consumer data industry association fair lending...
TRANSCRIPT
Consumer Data Industry Association Fair Lending Teleseminar
May 10, 2016
D. Jean Veta, Covington & Burling LLP
Michael Nonaka, Covington & Burling LLP
Marsha J. Courchane, Charles River Associates
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The CFPB’s Increasing Role in Fair Lending
CFPB 2015 Fair Lending Report (April 2016)
New HMDA Rules
Supreme Court’s Inclusive Communities Decision
Fair Lending and its Intersection with UDAAP
Indirect Auto Lending
Small Business Lending
Agenda
3
CFPB Examination and Enforcement Jurisdiction:
Insured depository institutions and insured credit unions with total
assets of more than $10 billion
Certain nondepository institutions that are “covered persons,”
including those who the CFPB has reasonable cause to
determine are engaging or have engaged in conduct that poses
risks to consumers with regard to the offering or provision of
consumer financial products or services and those who are larger
participants in certain markets for consumer financial services.
Credit bureaus;
Auto finance lenders;
Debt collectors
ECOA vs. FHA
The CFPB’s Role in Fair Lending
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The CFPB’s Role in Fair Lending
The CFPB’s Office of Fair Lending and Equal Opportunity
Patrice Alexander Ficklin, Director
From April 2014 – April 2015, fair lending enforcement actions
required institutions to provide approximately $224 million in
remediation to about 303,000 consumers
Number of fair lending enforcement actions by year: 2013 (two);
2014 (one); 2015 (four); 2016 (one to date)
Making policy through enforcement actions
E.g., Indirect Auto Lending
Leaked Memos
Use of Bayesian Improved Surname Geocoding (“BISG”)
Non-English Speaking Customers
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The CFPB’s Role in Fair Lending: Individual Liability
Broad scope – both individual liability and failure to admit
wrongdoing
Congress
DOJ – the Yates Memo; new FCPA pilot program
SEC under increasing scrutiny for failing to assess individual
liability
CFPB individual liability cases to date
Small entities; owner/operators
Clearly unlawful conduct
Potential sanctions
Civil money penalties
Sancho: ban from the “financial products industry”
Potential application in fair lending cases
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PHH’s appeal of Director Cordray’s ruling in captive reinsurance case has potentially broad consequences for industry and the Bureau
The facts
ALJ found that PHH violated RESPA through a mortgage reinsurance kickback scheme, and required disgorgement of $6.5 million
On administrative appeal, Director Cordray ordered a massive increase in disgorgement to $109 million. The increase was due to: Violation of RESPA every time PHH accepted a payment from a mortgage
reinsurer
Total gross revenue from premiums vs. profits
Did Cordray correctly interpret RESPA?
If so, did the industry have fair notice of his interpretation?
Contrary to HUD guidance
Contrary to widespread industry practice
Is there no statute of limitations applicable in CFPB administrative hearings?
Is the Bureau constitutional?
Single Director structure
Director can be fired only “for cause”
The CFPB’s Role in Fair Lending: CFPB v. PHH
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Fintech
Coming under increasing scrutiny
Relationship between fintech lenders and banks
Innovations in Underwriting
Big Data
Non-traditional data
New Small Business Initiative
Early stages
Fair lending risk
The CFPB’s Role in Fair Lending: The Future
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Changes to HMDA Reportable Data
Applicant/Borrower Characteristics Applicant/Borrower Age
Co-Applicant/Co-Borrowers
Collateral Characteristics Detailed Property Type – 1-Unit, 2-4 Units, 5+ Units
Detailed Occupancy Status – Differentiate b/w Investor Property & Second Home
Underwriting/Pricing Factors Applicant/Borrower Credit Score
Debt-to-Income Ratio
Loan-to-Value Ratio (via Property Type)
Combined Loan-to-Value Ratio
AUS Recommendation
80 Fed. Reg. 66128 (October 28, 2015)
http://www.gpo.gov/fdsys/pkg/FR-2015-10-28/pdf/2015-26607.pdf
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Additional HMDA Reportable Data (cont.)
Loan Characteristics Detailed Loan Purpose – Differentiate b/w Rate/Term & Cash-Out Refinance
ARM Features – Initial Fixed Rate Period
Prepayment Penalty Terms
Loan Term
Non-Amortizing Features – Balloon Loans, Interest Only Loans, etc.
Reverse Mortgages
HELOCs
Business Channel – Retail, Broker, Correspondent
Rates & Fees Note Rate
Total Points & Fees
Origination Charges
Discount Points
Lender Credits
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The new HMDA data will be mainly used to support disparate
impact theories under fair lending laws
This gives the government and private parties more data to
support fair lending claims
The ease of access through the CFPB’s website may also
advantage complaining parties
Conversely, a defendant will also have more data – including credit
characteristics – to respond to a complaint
Relying on the Inclusive Communities burden-shifting test—this
may give defendants a better chance of dismissing the action at
an early stage
Using history as a guide—the revised Regulation C will likely lead
to increased discrimination claims
Increased fair lending analyses by lenders will be required
The Likely Impact of the New Amendments to
Regulation C on Litigation
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Addresses the problem of “abusive disparate impact claims”— announced rules when litigating disparate impact litigation:
Courts must promptly assess the viability of a case
A mere “showing of a statistical disparity” is insufficient, as disparate impact litigation is not meant to impose “racial quotas” This is a significant requirement favoring lenders
The plaintiff must show that a statistical disparity was caused by the defendant’s policies and practices—causality required
The defendant may defeat a prima facie disparate impact claim by showing that the policy or practice at issue was “necessary to achieve a valid interest,” which may include “practical business choices and profit-related decisions”
If the defendant identifies such a valid interest, the burden shifts back to the plaintiff to show “that there is ‘an available alternative . . . practice that has less disparate impact and serves the [entity’s] legitimate needs.’”
Inclusive Communities Decision
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Underwriting Analysis
Detailed Loan Purpose
Detailed Occupancy Status
Detailed Property Type
Loan Amount
Debt-to-Income Ratio
Loan-to-Value Ratio
Combined Loan-to-Value Ratio
Applicant Credit Score
Automated Underwriting
Decision
Detailed Loan Product
Business Channel
Based upon a review of underwriting guidelines and rate sheets, develop
customized statistical models that may control for factors such as the following:
Pricing Analysis
Detailed Loan Purpose
Detailed Occupancy Status
Detailed Property Type
Loan Amount
Rate Lock Week
Loan-to-Value Ratio
Combined Loan-to-Value Ratio
Borrower Credit Score
Detailed Loan Product
Business Channel
MSA
Statistical Analysis of Underwriting and Pricing
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Protected
Class
Comparator
Group Protected Class
Odds
Ratio
Pseudo
R-Squared Model Total Denials Total Denials p-value
Conventional Mortgage Applications
African
American
Raw 44,126 11,459 3,008 1,493 2.810 0.000 0.013
Controlled 44,074 11,408 3,001 1,487 1.335 0.000 0.359
Hispanic Raw 44,126 11,459 3,999 1,433 1.592 0.000 0.003
Controlled 44,078 11,412 3,989 1,423 1.143 0.002 0.353
Hypothetical Lending Institution - Fair Lending
Analysis of 2014 HMDA – Incidence of Denial
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Protected
Class
Model
Comparator
Group
Loan Count
Protected
Class
Loan Count
Coefficient
(bps)
p-value
Adjusted
R-Squared
Conventional First Lien Mortgages
African
American
Raw 26,222 1,109 13.62 0.000 0.003
Controlled 26,222 1,109 3.32 0.000 0.765
Hispanic Raw 26,222 1,994 7.97 0.000 0.001
Controlled 26,222 1,994 1.27 0.016 0.767
Hypothetical Lending Institution - Fair Lending
Analysis of 2014 HMDA – APR Regression Results
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Fair Lending – Redlining Analyses
• Redlining investigations focus on geography – generally by looking at the
percentage minority in particular census tracts or by looking at the market
share on one institution compared to others offering the same product in the
same geography
• First look at originations for financial institution as % in majority minority tracts
compared to total originations.
• Next, calculate shares of financial institution in low, or high minority tracts
compared to peers’ shares in those tracts.
• Measure statistically significant differences.
Originations
Total
Count
Majority Minority
Tracts
Majority African
American and/or
Hispanic Tracts
Low Tract
Minority Share
High Tract
Minority Share
Low
Minority
Share /
High
Minority
Share Count % p-value Count % p-value % p-value % p-value
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There is no standard monitoring approach, but all involve an assessment
of the distribution of own institution’s lending activity during a given time
period within a defined geographic area versus a benchmark.
For own institution’s lending activity within the defined geographic area
determine the proportion that involved properties located in census tracts
with relatively high concentrations of minority residents.
Compare own institution’s proportion with that of lending activity for other
lending institutions operating in the same defined geographic area using
publicly available HMDA data from the same time period.
Prior to public release of HMDA data for given time period, monitor trends
by comparing own institution’s proportion during given time period with
own institution’s proportion during prior time period.
Monitoring of Redlining Risk
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First joint redlining consent order issued by CFPB/DOJ was for Hudson Savings Bank, September 24, 2015.
CMP of $5.5 million paid to CFPB
$25 million in “subsidy” fund
$200k advertising/marketing
$100k Financial Education
$750k CD partnerships
Required new branches be established
Required changes to CRA assessment areas to include full counties in NY and to add Camden NJ and Philadelphia
Drew heavily not only on own percentage applications in high minority areas, but explicitly compared to peers for majority-black-and-Hispanic tracts
Ignored higher than average approval rates and ignored purchased loans
CFPB/DOJ: Redlining Risk Remediation
Hudson Savings Bank
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Associated Bank – Conciliation Agreement dated May 22, 2015
$200 million settlement over 2008 – 2010 – focusing on allegedly
smaller “market share”
Subsidy assistance ($10 million – lower interest rates, down
payment assistance, closing costs)
$3 million to help home repairs
$1.35 million for community reinvestment and fair lending
education
$1.25 million for marketing/outreach (print media, radio, outreach)
4 new production centers in majority-minority areas (Milwaukee,
Chicago, Cook or DuPage counties)
3 new branches in majority-minority areas (Chicago, Milwaukee,
Racine)
HUD: Redlining Risk Remediation
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Lender Name
Total
Loans
Loans in
Majority
Minority
Tracts
Share in
Majority
Minority
Tracts
Institution A 40,000 2,400 6.0%
Institution B 35,000 2,275 6.5%
Institution C 30,000 1,500 5.0%
Institution D 24,500 1,470 6.0%
Institution E 13,750 688 5.0%
Institution F 12,500 563 4.5%
OWN INSTITUTION 7,000 210 3.0%
Institution G 6,500 488 7.5%
Institution H 3,750 113 3.0%
Institution I 3,500 350 10.0%
Institution J 3,500 525 15.0%
Institution K 1,250 63 5.0%
Institution L 750 68 9.0%
All Other Lenders 175,000 10,500 6.0%
Lenders with Similar Volumes 36,500 1,850 5.1%
Hypothetical Lending Institution – Redlining Risk
Assessment – Given Geographical Area -- 2014 HMDA
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Lender Name
Total
Loans
Loans in
Majority
Minority
Tracts
Share in
Majority
Minority
Tracts
Odds
Ratio p-value
OWN INSTITUTION 7,000 210 3.0% - -
All Other Lenders 175,000 10,500 6.0% 0.485 0.000
Lenders with Similar Volumes 36,500 1,850 5.1% 0.836 0.000
Hypothetical Lending Institution -- Analysis of Differences in
Proportions of Lending in Majority Minority Census Tracts
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Unfair – causes or is likely to cause substantial injury to
consumers; not reasonably avoidable; and injury not outweighed by
offsetting benefits (e.g. lower prices, more products). Substantial
injury involves monetary harm (e.g. costs or fees – even small
amount if large number of consumers impacted)
Deceptive – misleads or is likely to mislead in a material way
(central characteristics; expressed claims …); consumer’s
interpretation is reasonable (e.g. bait & switch). Evaluation with the
four P’s (prominence, presented in easy to understand format;
placement where consumers look; info in close proximity to claim) –
and may be interpreted relative to a particular target audience
UDAAP: Unfair, Deceptive and Abusive
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Materially interferes with consumer’s ability to understand a term or
condition
Takes unreasonable advantage of
Lack of understanding
Inability of consumer to protect its interest in choosing or using
product
Reasonable reliance of consumer on a covered person acting in
their interest
Abusive
23
Assess quality of compliance risk management – review of internal
controls and policies and procedures
Doc review:
lists of products, descriptions, fees, disclosures, account statements
Procedure manuals and written policies
Mgmt and Board meeting minutes
Internal control and monitoring information
Compensation
Scripts, marketing, promotional materials
Third party agreements
Identify acts or practices that materially increase risk of UDA
Review Complaints
Gather facts and determine violations
Examination Objectives
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Are products underwritten on basis of ability to repay?
Does product profitability depend on penalty fees or back-end
rather than upfront fees?
Does product have high rates of re-pricing or changes in terms?
Does combination of terms increase difficulty in understanding?
Are there penalties for terminating relationship?
Does consumer bear fees or costs to get information about own
accounts?
Is product targeting to particular populations without making sure
marketing, disclosures suit that population?
Transaction Testing High Risk Areas Identified
25
Indirect auto lending involves a prospective car purchaser’s
financing of the transaction through a third-party (bank, nonbank
affiliate of a bank, captive nonbank) that contracts with the dealer
Typical Process
Car dealer collects car purchaser’s information and forwards
information to indirect auto lenders that evaluate purchaser’s
creditworthiness
Indirect lenders decline to provide financing or give the dealer a
minimum interest rate at which the lender is willing to purchase
the financing agreement originated by the dealer
Indirect lenders may use discretion to modify interest rate, allow for
underwriting exceptions, or change other terms and conditions
Indirect lender may allow dealer to increase interest rate above
minimum rate and to receive compensation from the lender
based on the difference in the actual rate and minimum rate
Indirect Auto Lending
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CFPB Bulletin 2013-02 (Mar. 2013)
CFPB issued Bulletin 2013-02 applying ECOA to indirect auto lenders even though they do not interact with the car purchaser because they “participate” in decision to extend credit
Lenders may be liable for pricing disparities that exist on the basis of a prohibited class by (1) giving dealers discretion and incentive to increase actual interest rates and/or (2) allowing for discretion in modifying their own criteria and terms and conditions
Lenders may be liable under ECOA under disparate treatment and disparate impact theories of discrimination
Bulletin recommends steps to mitigate fair lending risk:
Impose controls on dealer discretion and compensation policies
Eliminate dealer discretion to increase interest rate and provide for alternative compensation structure
Robust fair lending compliance management program
CFPB Supervisory Highlights (Sept. 2014)
Pricing disparities are due to discretionary pricing adjustments
Indirect Auto Lending (cont.)
27
CFPB Larger Participant Rule
In June 2015, CFPB finalized rule to supervise larger nonbank auto finance companies and issued related examination procedures
CFPB already had supervised auto lending conducted by larger banks and thrifts
Nonbank auto finance companies that originate more than 10,000 loans or leases are subject to CFPB examination for compliance with, among others, ECOA, TILA, UDAAP, and Consumer Leasing Act
Examination focal points:
Disclosure of auto financing terms/fair marketing
Provision of accurate information to credit bureaus
Fair debt collection
Fair lending
Other areas of emphasis: ancillary products, use of service providers, and repossession/bankruptcy
Indirect Auto Lending (cont.)
28
Indirect auto lending has been major enforcement priority of CFPB:
Nonbank lenders – Westlake Services, LLC/Wilshire Consumer Credit
Security National Automotive Acceptance Company
First Investors Financial Services Group
Captives – Toyota Motor Credit Corporation (CFPB/DOJ)
American Honda Finance Corporation (CFPB/DOJ)
Dealers – Herbies Auto Sales
CarHop/Universal Acceptance Corporation
DriveTime/DT Acceptance Corporation
Banks – Fifth Third Bank
Ally Financial
U.S. Bank/Dealers’ Financial Services
Indirect Auto Lending (cont.)
29
CFPB’s fair lending enforcement initiative on auto lending includes
coordinated action with the DOJ
CFPB and DOJ have Memorandum of Understanding to
coordinate fair lending enforcement (information sharing, joint
investigations, referrals and notifications)
CFPB examines institutions for compliance with ECOA and is
required to refer matter to the DOJ if there is a pattern or practice of
discrimination
Indirect Auto Lending (cont.)
30
CFPB and DOJ fair lending enforcement actions relating to auto
lending typically include following findings and remedial provisions:
Minority borrowers typically paid higher dealer markups (i.e.,
interest rate increases) that were not based on creditworthiness
or transaction characteristics
Pricing discrepancies affected thousands of borrowers and were
attributable to discretionary pricing systems established by
indirect lenders with dealers
e.g., protected class borrowers charged 30 basis points more than similarly
situated non-Hispanic whites
Remedial provisions
Revised dealer compensation policy – pricing mark-up limited to 1.00-1.25%
depending on loan term
Remediation to customers who were “overcharged”
Retain settlement administrator to distribute funds
Indirect Auto Lending (cont.)
31
CFPB’s efforts to regulate indirect auto lending have been sharply
criticized and subject to controversy
Auto dealers carved out of CFPB jurisdiction in Dodd-Frank Act
Regulation via enforcement
Disparate impact
Proxy methods for race and gender (see appendix)
House passed bill to withdraw Bulletin 2013-02 and subject
further efforts to notice and comment rulemaking
Indirect Auto Lending (cont.)
32
ECOA applies to commercial credit and consumer credit – fair
lending protections apply to commercial credit applications and
originations
CFPB has started fair lending examinations of business lenders
while navigating Dodd-Frank statutory framework for CFPB
authority
Fair lending priorities: mortgages, auto loans, credit cards, small
business loans
Small Business Lending
33
Section 1071 of Dodd-Frank Act
Amends ECOA to establish a robust HMDA-like data collection
requirement for loan applications from women-owned, minority-
owned, and small businesses
Requires itemization of data fields such as application date, loan
purpose, action taken, census tract, gross annual revenue of
business applicant, race/sex/ethnicity of principal owners
Purpose is to facilitate enforcement of fair lending laws and identify
business and community development needs of certain businesses
Small Business Lending (cont.)
34
Section 1071 of Dodd-Frank Act
CFPB has delayed action on section 1071 rules several times
CFPB outreach and research
Interdisciplinary panel for rulemaking
Assistant Director, Small Business Lending position
Recent CFPB rulemaking agenda slated pre-rule activities for
September 2016
SBREFA panel
ANPR or NPR?
Public interest groups have pushed for expansive regulations
implementing section 1071
Small Business Lending (cont.)
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Questions
Jean Veta is described by Chambers USA as “one of the premier banking and financial regulatory
enforcement litigators in the country.” She defends financial institutions and their officers and
directors in civil and regulatory enforcement matters, government investigations, internal corporate
investigations, and congressional investigations. One client said, “[s]he brought a discipline and
toughness that was necessary in dealing with private litigants and was very experienced at dealing
with government litigations.” Based on her success in defending IndyMac Bank’s former CEO,
Jean was named Litigator of the Week by The American Lawyer, and was featured when
Covington was named an Am Law 2014 Litigation Department of the Year finalist. D. Jean Veta
+1 202 662 5294
Michael Nonaka advises banks, financial services providers, and non-bank companies on a broad
range of compliance, enforcement, transactional, and legislative matters. He has worked
extensively with federal and state banking agencies and with other federal agencies authorized to
regulate financial services. Mr. Nonaka has significant experience advising clients on issues arising
under financial services legislation such as the Dodd-Frank Wall Street Reform and Consumer
Protection Act. He has advised clients on, among other areas in Dodd-Frank, regulation as a
systemically important financial institution, resolution planning, the Federal Deposit Insurance
Corporation’s orderly liquidation authority under Title II, and the scope of the Consumer Financial
Protection Bureau’s authority.
Michael Nonaka
+1 202 662 5727
Dr. Marsha J. Courchane, Practice Leader of Financial Economics, specializes in financial
institution analyses for regulatory reviews and in support of litigation. Dr. Courchane is a leading
expert in the areas of mortgage and consumer lending and has worked with many of the largest
lenders in the US. Her client work and research focus on issues including fair lending, affordable
lending, credit scoring and the origination, pricing, securitization, and servicing of mortgages. Dr.
Courchane held a number of academic and professional positions prior to her consulting
experience. She served as Director of Financial Strategy and Research in the housing economics
and financial research department at Freddie Mac, and she was Senior Financial Economist at the
Office of the Comptroller of the Currency, focusing on fair lending and credit scoring matters for
large national banks.
Marsha J. Courchane
Charles River Associates
+1 202 662 3804
36
Appendix – BISG Proxy Calculations
http://www.census.gov/genealogy/www/data/2000surnames/index.html
Step 1: Surname
Race/Ethnicity Probabilities for surname
"Johnson" Race/Ethnicity Share
Hispanic 1.5%
African American 33.8%
Asian/PI 0.4%
American Indian 0.9%
White 61.6%
2+ Races 1.8%
Total 100.0% Source: Census Bureau
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Appendix – BISG Proxy Calculations
*Important – BISG does not use the Intra-tract shares commonly used in other geography-based proxies.
Step 2: Geography
18+ Population of Tract 0050.02 - Washington, DC
Race/Ethnicity Tract Counts
Intra-Tract
Shares*
U.S. 18+
Population Count Share of U.S.
Hispanic 1,340 24.5% 36,138,485 0.0037%
African American 1,008 18.4% 27,327,470 0.0037%
Asian/PI 307 5.6% 11,637,514 0.0026%
American Indian 15 0.3% 1,600,043 0.0009%
White 2,693 49.2% 157,123,289 0.0017%
2+ Races 109 2.0% 3,177,961 0.0034%
Total 5,472 100.0% 237,004,762 0.0023% Source: Census Bureau
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Appendix – BISG Proxy Calculations
1Elliott, Marc N. et al, “Using the Census Bureau’s Surname List to Improve Estimates of Race Ethnicity and Associated
Disparities,” Health Serv Outcomes Res Method (2009) 9:69–83.
Step 3: BISG1 Probabilities
BISG Calculation Example
Race/Ethnicity
Surname
"Johnson"
Tract 0050.02
Wash, DC
BISG
Probability
Vector
Hispanic 1.5% 0.0037% 2.3%
African American 33.8% 0.0037% 51.1%
Asian/PI 0.4% 0.0026% 0.5%
American Indian 0.9% 0.0009% 0.3%
White 61.6% 0.0017% 43.2%
2+ Races 1.8% 0.0034% 2.6%
Total 100.0% 0.0023% 100.0% Source: Census Bureau