The Feasibility and Functioning of Public Mortgage Insurance
An International Comparison
STi INSTTE
eh.TECHNOLOGY
FEB 15 2006
by
LIBRARIES
Liou Cao
Master in Urban Planning, Tsinghua University, P.R. China (2000)
Bachelor in Architecture, Tsinghua University, P.R. China (1997)
Submitted to the Department of Urban Studies and Planning in
Partial Fulfillment of the Requirements for the Degree of
DOCTOR OF PHILOSOPHY
IN
URBAN ECONOMICS AND URBAN INFORMATION SYSTEMS
AT THE
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
SEPTEMBER 2005
© 2005 Liou Cao. All rights reserved.
The author hereby grants to MIT permission to reproduce and to
distribute publicly paper and electronic copies of this thesis document in whole or in part.
Signature of Author:
"
C
Department of Urban Studies and Planning
~
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sJune
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102005
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Certified by:
p1i/'~
~Jo
reira Jr.
rfessor of Urban Planning and Operations Research
v'~y~ "·3
Dissertation Supervisor
Accepted by:
Frank Levy
Chairman, Ph.D. Committee
Department of Urban Planning and Planning
Abstract
Public mortgage insurance (MI) is one type of supply-side government intervention in housing
finance. It is an important component of the modem housing finance market, employed to expand
homeownership and provide credit enhancement to mortgage loans. This research explores the
feasibility and functioning of public MI, by conducting an international comparison of three
representative public MI programs: the U.S. Federal Housing Administration; the Dutch
Homeownership Guarantee Fund; and the Mexican Federal Mortgage Corporation. The main
purpose is to build an integrated framework for policymakers when considering a public MI
scheme, from institutional, financial, and operational perspectives. Research methodologies used
include case studies, interviews, Monte Carlo simulation models, and regression analyses.
The analytical framework of this research comprises three research questions: a) What are the
primary economic problems in housing and housing finance markets that cause market
inefficiency and hence call for government intervention in the form of public MI? b) What are the
implied liabilities imposed on the backing government of sponsoring a public MI enterprise? and
c) What are the potential economic problems that can result from the creation of a public MI
system?
Answers to these questions indicate that public MI can be an effective policy tool to address
particular housing market inefficiencies. However, a good fit between public MI and a nation's
housing and housing finance markets entails many factors, including economic, financial, legal,
political, institutional, and even cultural. Public MI should be designed and priced properly to
maintain its financial soundness over the long term, without imposing "hidden" liabilities on the
backing government. Certain institutional arrangements and operational strategies are necessary
to ensure public MI's relative independence and to control market distortions stemming from its
presence.
This research contributes to the knowledge base for any country considering a public MI scheme
to boost its housing market development. It is intended to offer much needed insight into the
economic rationale, financial viability, institutional and legal infrastructure, and operational
strategies of government-sponsored MI programs, and help policymakers make informed
decisions based on a holistic socio-economic view of the public MI.
-2-
Dissertation Committee:
Joseph Ferreira Jr. (Chair)
Professor of Urban Planning and Operations Research
Department of Urban Studies and Planning, MIT
Hugo Priemus
Professor of Housing, Urban and Mobility Studies
Dean, Faculty of Technology, Policy and Management
Delft University of Technology, The Netherlands
David Geltner
Professor of Real Estate and Finance
Director, Center for Real Estate, MIT
Department of Urban Studies and Planning, MIT
Lynn Fisher
Assistant Professor of Center for Real Estate, MIT
Department of Urban Studies and Planning, MIT
Robert M. Buckley
Senior Housing Advisor, The World Bank
-3-
Acknowledgement
The completion of this dissertation is one of the most significant milestones in my life. It would
not have been possible without the generous support of many people.
First, my deep gratitude goes to Professor Joe Ferreira for his mentorship and friendship. He has
fostered an inquisitive, open-minded academic attitude and quantitative spirit in me. He has also
encouraged me to explore a wide range of research areas, which has helped make my five-year
stay at MIT such a wonderful journey. Thanks also go to my other dissertation committee
members. Professor Hugo Priemus has offered me the great opportunity of conducting research at
the Delft University of Technology in the Netherlands. His academic insights and rigorous
research methods have benefited me far beyond this dissertation. Professor David Geltner and
Professor Lynn Fisher have helped me structure the research and turned several interesting ideas
into a coherent dissertation. Meetings with them have always been sources of inspiration. Mr.
Robert Buckley, with his research expertise in international housing finance and decades of
experience in the field, has helped me gain a deeper understanding of the international context of
this research and guided me from the very beginning.
Thanks also go out to those who have contributed enormously to this research: Ms. Kasia
Kozinski and Mr. Charles Capone, who are my great mentors and best of friends; Ms. Judy May,
Mr. Ed Szymanoski, and Mr. Darryl Getter at the FHA; Mr. Hans Mersmann at the WEW; Mr.
Sybo Bruinsma at the WSW; Ms. Marja Elsinga and Ms. Marietta Haffner at the OTB of TU
Delft; Mr. Guillermo Babatz Torres, Mr. Jesus Alan Elizondo Flores, and Mr. Gerardo Bazan
Morante at the SHF; and Mr. Roger Blood at Mercer Management Consulting. Their support
made this research possible.
I am also indebted to the community of the Department of Urban Studies and Planning at MIT,
especially Professors Bill Wheaton, Karen Polenske, and Larry Vale; and Sandy Wellford, Karen
Yegian, and numerous other individuals. Acknowledgements also go to special friends such as
Lijian Chen, Ruijue Peng, Zhan Guo, Ning Ai, Jinhua Zhao, Xiaoyu Shi, Zhanbin Jiang, Ming
Guo, Ayman Ismail, Uri Raich, Ari Goelman, Pierre Fallavier, Yan Zhang, Jiawen Yang, Mizuki
Kawabata, and more. They have made my life the more colorful.
I dedicate this dissertation to my loving parents, Kangping Wu and Hua Cao, and to my sister,
Likun Cao. Their love, encouragement, and confidence in me have always pushed me forward
and held me when I fall. Lastly, I am deeply grateful to Duncan, who has been a wonderful
sounding board for ideas, and has been extremely caring, encouraging, patient, and loving.
-4 -
List of Abbreviations
ADB
Asian Development Bank
ARM
Adjustable-Rate Mortgage
BKN
Government-Sponsored Guarantee Fund in Sweden
CMHC
Canada Mortgage and Housing Corporation
CONAFOVI
National Housing Commission
DIM
Dual-Indexed Mortgages
EU
European Union
Fannie Mae
Federal National Mortgage Association
FCRA
Federal Credit Reform Act
FGI
Financial Guarantee Insurance
FGIC
Government-Sponsored Guarantee Fund in Kazakhstan
FHA
Federal Housing Administration
FIEH
Fuente Integral de Estadistica Hipotecaria (Statistical Mortgage Integral Source)
FONHAPO
Low-Income Housing Fund
FOVI
Financial Housing Aid Fund
FOVISSSTE
Housing Fund for the Social Security Services Institute of the Public Workers
Freddie Mac
Federal Home Loan Mortgage Corporation
FRM
Fixed-Rate Mortgage
FY
Fiscal Year
GDP
Gross National Product
Ginnie Mae
Government National Mortgage Association
GSE
Government-Sponsored Enterprises
HKMC
Hong Kong Mortgage Corporation
HLGC
Home Loan Guarantee Company
HOC
Homeownership Center
HUD
The U.S. Department of Housing and Urban Development
IFC
International Finance Corporation
IIF
Insurance-In-Force
IMGC
India Mortgage Guarantee Company
INFONAVIT
Institute for National Housing Fund for Public Workers
LTV
Loan-To-Value Ratio
-5-
MBS
Mortgage-Backed Securities
MC
Marginal Cost
MI
Mortgage Insurance
MMIF
Mutual Mortgage Insurance Fund
MPB
Marginal Private Benefit
MPC
Marginal Private Cost
MW
Minimum Wage
NAHA
National Affordable Housing Act
NGO
Non-Governmental Organization
NHB
National Housing Bank
NHG
National Mortgage Guarantee
NIBUD
Consumer Credit Counseling Service
NPV
Net Present Value
PMI
Private Mortgage Insurance
RHS
Department of Agriculture's Rural Housing Service Housing
SGFGAS
Government-Sponsored Guarantee Fund in France
SOFOLES
Financing Societies with Limited Purposes
SHF
Sociedad Hipotecaria Federal (Federal Mortgage Corporation)
U.S.
The United States
UDI
Unidades de Inversion (an investment unit in Mexico)
UGC
United Guarantee Company
UPB
Unpaid Principal Balance
VA
Department of Veteran Affairs
VaR
Value-at-Risk
VNG
The Association of Netherlands Municipalities
VROM
The Ministry of Housing, Spatial Planning and the Environment
WEW
Stichting Waarborgfonds Eigen Woningen (Homeownership Guarantee Fund)
WSW
Stichting Waarborgfonds Sociale Woningbouw (Social Housing-Building
Guarantee Fund)
-6-
Table of Contents
Chapter 1 Introduction ...........................................................
11
I.
What Is Public Mortgage Insurance ........................................ .........................................
11
II. The Purpose and Significance of This Research .............................................................. 14
III. Three Research Questions ................................................................
15
IV. The Structure of the Dissertation ................................................................
15
Appendix I. 1 Various Types of Insurance in Mortgage Finance.................................
17
Chapter 2 Theoretical Framework ...........................................................
19
I.
Housing Finance Market Imperfection and Government Intervention............................ 19
II. Credit Risks in Mortgage Contract and Mortgage Insurance ........................................... 22
III. Comparative Research on Different Housing Finance Systems and International
Mortgage Insurance Models ................................................................
26
IV. Added Value of This Research ................................................................
27
Chapter 3 Research Methodology and Three Study Cases...................................
29
I.
II.
Research Methodology .........................................
29
Three Study Cases .........................................
29
A. The United States and the Federal Housing Administration (FHA) .......................... 30
B. The Netherlands and the Homeownership Guarantee Fund (WEW) ......................... 33
C. Mexico and the Sociedad Hipotecaria Federal (SHF).
..................................
35
III. Choice of the Three Cases ................................................................................................ 38
Chapter 4 Market Inefficiency And Economic Rationales For Public MI ............... 41
I.
Market Inefficiency and Uncertainty Risks ................................................
41
A. Public MI Addresses Market Inefficiency .............................................................. 41
B. Uncertainty Risks ..............................................................
42
II. Different Stages of Housing Finance Market Development and Public MI..................... 45
III. Economic Rationales for Public MI - Case Studies ......................................................... 48
A. The U.S. FHA Program ........................................
......................
48
B. The Dutch WEW Program..............................................................
52
C. The Mexican SHF Program ..............................................................
57
IV. Comparison of the Three MI Programs' Economic Rationales.
............................. 62
Appendix 4,.1 Mexican Housing Finance Main Market Participants ........................................ 67
Chapter 5 Analysis of Implied Government Liabilities.............................................. 69
I.
Implied Government Liabilities In Supporting Public MI ............................................... 69
II.
Methodology ..............................................................
73
A. Existing Methodologies - Literature Review ............................................................ 73
III.
B. My Approach ..............................................................
Model Setup ..............................................................
A. The Model .
............................................ .... ..........
B. Modeling Processes ..............................................................
C. Parameter Settings ..............................................................
-7-
74
75
..................................75
76
79
IV. Modeling and Simulation Results ........................................
.........................
A. The U.S. FHA Program .................................................................
81
81
B. The Dutch WEW Program.................................................................
99
C. The Mexican SHF Program .................................................................
116
V. Comparison of the Three Cases and Implications .......................................................... 129
Appendix 5.1 Regression Analysis of FHA Cohort Prepayment Rates, 1975-2003 ............... 134
Appendix 5.2 Components of the Cash Flow Analysis for the U.S. FHA Program ............. 135
Appendix 5.3 Regression Analysis of FHA Multi-Year Cohort Default Rates, 1975-2003 ... 136
Appendix 5.4 Components of the Cash Flow Analysis for the Dutch WEW Program........... 137
Appendix 5.5 Regression Analysis of WEW Multi-Year Cohort Default Rates, 1981-1999. 138
Appendix 5.6 Explanations of Some Parameters Used in the Cash Flow Analysis of the
SHF Mortgage Guarantee...................................................................
139
Appendix 5.7 Components of the Cash Flow Analysis for the Mexican SHF Program......... 140
Chapter 6 Potential Economic Problems Resulting from the Establishment of
Public MI .............................................................
I.
Potential Economic Problems .........................................................................................
143
143
A. Adverse Selection ........................................
143
B. Moral Hazard ........................................
144
C. Non-optimal Risk Taking and Risk Allocation.....
.........................
145
D. Constraining Participation and Development of the Private Sector....................... 146
E. Abuse of Public MI for Political Reasons..................................
147
II. Addressing Potential Economic Problems - Case Studies
......................... 148
A. The U.S. FHA Program ........................................
148
B. The Dutch WEW Program..................................
155
C. The Mexican SHF Program.........................................
162
III. Comparison of the Three MI Programs' Potential Economic Problems .................... 166
Chapter 7 Conclusions And Policy Implications ...................................................
171
I.
Research Purpose and Method ........................................
171
II.
Major Research Findings ................................................................................................
172
A. Economic Rationales for Public MI.......................................
172
B. Implied Government Liabilities of Backing Public MI ........................................ 174
C. Potential Economic Problems Resulting from Public MI ........................................ 178
III. Policy Implications and Recommendations ........................................
179
A. The Government Role in MI Provision .....
...............................
B. Public MI as a Housing Policy ....................................
C. Public Versus Private ........................................
IV. Future Research .................................................
References ......................................................................................................................
-8-
180
181
182
184
187
List of Tables
Table 1.1
Table 4.1
Table 4.2
Table 4.3
Table 5.1
Table 5.2
Table 5.3
Table 5.4
Table 5.5
Table 5.6
Table 5.7
Table 5.8
Table 5.9
Table 5.10
Table 5.11
Table 5.12
Table 5.13
Table 5.14
Table 5.15
Public Mortgage Default Insurance Schemes Worldwide .................................
Development Stages of the Three Public MI .................
..........................
Market Imperfections Addressed by the Public MI ......................................
Prerequisites for Public MI to Function.......................................
...............
Parameters Categories and Explanations ..............................................
Cash Flow Components ...................
...............
Cumulative Default Rates of FHA-Insured 30-Year Fixed-Rate Mortgages ...........
Cumulative Prepayment Rates and Total Termination Rates of FHA-Insured 30Year Fixed-Rate Mortgages .................................................................
Parameters Used in the Cash Flow Analysis of the FHA Program ......................
Erlang Parameters for Annual Unconditional Default and Prepayment Rate
Distributions - The FHA .. .....
..............................................................
Simulation Results of the NPV and Profitability Rate of the FHA 2003 Cohort ......
Multi-year NPV and Profitability Rate Distributions - The FHA ......................
Multi-year NPV and Profitability Rate Distributions from the "Loss Zone"...........
The Sensitivity Analysis of Alternative Scenarios - The FHA ...............
....
Projected Cumulative Default Rates of WEW Guaranteed Cohorts, 1981-2003......
Parameters Used in the Cash Flow Analysis of the WEW Guarantee ...................
Erlang Parameters for Annual Unconditional Default and Prepayment Rate
Distributions - The WEW .....................................................................
Change of Monthly Payment Due to Interest Rate Reset .................................
Simulation Results of the NPV and Profitability Rates of the WEW's Three Types
ofCohorts
................................................
Table 5.16
Table 5.17
Table 5.18
Table 5.19
Table 5.20
Table 5.21
Table 5.22
Table 5.23
Table 5.24
Table 6.1
Table 6.2
.......
.
13
48
63
65
80
80.........
82
83
84
88..
88
89
91
94
97
101
102
106
107
.............. 108
Multi-year NPV and Profitability Rate Distributions - The WEW ......................
The Sensitivity Analysis of Alternative Scenarios - The WEW .........................
Marginal Default Rates of FOVI-Guaranteed Cohorts .......
.............................
Projected Cumulative Default Rates of SHF Guaranteed Cohorts, 1996-1998.........
Parameters Used in the Cash Flow Analysis of the SHF Mortgage Guarantee.........
NPVs and Profitability Rates of the SHF-Guaranteed Cohort Under Different
111
115
118
119
120
DefaultScenarios.................
........................
125
The Sensitivity Analysis of Alternative Scenarios - The SHF ...........................
Comparison of the Modeling Results for the Three MI Programs .......................
Sustainability of the Three Public MI Programs in the Worst Case Scenarios.........
Potential Economic and Social Problems Resulting from Public MI ....................
Operational Strategies to Control Adverse Selection and Moral Hazard ...............
128
130
133
167
168
.................
-9-
...
.........
List of Figures
Figure 4.1
Figure 4.2
Figure 4.3
Figure 4.4
Figure 4.5
Figure 4.6
The Market for Residential Mortgage Credits in Developing Countries................
43
Evolution of the Housing Finance Market and the Position of the Three Countries... 46
Credit Enhancement for Government Insured Secondary Mortgage Market...........
52
Foreclosure Sales in the Netherlands, 1975-2003 .........
.................
........... 54
Capital Sources for Mortgage Loans in Mexico .................
.........
......... 58
SHF's Evolving Role ...............................................................
61
Figure 4.7
SHF's Role in the Secondary Mortgage Market ............................................
Figure 5.1
Figure 5.2
Figure 5.3
Figure 5.4
Figure 5.5
Figure 5.6
Implied Government Liabilities of Supporting a Public MI ..............................
70
Modeling Processes .................................................................
..... 78
Modeling Parameters .................................................................
..... 79
The Distribution of Macro Economic Conditions by Loan Age - The FHA ...........
82
The Frequency Distribution of FHA Cohorts' Ultimate Default Rates ..................
86
The Frequency Distribution of 10,000 Simulated Ultimate Default and Prepayment
Rates - The FHA ...............................................
..........
.............
87
The Probability Distribution of Annual Unconditional Default and Prepayment
Rates - The FHA ...............................................
..........
.............
88
The Frequency Distribution of the Simulated Profitability Rates .........
............. 90
Frequency Distributions of Multi-year Cumulative Profitability Rates ..................
92
The Frequency Distribution of 6-year Cumulative Profitability Rates ..................
94
Revenue Components of the FHA MMI Fund, FY2003 .........
..................
97
The Distribution of Macro Economic Conditions by Loan Age - The WEW .........
99
The Frequency Distribution of WEW Cohorts' Ultimate Default Rates ................
104
The Frequency Distribution of 10,000 Simulated Ultimate Default Rates - The
Figure 5.7
Figure 5.8
Figure 5.9
Figure 5.10
Figure 5.11
Figure 5.12
Figure 5.13
Figure 5.14
W EW ......................................................................
Figure 5.15 The Probability Distribution of WEW's Annual Unconditional Default Rates .........
Figure 5.16 Frequency Distributions of Profitability Rates for WEW's 10-year, 15-year, and
30-year Fixed Rate Cohorts...................................................................
Figure 5.17 Development of House Price and Long-term Equilibrium Price, 1965 - 2000) ........
Figure 5.18 Cumulative Mortgage Loan Delinquency Rate of Consolidated Commercial Banks
in Mexico ......................................................................
Figure 5.19 The Prepayment Pattern of the SHF-Guaranteed Mortgages ..............................
Figure 5.20 Projections of Annual Unconditional Default Rates of the SHF 2003 Cohort .........
Figure 5.21 Projections of Annual Unconditional Prepayment Rates of the SHF 2003 Cohort....
Figure 5.22 Relationship Between Cohort Ultimate Default Rates and Profitability Rates.........
Figure 6.1 The Institutional Structure of the FHA ........................................................
Figure 6.2 The Loss Mitigation Procedure Required by the FHA ....................................
Figure 6.3 The Institutional Structure of the WEW ...............
.......................................
Figure 6.4 The Distribution of WEW-Guaranteed Loans and Owner-Occupied Dwellings'
Prices within WEW's Loan Limit, 2003.....................................................
Figure 6.5 The Average Mortgage Delinquency Rates in EU Countries, 1994-2001...............
Figure 6.6 The Institutional Structure of SHF...........................................................
Figure 7.1 The Interpolated U.S. Treasury Yield Curves of 1981, 1986 and 1993.................
Figure 7.2 Projected Six-Year Cohort Ultimate Default Rates Starting from 1981, 1986 and
1993...................
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62
105
106
109
115
118
120
123
124
125
148
153
155
157
162
163
176
1...................7.............
Chapter 1 Introduction
I.
What Is Public Mortgage Insurance
Housing plays a significant role in the social and political arenas of most societies.
Almost all governments intervene in housing markets through an array of policies and
programs intending to stimulate housing provision and consumption. Public mortgage
default insurance (MI) is one type of program promoting access to formal and affordable
housing finance to the general population or targeted groups. Mortgage default insurance
is a credit guaranty for financial institutions making residential mortgage loans to
individuals. The credit guaranty is payable if the specified loan becomes delinquent and
enters into foreclosure, and the lender fails to recoup its outstanding principal and other
associated costs. The guarantee is payable in an amount up to the level of "coverage"
specified in contract between the lender and the insuring entity. Claims may include not
only the principal, but also delinquent interest, foreclosure and auction costs, and routine
maintenance (Merrill and Whiteley, 2003). MI is one of a range of insurance products
that serve to protect lenders and investors from loss (Blood, 2001).1 A public MI
enterprise, directly operated or backed by the government, offers to take over from
private lenders all or a portion of the credit risks associated with all or a targeted sector of
mortgage loans.
Public MI represents supply-side government intervention in the housing finance market.
It is employed for two main reasons:
1. To encourage and expand homeownership, especially for lower and medium income
households. Access to homeownership has become a policy priority in many
developing as well as developed countries. The economic rationale for increasing
homeownership is the belief that it has positive externalities, namely that ownership
makes people better citizens and causes them to invest more in their neighborhood
The other lines of insurance products include fire and extended coverage, borrower life and disability of
liability insurance, lender errors and omissions insurance, fidelity bonding, flood and earthquake (special
hazard) coverage, and title insurance. It may be useful to consider MI in the broader context of all possible
events that can result in non-repayment of a home loan (Blood, 2001). Detailed explanations of these
products can be found in Appendix 1.1.
and human capital. A number of recent studies attempt to measure the benefits to
homeownership, such as increase in the success of children (Green and White, 1997),
citizenship (DiPasquale and Glaeser, 1999), and a variety of family outcomes and
attitudes (Rossi and Weber, 1996). Acting on the economic rationale that increased
homeownership boosts total social welfare, public MI addresses the issue of
homeownership accessibility and affordability.
2. To provide credit enhancement to mortgage loans, thereby facilitating the
development of a mortgage securitization market. By removing or reducing credit
risks involved in mortgages, public MI reduces investment risks in the secondary
mortgage market and mortgage-backed securities (MBS). It can stimulate the flow of
funds from capital markets into the housing and mortgage sectors. This reason tends
to receive greater attention in developing countries today, as they are eager to develop
robust secondary mortgage markets to channel funds.
In countries where public MI already exists, the historical experience has been for public
MI first to help expand and improve the primary mortgage market. Then, as a by-product
of that primary market momentum, public MI has tended to help accelerate development
of the secondary market and MBS (Blood, 2001). However, evidence from the most
recent development of public MI in emerging economies (like Mexico) has shown that
the government weighs the two reasons equally important and tries to achieve them
simultaneously when establishing a public MI program.
A mortgage default insurance system plays an important role in the modem housing and
mortgage finance markets of many developed countries, including the United States,
Canada, Australia, New Zealand, United Kingdom, France, The Netherlands, China's
Hong Kong, and Sweden. They have demonstrated that a MI system offers a workable
and versatile credit enhancement to support expanded homeownership. Since the mid
1990s, a growing number of emerging countries and transitional economies have
expressed interests in building such a system of their own. Mexico, South Africa and
Lithuania have just established MI programs; in Kazakhstan, India, Latvia, and Thailand,
MI is under active development; and in Russia and Poland the preliminary research and
- 12-
dialogues have begun to explore the feasibility of setting up a MI model. A commonality
of these new or expected MI models in emerging and transition markets is the initial
active involvement of the government. The forms include government-run MI programs,
government-backed MI enterprises, or public-private partnership with international
finance institutions providing support. Table 1.1 summarizes all existing and expected
(under active development) public MI schemes worldwide.
Table 1.1 Public Mortgage Default Insurance Schemes Worldwide
Country with
public MI
Start of
the
program
Targeted
population
Government involvement
Premium
structure (for the
major product)
Loan
coverage
Maximum
Loan-tovalue ratio
1934
Low
incomes
Upfront 1.5%
Annual 0.5%
100%
100%
1944
Veterans
Operated by the U.S.
government - The Department
of Housing and Urban
Development
Operated by the U.S.
government - The Department
of Veterans Affairs
1954
1993
Operated by the government
insurer, Canada Mortgage and
Housing Corporation
Government-managed fund:
SGFGAS
95%
France: SGFGAS
Low and
medium
incomes
Low
incomes
Up to
100%,
varies
with
LTVs
100%
100%
Canada: CMHC
Borrowers pay a
funding fee to help
defray costs of the
VA home loan,
including the MI.
Varies according
to LTV. Highest
upfront 4.50%
Upfront 2%
Annual 0.15%
100%
100%
The Netherlands:
WEW
1995
Low and
medium
incomes
Housing
associations
Low and
medium
incomes
Low and
medium
incomes
A private, non-profit fund
backed by the government
Upfront 0.3%. To
be lowered to
0.28% in 2005
0.28%
100%
112%
100%
-
Annual 0.5%
30%
95%
Floating rates,
around 2.50%
30% or
less
90%
Annual 0.69% for
UDI loans
Annual 0.83% for
Peso loans
Maximum upfront
4.43%
25%
90%
Initially
100%. In
2002 it
was cut to
25%
30%
initially
95%
United States:
FHA
VA
WSW
1983
Sweden: BKN
1992
Hong Kong:
HKMC
1999
Mexico: SHF
2002
75 percent of
all income
levels
Lithuania
2002
Low
incomes
Kazakhstan: FGIC
(expected)
Under
active
developm
ent
Under
active
developm
ent
Low and
medium
incomes
India (expected)
-
A private, non-profit fund
backed by the government
Operated through the National
Housing Credit Guarantee Board
under the Ministry of Finance
Operated through Hong Kong
Mortgage Corporation Ltd., a
public sector entity created to
add liquidity to the Hong Kong
residential mortgage market
Operated by SHF, a government
development bank for housing.
However, the government's
backing will expire in 2013.
Operated by the governmentowned Lithuanian Mortgage
Insurance Company
FGIC will be established as a
public company first. Ultimately
it is to be privatized or evolve
into a public private partnership.
Operated through India
Mortgage Guarantee Company.
The National Housing Bank will
hold 26% stake in the company.
Other stakeholders are private
MI companies and international
-13-
Single premiums
will be offered
initially
-
85%
Up to 100%
financial institutions (ADB,
IFC).
Latvia (expected)
Thailand
(expected)
Poland (expected)
Russia (expected)
Note: Countries that only have private MIs are: Australia, New Zealand, United Kingdom, Italy, Spain, Israel, and South Africa (operated
by a NGO). In Australia, the public MI had existed until 1997 and then was taken over by the private sector.
II.
The Purpose and Significance of This Research
The burgeoning public MI development, especially the start-up efforts in developing
countries, has motivated this comparative research because the author believes that there
are many important economic issues related to the establishment of public MI that are not
well understood by policymakers. The questions that need to be answered include: What
are the housing market failures or incompleteness that justify the government
intervention? Can these imperfections be effectively and efficiently addressed through a
public MI scheme? Does the government understand the financial liabilities involved in
public MI provision and how they fluctuate under various economic conditions? Does the
government have the administrative capability to manage or monitor a public program
and deal with potential economic problems that could result from the creation of a public
MI?
The choice of setting up a public MI program, among other alternative housing policies,
must be based on a thorough understanding of the underlying causes of a nation's
housing market, and opportunity costs and risk factors for the backing government. In
some countries, policymakers' preference to finance-linked housing subsidies and
programs, such as public MI, is due to the "advantage" that the real costs of these
programs can be "hidden," off-budget or deferred and cannot be easily quantified.
However, this kind of thinking is dangerous because: i) public MI may not address the
fundamental problems in the housing market; ii) there are high, often uncertain and
volatile costs to the government for sponsoring a public MI program or enterprise. Even
though those costs may not show up in the current budget, they have to be borne by the
government (hence taxpayers) eventually; and iii) public MI may pose restrictions on the
efficiency of housing finance markets and create market distortions.
- 14-
This research builds an integrated analytical framework for policymakers to consider the
feasibility and functioning of a public MI scheme. This framework accommodates three
perspectives: institutional, financial, and operational. By conducting an international
comparison of three representative public MI programs - the U.S. Federal Housing
Administration (FHA), the Dutch Homeownership Guarantee Fund (Stichting
Waarborgfonds Eigen Woningen, abbreviated to WEW), and the Mexican Sociedad
Hipotecaria Federal (SHF, Federal Mortgage Corporation) - this study analyzes
fundamental economic issues with regard to the creation of public MI, government risks
in supporting it, and potential economic problems resulting from it. The ultimate purpose
of this research is to better inform practices in other countries, especially developing
countries and transitional economies that are showing great interest in setting up their
own mortgage insurance programs. Understanding the questions raised in this research
bears important policy implications and helps policymakers make informed decisions.
III.
Three Research Questions
The three research questions reflect three fundamental aspects confronting policymakers
in any country wishing to create or maintain an effective and cost-efficient public MI
program. They comprise the analytical framework of this comparative study:
*
What are the primary economic problems in the housing finance market that cause
market inefficiency or market imperfections and hence call for government
intervention in the form of a public MI?
* What are the implied liabilities imposed on the backing government (hence
taxpayers) of sponsoring a public MI enterprise?
*
What are the potential economic problems that can result from the creation of a
public MI system?
IV.
The Structure of the Dissertation
The dissertation is organized as follows: Chapter 2 reviews the literature on housing
market imperfection and government intervention, option pricing theories that evaluate
credit risks in mortgage contracts, and comparative research on housing finance systems.
They constitute the theoretical foundation for this research. Chapter 3 presents research
- 15 -
methodology, three study cases and the rationale for their selection. Chapter 4
investigates the first research question by analyzing market imperfections in each of the
three countries that justify the establishment of public MI. Chapter 5 answers the second
research question by applying quantitative models and simulation techniques to measure
the implied liabilities imposed on the public MI program and the supporting government
under the current program regime. Multi-year and sensitivity analyses are also conducted
to evaluate the magnitude of potential liabilities under different economic scenarios.
Chapter 6 explores the final research question by investigating the major potential
economic problems that can result from the creation of a public MI program: adverse
selection, moral hazard, non-optimal risk taking and risk allocation, constraining private
sector development, and the abuse of public MI for political purpose. To address these
market distortions, institutional and regulatory improvements and management strategies
are discussed. Chapter 7 concludes the research and discusses policy implications for any
country considering MI.
- 16-
Appendix 1.1 Various Types of Insurance in Mortgage Finance
Insurance Type
Title insurance
Definition
Title insurance for mortgage lenders and investors provides a
Risk factors
Value or marketability of
guarantee that they have a valid and enforceable lien on the
the property securing
residential real estate covered by the policy, including the
mortgage is compromised
priority of their lien relative to others. Title coverage assures
by a defect in the title.
that, in the event of borrower default and foreclosure, the value
of the underlying property serving to secure the mortgage loan
will not be lost as a result of a title defect or an undisclosed
encumbrance that interferes with the realizable value or
marketability of the property.
Title/closing
agent's
A special title insurance/loan
closing agents' errors and
Mistakes by the loan
E&O insurance
omissions policy has been designed for damages caused by
negligent acts committed by agents for title insurance
companies. This specialized professional liability coverage also
includes legal defenses.
This coverage extends to the loan closing process itself.
Wrongful acts relating to the loan closing-e.g. documentation,
disclosure, or funds disbursement--can undermine the integrity
of the mortgage security such that mortgage guaranty insurance
coverage could be jeopardized, or lender liability under
consumer protection laws might be triggered.
closing agent compromise
the value/integrity of the
newly created mortgage
security.
Errors & omissions
insurance (E&O)
Mortgage
impairment
insurance
Mortgagees errors and omissions coverage (an expanded form of
which is also known as mortgage impairment insurance) is a
specialized form of professional liability insurance which
protects mortgage lenders and their secondary investors against
certain liabilities and losses resulting from an error or accidental
omission in the performance of certain customary operations.
Failure of the mortgage
servicer to perform
necessary actions to
maintain the investment
quality status of the
mortgage.
Directors and
officers liability
insurance
(D&O)
Directors' and officers' liability insurance is a form of
professional liability insurance which can serve the needs of
financial institutions, including those engaged in mortgage
lending and the servicing of mortgage loans for third party
investors. D&O coverage written for mortgage bankers may
have special provisions especially applicable to this particular
business.
D&O protects those engaged in home mortgage lending, selling
and servicing against loss exposure arising from the wrongful
(though not dishonest or criminal) acts of the business
enterprise's executive managers and outside directors. The D&O
policy covers damages, judgments and settlements.
Failure of the mortgage
lending and servicing
institution's management
to fulfill their fiduciary
obligations.
Homeowners'
property insurance
Individual homeowners almost always carry their own
homeowners' property insurance policy which protects against
loss by fire and, typically under 'extended coverage' provisions
of the policy, a variety of other perils associated with
homeownership. Extended coverage normally refers to theft, and
personal liability and causes of physical damage other than fire.
Value of the property
securing the mortgage is
reduced by fire, other
physical damage, or
related perils.
Homeowners flood
insurance
Flood insurance is a specialized form of property and casualty
insurance which in the U.S. involves a unique participation of
the federal government in the underwriting and assumption of
Value of property securing
the mortgage is reduced by
flood damage.
financial risk. Flood insurance for residential dwellings covers
loss from damage or destruction of a home caused by
temporarily rising waters, unusual and rapid runoff of surface
water, or mudslides or collapse of waterfront land caused by
abnormal waves or currents. Coverage can be extended for both
the building itself and its contents.
17-
Mortgage
redemption life and
mortgage disability
insurance
The closely related life and health insurance lines, respectively,
provide personal coverage for the home mortgage borrower in
the event of his death or disability. Mortgage life and disability
coverage offers some additional assurance of timely repayment
in the event that the borrower were to die or be disabled.
Mortgage redemption life insurance basically pays off the full
outstanding balance of the mortgage loan if the covered
borrower dies. Companion disability insurance normally makes
monthly mortgage payments for a stipulated maximum term in
the event of borrower disability, i.e. inability to continue
working.
Mortgagor is unable to
make scheduled monthly
payments due to death or
disability.
Cash flow insurance
Financial guaranty
insurance
Cash flow insurance is a corollary coverage to mortgage
guaranty insurance that is especially important to institutional
investors in mortgage-backed securities. These investors are
relying upon the timely, as well as the ultimate, repayment of
their invested capital. Although private insurers today do not
normally provide such cash flow insurance, investor
requirements for timely payment are satisfied by timely payment
guarantees on their mortgage-backed securities from Fannie Mae
Payments received on
loans in a mortgage pool
fail to produce cash flow
needed for required
payments on mortgagebacked security.
and Freddie Mac or, alternatively by the federal government's
GNMA cash flow guaranty in the case of government securities
backed by pools of mortgages loans that carry governmentsponsored (FHA) mortgage guaranty insurance. Financial
guaranty insurance guaranties the timely payment of principal
and interest, as well as ultimate repayment, on privately issued
mortgage-backed securities that are not issued by Fannie Mae or
Freddie Mac.
Source: http://www.usaid.gov/locations/europeeurasia/ demgov/localgov/pdf
- 18 -
ddocs/annexb.pdf
Chapter 2 Theoretical Framework
This research is mainly built on three strands of literature, which comprises the
theoretical foundation of the study: i) imperfection of the housing market and housing
finance market, and government intervention; ii) evaluating credit risks in mortgage
contracts and mortgage insurance based on option-pricing theories; and iii) comparative
research on different housing finance systems and international mortgage insurance
models.
I.
Housing Finance Market Imperfection and Government Intervention
Broadly speaking, housing finance market imperfection is mainly caused by a whole
class of "information costs." One such information cost resulting in market failures is
"credit rationing". The theory of credit rationing is developed by Jaffe (1971), Jaffee and
Modigliani (1971), and by Stiglitz and Weiss (1981). They argue that lenders' concerns
about credit risk cause them to ration credit rather than to charge higher interest rates.
The resulting equilibrium of a loan market is characterized by excess demand over
supply. Potential borrowers who are denied loans would not be able to borrow even if
they indicated a willingness to pay more than the market interest rate, or to put up more
collateral than is demanded of loan recipients. Lenders fear adverse selection - higher
rates will attract risky borrowers and drive away good borrowers; thus higher rates do not
lead to greater returns. As a result, they apply various nonprice rationing techniques
(underwriting and collateral requirements) that limit default risk. These binding credit
constraints reflect lenders' uncertainty about borrower abilities to repay the loan or
uncertainty about the collateral value behind the loan (e.g., location in a declining
neighborhood).
In the U.S., some empirical studies compare the characteristics of FHA and conventional
borrowers. The study by Canner, Gabriel, and Wolley (1991) evaluates the likelihood of
obtaining a conventional versus an FHA loan as a function of various borrower and
neighborhood characteristics that reflect default risk. They find that young and lowerincome borrowers living in lower-income census tracts are more likely to choose FHA, as
- 19-
are borrowers with a high probability of mortgage payment delinquency. Other studies
have also found that FHA financing mitigates the effects of credit rationing and redlining
(Rosenthal, Duca, and Gabriel, 1991; Shear and Yezer, 1983; Holmes and Horvitz, 1994).
Another type of "information cost" is the uncertainty risk of the housing market as a
whole. During the 1930s in the U.S., high information costs of each mortgage and the
local housing market made housing finance a very local industry, resulting in an
imbalanced capital demand and supply across regions. However, this kind of information
cost hardly exists today in advanced economies, thanks to the development of
communication and transportation technologies and the deregulation of a nationalized
housing finance market. But high information costs do exist in developing countries and
emerging economies where there is no history of private lending and tracking of
borrower credit. Information costs there can be so high that the private sector is reluctant
to invest in housing finance markets. Uncertainty risks are a special type of information
cost. Foster and Herzog (1981) argue that the ability of individual homeowners to repay
their loans is highly dependent upon the economy of the nation. Therefore mortgage
insurance is an economic (or fundamental) risk. The uncertainty risks of the whole
market's behavior in the future suggest that mortgage insurance involves risks that are
not appropriately treated through the private sector, and therefore should be handled by
the government.
Much has been writen on government intervention and subsidy programs for housing
finance markets. Hoek-Smit and Diamond (2003) provide a good summary of various
housing subsidy policies and programs. They emphasize that in overcoming policy or
market failure or extending incomplete markets, policymakers must understand the
causes of the supply or demand constraints. If poor access to, or high cost of, housing
finance is the main bottleneck, relevant policy or regulatory changes and/or financelinked subsides may be considered - among them public default insurance. However, if
the high cost of finance is related to monopolies or other major inefficiencies, subsidies
will merely pay for these inefficiencies. In their view, state-sponsored insurance or
guarantees can be used as a policy tool to encourage mortgage lending in general in the
- 20 -
face of legal, economic or political uncertainties. However, they argue that it is very
important not to use it when the underlying problem is such that the legal, social or
political environment does not permit management of default risks.
Academic discussions on the economic and social welfare impacts of government
intervention are abundant. Within the context of public mortgage insurance, the
comprehensive study by Bunce et al (1996) concludes that FHA helps complete the
mortgage lending market by serving the populations that are unserved or underserved by
the private sector. They argue that the range of service provided by FHA extends beyond
that available from private MIs enabling it to accommodate higher risk borrowers.
Pennington-Cross and Yezer (2000) analyze FHA's role within the U.S. mortgage
markets as creating regional stabilization, information externalities in mortgage scoring
schemes, reducing lending discrimination, and generating homeownership externalities.
Follain and Szymanoski (1995) discuss the government's evolving role in the U.S.
multifamily mortgage markets and the experience of the FHA program in multi-family
insurance. Diamond and Lea (1992) argue that the distortionary effects of the U.S.
government guarantees in the mortgage market are declining rapidly as the capital
requirements for banks and federally sponsored agencies have been strengthened and
FHA premiums raised.
Government intervention can cause problems in the housing finance marketplace as well.
Adverse selection and moral hazard have been thoroughly discussed by Kihlstrom and
Pauly (1971), and Spence and Zeckhauser (1971). Pauly (1974) further explores the
overinsurance and public provision of insurance and argues that in the presence of
adverse selection and moral hazard, the competitive outcome in the markets for insurance
may be nonoptimal. One solution to this nonoptimality is some form of public
intervention. But he emphasizes that the advantage of public provision lies in its ability to
generate a particular kind of information. Through public provision of insurance, firms
are provided with information on the total amount of insurance bought by each
prospective purchaser. Either this information could be provided directly, or a law could
require the purchaser to provide correct information on total purchases to all firms. With
- 21 -
this information an optimal market outcome may occur. Diamond and Lea (1992) made
an international comparison of five developed countries' government intervention of
housing finance markets in the form of "special" circuits, characterized by a significant
degree of regulation, segmentation from the rest of the financial markets, and often
substantial government subsidy. They conclude that many of these special circuits created
certain degrees of distortion and inefficiency in the housing finance systems, and all
subject countries have converged significantly toward competitive market allocation of
mortgage credit since the 1980s. However, they also point out that in many developing
countries where barriers to the market-oriented operation of housing finance are serious,
the government intervention may still be necessary to overcome the difficulties of the
market in pricing and allocating risk. The study by Hoek-Smit and Diamond (2003)
compares various demand- and supply-side government interventions for housing finance
from aspects of efficiency, equity, transparency and implementation. They point out that
government-sponsored insurance or guarantee for primary mortgage market risks can be
a relatively efficient way of encouraging mortgage lending, especially to targeted groups.
However, such programs can be highly damaging if they deter the resolution of economic
and political issues that are elevating default risks. Also, without any objective risk
assessment such schemes can be very expensive in the long run.
II.
Credit Risks in Mortgage Contract and Mortgage Insurance
The major concern for mortgage insurance is credit risks from borrower default. Many
studies view the mortgage contract as containing a compound put (default) and an
American call (prepayment) option. The contingent claims models, developed by Black
and Scholes (1973), Merton (1973), Cox, Ingersoll, and Ross (1985), and others, provide
a coherent motivation for borrower behavior. A number of studies have applied this
model to the mortgage market. Hendershott and Van Order (1987) and Kau and Keenan
(1995) have surveyed much of the literature related to mortgage pricing. Early studies
using option models focused on explaining either prepayment or default behavior, but not
both. A series of papers by Kau, Keenan, Muller, and Epperson (1992, 1995), Kau and
Keenan (1996), Titman and Torous (1989), and Deng, Quigley, and Van Order (1996,
2000) have provided theoretical models and empirical tests that emphasize the
- 22 -
importance of the jointness of prepayment and default options. In recent years, survival
analysis (especially proportional hazard estimation) with prepayments and default treated
as competing risks has become the preferred empirical approach to investigating
mortgage credit.
Some researchers explore the "nonruthless" exercise of the mortgage default or
prepayment options. Deng, Quigley, and Van Order (2000) show that there exists
significant heterogeneity among mortgage borrowers; a pure option model is not enough
in itself to predict borrower behavior. Vandell (1992, 1995), Vandell and Thibodeau
(1985), Foster and Van Order (1985), and Lekkas, Quigley, and Van Order (1993) have
also found evidence of nonruthless default behavior. Four facts about the nature of the
decision-making process with respect to prepayment and default impact the
nonruthlessness in exercising mortgage options (Vandell, 1995): various transaction costs
among borrowers and lenders; borrower's solvency or ability to service the debt; loss
mitigation efforts; and the value of waiting.
Literature on credit risks of mortgage portfolios and mortgage insurance is mainly
focused on risk-based capital requirements or insurance pricing, built upon the underlying
mortgage portfolio performance. The performance measures include conditional
probabilities of delinquency, foreclosure, and prepayment. Existing models of measuring
credit risks can be categorized into three broad types: econometric models, stochastic
simulation models, and "hybrid" models incorporating the two.
The econometric models estimate mortgage default and prepayment rates based on
empirical loan data (LTV, loan type, etc.) and various borrower characteristics
(downpayment, income, race, age, neighborhood, etc.). Then the estimated coefficients
are applied to new portfolios of loans to project the adequate capital requirements or
mortgage insurance premium. Among them, multi-variant regression models are often
used by government agencies and auditors. Academic research using the econometric
models generally includes proxy variables for the default and prepayment options,
measuring the extent to which these two options are "in-the-money. " The contingent
- 23 -
claim model specifies the probability of exercising these options as a function to the
extent to which they are in-the-money, and the "trigger events" that effect the decision on
how far the options need to be in-the-money for it to be optimal to exercise. Several
recent empirical studies have applied the Cox Proportional Hazard model (introduced by
Cox and Oakes, 1984) to estimate mortgage risks (e.g. Quigley and Van Order, 1991;
Deng, Quigley, and Van Order, 1995, 2000; and Tiwari, 2001; among others). Instead of
solving for the unique critical values of the state variables in the contingent claim model,
the proportional hazard model assumes that at each point of time during the mortgage
contract period, the mortgage has a certain probability of termination, conditional on
survival of the mortgage.
The stochastic simulation models use option-pricing techniques to determine the impact
of changes in the mortgage contract or in the economic environment on mortgage
insurance values. The value is obtained numerically as the solution of a two-dimensional
partial differential equation in backward time, whose terminal and boundary conditions
embody the terms of the contract. In particular, two stochastic processes for interest rate
movements and housing price fluctuations are simulated, as well as the correlation
between the two. Both of the decisions to default and prepayment are simulated through a
set of decision rules based on numerical models - certain algorithms take discrete
increments (or decrements) in the housing price and interest rate in every specified
interval until the insurance contract expires. If the house price hits certain critical value
defined by boundary conditions, borrowers default or prepay. The net present value of
losses from a portfolio of loans is to be compensated by the insurance premiums charged.
Cunningham and Hendershott (1984) apply this approach to the pricing of FHA mortgage
insurance. Kau, Heenan and Muller (1993) use a similar model to price private mortgage
insurance value through a set of numerical solutions with different economic
environments and mortgage contracts. Kau and Keenan (1996) improve the model by
introducing catastrophic events into the model, incorporating a Poisson distribution into
the building process.
- 24 -
The third type of models combines part of the econometric model and part of the
stochastic model, creating a "hybrid" model incorporating both historic experience and
simulation techniques. Calem and LaCour-Little (2001) develop estimates of risk-based
capital requirements for single-family mortgages held in portfolio by financial
intermediaries. They first use Cox's proportional hazard model to estimate the probability
distributions of default and prepayment, and then apply the estimated coefficients in
stochastic simulation models to project credit loss probability distributions. Similar
approaches are employed by Rodda, Lam and Youn (2004) to study the FHA HECMs
(Home Equity Conversion Mortgages) refinancing option. A series of papers by Capone
(2000, 2002, 2003) simulate the distribution of potential lifetime default and prepayment
rates and their patterns based on historic FHA loan data, and then analyze the cash flows
associated with each default rate. The simpler version of his model (2000) is to
parameterize the default rate distribution to match the limited information available on
expected lifetime default rates of the FHA historical books of business. With some simple
prepayment rules and assumptions, simulations using random draws from the default rate
distribution are used to develop a distribution of mortgage insurance values. Capone
(2003) further improves the model by incorporating a stochastic economic model based
on a vector autoregressive system of equations.
At the institutional level of valuing public MI provision, a recent study by Buckley,
Karaguishiyeva, Van Order and Vecvagare (2001) has extended the early work by
Merton (1977) that demonstrated an isomorphic correspondence between loan guarantees
and common stock put options. Buckley et al. assume that government provides implicit
guarantee (backing) to the public mortgage insurance enterprise, in case it cannot cover
its liabilities. Public transfers arise when the value of the mortgaged housing and the
insurance company's capital is less than the value of outstanding loans. The probability
of exercising this option by the insurance company imposes a risk to the government. The
authors apply Merton's model to the terms and conditions of 12 forms of mortgage
insurance now in use in 9 countries and discuss the relative risks of the various programs.
- 25 -
III.
Comparative Research on Different Housing Finance Systems and
International Mortgage Insurance Models
Comparative studies of various housing markets and housing finance systems is a recent
addition to the literature of housing finance. Diamond and Lea's study (1992) compares
the housing finance markets between the United States and United Kingdom, to conclude
that these countries have exhibited a significant degree of convergence toward
competitive market arrangements in the last decade. However, significant differences
remain, and those differences seem to derive partly from historical precedents and partly
from different goals of public policy. Jaffee and Renaud (1996) have conducted a
comparative study among transitional economies on their mortgage market development
and concluded that a housing finance system is an essential component of the
development of an efficient financial system in a transitional economy. However, a
housing finance system is unlikely to emerge without government support.
With growing interest in mortgage insurance systems in many developing countries since
the 1990s, more research has been done analyzing particular mortgage insurance schemes
in some countries. Blood (1998, 2001) analyzes the feasibility of building a mortgage
guarantee system in Mexico and Poland, identifying certain prerequisite market
conditions for a viable mortgage guarantee model, including economic and financial
stability, contract enforceability, data availability, title to real property, loan underwriting
and servicing, real estate markets, regulation, and credit and homeownership culture.
Blood and Whiteley' s (2004) report for the Russian Federation analyzes the market
readiness for a mortgage insurance system in Russia and concludes that a public MI is
hard to justify in the immediate near term even if needed laws and data are present.
Papers by Merrill and Whiteley (2002, 2003) are focused on the experience of
establishing Guarantee Insurance Fund for Mortgage Credit (FGIC) in Kazakhstan and
how to export that to other transitional and emerging markets. Several conference
presentations provide an overview of existing mortgage guarantee models in Hong Kong
(Lamoreaux, 1999), The Netherlands (Mersmann, 2001), to name a few.
- 26 -
Among the existing studies, however, very few have taken the task of comparing various
guarantee models in a comprehensive way. Blood (2001) provides a good general
international overview of the existing mortgage default insurance programs and some key
issues for their survival or success, and points out that government supportive policies are
indispensable. However, his research is more focused on empirical summaries than
theoretical analysis. Buckley et al (2001) compare several mortgage guarantee schemes
from one specific perspective: the implied housing market volatility that can be
accommodated by a public MI model if that model does not impose liabilities on the
government. The model they use for comparison is highly simplified and static in the
sense that the interest rate is considered fixed and the fluctuation in the value of the
"debt" (the liability to the insurer) is ignored. Nonetheless, their model suggests how to
quantify government credit risks in sponsoring a public MI program, and how to conduct
comparable analysis across countries.
IV.
Added Value of This Research
This research adds significantly to the existing literature of housing finance and
international comparative study. As an important component of the increasingly
unbundled mortgage finance market, mortgage insurance plays a significant role in both
primary and secondary markets. However, there are very few studies on the systematic
and quantitative comparison of different mortgage insurance models. As more papers
introduce individual cases of new MI models, the development of a holistic and
comparative view on some representative MI systems becomes increasingly necessary.
This research fills gaps in the current academic literature in the following respects:
* It is the first integrated framework for analyzing the feasibility and functioning of
public: mortgage insurance systems. It explores the three fundamental questions of
Chapter 1 regarding a public MI model, and answers them from three distinct
perspectives: institutional, financial, and operational. The framework has general
implications for a country wishing to establish or is in the process of developing
its own public MI system.
* It is the first comprehensive comparative study of three public MI models at
different development stages, ranging from a mature model to a model still in its
- 27 -
infancy, in developed countries as well as the developing world. This
international comparison can shed new light on the socio-economic rationales and
functioning of a public MI model for a wide range of audience.
* It is among the first few studies that apply numerical simulation models to
quantify the scale of credit risk in supporting a public MI system, and analyze the
magnitude of that risk under different economic scenarios and across different
countries.
- 28 -
Chapter 3 Research Methodology and Three Study Cases
I.
Research Methodology
The nature of this research lends itself to both qualitative and quantitative methodologies.
I have used a multi-case study approach for this research and put it into a comparative
framework to demonstrate the commonalities and variations among the three public MI
systems. Qualitative methodologies include intensive semi-structured personal and phone
interviews, conducted not only with personnel from the three public MI entities, but also
with their regulators, competitors, close partners and lenders. In quantitative analysis,
relevant data are collected from monthly and annual management reports, financial
statements, actuarial review reports, various documents and mortgagee letters, and
program websites. Methodologies deployed are regression analysis, Monte Carlo
simulations and discounted cash flow analysis. The quantitative analysis does not intend
to provide an accurate projection of the implied liabilities for the public MI program and
the backing government in each of the three countries. Rather, it serves as an analytical
tool to demonstrate and compare the magnitude of credit risks imposed on the
government under different program design and country characteristics, in a consistent
and quantifiable manner.
II.
Three Study Cases
I have chosen three existing public MI programs as my study cases: the U.S. Federal
Housing Administration (FHA), the Dutch Homeownership Guarantee Fund (Stichting
Waarborgfonds Eigen Woningen, abbreviated to WEW), and the Mexican Sociedad
Hipotecaria Federal (SHF, Federal Mortgage Corporation). The following is a brief
introduction of each of the three countries' housing and housing finance markets, housing
policies, and government involvement, followed by the description of their public MI
programs and the reasons for their selection.
- 29 -
A.
The United States and the Federal Housing Administration (FHA)
The U.S. housing and housing finance markets
There are about 290 million people and 121 million housing units in the United States.
The U.S. housing market features free market forces and very moderate government
intervention. Public housing accounts for less than 5 percent of the total housing stock.
Almost 65 percent of all houses are detached and slightly less than 15 percent are built in
a complex comprising more than 5 dwellings (The WEW special report, 2004). The
homeownership rate was quite high, at about 69 percent in 2004. The rental sector is run
by private landlords.
The U.S. housing and mortgage markets play an important role in the U.S. economy.
Housing is the primary source of wealth for most Americans. Spending within the
housing sector (including rents, utilities, furnishings, maintenance, repair, and
remodeling) has accounted for about one-fifth of the national economic activity for
decades. The U.S. government has been actively promoting homeownership. As a result
of that, and also the strong house price appreciation, U.S. mortgage debt outstanding is
currently around 6.1 trillion dollars, having grown at a 7.5 percent compound annual rate
since 1994 even after adjusting for inflation. Mortgage debt now accounts for 43 percent
of residential value, up from 31.6 percent in 1980.
The U.S. housing finance system is often regarded as one of the most efficient and
effective capital market structures in the world. Between the end of World War II and the
1970s, American mortgage markets were dominated by depository institutions (mainly
savings and loan associations or, more broadly, thrift institutions). The mortgages were
financed with low-cost, short term, government-insured deposits. The rise in the
secondary markets in the 1970s and especially in the 1980s came about largely because
of standardization of pools of mortgages brought on by three government-sponsored
enterprises (GSEs): the Federal Home Loan Mortgage Corporation (Freddie Mac), the
Federal National Mortgage Association (Fannie Mae), and for government-insured loans,
the Government National Mortgage Association (Ginnie Mae). Annual sales of
mortgages to these three institutions have risen to over $700 billion in 1998; they now
- 30-
own or are responsible for about half of the outstanding stock of single-family mortgages
in the U.S. The U.S. government is actively involved in the housing finance markets
mainly through operating the Federal Housing Administration (FHA) and Ginnie Mae,
and regulating Fannie Mae and Freddie Mac.
The FederalHousingAdministration
The history of the FHA finds its origin in the Great Depression with the failure of the
housing and financial systems. In 1934, Congress passed The National Housing Act and
established the Federal Housing Administration (FHA). The objective of the FHA was to
encourage banks, building and loan associations, and other approved institutions to make
loans for building homes, small business establishments, and farm buildings, by insuring
all loans underwritten to its standards. Since its inception, the FHA has experienced
periods of great success and failure.
The historic impact of the FHA mortgage insurance program has been significant. In
addition to laying the financial infrastructure for the vibrant secondary market that exists
in the U.S. today, the FHA helped raise physical housing standards by publishing the
FHA Minimum Property Standards, which provided detailed specifications for new
middle-income housing. Later, the FHA was essential to developing appraisal standards
that help set market values, minimize fraud, and establish quality control features that are
used today.
Currently, the FHA is one of the largest credit programs within the U.S. government. It
continues to provide mortgage insurance to encourage lenders making credit available to
expand homeownership to unserved or underserved populations. The FHA covers 100
percent loan losses due to borrower defaults. Since its inception, the FHA has provided
mortgage insurance to 32.6 million single family households and 45,000 multifamily
projects containing 5 million units of housing. The FHA currently has 5.3 million insured
single family mortgages and 12,706 insured multifamily projects in its portfolio (FHA
Management report, FY2003). In FY 2003, FHA insured about 1.3 million loans, a value
of 159 billion dollars of new mortgage originations, and its total insurance-in-force (IIF)
-31 -
was 490 billion dollars. 88.9 percent of its IIF is the single-family mortgage insurance
business.2 The FHA's major insurance fund, the Mutual Mortgage Insurance (MMI) fund,
was estimated with an economic value of 22.7 billion dollars at the end of FY 2003. 3
Targeting lower income borrowers, the FHA-insured loans have a ceiling that is 87
percent of the conforming loan 4 ceiling or 312,896 dollars in 2005 for high-cost areas and
48 percent of the conventional loan limit in low cost areas or 172,632 dollars in 2005. As
with conventional loans, the financing limit is higher for properties with multiple units two-to-four with at least one owner-occupied.
The challenge of the FHA is to remain meaningful and valuable to the nation's housing
system, while relinquishing business that can be well-managed by a viable private MI
sector. Today the mission of the FHA MI program within national mortgage markets is
to:
* Encourage homeownership among low-income, first-time homebuyers and
minority borrowers;
* Counteract lending discrimination nationwide;
* Establish mortgage loan quality standards and explore new financial instruments
and insurance products;
* Generate useful information and develop technology that will positively impact
the provision of housing; and
* Support multifamily projects for affordable housing.
While the main business of the FHA program is to provide insurance for single-family
and multifamily residential housing and healthcare facilities, other businesses include:
property acquisitions, maintenance and servicing of the property, foreclosure sales (for
FHA portfolios borrower defaults and loan foreclosure), and loss mitigation incentives
and mortgage notes assignment activities.
2
FHA's multifamily and health care insurance business comprised 10.8 percent of its IIF.
3 At the end of FY2003, the MMI Fund comprised 82.48 percent of the FHA Insurance Fund. The other
funds are: the General Insurance (GI) Fund, 16.55 percent; the Special Risk Insurance (SRI) Fund, 0.92
percent, and the Cooperative Management Housing Insurance (CMHI) Fund, 0.05 percent.
4 If a borrower borrows at or below the conventional loan limit for non-government mortgages, he/she
would have what is generally known as a "conforming" loan. If the amount borrowed is above the
conventional loan limit, he/she would have a "jumbo" loan and face a somewhat higher rate because larger
loans imply more lender risk.
- 32 -
B.
The Netherlands and the Homeownership Guarantee Fund (WEW)
The Dutchhousing and housingfinance markets
Over 16 million people live in the Netherlands. The Dutch housing system is known for
being effectively and stringently regulated by the government, mainly through housing
policies and spatial planning policies. The Dutch housing market has several defining
characteristics: the strong position of the social rental sector, broad rental subsidies and
rent controls.,and significant provision of developable land by municipalities. At the end
of 2003, the Dutch housing stock consisted of about 6.7 million dwellings, of which 2.4
million were in the social rental sector (35 percent); the owner-occupied sector comprised
54% of the housing stock; and the private rental sector was much smaller, only about
11%.
Traditionally the Netherlands has not had a large owner-occupied housing sector and
government policy was more focused on constructing social rental housing owned and
managed by housing associations. Since 1989, the Dutch housing policy has changed in
favor of market forces and less government intervention. As a result, demand for
homeownership surged. In 2000, the Dutch Parliament accepted a policy paper "What
People Want, Where People Live" that defined national housing goals through the year
2010. One of the core objectives is to increase homeownership to 65 percent by 2010.
The establishment of the WEW guarantee fund for homeownership is one of the
government measures to achieve that goal.
The Dutch mortgage market has grown enormously over the past decade, in line with the
owner-occupied market. Outstanding mortgages for housing were 363 billion euros,
accounting for 81 percent of the national GDP in 2001 (The WEW special report, 2004).
The major players in the housing finance market are commercial banks, special mortgage
banks, insurance companies and savings banks. The government involvement in housing
finance is embodied in two guarantee funds: the WEW (Stichting Waarborgfonds Eigen
Woningen) fund to facilitate homeownership, and the WSW (Stichting Waarborgfonds
Sociale Woningbouw) fund to ensure the financial stability of housing associations.
- 33 -
The Homeownership Guarantee Fund (WEW)
The WEW was created by the Dutch Ministry of Housing, Spatial Planning and the
Environment and the VNG (the association of Netherlands municipalities) in November
1993. It is a private, non-profit organization with close ties to the central and local
governments through their continuous backing. 5 Since 1995, the WEW has been issuing
its insurance product - the National Mortgage Guarantee (NHG) - to provide mortgage
guarantees to borrowers who purchase an existing or newly built owner-occupied house.
The guarantees for home improvements were issued from the beginning of 1999. WEW's
guarantee covers 100 percent of the loan amount. As a result, borrowers can obtain high
loan-to-value ratio (LTV) loans, even with LTV exceeding 100 percent. Lenders also
benefit from WEW guarantees because of the "zero solvency" with the guarantee, so that
they do not need to keep capital reserves on their balance sheets.
The WEW aims to serve low and middle income populations. The loan ceiling for
WEW's guarantees is C230,000 in 2004, which includes all the transaction costs such as
notary costs, commission fees, etc. Loans for home improvements fall under the same
conditions. On average, the Fund provides approximately 60,000 guarantees a year. In
2003, the WEW provided an all time high of 73,889 guarantees of Cl billion loan value.
The potential market share (the market segment qualifying for WEW guarantees) of the
WEW is about 50 percent of the total mortgage market in the Netherlands, within which
the Fund actually reached about half of it. Therefore, its guarantee product penetration is
around 25 percent. Since its inception in 1995, the Fund has issued 521,181 guarantees
with a value of C60 billion. As of the end of 2003, the WEW's equity was C286 million.
This amount serves as capital reserves to cover the risk of a nominal amount of about C52
billion guaranteed mortgages (i.e., insurance-in-force). The capital ratio of the WEW
fund in relation to its guaranteed capital was approximately 0.55 percent in 2003.
5 They agree to provide the WEW interest-free loans to top up its capital reserves if these reserves fall
below a minimum obliged level. The critical limit of the fund reserves is fixed at 1.5 times the average loss
level during the last 5 years. If the fund reserves drop down below this critical level, both the central
government and participating municipalities will provide the fund with subordinate interest-free loans.
Therefore, the Dutch Central Bank considers the guarantee as a government guarantee.
- 34-
The WEW is a lean organization with the sole business of providing mortgage guarantees
to single-family owner-occupied houses, for purchases or home improvements. It
stipulates regulations for the guarantee issuance, and delegates all other tasks to lenders,
including underwriting the guarantees, property acquisition and foreclosure sales, and
recourse to defaulted borrowers.
C.
Mexico and the Sociedad Hipotecaria Federal (SHF)
The Mexican housing and housing finance markets
As a developing country with huge housing pressure, Mexico is at a crossroads.
Restoration of macroeconomic stability after the 1994-1995 financial crisis, coupled with
strong government commitment to housing reforms, provides the nation with a unique
opportunity to strengthen its housing finance markets and improve housing conditions to
meet increasing demand.
The current population of Mexico is about 105 million, most of whom live in cities larger
than 100,000. The estimated shortage of housing amounts to over 10 million houses and
the needs of new financial and infrastructure programs are huge. 750,000 new homes are
needed annually in Mexico just because of household formation. Mexico's housing
market is highly segmented. 6 Its housing stock consists of about 24.5 million homes, twothirds of which are in the bottom three market segments (Minimum, Social and
Economic). However, half of the housing stock value is concentrated in the higher-end
houses in the Middle, Residential and Residential Plus segments (The State of Mexico's
Housing, 2004). The substantial value contained in the existing housing stock is highly
under-leveraged. Only 12.6 percent of the housing stock is currently mortgaged. The
6 Mexico's
existing housing stock and its value distribution
Price range
(000s of pesos)
Minimum
Avg. price
(Pesos)
Price
(minimum wage
equivalents)
5.4 MW
Number of
units
(Millions)
1.85
Value
(Billions of
pesos)
$14.6
<80
79,000
80-200
151,000
10.4 MW
7.17
$108.1
Economic
Middle
200-380
380-1,000
217,000
568,000
14.9 MW
39.0 MW
6.46
6.96
$139.9
$395.2
Residential
1,000-2,000
1,230,000
84.6 MW
1.18
$145.4
>2,000
3,628,000
249 MW
0.81
$292.8
24.43
$1095.9
Social
Residential plus
Total
Source. Softec 2003
35 -
primary emphasis of Mexican housing policy for at least the last 30 years has been
homeownership, and with considerable success: 78 percent of households own their own
homes, and about 13 percent of households rent. However, The most significant
challenge in Mexican housing markets continues to be the presence of a large informal
sector, creating substantial negative externalities.
Mexico's current housing finance system is probably more complex, fragmented, and
"outside the banking system" than in any other emerging markets. Access to housing
finance has been dominated by political and administrative processes rather than market
driven. Major players in the housing finance system are segmented according to markets:
i) low income (people earning less than 3 minimum wages (mws); ii) social interest
(earning 3 to 8 mws); and iii) middle and upper income (earning 9 mws and above). A
spectrum of private to public entities serves their targeted populations. Overall, the
housing finance system in Mexico serves only a small portion of the total potential
market, and relies on government-sponsored financial institutions and mandated housing
finance revenue streams (The State of Mexico's Housing, 2004). Public or publicly
mandated institutions provide the majority of housing finance. The housing sub-accounts
of the pension programs (INFONAVIT and FOVISSSTE) are financed through
mandatory contributions by firms and employees. They primarily finance the sale of new
homes to low-wage employees in formal private and public sector jobs. Currently, the
private sector provides very little capital for housing finance because of the damage
caused by the 1994-95 peso crisis.
The Sociedad Hipotecaria Federal (SHF)
The SHF was chartered in 2001 as a federal development bank and is currently owned by
the federal government. Its predecessor, FOVI (Fondo de Operacion y Financiamiento
Bancario a la Vivienda), was established in 1963 as a trust fund to channel federal
government money to housing. From February 2002, the SHF took on FOVI's trust role.
The SHF was created in order to grant loans and guarantees to intermediary finance
organizations. It does not have a retail operation, but channels funds through Mexican
- 36 -
commercial banks and SOFOLes,7 acting as a second-tier bank. Currently SHF offers
loans for purchase of new or existing homes by individuals for ownership or rental, land
development and commercial infrastructure (The State of Mexico's Housing 2004). It
serves a broad target market of households earning between 2 and 50 minimum wages
(3,000 to 75,000 U.S. dollars), qualifying 75 percent of all families in Mexico, and
specifically, 91 percent of families in the 80 largest cities.
Over the most recent few years, the management and strategic direction at the SHF has
changed such that mortgage default insurance has become its main focal point. Now its
primary mandate is to develop the mortgage securitization market in Mexico by granting
guarantees and standardizing the origination and administration of mortgage loans. The
SHF provides intermediary financiers with a 25 percent first-loss mortgage default
guarantee, in addition to the other two guarantee products it offers - the construction loan
guarantee and on-time payment guarantee for MBS. SHF's guarantees carry the full faith
and credit of the Mexican government until October 2013. After then, whatever liabilities
undertaken by the SHF will be supported by the institution's own financial strength.
Eventually the SHF hopes to see private MI companies come into the Mexican mortgage
market after it demonstrates the viability of the MI business and it then can become a
resinsurer and share the risks with private partners.
The SHF's MI product has been in existence for only a little more than two years. 8 From
2002 to 2004 (August 31), SHF issued about 140,000 guarantees of a total loan value of
35 billion pesos (about 3 billion U.S. dollars). In 2003, it issued 53,149 loans with a total
amount of 15.5 billion pesos (equivalent of 1.36 billion U.S. dollars). As of 2003, SHF's
capital reserves for its first-loss mortgage guarantees are about 197 million pesos, to
cover the potential losses of its 24.3 billion pesos insurance-in-force, indicating a capital
ratio of 0.81 percent.
7
Sofoles are special purpose financial companies, also known as non-bank banks. Since their inception,
mortgage Sofoles have concentrated on originating and servicing FOVI (now SHF) products. Sofoles are
the most dynamic sector of the Mexican housing market. They have grown from zero in 1995 to controlling
over 95 percent of the SHF program by December 2002, originating, underwriting, and servicing almost
300,000 loans in that period.
8 There was an old MI product, the Pari-Pasu, under the previous FOVI regime. Some of the Pari-Pasu
guarantees are transferred to be under the SHF's first-loss guarantees.
- 37 -
The SHF's mortgage guarantee program will likely exert big influences on Mexico's
housing and mortgage markets, such as the following:
* Establish consistent mortgage origination standards;
*
Consolidate consumer credit information across a large number of institutions;
* Develop standardized servicing guidelines; and
* Build up the market confidence and set up clear rules for the private sector to
participate in mortgage insurance business or invest in MBS.
III.
Choice of the Three Cases
I choose the FHA, WEW and SHF as my study cases for comparison, for the following
reasons:
a) They are representative models. The world of public MI programs can be categorized
by two main criteria: the direct operator of the program and the macro economic
context of the country where the program is operated. As shown below, the three
cases are exemplars of each of the three existing scenarios:
Government-
Government-
run public MI
backed, privately
rpublic MI
FHA
i)~
w
Developed
countries
Non-existent
Developing
FHA: directly run by the government, operating
in a developed economy
WEW: backed by the government and run by a
privately foundation, operating in a developed
economy
SHF: directly run by a government agency,
operating in a developing country
countries
The U.S. FHA is a full-fledged public MI program completely managed by the
government. It operates in a developed economy with the fully private housing
market and mature housing finance market, including both the primary and secondary
mortgage markets. Similar public MI models can be found in Canada and Hong
Kong. The Dutch WEW is operated by a private, non-profit organization with the full
backing of the central and local governments. It functions in an advanced economy
with strong government intervention in the housing and housing finance markets. The
WEW is to reduce barriers to homeownership rather than to promote it. As a result of
Dutch government policy in the past, a significant portion of the housing stock is
- 38 -
social rental housing. Similar context is shared to some extent by many other
European countries, such as France. The Mexican SHF is a government development
bank with a limited period (until 2013) of full government backing. It is developing
fast as a major player in Mexico's primary market and to jump-start the development
of the secondary market. By issuing mortgage insurance, the SHF hopes to achieve
multiple goals such as accumulating market data, setting clear rules, and building the
necessary legal, social and financial infrastructure. Challenges faced by the SHF
reflect a lot of common legal and socio-economic problems shared among developing
countries and emerging economies. In short, the FHA stands for a mature and
complex public MI entity fully embedded within the government in an advanced and
developed private housing market; the WEW represents a relatively young and simple
public MI entity, in the form of a private, non-profit organization, that operates in a
developed economy with both the private housing market and social housing sector;
and the SHF typifies a nascent yet aggressively developing public MI program run by
the government, in an emerging economy facing severe housing shortage and housing
finance market flaws.
b) They are at different stages of the public MI development. The U.S. FHA has existed
since 1934 and its purposes and products have been adapting to housing policy and
housing market changes ever since. The Dutch WEW has been operating in its current
form only since 1995, and has not yet been through any severe economic downturns.
The Mexican SHF was created recently, post the severe financial crisis of 1994-1995
that essentially damaged the confidence of the mortgage market. The SHF is expected
to restore and stimulate mortgage lending and channel much-needed funds to the
housing sector. The comparison among the evolutionary paths of the three public MI
schemes can provide valuable insights applicable to a wide range of countries.
FHA: mature
WEW: developing/stabilizing
SHF: sarting
Moreover., the evolution of a public MI system cannot be separated from the broader
housing finance market development and changes of the national housing policy. The
three chosen countries have similarities and differences in these aspects as well.
- 39 -
c) The structure and design of their public MI systems differ in significant and
interesting ways. The differences range from institutional context, organizational
framework and operational management, to MI product details such as the premium
structure, target populations, loss coverage, claim payment method and timing, and
risk containment. The variations among the three cases illustrate that a public MI
scheme is country-specific. The comparison shows how they fit into the unique
context of their nations' housing markets and housing finance systems and the
mechanism they use to address specific housing finance problems.
- 40-
Chapter 4 Market Inefficiency And Economic Rationales For Public MI
I.
A.
Market Inefficiency and Uncertainty Risks
Public MI Addresses Market Inefficiency
Economic theories distinguish four main types of market inefficiency: externalities,
information imperfections, market power, and public goods. Public MI, as one type of
government intervention, has to be justified by certain economic rationales that address
housing finance market inefficiency. As discussed earlier, public MI is usually
established for two main purposes: promote or expand homeownership, and provide
necessary credit enhancement to facilitate secondary market development. In a complete
housing finance market, as usually found in developed countries, the fundamental reason
for government involvement in increasing homeownership is its positive externalities.
This suggests that homeownership is likely to be undersupplied from a social perspective
because private sectors cannot reap the full benefits of homeownership and therefore will
not provide it to the socially optimal level. The government steps in to provide MI to
private lenders, lowering their investment risks and the cost of capital, and thereby
boosting the supply of mortgage credits or lowering the barrier (e.g. downpayment
threshold) to obtaining a mortgage. In many cases, the housing finance market is
incomplete in emerging and developing economies. Only a small proportion of
households can afford newly constructed housing - recent calculations show that this
figure is around the
7 0 th percentile
of the income distribution in countries like Brazil,
Mexico and Indonesia (Hoek-Smit and Diamond, 2003). Owing to the lack of access to
formal housing finance, most newly formed households in these countries are left with
the choice of doubling up with relatives, building informal and substandard housing
(slum formation), or squatting. In these situations, the public MI brings foremost public
goods such as better public health, improved fairness and societal stability, in addition to
expanding the boundary of homeownership affordability to create positive externalities.
In providing credit enhancement for developing the secondary mortgage market,
especially mortgage securitization, public MI addresses the market inefficiency of
information imperfections. In developing countries and emerging economies, there is
- 41 -
often little history of formal mortgage lending and no tracking of borrower credit.
Investors lack confidence in the country's macro economy and its mortgage market
stability, or they are concerned about the quality of the mortgage portfolio backing the
MBS issuance because of asymmetric information. These concerns of evaluating
uncertain risks involved in mortgages can not only lead to a prohibitive level of private
cost of capital in mortgage lending, but also hamper the advancement of the mortgage
securitization market through which many developing countries hope to channel much
needed capital to the housing sector. A government-sponsored
MI system lowers the
information costs for private lenders and eases the uncertainty concerns of MBS investors
by essentially reducing their need for mortgage performance information, because part or
all of the default-related credit risks are borne by the government.
B.
Uncertainty Risks
Why cannot the functions of a public MI system be completely fulfilled by the private MI
sector? Why do developing countries jumpstart the MI provision with a public entity?
The key reason is the various uncertainty risks involved. The capital market for housing
credits can be highly restricted when there is potential risk for high default rates, due to:
i) political or economic uncertainty; ii) legal framework (or lack of thereof) allowing for
valid collateral and security interests; or iii) sub-market of borrowers with limited wealth
or volatile income for which private capital would be very expensive if available. These
risks could result in an unviable private housing finance market in general or among
particular population groups.
The uncertainty risks involved in the MI provision differ between developed countries
and developing economies. In developing countries, uncertainty risks are a function of a
country's political and economic instability, that is, the idiosyncratic risks of potential
catastrophes such as financial crisis or depressions. In developed countries, the level of
catastrophic uncertainty risks due to economic or political instability is very low. There,
the uncertainty risks are with regard to expanding the underwriting envelope to include
marginal borrowers (low-incomes, low credit score borrowers, etc.). For private mortgage
insurers, these borrowers are vulnerable to economic shocks or recessions, which would
- 42 -
incur potential huge losses. These losses are either hard to project with some certainty, or
the necessary insurance premium would be too high. As a result, the private MI sector
will not supply insurance to certain borrower classes (credit rationing). For the
government to take risks that the private sector is not willing to take, there must be some
economic rationale. The belief must be that the marginal social benefit of including
marginal borrowers into homeownership exceeds the marginal cost of bearing the
additional credit risks by the government. To illustrate this argument, I set up a model as
following (Figure 4.1):
Figure 4.1 The Market for Residential Mortgage Credits in Developing Countries
Cost of
homeownership
risks
cepremium+ costof capitalfor political/economic
r
risks)
lium+ u*(costof capitalforpolitical/economic
emium- PMIpremium)- (1- u)*(costof capitalfor
rL
rP
inds+ PublicMIpremium
self-insurance
premium- PublicMIpremium)- (costof
orpolitical/economic
risks)
rO
rl
rc
K
Supply of mortgage
credit
So: supply schedule of mortgage credit without MI
S : supply schedule of mortgage credit with only the private MI sector
S: supply schedule for borrowers of mortgage credit with the presence of a public MI
S: supply schedule of lenders of mortgage credit with the presence of a public MI
D: demand schedule of borrowers
MPC: marginal private cost
MC: marginal cost
MPB: marginal private benefit
K: supply of mortgage credits
r: cost of homeownership - using mortgage interest rate as the proxy
In a housing finance market of developing countries and emerging economies,
households' demand for homeownership is depicted by the demand schedule D, which
represents the marginal private benefit of owning a home. The cost of homeownership is
- 43 -
represented by the mortgage coupon rate r (including the mortgage insurance premiums
paid by borrowers). Without any form of mortgage insurance, private lenders provide
mortgage credit according to the supply schedule of So. Their marginal private cost has
three components: cost of funds, implied premium for self-insurance, and cost of capital
for bearing uncertainty risks of political and/or economic instability. The last risk
component is fairly high in those countries for private lenders. Therefore the resulting
market equilibrium is at (Ko, ro),with restricted mortgage credits and high mortgage
coupon rate.
If there exists a private mortgage insurance industry, the marginal cost for lenders
becomes the sum of cost of funds, private mortgage insurance premiums, and a
proportion (v ) of the cost of capital for political and/or economic risks, because the
private mortgage insurer takes on a small part of those risks. However, v is only slightly
less than 1 (100 percent) for lenders because private mortgage insurers cannot (or are not
willing to) take a substantial part of the uncertainty risks, due to the same reason - the
cost of capital for insuring those risks is too high. Usually private MIs only provide
partial coverage for lenders. Compared to the situation of lenders' self-insurance, the
marginal cost for lenders is slightly lowered mainly by the efficiency gain from the
private sector: (self-insurance premium - PMI premium), because, presumably, private
MIs have better risk management skills and diversification capacity than individual
lenders. However, the magnitude of this efficiency gain is limited so that it expands the
mortgage credit supply only modestly and moves the supply schedule to Sp. The new
market equilibrium is then at (K1 , rl). The increased supply of mortgage credit lowers the
mortgage rate to r.
However, the government wants to further expand the mortgage credit supply and
considers the optimal equilibrium in the residential mortgage credit market be at (K*, r*),
because it believes that positive externalities with increased homeownership increase
total social welfare. At (K*, r) the marginal cost of providing mortgage credit and the
marginal social benefits (instead of marginal private benefit) of homeownership equate.
But private mortgage insurers will not take on more risks to reach the credit supply level
- 44 -
of K*, because then the insurance premium they will have to charge to compensate for the
cost of capital would be too high, resulting in an unaffordable mortgage rate of r. To
achieve the supply schedule depicted by S*, the government establishes a public MI
program that usually provides 100 percent coverage and therefore completely removes
uncertainty risks of political and/or economic catastrophes from lenders. Now the
marginal cost for lenders becomes cost of funds plus public MI premium, which is
substantially lower than the original marginal private cost because of the removal of cost
of capital for uncertainty risks. The new equilibrium (K*, r*) greatly expands the
mortgage credit supply and lowers the mortgage rate. The government-sponsored public
MI charges an insurance premium of (r*-r() to cover the expected losses under normal
economic conditions, while the potentially large credit risks imposed on the government
(represented by the segment of rL-r ) are borne by taxpayers collectively. Again, the
economic rationale for bearing these risks by taxpayers is the belief that positive
externalities to the society as a whole outweigh potential costs. The model of the market
for mortgage credits in developed countries is similar to the model presented before
(Figure 4.1), except that the uncertainty risks are with regard to including marginal
borrowers into homeownership, and the cost of capital for doing so is too high for both
lenders and private mortgage insurers.
In some cases, the strongest economic rationale for the public MI provision lies in
addressing market failure rather than reducing uncertainty risks. For example, when the
banking regulations require only high down payment loans and/or bank-lending markets
are geographically concentrated - as they were in the U.S. in the Depression - then
public provision of MI can be the only supply curve for the credit market of low down
payment mortgage loans, not the shifted supply curve from the private provision.
II.
Different Stages of Housing Finance Market Development and Public MI
Today public MI is an important component of the broader housing finance market. The
discussion of economic rationales for public MI must be put into the context of the
overall housing finance market development. So, first I examine the different
development stages of the housing finance market (Figure 4.2).
- 45 -
Figure 4.2 Evolution of the Housing Finance Market and the Position of the Three
Countries
,ii........................ii..............
Stage I
i Stage II
. Self-finance
. Informal sector
Direct finance
Primary market with two
L
parties: lenders and
I borrowers
The housing finance
system in many developing
A variety of countries'
housing finance systems are
countries is still largely at
Stage I, with a large
informal sector.
mostly at Stage II, including
A ads
lIICeniCall
Mexican market
both developed and
developing countries
ad
...
L
X
Stage III
Indirect finance
Secondary market with
financial intermediaries unbundling of the market
Majority developed economies
have well-functioning
secondary markets, although
some emerging nations are
trying to establish it too.
.~-1-
IIIarKei
Dutch maiket
i
b~~~~~~~~~~~~~l
The evolution of the housing finance market can be divided into three stages: selffinance, direct finance and indirect finance. Self-finance is prevalent in developing
countries with a large proportion of very low income households that cannot afford a
mortgage. Instead, they self-build houses with the assistance of extended family, financed
primarily with cash to buy building materials and get some technical assistance from
people trained in design and construction. Self-built housing usually lacks clear land title
and is not formally connected to urban services, posing threats to public safety and
health. Self-financed and self-built housing comprises roughly half of all newly
constructed housing and nearly two-thirds of the existing stock in Mexico.
Direct finance involves two parties: mortgage lenders and borrowers. Lenders, usually
banks, provide all the major aspects of mortgage lending - they originate loans, service
them (i.e., collect payments and manage defaults) and are the ultimate investors. They
hold mortgages in their portfolio and finance them through short-term deposits or issuing
long-term bonds. Therefore, lenders accept all risks including credit risk, interest rate
risk, business risk, and management and operation risk. This form of housing finance
prevails in both developing and developed countries and is usually the predominant form
of financing. In the Netherlands, around 85-90 percent of the mortgage funding resources
are supported by general funding methods including banks' savings accounts, other
- 46 -
accounts, bank bonds, loans from other mortgage finance institutions and insurance
premiums (EMF HypoStat 2001). In the United States, 25-30 percent of the mortgages
are retained in lenders' portfolios and funded by their resources.
Indirect finance refers to the existence of secondary mortgage markets where mortgage
portfolios are sold to secondary market entities or pooled together with other similar
mortgages to form mortgage-backed securities (MBS). The secondary markets have been
proven an efficient and low cost way of raising money and managing cash flows,
primarily because of economies both in raising money "wholesale" in the capital markets,
in processing the purchase and servicing of large numbers of mortgage loans, and in
managing risks through diversification and in some cases implicit government guarantees
(Van Order, 2000). The unbundling of the mortgage markets results in specialized
intermediaries such as mortgage originators, servicers, secondary market institutions and
mortgage insurers, between the final investors of MBSs (funding sources) and mortgage
borrowers. Secondary market financing mostly exists in developed economies with sound
legal and regulatory infrastructures and good market information. It has become a major
source of mortgage funding in a few countries such as the U.S. where 73 percent of the
2..5trillion dollar new mortgage originations in 2002 were funded through MBS issuance
(about 1.8 trillion dollars). In the Netherlands, the annual gross lending against mortgage
in residential property was about 96 billion euros in 2002. During the same year, 5
million euros of mortgage bonds and 17.6 billion euros of MBSs were issued, comprising
about 18 percent of the total lending. The growth of MBS issuance in the Netherlands has
been rapid, increasing from mere 227 million euros in 1996 to about 17.6 billion euros in
2002. Developing countries are active in jumpstarting their secondary markets in order to
channel more investments and funds to their housing sector. In Mexico, the first MBS
issuance was done by Su Casita, one of the biggest Sofoles, in December 2003. The first
tranche was sold for 0.5 billion pesos and the second tranche (sold in Aug 2004) was
about 1 billion pesos. More MBS deals are expected in 2005 with the involvement of the
SHF guarantees.
- 47 -
III.
Economic Rationales for Public MI - Case Studies
The economic rationales for public MI change over time as the housing finance market
evolves and housing policy changes. Market inefficiencies that caused the creation of
public MI may be very different from those that justify its continuation. In this section, I
answer the first research question: What are the primary economic problems in the
housingfinance market that cause market inefficiency or market imperfections and
hence call for government intervention in the form of a public MI? I analyze the three
public MI programs to compare the housing market inefficiencies they address at
different stages of the housing finance market respectively, from their policy context,
market imperfections, program goals and barriers, prerequisites, institutional framework,
and the relationship with other supplementary housing policies. The development
timeline of public MI for each study case, defined by the three stages of each country's
housing finance market, are outlined below (Table 4.1).
Table 4.1 Development Stages of the Three Public MI
Stage I
FHA
WEW*
SHF*
1963-now
Stage II
1934-now
1956-now
1963-now
Stage III
1970s-now
Mid 1990s-now
2003-now
: startingfrom theirpredecessor'sestablishmenttime
A.
The U.S. FHA Program
Stage II: Primary market stimulation
The FHA was created in 1934 to fill the gap caused by the failure of the housing finance
systems and private mortgage insurance industry due to the Great Depression. Prior to the
depression, there was very little federal intervention in or supervision of mortgage
lending transactions, which were viewed essentially as local in character. The lack of
regulation and discipline in mortgage lending and mortgage insurance, combined with the
severity of the depression, resulted in wide-spread losses that were not capable of being
recouped by lenders. As a result, mortgage lending virtually ceased, and government
intervention was required to stimulate the housing sector. Therefore, the original
objective of the FHA was to encourage banks, building and loan associations, and other
approved institutions to make loans for building homes, small business establishments,
- 48 -
and farm buildings, by insuring all loans underwritten to its standards. In many ways, the
focus was on economic stimulus through housing construction and hence mortgage
lending. Lack of confidence in the marketplace among lenders was the fundamental
market inefficiency.
However, the FHA insurance product, while stimulating construction, did not stimulate
the desired level of mortgage lending. Lenders did not trust the insurance product, and
wanted to remove all the "mortgage risk" from their balance sheets. Therefore, Congress
established The Federal National Mortgage Association (in 1938), with the objective of
improving credit flows by putting cash back to the banks via purchasing FHA-insured
loans, and also removing funding risks from the lenders' balance sheets. With the
creation of the Federal National Mortgage Association (now known as Fannie Mae), the
federal government, in effect, legislated a market for mortgage insurance9 and added
liquidity to the market. A very important contribution of the FHA program was the
establishment of the innovative mortgage product with lower down payments, higher
payment-to-income ratios, and longer terms to maturity than had been observed before.
In the 1950s and 1960s, as concerns with inner city housing conditions rose the economic
rationales for the FHA have changed from stimulating lending based on quite stringent
and economically sound criteria (as a substitute for failed private MIs) to promoting
adequate, owner-occupied housing. FHA's mortgage insurance was deemed as one form
of subsidy in mortgage financing. The 1960s saw FHA mortgage insurance increasingly
combined with other housing programs. Supply-side housing policy had identified capital
subsidies, usually below market interest rate financing, as a major tool for either
extending homeownership or subsidizing multifamily units. FHA's purpose in this was to
insure these financial instruments (Pennington-Cross and Yezer, 2000). The economic
rationale for the FHA's activities was the belief that homeownership generates positive
externalities and that it should be subsidized. However, the large number of economically
unsound, very high-risk mortgages insured in support of special housing subsidy
9 As Fannie and Freddie are prohibited - in their congressional charters - from purchasing a loan that
exceeds 80% loan-to-value ratio unless the loan has private mortgage insurance or other credit
enhancement for the portion of the loan above 80% of value.
- 49 -
programs led to accusations of fraud and abuse in FHA programs. The negative
externalities associated with high risk mortgages, such as the concentration of defaulted
and foreclosed properties in certain communities and the resulting deterioration of the
housing stock, were not carefully evaluated.
The 1980s and 1990s saw the gradual narrowing down and focus of the FHA insurance
on low-income borrowers, partly because of the national housing policy and partly
because of increasing competition from the conventional market and private MIs. The
goal of the FHA is to complement not compete with the private sector, although in reality
there is considerable overlap between the households served by FHA and conventional
lenders (Ambrose, Pennington-Cross and Yezer, 2000; Canner and Passmore, 1995). The
target population has increased the FHA's average loan-to-value ratio substantially and
therefore its vulnerability to economic downturns. The 1980s recession caused huge
losses to FHA. By 1990, the estimated actuarial value of its insurance fund was just 1
percent of the insurance in force, and even that was projected to evaporate by the year
2000. Every book of business underwritten from 1980 to 1989 was either already losing
money or was expected to over the course of its life. It was clear in 1990 that the FHA's
major insurance fund, the MMI Fund, was no longer sound (Capone, 2001). To restore its
financial health, FHA doubled its premiums and adapted its mortgage limits to local area
housing costs. With the "implicit" support from Fannie Mae and Freddie Mac due to their
affordable housing goals set by their chartersl° and the robust economy after the
recession, the FHA has returned to economic soundness.
Since the 1990s, the FHA has been focusing on the provision of credits to households not
served or underserved by the private sector, most notably first time and minority
homebuyers. In FY 2003, 77 percent of FHA-insured purchase loans involved first-time
homebuyers, and 37 percent of those first-time buyers were minorities. The main
economic rationale for the FHA to target these populations is still homeownership
10The Federal Housing Enterprises Financial Safety and Soundness Act of 1992 requires Fannie Mae and
Freddie Mac to meet annual percent-of-business housing goals established by HUD for three categories:
low- to moderate-income households (50% in 2003); underserved areas (31% in 2003); and special
affordable housing (20% in 2003). These mandates have forced the GSEs to lend to a sector of the low-tomoderate-income population likely to need low down payments and MI.
- 50 -
externalities and social equity. But the new underwriting criteria stipulated by the Federal
National Affordable Housing Act of 1990 requires that each new book of business in the
MMI Fund must be fully funded at the time it is endorsed, i.e. be self-supporting, to avoid
repeating the 1980s experience. Another economic rationale is regional stabilization and
balance of mortgage credit provision. Some researchers (Canner and Passmore, 1995;
Caplin, Freeman and Tracy, 1997) have argued that private MIs do not penetrate far into
the higher-risk mortgage markets or declining housing markets in areas experiencing
falling wages and rising unemployment. The resulting market failure is the potential
vicious cycle of lenders lowering LTVs (more downpayments required), therefore
lowering housing prices that can be afforded by local residents, which could cause
lenders to further lower required LTVs. Since the FHA has the same underwriting criteria
across all regions, without any discriminations against declining markets, to some extent
it helps "attenuate housing market slumps by serving as lender of last resort."
(Pennington-Cross and Yezer, 2000).
Stage III: Secondary market credit enhancement
FHA mortgage insurance plays an important role in the U.S. secondary mortgage market
by addressing the key element of the principal-agent problem: information asymmetry. In
the U.S., virtually all government-insured loans become mortgage-backed securities. The
MBS investors are concerned with credit risks of the mortgage pool because mortgage
originators (or sellers), with superior information about loans, may adversely select
against them by keeping good loans and selling the ones that are riskier than they appear
to be to the secondary market. To remove the burdens of monitoring credit risks of
individual loans off the MBS investors, FHA covers the bulk of credit risks related to
borrower defaults, and Ginnie MaeI1 guarantees timely payment of principal and interest
on MBS even if the issuer does not make the payments. Together, the two layers of
guarantees provide enough credit enhancement to Ginnie Mae MBS so that it is
" Ginnie Mae (the Government National Mortgage Association) is one of the three government agencies
that brought on the standardization of pools of mortgages to form mortgage-backed securities. It was
created in 1968 as a successor to the old Fannie Mae when it was privatized. What Ginnie Mae does is
guaranteeing investors the timely payment of principal and interest on MBS backed by federally insured or
guaranteed loans -- mainly loans insured by the Federal Housing Administration (FHA) or guaranteed by
the Department of Veterans Affairs (VA). Ginnie Mae securities are the only MBS to carry the full faith
and credit guaranty of the United States government.
51 -
considered one of the safest investments in the capital market and helps channel funds
into the mortgage market for the affordable owner-occupied housing sector. Figure 4.3
illustrates the structure and mechanism of the credit enhancement for government-insured
MBS markets.
Figure 4.3 Credit Enhancement for Government Insured Secondary Mortgage Market
........ . ...
..
.W
..-...........................
:
_Insura
fe
ta
o1." s
prilction. .
Mortgage
pit
Insurers:
FHA
VA,&
:RHS*
: : ::
:
iT__
Paying Agent:
Receives and
- i
___ _
Wall Street
Investment Banks package MBS in
issuer's name and
market to investors
Ginnie Mae MBS
Investors
.
Guarantqefees-
...
:
MBS
i
payments
Guarantor:
.
Ginnie Mae
*
Timely payment
Principal and interest
pass throughs
:........
. i
:
disburses mortgage
,
.
.
.
--...
*: VA: Department of Veteran Affairs; RHS: Department of Agriculture's Rural Housing Service Housing
B.
The Dutch WEW Program
Until 1956, only specialized mortgage banks were allowed to lend mortgages in the
Netherlands. House purchase was difficult for households without sufficient savings.
Downpayment proved to be a big barrier to homeownership and caused market
inefficiency. In 1956, a municipal guarantee system backed by the central government
was founded to support the availability and affordability of homeownership. At first, the
guarantee was only available for mortgages of purchasing newly built housing. Later in
1973, the municipal guarantees were extended to mortgage loans for the purchase of
existing dwellings.
The guarantee made mortgage lending attractive to other types of financial institutions
and commercial, co-operative and savings banks entered the mortgage market. In the
- 52 -
municipal guarantee system, the central government and municipalities share the debt
risks fifty-fifty. As a result of the guarantee, the downpayment necessary for the
financing of a home was reduced from 30 percent to 10 percent. The demand for
mortgages increased greatly and led to an increased competition for funds between
lenders. The cost of mortgage credit supply was lowered, therefore lowering mortgage
interest rates for borrowers as well. However, this fragmented municipal guarantee
system created new market inefficiencies while addressing inadequate homeownership,
due to its structural and operational flaws and lax regulations.
The municipal guarantee system was inefficient (the application process could last 6
months) and lacked transparency because each municipality set its own standards and had
its own guarantee products, causing inconsistency in the marketplace. Moreover, no
capital reserves were created either in municipalities or the central government for the
guarantees issued. The lack of risk assessment resulted in potential resource allocation
inefficiency in the governments. Despite these inefficiencies and significant flaws in
program design, this guarantee system had worked well until the beginning of the 1980s,
largely owing to a booming economy and rising home prices. In the period of 1978 1982, the Dutch economy went into a recession. Home prices dropped significantly; longterm interest rates increased and unemployment figures soared. Not surprisingly, the
number of defaults increased sharply. After some years (mid 1980s), the number of
foreclosure sales of dwellings reached 2,500 on an annual basis with an estimated loss of
£56.8 million (Boelhouwer and Neuteboom, 2003), compared to only a few hundreds
normally (Figure 4.4). Mortgage lenders experienced increased losses during the late
1980s when interest rates were consistently above 10 percent and some borrowers were
unable to maintain payments on their loans. Municipalities and the central government
covered all losses of default claims from the guarantees issued.
- 53 -
Figure 4.4 Foreclosure Sales in the Netherlands, 1975-2003
0000
0000
3
0000 %
0o
0Z
o
0000
0000
8
o,
E
z
o
'E
Year
-- number ofdefault
-- '--claim
amount
Source: WEW database
The bad experience made local and central governments re-evaluate weaknesses in the
structure of the guarantee provision, and led to the decision to make the mortgage
guarantee program organizationally efficient and financially self-supporting. The
restructuring emphasized three features: standardization of rules, efficiency in the fund
operations, and solid capital reserve fund management. The result was the formation of
the Homeownership Guarantee Fund (abbreviated to WEW in Dutch) in November 1993
by and under the supervision of the Ministry of Housing, Spatial Planning and the
Environment (VROM) and the VNG (the Association of Netherlands Municipalities).
The WEW is a private, non-profit organization backed by the full faith and credit of both
central and participating local governments. It took over all the existing guarantees from
the participating municipalities. As a result, nearly all municipalities stopped issuing new
guarantees. For the mortgage default risks of the existing guarantees transferred to the
WEW, the central government and municipalities deposited some capital as a separate
reserve to cover claims from the old municipal guarantees when WEW started operation
in 1995. The amount was based on projections of the old loans' performance. If the actual
payouts exceed the reserves, WEW has the right to ask for more funds from the
governments.
The relationship between the WEW and the central government and participating
municipalities is structured in a backstop agreement. This agreement serves to secure the
payment of debts for lenders in case the fund's reserves drift into a state of illiquidity. In
- 54 -
that case, the government provides the WEW unlimited interest-free loans to pay off the
accepted debt claims. Through this backing the Dutch Central Bank considers the
National Mortgage Guarantee (NHG) as a government guarantee. Contrary to the
municipality guarantee, the NHG issued by the WEW is organized on a national scale.
Therefore, all borrowers in the Netherlands abide by uniform terms and standards.
The institutional change of the MI provision did not alter the crucial role played by both
the central and local governments. The Guarantee Fund's board of supervisors is
appointed by the central government, the association of municipalities, the union of
financiers, and the national consumers' association of owner-occupants. The WEW's
premium levels and contract structure must be approved by the Ministry of Housing and
the union of municipalities every year. The Fund is responsible for the policy and
implementation of the NHG. It draws up regulations for issuing the NHG, which require
the approval of the Minister of VROM and the VNG.
Historically the Netherlands had a low homeownership rate, lagging behind most other
European countries. The main purpose of the WEW is to reduce barriers to
homeownership, especially among lower and middle-income populations in the
Netherlands. The policy goal, however, is not pure promotion of homeownership, but
more of increasing the citizen's freedom of choice in housing tenure decisions. It is
closely related to the history of the Dutch housing shortage and the consequent housing
policy of emphasizing quantity rather than quality of housing and the strong presence of
social rental housing.' 2 As the Netherlands has overcome the acute housing shortage that
dictated its housing policy for many decades, people are no longer satisfied with the
limited choices in terms of residential environment and housing supply that has been
strictly controlled by the government. Many have indicated a preference to own their
homes'
3
and have a say in "how and where they live." It was clear that past housing
policies constrained people's options in housing consumption and therefore the total
12 At the end of 2003, the Dutch housing stock consisted of about 6.7 million dwellings, of which 2.4
million in the social rental sector (35 percent); the owner-occupied sector comprised 54% of the housing
stock; and the private rental sector became much smaller, only about 11%.
13However,
most recently the demand for owning a house has tempered and even shifted a bit to renting as
a result of the economic stagnation.
- 55 -
social welfare was not maximized. This inefficiency was caused by decades long housing
policies that restricted free market forces. The Dutch government has been making
housing policy transitions since the late 1980s and early 1990s towards more market
forces and less government intervention, and implemented tenure-neutral policies. The
aim of the government is to boost homeownership to around 65 percent by 2010. WEW
mortgage guarantee is one of several approaches to stimulate homeownership. It not only
helps households to buy new homes more easily, but also makes the transition from
renting to buying smoother.'4
Unlike the U.S. FHA program, WEW's target populations do not include low-credit
quality, high-risk borrowers. In fact, it has strict rules on applicants' qualification and
checking of their creditworthiness. WEW guarantees do not specifically extend to
minority households or first-time homebuyers. The WEW stipulates that eligible
applicants for its guarantee must have the Dutch nationality or the nationality of a
member state of the EU or the European Economic Area Agreement, or have a Dutch
residence permit for an indefinite period of time. These requirements exclude many
immigrants from countries like Turkey and Morocco who do not acquire the status of
Dutch citizens. This segment of the population is left for the social rental market.
Whether there is market inefficiency in this arrangement needs further research and is
beyond the scope of this study.
The secondary mortgage market, in the form of MBS, started in 1996 in the Netherlands
and grew rapidly. In 2003, the volume of residential MBS issued in the Netherlands
totaled 23.4 billion euros (approximately 30.3 billion dollars). However, its credit
enhancement is provided by various subordinate classes carved within the MBS plus the
reserve fund of the issuer. WEW guarantee is not utilized.
14
The Dutch government encourages the development by parties of intermediate forms that are neither
lease nor purchase, or a more fluid transition from renting to buying. In particular, constructions granting
tenants the right to buy the dwelling at a later date (wholly or partially, via an ownership share) are being
considered. Starting in 2001, such intermediate forms qualified for the National Mortgage Guarantee
scheme. This facilitated the development of intermediate forms ("What People Want, Where People Live"
report). The WEW guarantees are granted to owner-occupied dwellings that are at least for 50 percent in
the hands of the owner.
- 56 -
C.
The Mexican SHF Program
The Mexican housing finance market has fluctuated significantly over the past half
century with the government entities and private sector taking control alternately. In the
1960s, commercial banks were quite active in mortgage lending. High inflation
discouraged private capital in the 1970s, prompting the formation of federal public and
publicly mandated housing finance programs. During the 1980s, banks, newly under the
ownership of the federal government, had required quotas for social interest housing
loans. Then following re-privatization in the early 1990s, banks rapidly expanded
mortgage lending, but were devastated by the peso crisis in 1994-1995. Currently the
private sector provides very little capital for housing finance, although in recent years it
expressed interest in returning to the market. Public or publicly mandated institutions'5
provide the majority of housing finance (The State of Mexico's Housing, 2004). A
description of the current main market participants in the Mexican housing finance
system is provided in Appendix 4.1.
StageI and I: Informal and primarymarkets
A wide range of market inefficiencies exist in Mexico's informal housing sector and
primary mortgage markets. In the informal sector (50 percent of the housing stock),
formal lending programs and mortgage credits are not accessible to low-income or selfemployed workers, so that they cannot purchase a house that requires financing. Their
only option, progressive self-built houses, exerts negative externalities on surrounding
areas and deteriorates urban environment.
The primary mortgage market has three major imperfections. The first is the lack of
confidence in the marketplace among both borrowers and lenders following the financial
crisis. The inflationary macro economy and real estate bubble bursting caused banks
experiencing massive delinquencies and severe losses in mortgage loans. Borrowers
There are three major pubic or publicly mandated institutions. The housing sub-accounts of the pension
programs (INFONAVIT and FOVISSSTE) are financed through mandatory contributions by firms and
employees. The housing trust fund, FOVI, has evolved into the national mortgage bank, SHF. SHF receives
direct budget appropriations and funding from international donors, which it channels through the
15
SOFOLes, which are privately-owned
specialized financial institutions, to reach new markets (The State of
Mexico's Housing, 2004).
- 57 -
found themselves in deep negative equity position with no possibility of ever paying off
the loan. To this day, bank mortgage lending has not returned to the pre-crisis level and
households are very wary of mortgages provided by banks. The second problem is the
non-transparency and potential corruption in resource allocation within publicly
mandated housing finance funds or institutions. FONHAPO (a federal social housing
agency), INFONAVIT and FOVISSSTE (off-budget provident funds that receive
mandatory contributions from, respectively, formally employed private sector employees
and federal government employees) currently provide about 80-85 percent of the total
mortgages issued each year. They operate with their own agenda and target narrow
population segments, providing subsidized mortgage loans. Their funds come from the
federal government or mandatory worker contributions (Figure 4.5). As demand for these
types of subsidized loans exceeds supply, the allocation process breeds corruption and
distorts market forces. Private market players such as banks are not mobilized in the
primary markets.
Figure 4.5 Capital Sources for Mortgage Loans in Mexico
Public
Private
Banks
INFONAVIT
SHF
.4
FOVISSSTE
FONHAPO
Public capital;
Public
management;
Public sector
borrowers;
Public capital;
Public
management;
P
;
Private capital;
Private
management;
No subsidy
Private capital;
Private-public
representation on
board; publicly
mandated mission;
Private sector
borrowers
Interest rate subsidy
Public and private
capital;
Public
management;
No subsidy
Market catalyst
Upfront subsidy
Interest rate
subsidy
from
"The
State
2004"
ofMexico's
.......................................
Housing,
adapted
Source: adapted from "The State of Mexico's Housing, 2004"
The last issue regarding Mexico's primary market is the inefficient use of private capital
in the housing sector and the resulting serious under-leverage of properties. Mexico's
homeownership rate is around 79 percent, higher than that of the U.S. (70 percent) and
the Netherlands (54 percent). However, its mortgage debt as percentage of Gross
Domestic Product is only about 2 percent in 2003, compared to about 70 percent in the
U.S. and 88 percent in the Netherlands. Other developing countries such as Brazil (4%)
and Chile (12%) have much higher percentage than Mexico. This is due to the fact that a
- 58 -
large portion of Mexican housing stock is not formally financed. Indeed, only 15 percent
of the housing stock has ever had a mortgage. The total value of the unleveraged housing
stock is estimated at 700 billion U.S. dollars (Merrill Lynch report, 2003).
The SHF, established in 2002 as a government development bank for housing to replace
FOVI,1 6 addresses housing finance market inefficiencies in several ways:
i) It extends its products to a broad range of populations, serving households earning
between 2 and 50 minimum salaries (3,000 to 75,000 U.S. dollars, covering 75
percent of the households in Mexico). It is currently the only formal lender that offers
loan products for which informal workers are eligible. Its mortgage insurance covers
a wide spectrum of loans with the lower range competing with INFONAVIT loans
and higher end overlapping with commercial bank loans, therefore filling the previous
market gaps;
ii) It tries to restore market confidence and reduce uncertainty in mortgage lending by
building essential infrastructures in market information, mortgage standardization,
legal framework and operational transparency. The SHF has been building Mexico's
first centralized mortgage loan database, FIEH (Fuente Integral de Estadistica
Hipotecaria), to identify, control and monitor loans covered by its guarantees. The
database will have four types of information: socio-demographic information about
the borrower, information on his monthly payments; information on the property, and
information on the loan recovery once it becomes a delinquent loan. This centralized
information center has significant implications to all current and future market
participants including banks, rating agencies, regulators, credit risk insurers, and
MBS investors. It lowers uncertainty risks of all parties. The SHF also establishes
minimum criteria and universal standards in mortgage origination, servicing and
documentation for approved lenders and intermediaries (Sofoles), as well as clear
rules of capitalization and reserves for them. This brings transparency to the market
and offers a level playing field. In terms of legal infrastructure, the SHF is improving
16
FOVI was established in 1963 as a trust fund to channel federal government money to the housing sector.
FOVI obtained its funds from the Mexican Central Bank and distributed them to SOFOLes. In 1994, the
Central Bank became independent (autonomous, not depending on the government) and FOVI entered a
transitional period. In 2002, SHF replaced the FOVI.
- 59 -
foreclosure laws and appraisal and registry systems. It is looking to adopt
international standards in operational and risk practice for its MI business, and
encourages international private MIs to come in to Mexican markets; and
iii) With the full faith and credit of the Mexican government behind its mortgage
insurance and financial guarantees,' 7 SHF acts as a market catalyst to mobilize
private capital and increase leverage in housing stock. With its MI coverage, the
maximum LTV is raised to 90% with a term of 25 years. SHF is gradually phasing
out previous FOVI's loan granting function' 8 and instead pushing intermediaries to
seek private market capital from commercial banks, international financial institutions
(the World Bank, Inter-America Development Bank, etc.) and secondary market
investors, with the support of its insurance products. The SHF is trying to correct the
market distortion created by predominant public entities historically existing in
Mexico's housing finance sector, and encourage the system to evolve so that in the
long run it will look to the secondary market and long-term bond investors for capital.
The goal of SHF's MI program is to stimulate private lending and jumpstart the housing
finance market development by standardizing the market and expanding funding
mechanisms. The SHF views its role as an evolving one, comprising three phases (Figure
4.6):
This backing of the federal government will expire on October 2013. After that, the guarantees will be
supported exclusively by SHF's own financial strength.
18 Currently, as a transitional period, the SHF lends money to registered financial intermediaries, banks and
mortgage banks (Sofoles), which in turn lend SHF-guaranteed funds to individual borrowers. The SHF also
17
provided construction lending to private housing developers. Starting from January 2003, SHF stopped
funding construction loans but instead provide loan guarantees. Its ability to grant mortgage loans only
extends to October 2009. From then on, the only function of the SHF will be granting MI and guarantees.
- 60-
Figure 4.6 SHF's Evolving Role
Source: SHF Presentation 2003.
*
*
Short Term: SHF offers MI and financial guarantee insurance, looking for reinsurance with
specialized international insurance companies.
Medium Term: Private insurance companies, including affiliates of international firms, offer cover for
*
risks not covered by private monoliners.
Long Term: Private insurance companies offer cover for all market segments, SHF offers reinsurance
residential and medium segments of the market, while SHF offers cover for social housing and special
to these companies to undertake risks that private markets are not willing to hold.
Stage III: Secondarymarketcatalyst
The SHF is also expected to be the catalyst for the secondary market development in
Mexico, through its two major products: mortgage default insurance, and on-time
payment guarantee for MBS similar to those offered by U.S. Fannie Mae.' 9 Promoting
mortgage securitization was a main incentive for the creation of the SHF due to the
awareness that Mexican pension funds are and will be a significant element of the
country's long-term financial savings and they must be exploited to finance housing.20
Also private capitals and foreign investments must be channeled into the housing finance
sector more actively, breaking the current dominance of government funds and entities.
The market imperfections of the nascent Mexican secondary market are multi-fold,
including mainly the lack of market information and investors' confidence, and the
asymmetric information problem similar to the U.S. secondary market situation. SHF's
19 However,
SHF's on-time guarantee of MBS payments differs from that of Fannie Mae's in that it would
not be a 100% guarantee, since, in principle, partial classification must be maintained in such a way that a
fraction of the risk will be assumed by the intermediary financiers and another part by the investors.
Although SHF-guaranteed MBS should be triple A bonds, they should not be risk-free bonds, in order to
force the market to undertake the necessary risk monitoring (Babatz, 2004).
20Laws require these pensions funds to be invested in bond securities and prohibit them from being
invested directly in individual mortgage loans.
-61 -
MI and financial guarantee insurance (FOI) provide necessary credit enhancement to get
MBS deals to be AAA rated on local scale. SHF's role as a catalyst for standardization of
documents and origination and servicing procedures will be played through its role as
guarantor either through MI, FOI or both. The first MBS issuance in Mexico of 1.5
billion pesos occurred in 2003 by Su Casita, one of the biggest Sofoles, as a demo
issuance. The standard agency MBS carrying SHF insurance and guarantees is expected
to launch in 2005 by Sofoles. The SHF is the market maker, insurer and information
provider (Figure 4.7).
Figure 4.7 SHF's Role in the Secondary Mortgage Market
Financial
Guarantee
Insurance
Companies
Intermediaries
Debt capita~
Markets ~
SHF
Market Makers
Originators
(Special Purpose
Vehicle)
Capital at
Risk
bj
K
...
Investors
-I
Universal
Type
______
1
1
1
1
•
1
Rating Agencies
L
L
_
Mortgage
Insurance
Companies
•
Source: SHF presentation
IV.
Derivatives
MBS
Trusts
Servicing
Instruments
+
& Cash
Regulators
J
----------~
Information
I
"Opportunities in Housing in Mexico UCLA May 2004.ppt"
Comparison of the Three MI Programs' Economic Rationales
Previous case studies offer valuable insights into each of the three countries' housing
finance market inefficiencies that justified the establishment of the public MI and the
evolution of these economic rationales (Table 4.2).
- 62-
Table 4.2 Market Imperfections Addressed by the Public MI
FHA
Primary market
At establishment
Now
· Lack of market confidence
· Inadequate homeownership
and huge uncertainty risks
for lower-income families,
after the Great Depression.
minority households and
· Insufficient housing
downpayment barrier for
construction and mortgage
first-time homebuyers.
lending.
· Lending discrimination in
· Gaps in the market caused by
unserved or underserved
the disappearance of private
regions.
·
Secondary market
Nowa
. Information
asymmetry for MBS
investors.
MIs.
Lack of sound regulations
and standards in mortgage
underwriting.
WEW
*
SHFb
Inefficiency and nontransparency of the existing
municipal guarantee systems.
Lack of MI risk assessment
*
*
Limited housing style and
tenure choices for historical
reasons.
Inadequate homeownership
and regulations on capital
for the overall populations,
requirements, resulting in
resource allocation
inefficiency in governments.
especially lower-income
households.
Lack of market confidence and huge uncertainty risks (macro
economic and political) after the financial crisis of 1994-1995.
Lack of trust among lenders in previous FOVI's loan default
guarantees.
·
·
·
·
·
·
·
Not utilized.
· Lack of confidence for
the investors - needing
credit enhancement
. Information
Serious shortage of funds for mortgage lending due to the
asymmetry for MBS
complete retreat of commercial banks.
No access to formal financing for informal workers and very
low income populations.
Low affordability of mortgage financing for majority of the
population.
Non-transparency and distortion of the housing finance market
by public or publicly mandated funds/entities.
Inefficient use of private capital and under leverage of
residential properties.
Lack of sound regulations and standards in mortgage
underwriting. Lack of laws in foreclosure, appraisal and MI
business.
Lack of data on housing and mortgage markets.
investors.
Note:
a: The secondary market did not exist in either of the three countries when the public MI was created.
b: The SHF was established in 2002. The market inefficiencies then were similar to the current situation.
The comparison indicates some commonalities in the economic rationales for public MI
among the three countries. The most common market imperfections to be addressed by
public MI include: high uncertainty risks perceived by market participants due to macro
economic or political instability; lack of regulation and standardization in mortgage
- 63 -
markets; inadequate homeownership among general population or specific groups; and
lack of market information to assess risks. All these market inefficiencies tend to cause
credit suppliers reluctant to enter the market as it cannot carry higher rates to compensate
for the high uncertainty.
For a generic country to start a public MI program, policymakers must first understand
the causes of the supply or demand constraints in the housing finance market to decide
whether the problems can be tackled by a supply-side intervention as public MI.
"Unaffordable" housing can be because (i) prices of dwellings are too high; (ii) cost of
financing is too high; or (iii) incomes of some populations are too low and redistribution
is desired in the specific form of housing. Thorough analysis is needed to provide clear
understanding of the reasons. For instance, if low or unstable income is the dominant
constraint in the market for owner-occupied housing and not access to housing finance,
then demand-side subsidies on increasing households' ability to consume housing may be
more effective. These subsidies can be housing allowances or vouchers for rental or
owner-occupied housing, upfront grants tied to housing finance, or tax benefits that lower
the recurring cost of housing payments. Even if the problems lie in the supply side of
housing, the underlying causes may still render different strategies than public MI, or in
addition to public MI. For example, if the housing finance problems are due to public or
private monopoly systems or the regulatory environment (in the reliability and speed of
foreclosure procedures, planning and building standards, etc.), setting up a public MI
scheme alone is not likely to mitigate the problems. In such cases, developing or
reforming institutions and policies, and improving the regulatory and legal systems will
be needed before implementing public MI. Simply put, if government does not do what is
necessary to encourage the housing finance industries to function efficiently, housing
supply cannot respond to price signals, and higher incomes or subsidies will not translate
into better housing (Hoek-Smit and Diamond, 2003).
The comparison also illustrates the necessary prerequisite conditions for public MI to
function (Table 4.3). Policymakers need to check the readiness from multiple
perspectives of their housing market and infrastructure to determine the best timing for
- 64 -
launching a public MI. As shown in the following table, the newly established Mexican
SHF has been making great effort to catch up in several key aspects of the infrastructure
building for its MI program.
Table 4.3 Prerequisites for Public MI to Function
Prerequisites
Political and Social
Political initiatives and
attitudes
Government. housing policy
Social environment
FHA
WEW
SHF
Focus on the provision of
credits to households not
served or underserved by
Facilitate
homeownership
among low- and
Expand access to formal
housing finance to the
general population and
the private sector
medium-income
populations
encourage private capital
into the sector
One of the most important
components in the U.S.
An important
component among
The leading factor to
mobilize the market and
housing policy
other policies, such
initiate changes in primary
as a counterpart in
social rental housing
market and jumpstart the
secondary market
sector
Borrowers, lenders
and investors trust
the government
backing - zero risk
reserves required
Lack of trust in financial
institutions and
government guarantees.
Improving gradually
Borrowers, lenders and
investors trust the
government backing
Regulation and Legal
Regulation of banks
Regulation of insurance
Yes
Yes
Yes
Yes
Yes
Yes
Regulation for mortgage
Yes. Must be mono-line
Non-existent
Non-existent. In the
insurance
business, with specific
capital requirements
Laws (especially foreclosure)
Sound
process of stipulating
regulations and adopting
international standards
Sound
Not sound but improving.
In many places
foreclosure is still
impossible or takes a long
time.
Court system
Information
Market information &
Mortgage lending
performance
Pricing model data
Sound
Sound
Not tested
Good market information
gathered through FHA's
various databases and
reporting systems
Centralized in-house
loan databases
Good quality market data
non-existent before. Now
Data available
Data available
start to build housing price
index and FIEH database
by SHF
requirements
Not enough data. Pricing
model based on some
assumptions
Credit reporting (credit
bureau)
Internal Scorecard system
and external credit
reporting from Fair, Isaac
Credit information
& Co., a credit scoring
Office
bureau from the 1950s
- 65 -
from the Central
Credit Registration
Non-existent. Start to
develop.
Property title registration
Sound
Sound
Lack of agreement on
basic legal and
administrative principles.
Antiquated systems.
Real estate appraisal
Yes
Yes
information
Source: the author, some items adaptedfrom Blood and Whiteley, 2004
Non-existent. Now start to
gather the information
When addressing housing finance market imperfections, all three public MI programs
emphasize actuarially sound pricing and sustainability over the long term under normal
market ups and downs. Public MI becomes a subsidy only when the market encounters
catastrophic economic or political events, that is, to cover the systemic risks. This is to
control the uncertain and mostly "hidden" costs to the backing government, keep the
system transparent, and reduce the distorting impacts on the private market of the
intervention, so that public MI does not create new market inefficiencies while curing the
existing ones. It is very important, however, to understand and be able to measure the
potential costs to the government under different risk scenarios including rare, adverse
economic situations because the program can be very expensive in the long run. The next
Chapter analyzes implied liabilities of public MI in details.
- 66 -
Appendix 4.1 Mexican Housing Finance Main Market Participants
Infonavit
_Z_*[__P
=111611
r
Infonavit is a mandated pension
programme for private sector
workers. Infonavit has
approximately
15million workers affiliated
ffmwtw
Funded by a
mandatory 5% levy
on private employee
wages
Typical loans are in the
US$10,000 to US$50,000
fr.n
Lends typically at
Planning to grant
inflation + 6%
300,000 loans in
2003, up from
For buyers that
qualify, Infonavit
275,000 in 2002
terms often make it
the most attractive of
the lenders
Currently issues
range
around 75% of all
new mortgage
volume in
Mexico
Infonavit finances almost 80%
of all government housing
credits in Mexico
FOVISSSTE
FOVISSSTE (not to be
Funded by a
Lends typically at
Around 20,000
confused with FOVI) is a
mandatory 5% levy
inflation + 6%
loans per annum
mandated pension programme
for public sector workers.
on public employee
wages
Lends at inflation +
11%
Funds construction
and around 50,000
end-user loans per
annum
Typically at inflation
+ 12%
Around 5,000
loans per annum
Typically at inflation
+ 12%
Around 10,000
loans per annum
Fovissste has 1.5 million
affiliates
Typical loans are in the
US$10,000 to US$50,000
range
SHF (formerly
FOVI)
Formerly FOVI, the SHF funds
developers and end buyers,
primarily distributed by Sofoles
Loans are in the US$10,000
Funded by
contributions from
the federal budget, as
well as capital market
issuance
to US$150,000 range
Moving to a role as a
Mexican "Ginnie
Mae," insuring MBS
issues, but not
providing direct
funding itself
Sofoles
Lend mostly to lower and some
middle income segments,
Funded by SHF
(former FOVI),
although now starting to
capital markets, bank
compete head to head with
banks
loans, development
banks and, more
recently,
Typical loans are in the
securitization
US$50,000 to US$300,000
range
Banks
Lend mostly to middle and
upper income segments
Funded from bank
deposits, capital
markets and
Typical loans are in the
securitization.
US$50,000 to US$300,000
to FOVI/SHF funds,
range, although banks are now
offering to lend as little as
but not active
US$24,000
Source: Softec, Merrill Lynch
- 67 -
Access
- 68 -
Chapter 5 Analysis of Implied Government Liabilities
I.
Implied Government Liabilities In Supporting Public MI
Understanding the essence of risks and the magnitude of implied liabilities imposed on
the government under various economic scenarios is extremely important for
policymakers in evaluating the feasibility and soundness of a public MI program. There
are two characteristics of a public MI program that merit especial attention. First is the
long time horizon of mortgage insurance commitments. Typically mortgage loans span
over fifteen to thirty years, during which the nation's macro economy could experience
recessions or even depression. Therefore it is dangerous for policymakers to think that a
public MI program costs the government (taxpayers) nothing even if it is currently selfsupporting. Second, since most government-backed MI programs or enterprises do not
aim to maximize profits and usually target lower to middle income households, their
premium levels vis-h-vis the risk profile of their borrowers make them much more
susceptible to adverse economic shocks than their private counterparts, and their potential
losses are more severe too. Naturally, the important question to ask is: What are the
implied liabilities imposed on the government (hence taxpayers) of sponsoring a public
MI program or backing such an enterprise? This is the second research question of this
dissertation, and constitutes the second component of the integrated analytical
framework.
A conceptual model (Figure 5.1) is constructed to depict the relationship between various
macro economic conditions, indicated by the mortgage loan portfolio's ultimate
cumulative default rates, and the magnitude of mortgage insurers' (both private and
pubic) liabilities.
- 69 -
Figure 5.1 Implied Government Liabilities of Supporting a Public MI
Depression
scenario
Recession
scenario
Star! to eat up
capiial
!
Point of
insolvency~)
...................
.....
"'i
~
~~.::
Normal economic
conditions
:
...
9
:~
.
~~ii!~!j! i! i!~
. + ..
roba~ility distribution of portfolio'
ultimtte default rates - developed countries
y
*
Pflibability distribution ofporlfolio. ultimate
ielfUllt rates - developing countries
Default rates*
Public: NPV > 0
NPV of public
mortgage insurance
NPV of private
mortgage insurance
*: The default rate numbers are based on the U.S. mortgage market experience, as an example.
The upper part of Figure 5.1 is the probability distribution of a mortgage portfolio's
cumulative ultimate (at the end of the loan term) default rates, divided into three zones:
normal economic conditions, recessions, and depressions. The distribution is skewed to
the left with the mean portfolio default rate of J.1 at point D. The probability that default
rates will be less than J.1 is 0, indicating a favorable portfolio performance. Taking the
general U.S. mortgage market portfolios for example, under normal economic conditions,
a cohort's (a book of business originated in one year) ultimate default rate varies between
2% and 10%, with the mean around 6%.21 If a cohort is hit by recessions during its early
loan ages (usually between year 3 and year 7), the most vulnerable period in terms of
defaults, its ultimate default rate can increase to the level between 10% and 15%. In the
worst condition when serious recessions or even depressions hit the national economy
during the early years of the mortgage cohort, a small probability event represented by
These default rate figures serve illustrative purposes as examples of the general U.S. mortgage market.
On average, the FHA mortgage market segment will have higher cohort default rates than the figures
presented here due to its borrowers' risk profile. For instance, the estimated mean cohort ultimate default
rate of FHA cohorts (l975-2003) is around 9.76% as presented later in this Chapter, instead of 6% assumed
here. Also these figures cannot represent the mortgage market performance in other countries.
21
- 70-
the long tail of the probability distribution, the cohort ultimate default rate could further
escalate to more than 15%.
Over the lifetime of an insured mortgage portfolio, if the MI provider is to make zero
profit, the net present value (NPV) of the mortgage insurance premiums it receives
should exactly cover the expected losses (claims) from defaulted loans, i.e., NPVMI =
E[losses]. In other words, a non-profit-seeking MI provider's break-even point for its
insurance business is at point D, where NPV MI equates expected losses when the
ultimate default rate is at the mean u.
Private mortgage insurance companies are in the business to make money and therefore
quite risk-averse. They charge insurance premiums so that the net present value of their
mortgage insurance business is zero (breaking-even
2 2)
at point B, to the left of point D,
meaning that higher default rates are needed before the private insurers stop earning
profits. For private MIs, when default rates distribute within zone 0 and y around the
mean default rate ji, their business generates profits and has "surplus" that can be put into
loss reserves. When the economy deteriorates and default rates increase to levels
represented by zone r, the NPV of their mortgage insurance on a cohort becomes
negative. Excess reserves have to be used to pay out claims. If the recession scenario
persists, the escalating losses could deplete private MI companies' loss reserves and start
to eat up their private equity. If things worsen further, i.e., under the depression scenario,
the cohort's default rate may increase to zone a and private MIs could become insolvent
and go bankrupt, as exemplified by the Great Depression in the U.S. in the 1930s. Profit-
maximization and risk-aversion of private MI companies make them set the premium
levels so that their break-even default rate (point B) is far away from the mean default
rate (point D) to create a comfortable "cushion" in order to accommodate economic
fluctuations and housing market volatility.
22 "Break-even"
is defined by a NPV calculation that discounts at the cost of capital, that is, the cost of
holding its equity capital reserves.
-71 -
Compared with private MI companies, public MI programs are much more risk-tolerant.
In many countries, public MI embodies the government's role in promoting formal
housing finance among lower-income families. It is usually free from the pressure of
earning profits. Also, the public MI might hold loss reserves, but it does not have to hold
equity capital, which by itself is a big cost saving compared to the private sector.
Therefore, when serving similar segments of clients, public MI charges a lower premium
than its private counterparts. The break-even cohort default rate of the public MI is very
close to the mean default rate u, with a slight cushion. In the example shown in Figure
5.1, the break-even default rate is around 7% at point C when the mean default rate is 6%
at point D. Under stable economic conditions the probability distribution of cohort
default rates is concentrated around
or less than u (within zone 0), and the public MIs
generate positive NPV for writing mortgage insurance. However, this NPV can easily
become negative due to the small "buffer" space between point C and D, even under
normal economic scenarios (within zone y). Therefore, public MI is vulnerable to
unfavorable economic situations (depicted by the shaded areas with default probability of
a, and fl). This vulnerability is worsened by its borrowers' profile and characteristics. The
losses beyond public MI's own loss reserves become liabilities imposed on the
government and have to be shouldered by taxpayers collectively.
It is crucial for policymakers to understand the shape of the distribution of cohort
ultimate default rates, the implied break-even default rate based on the MI program
design, and the magnitude of potential losses imposed on the government under various
economic scenarios. In developing countries and emerging economies with relatively
risky and unstable macro economy, mortgage default rates tend to be higher and more
volatile than in developed countries. Hence the shape of the probability distribution of
cohort default rates in those countries tends to have a longer and fatter tail (the dotted
curve in Figure 5.1), indicating the larger probability for those markets to experience
recessions or depressions. For both developed and developing countries, it is particularly
important to be able to measure the potential liability impact on the government budget
resulting from sponsoring a public MI program.
- 72 -
II.
A.
Methodology
Existing Methodologies - Literature Review
There are two major methodologies to assess the contingent liability and capital adequacy
of housing finance companies (such as Fannie Mae and Freddie Mac) and mortgage
insurance enterprises: stress tests and value-at-risk measures. Stress tests are widely used
by rating agencies and government regulators in the U.S. and abroad. The method
postulates stressful economic situations and estimates the amount of capital necessary to
survive these situations. The advantage of the stress test approach lies in that it is easy to
understand and relatively straightforward to implement without requiring too many
historical data. The disadvantage, however, is that it does not attach a probability of
failure to the computed capital adequacy standard. Therefore it is difficult to tell if
different stress tests are equally probable, and hard to judge their comparability when
applied to programs in different countries.
The value-at-risk (VaR) method applies more sophisticated models (such as simulation
models) to analyze the probability of "failure" for the overall portfolio. With this method,
the capital adequacy is defined as the amount of capital needed to limit the probability of
"failure" (zero or negative NPV) over a specified period of time to a low level. The value
of a portfolio that is "at risk" is the amount that could be lost were a given bad event to
occur within a defined period of time. That event is defined by a critical value along the
frequency distribution of outcomes, where the critical value identifies a risk-tolerance
level. VaR models so far have mainly been used to evaluate the risks of trading
investment portfolios. They are not as widely used as stress tests because of their data
requirements and complexity. Enough data need to be available to derive the frequency
distribution of negative events. Their application in analyzing the contingent liability of
the mortgage insurance industry is at a nascent stage. In the U.S., a few academic studies
explored this method with stochastic models for the mortgage insurance business (Kau,
Keenan and Muller, 1993; Kau and Keenan, 1996; Calem and LaCour-Little, 2001;
Capone, 2000, 2003). In other countries, such models are absent (in the sphere of public
information).
- 73 -
The implementation of the VaR method is directly related to the statistical models that
evaluate mortgage default and prepayment behaviors. Like other duration- or agedependent processes, mortgage terminations are highly amenable to analysis using a
variety of statistical survival-time models, including parametric hazard models, semiparametric or proportional hazard models, and discrete-time models (Calhoun and Deng,
2002).
B.
My Approach
In this research, I choose the value-at-risk approach and apply parametric models to
simulate the mortgage cohort performance. Parametric models provide a complete
parameterization of the probability distribution of survival times (or default occurrences).
Examples include the exponential, Weibull, gamma, log-logistic, and various mixtures of
these and other parametric distributions. I follow and improve the methodology proposed
by Capone (2000) to conduct a cross-country comparison of the magnitude of implied
government liability (the implied "value-at-risk") for the three public MI programs. I
choose this methodology because: i) it provides a comparable approach to comparing
different MI models with regard to their credit risks imposed on the government; ii) it
incorporates both historical experience of mortgage loan performance and stochastic
simulation techniques, therefore having certain advantages over the discrete-time models
or pure simulation models where the interest rate and housing value movements are
assumed to follow some predefined stochastic processes; iii) it has the flexibility of
controlling country-specific characteristics through various parameter settings and
therefore allows sensitivity analysis; and iv) from the practical point of view, this
methodology is data-parsimonious, since relatively few parameters are needed to describe
the distribution of portfolio performance (defaults and prepayments). Some other
methods such as proportional hazards models or econometric models are data demanding,
requiring detailed loan-level characteristics and borrowers information. These data are
either confidential, or simply do not exist in many developing countries and emerging
economies, and therefore would greatly limit the applicability of this research to a
broader audience. In short, the proposed methodology provides a flexible and workable
- 74 -
way to obtain some quantifiable measures of implied liabilities in providing public MI
across countries.
Applying the VaR method and parametric models, I intend to examine the following
questions in the three study countries:
i)
Whether their public MI scheme is properly designed so that its pricing covers
expected risk exposure - the solvency of the public MI enterprise;
ii)
Whether the government understands and is ready to bear the implied liabilities
resulting from a catastrophic event if the public MI's loss reserves become
insufficient - what is the implied capital outlay for the government under situations
with loss probabilities like a or [3in Figure 5.1; and
iii)
What is the magnitude of potential liabilities over a longer period of time or under
different macroeconomic situations - multi-year analysis and sensitivity analysis
that can serve as the basis for further discussions of the public resource allocation
efficiency.
III.
A.
Model Setup
The Model
The model is constructed to find the net present value of the mortgage insurance contracts
on a particular book of business (cohort) that involve uncertain, but measurable cash
flows. Each possible economic path, n, produces a net present value, or NPV(n):
NPV(n) =
f (P(n,t) - C(n,t))erttdt
Where T is the life of each insurance contract (life of loans); Pt is net premium income of
each time period, t; Ct is net claims expense; and rt is the spot, risk-free interest rate used
for discounting. Across all possible states of the world, the expected value summary
statistic is:
E[NPV]= f (n)f (P(n,t) - C(n,t))er"dtdn
Wheref(n) is the probability of each economic path, n.
- 75 -
The various economic paths that a cohort of loans can potentially experience are the
result of a combination of macro economic environment, public MI program designs and
portfolio performance, which can be measured by cohort default and prepayment rates
and their patterns. Forecasts of the cohort performance provide the foundation for setting
up calculations of cash flows from public mortgage guarantees.
B.
Modeling Processes
For comparability purposes, I choose to model the performance of the 2003 mortgage
loan cohort (all the loans originated in year 2003) that is guaranteed by public MIs of the
three countries.
1)
The modeling processes can be divided into three main stages:
Simulate the cohort's ultimate default and prepayment rate distributions and their
annual patterns, by parameterizing frequency distributions for lifetime default and
prepayment rates to match the limited historical experience and expected future
performance for the existing books of business;
2)
Based on assumptions of macro economic conditions, housing market performance,
and program characteristics, calculate discounted cash flows for the public MI in
providing guarantees for the 2003 cohort. Obtain the NPV of the 2003 cohort for all
simulated pairs of cohort default and prepayment rates; and
3)
Conduct multi-year NPV analyses for the public MI programs to measure the longterm liabilities on the backing government. Regression models are used to explore
the correlation of cohort ultimate default rates between consecutive years and the
impact of interest rate movements. Then the cumulative NPV of multiple years is
calculated for each simulated pair of cohort default and prepayment rates. In the
analysis of the worst-case scenarios, repeated samples are drawn only from the
negative tail portion of the NPV distribution obtained from Step 2 - the "loss zone."
It embodies the interdependency of higher default rates during tough economic
situations.
23 The
choice of 2003 cohort is due to the fact that it is the latest year for which all the data are available for
all three countries.
- 76 -
The detailed implementation of the modeling processes above is illustrated in the
following flow chart (Figure 5.2). Parameters used in the modeling process are described
as well (Figure 5.3). They are further explained in the next section.
- 77 -
Figure 5.2 Modeling Processes
Data Examination
Macro economic conditions by loan age,
across all outstandin cohorts
filii
......
The Flow Chart of the Modeling Processes
.
,
.[).
Distribution of Cohort Ultimate Default Rates
Fit an Erlang distribution based on historical
experience of COhO~:~~ti:~w~!~Ult
I
rates
~--....-~.
~~~,..J
Monte Carlo Simulation of Cohort Ultimate
Default Rates based on the Erlall2 distribution
Simulation of Cohort Ultimate Preoavment Rates
Obtain 10,000 cohorts' ultimate lifetime prepayment
rates based on the simulated ultimate default rates
Draw 10,000 random samples from the Erlang
distribution of cohorts' ultimate lifetime default rates
f .. ,.-.....-.~.,
..
Regression analysis to explore the correlation
between ultimate default and prepayment rates
,~~
~
Patterns of Annual Unconditional Preoavment Rates
Patterns of Annual Unconditional Default Rates
The distribution of cohort's annual unconditional
prepayment rates based on historical experience, under
various economic conditions
The distribution of cohort's annual unconditional
default rates based on historical experience, under
various economic conditions
f=I~~--, •.
---
.[).
.[).
1':
~
......
.
I: ~' .•
-:
: ~
Discounted cash flow (DCF) analysis for
each simulated cohort ultimate default rate
NPV Distribution of the 2003 Cohort
The NPV (or profitability rate) distribution of the
2003 cohort based on the discounted cash flow analysis
-,..~
.-....-.-
.....-----
.[).
Correlation between multi-year cohorts' ultimate
default rates - regression analysis to project future
6-year and 10-year cohorts' default rates and
calculate the cumulative NPV
Multi-vear NPV Analvses
The cumulative NPV (or profitability rate)
distribution over the 6-year and 10-year
cohorts
Worst-case scenarios: resampling multi-year NPV
sequences only from the negative tail portion of the
NPV distribution to simulate the recession scenario
Sensitivity Analvses
Explore how the NPV of 2003 cohort will
change if some modeling parameters change
- 78 -
Figure 5.3 Modeling Parameters
Macro Economic
Environment
* Various duration
Treasury yields
* 30-year mortgage rate
* Inflation rate
· Housing price index
Public MI Program
Characteristics
* Program size
* Loan portfolio
composition
* Insurance products and
pricing
or
-
1
40.
o.
Portfolio Performance
Measures
* Ultimate cumulative
default rate and
prepayment rate
* Annual distribution
factors
...........................................
· Macro economy
· Housing market
conditions
· Program
management
............................................
bs
- -----flaim
- fa1c
ll.ll
Ctors S
... -£_.
* utner reatures or ne
program design
w
factors
Interpolation of other
Doints on the vield curve
Historical
cohorts' records
Project the Portfolio Performance of the 2003 Cohort
* Default and prepayment rates distribution
* Claim expenses and property recoveries
Compute discount factors
for converting future cash
flows into present values
as of insurance
endorsement year
I Fu
Future cohorts'
roiection
Time to foreclosure
v I
I Property disposition time
Property value recovery
rate
I~~~~~
-JhJ
I
-
alcae
-
Cash flow
rcmnnnent
_-L_~
- 1
- 1- ...
-11
me lnrv lstnouon OIme lvll provision I_
--
for the 2003 cohort of business
*: These items may be specific to some public MI models.
Source: adaptedfrom Capone (2002), andfrom the author
C.
Parameter Settings
The parameters used in the modeling process can be grouped into three categories: macro
economic environment, public MI program characteristics, and portfolio performance
measures and projections (Table 5.1). Their settings reflect a country's characteristics
(economic outlook, legal and financial infrastructure, borrowers profile, etc.) and its
public MI program's uniqueness. The country's future economic outlook will impact the
parameters values and the corresponding cash flows (Table 5.2).
- 79 -
Table 5.1 Parameters Categories and Explanations
Parameters
Meaning
Macro Economic Environment
Interpolated curve from 1-year, 5-year, 10-year and
Yield curve
30-year Treasury bonds (U.S.) or government bonds
An indication of the macro economic stability.
Directly related to mortgage performance
Interest rate charged on fixed rate mortgages (FRM)
Mortgage rate
with long terms (10-year, 15-year, 25-year, or 30year)
Indication of housing market conditions: appreciative
Housing price index
or depreciative market
Public MI Program Characteristics
The volume of new loans (number and dollar amount)
Program size
Inflation rate
Loan portfolio
insured in a particular year - the cohort size
The composition of the 2003 cohort, classified by
composition
various criteria: LTV classes, the length of interest
Insurance products and
pricing
rate fixation, mortgage types, etc.
The terms of the insurance contract and its pricing
structure - upfront fee and/or annual premium
Examples are: upfront premium refund, recourse to
Other features of the
defaulted borrowers, etc.
program design
Portfolio Performance Measures
The cumulative cohort default rate and prepayment
Ultimate cumulative
Future conditions
Unfavorable
Favorable
economic outlook
economic outlook
Upward trending
Flat or even
downward
trending
High and volatile
Relatively low and
stable
Fixed nominal rate
(developed
countries)
Appreciating
Fixed real rate
(developing
countries)
Depreciating
Tend to be large
Uncertain
N/A
N/A
Pricing tends to go
down
Pricing tends to go
up
N/A
N/A
Low ultimate
High ultimate
default rate
default rate
default rate and
rate at the end of the loan term
prepayment rate
Annual distributions of
The annual unconditional default and prepayment
Annual default
Annual default
defaults and
rates throughout the loan term for the cohort - the
rates peak early
rates may remain
prepayments
shape of the distribution is specific to each country
and narrowly
Claim expenses
The costs of paying out insurance claims due to
borrower defaults, including unpaid balances,
foreclosure-related expenses, and accumulated
interests
Low
relatively high for
a prolonged period
of time
High
Property recovery rate
The ratio of the sales revenues from foreclosed
High
Low
properties over the total claim payment
on defaults
Note: N/A means "not applicable".
Table 5.2 Cash Flow Components
Cash Flow Components
Meaning
Future potential changes
Favorable
Unfavorable
economic outlook
economic outlook
Cash inflows
Premium income
Upfront fees and/or annual premium paid by
insured borrowers
Income stream
larger due to
increased volume
Income stream
smaller due to high
defaults
Net revenues from
property sales (per
After the foreclosure, pubic MI enterprises take
over the property and sell it to make up for the
High due to
housing market
Low due to
housing market
property)
Recourse to borrowers*
delinquent payments.
Mortgage insurers can capture other assets of
defaulted borrowers to cover the debt
appreciation
N/A
depreciation
N/A
Insurance claim
Claim payments when insured borrowers default on
Low
High
payments
their mortgage loans
Cash outflows
80-
Premium rebates*
Rebate of part of the upfront premiums to early
prepayers
*: These items may be specific to some public MI models.
Note: N/A means "not applicable".
IV.
A.
N/A
N/A
Modeling and Simulation Results
The U.S. FHA Program
A.1 The data
Data used for the modeling and simulation are mostly obtained from the Fiscal Year (FY)
2003 Actuarial Review of the FHA Mutual Mortgage Insurance (MMI) Fund, which
includes most of the FHA's unsubsidized single-family mortgage insurance business. At
the end of FY 2003, the MMI Fund comprised 82.5 percent of the FHA insurance funds.
The MMI Fund Actuarial Review provides both historical portfolio performance data on
insured books from 1975 to 2003,24 and projections at the end of the loan term for each
outstanding book of business. Projections of ultimate default (claim) rates on books
underwritten between 1975 and 1993 will be most accurate because they are all seasoned
for at least 10 years. Default projections on cohorts seasoned between 5 and 10 years
(1993-1998 books) will also be fairly accurate. Other data are collected from the FHA's
Management Report (2003), internal website, and interviews. For this analysis, I focus on
the 30-year fixed rate mortgages, the mainstay of FHA-insured loan products.
The period of 1975-2003, a 29-year time horizon covered by the available data, is quite
representative of the U.S. macro economy, with three recessions (1980-1982, 1990-1991,
and 2001-2002) and three booms (1976-1978, 1983-1984, and 1997-1999). The
experience of economically favorable, normal, and unfavorable years is distributed quite
evenly across various loan ages (Figure 5.4), and therefore should not be of a concern of
causing bias in deriving default rate and prepayment rate distributions.
-
24 Although FHA has been in existence since 1934, the agency does not trust its records on early books of
business, meaning anything before 1975 (Capone, 2000).
81 -
Figure 5.4 The Distribution of Macro Economic
Conditions by Loan Age - The FHA
Macro Economic Conditions by Loan Age
• Favorable
economies
C Normal economies
C Unfavorable
economies
120%
100%
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
80%
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- - -
60%
-
-
-
-
-
-
-
-
-
-
I-
-
-
-
-
-
- - ~
40%
-
-
-
-
-
-
-
-
-
-
-
- - - -
-
-
-
-
-
-
-
-
-
I-
-
20%
-
-
-
-
-
-
-
-
-
-
-
-
-
0%
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17 18 19 20
Loan Age (1st - 20th Year)
A.2 Modeling parameters for the 2003 cohort
Ultimate default rates
The cohort's ultimate cumulative default rate,defined as the ratio of the number
defaulted over the total number
of insured loans in the cohort in itslifetime, has the
biggest impact on the performance
MMI
of loans
of the insurance fund. Throughout
FHA's
Fund cohorts' potential default rates for 30-year fixed rate mortgages
history, its
of each
outstanding book of business exhibit a large variance, ranging from a low of 4.01 percent
for the 1977 book of business to a high of 20.53 percent for the 1981 book (Table 5.3).
The mean default rate is estimated around 9.76 percent.
Table 5.3 Cumulative
Origination
Year
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
"Default Rates of FHA-Insured
To-date (2003) cumulative
default rate (%)
4.50
4.32
4.00
5.61
9.53
14.85
20.51
19.06
15.69
19.32
17.64
13.28
9.97
10.70
9.48
30-Year Fixed-Rate Mortgages
Projected 30-year default
rate (%)
4.50
4.33
4.01
5.61
9.54
14.86
20.53
19.09
15.71
19.36
17.70
13.36
10.09
10.88
9.69
- 82-
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
8.19
7.27
2000
4.09
2001
1.92
0.25
0.02
8.43
7.54
6.34
5.86
6.75
6.39
6.83
8.72
8.63
6.17
7.58
7.16
7.94
7.04
6.27
4.85
4.17
2002
2003
Mean
Median
Standard Deviation
6.78
7.43
5.81
6.74
8.62
9.76
8.43
4.75
Source: Actual (to-date) and projected default rates arefrom FHA Annual Actuarial Review of the MMIF, FY 2003, by
Deloitte & Touche
Ultimateprepaymentrates
The ultimate prepayment rate of an insured cohort also has a significant impact on the
insurance fund, as borrowers stop paying annual insurance premiums when they
terminate their mortgages earlier. In a low interest rate environment, borrowers tend to
exercise their "call option" embedded in the mortgage contract and prepay the loan.
Considering both defaults and prepayments, the total termination rates before the end of
the loan term are quite high and relatively stable for FHA's 1975-2003 books (Table 5.4).
Table 5.4 Cumulative Prepayment Rates and Total Termination Rates of FHA-Insured
30-Year Fixed-Rate Mortgages
Origination
Year
1975
1976
1977
1978
1979
1980
Ultimate
Default rate
4.50%
4.33%
4.01%
5.61%
9.54%
Ultimate
Prepayment
rate
-86.51%
86.73%
86.24%
84.78%
Sum (Total
termination rate)
91.01%
91.06%
90.25%
90.39%
91.10%
92.19%
1981
1982
20.53%
81.56%
77.32%
72.37 %
19.09%
74.53%
1983
1984
1985
15.71%
19.36%
17.70%
78.63%
74.95%
94.34%
77.48%
95.18%
14.86%
92.90%
93.63%
94.31%
- 83 -
1986
13.36%
80.90%
94.26%
1987
1988
1989
1990
10.09%
83.41%
93.50%
10.88%
94.13%
8.43%
83.25%
85.10%
86.99%
1991
1992
1993
1994
1995
1996
1997
1998
1999
7.54%
88.12%
6.75%
88.95%
89.86%
9.69%
94.79%
6.83%
8.72%
8.63%
89.16%
87.74%
87.75%
95.43%
95.66%
95.70%
96.24%
95.99%
96.46%
96.39%
7.94%
88.54%
96.48%
89.07%
89.33%
96.12%
2000
7.04%
6.78%
7.43%
2001
5.81%
6.39%
96.11%
98.11%
90.68%
91.04%
6.74%
2002
8.62%
2003
Mean
Median
Standard deviation
96.84%
94.22%
87.48%
80.63%
89.25%
84.45 %
86.51%
5.30%
94.21%
94.34%
2.32%
Source: Actual (to-date) and projected prepayment rates are from FHA Annual Actuarial Review of the MMIF, FY
2003, by Deloitte & Touche
Borrowers' default and prepayment behaviors are both correlated with interest rate
movements, among other factors. A simple regression model is run to capture the
interdependency between cohort default rates and prepayment rates from 1975 to 2003
(Appendix 5.1). About 81 percent of the variation is explained by the model:
Ultimate Prepayment Rate = 0.942 -1.004*Ultimate Default Rate
Otherparameters
All additional parameters used for this simulation can be found in Table 5.5.
Table 5.5 Parameters Used in the Cash Flow Analysis of the FHA Program
Cohort size
Year
Cohort
LTV <=80
3.2%
97<LTV
16.8%
composition by
80 < LTV <= 90
15.2%
Streamline refinance
5.0%
loan types
90 < LTV <= 95
95 < LTV <= 97
20.0%
24.8%
ARM (all LTV)
15.0%
Premium
structure and
pricing
For all types of 30-yr fixed rate mortgage loans originated in FY 2003:
Up-front premium rates (% of original loan amount):
Annual premium rates (% of beginning of the year (BOY) balance):
1.5%
0.5%
Loss coverage (including unpaid balance, delayed interest payments and
100%
2003
Loan insured
(million $)
57,796
Number of loans
550,000
other miscellaneous fees:
- 84 -
Average size of
the loan ($)
105,084
Duration of the
annual premium
payment
FHA stipulates that borrowers can terminate their mortgage insurance when their mortgage
loan's LTV drops below 78% through amortization. Therefore, the duration of the annual
premium payment is (in years):
LTV <=80
7
95 < LTV <= 97
12
80 < LTV <= 90
7
97<LTV
30
90 < LTV <= 95
12
ARM (all LTV)
12
Upfront
premi urn refund
The FHA Commissioner determines how much of the upfront premium is refunded when loans
are terminated. For loans closed on or after January 1,2001, no refund is due the homeowner
after the fifth year. The refund factors (percent of total upfront premiums paid) are:
Year 1
Year 2
Year 3
Year 4
Year 5
Premium refund schedule:
0.85
0.65
0.45
0.25
0.1
Loan term: 360 months
30-yr fixed rate
6.375%
Loan interest rates:
30-yr streamline
6.375%
Mortgage loan
features
Default costs
and property
recoveries
Default rate
scaling factors
& Prepayment
rate scaling
factors
4.750%
Unpaid Balances + foreclosure
expenses (10%) + interest (8%)
Recovery rate on defaults: Fixed
68 %
These figures are obtained from
rate 30-:year loans
D&T Annual Actuarial Review,
Recovery rate on defaults:
74%
based on FHA's historical
Streamline refinance 30-year loans
experience
These scaling factors differentiate risk levels of default and tendency to be prepaid among
different mortgage loan categories. The overall sum of the weighted averages of default and
prepayment factors (the product of scaling factors and product weights in the cohort) equals 1.
Default scaling Weighted
factor
average
0.55
0.0176
0.78
0.1186
0.86
0.1720
0.95
0.2356
1.15
0.1932
LTV < 80
80<= LTV < 90
90 <=LTV <95
95 <= LTV < 97
97<=LTV
Streamline
refinance
ARM
Yield curve for
discounting
future cash
flows
30-yr adjustable
1.18
Claim cost (% of UPB)
LTV < 80
80 <= LTV < 90
90 <= LTV < 95
95 <= LTV < 97
97<= LTV
Streamline
refinance
ARM
_Prepayment
scaling factor
1.1
0.99
0.98
0.98
0.9
1.8
0.0900
0.9
1.15
0.1725
1.18
Sum:
1.0000
Sum:
Derived from 1-30 Year U.S. Treasury Constant Maturity Rates of 2003 (
http://www.federalreserve. gov/releases/h 15/currentlh 15.pdf)
I-year Treasury maturity rate
1.24%
7-year Treasury maturity rate
2-year Treasury maturity rate
1.65%
10-year Treasury maturity rate
3-year Treasury maturity rate
2.11 %
20-year Treasury maturity rate
5-year Treasury maturity rate
2.97%
Interpolated
U.S. Treasury
Curve of 2003
Weighted
average
0.0352
0.1505
0.1960
0.2430
0.1512
0.0450
0.1770
1.0000
3.52%
4.02%
4.96%
Yield
The smoothl y
upward trending
yield curve
projects a stable
economy
6.0
5.0
'Cft. 4.0
32
3.0
Q)
> 2.0
10
0.0
o
o
"
0
MahJrity
,
,
(t)
~
0
,
CD
0>
'r"
'r"
Source: Values in this table come from, or are based on, data in the FY2003 Annual Actuarial Review of the FHA MMIF,
prepared by Deloitte & Touche; FHA's 2003 Management Report; and Capone, 2000.
- 85 -
A.3 Modeling processes and results
Simulate the distributions of lifetime default and prepayment rates
A standard approach to bankruptcy simulations is to assume that available experience is
the best information on the distribution of potential default/claim rates, to draw repeated
samples of subset portfolios, and to look at resulting value-at-risk measures (Capone,
2000). Based on FHA's past experience of its 29 outstanding cohorts (1975-2003), I map
the frequency distribution of their ultimate default rates (Figure 5.5). Although the
number of observations is limited, they can be fitted to a continuous Erlang distribution,
from which random samples can be drawn. The Erlang distribution is one form of the
Gamma distribution, which serves well as a general measurement model of engineering
variables because of its shape flexibility, positive sample space, and closed form solution
(Bury, 1999).25
Figure 5.5 The Frequency Distribution of FHA Cohorts' Ultimate Default Rates
The Actualand Fitted (ErlangDistribution)FHA 30-Year
CohortDefaultRates (1975-2003)
I
. 0.2 0a
r 1_____
I
r
__
--
0.05 - __
O-
-
FHA 30-year mortgage
portfolios' ultimate default
rate distribution:
_1___
a)
0 0.1 -
---
Erlang parameters for the
- -Actual Data
FittedDatawithEdangDistnbution - n1 -f
___.__..__.._i
-----
--
.
r
r
Il
\
------r
.·
---
--------
E
\r\r
,.............................
Lifetime
N
im) te- efa)
N
LifetimeUMtm~le
MefaliltH~te )
)
N
N
N
)
CM
·
Mean:
Std dev:
9.76%
4.75%
Erlang b:
2.309
Erlang c:
4
Based on the fitted Erlang distribution, I apply Monte Carlo simulation techniques to
draw 10,000 random samples from it, representing 10,000 potential outcomes of the
25 That is, if nothing is known about the physics of the measurement phenomenon, the Gamma distribution
is a good choice tofit the data. The Erlang distribution takes two parameters: the scale parameter b and the
shape parameter c. Erlang distribution has closed form solution, because it requires that the shape
parameter be an integer. Assume the mean and standard deviation of the projected 30-year ultimate default
rates are x-bar and s, the Erlang parameters are computed as following:
b
2
_
- ; c
-
X
2
; andftx)= (x/b)l
S
exp(-x/b)
b(c -1)!
fix) is the probability density function of x.
- 86-
cohort ultimate default rate (Figure 5.6). The simulation has the benefit of broadening the
outcomes beyond the range of the observed limited experience, therefore allowing the
exploration of some out-of-range events with small probabilities (frequencies). 10,000
ultimate prepayment rates are then obtained through the relationship of
Ultimate Prepayment Rate = 0.942 -1.004*Ultimate Default Rate
Figure 5.6 The Frequency Distribution of 10,000 Simulated Ultimate Default and
Prepayment Rates - The FHA
The FrequencyDistributionof 10,000Ultimate
Prepayment
Ratesof FHA2003Cohort
The FrequencyDistributionof 10,000SimulatedUltimate
DefaultRatesof FHA2003 Cohort
1000…
.... -.
1200
1200 1000
-
.
800
60
=3
600
400
2
400
200
-
--
1
_
8---
0
CM
_
e
-_
200
0
CO
COC
0 11) C
'-0
t
Ultimate
Default
Rate)
Ultimate Default Rate
C'
CO)
LO
0)
)
U)
N
CD
0
t
UltimatePrepaymentRate
Simulatethe annual distributionsof unconditionaldefaultand prepaymentrates
The unconditional annual default rate and prepayment rate patterns - percentage of total
defaults and prepayments occurring in each year - are also modeled based on FHA's
historical experience using Erlang distributions. For each book of business, a pair of
Erlang parameters are computed based on the relationship between the cohort's age
(year) and annual unconditional default and prepayment rates. Considering that the
annual distribution patterns of a cohort's default and prepayment rate differ under
different economic scenarios, I classify the existing books' performance into three
categories: good (ultimate cohort default rate < 6%), normal (10% > ultimate cohort
default rate > 6%), and bad (ultimate cohort default rate > 10%). Within each group, I
choose the median value of the scale parameter b and shape parameter c to construct the
corresponding Erlang distribution. Table 5.6 presents the Erlang parameters for annual
default and prepayment rate distributions, and Figure 5.7 illustrates the patterns.
- 87 -
0
Table 5.6 Erlang Parameters for Annual Unconditional Default and Prepayment Rate
Distributions - The FHA
Good performance
Bad performance
Ultimate default rate < 6%
Normal performance
6% < Ultimate default rate < 10%
Annual distribution patterns of unconditional default rates
Scale parameter b
3.47
1.91
Shape parameter c
2
3
Ultimate default rate> 10%
Annual distribution patterns of unconditional prepayment rates
Scale parameter b
4.86
3.01
Shape parameter c
3
2
1.78
4
2.21
3
Figure 5.7 The Probability Distribution of Annual Unconditional Default and
Prepayment Rates - The FHA
Annual Unconditional Default Probability
(1975-2003)
I-.-Good performance -'-Normal
performance --'-Sad
performance
0.16
~ 0.14
:5 0.12
11l
.g
0.1
~ 0.08
~ 0.06
~ 0.04
0.02
-
o
~
MOO
......
01
Annual Unconditional Prepayment Probability
(1975-2003)
-+-Good performance -'-Normal
performance --'-Sad
performance
0.14
~
0.12
~
0.1
£.
0.08
.c
-
C
~ 0.06
>-
~ 0.04
-
<D
a:
I
Under the "normal performance"
scenario, the annual unconditional
default rates peak between the 3rd
and the 6th year, then fall rapidly.
Under
the "bad
performance"
situations, the annual distribution of
defaults has a wider peak window (a
flatter middle section) between the
3rd and the 8th year, and tapers down
more gradually, indicating a larger
probability of default during the
later lifetime of the loan.
Under the "good performance"
scenario, the annual unconditional
prepayment rates distribute more
evenly than other scenarios, which
may reflect the fact that portfolios
with low default rates contain many
loans originated in the low interest
rate environment, e.g. during the
recessIOns.
0.02
o
~
M
It)
......
01
~
M
It)
~ po1Tcy year
......
01
"1-30
Discounted cash flow analysis
Assuming the size of FHA's 2003 cohort of 30-year fixed rate mortgages is 50 billion
dollars, I conduct the discounted cash flow analysis and portfolio performance evaluation
(measuring annual conditional and unconditional default and prepayment rates, survival
rates, and cumulative rates) over the lifetime of this cohort. Monte Carlo simulation is
- 88 -
used to obtain 10,000 ultimate default rates. The parameters employed (Table 5.5)
represent the projections of future macro economic conditions and housing market
performance in the U.S., based on FHA's own experience and forecasts of its
independent auditors. Cash flows of different loan products, categorized by their LTV
ratios, are analyzed separately. All future cash flows, over the life of the loans or
guarantees, are discounted by the future spot rates implied by the Treasury yield curve as
of mid 2003. Appendix 5.2 describes the components of the cash flow analysis. The net
present value (NPV), both in the aggregated amount for the whole cohort and in the
percentage term as the NPV per dollar of insured loan, is calculated for each simulated
cohort ultimate default rate, resulting in a NPV distribution (Figure 5.8). For
comparability purposes, I use "profitability rate" to measure the NPV per dollar of
insured loan, which is the ratio of the NPV of the 2003 cohort's guarantee contracts over
the cohort's total dollar amount (face value). Table 5.7 summarizes the results.
Table 5.7 Simulation Results of the NPV and Profitability Rate of the FHA 2003 Cohort
Simulated ultimate
default rates
NPV of the guarantee contracts
on the 2003 cohort (million $)
Profitability rate (NPV per
$ of insured loan, %)
Statistics
Mean
Minimum
Maximum
Breakeven
9.2%
0.7%
34.2%
13.1%
427
1,656
-3,063
12.7
0.85%
-6.13%
3.31%
0%
Percentile
5 th
3.3%
1,245
2.49%
1 0 th
4.1%
1,119
2.24%
5.9%
8.4%
11.8%
15.4%
18.0%
22.9%
834
439
202
-323
-702
-1,416
1.67%
0.88%
0.40%
-0.65%
-1.40%
-2.83%
2 5 th
50
h
7 5 th
9 0 th
9 5 th
9 9 th
Breakeven
percentile
t
8 2 nd
percentile*
8 2 nd
percentile
*: This breakeven percentile means that losses (negative NPV) can be expected 18 percent of the time.
- 89
-
Figure 5.8 The Frequency Distribution of the Simulated Profitability Rates
-
The Frequency Distribution of Profitability Rates of
FHA 2003 Cohort (based on 10,000 simulated default
rates)
1200 IUU-
, 800
@
600
400
_
200
I
ppp_
n..
0
Profitabilityrates(NPVof Ml / Facevalueof the cohort)
For FHA's 2003 cohort, the simulation shows that its mean profitability rate is around
0.85%, fairly close to the break-even zero percent, which supports the argument that a
pubic MI's pricing usually only has a small cushion. Under the model's parameters
setting, the cohort's break-even default rate is around 13%. That represents the
8 2 nd
percentile event, meaning that losses can be expected 18 percent of the time, or once
every 5.6 years on average. If the experience of FHA's 1981 book of business, the worse
default experience in the last three decades with a projected ultimate default rate of
20.5%, were to repeat in the 2003 cohort, the estimated losses would be about 1 billion
dollars, or 2% of the cohort's total value.
Multi-yearresamplinganalysis
Given the long time horizon of mortgage loans and hence mortgage guarantees, it is
important to measure the cumulative potential liabilities over multiple years, to determine
how much the burden could be on the backing government within a long period of time.
Examining the projected 30-year ultimate default rates for the existing FHA cohorts
(Table 5.3), we can see that they are strongly correlated - good years or bad years tend to
come in bunches instead of randomly. The recession of the 1980s provides the best
evidence - the estimated ultimate default rates exceeded 10 percent for nine consecutive
years from 1980 to 1988. To explore this yearly interdependency and how external
factors such as interest rates influence the ultimate default rates, I conduct several
regression analyses and obtain the following model with the best fitness (Appendix 5.3):
- 90 -
Cohort ultimate default rate ,t = -0.005 + 0.635* Cohort ultimate default rate (,-) + 0.605* I-year Treasury bond rate
(-0.57) (7.71)
(4.80)
R' = 0.88
The model explains around 88 percent of the variations in cohort ultimate default rates.
The coefficients for the lagged default rate variable and the interest rate are both
statistically significant, indicating that a cohort's ultimate default rate is correlated with
the cohort default rate from the previous year and the current interest rate.
Based on the regression model, I conduct multi-year NPV analyses by picking every one
of the 10,000 simulated 2003 cohort default rates, projecting the next 5 (and 9) years' of
cohort default rates based on the regression model derived above - from 2004 to 2008
(and 2012), and calculating the aggregate NPV over the total 6-year (and 10-year) period.
The future 1-year Treasury rates are derived from the Treasury bond yield curve as the
forward interest rates. Repeating the process for 10,000 simulated cohort default rates, I
obtain multi-year cumulative NPV (or profitability rate) distributions for 6-year and 10year periods respectively (Figure 5.9). When a multi-year cumulative NPV (or
profitability rate) is positive, it means that FHA generates surplus from its insurance
business over this period. If the cumulative NPV is negative, it gives us a magnitude of
the potential losses over the specified period that have to be sustained either by FHA's
capital reserves or by the government in case that reserves are not sufficient. Table 5.8
summarizes the statistics of the multi-year NPV and profitability rate distributions.
Table 5.8 Multi-year NPV and Profitability Rate Distributions - The FHA
6-Year Accumulationa
Cumulative NPV
of the guaranteed
cohorts (million $)
10-Year Accumulationa
Profitability rate
(Cumulative NPV
over 2003 cohort's
face value,
Cumulative NPV
of the guaranteed
cohorts (million $)
Profitability rate
(Cumulative NPV
over 2003 cohort's
face value, %)b
%)b
Statistics
Mean
Minimum
Maximum
3,542
-4,479
6,801
7.08%
-8.96%
13.60%
5,030
-3,305
8,493
10.06%
-6.61%
16.99%
5 th
lOth
25t h
756
1,488
2,608
1.51%
2.98%
5.22%
2,047
2,828
4,034
4.09%
5.66%
8.07%
5 0 th
3,684
7.37%
5,191
10.38%
Percentile
-91 -
75th
90th
95th
99th
Breakeven percentile
4,696
5,425
5,749
6,275
97 percentileC
9.39%
10.85%
11.50%
12.55%
12.53%
14.07%
14.76%
15.87%
6,263
7,035
7,378
7,935
99 percentileC
Note:
a: Every one of the 10,000 simulated ultimate default rates for the 2003 cohort is drawn to project the next 5-year (and 9year) cohort default rates. Therefore, 10,000 multi-year cumulative NPV (or profitability rate) outcomes are included in
the distribution.
b: Profitability rate is calculated as the ratio of cumulative NPV over one year's business volume - 2003 cohort's face
value.
c: The breakeven percentile means that losses (negative cumulative NPV) can be expected 3 percent and 1 percent of the
time respectively, for 6-year and 10-year periods.
Figure 5.9 Frequency
Distributions
of Multi-year
Cumulative
Profitability
Rates
The Freque ncy Distribution of 6-Year Cumulative
Profitability Raes, Based on Regression Analys is
350
300
(/)
250
.~
g
200
Q)
::J
0'"
Q)
u:
150
100
50
o
'eft.
"#. "#.
'eft.
"#.
~
OJ)
t"?
"7
~
'eft. 'eft.
..-
C'Il
"#.
l[)
'eft. "#.
OJ
A'ofitability Rates
CJ
..-
'eft.
'eft. 'eft.
N~to(J)
"#.
The Frequency Distribution of 10-Year Cumulative
Profitability Rates, Based on Regression Analysis
350
300
(/)
250
Q)
.0
c
200
Q)
::J
g
LL
150
100
50
0
~
0
CJ
'"7
~
'eft. 0
O? ~
"#.
C?
'"7
~
0
C'Il
'eft.
l[)
0
~
0
~
0
OJ
CJ
~
N
~
~
0
~
0
to
'(J)
eft.
A'oUability Rates
The multi-year
simulation
under the expected
subsequent
"#. '..eft.
results show the financial
stable economic
strength of the current FHA program
outlook in the future for the 2003 cohort and its
cohorts. The mean profitability
rate is about 7% for the next 6-year cohorts
and 10% for the next 10-year cohorts, or on average about 1% profitability
- 92-
rate per
annual book of business over the 6-year and 10-year time frames. The break-even
percentile is very high, 97% for 6-year cohorts and 99% for 10-year cohorts, indicating
very small probabilities of losing money over the multi-year horizon.
The multi-year analysis captures the correlation between adjacent years' cohort
performance. If this yearly correlation has not been taken into consideration, the positive
cumulative NPV or profitability rate of the FHA cohorts will be underestimated. For
comparison purposes, I conduct the resampling analysis by drawing random multi-year
sequences repeatedly from the profitability distribution (or NPV distribution) derived
earlier (Figure 5.8). The results show that the 6-random-year period is estimated to
generate cumulative NPV of 2.5 billion dollars, or 5.06% profitability rate over one
year's cohort volume, compared to the NPV of 3.5 billion dollars (7.08% profitability
rate) when six correlated cohorts are analyzed, a substantial 40 percent underestimation.
Also, for the 10-year period, random draws generate a cumulative NPV of 4.3 billion
dollars (8.56%7 profitability rate), while the correlated cohorts are projected to have a
cumulative NPV of 5 billion dollars (10.06% profitability rate).
A caveat of the multi-year analysis is that it heavily depends on the projection of future
interest rates, which can be quite volatile as the yield curve changes constantly. An
important question to ask is how bad things can go over multiple years into the future,
based on FHA's past bad experiences. In other words, what will be the magnitude of
liabilities for the FHA if there were six or ten consecutive years of losses from its insured
cohorts? These events can be considered as catastrophic risks with very low probabilities
- 3% over a 6-year period of time and 1% over a 10-year timeframe based on the
previous analyses. To answer this question I adopt the method proposed by Carey (1998,
2001) in sampling only from the simulated outcomes with losses. Based on the
profitability rate distribution derived from the 10,000 simulated cohort default rates
(Figure 5.8), I take repeated samples only from the truncated negative tail part of the
distribution. Resampling from the " loss zone" for multi-year sequences simulates the
recession scenario and helps us understand how large the liabilities could be if FHA were
to experience a period of consecutive high default rates like the 1980s. Table 5.9
- 93 -
summarizes the results of resampling 6-year sequences from the negative profitability
rate and NPV distribution and Figure 5.10 shows the distribution.
Table 5.9 Multi-year NPV and Profitability Rate Distributions from the "Loss Zone"
6-Year Accumulation when resampling only from the "loss zone"*
Profitability rate (Cumulative NPV
2003 cohort (million $)
over 2003 cohort's face value, %)
Cumulative NPV of the guaranteed
Statistics
Mean
Minimum
Maximum
-3,107
-6.21%
-15.04%
-0.96%
-7,518
-478
Percentile
5 th
-5,230
h
25
-10.46%
-9.12%
-7.70%
-6.06%
-4,561
-3,848
-3,029
-2,271
-1,658
-1,367
th
0
th
th
75
50
90h
th
95
-4.54%
-3.32%
-2.73%
-1.80%
th
-900
*: 1,500 resampling sequences were drawn randomly only from the negative segment of the profitability/NPV
distribution. Each resampling sequence has 6 data points, or profitability rates, from the distribution.
99
Figure 5.10 The Frequency Distribution of 6-year Cumulative Profitability Rates
The Frequency
Distributionof 6-YearCumulative
Profitability
RatesWhenResampling
OnlyFromEventswithLosses
4~,ItU
140
120
*o 100
) 80
o 60
L.
40
20
0
°
c|
'
'-
I
C
t
C)rofi)P bility
o
Rates
ProfitabilityRates
The results indicate that if the 2003 cohort were to experience a recession of six
consecutive years with net insurance losses, the magnitude of liabilities on the FHA
would be around 3.1 billion dollars on average, or 6.2 percent of the cohort's value. The
maximum possible losses could amount to 7.5 billion dollars, or 15 percent of the
cohort's value. For one-year's book of business, the profitability rate is about -1.03
- 94 -
percent on average and -2.5 percent maximum. Therefore the current requirement of 2
percent capital ratio of the MMI Fund is sufficient to cover a six-year recession scenario.
However, this conclusion assumes that the future cohort size is similar to that of the 2003
cohort. If new insurance cohorts were larger and initiated during the bad years, the capital
ratio needed to maintain the overall solvency throughout would rise.
The 1980s saw seven years (1980 through 1986) in the history of FHA for which default
rates were high enough to exceed the breakeven level (13%) and incurred losses. We can
be reasonably confident that the outcome for books of business underwritten from 1987
to 2000, which have at least 5 years of seasonality, will have ultimate default rates below
their breakeven point. This means that the loss years of the 1980s represent 7 out of 67
cohort years (since the FHA's inception), or just over 10 percent of historical experience.
But all of the loss years came together. Thus the frequency is actually 1 out of 62
potential six-year periods in FHA's history, or about 1.6 percent.
Sensitivityanalysisof FHA's liabilities
The simulation analyses show that the FHA mortgage insurance business is in good shape
going forward, and its capital requirement can withstand a relatively rare event of sixyear recession. This conclusion, however, is contingent upon the modeling parameters
and thus sensitive to their changes. The first factor is the FHA borrowers' risk profile of
its new business. FHA insurance premiums are far below PMI premiums for high LTV
(above 95 percent) loans. The extent to which lower premiums will enable more marginal
borrowers to qualify for FHA-insured mortgaged home purchases as they now meet
payment-to-income requirements will have an impact on the risk profile of FHA's new
business. In recent years, FHA has been pushing hard to expand homeownership to firsttime homebuyers and minority borrowers, which increases the risk of its books of the
past several years. The movement to this direction has two direct impacts on the
modeling parameters: the composition of the new cohort in terms of different LTV
categories, and the projected ultimate default rate. As FHA brings in new business and
existing books mature, its portfolio will move into higher and higher risk categories,
leading to potentially higher default rates. In addition, its borrower profile is becoming
- 95 -
more vulnerable to economic fluctuations. The combination of the two could make a
repeat of the 1980s experience more likely than a mere 1.6 percent event in the future. As
an example, if the percentage of high LTV loans (those over 95 percent) doubles for the
2003 cohort, the NPV of the insurance on this cohort is projected to decrease by 44
million dollars, or 11% (Table 5.10).
The second factor is the premium structure. In many occasions policymakers are under
pressure to take actions based on short-term results instead of taking a long-term view
toward the public MI program. In 1990 when Congress learned that the FHA's MMI
Fund had been losing money for the past several years and was not considered financially
sound, it ordered to double FHA's insurance premium, among other measures. The FHA
has now been generating surplus for the government and the discussion has shifted to
lowering the premium levels. Without an understanding of the long time horizon of the
risks involved in sponsoring a public program like the FHA, any actions to raise or lower
premium levels based on short-term performance can be irrational and create unnecessary
volatility in the program performance. Table 5.10 shows that if FHA cuts its annual
premium rate in half, the NPV of insurance on the 2003 cohort will become negative 184
million, a decrease of 146%.
The size of the new business also matters. As discussed earlier, if the new insurance
cohorts were larger than existing books, say on the order of $100 billion instead of $50
billion set in the parameter, and if these new cohorts were to experience a multi-year
recession, the capital reserves (or profits generated by the earlier smaller cohorts) may
not be enough to cover the large amount of losses caused by the larger new cohorts.
Taking the FHA's 1980s experience for example, the accumulation of losses over
multiple years of bad insurance cohorts was especially bad because these books of
business were larger than the profitable books that preceded them (Capone 2000).
Needless to say, changes of the macroeconomic environment and national housing
market conditions play a big role in FHA liabilities. Housing market conditions directly
impact the recovery rate of foreclosed properties. During FY2003, the biggest revenue
- 96 -
Figure 5.11 Revenue Components
component for FHA's single-family insurance
of the FHA MMI Fund, FY2003
business was "Property Sales and Income,"
exceeding its premium income (Figure 5.11).
Business Revenue Components
of the FHA
HI FY03
"".1tC:M
An upward trending U.S. housing market and
Uninvested
Funds
SF
2%
Periodic
appreciating property values greatly helped
mitigate the FHA losses. The sensitivity
analysis shows that a 20% increase in FHA's
recovery rate of foreclosed properties will boost
54%
the NPV by 1300/0 (Table 5.10).
The macro economic outlook of the country will affect the mortgage interest rates and the
shape of the yield curve, which in turn exert comprehensive influences on many factors
such as the mortgage default and prepayment rates (implied value of the put and call
options in the mortgage contract) and the discount factors of the future cash flows. For
instance, if the future economic outlook becomes unfavorable, say, similar to the 1980s
situation, and if we use the inverted yield curve of 1981 for discounting cash flows, the
NPV of the 2003 cohort will decrease by 10% (Table 5.10).
Table 5.10 The Sensitivity Analysis of Alternative Scenarios - The FHA
Current Model
Current parameter settings
Borrowers
riskprofileLoan
composition
NPV
(mil $)
NPVI
cohort
face
value
Alternative Scenarios
New parameter settings
NPV
(mil $)
80 < LTV <= 90
15.2%
IfFHA moves toward higher L TV
compositions - double the percentage of
high L TV loans:
90< LTV <=95
20.0%
65<LTV
95 <LTV
24.8%
80< LTV <=90
65<LTV
<=80
<=97
97<LTV
Streamline refi
ARM
(allLTV)
3.2%
16.8%
404
0.8%
5.0%
15.0%
<=80
Premium
structure
1.5%
0.0%
95 < LTV <=97
49.6%
97<LTV
33.6%
Up-front premium
rates
Annual premium
rates
0.5%
- 97 -
-11%
-184
-0.4%
-146%
0.0%
5.0%
ARM (allL TV)
11.8%
Scenario 1:halve the annual premium
Up-front premium
rates
Annual premium
rates
360
0.0%
90< LTV <=95
Streamline refi
ChaIl2e
NPV I
cohort
face
value
0.7%
1.5%
0.25%
-1,253
Scenario 2: apply the Dutch premium
Up-front premium
rates
Annual premium
rates
0.30%
0%
Scenario 3: apply the Mexican premium
Up-front premium
rates
Annual premium
rates
Housing
market
conditions
Recovery rate on
defaults: Fixed rate
30-year loans
68%
Recovery rate on
defaults: Streamline
refinance 30-year
loans
74%
0%
0.70%
Scenario I: Favorable (20% increase in
recovery rate)
Recovery
defaults:
Recovery
defaults:
refi
rate on
FRM
rate on
Streamline
81.6%
88.8%
Scenario 2: Unfavorable (20% decrease
in recovery rate)
Recovery
defaults:
Recovery
defaults:
refi
rate on
FRM
rate on
Streamline
54.4%
59.2%
Scenario 1: 1981 (worst)
Interest rate
environment
- the Yield
Curve
Interpolated U.S. Treasury Yield
Curve of 1981
15
15
~ 14
Qj
:; 14
13
13
Maturity
Ol
~atunW
II)
N
N
Ol
N
Scenario 2: 1993 (best)
Interpolated U.S. Treasury
Yield Curve of 1993
8
::l!6
o
"0
]14
>
2
o
II)
Ol
- 98-
Matur1lY
N
II)
N
Ol
N
-2.5%
-210%
B.
The Dutch WEW
Program
B.1 The data
Most of the data are provided by the Homeownership Guarantee Fund (abbreviated to
WEW) and its 2003 Annual Report. Although the WEW has been issuing its guarantee
products, the National Mortgage Guarantee, only since 1995, municipal guarantee
programs were in place long before that (since 1956) and their guarantee liabilities were
transferred to the WEW at its establishment. Unfortunately, the data of earlier years were
not well organized due to the fragmentation of the municipal guarantee systems. At the
national level, the available and reliable data on guaranteed mortgage performance dated
back to 1981, therefore offering a 24-year history to conduct this analysis, within which
the latest nine years are under the current program regime. The macro economy of the
Netherlands has experienced three downturns (1981-1982, 1992-1993, and 2001-2003)
and two boom periods (1989-1990 and 1997-2000) between 1981 and 2003. WEW's
outstanding cohorts originated within this duration have experienced favorable, normal
and unfavorable macro economic situations fairly evenly throughout their early loan ages,
i.e., the first ten years of their loan life when majority of the defaults occur (Figure 5.12).
Therefore I view the distribution patterns of ultimate default rates derived from the data
of 1981-2003 cohorts as unbiased and representative of the Dutch macro economy.
Figure 5.12 The Distribution of Macro Economic Conditions by Loan Age - The WEW
Economic Conditions by Loan Age
(1st - 20th Year)
I•
Favorable
economies
C Normal economies
C Unfavorable
I
economies
120%
100%
80%
1-
-
60%
-
-
40%
-
- - - - - - - - - - -
-
-
f-
-
-
f-
-
-
-
-
-
20%
0%
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20
Loan Age (1st - 20th Year)
B.2 Modeling parameters for the 2003 cohort
Ultimate default rates of cohorts 1981-2003
- 99-
Historically, the default rate of the Dutch mortgage loans has been very low. Mortgage
portfolios have experienced negligible losses and low delinquency levels compared with
most other European countries and the United States. At the moment, the Netherlands has
the lowest foreclosures and loss rates in Europe. 26 Mortgage loans more than 90 days in
arrears range from 0.25% to 0.75%, although an upward trend has emerged in recent
years (FitchRatings, 2004). The worst experience so far was the 1981 cohort with 2.87
percent cumulative default rate in its 23 years of seasonality. After the recession of the
early 1980s, the Dutch economy and its housing market have been upward trending until
2001 or so when the market stabilized and became stagnant due to the minor recession. It
is fair to say that the WEW's guarantee products, issued since 1995, have yet to be tested
in tough economic environments.
The actual up-to-date cumulative default rates are available for WEW's 1981-2003 books
of business. However, there are no projections for the ultimate cohort default rates at the
end of the loan term, usually 30 years. According to the experience and projections of the
U.S. FHA fixed-rate 30-year portfolios during the same period (1980-2003), the
cumulative default rates within the first 10 years of the loan life capture about 88% of the
overall 30-year end ultimate default rates, and the first 15 years' cumulative default rates
represent 97% of the ultimate default rates. This is consistent with the general
observation that defaults occur in early stages of the loan life, usually between the
3 rd
and
the 7 t h year. I project the ultimate default rates for WEW's 1981-2003 cohorts (Table
5.11) based on the following assumptions:
Loan Age (seasonality)
Greater than 20 years
Between 15 and 20 years
Between 10 and 15 years
Less than 10 years
Up-to-date cumulative
Ultimate 30-year end cohort
default rate
default rate
x
(1.03 = 1/97%)
1.03*x
(1.14 = 1/88%)
1.14*x
Cannot be predicted accurately.
x
x
x
x
26
The main reasons for this are: (i) the sharp increases in property prices, which have helped increase
recovery amounts to levels that exceed the lender's default exposure; (ii) the buildup of equity in the
acquired property, which gives borrowers a strong incentive to protect that value and avoid default. In
addition, in the Netherlands there is still a strong (albeit declining) cultural aversion to defaulting on debts.
Borrowers perceive their relation with the lenders as a personal one, and have a strong commitment to
maintaining debt payments. Furthermore, Dutch borrowers benefit from a good, reliable social security
system that offers them protection in the event of unexpected situations such as unemployment
prolonged illness (FitchRatings Report, 2004).
- 100 -
and
Table 5.11 Projected Cumulative Default Rates of WEW Guaranteed Cohorts, 1981-2003
Origination Year
1981
To-date (2003) cumulative
default rate
2.87%
1982
1983
1984
1985
1986
1987
1988
1989
1990
Projected 30-year ultimate
default rate
2.87%
2.47%
2.55%
0.85%
0.85%
0.42%
0.29%
0.30%
0.22%
0.19%
2.47%
2.55%
0.85%
0.88%
0.43%
0.30%
0.31%
0.23%
0.21%
1991
0.18%
0.20%
1992
1993
1994
1995
1996
1997
1998
1999
0.11%
0.12%
0.08%
0.04%
0.08%
0.07%
0.05%
0.07%
0.12%
0.13%
0.09%
2000
0.11%
2001
0.11%
2002
2003
0.01%
0.00%
Mean
Median
Standard Deviation
Source: WEW and the author
0.83%
0.30%
1.01%
Ultimate prepayment rates
Dutch mortgage lenders typically limit the penalty-free annual prepayments to 10%-15%
of the original mortgage balance. A prepayment of this nature is permitted once a year
and, for any prepayment above this percentage, borrowers are required to pay a penalty
equal to the difference in the mortgage funding cost between the contracted rate and the
applicable market mortgage rate. They may also prepay the mortgage without penalty at
the reset date on a fixed-rate mortgage. 2 7 Historically, prepayments have been stable,
with annualized conditional prepayment rates ranging between 5% and 15%
(FitchRatings, 2004). Another factor impacting the prepayment rate is that contrary to
American mortgage contracts, Dutch mortgage interest rates are commonly fixed for a
27 Borrowers
may prepay without penalty under certain circumstances - for example, when moving house,
or in the case of divorce or death.
- 101 -
period between 5 and 20 years. At the end of each fixed-rate period, the mortgage rate is
reset to the prevailing market mortgage rate. This acts as an incentive for borrowers not
to prepay their mortgages until the reset date, to avoid significant fees. Although there
was a gradual upward trend in prepayment rates in recent years, it remains uncertain
whether the trend will continue in the future as interest rates are already at a historical
low and penalty fees are relatively high once certain prepayment limits have been
breached (FitchRatings, 2004).
As WEW's guarantee pricing only contains a one-time upfront fee of 0.3% without
annual premiums and any refund of upfront fees, the prepayment rate does not influence
the cash flow analysis in any significant way. The WEW does not have data on
cumulative prepayment rates of its outstanding books of business. Charlier and Van
Bussel's (2003) study on the prepayment behavior of Dutch mortgagors showed that in
2001, about 18% of the newly issued mortgages were refinances to replace existing loans.
The authors believed "these replacement numbers illustrate that, despite significant costs,
prepayment is an important feature when managing a Dutch mortgage portfolio." Based
on their research and the recent up trend in prepayment rates, I assume that on average
the ultimate prepayment rate of WEW-guaranteed cohorts is around 30%, which is
substantially lower than the FHA prepayment level of 84% on average.
Other parameters
All other parameters are constructed to reflect the characteristics of the Dutch mortgage
market in general and WEW-guaranteed segments in particular (Table 5.12).
Table 5.12 Parameters Used in the Cash Flow Analysis of the WEW Guarantee
Cohort size
Year
Loan insured
Number of loans
Cohort
composition by
loan types
2003
9,932
By LTV categories:
LTV <=80
80 < LTV <= 90
90 < LTV <= 100
100< LTV <= 110
LTV> 110
Average size of
the loan ()
(million C)
64,641
8.8%
5.6%
9.7%
44.7%
31.3%
- 102 -
153,649
By years of interest rate fixing:
Less than 10 years:
10-15 years:
15 to 20
33.4%
24.7%
21.4%
20 to 25
25 to 30
5.3%
15.2%
Premium
structure and
pricing
Mortgage loan
features
For all types of mortgage loans:
Up-front premium rates (% of original loan amount):
0.3%
Loss coverage (including unpaid balance, delayed interest payments
100%
and other miscellaneous fees:
Loss coverage for interest-only loans:
50%
Typical loan term: 360 months
Loan interest
rates:
average 10-yr fixed rate
average 15-yr fixed rate
4.30%
4.80%
average 20-yr fixed rate
5.30%
assume: after 10 years the rate will be reset to the implied
yield of Euro bonds for year 10 to 20
assume: after 15 years the rate will be reset to the implied
yield of Euro bonds for year 15 to 20
assume: after 20 years the rate will be reset to the implied
yield of Euro bonds for year 20 to 30
Note: these assumptions are based on the Euro yield curve.
Default costs
and property
recoveries
5.40%
5.50%
5.30%
Claim cost (% of UPB)
1.16
Unpaid Balances + foreclosure expenses
(10%) + interest (6%). (According to the Fitch
report, foreclosure expenses are around 5,000
Euros (3.25% of the average loan size) plus
6% of the property transfer tax. So in the
range of 10%. )
Recovery rate (average) on the
original loan amount
54.8%
This percentage is from FitchRating (2004),
defined as: (Property value after indexation -
Market value discount amount - Repossession
costs) / Current loan balance
Recourse of the claim (loss) by
5%
This is the level of year 2003. The recoveries
5 years
from recourse of defaulters' other assets are
very low currently, due to the difficulty of
tracing after defaulters, administrative
expenses, banks inertia, etc.
WEW's right of recourse will disappear after
WEW
Duration for recourse to
happen
5 years since foreclosure.
% of defaulted borrowers who
20%
are in good faith (exempt from
In 2002, 30% of the defaulted households
were in good faith. In 2003, 17%. In the first 3
Default rate
recourse)
months of 2004, 15%
These scaling factors differentiate risk levels of default and tendency to be prepaid among
scaling factors
different mortgage loan categories. They are based on the U.S. FHA experience and adjusted
& Prepayment
(interpolated) according to the high LTV categories of the WEW guaranteed loans. For the lack
rate scaling
factors
of loan-level data on the WEW portfolios, the author believes that the LTV levels exert similar
impacts on portfolio default and prepayment rates in both the U.S. and the Netherlands.
Default scaling Weighted
factor
average
Yield curve for
discounting
future cash
LTV < 80
80 <= LTV < 90
90 <= LTV < 100
0.55
0.78
0.86
100 <= LTV < 110
110<= LTV
Prepayment
scaling factor
Weighted
average
0.0482
0.0435
0.0835
LTV < 80
80 <= LTV < 90
90 <= LTV < 100
0.98
0.4380
100 <= LTV < 110
0.98
0.4380
1.24
Sum:
0.3875
1.0000
110<= LTV
0.9
Sum:
0.2813
1.0000
1.3
1.1
1
Euro Yield Curve as of 2003 (Obtained from
http://epp.eurostat.cec.eu.int/cache/ITY_PUBLIC/EYC/EN/page4.htm#yields)
- 103 -
0.1140
0.0614
0.0971
~~r="'"
...........
flows
Euro Yield Curve, 2003
The smoothly up
trending yield
curve projects a
stable economy
1
~
v
,....
~
0
co
M
Maturity
~
Ol
CX)
ll)
C\/
C\/
C\/
C\/
Source: Values in thistable come from, or are based on, data provided by the WEW,
"Dutch RMBS Default Model 2004" by Fitch Ratings.
WEW's
Annual Report of2003, and
B.3 Modeling results
Simulate the distribution of WEW
cohort default rates
Limited to the data availability of the portfolio performance
previously municipality-guaranteed)
of WEW-guaranteed
(or
mortgage
loans, and their seasonality levels, I only
have 14 data points (cohorts from 1981-1994)
on cohort ultimate default rates. They are
mapped
to a frequency distribution and a continuous Erlang distribution is constructed
based on this very small sample (Figure 5.13).28
Figure 5.13 The Frequency
Distribution of WEW
The Actual and Fitted (Erlang Distribution) WEW
Cohort Default Rates (1981-1994)
-Fitted
Data with Erlang Distribution - - - - - .Actual Data
0.25
~ 0.20
Cohorts' Ultimate Default Rates
Erlang parameters for the
ultimate default rate
distribution of WEWguaranteed cohorts:
'0
a5
0.15
:::l
CT
~ 0.10
u..
0.05
,,
, , ,
Mean:
Std dev:
Erlang b:
Erlang c:
0.832%
1.009%
12.224
1
0.00
~
M
~
~
~
~
~
M
~
~
~
~
~
~
~
~
N
M
N
~
N
~
N
~
N
Cohort Ultimate Default Rate (1/1000)
28 The frequency distribution of the historical cohort default rates of the WEW
exhibits a bimodal pattern,
which may not be accurately captured by an Erlang distribution. The fittedErlang distribution is likely to
concentrate density between the high default rates and the low default rates, where not enough historical
experience exists. Because of the short history of the WEW
data (only has one recession period and then all
economically favorable years since 1995), itis hard to justify the application of other forms of distribution
as well. For comparability purposes, I chose to stilluse the Erlang distribution to fitthe data. When more
data become available in the future, other alternative forms of distribution (such as Beta distribution) may
be more appropriate to fitthe data.
- 104 -
Although the representativeness of the derived Erlang distribution is questionable due to
the scarcity of the data, it is the best assumption I can make to reflect the ultimate cohort
default rate patterns. This analysis is to showcase a framework to understand the
magnitude of potential liabilities. As more data become available, they can be
incorporated into this framework to improve the projections.
Similar to the approach used in analyzing the U.S. FHA program, I apply a Monte Carlo
simulation to draw 10,000 random samples from the Erlang distribution of WEW
cohorts' ultimate default rates (Figure 5.14).
Figure 5.14 The Frequency Distribution of 10,000 Simulated Ultimate Default Rates The WEW
Simulated ultimate default rates
Statistics
The Frequency Distribution of 10,000
Simulated Ultimated Default Rates of WEW
2003 Cohort
4W
r
-
-
-
-
·---
· ·----
···---
Mean
Median
Maximum
Minimum
--
400
lb 350
·
1.2336%
0.8682%
12.3863%
0.0001%
300
Percentile
a) 250
a' 200
L 150
100
50
O
5 th
10th
25
a 'n
o
o
cD
0
C
U
-
o cD
0
-
a
!
U
-^
0a
o
)
N
~2
0
U
N C
7
.. ..I. .. I - 1
o
' o
o
co
0
\T
0a)
s)
I . II . . .1., .
-
o
I0
U)
'o
)
U)
I)
o
0
0
a)
)
Ultimate Default Rate
th
0.0647%
0.1347%
0.3585%
75h
0.8682%
1.7352%
90"
2.8086%
Qgth
9 9 th
3 -4N~o
50
5.5865%
Simulate the annualdistributionsof unconditionaldefaultand prepaymentrates
During the past two decades, the Dutch economy has experienced only one serious
recession in the early 1980s and some stagnant years between 1991 and 1993. Based on
the projections of ultimate default rates (Table 5.11) of 1981-1994 cohorts, I group them
into three categories in terms of their performance: good (ultimate cohort default rate <
0.3%), normal (2% > ultimate cohort default rate > 0.3%), and bad (ultimate cohort
default rate > 2%). For each cohort, I calculate the pair of Erlang b and c parameters
based on its annual marginal (unconditional) default rate. Then I select the median value
pair of the scale parameter b and shape parameter c within each group to construct the
- 105 -
corresponding annualized Erlang distribution of default rates. As for the distribution of
prepayment rates, there are no data available either at the aggregate level or annual level.
So I apply the Erlang parameters derived from the U.S. FHA's annual prepayment pattern
of the "good performance" group, as all the WEW cohorts are projected to have ultimate
default rates under 6%, the threshold defining the "good performance" group in the FHA
case. Table 5.13 summarizes the Erlang parameter settings and Figure 5.15 depicts the
annual distribution patterns for default rates.29
Table 5.13 Erlang Parameters for Annual Unconditional Default and Prepayment Rate
Distributions - The WEW
Good Performance
Ultimate default rate < .3%
Normal Performance
.3% < Ultimate default rate < 2%
Bad Performance
Ultimate default rate > 2%
Annual distribution patterns of unconditional default rates
Scale parameter b
Shape parameter
c
0.52
0.73
0.96
7
6
7
Annual distribution patterns of unconditional prepayment rates
Scale parameter b
4.86
4.86
Shape parameter
c
3
3
4.86
3
11
-~~~~~~
Figure 5.15 The Probability Distribution of WEW's Annual Unconditional Default Rates
AnnualUnconditional
DefaultProbability,
WEW30-yearloans
(1981-1994)
---U.Oa)
0.3
Goodperformance-----
Normalperformance
A
Badperformance
-- - -----1
I 0.25
° 0.2
v 0.15
o 0.1
0.05
0
______ _ - - -_-- -_
-
- - ce- a0U Nr0 a c 0 ---N~- 0)
-~~~~
-N
- a)
r m-
Policyyear 1-30
Figure 5.15 illustrates the annual unconditional default probability distribution for WEW
30-year fixed rate mortgage portfolios. However, most of WEW-guaranteed loans have a
period of interest rate-fixation less than 30 years. The length of the interest rate fixation
29 The annual distribution patterns for prepayment rates are the same as those of FHA cohorts presented in
Figure 5.7 ealier.
- 106 -
ranges widely from only one year (ARMs) to 30 years, with some apparent clusters: 5-7
years, 10-year, 15-year, 20-year, and 30-year. For simplicity, I analyze three most
popular types: 10-year, 15-year and 20-year fixed interest rates. I assume that after the
first 10 (or 15, 20) years, the mortgage interest rate will be reset to the prevailing market
rate then and keep fixed for the rest of the loan life. Based on the interest rate
assumptions (Table 5.12), there will be some "payment shock" after the initial interest
rate-fixing period, in the 11th year for the 10-year fixed rate loans and in the 16t h year for
the 15-year fixed rate loans (Table 5.14).
Table 5.14 Change of Monthly Payment Due to Interest Rate Reset
30-yr interest rate fixed mortgages:
monthly payment per euro of loan:
20-yr interest rate fixed mortgages:
monthly payment per euro of loan:
15-yr interest rate fixed mortgages:
monthly payment per euro of loan:
10-yr interest rate fixed mortgages:
monthly payment per euro of loan:
0.0056
First 20 years
monthly pmt
Next 10 years
monthly pmt
% of payment changes from
the 30-yr fixed loans
0.0056
First 15 years
monthly pmt
0.0052
First 10 years
monthly pint
0.0056
Next 15 years
monthly pmt
0.0055
Next 20 years
monthly pmt
0.0%
0.0049
0.0054
4.7%
9.7%
In order to account for the impact of payment adjustments on annual unconditional
default rates, I apply an adjustment coefficient to translate changes in monthly payments
(due to changes in the mortgage interest rate) into some proportional effect on the annual
default rate. I tested various adjustment coefficients ranging from 0.1 to 1 and they did
not change the default pattern in any significant way. The reason may be because
unconditional default rates are fairly low after year 10 as the property has always
appreciated in value and borrower incomes are likely to be higher and more stable. I use
0.5 as the adjustment coefficient to translate the payment shocks into proportional annual
default rate changes.
Discountedcash flow analysis
Unlike the FHA portfolios in its MMI Fund, the WEW 2003 cohort is far from being
homogeneous. It is a complex mix of various types of mortgage loan products with
different LTVs and interest rate fixing periods. I focus on three major types of loans in
- 107 -
WEW's portfolio: 30-year loan term, annuity-type mortgages with 10-year, 15-year or
30-year interest rate fixing period. For simplicity purposes, I first analyze them
separately, assuming that there are three cohorts of homogenous WEW-guaranteed
mortgages with face value of 10 billion euros each. Appendix 5.4 presents the details of
the cash flow analyses. The NPVs of the three types of cohorts and their profitability
rates are summarized as following (Table 5.15):
Table 5.15 Simulation Results of the NPV and Profitability Rates of the WEW's Three
Types of Cohorts
Simulated
NPV of the guarantee contracts
ultimate
(million C)
Groupedby the lengthof interest-ratefixing
default rate
Profitability rate (NPV per C of
guaranteed loan, %)
Groupedby the lengthof interest-ratefixing
10-yr
15-yr
30-yr
10-yr
15-yr
30-yr
1.234%
0.868%
12.386%
0.0001%
-22
-7
-416
29
-22
-7
-418
29
-24
-8
-422
29
-0.22%
-0.07%
-4.16%
0.29%
-0.22%
-0.07%
-4.18%
0.29%
-0.24%
-0.08%
-4.22%
0.29%
0.065%
0.135%
0.359%
0.868%
1.735%
2.809%
3.643%
5.587%
-102
-72
-43
-7
14
24
27
29
-102
-72
-43
-7
14
23
27
29
-126
-91
-46
-8
14
23
27
29
-1.02%
-0.72%
-0.43%
-0.07%
0.14%
0.24%
0.27%
0.29%
-1.02%
-0.72%
-0.43%
-0.07%
0.14%
0.23%
0.27%
0.29%
-1.26%
-0.91%
-0.46%
-0.08%
0.14%
0.23%
0.27%
0.29%
Statistics
Mean
Median
Maximum
Minimum
Percentile
5t h
10th
25 t h
50t
7 5 th
90 t h
95t h
99th
Breakeven
percentile*
Breakeven default rate
43
rd
0.71%
4 3 rd
0.70%
42
nd
0.68%
*: The breakeven percentile means that losses (negative NPV) can be expected 57 percent, 57 percent, and 58 percent
of the time respectively, for loans with 10-year, 15-year and 30-year interest rate fixing period, respectively.
108 -
Figure 5.16 Frequency Distributions of Profitability Rates for WEW's 10-year, IS-year,
and 30-year Fixed Rate Cohorts
Frequency Distribution
of Profitability
Rates
of WEW Cohort with 1D-Year Fixed Interest
Rate
900
800
en 700
.~ 600
c 500
~ 400
c:T 300
~ 200
u.. 100
en
Q)
0g
Q)
5Q)
u:
o
~o
~
e
~~ ~0
~ ~
0
I
0
~~
~
~
~
~
0
~
~~
~
~
~
~
0
N
~~
~
N
~
~
0
~
Portitabillty rates (NP\t per Euro alloan)
~~
I"-
~
0
N
c? ~
Frequency
Distribution
of Profitability
Rates
of WEW Cohort with 15-Year Fixed Interest
Rate
900
800
700
600
500
400
300
200
100
o
~o
o
~
e
~~
~
~0
<D
~
~
0
~0
~
~~ 0
~ ~~ ~0
0>
9 9 ~ ~ ~
~
CO
~
~
~
c?
~~
I"-
~
0
N
c? ~
Porfitability rates (NPV per Euro 01 loan)
Frequency
Distribution
of ProfitabIlity
Rates
of WEW Cohort with 3D-Year Fixed Interest
Rate
900
800
Q)
700
.0 600
~ 500
:J 400
300
200
100
en
g
u:
o
~
0
~
0
~
~~
0
~
0
0
<D
e 9 9
~
~
~~
0
I
~
0
~~
0
I
~
~
~~
0
I
~
0
~
~
0
~
~
CO
~
0
~
0
~
0
~
~l"0
~o
o
N
c? c? ~
Porfitability rates (NPV per Euro 01 loan)
The simulation results (Figure 5.16) show that the cohort performance does not vary too
much among 10-year, IS-year, and 30-year fixed rate mortgages. For all three types, the
clusters (bars) with highest frequencies are concentrated in the range of 0% to 0.30/0 in
profitability rates. The mean cohort profitability rate is around -0.20/0, meaning that the
cohort loses 22 to 24 million euros on the 10 billion euros face value on average. Out of
10,000 outcomes, about 42-430/0 have positive NPVs, indicating a breakeven cohort
default rate around 0.7%. If the worst experience so far, the 1981 book of business with
the cohort default rate of 2.870/0, were to happen again for the 2003 cohort, the projected
losses would mount to 74 million euros for the 10-year and IS-year fixed rates
respectively, and 93 million euros for the 30-year fixed rates.
These estimates are based on the simulated 10,000 ultimate default rates derived from the
mortgage loan performance between 1981 and 1994, which contains a major economic
downturn of 1980-1983. Since the early 1990s, the Dutch economy has been steady and
- 109 -
growing, so has its mortgage markets. The default rates have been extremely low for the
past decade. Between 1995 and 2003, 511,244 guarantees were issued and only 566, or
0.11%, defaulted. The simulation outcomes of default rates presented before may be
biased upwards. If we pick the worst performing cohort within the last 10 years,30 the
2001 book of business with an estimated ultimate default rate of 0.22%,3 1 the NPV of the
guarantee is projected to be around positive 20 million euros, or a 0.2% profitability rate.
This estimate is likely to be in the reasonable range, considering that since its
establishment in 1995 the WEW has been building up its capital reserves to about 247
million euros, from its 10 books of business (1995-2004).
Multi-yearNPV analysis
Similar to the analysis of the U.S. FHA program, I conduct the multi-year NPV analysis
to understand the range of potential liabilities on WEW-guaranteed cohorts. As there are
no projections of ultimate default rates for the WEW outstanding cohorts, I select the
cohorts with at least five years of seasonality (19 cohorts, from 1981 to 1999), and apply
the multipliers derived from the FHA historical experience to convert their up-to-date
cumulative default rates into the estimated ultimate cohort default rates. We can also
observe the pattern that higher default rates tend to come together, such as the early years
of the 1980s. A regression analysis is used to explore the correlation between adjacent
years and with the short-term interest rates (Appendix 5.5). The three months
Aibor/Euribor rates are used to represent short-term interest rates.32 The best fitting
model is the following:
Cohort ultimate default rate (, = -0.003 + 0.678* Cohort ultimate default rate (,.-)+ 0. 050* short-term interest rate
(-1.09) (7.01)
(1.30)
R2 = 0.83
30 This is, of course, an estimation as the most recent 10 cohorts are not seasoned enough to be accurately
projected in terms of their ultimate cohort default rates. Based on the limited available annual marginal
default rates of the most recent 10 cohorts, I apply the Erlang distribution of annual unconditional default
rates derived earlier to back out the 30-year end cumulative default rate.
31 For the 2001 cohort, the annual unconditional default rate is 0% for the 1 year, 0.029% for the 2 nd year,
and 0.083% for the 3'd year. Applying the Erlang distribution factors, we can get an estimate of 0.22% for
the cohort's ultimate 30-year end default rate.
32This is the only type of interest rate that has been available and consistent for the whole period of 1981-
1999. Data on other types of interest rates such as the 1-year Euro bond have been available only since
January 1999.
- 110-
The model explains about 83 percent of the variations in cohort ultimate default rates.
The coefficient for the lagged default rate variable is statistically significant, indicating
the interdependency of cohort default rates between consecutive years.
For simplicity, I assume that in the 2003 cohort and its following cohorts, 35% of the
guaranteed mortgages are 10-year fixed rates, 45% are 15-year fixed rates, and 20% are
30-year fixed rates.33 Based on the regression model above, I conduct multi-year NPV
analyses over the next 6 and 10 years respectively. The future short-term interest rates are
obtained from the Euro bond yield curve. Table 5.16 summarizes the statistics of the
multi-year NPV and profitability rate distributions.
Table 5.16 Multi-year NPV and Profitability Rate Distributions - The WEW
6-Year Accumulationa
Cumulative
NPV of the
guaranteed
cohorts
Profitability rate
(Cumulative NPV
over 2003 cohort's
face value, %)
(million C)
10-Year Accumulationa
Profitability rate
Cumulative
NPV of the
guaranteed
cohorts
over 2003
cohort's face
(million C)
value, %)
(Cumulative NPV
Worse Case Scenario
A repeat of the 1981-1983
recession: three consecutive
years of high default ratesb
Cumulative
NPV
Profitability
rate
(million C)
Statistics
Mean
Minimum
Maximum
55
0.55%
-1,091
-10.91%
2.02%
202
167
-1,090
-10.90%
327
3.27%
318
309
280
214
3.18%
3.09%
1.67%
Percentile
5 th
10
25
50
75
194
186
159
98
th
th
th
th
-4
9 0 th
95
99
-117
-200
-403
th
th
Breakeven percentile
Note:
1.94%
1.86%
1.59%
0.98%
-0.04%
-1.17%
103
-21
-113
-335
-2.00%
-4.03%
75 percentile'
-210
-2.1%
2.80%
2.14%
1.03%
-0.21%
-1.13%
-3.35%
89 percentilec
a: The statistics of the 10,000 simulated ultimate default rates for the 2003 cohort are used to project the next 5-year (and 9-year) cohort
default rates and calculate the cumulative NPV and profitability rate.
b: The ultimate default ratefor the 1981 cohort is estimated to be 2.87%, for the 1982 cohort, 2.47%;for the 1983 cohort, 2.55%.
c: The breakeven percentile means that losses (negative cumulative NPVs) can be expected 25 percent and 11I percent of the time,for6-year
and 10-yearperiods respectively.
33 The
actual composition of the 2003 cohort in terms of interest rate-fixing periods is as following:
Less than 10 years:
10-15 years:
15 to 20
20 to 25
25 to 30
Actual figures
33.4%
24.7%
21.4%
5.3%
15.2%
My assumption
35%
45%
20%
111 -
The multi-year analyses show that under the current stable economic outlook indicated by
the Euro yield curve, the 2003 cohort and its subsequent cohorts are expected to generate
positive values of 55 million euros for the next 6 years and 167 million euros for the next
10 years. The mean profitability rate is about 0.55% for the 6-year cohorts and 1.67% for
the 10-year cohorts, or it ranges within 0.09% - 0.17% for one-year's book of business on
average going forward. The break-even percentile is around 75 percent for 6-year cohorts
and 89 percent for 10-year cohorts, which means that under the current program design
and economic outlook, the probability of net losses over the next 6-year cohorts is 25
percent, and 11 percent over the next 10-year cohorts. How bad could things go? If the
worst experience so far, the recession of three consecutive years between 1981-1983,
were to happen again, the estimated total losses would amount to around 210 million
euros for the WEW.
Uniquenessof Dutchmortgageproductsand sensitivityanalysis of WEW's liabilities
The previous NPV analyses made some simplified assumptions about WEW's 2003
cohort. In reality, the cohort is a complex mix of various types of mortgage products with
very different risk profiles that are not captured in the cash flow analysis.3 4 Savings
34 The Dutch tax system allows full deductibility of mortgage interest and tax exempt returns, with no
capital tax on linked repayment vehicles. It therefore encourages homeowners to increase their leverage for
purchasing a property rather than use equity. As a consequence, in the 1990s an extensive system of
mortgage types was developed that offered maximum advantage of the tax opportunities. The mortgage
products currently available are as following:
Definition
Mortgage types
Linear/Annuity Mortgage
Savings Mortgage
Life Insurance Mortgage
These mortgages require borrowers to make principal repayments to amortize the
principal balance of the loan on a monthly basis.
Under a savings mortgage a borrower pays the principal into a savings account, which
accrues the same rate of interest as the one on the mortgage. The principal built up in this
account simulates an annuity profile. Unlike insurance and investment funds, the
principal balance of the savings fund cannot decline; it is therefore, effectively, a
'synthetic' amortizing loan.
This product is a combination of an interest only loan and a life insurance policy, into
which the borrower pays monthly premiums. Amounts accumulated by the policy are
used to repay the mortgage on its maturity date. The provider of the policy can guarantee
a minimum yield on amounts paid in by the borrower. However, the overall maturity
amount depends on the insurance company's success in achieving an assumed
benchmark return.
- 112-
% in 2003
cohort
12%
27%
12%
mortgages are essentially the same as traditional annuity mortgages. Two of them
combined represent about 40% of the portfolio. The remaining 60% loans are riskier than
annuity type loans. Life insurance mortgages and investment mortgages link the
borrowers' monthly payment to an insurance policy or an investment vehicle. Borrowers'
ability fully to repay the loan is therefore connected to the performance of these
investments, which adds volatility to the default rate of the cohort guaranteed by the
WEW. The interest-only loans (33% of the cohort) are the most risky ones. If the housing
market is trending downward and prices drop, borrowers with interest-only loans will
have negative equity in their house much more quickly than borrowers with amortizing
loans, a strong incentive for borrowers to default. Taking these mortgage types into
account, the magnitude of potential liabilities computed earlier for the 2003 cohort is
likely to be underestimated.
Fortunately, there are several risk mitigating measures taken by the WEW. For instance,
it only provides 50 percent coverage to interest-only loans instead of 100 percent, and the
LTV of interest-only loans cannot exceed 70 percent. Also, guarantees provided by the
WEW amortize over time on a monthly 30-year annuity basis, even if the mortgage itself
does not amortize at the same schedule. This rule limits the claim amount to WEW
regardless of the underlying mortgage products, while creating a potential shortfall in
protection for lenders. To some extent, these factors counteract the increase in cohort
riskiness resulting from the cohort loan composition. Further analyses are needed to
explore how much they mitigate each other, which is beyond the scope of this study.
Investment Mortgage,
Switch mortgages, and
Miscellaneous
Investment mortgages involve an interest only loan, whereby, on a monthly basis,
borrowers only make interest payments to the mortgage lender and pay, separately,
premiums to an investment policy. Amounts built up in the policy are earmarked for
redemption of the mortgage on its maturity date, but are entirely dependent on the
performance of the investment fund.
16%
Instead of choosing beforehand between a savings and an investment mortgage,
mortgagors can switch between building up the principal amount through a savings
account or by an investment account. Similar to a savings or an investment mortgage, the
borrower does not pay back any principal during the term of the contract.
Interest-Only Mortgage
With an interest-only mortgage borrowers are only required to pay interest to the lender.
No scheme is arranged for the repayment of the principal; hence the responsibility of the
final mortgage repayment lies exclusively with the borrower. Lenders will only offer
interest only loans to a 75% loan to foreclosure value, beyond which some form of
repayment fund is required.
- 113-
33%
Insurance pricing always plays a very significant role in the liability analysis. Thanks to
the booming housing market and the extremely low default rates, the WEW has been
generating surplus and building up capital reserves since its inception. In 2005, WEW is
going to lower its one-time upfront insurance premium to 0.28% from its current level of
0.3%. Based on the mean cohort default rate simulated earlier, this change will lead to a
9% decrease in insurance NPV, or 2 million euros more in losses, from its previous 22.4
million (Table 5.17).
The management of the program exerts big impacts on its performance as well. The right
of the WEW to recourse to borrowers' other assets if they default on their mortgages
strengthens the WEW's position. But currently this ability has yet to be fully utilized as
only about 5% of the losses were recovered through recourse in 2003 for the WEW.
However, the awareness of the WEW's right of recourse may act as a deterrent on
borrowers and therefore reduce the defaults. The WEW relies on lenders to trace after
defaulted borrowers and the process has not been working effectively so far. If the WEW
could double the recovery rate from recourse, from the current 5% of the claim amount to
10%, the cohort insurance NPV will increase by 27% (Table 5.17).
Since 1991, the favorable economic situation in the Netherlands has brought about a
combination of rising incomes and falling interest rates. As a result, the housing market
has taken off and sustained continuous price increases (Figure 5.17). However, this
continuous price increase of the previous years has been broken since the second half of
2001 (Boelhouwer and Neuteboom, 2003). In 2002, real housing prices seemed to be
stabilizing, and even falling slightly. During the history of 1966 to 2002, the real house
price volatility in the Netherlands was around 17%, ranking the third (only after Finland
and Ireland) in EU countries.
- 114-
Figure 5.17 Development of House Price and Long-term Equilibrium Price, 1965 2000)
Source: Boelhouwer et al (2001 )
The volume of mortgage debt has increased by 35% between 1996 and 2002. As of 2002,
the mortgage debt to GDP ratio in the Netherlands was 88%, the highest in all ED
countries (Hypostat 1992-2002). With this large volume of mortgage debt, the fluctuation
of housing prices would exert great influence on the national economy. For example, if
the housing prices increase by 20%, i.e., the sales of foreclosed properties up by 20%, the
cohort NPV will increase by 540/0 (Table 5.17).
Table 5.17 compares the NPV ofWEW's
2003 cohort35 under different parameter
settings and economic scenarios. The cohort's volume is 10 billion euros at origination.
Table 5.17 The Sensitivity Analysis of Alternative Scenarios - The WEW
Model (assuming the mean default rate of 1.23%)
NPVI
NPV
cohort
Current parameter settings
face
(mil €)
value
0.3%
One-time up-front
Alternative
Current
Premium
level
-22.4
premium rates
Housing
market
conditions
Recovery rate on
defaults
-0.22%
Scenarios
-24.4
-9%
-10.2
-0.1%
54%
NPV
New parameter settings
New premium since 2005:
Up-front premium rates
Recovery rate on
defaults
(mil $)
0.28%
Scenario 1: Favorable (20% increase in
recovery rate)
54.8%
Cball2e
NPV I
cohort
face
value
-0.24%
65.8%
Here I assume the "simplified version" of the WEW's 2003 cohort that only contains annuity type
mortgages. It is composed of 35% of the 10-year fixed rates, 45% of the IS-year fixed rates, and 20% of the
30-year fixed rates.
35
- 115 -
Scenario 2: Unfavorable (20% decrease
in recovery rate)
Recovery rate on
defaults: FRM
Program
management
Interest rate
environment
-the Yield
Curve
-0.35%
43.8%
Scenario I: Stronger recourse recoveries
% of losses recovered
through recourse
Duration of recourse
-34.6
-16.4
5%
% of losses recovered
through recourse
Duration of recourse
5 years
10%
5 years
If the future economic outlook is
unfavorable: the yield curve of U.S. 198 I
Treasury bills
Euro Yield Curve, 2003
- I 2;8
15
"0
Q;3
>=
15
2
~ 14
Qi
>= 14
13
13
C.
The Mexican SHF Program
C.I The data
The Mexican SHF (Federal Mortgage Corporation) was created in February 2002.
Currently the SHF operates in essentially the same way as its predecessor, FOVI, used to,
i.e. granting loans to intermediary finance organizations and issuing mortgage insurance
and loan guarantees. But its primary mandate is to standardize mortgage origination in
the primary market and promote development of the secondary market by granting
mortgage guarantees. The SHF is moving out of the loan granting role to be solely
focused on three major guarantee products: mortgage insurance, construction loan
guarantee, and on-time payment guarantee for MBS. The implied government liabilities
to support SHF's new guarantees will expire in October 2013. From that point on,
whatever guarantees the SHF undertakes will be supported by the institution's own
financial strength. Therefore, this analysis is focused on the projections of the cohort
performance for 2002 to 2013 books of business, on the SHF's first-loss partial mortgage
guarantees.
-116-
Reliable data on the mortgage portfolio performance, such as default rates and losses, and
prepayment rates, are very limited in Mexico. Although before the establishment of the
SHF, FOVI had been granting mortgage guarantees under its "Pari-Passu" program, the
poor management and non-standardized underwriting process compromised the data
credibility. For example, no claims were ever submitted or paid under the old "PariPassu" program, despite numerous defaults suffered by lenders during Mexico's 1995
currency crisis.3 6 Another reason for the lack of usable historical data is due to the fact
that after the crisis the mortgage products changed in 1996. The Mexican government
provided several measures to increase liquidity to banks and payment support for
mortgage holders, among which was to allow borrowers to restructure their mortgage
loans into an inflation adjusted accounting unit, the Unidades de Inversion (UDIs) at a
low (subsidized) fixed real interest rate. The restructuring of the mortgage product
changed the portfolio risk profile. Of the SHF-guaranteed 2003 cohort, 93 percent are
UDI denominated mortgage loans.
Considering the factors above and data availability, I focus my analysis on the first book
of business, the 2003 cohort, guaranteed by the SHF's new MI product. Most of the data
are provided by the SHF extracted from its newly established 300,000-loan database,
known as Fuente Integral de Estadistica Hipotecaris (FIEH, Statistical Mortgage Integral
Source). A lot of information is also obtained through field work and interviews. Data
date back to 1996 when UDI loans started.
C.2 Modeling parameters for the 2003 cohort
Historical default rates, 1996-2003
Even before the financial crisis of 1995 - 1997, the Mexican real estate market showed
signs of weakening as the previous property price bubble generated between 1991 and
36 This
is because under the FOVI's Pari-Passu guaranty program, lenders can only collect money after
they foreclose the loan, obtain the property, sell it, and then fill out all the documents. This process in itself
was extremely difficult, if not impossible. In addition, lenders had to prove that their origination documents
fulfill the requirements of FOVI, which was impossible due to the non-standardization of mortgage loan
origination and the lack of monitoring from the FOVI. As a result, lenders lost trust in FOVI's guarantee
program.
- 117-
1993 burst.3 7 The mortgage delinquency rate has been climbing up since 1993 and
skyrocketed during the crisis and the years afterwards (Figure 5.18).
Figure 5.18 Cumulative Mortgage Loan Delinquency Rate of Consolidated Commercial
Banks in Mexico
40%
33.7%
35%
34.5%
V 28.1%
25'%
20%
15%
10%14
N0.2%
'
C
--
3%
C
C
-..
Sl'turcl':
1lltcl t.. N.ixico.
lIt.S..
.
Not In Jantluar I 97.
M hNexicadtkpt l ISk ( .A\
AP aountii
standardsandlthusbeganincluditi
entir laince in pa.t dc
lo I l as part o' th1kdlinqt enlt ptrllklit).
lhe
The Mexican Banking Commission (CNBV) estimated that the percentage of delinquent
loans as compared to current loans was as high as 13.9 percent by the end of 1993. After
the crisis, as shown in the graph, the delinquency rate of some consolidated commercial
banks reached around 35% of the total outstanding mortgage portfolio in 1997. Broken
down by cohorts, i.e. origination years, the marginal default rates of previous FOVIguaranteed cohorts are as the following (Table 5.18):
Table 5.18 Marginal Default Rates of FOVI-Guaranteed Cohorts
Origination year 1996
1996
P.00%
1997
1998
1997
1998
1.14%
3.51%
0.02%
Age of Loan
1999
2000
3.86%
4.36%
19.20%
0.53%
1.73%
1.96%
2.22%
2.88%
9.33%
0.00%
0.47%
1.36%
1.66%
2.13%
5.62%
0.42%
1.70%
2.77%
3.77%
8.66%
1.32%
2.35%
0.14%
0.73%
4.00%
0.86%
0.01%
0.01%
0.33%
2001
2002
-`
Y,
--
Cumulative default
rate to-date
2.89%
2000
.
2002
3.44%
1999
"
2001
-
Source: 1ne nr
37Between 1993 and 1995, urban land prices decreased about 10 percent in real terms and social-interest
housing prices declined similarly. Residential, medium, and economic housing - the three categories on
which commercial banks concentrate their lending - suffered a much deeper price decline at about 30
percent in real terms (Pickering 2000).
- 118-
These cohorts had been transferred to SHF's guarantee program. As the SHF-guaranteed
2003 cohort has only one-year history, I will base my assumption of its ultimate cohort
default rate on the range of the default rates projected from the existing books of business
since 1996. The 1996-1998 cohorts have more than 5-year seasonality so far and provide
a good starting point for estimation. Using the experience of the U.S. FHA fixed-rate 30year portfolio's cumulative default rates as a reference, I obtain the following factors: the
cumulative default rates within the first 5 years of the loan life capture about 37% of the
overall ultimate default rates, the first 6 years represent 50%, and the first 7 years
represent 61%'oof the ultimate default rates. Based on these percentages I project the
ultimate default rates for the 1996-1998 cohorts (Table 5.19).
Table 5.19 Projected Cumulative Default Rates of SHF Guaranteed Cohorts, 1996-1998
Origination Year
1996
1997
1998
To-date (2003) cumulative
default rate
Projected ultimate default
rate
19.20%
9.33%
5.62%
31.5%
18.7%
15.2%
Prepaymentrates, 2000 -2003
Data on mortgage prepayment rates were not available before 2000. A close examination
of the mortgage performance during and after the financial crisis of 1995 suggests that
the prepayment rate should be fairly low. During that period, nearly all mortgages
originated in Mexico were dual-indexed mortgage (DIM), which allowed lenders to earn
a variable market interest rate on loans while also keeping payments affordable for
borrowers.38 However, the financial crisis and the resulting highly inflationary
environment, worsened by declining housing prices, have caused accelerated negative
38 The
novel feature of the DIM is that the monthly payment is not determined by the "effective" or
"debiting" rate but instead by a separately calculated "payment" rate. The debiting rate determines the
interest accrued on the outstanding debt at any time, and is the interest owed by the borrower over the life
of the loan. The payment rate, on the other hand, specifies the amount paid by the borrower in any given
month. DIM payment rates grow over time. Payments are adjusted periodically, usually at longer intervals
than the debit rate, and determine the rate of amortization of the loan at any given time. Payment
adjustments in Mlexicohave typically followed minimum wage adjustments. These mortgages were
designed to negatively amortize over the early period of the loan's life, allowing banks to keep payments
affordable (Pickering 2000).
- 119-
amortization in a large number of mortgages. Excessive negative amortization induced
high default rates while discouraged prepayments.
The SHF's new database has information on monthly prepayment rates of its portfolio
(Figure 5.19). Considering both complete prepayments and partial prepayments, SHF
portfolio's cumulative prepayment rate for the last 48 months was around 15 percent. For
lack of better data, I assume the prepayment pattern of the SHF cohort is similar to that of
the FHA's. That is, the first 4-year's cumulative prepayment rate represents about 33% of
the ultimate prepayment rate. So the projected ultimate cohort prepayment rate
approximates 450/0.
Figure 5.19 The Prepayment Pattern of the SHF-Guaranteed Mortgages
Prepayment
Rate of SHF-Guaranteed
Mortgages
_
Curtailment
-Log. (Prepayment Curve)
_
Total Prepayment
-Prepayment
Curve
Annual prepayment rate
0.60%
15t
2nd
0.50%
0.40%
3rd
4th
Cumulati ve 151 -4 th
0.30%
0.20%
0.00%
15.08%
year
0.10%
~
~
~
~ ~
~
~
~
~
Month
re
M
~
~
~
~
~
~
1.99%
4.22%
4.50%
4.38%
Projected ultimate
prepayment rate
45.3%
Source: The SHF
Other parameters
As a new entity that started operation in 2002, the SHF has much less historical data on
its loan performance than the U.S. FHA program or the Dutch WEW fund. Table 5.20
presents the other parameters used in the analysis. Assumptions are stated when there are
no actual data. Explanations of some parameter values are presented in Appendix 5.6.
Table 5.20 Parameters Used in the Cash Flow Analysis of the SHF Mortgage Guarantee
Cohort size
Year
2003
Loan insured
(million)
15,519 Pesos, or
1,361 U.S. $
Number of loans
53,149
- 120 -
Average size of the loan (Pesos)
Prosavi
Profivi
Peso
79,288
358,559
607,702
Cohort
composition by
loan types
There are three types of mortgage loans guaranteed by the SHF: Prosavi loans, UDI loans, and
Peso loans. Prosavi (Programa de Subsidios a Vivienda) loans are subsidized mortgages offered
to families with incomes below 5 minimum wages, while Profivi (Programa de Financiamiento
a Vivienda) loans are unsubsidized. Both Prosavi and Profivi loans are denoted in UDIs. Peso
loans are unsubsidized mortgages denoted in Pesos. More details on SHF mortgage products
can be found in Appendix 5.6.
Product type
Prosavi
Profivi
Peso loan
Premium
structure and
pricing
Mortgage loan
features
Percent
6.4%
92.8%
0.8%
Average LTV
73%
86%
86%
Average loan term (year)
25
25
25
Up-front premium rates:
Annual premium rate charged on the outstanding loan balance:
0%
UDI loans
Peso loans
0.70%
0.83%
Loss coverage (percentage of unpaid balance plus the capital,
ordinary interests, delayed payments and the collection and cover
commissions, as well as the other miscellaneous costs):
Typical loan term: 300 months
Lan interest
25-yr fixed real rate (UDI)
25-yr fixed real rate (Peso
25%
13.7%
17.5%
Note: majority of the loans are UDI loans, fixed in real rate (variable rates when
inflation fluctuates). That is, the principal is adjusted with inflation each month. If
the real interest rate is constant, monthly payments are adjusted only by the rate of
inflation (which increases the principal).Real interest is then charged on this
inflation-adjusted capital.
Inflation and
implied UDIPeso
relationship
2003
Inflation (% change)
UDI - Peso rate (1 UDI)
2004
3.9
3.272
3.6
3.390
2005
3.8
3.518
2006
3.4
3.638
2007
onwards
3
3.747
Source: Mexican government. Bank of Mexico.
Default costs
and property
recoveries
UDI Loans:
Claim cost (% of UPB)
1.56
Recovery rate (average) on
the original loan amount
56%
Unpaid Balances + foreclosure-related expenses
(24%) + interest for 2.3 years (13.7%*2.3)) -- the
average foreclosure time lasts for 27 months, i.e.
2.3 years.
Suppose the original house value is 100 UDIs. Get
a loan of 85% LTV, i.e., the loan amount is 85
UDIs. As the majority of defaults occur between
the 3 rd and the
5th year:
7 th
year, I assume it happens in the
UPB = 0.9710 * 85 = 82.5 UDIs outstanding
Default amount = 82.5*1.56 = 128.75 UDIs
Assume the house value depreciates by 2% per year
(see Appendix 5.6). At the 5th year, the house value
is 100*(1-5*2%) = 90 UDIs, if no depreciation.
Suppose the house can be sold at 80% of that value.
Then the recovery rate = 90*80%/128.75 = 56%
- 121 -
Peso Loans:
Default rate
scaling factors
& Prepayment
rate scaling
factors
Claim cost (% of UPB)
1.64
Recovery rate (average) on
the original loan amount
87%
Unpaid Balances + foreclosure-related expenses
(24%) + interest for 2.3 years (17.5%*2.3»
-- the
average foreclosure time is 2.3 years.
Suppose the original house value is 100 Pesos. Get
a loan of 85% LTV, i.e., the loan amount is 85
Pesos. As the majority of defaults occur bIt the 3rd
and 7th year, I assume it happens in the 5th year:
UPB = 0.9844 * 85 = 83.67 Pesos outstanding
Default amount = 83.67*1.64 = 137.22 Pesos
Assume the house value appreciates by 10% per
year (see Appendix 5.6). At the 5th year, the house
value is 100*(1 +5*10%) = 150 Pesos, if no
depreciation.
Suppose the house can be sold at 80% of that value.
Then the recovery rate = 150*80%/137.22 = 87%
Both UDI and Peso loans are 25-year term, fixed rate (real vs. nominal) with an average LTV of
85%. They behave differently because: 1) the early stage monthly payment is different. Peso
loans' payment burden is larger than UDI loans for the first 8 years; 2) there exists negative
amortization in UDI loans but not in Peso loans.
Risk profile of UDI vs Peso loans: a) UDI loans have a swap protection against inflation for
borrowers; 2) initial payments in Peso loans are bigger than UDI loans -- initial years with high
default probability; 3) If inflation shoots up again, fixed rate Peso loans will be repaid fast.
Default scaling Weighted
factor
average
VOl loans
0.99
0.9821
UOI loans
Peso loans
1.2
0.0096
Peso loans
Sum:
1.0000
Therefore I assume the following scaling factors:
Yield curve for
discounting
future cash
flows
_Prepayment
scaling factor
0.99
1.2
Sum:
Weighted
average
0.9821
0.0096
1.0000
Government Bond Yield Curve as of 2003 (Obtained from Bank of Mexico.
(http://www.banxico.org.mx/siteBanxicoINGLES/elnfoFinancieralFSinfoFinanciera.html)
8.0
7.0
6.0
:!2 5.0
~ 4.0
3.0
2.0
1.0
0.0
Interpolated Yield Curve, 2003
..
...-...
"
'"
-:
..
.
./
-
,
~
r--
0
(')
MaturitY
~
~
C\l
C\l
The yield curve
slopes gently
upward,
indicating a
stable economy
LO
C\l
Source: Values in this table come from, or are based on, data provided by the SH F, interviews conducted by the author, and
reference from the U.S. FHA experiences.
C.3 Modeling results
Cohort ultimate default and vrevayment rates and their annual distributions
Based on the previous analysis (Table 5.19), I assume the 2003 cohort default rate falls
within the range of 10% to 35%. The lower bound of 10% is based on the improving
- 122 -
performance of SHF-guaranteed cohorts. The SHF's own estimate of the cohort's
ultimate default rate is around 22.8%. The annual distribution of the marginal
(unconditional) default rate is modeled after the Erlang scale and shape parameters from
the U.S. FHA cohorts with ultimate default rates over 10%, the "bad performance" group
(b = 1.78, c = 4).
The SHF made its own projection regarding to the default rate distribution, based on its
limited loan performance data to-date (66 months of data) and the U.S. Freddie Mac
portfolio experience.3 9 My projection of the cohort default rate distribution for the SHF is
based on the U.S. FHA cohort experiences. I believe it is better than the projection based
on the Freddie Mac data because: i) FHA serves lower-income and higher-risk borrowers
while Freddie Mac's market segment is conforming loans. FHA borrowers' risk profile
bears more similarities to that of SHF borrowers; ii) given the high ultimate default rate
of the SHF cohort, a relatively wider default "peak window" (a curve with wider mid
peak section) between the 2n d and the 8th year is more likely than the narrow peak zone
under favorable economic conditions, as depicted by the projected curve based on
Freddie Mac experience; and iii) my projection fits the actual data from SHF's 2003
cohort quite well (Figure 5.20).
Figure 5.20 Projections of Annual Unconditional Default Rates of the SHF 2003 Cohort
--
---
Estimated Distribution of Annual Unconditional Default
Rate (Assume Ultimate Default Rate = 20.6%)
---
SHF Experience
andProjection
---
My Projection
]
3.5%
3.0%
- ..
_PrjelonsJasednnI S
-- ...--
Freddie Mac experience
projections0based on
1.5%.
1.0%
aj/~~~
~~1.5%
|U.S.
.-
0.5%
*
FHA
experience
iMyprojections
based on
.
0.0%
co)
LO
N_
0)
n Age
c'
L
0Loa
Loan Age
39 The
SHF's detailed forecasting model for future default rates based on Freddie Mac experience and
models is considered confidential and thus not provided to the author. Its projected annual conditional
default rates are graphed in Figure 5.20.
- 123 -
As discussed in the previous section, I assume the cohort ultimate prepayment rate to be
around 45.30/0. Based on the Erlang distribution from the U.S. FHA cohorts (bad
performance group, b = 2.21, c = 3), I project the SHF cohort's annual unconditional
prepayment rates over the lifetime of the loan (Figure 5.21). Compared with SHF's actual
prepayment rates for the past four years, my projection is biased upwards. This may be
due to the fact that Mexico's mortgage market is still in its early development and
borrowers are not sophisticated enough to exercise their "call option" (prepayment
option) whenever it becomes in-the-money. The situation can change as the mortgage
market matures in the future.
Figure 5.21 Projections of Annual Unconditional Prepayment Rates of the SHF 2003
Cohort
Estimated
Prepayment
Distribution
of Annual
Rate (Assume
Ultimate
Unconditional
Prepayment
Rate
=
45.3%)
-+- My
*
a:
E
Projection
__
SHF Experience
6.0%
5.0%
4.0%
Q)
E
>co
ar
0:
3.0%
2.0%
1.0%
0.0%
M
C\l
Lt)
C\l
Discounted cash flow analysis
For simplicity, I round up the numbers and assume that in 2003 SHF guaranteed 15
billion pesos, or 4.6 million UDls, of mortgage loans. Within the cohort, there are three
types of loans: Prosavi, Profivi, and Peso loans. Prosavi and Profi vi loans are quite
similar, both denoted in UDls, unsubsidized in their interest rates, with payments linked
to the minimum wage and protected by the Swap fund.4o Hence, I group them together as
UDI loans. They are designed for the volatile inflation environment. As Mexico's macro
Borrowers pay in minimum wage terms, but the intermediary financier receives flow in UDI, and in the
middle is the Swap offered by the SHF. The Swap fund works as follows: the SHF charges a premium of
71 basis points and uses that to create a fund so that if the minimum wage falls behind inflation, the fund
will cover those losses. The pricing of the swap is designed such that it can sustain a fall of 25% of the
minimum wage compared with inflation over a period of 30 years (Babaiz, 2004).
40
- 124 -
economy stabilizes and inflation is put under control, the mortgage market will see the
growth of the more traditional, fixed-rate long-term mortgages denoted in the local
currency, Pesos. Even though Peso loans only made up about 0.80/0 of the 2003 cohort,
they are expected to grow fast in the future. The SHF is taking great effort to promote
Peso loans. For this analysis, I distinguish the cash flow calculations between 001 loans
and Peso loans. Table 5.21 summarizes the NPVs and profitability rates under different
default scenarios for UDI loans, Peso loans and the cohort as a whole. Figure 5.22
illustrates the relationship between the cohort ultimate default rate and the profitability
rate. Appendix 5.7 presents details of the cash flow calculation.
Table 5.21 NPVs and Profitability Rates of the SHF-Guaranteed Cohort Under Different
Default Scenarios
UDI Loans: 14,880 mil peso
Ultimate
default rate
NPY (mil Peso)
10%
15%
PESO Loans: 120 mil peso
677.1
Profitability
rate
4.6%
509.9
3.4%
20%
342.6
2.3%
3.1
25%
175.4
1.2%
30%
8.2
0.1%
35%
-159.0
-1.1%
The Cohort:
15,000 mil peso
NPY (mil Peso)
Profitability
rate
NPY (mil Peso)
5.3
4.4%
682.4
4.2
3.5%
514.1
3.4%
2.6%
345.8
2.3%
2.1
1.7%
177.5
1.2%
1.0
0.8%
9.2
0.1%
-0.1
-0.1%
-159.2
-1.1%
Profitability
rate
4.5%
Break-even default rates:
30.3%
-1.86
-0.01%
0.91
0.76%
-0.95
-0.01%
34.5%
-142
-0.96%
0.00
0.00%
-142
-0.96%
1.7%
2.5
2.1%
251.5
1.7%
Expected 2003 cohort NPV:
248.9
22.8%
Figure 5.22 Relationship Between Cohort Ultimate Default Rates and Profitability Rates
Profitability
I --+-- UDlloan
Rate Under Various
-----
Peso loan
Default Rates
-6--
Cohort aggregate
I
5.0%
~
~
15
.!B
4.0%
3.0%
2.0%
~
1.0%
a:
0.0%
-1.0%
-2.0%
Ultimate
Default Rate
- 125 -
The analysis shows that at the current pricing level the cohort's NPV is estimated to be
252 million pesos, or 1.7 percent profitability rate, assuming the cohort default rate is
22.8 percent as projected by the SHF. The cohort as a whole can sustain a 30 percent
cumulative default rate and just breaks even. In other words, the 2003 cohort could
survive a recession or crisis almost as severe as that of 1995-97.
The cohort profitability rate ranges from -1.1% to 4.5%, or NPV from -159 million to
682 million pesos of the 15 billion pesos cohort, assuming the cohort default rate falls
within 10% to 35% sphere. In the cohort, SHF's guarantees on peso loans have a higher
profitability rate than on UDI loans (Figure 5.22). The guarantee pricing level on peso
loans has a break-even default rate of 34.5 percent, higher than that of UDI loans (30.3
percent). On the other hand, peso loans have higher risk than UDI loans because the UDI
loan design protects borrowers from the possible mismatch of inflation and minimum
wage adjustments. The swap coverage, provided by the SWAP fund, on UDI loans
enables borrowers' payments only to increase in accordance with the minimum wage,
despite the loan being stated in UDI that traces inflation. But peso loan payments are
fixed every month in the nominal term. If there are unexpected or permanent drops in real
terms in the minimum wage, UDI borrowers will not be adversely affected, as the
shortfall in payments will be covered by the swap fund. Again, as Mexico's economic
environment improves, the risk premium charged on peso loans may reduce.
Sensitivity analysis of SHF liabilities in its mortgage guarantee business
Over the most recent few years, the climate for housing finance in Mexico has improved
and the government has determined to strengthen housing supply and affordability. As a
newly established entity, the SHF has progressed in full speed rolling out a range of new
products in mortgage guarantee, construction loan guarantee, and MBS on-time payment
guarantee. Focused solely on its first-loss, partial mortgage guarantee products, this
analysis identifies several factors that can influence SHF's potential liabilities
significantly.
- 126 -
The foremost factor is the legal infrastructure and social recognition for foreclosure.
There are still some states in Mexico where the process of foreclosure and eviction for
collateral recovery purposes remains basically unachievable. Even for places where
foreclosure is possible, it takes on average 27.6 months from payment delinquency to
foreclosed property sale or auction. The accumulated delayed payments, interests, and
other fees and insurances during the foreclosure process are substantial. In comparison,
the average foreclosure time in a mature market like the U.S. FHA program is less than
half of that length, about 12 months. As Mexico's mortgage finance market develops,
home foreclosure and collateral recovery should become a practical and predictable
process, which means cost-savings for the SHF's first-loss partial MI product. For
instance, if the foreclosure process shortens to one year, the 2003 cohort's NPV will
increase by 9%, from 252 million pesos to 275 pesos (Table 5.22).
SHF's own operation and management are evolving rapidly, which has important
implications on its liabilities as well. In analyzing the 2003 cohort, I assume that the SHF
grants an advance claim payment in the sixth consecutive month that borrowers have
been delinquent, which is equivalent to 25 percent of the amount owed by the defaulted
borrower. Then the property will be foreclosed and sold/auctioned, and the actual claim
amount (losses) is the difference between the value of the sale and the amount owed by
the borrower plus administrative costs incurred. The SHF should only cover up to 25
percent of the updated gap, or actual losses incurred to the intermediary. If the advance
granted by the SHF earlier was greater than the updated amount, the intermediary would
have to reimburse the difference to the SHF. Starting from the 2004 cohort, the SHF
changed the rules and decided to defer the point of its claim payment from the time at
which a delinquent loan reaches six months in arrears to the time when the lender has
secured a court foreclosure or other legally assured control of the collateral property.
Though not preferred by lenders, this change improves SHF's MI program from both a
risk management and operational standpoint, while at the same time giving lenders
greater certainty and finality with regard to the claims proceeds they receive (Blood, WB
report 2004). A simple analysis shows that if the claim payment time is shifted from the
- 127 -
sixth month in delinquency to the end of the 27th month when the foreclosure process
completes, the cohort NPV will increase by 170/0 (Table 5.22).
Another key parameter, the long-term projection of the baseline frequency of defaults
(claims) over the lifetime of the cohort, was set to be 22.8 percent. It is quite high, both in
terms of international experience - even in times of stress - and also a bit pessimistic
reading of limited Mexican experience. Lenders' improved ability to foreclose and
recover collateral property - which will influence future borrower behaviors - together
with the continued strengthening of financial sector and the overall economy suggest that
the ultimate default rate of SHF cohorts will trend down. Also, a larger percentage of
defaulted loans could be "cured" by various risk mitigation methods that are currently
being developed by the SHF rather than going to foreclosure. If the SHF's 2003 cohort
default rate lowers to be similar to that of the FHA's average default rate over the past 30
years, 9.8%, its NPV will increase by 173% and its profitability rate will reach 4.6%
(Table 5.22).
Table 5.22 illustrates the NPV of the SHF's 2003 cohort under alternative parameter
settings and future economic outlooks. The cohort size is set to be 15 billion pesos at
origination.
Table 5.22 The Sensitivity Analysis of Alternative Scenarios - The SHF
Current
the mean default rate of 22.8%)
NPVI
NPV
cohort
(mil
Current parameter settings
face
Peso)
value
Premium
structure
Model (assumin
Up-front premium rates:
Annual premium rate:
UOI loans
Peso loans
Foreclosure
process
Foreclosure process takes about 2 years
on average.
Timing of
the claim
payments
In 2003' s cohort, the SHF pays claims in
the sixth month of the borrower
delinquency.
Cohort
default rate
Mean default rate
Scenarios
New parameter settings
If add an upfront premium - similar to
the FHA premium structure:
0%
0.70%
0.83%
Alternative
252
1.7%
= 22.8%
- 128 -
Up-front premium rates:
1.5%
Annual premium rate:
UOI loans
0.50%
Peso loans
0.50%
If the process shortens to I-year
NPV
(mil $)
226
275
From 2004 onwards, SHF pays tclaims at
the end of the 27th month when the
foreclosure process completes
295
Mean default rate = 9.8% (The U.s. FHA
experience)
689
4.6%
173%
Housing
market
conditions
Recovery rate on
UDlloans
Recovery rate on
Peso loans
Scenario I: Favorable (20% increase in
housing prices)
56%
87%
Recovery rate on
67%
UDlloans
Recovery rate on
105%
Peso loans
Scenario 2: Unfavorable (20% decrease
in housing prices)
Recovery rate on
45%
UDlloans
Recovery rate on
70%
Peso loans
If the future economic outlook is
unfavorable: the yield curve of U.S. 1981
Treasury bills
Interest rate
environment
- the Yield
Curve
Interpolated
328
2.2%
30%
176
1.2%
-30%
133
0.9%
-47%
Yield Curve, 2003
Interpolated U.S. Treasury Yield
8.0
Curve
15
6.0
of 1981
15
'0
~ 4.0
~ 14
CD
14
2.0
>=
0.0
r-.
0
M
13
<0
Maturity ....
13
....
II)
Ol
The implied liabilities on the government by supporting SHF's mortgage guarantee
programs will be constantly changing as the SHF progresses to establish freestanding
financial credibility in the future marketplace, independent of the current full government
backup. That backup is scheduled to stop by 2013. Meanwhile, the SHF is actively
seeking private sector and international participation, even competition, in Mexico's
mortgage credit enhancement business. Beyond its recent dialogue with many U.S.
private mortgage insurers, with an initial eye toward early risk sharing through quota
share reinsurance, the SHF has stated the intent to reorganize its credit guaranty activities
under an insurance company charter, in contrast to the current development bank
structure (Blood, 2004). All these future legislative, regulative, and structural changes of
Mexico's mortgage market and the SHF itself will have important implications on its
liabilities.
V.
Comparison of the Three Cases and Implications
The simulations and analyses of the three public MI programs appearing above provide a
framework for understanding the magnitude and volatility of implied liabilities imposed
on the government to sponsor a public MI enterprise. The three programs are analyzed in
- 129 -
a comparable manner on their 2003 cohort's NPV distribution, multi-year NPV
distribution, and potential losses in worst-case scenarios (Table 5.23).
Table 5.23 Comparison of the Modeling Results for the Three MI Programs
FHA
WEW
SHF*
9.2%
1.23%
22.8%
0.85%
-0.22%
1.7%
13.1%
0.7%
30%
82 percentile
43 percentile
7.08%
0.55%
2003 Cohort
Simulated (or projected) mean
default rate
Cohort profitability rate at
mean default rate
Break-even default rate
Break-even default rate
Break-even default rate
percentile
Multi-year Analysis
6-year accumulation - mean
profitability rate (over one
year's business volume)
Break-even percentile of 6year accumulatiole
10-yearaccumulation - mean
profitability rate (over one
year's business volume)
Break-even percentile of 10year accumulation
75 percentile
10.06%
1.67%
99 percentile
99 percentile
89 percentile
89 percentile
Worst-Case Scenarios
6-year accumulation when
resampling only from the "loss
zone" - mean profitability rate
-1.03%
(over one year's business
volume, annualized number)
The worst experience so far,
the recession of 1981-1983,
were to happen again, the
profitability rate (over one
-0.7%
year's business volume,
annualized number)
The high default rates during
the 1995-1997financial crisis,
for 3 consecutive years (over
-1.07%
one year's business volume,
annualized number)
*: There is not enough historical data to simulate the distribution of cohort ultimate default ratesfor Mexican SHF.
Therefore many entries are blank.
Now the questions raised at the beginning of this Chapter can be answered. First, can the
public MI design and pricing cover expected risk exposure for a single cohort? At the
mean cohort default rate, FHA and SHF's 2003 cohort will have a positive profitability
130 -
rate of 0.85% and 1.7% respectively, indicating that both of them are expected to
generate positive NPV (profit) from their MI contracts. FHA's single cohort profitability
rate is quite close to zero, the break-even point, supporting the argument that public MI
programs tend to have a smaller "buffer" zone against losses and are not profit
maximizing. Mexico's SHF MI program has a higher cohort profitability rate of 1.7%,
which can be due to the fact that as a newly established entity the SHF is trying to build
up its capital reserves and make sure that its MI program is self-sufficient. It may also
reflect the relatively higher risk and more volatile macro economic environment in
Mexico.
At the simulated mean default rate of 1.23%, the Dutch WEW-guaranteed cohort will
have a negative profitability rate, or loss rate, of 0.22%. This means that if the
distribution of WEW's cohort ultimate default rates, derived from the historical
experience of the 1981-1994 books of business, is a good indication of the WEW's future
cohort default rate patterns, then its current pricing level is insufficient to cover expected
losses. However, as discussed earlier in Section B.3, the Erlang distribution of ultimate
default rates based on only 14 data points can raise a lot of concerns over its validity of
accurately estimating the mean cohort default rate for the future. Moreover, the 1981-
1994 cohorts were originated under the old municipal guarantee systems and later
transferred to the WEW. So they may not be underwritten to the WEW's standards,
which could have an implication on their lifetime default rates. For instance, if WEW's
underwriting requirements are higher and stricter than those of the municipal guarantee
systems, then the estimate of mean cohort default rate based on old books' performance
is likely to be upward biased. Unfortunately, there are not enough seasoned cohorts
originated under the WEW's regime (since 1995) at this point to conduct the distribution
pattern analysis. Judging from the satisfactory, yet short, performance of the WEW's
cohorts originated after 1995, we can conclude that if a cohort default rate varies between
0.1% and 0.2%, a reasonable guess based on WEW's recent cohorts' figures, it is
estimated to generate a positive profitability rate around 0.2%, quite close to the breakeven point as well.
- 131 -
Secondly, what is the scale of potential liabilities over a longer time horizon? The
analyses show that under normal conditions, on average both FHA and WEW programs
are financially sound over the next six-year and ten-year period, generating positive
profitability rates.4 1 Comparatively speaking, the WEW is exposed to greater potential
risks than the FHA over a period of multiple years. For instance, the probability of net
losses over a 6-year period is 25 percent under the WEW program (75 is the breakeven
percentile), while it is only 3 percent under the FHA program (97 percentile breakeven).
The breakeven percentiles for 6-year periods are lower than the 10-year percentiles for
both FHA and WEW, indicating that there is a larger probability of incurring losses over
6 consecutive years than over 10 years. In particular, WEW's 10-year breakeven point is
projected to be at the 89 percentile, versus its 6-year breakeven point of 75 percentile - a
14 percentage points difference - meaning that the probability of WEW's cohorts losing
money in a 6-year period is estimated to be 14 percent higher than in a 10-year duration.
These numbers indicate that the two programs' financial strength is stronger over the
long term than the short term. This conclusion has important policy implications. If in the
future there are one or several new books of business that are predicted to incur losses, it
may not be necessary for policymakers to increase premium rates or take other immediate
measures because the program can still be self-sufficient over the long term. On the other
hand, policymakers should also be cautious about reducing premium rates if the program
has experienced some good years, unless the probability of losses over the long term is
truly small.
The last question is that if the worst scenario happens, what is the implied capital outlay
for the backing government (taxpayers) to bail out the program? In other words, can the
program survive recession or catastrophic events, and for how long? The first layer of
protection is the loss reserves of the public MI program. If the reserves are depleted, the
remaining liabilities are to be borne by the government. Table 5.24 presents the
comparison among three programs. For each program, the worst-case scenario is based
on the most severe historical experience of that country's mortgage market. The analysis
shows that the FHA and the WEW can survive the assumed worst-case scenarios without
41 There is not enough historical data to conduct multi-year analysis for the Mexico SHF program.
- 132 -
imposing additional liabilities on the backing governments. However, their levels of
sustainability differ. The FHA can survive about 22 consecutive years of net losses as bad
as its 1980s experience, while WEW can only be self-sufficient for less than 4 years. The
SHF's current reserves can only sustain one cohort of high default rate as severe as the
1997 experience. If the crisis scenario were to persist for 3 consecutive years, the
Mexican government would have to bear the extra liability of approximately 280 million
pesos.
Table 5.24 Sustainability of the Three Public MI Programs in the Worst Case Scenarios
Loss reserves for its MI
FHA
(Billion $)
WEW
(Billion C)
SHF
(Billion Peso)
22.742
0.245
0.197*
program, as of 2003
Insurance-in-force
(IIF)
Capital ratio (reserves/IIF)
Worst-case scenario
Potential losses
Implied liabilities on the
42
435.7
24.3*
5.21%
9 consecutive years of
0.58%
Similar to the recession
0.81%
High default rate of 35%
high default rates (>10%)
of the 1980s - 3
for 3 years, similar to the
as experienced between
1980-1988
consecutive years of
high default rates
situation of 1997 after the
financial crisis
9.2
0.21
0.48
22 years42
3.5 years
1 year
backing government
Sustainable period if the
worst-case situation were to
persist
* Here I only include mortgage loans (Prosavi and Profivi loans) guaranteed by the SHF's 1S'loss MI product.
Of course, the program's ability to sustain economically stressful situations is closely
related to its age. The longer the program has existed, the more likely it is able to build up
loss reserves and survive economic downturns.
42
The FHA took a $7 billion hit to its insurance Funds in 2004 because it had over-valued the positive NPV
of its existing books of business and therefore over-promised the Treasury of its cash inflows. If we take
that into account, the FHA's capital reserves for its MI business was around $15.7 billion as of 2004, which
would sustain the program for the worst case scenario for about 15 years.
- 133 -
Chapter 5 Appendices
Appendix 5.1 Regression Analysis of FHA Cohort Prepayment Rates, 1975 - 2003
Origination Ultimate Default
Year
rate
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
4.50%
4.33%
4.01%
5.61%
9.54%
Ultimate
Prepayment rate
86.51%
86.73%
86.24%
84.78%
81.56%
77.32%
72.37%
The model:
Ultimate Prepayment Rate = a + b*Ultimate Default Rate
74.53%
78.63%
74.95%
77.48%
Regression result:
14.86%
20.53%
19.09%
15.71%
19.36%
17.70%
13.36%
10.09%
10.88%
80.90%
83.41%
83.25%
85.10%
86.99%
88.12%
9.69%
8.43%
7.54%
6.75%
88.95%
6.39%
6.83%
89.86%
89.16%
87.74%
8.72%
8.63%
87.75%
7.94%
88.54%
89.07%
7.04%
6.78%
2000
7.43%
2001
2002
2003
5.81%
Ultimate Prepayment Rate = 0.942 -1.004*Ultimate Default Rate
89.33%
90.68%
91.04%
87.48%
80.63%
6.74%
8.62%
-
Regression output:
Regression Statistics
Multiple R
0.8994045
R Square
0.8089285
Adjusted R Square
0.8018518
Standard Error
0.0235789
Observations
29
ANOVA
df
SS
Regression
MS
1
0.063551
0.063551
Residual
27
0.015011
0.000556
Total
28
0.078562
Coefficients Standard Error
Intercept
X Variable 1
t Stat
F
114.3084
P-value
SignificanceF
3.3186E-11
Lower 95%
Upper 95%
0.9424341
0.010151
92.84054
2.29E-35
0.921605761
0.96326243
-1.0036671
0.093875
-10.6915
3.32E-11
-1.196282923
-0.81105134
- 134-
Appendix 5.2 Components of the Cash Flow Analysis for the U.S. FHA Program
FHA 30-year fixed rate mortgages
- Cash flow analysis
for Cohort
2003
Assume a cohort of $50,000,000,000 (50 billion) 30-year fixed rate FHA-guaranteed
Within this cohort, there are the following 8 types:
65<LTV <=80
80 < LTV <= 90
90 < LTV <= 95
95 < LTV <= 97
loans.
97<LTV
Streamline refinance
ARM (all LTV)
Investor
Basic Loan Amortization Factors
* Year
* loan age at midyear (months)
6
2
3
4
5
6
30
18
30
42
54
66
354
* loan balances,
BOY
1.000
0.9886
0.9764
0.9634
0.9495
0.9348
0.0723
* loan balances,
mid year
0.9944
0.9826
0.9700
0.9565
0.9422
0.9270
0.0367
* loan balances,
EOY
0.9886
0.9764
0.9634
0.9495
0.9348
0.9190
0.0000
Unconditional
rates
Default
and Prepayment
Rates for Cohort:
scaled
by ultimate
Annual unconditional
Default rate
0.0026
0.0061
0.0082
0.0086
0.0080
0.0068
0.0000
Annual unconditional
Prepay rate
0.0697
0.1000
0.1076
0.1030
0.0923
0.0795
0.0001
Cash Flow Analysis:
Cash Flows
Commitments
(loan amount, face value)
(+)
Upfront premiums received
Annual premiums
Other inflows
Year 2
Year 3
Year 4
Year 5
Year 6
Year 30
1600
24
7.68747
(+)
6.84202
5.8346
4.8286
3.90867
3.111
0
0
(+)
2.6927
Default payments (-)
Recoveries on defaults
Year 1
by loan product
6.3076
8.3038
8.6289
7.8724
6.6113
0.0000
1.83102
4.28919
5.6466
5.86767
5.3532
9.28E-05
1.68678
1.24041
0.64998
0.22964
0.00143
0.00338
0.0045
0.00474
0.00439
0.0037
3.27E-07
0.07671
0.11005
0.11841
0.11325
0.10155
0.0874
0.000151
0.00143
0.00481
0.00931
0.01405
0.01845
0.0222
0.033539
0.07671
0.18675
0.30516
0.41841
0.51996
0.6074
0.959542
0.92187
0.80844
0.68553
0.56753
0.4616
0.007069
0.006919
(+)
1.556
Premium refunds (-)
0
0
0
Other outflows (-)
Loan Performance
Unconditional
default rate
Unconditional
prepayment
rate
Cumulative default rate
Cumulative
prepayment
rate
1
BOY survival rate
EOY survival rate
0.92187
0.80844
0.68553
0.56753
0.46159
0.3704
Conditional
default rate
0.00143
0.00367
0.00557
0.00692
0.00774
0.0081
4.62E-05
Conditional
prepayment
0.07671
0.11937
0.14647
0.1652
0.17893
0.1894
0.021298
Mid-year payers rate of annual premium
0.96093
0.86515
0.74698
0.62653
0.51456
0.416
0.006994
30-year CUM. DEF. RATE
0.03354
30-year CUM. PREPAY. RATE
0.95954
rate
Note: The same cash flow components were analyzed for the rest types of loan products by L TV categories.
cumulative cash flows were obtained by aggregating all loan types.
- 135 -
Then the
Appendix 5.3 Regression Analysis of FHA Multi-Year Cohort Default Rates, 1975 2003
Projected
30-year
Origination
Year
I-yr
T yr
Treasury
bond rate
Default rate
default rate
-
4.50%
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
4.33%
4.01%
5.61%
9.54%
14.86%
20.53%
19.09%
15.71%
19.36%
17.70%
13.36%
10.09%
10.88%
9.69%
8.43%
7.54%
6.75%
6.39%
6.83%
8.72%
8.63%
7.94%
7.04%
6.78%
7.43%
2000
2001
2002
2003
5.81%
6.74%
8.62%
4.50%
4.33%
4.01%
5.61%
9.54%
14.86%
20.53%
19.09%
15.71%
19.36%
17.70%
13.36%
10.09%
10.88%
9.69%
8.43%
7.54%
6.75%
6.78%
5.88%
6.08%
8.34%
10.65%
12.00%
14.80%
12.27%
9.58%
10.91%
8.42%
6.45%
6.77%
7.65%
8.53%
7.89%
5.86%
3.89%
3.43%
6.39%
6.83%
5.32%
5.94%
8.72%
8.63%
5.52%
5.63%
7.94%
7.04%
6.78%
7.43%
5.81%
5.05%
5.08%
6.11%
3.49%
2.00%
6.74%
1.24%
The model:
Cohort ultimate default rate (,)=a + b* Cohort ultimate default rate (,4~
+ c* -year Treasury bond rate
Regression result:
Cohort ultimate default rate ,) = -0.005 + 0.635* Cohort ultimate default rate (,4o
+ 0.605* I-year Treasury bond rate
Regression output:
Regression Statistics
Multiple R
0.93679173
R Square
0.87757875
Adjusted R Square
0.86778505
Standard Error
0.01716896
Observations
28
ANOVA
df
Regression
Residual
Total
2
25
27
SS
0.052827
0.007369
0.060196
Coefficients Standard Error
Intercept
X Variable 1
X Variable 2
-0.00486747
0.63530555
0.60468706
MS
0.026414
0.000295
t Stat
F
Significance F
89.60646
3.965E-12
P-value
0.008587
-0.56685
0.575872
0.082363
0.125931
7.713491
4.801718
4.54E-08
6.23E-05
- 136 -
Lower 95%
-0.0225525
0.4656761
0.3453267
Upper 95%
0.012818
0.804935
0.864047
Appendix 5.4 Components of the Cash Flow Analysis for the Dutch WEW Program
WEW-guaranteed
Mortgage Cashflows analysis for Cohort 2003
Assume a cohort of Euro 10,000,000,000 (10 billion)
WEW-guaranteed loans.
Within thiscohort,there are the following5 types of LTV loans --Iassume thatLTV isthe most important factorimpacting
defaultrates.
Other factorsinclude:the eriodof interestratefixing;mortgage types.
LTV <=80
0< LTV <= 90
0< LTV <= 100
100 < LTV <= 110
LTV>110
Basic Loan Amortization Factors
*
Year
*
loan age at midyear (months)
6
30
42
54
*
loan balances, BOY
1.000
0.9860
0.9713
0.9558
0.9394
0.9221
0.0648
*
loan balances, mid year
0.9931
0.9788
0.9636
0.9477
0.9309
0.9131
0.0328
*
loan balances, EOY
0.9860
0.9713
0.9558
0.9394
0.9221
0.9039
0.0000
Annual unconditionalDefaultrate
Annual unconditionalPrepay rate
2
18
3
4
Unconditional Default and Prepayment
ultimate rates
0.0001 0.0018
0.0031 0.0025
0.0028 0.0082
0.0139 0.0184
5
6
66
30
354
Rates for Cohort: scaled by
0.0014
0.0215
0.0006
0.0232
0.0000
0.0005
Cash Flow Analysis: by loan product
Cash Flows
Commitments (loanamount, face value)
Upfront premiums received (+)
Other inflows(+)
Defaultpayments (-)
Recoveries on defaults(+)
Recoveries through recourse (periodpmt)
Recoveries through recourse:cumulative (+)
Loan Performance
Unconditionaldefaultrate
Unconditionalprepayment rate
Cumulative defaultrate
Cumulative prepayment rate
BOY survivalrate
EOY survivalrate
Conditionaldefaultrate
Conditionalprepayment rate
Mid-year payers rateof annual premium
30-year CUM. DEF. RATE
30-year CUM. PREPAY. RATE
Year 1 Year 2
876.7319
2.6302
0
0.0791
0.00063
7.78E-05
0.00358
7.78E-05
0.00358
1
0.9963
7.78E-05
0.00358
0.99817
0.00547
0.38729
Year 3
Year 4
Year 5
Year 6
Year 30
1.0070
1.6630 1.3533 0.7468 0.3222
0.04336 0.55184 0.91134
0.7416 0.40925
0.00806 0.01330 0.01083 0.00597 0.00258
0.00063 0.00869 0.02199 0.03282 0.03879
0.0000
2.24E-17
3.02E-20
9.18E-16
0.001
0.01071
0.00108
0.0143
0.99634
0.98462
0.00101
0.01075
0.99048
5.73E-20
0.000703
0.005469
0.387289
0.607945
0.607241
9.43E-20
0.001157
0.607593
0.00168
0.01802
0.00277
0.03232
0.98462
0.96492
0.00171
0.01831
0.97477
0.00139
0.02396
0.00416
0.05628
0.96492
0.93956
0.00144
0.02483
0.95224
0.00078
0.02799
0.00494
0.08427
0.93956
0.91079
0.00083
0.02979
0.92518
0.00034
0.03014
0.00528
0.11441
0.91079
0.8803
0.00038
0.03309
0.89554
Note: The same cash flow components
were analyzed for the rest types of loan products, firstby the interest rate-fixation
period, then by L TV categories. Then the cumulative cash flows were obtained by aggregating allloan types.
- 137 -
Appendix 5.5 Regression Analysis of WEW Multi-Year Cohort Default Rates, 1981
- 1999
Origination Projected
Year
30-year
default rate
Default rate
1-yr
(t- 1)
Euribor rate
1981
1982
1983
1984
1985
1986
1987
default rate
2.874%
2.474%
2.552%
0.850%
0.875%
0.429%
0.303%
2.874%
2.474%
2.552%
0.850%
0.875%
0.429%
10.69%
11.59%
8.37%
5.65%
6.13%
6.36%
5.68%
1988
0.306%
0.303%
5.36%
1989
0.227%
0.306%
4.82%
1990
0.212%
0.227%
7.39%
1991
1992
1993
0.201%
0.123%
0.134%
0.212%
0.201%
0.123%
8.68%
9.28%
9.35%
1994
1995
1996
0.093%
0.049%
0.097%
0.134%
0.093%
0.049%
6.82%
5.17%
4.37%
1997
0.090%
0.097%
3.00%
1998
0.077%
0.090%
3.34%
1999
0.131%
0.077%
3.45%
The model:
Cohort ultimate default rate (, = a + b* Cohort ultimate default rate (,u+ c* short-term interest rate
Regression result:
Cohort ultimate default rate (,)= -0.003 + 0.678* Cohort ultimate default rate (,)
+ 0. 050* short-term interest rate
Regression output:
Regression Statistics
Multiple R
0.913333
R Square
0.834176
Adjusted R Square
0.812067
Standard Error
0.003323
Observations
18
ANOVA
df
2
SS
0.000833
MS
0.000417
Residual
15
0.000166
1.1E-05
Total
17
0.000999
Regression
Intercept
def (t-l)
Interest rate
Coefficients Standard Error
-0.00259
0.002373
0.678413
0.09683
0.050297
0.038648
t Stat
-1.09281
7.006237
1.301399
- 138-
F
Significance F
37.7288
1.40393E-06
P-value
0.291716
4.24E-06
0.212756
Lower 95%
Upper 95%
-0.00765243
0.00246509
0.472024886
0.88480107
-0.03208014
0.13267402
Appendix 5.6 Explanations of Some Parameters Used in the Cash Flow Analysis of
the SHF Mortgage Guarantee
1. SHF Mortgage Products for Individual Home Purchase
Product
Price limit
in UDI
Price limit
in Peso
Down
payment
Subsidy
Payment
adjusted
Prosavi
45,000
$140,400
2.5%
With MW
Profivi
250,000
350,000
500,000
$ 780,000
$ 1,092,000
$ 1,560,000
10%
15%
20%
Up to
25,000 UDI
No
No
No
No
I
Peso
With MW
With MW
With MW
No
Initial monthly
payment
(Peso/1000)
$15.4
$12-13
$12-14
$12-15
Source: Mexican Housing Overview, 2003 (by Softec)
2. House Value Appreciation/Depreciation Rates Derived from Historical Housing Price
Changes in Mexico
Housing Price Appreciation after the Crisis
Average unit price per m2 in UDIs, 1997-2002
Home prices ('000)
1997
1998
1999
2000
2001
2002
Annual
appreciation
Social
1,229
1,180
1,101
1,206
1,141
1,054
-2.85%
Economic
1,396
1,322
1,273
1,449
1,285
1,270
-1.81%
Middle
1,734
1,898
1,761
1,887
1,758
1,820
0.99%
1998
1999
Home prices ('000)
1997
Social
2,243
2,489
2,815
Economic
2,548
2,788
3,257
Middle
3,164
4,003
4,504
Source: Mexican Housing Overview, 2003 (by Softec)
2000
3,084
3,706
4,827
2001
3,423
3,854
5,273
2002
3,320
4,000
5,733
Average unit price per m2 in Pesos, 1997-2002
Annual
- 139-
appreciation
9.60%
11.40%
16.24%
Appendix 5.7 Components of the Cash Flow Analysis for the Mexican SHF Program
SHF-guaranteed
Mortgage Cashflows
analysis for Cohort 2003
Assume a cohort of Peso $15,000,000,000
*
(15 billion) SHF-guaranteed
Cohort face value ($)
loans.
15000 million Pesos
4585 million UDls, using the mid-year (2003) UDI-Peso rate.
Or
Within this cohort, there are the following 2 types of loans -- UDls and Pesos (assuming average LTV of 85%).
Scenario 1 - with tile UDI-MW swaR fund: sUp'P.Qse25-~ar
fixed rate of lTV 85%
UDI: Basic Loan Amortization
Factors - All units are in UDI
* Year
1
2
3
4
* loan age at midyear (months)
6
18
30
42
* loan balances,
BOY
1.000
0.9950
0.9892
0.9827
0.9751
0.9665
0.1317
* loan balances,
mid year
0.9976
0.9922
0.9861
0.9790
0.9710
0.9617
0.0681
* loan balances,
EOY
0.9950
0.9892
0.9827
0.9751
0.9665
0.9566
0.0000
3.2554
3.3531
3.4574
3.5475
3.6216
3.6920
0.0000
* 1 UDlloan balance, EOY -- in Pesos!
(from 03) Loan size
1 UDI, 3.2718 Pesos
=
Peso Basic Loan Amortization
5
54
6
25
66
294
Factors - All units are in PESO
* Year
1
2
3
4
5
6
25
* loan age at midyear (months)
6
18
30
42
54
66
294
* loan balances,
BOY
1.000
0.9975
0.9945
0.9910
0.9868
0.9818
0.1616
* loan balances,
mid year
0.9988
0.9961
0.9928
0.9890
0.9844
0.9789
0.0843
* loan balances,
EOY
0.9975
0.9945
0.9910
0.9868
0.9818
0.9758
0.0000
3.2636
3.2539
3.2423
3.2286
3.2122
3.1927
0.0000
* 1 UDlloan
balance, EOY -- in Pesos!
(from 03) Loan size
1 UDI, 3.2718 Pesos
=
Unconditional Default and Prepayment
ultimate rates
Rates for Cohort: scaled by
Annual unconditional
Default rate
0.0009
0.0043
0.0083
0.0112
0.0125
0.0123
0.0000
Annual unconditional
Prepay rate
0.0133
0.0340
0.0486
0.0549
0.0546
0.0500
0.0002
Cash Flow Analysis: by loan product
IUDI Loans - First all comDutina in UDls J
Cash Flows
Commitments (Joan amount, face value)
Upfront premiums received (+)
Annual premiums (+)
Year 1
Year 2
Year 3
Year 4
Year 5
Year 6
30.6278
28.9673
26.8687
24.6142
22.4245
1.8999
77.5158
85.6624
83.6535
0.0191
Year 25
4547.955
0.0000
31.6104
Other inflows (+)
0.0000
Default amount
SHF's claim payment (-) - pay at the 6th
month
6.6490
30.1774
57.7374
1.6623
7.5444
14.4344
19.3790
21.4156
20.9134
0.0048
Recoveries on defaults
2.9921
13.5798
25.9818
34.8821
0.0542
The "Real" amount of loss
3.6570
16.5976
31.7556
42.6337
0.0662
The "correct" amount SHF should pay
0.9142
4.1494
7.9389
10.6584
0.0166
Refund to SHF (+)
0.7480
3.3950
6.4955
8.7205
0.0135
Other outflows (-)
0
Loan Performance
- 140-
2.0417E-05
Unconditional default rate
0.0009
0.0043
0.0082
0.0111
0.0124
0.0122
Unconditional prepayment rate
0.0132
0.0336
0.0481
0.0544
0.0541
0.0495
..
1.5870E-04
Cumulative default rate
0.0009
0.0052
0.0134
0.0246
0.0369
0.0491
.
9.8977E-02
Cumulative prepayment rate
0.0132
0.0468
0.0949
0.1493
0.2034
0.2529
.
4.4805E-01
BOY survival rate
1.0000
0.9858
0.9480
0.8916
0.8261
0.7597
...
4.5315E-01
.
4.5297E-01
...
EOY survival rate
0.9858
0.9480
0.8916
0.8261
0.7597
0.6979
Conditional default rate
0.0009
0.0043
0.0087
0.0125
0.0150
0.0161
Conditional prepayment rate
0.0132
0.0341
0.0508
0.0610
0.0654
0.0652
..
3.5021 E-04
Mid-year payers rate of annual premium
0.9929
0.9669
0.9198
0.8589
0.7929
0.7288
...
4.5306E-01
30-year CUM. DEF. RATE
0
30-year CUM. PREPAY. RATE
0
4.5054E-05
Convert UDI payments to Pesos
Commitments (loan amount, face value)
Upfront premiums received (+)
Annual premiums (+)
14880
0
103.423
Other inflows (+)
0.0000
SHF's initial claim payment (-)
5.4386
103.816 101.918 97.7487
12.8584
25.572 50.7857 70.5009 80.2476
80.7166
·.
2.6318 12.3509 24.3395
33.6575
·*
Refund to SHF (+)
Other outflows (-)
92.2334 86.5489
0.0323
-0.0917
0
Note: The same cash flow components were analyzed for Peso loans. Then the cumulative cash flows were obtained by
aggregating both UDI and Peso loan types.
- 141 -
- 142 -
Chapter 6 Potential Economic Problems Resulting from the
Establishment of Public MI
I.
Potential Economic Problems
All housing subsidies distort markets to some degree. Because of this, subsidy programs,
particularly in developing countries and emerging economies, should be designed
thoughtfully to avoid the unhealthy development of housing finance, housing
development, and land markets (Hoek-Smit and Diamond, 2003). Public MI, usually
considered as a supply-side government intervention, can create new market
inefficiencies, undermining the benefits it brings through increased homeownership and
credit enhancement for the secondary mortgage markets. Essentially these new
inefficiencies result from the change of behaviors of the participating lenders and/or
borrowers, and the role of the private sector in the presence of a public MI system. The
major potential economic problems include: adverse selection and moral hazard by
lenders, non-optimal risk-taking by borrowers and sub-optimal risk allocation in the
society as a whole, and the constraint of participation and downward expansion of the
private MI sector.
A.
Adverse Selection
Adverse selection occurs when the insurer bases its premiums on the average risk of a
sample of participants in which the higher-risk participants are under-represented. This
results in a higher average risk level than what was estimated by the insurer. Adverse
selection is the result of asymmetric information: the insured (lender) in the insurance
contract has information that is not available to the insurer. This situation occurs mostly
in voluntary insurance programs that have large pools of potential customers with
differing levels of risk (Follain and Szymanoski, 1995). Mortgage insurance is
susceptible to adverse selection, especially the public MI backed by the government and
targeting lower income households. For example, in the U.S., some mortgage lenders will
keep the high LTV loans with good borrower's credit rating (hence lower risks) in their
- 143 -
own portfolio by offering the borrower an 80-10-10 loan4 3 (a form of lender's self-
insurance), while requiring those low-credit quality borrowers to obtain private MI or
FHA insurance. Insurers in life and health insurance markets deal with this problem by
gathering information about those being insured in order to reduce the information gap.
Similarly, private mortgage insurers also gather a lot of information through the
insurance underwriting process in order to learn about loan applicants and to rate more
accurately the risk of the loan. For public MI, the situation is different from private
companies. In developed countries, public MI often facilitates higher-risk (lower-income
or lower-credit quality) populations to become homeowners. Without a public MI, those
borrowers are rationed out of the housing finance market. In emerging markets, there are
usually no private MI companies and major lenders only serve the upper end of the
market (high-income populations), leaving a large proportion of the population without
formal financing approaches. Public MI is used as a tool to provide access to formal
financing for the majority of the households. Therefore, for both developed and
developing countries, if a public MI program understands its expected insurance
population and bases its insurance premium on the average risk of this particular group of
borrowers instead of on the average risk of the whole spectrum of mortgage borrowers,
the problem of adverse selection will not impose unexpected threats.
B.
Moral Hazard
In the context of mortgage insurance, moral hazard arises if the behavior of the insured is
riskier with insurance than without it. Critics argue that the knowledge that mortgage
insurers' coverage of losses will be made available in the event of loan defaults makes
defaults more likely to occur. Follain and Szymanoski (1995) argue that moral hazard
problems can result from the provision of MI in one of two ways. The first involves the
behavior of the borrower. The borrower pays the cost of MI, either as an explicit
mortgage insurance premium or as an implicit amount embedded in the interest rate of
the mortgage. Once the loan is made, the default insurance has the effect of giving the
borrower a "put option" with which he or she may force a "sale" of the property to the
43 The "80-10-10"
loan means that the borrower obtains a mortgage for 80% of the purchase price (LTV of
80%), puts 10% down and borrows the remaining 10%. That second loan is called a "piggy back loan."
- 144 -
lender at a price equal to the unpaid balance of the mortgage. In situations in which the
borrower has negative equity in the house, it creates moral hazard incentives. The second
type of moral hazard involves the behavior of financial intermediaries, such as insured
lenders. With MI coverage, lenders may become less responsible when underwriting
financially sound loans as they can earn substantial upfront fees from mortgage
originations. Also, they may not monitor the performance of the insured loans as closely
as they would have without the MI. Public MI programs with 100 percent coverage are
more prone to experiencing moral hazard problems than their private counterparts that
usually only provide partial coverage of losses. However, some public MI programs have
the right of recourse to defaulted borrowers (like the WEW in the Netherlands), which
decreases the risk of moral hazard substantially.
C.
Non-optimal Risk Taking and Risk Allocation
Although public MI enables relatively low-income people to enter homeownership
earlier, in particular to overcome the barrier of downpayments, a balance needs to be
struck between the positive externalities from increased homeownership and the negative
effects of expanding the envelope too far. Some criteria must not be compromised (such
as a minimum income-to-payment ratio), in order to maintain a sustainable
homeownership in the long term for the low-income households. Public MI should not be
considered a subsidy. Therefore it should help households with the timing of entering
homeownership (earlier than later), but not the underlying ability of affording monthly
mortgage payments, which may be addressed with other types of housing subsidies.
Entering homeownership and affording it are two different things and thus need to be
distinguished. Otherwise a public MI system may unintendedly encourage some
borrowers to consume much more housing than they can reasonably afford and therefore
take more housing risk, which in turn can result in a sub-optimal risk allocation for the
society as a whole. For example, within the U.S. FHA program, high LTV borrowers (9597%) have much higher default rates than relatively low LTV borrowers (80-95%). On
the one hand, this fact may simply exhibit people's rational behavior of exercising their
default options, as high LTV borrowers have less equity in the house than low LTV
borrowers. When housing prices fluctuate (decline), high LTV borrowers' option
- 145 -
becomes "in-the-money" faster than that of low LTV borrowers. On the other hand, it
indicates the financial vulnerability of high LTV borrowers and raises the question of
whether their risk-taking level in housing consumption is optimal for themselves and for
the society as a whole. Unnecessarily high levels of default and foreclosure create
deadweight losses to the society due to legal fees and the costly foreclosure process,
impairment to borrowers' credit history, and the destabilizing impacts on certain low-
income communities with concentrated public MI-insured properties. These deadweight
losses should be carefully weighed against the potential social benefits from increased
homeownership and better credit enhancement in the marketplace, in order to see whether
public MI, as a housing policy, is cost-efficient.
Constraining Participation and Development of the Private Sector
D.
The presence of a public MI entity may create distortion in the marketplace by
influencing the participation and development of private housing finance players and
limiting the downward expansion of the private sector. In the U.S., the FHA avoids
crowding out private sector MI providers by setting its loan ceiling for single-family
mortgages to 261,609 dollars in high-cost areas and 144,336 dollars in lower-cost areas in
2003. But there is still considerable overlap between the FHA and private MI providers in
the lower end of the mortgage market. However, this does not seem to adversely impact
the private MIs as their market share has been increasing over the recent years.4 4 In the
Netherlands, the WEW is the sole MI provider. Its loan ceiling of 230,000 euro in 2003
44 The
market shares of FHA, VA, and Private MIs are as follows:
a. Number of Insured Mortgage Originations in the U.S., 1998-2002
VA44
Private
Private MI as a % insured
2002
2001
2000
1,246,561
1,062,867
783,990
FHA
328,502
281,510
186,695
2,305,709
2,035,546
1,236,214
59.41%
60.22%
56.02%
1999
1,138,086
441,642
1,455,354
47.95%
1998
1,110,530
384,601
1,473,344
49.63%
b. Dollar Volume of Insured Mortgage Originations ($ in millions), 1998-2002
2002
2001
2000
FHA
VA
Private
Private MI as a % insured
145,053
131,240
78,669
41,945
35,443
22,208
337,053
282,506
163,136
64.32%
62.89%
61.79%
1999
108,106
49,580
188,871
54.50%
1998
103,165
42,584
187,437
56.26%
Source: MICA Fact Book 2003-2004
- 146 -
covers about 50 percent of the mortgage market. The absence of private MIs may reflect
the lack of demand for mortgage insurance, as homeownership has not been a favored
choice for lower income families and the credit enhancement for MBS is currently done
through structured finance. It may also reflect the fact that lenders prefer to self-insure
mortgage loans implicitly because of the historically satisfactory loan performance (low
default rates) and therefore the perception of low risks in insuring them. In addition, the
very low insurance premium charged by the WEW is likely to make the private sector
unviable in the lower-end of the market. The WEW is pushing to increase its loan limit,
which would further expand its market reach. In Mexico, currently the SHF's MI loan
ceiling accommodates about 75 percent of all the mortgages. No private MIs ever existed.
In contrast to the Dutch situation, the SHF is actively creating the necessary environment
for the private sector to enter, with the vision of ultimately becoming a reinsurer for
private MIs.
E.
Abuse of Public MI for Political Reasons
The biggest potential problem of public MI is the abuse of the program to suit changing
political agendas. If the risk management or financial soundness of public MI is
compromised, the hidden costs of the program can impose huge inefficiency on the
housing finance system as a whole and create unnecessary market fluctuations. A
classical example is during the 1950s and 1960s, when the FHA program took on a wide
range of narrowly targeted programs to support piecemeal housing policies generated by
a hyperactive political process. When urban blight became an issue and urban renewal
was seen as the answer, the FHA was to facilitate both relocation of residents and
resettlement of new residents. Consequently, special programs for cooperatives,
condominiums, nursing homes, and home improvement were added to FHA's portfolio
(Pennington-Cross and Yezer, 2000). The short-term fiscal soundness of the FHA's
insurance fund led to the perception that it could support virtually any aspect of housing
policy. That situation did not last long, and the large number of economically unsound
mortgages insured and resulting losses caused a quick retreat in high-risk insurance
activities in the 1970s. The FHA was criticized as producing negative as well as positive
neighborhood externalities. The lesson to be learned is that the clear objectives and
- 147 -
principles of public MI, especially its financial independence, have to be maintained at all
times.
II.
Addressing Potential Economic Problems - Case Studies
The three public MI programs considered have addressed some of the potential economic
issues while perhaps remaining susceptible to other problems. A comparison of the three
models in their institutional structure and operational strategies provides valuable insights
and recommendations for other countries.
A.
The U.S. FHA Program
MaintainFHA's financialsoundnessundervariouspoliticalenvironments
The U.S. FHA program lies within the Department of Housing and Urban Development
(HUD) (Figure 6.1). Because it is directly operated by HUD, FHA has sacrificed some
operational efficiency. The FHA budget must be approved by Congress each year;
therefore appropriations, policy changes and new product development requires
approvals that would not be required by the private sector.
Figure 6.1 The Institutional Structure of the FHA
As part of a government agency, the FHA is inevitably influenced by the changing
housing policies as previously discussed. Its insurance funds (capital reserves) fluctuated
greatly because of the shifts of political emphases in housing policy throughout the 1950s
to the 1980s. Worsened by the 1980s' recession, the capitalization of the FHA insurance
fund caused serious concerns and led the U.S. Congress to pass two landmark pieces of
legislation in 1990: the National Affordable Housing Act (NAHA) and the Federal Credit
- 148-
Reform Act (FCRA). These two laws have a significant impact on how the FHA
measures its books of business and maintains its financial soundness.
Before the Credit Reform Act, the FHA recorded its business activities on a cash-flow
basis and was reflected in the U.S. Government Budget in its cash-based, current-year net
income. After Credit Reform, the FHA is required to project the lifetime net present value
(NPV) of each year's new book of business (cohort), prior to insuring any loan in a given
fiscal year. Currently, only the FHA's multifamily insurance business may have cohorts
with projected negative NPVs (meaning the government subsidies the insurance beyond
the premiums paid by borrowers). All basic single-family insurance cohorts, excluding
the special programs' higher risk cohorts, must be self-supporting and generate positive
NPVs (creating an inflow of funds to the government). When positive NPVs are
expected, the additional premiums are transferred by the FHA to the U.S. Treasury as the
FHA reserves. In the case of projected negative NPVs, the funds are transferred from the
U.S. Treasury to the FHA. Also, before Credit Reform there was no limit on how much
the FHA could borrow from the Treasury. If the FHA did not have enough money to pay
claims, the Treasury would cover the losses because the FHA program was backed by the
full faith and credit of the U.S. government. The Credit Reform Act established the rule
of "pay as you go," which means that the FHA loan guarantees must be fully funded at
the time they are endorsed. Now, the bulk of the FHA's business - the single-family
mortgage insurance - must be self-supporting. If the FHA was to run out of money to
pay claims, Congress will need to appropriate the money and then give FHA the authority
to borrow it from the Treasury.
The National Affordable Housing Act (NAHA) set the capital requirements for the
FHA's MMI fund, and requires it to maintain a capital ratio of at least 2 percent.45 This
capital ratio is expected to be sufficient to withstand unexpected losses without exposing
45 This
capital ratio is calculated as: Economic net worth of the MMI Fund
Unamortized insurance-in-force
"Economic net worth" means the current cash available to the MMI Fund, plus the net present value of all
future cash inflows and outflows expected to result from the outstanding mortgages in the MMI Fund.
- 149 -
the taxpayers to financial risk. For each fiscal year, an independent audit and actuarial
review is mandated by Congress to check the MMI fund's soundness.
This legislation aims to avoid the abuse of the government MI products because of
changes in political environment. It has been effective since the 1990s to keep the FHA's
capital reserves at a comfortable level.46 However, it is not flawless. The projection of
NPVs over the lifetime of a cohort at its endorsement often proves far off the actual
performance and therefore adjustments to the projection and the resulting budget /
appropriation have to be made annually. Transfers between the Treasury and the FHA
add a lot of operational complication and inefficiency to the system.
Controladverseselectionand moral hazard
One of the most distinctive features of the U.S. secondary mortgage markets is the virtual
mandate by GSEs that all home loans over 80 percent LTV be insured. This mandate
serves to restrain adverse selection of risk by lenders and assure a large and growing
volume of new insurance. Then there is the choice between private MI providers and the
FHA. Since the FHA targets low-incomers and minority households, and these
populations are usually shunned by the private MIs for their high risks, adverse selection
does not appear to be an unexpected problem. FHA's insurance premium pricing is
slightly higher than that of a private MI company. But this will not result in "even worse
adverse selection" as argued in the literature47 because many of the targeted borrowers
have no access to mortgage loans without the public MI. There is simply no other choice
for them. However, in good economics times, some high-LTV borrowers with slightly
better credit quality may be able to get private MI-insured or lender self-insured loans,
which could hurt the FHA's performance. As analyzed earlier in Chapter 5, if all
borrowers with LTV lower than 95 percent leave the FHA, the program's single cohort's
profitability rate will decrease by 11%.
46
At the end of FY2003, the MMI Fund's capital ratio was estimated at 5.21 percent, well above the 2
percent minimum requirement.
47 Literature on adverse selection argues that the insurer may not be able to correct the adverse selection
problem by merely raising premiums for everyone, because the higher premiums may cause lower-risk
individuals to leave for other choices, leaving only the higher-risk individuals in its portfolio and further
raising the average risk of the participating group.
- 150-
Moral hazard proves to be a real concern. It can manifest itself in several ways such as in
the increase in cohort default rates or the decrease in recovery rates of the defaulted
properties due to the lack of responsibility of the insured lenders and/or borrowers. The
impact is substantial. For instance, if the FHA's 2003 cohort's ultimate default rate
increases by 20%, from 9.2% (the estimated mean default rate) to 11%, the NPV of the
insurance contracts will decease by 48.5%, or by 207 million dollars on the 50 billion
dollars cohort face value. If the recovery rate of defaulted properties decreases by 20%,
from currently assumed 68% to 54.4%, the cohort's insurance NPV will decrease by
130%, resulting in losses of 121 million dollars.
Moral hazard is managed by the FHA through a variety of techniques and operational
strategies. The first method of preventing moral hazard is the screening and approval
process of the participating lenders prior to their involvement in the insurance program.
There are approximately 12,000 approved lenders (mortgagees) for the FHA programs.
The approval depends mainly on the mortgagee's financial strength and past
performance. A mortgagee must have had an acceptable claim and default record for at
least 2 years prior to its application for participation in the FHA Lender Insurance
Program.4 8 When a lender applies for FHA, he needs to submit his financial documents,
photos of facilities, and some fees. On-site inspections are conducted at selected lenders'
shops to verify their financial strength.
The second strategy to reduce moral hazard is post approval lender monitoring,
inspection, and recertification. The FHA set up an institutional and informational
structure to hold lenders responsible even though the full coverage of losses is available
through its MI provision when borrowers default. FHA monitors lenders through its
office of lender activities and program compliance in the HUD headquarters, 4
homeownership centers (HOCs), and about 140 monitors stationed in the field throughout
The acceptable claim and default record is determined by HUD. Right now they are as follows: (1) A
mortgagee is eligible for the Lender Insurance program if its claim and default rate is at or below 150
percent of the national average rate for all insured mortgages. (2) A mortgagee that operates in a single
State may choose to have its claim and default rate compared with the average rate in the State in which it
operates, in which case the Single State mortgagee is eligible for the lender Insurance program if its claim
and default rate is at or below 150 percent of the Sate average rate for insured mortgages.
48
151 -
the United Sates. Lenders are evaluated every year to decide whether their participation
in FHA insurance programs will be renewed. Inadequate performance or reserves can be
grounds for revocation of delegated underwriting arrangements, thereby providing an
incentive for lenders to perform. The lender approval and recertification division checks
and approves 2000-3000 mortgagees a year on average, and terminates unqualified
mortgagees at about the same scale. The quality assurance division, together with 4
HOCs, monitors lenders for program compliance. Every HOC targets its own lenders
according to risk-based criteria, while the division at the headquarter sets policies and
oversees the overall performance.
The headquarter staff monitor lenders in two ways: on-site monitoring and a computer
system called "Neighborhood Credit Watch." The decision of whom to pick for on-site
monitoring is based on many risk factors related to a lender, including: defaults, claims,
underwriting review, higher-risk programs, internal referral, external referral, fluctuations
of the volume of business, late MI premiums, etc. This division usually does about 10
percent on-site monitoring of all the approved mortgagees per year. In addition to on-site
monitoring, this division also uses two computerized monitoring systems: Neighborhood
Credit Watch and Third-party Appraiser Watch. The Neighborhood Credit Watch System
helps identify poorly performing lenders. It was initiated in 1999 and now covers about
30,000 branches. It serves to warn marginally performing lenders to improve their
performance if they wish to maintain their status as approved FHA lenders, helps
screening out problematic mortgagees for on-site review, and refers the really bad
performers to the mortgagee review board, who then decide on civil monetary penalties,
status terminations, or other punishments. The Appraiser Watch Monitoring System is
used to identify appraisers who either knowingly or unintentionally put homeowners at
risk for losing their homes to foreclosure because of inflated valuations. The Appraiser
Watch monitoring system relies on historical risk factors to help identify appraisers
whose work will be reviewed by FHA staff. Included among the risk factors are an
appraiser's association with mortgages of high default rates, or a high volume of
appraisals on higher risk mortgage programs such as loans for multi-unit properties. The
- 152-
use of technology and advanced information systems improves FHA's operational
efficiency and helps control the extent of moral hazard.
The third approach to controlling moral hazard is implementing mandatory loss
mitigation efforts among lenders, when borrowers become delinquent in their payments.
Without any enforcement of loss-reducing procedures, lenders may simply choose to
assign the delinquent loans to the FHA as defaulted ones and collect the full loss
coverage. This has two negative results: borrowers will lose their homes and the FHA
will suffer big losses due to the costly foreclosure - the prolonged foreclosure process,
accumulated claim amount, and servicing and maintenance after the property acquisition
by the FHA. In answer, the FHA launched the Loss Mitigation Program which requires
lenders to first go through the loss mitigation procedure before they can file insurance
claims. The procedure is composed of five types of strategies: special forbearance, loan
modification, pre-foreclosure sale, partial claims, and deed-in-lieu (Figure 6.2). Under
this program, lenders must provide more options to help some homeowners keep their
homes. When borrowers default on their FHA-insured mortgages, they can resolve a
default (90-day delinquency) in several ways short of foreclosure: by paying down the
delinquency (cure), by a pre-foreclosure sale with the FHA perhaps paying an insurance
claim in the amount of the shortfall, or by surrendering a deed in lieu of foreclosure.
Figure 6.2 The Loss Mitigation Procedure Required by the FHA
Source: Office of Evaluation, U.S. Department of Housing and Urban Development (HUD)
If after all the loss mitigation alternatives have been exhausted and a defaulted loan still
cannot be cured, it then goes into foreclosure. On average the FHA single-family loan
153 -
foreclosure process takes about 1.3 months. After the foreclosure is completed, the lender
files a claim to the FHA and also files a deed to the Secretary of HUD. Then FHA pays
out the claim, takes possession of the property, and has its Management and Marketing
(M&M) contractors mange and sell the property. The FHA's loss mitigation program has
effectively lowered the foreclosure rate among all 90-day delinquencies from 77 percent
in 1998 to around 50 percent in 2003.
Manage other "non-transparent"risks
As a public program with social objectives, the FHA is exposed to some "non-
transparent" risks, which to some extent create market distortion within the low-income
segment of the housing market. For example, although FHA allows sellers to pay certain
costs on behalf of borrowers, the practice entails risks that sellers may inflate the sales
price of the home, which is why FHA limits the amounts of these concessions. With
regard to its minimum borrower downpayment requirements, the FHA does not allow
seller gifts, although it does permit relatives, local governments, and non-profit
organizations to provide downpayment assistance as a means of assisting first time
buyers attain the benefits of homeownership. In recent years, certain non-profit
organizations have instituted downpayment assistance programs for borrowers funded
directly by donations received from property sellers. This practice has had the effect of
circumventing the FHA's policy against seller downpayment assistance, and becomes a
new potential risk factor that could raise FHA defaults and claims to higher levels than
predicted. It influences the FHA's risk profile and reinforces both the adverse selection
and moral hazard problems. The FHA is stepping up its effort to discern the involvement
of non-profit organizations in downpayment assistance with the help of its advanced loan
evaluation system, the Total Scorecard. This system is embedded in automated
underwriting systems used by lenders. It evaluates mortgage applications and predicts the
likelihood of borrower default in an objective and consistent manner. If loan applications
obtain a score above certain threshold, they can pass the system for automatic
underwriting. Otherwise, the applications will get "referred decision" because of their
low scores. The lender must manually underwrite these loans to ensure that the applicant
receives maximum consideration.
- 154-
B.
The Dutch WEW Program
Relative independence of the WEW
The WEW is a private, non-profit organization, supervised and backed by the central
government and local municipalities in the Netherlands. It has a relatively simple and
straightforward institutional structure (Figure 6.3).
Figure 6.3 The Institutional Structure of the WEW
Supervised by Ministry of
Housing, Spatial Planning
and the Environment and the
Association of Netherlands
Municipalities
Being a private entity helps maintain the WEW's financial independence and makes it
less prone to political pressure. Moreover, the WEW has been focused on only two types
of products since its inception in 1995: guarantees for owner-occupied single-family
mortgages and for home improvement loans.49 Unlike the FHA's experience, no other
miscellaneous or special standalone housing programs are imposed on the WEW, mainly
because the WEW's current policy priority is to expand homeownership across both low
and middle income segments. For instance, the policy could serve as an instrument to
encourage relatively higher-income tenants in the social rental sector to move to the
purchase sector (Boelhouwer and Neuteboom, 2003).50The situation is determined by the
history of Dutch housing policies and its current housing market composition with a
significant portion of the housing stock in the social rental sector. The WEW is
facilitating the transition toward market-oriented housing supply and demand.
49 Guarantees on home improvement
50 Until
loans started later, in January 1999.
the 1990s, there was a marked difference in rental and housing-for-purchase
markets. In the 1990s,
the important distinction was no longer drawn between rental and purchase, but rather between subsidized
and unsubsidized housing and the extent to which the government should provide financial support
(Boelhouwer and Neuteboom, 2003).
- 155-
The supervision and monitoring of WEW's performance and financial soundness are
conducted by a consortium of two public entities, the Ministry of Housing, Spatial
Planning and the Environment and the Association of Netherlands Municipalities.
There is a long tradition of collaboration and compromise among different political
parties in the Netherlands. Plus, the central government and local governments share the
backstop responsibility of supporting WEW's guarantees 50-50. The risk sharing aligns
the interests between the two in supervising the performance of the WEW.
Managepotentialadverseselectionand moral hazard
Unlike in the U.S., there is no requirement that high LTV mortgage loans must carry
mortgage insurance in the Netherlands. Therefore, lenders have the option between self-
insurance and the WEW guarantee, which may promote "cherry-picking," or adverse
selection, among loans. They might be inclined to self-insure good credit-quality loans
but require the weaker borrowers to obtain a WEW guarantee. To some extent, this is
shown in the distribution of WEW-guaranteed loans vis-a-vis the sale price distribution
of owner-occupied houses within the WEW-guaraneeable range in 2003 (Figure 6.4). The
guaranteed loans were concentrated in the lower part of the WEW-guaranteeable range,
while the dwelling's sale prices nationwide
s
within the WEW's loan limit were
comparatively more concentrated in the upper part of that range. For instance, mortgage
loans below 140,000 euros accounted for about 39% of the total WEW-guaranteed loan
amount in 2003, while the general loan distribution below 140,000 euros was about
29.7%. On the other hand, WEW-guaranteed loans over 180,000 euros (but below its
ceiling of 230,000 euros) represented 32% of its total guaranteed loans while this figure
was about 40% within the general, WEW-guaranteeable
loans. The over-representation of
smaller-sized loans and the under-representation of relatively larger-sized ones within
WEW's guaranteeable range indicate the possible existence of lenders' self-insurance:
purchasers of more expensive houses (hence larger loan sizes) are usually relatively
higher-incomers, considered in good financial standing and less risky. They are more
likely to be self-insured by lenders and less often appear in the WEW-guaranteed
A dwelling's sale price is an approximation of the mortgage loan size because of the commonly high
LTV mortgages in the Netherlands.
51
- 156-
portfolio, while the less expensive houses (hence smaller-sized loans) are more likely to
be purchased by lower-income people and insured by the WEW. However, a caveat is
that it is hard to judge the extent of potential adverse selection based on only one year's
data. The more recent data from Kadaster (the database recording transactions of owneroccupied houses in the Netherlands) and WEW show that the price distribution of houses
sold had similar patterns to the corresponding price categories in the WEW guaranteed
loans. For example, around 58% of all WEW guarantees were granted in the price range
above 155,000 euros for the first quarter of 2005, while the percentage of owneroccupied houses sold in that range accounted for about 60% in the Kadaster database for
the year 2004.52 So there was almost no over-representation
of the WEW-guaranteed
loans in the lower half of its guaranteeable universe, compared to the general mortgage
markets in the Netherlands. Another argument is that the WEW guarantees are more
concentrated in the four largest cities in the Netherlands, where there is overrepresentation of lower priced houses. Therefore the mismatch between patterns of house
sales prices nationwide and WEW -guaranteed loan sizes may merely reflect the
differences in the geographic distribution rather than the presence of adverse selection by
lenders. More time-series data are needed to explore the existence and extent of the
potential adverse selection.
Figure 6.4 The Distribution of WEW -Guaranteed Loans and Owner-Occupied
Dwellings' Prices within WEW's Loan Limit, 2003
Owner-occupied
within
Dwellings
the WEW's
by Sales Price,
Loan
Loan Limit (2003)
200,000-220,000
4%
180,000-200,000
7%
'"
18.3
140,000-160,000
E
::I
0
c:
(tJ
0%
5%
10%
142,941-154,285
17%
100,000-120,000
11%
0
...J
7%
up to 80,000
15%
.1110/_
0%
20%
Source: WEW, aTB
52
10%
10%
10%
~%
167,899-180,000
7.6%
up \0 100,000
0
8%
w
E
.1%
100,000-120,000
by WEW (2003)
4
«
1~ 0%
120,000-140,000
Guaranteed
'0 190,588-200,000
:;
~
11.9%
160,000-180,000
Ranges
These figures are provided by the WEW.
- 157 -
5%
10%
15%
20%
The WEW controls adverse selection by requiring lenders to check borrowers' credit
worthiness before endorsing guarantees. The lender must request a statement from the
Central Credit Registration Office for every applicant. In order to be eligible for the
WEW guarantee, the income standards drawn up in co-operation with the Consumer
Credit Counseling Service (NIBUD) also have to be met. The borrower can only
participate in the WEW guarantee if he meets the income requirements adopted by the
NIBUD. 53 Many recent immigrants who have not yet established their credit history, and
other low-incomers who have impaired credit quality are excluded from the WEW
guarantee provision.
Moral hazard has not been a serious problem so far for the WEW, as the portfolio default
rates have been very low (less than 1 percent). When a lender files claims to the WEW, it
will examine the loan documents processed by the lender. If the lender did not issue the
guarantee according to WEW's underwriting requirements, the WEW will ask for
reimbursement from the lender for the claim amount paid out. This acts as an incentive
for lenders to maintain good faith. Currently, the WEW is contemplating the launch of a
new program called "home expense program," in order to promote "sustainable
homeownership." This program may increase moral hazard occurrences of both lenders
and borrowers. Similar to the payment insurance program in some other countries like
Australia and the UK, the program brings additional safeguards to the borrower. It works
as follows: currently if a borrower cannot make his mortgage payment for nine months,
his house will be sold forcibly, either before a foreclosure or in an auction. Under the new
program, this period is extended to two years before any forced sale. Lenders will work
out a payment reduction plan with the borrower in this two-year period - an extra loan
will be granted from the lender to the troubled borrower. The WEW provides guarantee
to the new extra loan without charging any extra guarantee fees. The rationale for this
program is the belief that most borrowers cannot make their payments because of
economic reasons (e.g. unemployment). Giving them more time will help them recover
from the bad situation and resume payments again later, therefore avoiding the costly
-
There is only one income requirement: the home expenses-to-income ratio. The maximum loan amount
that can be accepted as safe and sound is about 4 - 4.5 times the annual household income.
53
- 158
foreclosure. However, the added risk of this program is substantial. It may cause both
borrowers and lenders not to take the first 9-month delinquency seriously and result in
bigger losses to the WEW later. It will take some time to learn whether its benefits
outweigh its costs.
Full recourseand problemsof risk allocation
In the Netherlands, unlike in many other EU jurisdictions, there is full recourse against
borrowers who have defaulted on a mortgage. During foreclosure, one method of
recourse is through a borrower's earnings. Under the Civil Code and Code of Civil
Procedures, a lender in the Netherlands is allowed to apply for an attachment to the
borrower's income at any stage during the foreclosure process. This allows the lender
legal recourse to a proportion of a borrower's salary and other income decided by the
judge at court. Lenders' right of full recourse to borrowers' non-housing assets and
income can result in non-optimal risk allocation of the society as a whole. With 100
percent guarantee coverage and very low premiums, households may obtain a bigger
mortgage (higher LTV) than they would under a partial guarantee program or higher
insurance premium. Therefore, they have a larger risk exposure to the housing market
volatility, and are more likely to default during economic downturns. With full recourse,
the losses from default are ultimately borne by the borrower. That is, individual
households actually face the risks of the whole housing market fluctuation. This raises the
question of whether such a public MI system allocates housing market risks efficiently. A
nationwide public MI enterprise should have a much better risk diversification capacity
than individual households, and can cross subsidize borrowers of different LTV classes
and loan cohorts. Therefore, by concentrating risks on individual borrowers, the WEW
system with strong recourse is likely to create sub-optimal risk allocation. This
inefficiency, however, is mitigated by the WEW's policy to forgo the right of recourse to
those defaulted borrowers who are believed in "good faith." That is, if in the opinion of
the WEW, the forced sale cannot be attributed to the borrower and the borrower has
assisted in limiting the residual debt as far as possible, the WEW will waive the residual
debt. However, the majority of defaulted households carry their debt forward. In 2002,
- 159-
only 30 percent of the defaulted households were remitted from the debt. In 2003, the
figure was about 17 percent.
On the other hand, an argument may be made that borrowers will take the potential
recourse into account and consider the shadow price of the mortgage insurance premium
accordingly. However, there is the concern of information efficiency. Households may
not be able to project properly the shadow price of the mortgage insurance with recourse,
due to their lack of information on the national or regional housing market conditions.
Individual households' mispricing of the insurance premium with the recourse clause can
result in negative externalities (such as general over-exposure to the housing market
risks) that will not be internalized by individual households, but to the society as a whole.
Again, a nationwide organization like WEW should have better market information and a
more holistic picture than individual households.
The risk allocation inefficiency because of recourse to borrowers only becomes
prominent during economic downturns when defaults increase, which has not happened
since the WEW's establishment. The process of recourse is time-consuming and staffextensive. As a lean organization, the WEW has to resort to the lender (usually a bank) to
follow up with defaulted borrowers and manage the recourse process. Banks may not
have enough incentives to do the job well while the WEW does not have the expertise or
staff to handle recourse by itself. As a result, in many cases the WEW gave up the
recourse to borrowers after a short period because the recoveries may not even cover the
operation costs of tracing these borrowers. In 2003, the WEW recovered 27,110 euros
from recourse, which accounted for only about 5 percent of the total claim amount paid
out. Therefore it is safe to say that so far the inefficiency in risk allocation due to recourse
is negligible in the Netherlands. Nonetheless, policymakers ought to be aware of its
potential impacts should the economy worsen and defaults increase.
Privatesectordevelopment
There is no private MI sector in the Netherlands, mainly because of the two reasons
discussed earlier. First, there is no regulatory requirement for high LTV mortgage loans
160 -
to carry MI, unlike the situation in the U.S. As a result, lenders have the incentive and the
option to sel'-insure loans in the higher-end of the market where borrowers' income and
credit quality are relatively high. Second, in the lower end of the mortgage market, the
WEW has the funding advantages of enjoying the backing of the full faith and credit of
the Dutch governments and also being free from earning profits. Therefore, the WEW's
insurance premium can afford to be so low that private MIs cannot compete in pricing.
Even though it is not as well capitalized as the U.S. FHA in terms of capital ratio, defined
as the ratio of capital reserves over the total insurance-in-force,
54
the WEW guaranteed,
securitized mortgage portfolios enjoy triple-A rating from major rating agencies.
Some discussions are raised as to whether the WEW should be completely privatized,
without government backing. The fundamental question is whether the functions
provided by the WEW right now can be accomplished by the private sector at a similar or
lower premium rates, stably and sustainably. The private insurers can add more efficiency
and risk diversification to the market, as most major private MI companies are
internationally diversified. The disadvantage of a complete privatization is the loss of the
WEW's functions in stabilizing the national economy and the low premium rates because
of its non-profit status. There are risks that the private MI sector will not be able or
willing to take on lower credit quality borrowers (e.g., immigrants). But these risks are
not currently dealt with by the WEW either.
There is potential market space for private mortgage insurers in the Netherlands, because
the WEW has a loan limit which disqualifies guarantees to about 50 percent of the
mortgages in the Netherlands. Currently WEW's market penetration rate is about 29%,
covering the lower-end of the market. The private MI sector can enter the higher end of
the mortgage market, a potential size of 50%-70% of the entire market, and further
expand mortgage credit provision. In the future, lenders may see the benefits of
unloading mortgage default risks to private mortgage insurers who can manage them
more internationally and efficiently, especially considering the increasing integration of
54
In FY2003, the capital ratio of the FHA's MMI Fund was 5.21%, while the capital ratio of the WEW was
0.58%.
- 161 -
the European Union (EU). These trends might change the long-standing Dutch culture of
honoring one's debt and timely payments, and lead to higher default rates as those exhibit
in other ED countries (Figure 6.5).
Figure 6.5 The Average Mortgage Delinquency Rates in EU Countries, 1994-2001
Average
Mortgage
Delinquency
Countries,
Rates in EU
1994-2001
Netherlands
Austria
UK
Luxembourg
Denmark
France
Portugal
5.00
5. Yo
.8%
6.0%
Belgium
Italy
Ireland
Spain
10.5%
Finland
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
Source: Diaz-Serrano, 2002; the European Community Household Panel (ECHP).
c.
The Mexican SHF Program
Institutional structure to keev MI's indevendence
The SHF was chartered in 2001 as a federal development bank and is currently owned by
the Mexican federal government. Compared to its predecessor, FOVI, which was a trust
fund within the Central Bank receiving subsidized funding from the Bank of Mexico,
SHF has much greater financial independence. It received one-time funding from
previous FOV!' s assets and some loans from the World Bank and the Inter-America
Development Bank at its establishment.55 From that point on, the SHF is responsible to
raise its own funding in the capital market by issuing bonds. SHF's guarantee products
enjoy the full backing of the government until 2013. After that, SHF is fully responsible
for its liabilities. The SHF is regulated by CONAFOVI - the National Housing
Commission in charge of developing housing policy and coordinating national housing
institutions (Figure 6.6).
55 In February 2002, the SHF was created and took $10 billion pesos from FOVI's. Four months after its
establishment, SHF received U.S.$500 million loan from the World Bank and U.S. $500 million from the
Inter-America Development Bank.
- 162 -
Figure 6.6 The Institutional Structure of SHF
..............................
International private
* MIs as insurers
Regulated by CONAFOVI
(National Housing Commission)
l
SHF
Mexican
government
*: SHF has stopped granting construction
loans and is gradually phasing out direct
loans to Sofoles for individual mortgages.
They are encouraged to raise money through
securitization.
The wide range of financial guarantee products currently offered by SHF raises potential
problems of liability overlap and conflict of interest among different programs. For
example, the UDI-minimum wage swap fund supports SHF's guarantees to cover
possible unexpected or permanent drops in real terms in the minimum wage, so allowing
borrowers to pay a mortgage stated in UDI in minimum wage terms. The fluctuations of
the gap between inflation and the minimum wage also impact the borrowers' default
behavior, therefore related to SHF's MI products. Currently these two guarantee products
are both offered by SHF. Although there is separation between its capital reserve funds
for the first loss guarantee and for the UDI swap program within the organization, there is
still potential risk of violating the rules internally in stressful circumstances, especially
given the fact that the federal government provides backup for both guarantees.
One solution to the problem is to establish permanent separation of the two programs by
putting them in different entities. SHF may be able to convert its first-loss MI program's
legal entity from within a development bank to an insurance company (or subsidiary of
the bank) and to seek adoption of new regulations specifically applicable to mortgage
default insurance. As the Mexico macro economy further stabilizes, SHF should phase
out the UDI swap program as rapidly as the market will permit. The high cost of this
product to the borrower, especially when superimposed upon the cost of the fist loss
mortgage insurance, reduces affordability and demand (Blood, 2004).
- 163 -
Controladverseselectionand moralhazard
Currently, commercial banks only lend to the top income percentile populations who do
not need credit enhancement to make good on their mortgages. The SHF channels most
of its funds to Sofoles to fund individual mortgage loans for middle- to low-income
households, covering 75 percent of all households in Mexico. The funding automatically
carries SHF's mortgage insurance. Therefore adverse selection by lenders, in this case the
Sofoles, does not exist. However, situations can change soon as the SHF is encouraging
Sofoles to obtain mortgage funding from the capital markets by securitization. SHF is
planning to reduce the direct funding dramatically since 2005, and will provide MI to
mortgage loans whose funding comes from sources other than SHF.
From the beginning of its operation, SHF emphasizes and requires individual loan-level
review as the centerpiece of its mortgage insurance underwriting program, in order to
establish its credibility and discipline in the marketplace. SHF plans to have "delegated
underwriters" when portfolio and customer risk experience justifies a prudent clientbased transition to a lower-cost, more rapid turnaround business model involving
delegated insurance approval authority, which will be accompanied by on-site follow-up
audits of insured loan files. SHF will establish its performance criteria required for
lenders to earn the elevated status of "delegated underwriter." Periodic renewal of this
status can act as an incentive to prevent moral hazard.
Another effective approach of controlling moral hazard is through risk sharing with the
insured. SHF's first-loss guarantee provides coverage of losses up to 25 percent of the
unpaid balance of the defaulted loan. Therefore lenders have incentives to monitor the
performance of their insured loans to prevent losses exceeding the coverage threshold.
SHF also keeps a close eye on its counter party risks and monitors Sofoles' financial
strength regularly. It uses a standardized model called "CAMEL" to examine the
financial health of each Sofol, by examining the balance sheet of the Sofoles on five
aspects: Capital, Asset, Management, Earnings, and Liquidity. The system rates the
factors and derives a composite score for each Sofol. SHF also requests each Sofol
- 164 -
submit its financial information to rating agencies and be ranked by them. Rating
agencies assign an investment grade to each Sofol every three months. In addition, SHF
developed a multi-variable regression model to assign weights to different variables that
are linked to the default rates. By conducting these monthly evaluations, SHF
accomplishes three goals: i) controlling the risk of moral hazard by constant monitoring
and inspection of lenders (Sofoles); ii) gathering data input about lenders into its default
projection models; and iii) increasing the credibility of its MI product by involving
internationally acknowledged rating agencies.
Private sector development
SHF acts as a catalyst to encourage and facilitate the development of the private MI
sector. It is in active talks with some North American MI companies to attract them to
enter the Mexican mortgage market. SHF is laying the informational, legal, and
regulatory infrastructure so that private participants will be ready to take on the credit
risks. SHF will remain in the market to provide MIs only if there are no private bodies
interested in doing this. It is likely to become the ultimate backing for private MI
providers in the future to maintain investors' confidence. This arrangement, very
different from the FHA and WEW's situations, minimizes the impact of public MI
provision on the private sector development and aims to fully privatize the MI sector if
feasible. In the short- and medium-run, this process can benefit the Mexican mortgage
market and improve its transparency and efficiency, because of the unique, historical
housing finance market distortions caused by the long-time dominance of public funds
and housing entities. In the long run, however, public MI should still remain in the
Mexican mortgage market to serve the very large lower-income, higher-risk segments for
which the private MI would be too expensive.
Risk allocation efficiency
The Mexican housing stock is severely under-leveraged as many households have no
access to formal housing finance. Only about 12.6% of the housing stock is currently
mortgaged (Merrill Lynch report, 2004). Lower income and self-employed persons and
those in the informal sector have almost 100 percent equity in their houses. It not only
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greatly limits the liquidity of the housing finance market, but also results in inefficient
risk allocation because those households bear all the housing market risks individually
without any cushion or risk pooling. SHF's MI products are expected to expand the
underwriting envelope and allow more people to leverage their savings in housing
consumption (through higher acceptable LTVs by lenders). This will distribute risks
among SHF, lenders (Sofoles or banks) and borrowers. The diversification capacity of the
SHF is much better than both lenders and individual borrowers, which should result in a
better risk allocation in the market as a whole. The key is to price correctly the risks SHF
is taking in its MI premiums, so that the federal government does not end up paying for
unexpectedly large liabilities.
III.
Comparison of the Three MI Programs' Potential Economic Problems
The analyses of the three public MI programs illustrate the major potential economic and
social problems that can result from the establishment of public MI (Table 6.1). Its
distorting effects on the housing finance market reflect the "indirect" costs of
implementing such a government intervention, in addition to the direct costs of managing
and operating it and the financial liabilities of backing it. Policymakers should understand
the scale of the total costs, direct as well as indirect, and weigh them against the potential
benefits to decide whether such a program is the most cost-efficient option, now and in
the future. A country's historical context in housing policy and the uniqueness of its
housing markets matter.
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Table 6.1 Potential Economic and Social Problems Resulting from Public MI
Adverse selection
FHA
WEW
SHF*
Moral hazard
Risk allocation
inefficiency
Medium
High
Medium
Constraint on
private sector
development
Low
All loans over 80%
LTV have to be
100% loss
coverage.
Unnecessarily
high level of
Private MI
sector is
Historically the
program was
insured if to be
Lenders become
defaults exists
gaining market
heavily
sold to the GSEs.
less responsible
among higher-
share and serve
impacted by
Program targets
lower-income,
higher-risk
populations.
in underwriting
and monitoring
insured loans.
risk borrowers.
majority of the
market.
various housing
policies.
Medium
No requirement for
carrying MI.
Guaranteed loans
concentrate on the
Low
Historical culture
of treating debt
and payments.
Default rates
Potentially High
The right of full
recourse to
defaulted
borrowers can
High
Its loan ceiling
covers 50
percent of the
market, with
Low
A private
organization.
Relatively new
program with
lower end of the
have been low,
result in
very low
only two types
guaranteeable
range.
which may
change as EU
inefficient risk
allocation of the
premiums and
government
of MI products.
becomes more
society. It is
backing.
integrated. The
right of recourse
reduces moral
hazard.
mitigated by the
fact that some
borrowers are
exempt from the
recourse.
Low
All mortgage
funding from SHF
carries its
guarantee
Medium
Only 25% loss
coverage.
Banks do not
utilize SHF,
Low
SHF helps
increase the
overall risk
allocation
Low
SHF is laying
the foundation
for private MI
providers to
High
Historically
government
policies
dominate public
automatically.
In the future
Sofoles may obtain
while Sofoles are
completely
dependent on it
efficiency by
encouraging
more leverage in
come in to the
market. Private
sector
housing entities.
Many guarantee
products are
funding from other
sources and the
adverse selection
currently.
housing
consumption.
development is
strongly
encouraged.
currently
offered by SHF
and backed by
can be a concern.
Abuse of the
program for
political reason
High
the government.
*: SHF is undergoing rapid development and changes. For example, it will dramatically reduce its mortgage funding to
Sofoles if they start to raise capital through securitization, hopefully in 2005. This may change the extent of impact of
some problems above, such as adverse selection and moral hazard.
The three public MI programs have deployed various operational strategies to cope with
the actual and potential economic problems described above. Most efforts are focused on
controlling and reducing adverse selection and moral hazard (Table 6.2).
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Table 6.2 Operational Strategies to Control Adverse Selection and Moral Hazard
FHA
·
·
·
·
·
Screen and approve lenders prior to their participation in the insurance program.
Post-approval lender monitoring and inspection: on site and electronically.
Annual recertification of participating lenders.
Apply information systems and databases to monitor lender and appraiser performance.
Implement mandatory loss mitigation efforts when borrowers become delinquent.
WEW
·
·
Require lenders check borrowers' credit worthiness and make sure it meets certain criteria.
The right of full recourse to defaulted borrowers' other assets, as a disincentive to prevent
borrowers' moral hazard.
·
The right of WEW for reimbursement from lenders after the claim payout, if lenders did not
abide by its underwriting rules.
SHF
·
·
·
·
·
·
Automatically impose its insurance on all the funding distributed by SHF.
Risk sharing with insured lenders: MI only covers losses up to 25% of the unpaid loan
balance.
Individual loan-level review and underwriting by SHF.
Closely monitor Sofoles, its counter party, on its financial strength and management quality.
Require credit rating of Sofoles from internationally acknowledged rating agencies.
Apply information systems and databases to track the relationship between lenders evaluation
and default rates.
They share some common strategies that prove to be effective in practice and can be
emulated by other countries, such as active monitoring and recertification of lenders,
using information systems to track lenders performance and link it to loan default rates,
etc. Also, various forms of risk-sharing techniques are applied in each of the three models
depending on that country's characteristics and its mortgage market's uniqueness, such as
the mandatory loss mitigation process in FHA, the right of reimbursement from lenders in
case of unqualified underwriting and the right of full recourse to ill-intentioned borrowers
in WEW, and the first-loss partial coverage in SHF. Risk sharing creates incentives for
the insured party to remain responsible and diligent. Such mechanism should be
incorporated in the public MI design, especially in developing countries and emerging
economies. Loss mitigation has proven quite successful with the FHA program - not only
reducing monetary payouts of the FHA but also bringing positive social impacts by
helping borrowers keep their homes. In places where foreclosures are difficult or timeconsuming, a very common problem in many developing countries, loss mitigation
efforts can be particularly effective. In Mexico, the SHF encourages alternatives such as
restructuring the delinquent loan through Sofoles, or transferring the loan and the
property to another borrower, rather than going into the expensive and lengthy
foreclosure process.
- 168 -
The issues of constraining private sector development and potential abuse of public MI
for political reasons can be addressed in one of two ways: i) establishing sound
institutional framework to ensure the independence of the public MI enterprise and limit
unfair or unreasonable competition to private MI providers; or ii) eventually privatizing
the public MI when the market matures and the private sector is ready to take on the
risks, maybe with the government providing some type of catastrophic risk backup. The
U.S. FHA and the Dutch WEW chose the first approach; and the Mexican SHF clearly
adopted the second one. The choice depends on a country's housing policy history and
housing market context, housing finance market maturity, government credibility and the
availability of various legal and regulatory infrastructures. For developing countries and
emerging economies with perceived risks of corruption or bureaucracy of government
agencies, the staged privatization of public MI can be a better and more realistic approach
in the long run.
- 169 -
- 170-
Chapter 7 Conclusions And Policy Implications
I.
Research Purpose and Method
This research explores the feasibility and functioning of the public mortgage insurance
programs and the role played by the government. The significance of this work lies in
that it is the first comprehensive effort to provide an integrated analytical framework for
policymakers to evaluate the prerequisites, potential costs and benefits, and possible
consequences of employing public MI to advance housing finance markets. It proposes a
quantitative, adaptable modeling process to measure and project the potential liabilities
imposed on the government that backs the public MI. As one of many finance-linked
housing policy alternatives, public MI can be an effective catalyst to promote
homeownership affordability in primary mortgage markets and enhance credibility and
liquidity of secondary markets. However, a good fit between public MI and a nation's
housing markets and housing finance markets entails many factors, including economic,
financial, legal, political, regulatory, institutional, geographic, and even cultural. This
research comes at a time when a growing number of developing countries and emerging
economies have expressed strong interest in establishing a public MI program, therefore
offering important practical worth.
The research uses a multi-case analysis approach and conducts an international
comparison among three representative public MI programs: the U.S. FHA, the Dutch
WEW, and the Mexican SHF. They are exemplars of the three partitions of the existing
public MI programs worldwide. They represent three models at the different stages of MI
evolvement, within different housing policy context and housing market situations.
The main research methodologies include case studies and interviews, quantitative
simulation models (the Monte Carlo simulations), and regression analyses. The value-atrisk (VaR) analyses used in this research have two major advantages over the traditional
accounting-like assessments and stress tests of the three programs. First, VaR analyses
not only measure the magnitude of potential losses at different levels on an insured
cohort, but also assess the probability associated with specific levels of losses, therefore
- 171 -
giving policymakers a better idea of the likelihood and degree of losses. The VaR
analyses offer the frequency distribution of potential results, rather than a single value
point on that distribution as provided by the traditional methods. Second, this research
combines the VaR analyses with the regression analyses to take into account the yearly
correlation in cohorts' performance. When analyzing the NPV of mortgage insurance
contracts over multiple-year cohorts, the VaR method with annual correlation factors
measures more accurately the gains/losses over a long time horizon. As discussed earlier
in Chapter 5, if the multi-year cohort performance correlation is ignored, the 6-year
cumulative cohorts' NPV of the FHA mortgage insurance contracts will be
underestimated by about 40 percent.
In this study, I develop an integrated framework to analyze and compare public MI
programs from institutional, financial and operational perspectives. The framework is
composed of three inter-related research questions: a) What are the primary economic
problems in the housing finance market that cause market inefficiency or market
imperfections and hence call for government intervention in the form of a public MI? b)
What are the implied liabilities imposed on the backing government (hence taxpayers) of
sponsoringa public MI enterprise?and c) Whatare thepotential economicproblems
that can result from the creation of a public MI system ? In the section that follows, I
provide answers to these questions in major research findings.
II.
A.
Major Research Findings
Economic Rationales for Public MI
There are two main purposes of utilizing a public MI scheme - to promote or expand
homeownership, and/or to provide necessary credit enhancement to facilitate secondary
mortgage market development. I differentiate the economic rationales for establishing
public MI in developed countries and developing countries, by focusing on their
uncertainty risks in the marketplace. A simplified model is developed to show that in
developing countries, uncertainty risks include a country's political and economic
instability - the idiosyncratic risks of potential catastrophes such as financial crisis or
depressions. In developed countries, the uncertainty risks are taken when expanding the
- 172 -
underwriting envelope to include marginal borrowers - populations unserved or
underserved by the private sector because of their perceived higher risk profile. In both
cases, public MI takes on risks that are shunned by the private sector, because of the
government's belief that positive externalities to the society as a whole from a more
complete housing finance market, increased homeownership, and information efficiency
outweigh potential costs.
The answer to the first research question - the housing market inefficiencies that justify
the establishment and continuing existence of the public MI - differs across three
countries. In the U.S., the main initial inefficiency was the lack of confidence in the
housing market and the hesitance in mortgage lending following the Great Depression.
Now the market,inefficiencies mainly lie in the inadequate homeownership among lowerincome populations and in the information asymmetry in the secondary mortgage market.
The FHA provides important credit enhancement to government-backed GinnieMae
MBS. In the Netherlands, the housing market imperfections addressed by the public MI
are the historically limited choices in housing tenure and the resulting low
homeownership rate among the general population, especially low and medium income
households. The WEW is used as one of many policy tools to redirect the Dutch housing
market from its previous housing market model of heavy government regulation and
subsidies, and mass production of social housing to a more market-oriented housing
production and consumption model. Public guarantees, instead of direct subsidies,
facilitate increased homeownership. In Mexico, the major inefficiencies in housing and
housing finance markets that called for the establishment of SHF are multi-faceted. These
inefficiencies include the great uncertainty of the country's macro economy and political
stability, the serious shortage of capital in mortgage lending, and the lack of formal,
affordable mortgage financing for the majority of the population, and the dearth of data
for both primary and secondary mortgage markets.
The comparison shows that as a nation's housing market develops and housing policy
changes, the market inefficiencies addressed by public MI evolve as well. Three common
market imperfections that can be effectively mitigated by public MI are: a) perceived
- 173-
high uncertainty risks due to macro economic and/or political instability, or due to the
vulnerability of certain population groups; b) lack of standard market information and
data; and c) absence of standardization and regulation in mortgage markets. In particular,
the market information asymmetry for MBS investors is the most prominent reason why
public MI is utilized in the secondary mortgage market for credit enhancement.
In many developing countries, establishing a public MI program/enterprise can jumpstart
the necessary regulatory and legal infrastructure for mortgage markets (such as
establishing or improving laws on foreclosure, property title, appraisal, lenders' capital
reserve requirements, etc.), can standardize mortgage loan origination and servicing, and
can collect market data centrally and systematically. The comparison concludes with
those prerequisites for public MI feasibility: a country's political and social, regulatory
and legal, and information conditions.
B.
Implied Government Liabilities of Backing Public MI
To answer this question, first I analyze the economics of the government in supporting a
public MI program. The risk tolerance and loss magnitude between a public MI program
and a private MI provider under various economic conditions is then compared. The
model shows that public MI programs are more risk-tolerant than the private sector, and
therefore their break-even cohort default rate is much closer to the mean portfolio default
rate than that of the private sector.
The answer to the second research question - the implied liabilities imposed on the
government in backing public MI - is provided from these perspectives: a) whether the
current public MI design and pricing can cover expected losses for a single cohort; b)
what is the potential governmental liability under adverse events with small probabilities
(recession scenarios and worst case scenarios); and c) what is the magnitude of
government liability over multiple years. The value-at-risk method with parametric
models is employed to analyze these questions for each of the three cases.
- 174 -
The results reveal that at the mean cohort default rate based on each country's historical
mortgage portfolio performance, the FHA and SHF will have a small positive
profitability rate (generating profits) for their 2003 cohort, while the WEW will have a
slightly negative profitability rate, incurring some losses. However, a big caveat is that
the mean cohort default rate in the WEW case is estimated based on very limited data
firom the pre-WEW guarantee regime, which could cause an upward bias and
overestimate the default rate. All three programs' profitability rates are very close to the
break-even point, supporting the argument that public MI does not aim to maximize
profit and is priced at the level such that the "buffer" zone against a single cohort's
insurance loss is quite small.
The multi-year NPV analyses on the FHA and the WEW show the financial soundness of
both programs.56Over a six-year period and ten-year period respectively, both programs
generate positive net present values, with FHA boasting a stronger result. For instance,
the FHA cohorts for the next six years are projected to generate a profitability rate of
7.08%, with 3 percent probability of incurring losses; and the WEW cohorts are
estimated to have a profitability rate of 0.55%, with 25 percent probability of losing
money. The analyses reveal that the two public MI programs' financial strength is
stronger over the long term than the short term. Therefore, the premium pricing of public
MI should be based on business projections over a longer horizon, rather than narrowly
focusing on yearly fluctuations. The multi-year NPV analyses also emphasize the
importance of the current economic environment and its future outlook at the time when
projections are made, as the macro economic conditions will render different trajectories
for multiple, correlated cohorts' performance. Take the FHA's 2003 cohort as an
example. Its projected cohort ultimate default rate is around 8.62%. If for the next six
years, the U.S. macro economy remains positively stable as indicated by the smoothly
upward trending yield curve,57 the estimated 6-year cumulative cohorts' NPV is about
positive 3.6 billion dollars. However, if the economy enters a serious recession in the
There is not enough historical data to conduct the multi-year simulations for the Mexican SHF program.
implied Treasury rates for different maturities are derived from the yield curve as the following: 1year rate: 1.24%; 2-year rate: 1.65%; 3-year rate: 2.11%; 4-year rate: 2.54%; 5-year rate: 2.97%; and 6-year
rate: 3.25%.
56
57 The
- 175 -
next six years, similar to that of the early 1980s,58 the next 6-year cumulative cohorts'
NPV will be around negative 3.8 billion dollars - a substantial 7.4 billion dollar
difference over the next six consecutive cohorts between the two different economic
paths.
A nation's macro economy and housing markets tend to experience cyclical movements.
It is important to understand where the national economy stands in these cycles - whether
it is heading to a recession, at the bottom of a recession and ready to pull out, or in a
steady upswing trend. Based on the modeling procedures proposed in this research, it is
possible for policymakers to make these projections. The shape of the current yield curve
paints a picture of the future economy, and can be used to derive short-term interest rates
over the next few years.5 9 Then based on the yearly correlation between adjacent cohorts'
performance and short-term interest rates, a series of projections of cohort ultimate
default rates can be made to compute the cumulative NPV of the insurance contracts on
these cohorts. As an example, let us examine the recession period of the early 1980s and
the recovery period afterwards in the U.S. The yield curves in the years of 1981, 1986
and 1993 (Figure 7.1) imply that the national economy was heading to a recession in
1981 (the inverted yield curve), gradually recovering in 1986 (the flat and slightly
Figure 7.1 The Interpolated U.S. Treasury Yield Curves of 1981, 1986 and 1993
Interpolated
U.S.Treasury
YieldCurveof
1986
Interpolated U.S. Treasury YieldCurve of
1981
IU
145--
!,
, - .. .....
Interpolated U.S. Treasury YieldCurveof
1993
"' , -
.....................
........ . . . .......
.
.....................
6
8
4
`
5;13.5
b-
6
.Z_ 3
- 4
130
2
125
0
>2
1
0 11.11... I....
M-v t
Nbturdy
N-
Maturity
N-
"
.
r~- 0
I--,
)
... 11- I (D
Co
N
n
(D
Maturity
The implied interest rates derived from the 1981 Treasury yield curve are as the following: 1-year rate:
14.80%; 2-year rate: 14.57%; 3-year rate: 14.46%; 4-year rate: 14.36%; 5-year rate: 14.25%; and 6-year
rate: 14.16%.
58
59
The future short interest rates can be inferred from the yield curve of zero-coupon bonds as the
(1 + y ),
where n denotes the period in question and y, is the yield to maturity of a zero-coupon
(1 + Yn-1 )
bond with an n-period maturity. r, is the short interest rate in period n.
following:
=
- 176-
upward yield curve), and growing in 1993 (a normal, upward trending yield curve). If
policymakers were contemplating an FHA program in those years, they could project the
program's potential performance over the first six years of the program's existence as
follows: i) infer the future six-year interest rates from the yield curve; ii) make a baseline
projection of the first cohort's ultimate default rate (mostly based on historical
experience); iii) project the next five cohorts' ultimate default rates based on the multiyear correlation of cohort ultimate default rates (involving the previous year's cohort
performance and the current short-term interest rate); and iv) calculate the estimated
cumulative NPV over the first six years of the program launch. Suppose the baseline
projection of a cohort's ultimate default rate is 10% in the U.S. As shown below (Figure
7.2), if the FHA program was launched in 1981, it would incur losses of about 3.5 billion
dollars on its first six insured cohorts. If the program were to start in 1986 or 1993, it
would generate profits of about 1.9 billion and 2.3 billion dollars respectively.
Figure 7.2 Projected Six-Year Cohort Ultimate Default Rates Starting from 1981, 1986
and 1993
Projected
6-yearCohortUltimateDefault
Rates,In1981
a 25% -
25%
25% -
20% -
20% -
15%
15%
E, 10%
X.
10%
15%
0%-
Projected
6-yearCohort
Ultimate
Default
Rates,in1993
Projected
6-yearCohortUltimate
Default
Rates,in1986
E,
D-
E 5% -
5%-
E5%
3 0%
0%
0%
1981 1982 1983 1984 1985 1986
1986
1987
1988
1989
1990 1991
1993 1994 1995 1996 1997 1998
Six-year cumulative NPV (million $):
Starting FHA in 1981
Starting FHA in 1986
Starting FHA in 1993
-3,505
1,940
2,283
The example above illustrates that the timing of launching a public MI program is crucial
for its initial success. For developing countries to decide the appropriate start-up time of a
public MI program, policymakers need to conduct similar analyses to look at the current
economic situation and the future economic outlook, the cyclical market behaviors, and
the correlation between multiple years' cohort performance.
- 177 -
To further understand the magnitude of potential liabilities under unfavorable macro
economic conditions, worst-case scenarios are analyzed in all three public MI programs,
assuming that the most severe experience of economic depression so far in each country
were to happen again. The results show that all three programs can survive the worst-case
situation without imposing immediate burden on the backing government, but to different
degrees. The FHA can survive about 22 bad years resembling the 1980s' books of
business, and the WEW can be self-supporting for about 3.5 years if it encounters the
recession of the 1980s again. The SHF's current capital reserves can only sustain one
year of the post-1995-crisis situation. The comparison indicates that the longer the
program has existed, without unusually bad times, the longer it can remain selfsupporting during economic downturns. Even if a public MI program is new and does not
have enough capital reserves, as long as it can borrow from the federal government, then
what matters is a long-run actuarially sound premium structure. If losses are incurred in
the next few years, they can be paid back by earnings on future cohorts. This is ultimately
the value of the government insurance: the insurance fund always has a credit rating
equal to that of the federal government because it can borrow and lend to the federal
government (or Treasury) at sovereign debt rates.
C.
Potential Economic Problems Resulting from Public MI
Public MI, as a form of government intervention, inevitably distorts the housing markets
to various degrees. The answer to the last research question - potential economic
problems that may result from the establishment of public MI - is provided by first
discussing five major types of potential economic and political problems and then
analyzing the three study cases respectively. The five types of issues are: adverse
selection, moral hazard, non-optimal risk taking and risk allocation, restraining the
private sector development, and political abuse of the public MI program.
Thorough analyses of the three cases show that for the FHA program, the main concerns
are controlling moral hazard, reducing the political dominance over the program's
development, and managing "non-transparent" risks; for the WEW, dominant issues
include adverse selection, risk allocation inefficiency, and constraints on the private
- 178-
sector development; for the newly established SHF, the foremost tasks are ensuring the
political and financial independence of the program and controlling moral hazard.
In answering the research question, I also examine the institutional context and
arrangement of the three public MI entities - their supervisors, auditors, close partners
and government backing - that can help maintain the relative independence and financial
soundness of these MI programs. The comparison of the three cases on what types of
operational strategies each of them utilizes to manage and mitigate the aforementioned
market distortions reveals some commonalities. The effective ones include: active
monitoring and recertification of participating lenders, using advanced information
systems and databases to track lenders performance and link it to portfolio default rates,
and most importantly, some form of risk-sharing techniques that gives the insured lenders
incentives to behave responsibly. Such techniques include mandatory loss mitigation
efforts of FHA, the right of reimbursement of claim payout from lenders who did not
underwrite the defaulted loans up to the standards of WEW, and the first-loss partial
coverage of SHF. Some unique measures are adopted in each country to address the
potential market distortions stemming from public MI presence. For instance the full
recourse to defaulted lenders in the Netherlands, which are decided by that country's
historical housing market conditions and the legal and cultural context. For developing
countries where mortgage market risks are substantial, the implied co-insurance between
the public MI and private lenders (through the partial loss coverage) adopted by the
Mexican SHI program seems to be an effective strategy.
III.
Policy Implications and Recommendations
This research has important practical policy implications for countries that are
considering building a public MI system or in the process of doing so, especially
developing countries and emerging economies that wish to develop their primary as well
as secondary mortgage markets. The proposed framework and findings from comparing
three public MI programs give policymakers a toolkit and benchmark to evaluate the
feasibility and functioning of public MI in their own country. In the following section I
- 179 -
expand the discussion of public MI as a policy tool and present some policy implications
and recommendations that are relevant to this research.
A.
The Government Role in MI Provision
The government's involvement in MI provision can vary greatly, ranging from no
involvement (such as in Australia where all MI is provided by private MI companies) to
complete control (such as the U.S. FHA). Among the three case studies in this research,
the U.S. government is actively involved in both financial and operational management
of the program. The program's cost and efficiency may not be optimized. Nonetheless,
when FHA-insured loans are sold as Ginnie Mae securities, the full faith and credit of the
U.S. government provides an AAA guarantee that credit losses are covered and investors
and financial partners consider MBS a good investment. The Dutch and Mexican MI
models enjoy the full backing of the government as well, but their public MI entities are
more stand-alone and independent, with a lot of the work delegated to lenders or
intermediary financiers. What should be the role of the government in MI provision direct operator and manager, supervisor, backstop supporter, or a combination of them? It
certainly depends on a country's specific housing market history and government
credibility and capacity. In developing countries where political instability, government
corruption and inefficiency are still of big concern, the government role is recommended
to be clear and straightforward, such as only being the backstop supporter and program
supervisor, rather than being the full fledged manager of the program like in the U.S. In
this sense, the Dutch WEW model as a non-profit, private foundation provides a
workable solution. The Mexican SHF has also started working on the creation of a
mortgage insurance company as a subsidiary of the current SHF, therefore giving the
insurance program more independence. Other alternative forms of a MI entity could be a
government-owned joint stock company or a government-chartered special insurance
company.
Market size (annual numbers of homes insured, the average loan balance, etc.) also
matters in deciding the government's role, as it impacts the program's operation scale and
whether business revenues can cover costs. In countries where mortgages have lower
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loan balances and less premium income, fewer loans, and smaller insurance fund
reserves, the public insurance program may not generate sufficient income to support a
FHA-style operation.
B.
Public MI as a Housing Policy
Public MI as a government housing policy is built upon many prerequisites besides the
government's good will and supportive policies. These include a sound legal system for
land titles and property foreclosure laws; a well-functioning primary mortgage market;
and a culture of credit and availability of credit history. In many countries where these
legal and regulatory conditions are absent, a mortgage guarantee model may not work.
That is exactly the reason why in the Russian Federation "early introduction of MI is not
recommended as substitute for resolving, even temporarily, serious legal and regulatory
problems with home mortgage lending in Russia," (Blood, 2004) even though improving
the country's system of housing finance and expanding mortgage credit supply have been
a top economic development priority there. In most emerging economies, housing finance
and housing production systems are marred by inefficiencies and inequities (Hoek-Smit
and Diamond, 2003). For these economies, the policy priority is to improve the efficiency
and transparency of the markets rather than establishing a public MI in an inefficient
sector.
Public MI alone can only address some of the housing market imperfections in the
mortgage credit supply side. In many developing countries, the great majority of the
households cannot afford the lowest priced house in the formal housing market sector,
even if the financing is accessible. In that case, the main constraint in the housing market
is the low income at the demand side, and the effective housing policies are more likely
to include income subsidies, mortgage tax deductions, and upfront grants rather than MI.
In some developing countries, such as in Mexico, both demand and supply sides of the
housing finance system are defective. A combination of policy measures should be
employed to reach the most effective remedy. In short, MI is a rather narrow and
specialized component of the larger scheme of housing finance. It should be developed
with an eye toward its proper place in the larger system, being combined and augmented
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with other housing policies. Moreover, MI is not just a short-term "quick hit."
Governments need policies that support a long-term and sustainable commitment to the
housing sector.
C.
Public Versus Private
There has always been policy debate as to whether a government-sponsored
MI system
should be privatized and rely on private capital to function without imposing any
potential liabilities on the government. The bottom line is whether the benefits provided
by public MI would be cost-effective and compelling for the private sector to provide.
Take the U.S. FHA program for example. The advantages of the FHA remaining public
are several:
1) The FHA does not need to earn a risk-adjusted profit to shareholders, which is FHA's
principal cost advantage over the private MIs in serving riskier borrowers.
2) The FHA provides stability because its non-discriminatory underwriting policies offer
all qualified borrowers the same price. When a region experiences economic
downturns, private MIs may choose to reduce their business in that region or leave
there completely, but the FHA will always provide MI, hence stabilizing the national
housing markets.
3) The FHA provides social benefits such as expanding homeownership to lower-
income people, first-time homebuyers and minority families. If the FHA loans were
to be made through private MIs, the combined insurance premiums and GSE
guarantee fees necessary to accommodate and integrate FHA's risk profile into the
conventional market would be substantially higher than the combination of FHA's
current premiums and the Ginnie Mae guarantee fee. FHA borrowers who attempted
to use private MI would be declined or pay higher fees and enjoy less desirable terms.
On the other hand, there are disadvantages of the FHA remaining public:
1) If the FHA does not price its risk properly, or experiences abnormally high losses, the
government may need to assume the liability. This means taxpayers would pay for
such insurance losses. A welfare transfer from all taxpayers to people who took out
FHA loans may not be the most appropriate use of social welfare.
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2) As a program directly operated by the government, FHA has sacrificed some
efficiency.
The choice between public versus private provision of mortgage insurance is changeable.
There are examples of an initially public MI system and its subsequent transition to the
private sector. In Australia, MI began with a government-owned
MI fund operating
successfully for several decades before being sold outright to a private firm in the late
1990s. Banks using both government and private MI received risk-based capital
incentives. Moreover, for developing countries and emerging economies, the form of
ownership in MI provision can be very flexible and innovative, adapting to each
country's unique situation. In India, the proposed India Mortgage Guarantee Company
(IMGC) is co-owned by the Indian National Housing Bank (NHB), the Canada Mortgage
and Housing Corporation (CMHC), the Canada-based United Guarantee Company
(UGC), the Asian Development Bank (ADB), and the International Finance Corporation
of the World Bmank(IFC). In this partnership format, CMHC and UGC, as experienced
and successful public and international MI provider respectively, will lend their expertise
for the day-to-day operations of the new company, while the two international financiers,
ADB and IFC, will be the strategic partners and funding-providers. The NHB will hold
the majority stake of the proposed MI entity and take the initiative to promote MI in
India, with the aims of mitigating severe housing shortage and developing secondary
mortgage markets. In South Africa, the Home Loan Guarantee Company (HLGC) has
been working in its own unique way to broader homeownership financing for the past
decade. HLGC operates as an independent, non-profit, non-governmental organization
(NGO). It pays neither taxes nor dividends, with all retained earnings accruing to its
reserves (Blood, 2001). HLGC has been able to secure reinsurance for its guarantees
from Centre Re, a subsidiary of Zurich Re. With this credit enhancement and its own
conservative reserves, HLGC has earned an AA+ investment grade rating from major
rating agencies and gained lender and investor acceptance. These examples of "hybrid"
public MI provision in developing countries today emphasize that the fundamental issue
is not so much about whether a MI scheme should be public or private, but about which
form can best suit the country's housing markets and housing finance system, and work
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efficiently with other components of the existing systems. One country's MI model is not
directly replicable in another market (nor should this be encouraged). MI needs to be
considered in the context of the unique historic needs and political objectives to ensure
the success of the program.
IV.
Future Research
This research can be furthered in a number of directions, as there are many interesting
questions yet to be addressed. Pertinent research questions for the future include: (1) how
to quantify the aggregated costs and benefits of public MI, including both direct and
indirect components? (2) how does public MI compare with other finance-linked housing
subsidies in efficiency, equity, and effectiveness, especially in developing countries and
emerging economies? and (3) what synergies can public MI, in combination with other
demand and supply side housing subsidies, create to address the incomplete housing
finance markets in developing countries?
In this dissertation I quantitatively analyze the implied governmental financial liabilities
of sponsoring a public MI program in various economic scenarios. However, this is only
one part of the total costs. Other indirect or induced potential costs can be quite
substantial, including: the deadweight losses due to increased foreclosures among certain
population groups or in certain communities; non-optimality of some borrowers' housing
consumption based on their utility functions that factor in the presence of mortgage
insurance coverage; and losses due to distortions introduced in the housing finance
markets. At the same time, better quantifiable measures ought to be developed to
understand the benefits of public MI. In this research, I generalize two main benefits of
public MI: expanded homeownership and credit enhancement. There are other potential
positive social externalities such as gains in housing market efficiency, liquidity,
completeness or extension; redistributional improvements; and maybe even public health
outcomes from better housing. A limitation of this research is the lack of systematic and
quantifiable measurements to analyze the benefits from public MI, which is an
indispensable component to conduct a holistic cost-benefit analysis. More efforts are
worth putting into this aspect.
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The second and third research questions are focused on housing markets and housing
finance systems of developing countries and emerging economies, where the physical
shortage of housing stock, deteriorated dwellings or informal housing stock, and
unaffordable housing costs are commonly shared problems. Housing, and housing
finance in particular, is always considered a top priority on those countries' list of
economic development goals and expected to become one of the growth engines. To
achieve that goal, a great variety of policies and subsidy approaches can be applied and
public MI is but one of them. A careful evaluation of the opportunity costs and risk
factors of other housing subsidy alternatives besides public MI, and a thorough
comparison among them with respect to their efficiency, equity and transparency are of
great interest and importance to policymakers. This research provides a solid starting
point for further discussions and comparisons of various housing policies. The analytical
framework proposed here can be applied to a wider range of finance-linked housing
intervention programs in analyzing their costs, benefits, and applicability to developing
countries' housing markets. Further comparative analyses on how developing countries
utilize different combinations of policy tools and programs to advance their housing
markets and housing finance systems are of great significance. As an initial step, this
research shows that mortgage insurance is but one component of housing finance - an
important contributor, if implemented properly at the right time.
- 185 -
- 186-
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