analyses such as actuarial

rate calculations, and averaged annualized lost estimates, accounting for

real world coverage totals.

b) At such a resolution, mitigation strategy scenarios can also be triggered

within the framework at a structure level such as buyout or structure

elevation.

Best Case Scenario

Difficulties and Options

1. Geocoding of Policy, Claims, Repetitive Loss, Severe Repetitive Loss,

Elevation Certificates and Additional Localized Data Sets Such as Known

Locations of Severe Damage as a Result of a Storm.

a) Size of the Study and Ability to Conflate Geocoded Data should

directly influence the route taken within the framework – specifically

the resolution which is chosen as the ‘hub’ for reporting (CBG,

Community etc.).

2. Lack of Policy Data

1. In some jurisdictions, policy data may not be available due to FEMA

denial or a lack of community engagement on the issue.

1. the framework, through the abstractions in the system, can

accommodate an alternate sources such as the tax assessors

database for property locations within the SFHA.

Implications to North Carolina

59 | April 30, 2013

PDF maps identifying hotspots in a few selected areas;

A set of metrics to help guide communities about the benefits in obtaining a better CRS

rating;

Information to help communities identify viable mitigation funds and projects for

mitigation activities;

New Higher Standard actions for communities to help reduce affect from BW12 on

newly-mitigation properties and future development; and

Outreach presentation slides for use by the NCFMP at various meetings with

communities and/or other government agencies.

Community Outreach Toolbox

Mobile-friendly application for mobile web browsers (agnostic of OS type)

Address-based results providing homeowners in BW12 Impacted areas, advice on the

following:

Elevation to reconstruct post-Disaster, or Optionally

Cost impacts of reconstruction

Cost impacts to annual insurance premiums based on various First Floor Elevation (FFE) scenarios

Savings or Losses over a 15 and 30 year period based on various reconstruction scenarios

Qualitative estimate of confidence in the results presented to the user

Option to refine results by allowing users to identify a specific property on a map and by providing their

property’s first floor elevation (FFE) and other building information

Customized/Contextualized Help within App User Interface (UI)

Scalable:

Advisory services for additional areas of interest

Additional tools and features that assist the users with their decisions towards resilience

The Best Defense

Geocoding Service Reverse

Geocoding Service

Advisory Info

Service LAG Service

Advisory Model

Service

ArcTool box 10.1

(GP Rebuild

Tools)

Map Layers

Arc

GIS

Se

rve

r 1

0.1

SQL Server 2012

/SDE

Rebuild Mobile App Geoprocessing Services IIS Web Server

ArcGIS Web

Adaptor

Applications Users (Mobile and Web Browsers)

Clie

nt

ArcGIS Online (AGOL) - Gateway

Map Layers

Application Architecture

Map Service

Web Processing Services

Discoverable on

FEMA AGOL