220 Week 7 F /For WIZARD KIM

After reading this week’s material, consider, research and address the following:

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Please conduct independent Internet research on the most recent mortgage foreclosure crisis. This crisis impacted both the residential and commercial real estate finance markets.

1. Real estate foreclosures in some areas climbed to very large percentages (as high as 50%) of the housing stock.

2. Banks voluntarily attempted mortgage modification programs for some real estate owners.

3. The federal government required mortgage modifications programs, or provided funds for such programs, in an effort to lessen the impact of the foreclosure crisis.

How did the traditional foreclosure process fail under these unusual circumstances? Please discuss this question along with one of the three points listed above.

250 word minimum, USE THE ATTACHED REFERENCES AS WELL AS OTHER REFERENCES to answer the questions, cite in APA.

CHAPTER 14 Ten-Plus “Must-Knows” About Foreclosure 221

Chapter 14

IN THIS CHAPTER

» Opening your eyes to your situation
and options

» Knowing the best sources for
objective information and advice

» Understanding the realities of
foreclosure investing

Ten-Plus “Must-Knows”
About Foreclosure

Lenders have a contractual right to take over ownership of a property (fore-close) if the borrower can’t make required payments

Even in the best of times, some foreclosures occur, but the number of foreclosures accelerates
during soft real estate markets or because of risky loans. From 2006 through 2010,
the number of foreclosures increased tremendously as real estate prices declined
and numerous borrowers found themselves saddled with high-cost mortgages.

In Las Vegas, home prices plunged by more than 60 percent from early 2006 to
2011 — the greatest percentage decline in home prices of the 50 largest metro-
politan areas in the nation. With that incredible decrease in home value, it’s easy
to understand the record number of home foreclosures because many homeown-
ers who hadn’t owned for long found that they were living in homes worth less
than the amount they owed on their mortgage.

Having the home in which you’re living end up in foreclosure is a nasty, unpleas-
ant experience for most folks. In most instances, homeowners become overex-
tended with their bills or lose some or all of their income(s) and simply can’t
afford to muster their mortgage payment. Meanwhile, some homeowners whose
properties end up in foreclosure aren’t in dire financial straits. Instead, they
choose to walk away from a property that dropped in value and is worth less than
the outstanding mortgage amount. As we note in Chapter 3, either course of action

Griswold, R. S., Tyson, E., & Tyson, E. (2017). Mortgage management for dummies. Retrieved from http://ebookcentral.proquest.com
Created from apus on 2020-05-21 07:43:19.

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222 PART 5 The Part of Tens

will probably have severe repercussions on your credit score and ability to borrow
in the future.

In other cases, overextended investors walk away from multiple properties that
declined in value. (This was the major factor in the Las Vegas market crash with
more than 40,000 homes being purchased by investors seeking rapid appreciation
only to see the market plummet.) Instead of continuing to make payments on
property that’s worth less than they paid for it, some investors cut and run.

Although we feel sorry for some investors caught up in the pre-2007 frenzy of
speculative buying of rental homes, this chapter is really geared to homeowners
who may be in danger of losing their home to foreclosure. Some tips also apply to
folks attracted to investment opportunities on property in foreclosure. While the
number of foreclosures is significantly lower throughout the country since 2010,
some homeowners are going to be unable to meet their loan obligations and a
short sale or foreclosure is in their future.

Deal with Reality
Just as a lot of folks do when consumer debt (on credit cards and auto loans) gets
overwhelming, many people falling behind on their mortgage payments want to
run and hide. Mortgage statements and bills go unopened and calls from the
lender go unanswered and unreturned. Some folks with excessive credit card bills
do the same thing. Sticking your head in the sand when it comes to mortgage pay-
ments does you no good. You’ll lose your home if you don’t take action now.

The sooner you contact your lender and level with them about your problems, the
better. Explain your financial situation, debt burdens, and what you can afford to
pay monthly on your mortgage. That said, don’t allow any person at a financial
institution to berate or verbally abuse you. Find a way to do the best you can. Avail
yourself of financial counseling and try negotiating better mortgage terms (we
cover both of these topics later in this chapter).

Heed this sage advice from veteran mortgage professional Chris Bruno:

Whether one is in foreclosure, contemplating foreclosure, or buying a foreclosed
property, getting competent professional help early in the process is extremely
important for a more favorable outcome. I have seen many people come to me at
the 11th hour having never responded to the foreclosure documents from the
lender. Needless to say, it’s very stressful, and the delay only limited their options
and made the whole process much more expensive.

Griswold, R. S., Tyson, E., & Tyson, E. (2017). Mortgage management for dummies. Retrieved from http://ebookcentral.proquest.com
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CHAPTER 14 Ten-Plus “Must-Knows” About Foreclosure 223

Review Your Spending and Debts
The first step in taking the bull by the horns when you’re drowning in mortgage
debt is to zoom out to 30,000 feet and look at your entire financial situation.
Tabulate all your debts and spending. Identify expenses you can most easily
reduce. Although your housing expenses are a significant portion of your total
expenditures, they’re probably less than the majority of your typical monthly
expenses.

Complete the worksheets in Chapter 1 of this book to help you identify ways to
reduce your spending and debts, including consumer debts. For detailed assistance
with analyzing your spending and debts, see the latest edition of Eric’s Personal
Finance For Dummies (Wiley).

Beware of Foreclosure Scams
Perhaps the only thing worse in the real estate world than falling behind on your
payments and entering the foreclosure process is falling prey to the circling vul-
tures trying to take advantage of your hardship and lack of financial knowledge.
In the late 2000s, increasing numbers of scoundrels and hucksters made claims
that they could stop foreclosure no matter what the situation. After charging fees
of $1,000-plus and doing little if anything, in the worst cases, unsuspecting
homeowners sign over ownership of their property (and begin making rental pay-
ments) to the con artists!

Only make use of counselors approved by the U.S. Department of Housing and
Urban Development (HUD). We explain how to find these good guys and gals in
the “Make Use of Objective Counseling” section, later in this chapter.

Consider Tapping Other Assets
As long as you’re not going to declare bankruptcy (check out the “Understand
Bankruptcy” section, later in this chapter), you should make a list of assets you
might tap to help meet your mortgage payments. These assets may include bank
saving accounts, mutual funds, stocks, bonds, cash value life insurance policy
balances, 401(k) plans, unneeded personal property you could sell, and so on. Be
sure you fully understand all tax consequences before liquidating any investments
to help make mortgage payments.

224 PART 5 The Part of Tens

In the unlikely event that you’ll file for bankruptcy protection, don’t use the pro-
ceeds from your other assets for home payments. The reason: You may be able to
protect and keep those other assets if you file for bankruptcy.

Make Use of Objective Counseling
A number of nonprofit organizations offer low-cost or free counseling to home-
owners in danger of losing their homes to foreclosure. The best way to find those
agencies is to contact the U.S. Department of Housing and Urban Development
(HUD) “housing counseling agency locator” at 800-569-4287. Select the option
for mortgage delinquency counseling and then enter your five-digit zip code to
obtain the name and phone number of approved counseling agencies near you.

Alternatively, you can visit the HUD website (www.hud.gov) and then click the
HUD Approved Housing Counseling Agencies link under the Resources tab on the
home page. In addition to helpful articles, the website enables you to find multiple
area counseling agencies.

Negotiate with Your Lender
Smart lenders don’t want your property to end up in foreclosure, especially if the
mortgage balance exceeds what the lender could reasonably expect to net (after
selling and other expenses) from selling the property. If your current mortgage
terms appear to doom you to foreclosure, contact your lender immediately and
plead your case to have your loan modified. Sure, the modification will hurt your
credit, but it’s likely already damaged if you’re facing foreclosure. Also, remember
that the modified (lower) payments may help you keep your home while you
rebuild your credit.

You also may want to see whether you qualify under the specific and limited con-
ditions for borrowers seeking to restructure or refinance homes with low equity,
no equity, or negative equity (the home is worth less than the loan). You may qual-
ify for a government program, such as the Home Affordable Refinance Program
(HARP), that allows homeowners to refinance their loan. Note that these pro-
grams are constantly evolving and the terms may change periodically, so you need
to continually see whether you qualify. For example, the HARP program started in
2009, but by late 2011, it was modified and referred to as HARP 2.0. Then, Presi-
dent Obama proposed further changes and a revised HARP 3.0, but Congress never
approved that proposed program. Our point here is that you’ll hear a lot of hype
about government programs that will solve all your problems. Be cautious and

http://www.hud.gov

CHAPTER 14 Ten-Plus “Must-Knows” About Foreclosure 225

skeptical, but seek the advice of an objective counselor (as we suggest earlier) as
there may genuinely be some help out there for you.

For ideas on how to customize your current loan terms to help you afford your
home, consult with a local HUD-approved counselor, as discussed in the previous
section. Most lenders can make your current loan more attractive through a modi-
fication (by reducing the interest rate or changing the rate to a fixed-rate from an
adjustable, for example) if doing so will keep you out of foreclosure and keep you
making monthly payments.

Understand Short Sales
If your home is worth less than the amount you owe on the mortgage(s), it is said
to be underwater or upside down. You may think you can’t sell the home because
you won’t clear enough money to satisfy the lender(s), real estate agents, and
closing costs, and therefore foreclosure is the only option. But, thankfully, you can
opt for a short sale.

A short sale means you can sell your house (avoiding foreclosure) and pay off the
lender(s) for less than what they are owed. The lender(s) is getting a payoff that
is “short” of what it is owed — hence the name short sale.

The lender has to approve a short sale, but it happens regularly. Why would the
lender do this? Because it’s easier and less expensive for them than processing a
full foreclosure. Why would you do this? Because, compared with foreclosure, it is
better for your credit (see the “Consider the Future Impact to Your Credit Report”
section later in this chapter.)

Seek Legal and Tax Advice
If you’re confronted with or considering foreclosure, talk to an experienced real
estate lawyer and tax advisor before you agree to a foreclosure or short sale (see
the preceding section). In fact, seek their advice before you even start skipping
mortgage payments. There are state and federal laws involved, and you need
to know

» If the lender loses money on the foreclosure or short sale, can they come after
you for the difference (called a deficiency judgment)? This varies from state to
state and is a question for the attorney.

226 PART 5 The Part of Tens

» If the lender loses money on the foreclosure or short sale, can the IRS tax you
on the amount the lender loses? It may sound crazy, but the IRS may consider
the loss that the lender suffers (which they write off) as a taxable benefit
to you. It is called debt relief. This may or may not apply at the time you’re
reading this, so find out by asking your tax advisor.

Understand Bankruptcy
To make the best decision you can, consider a range of options. When mortgage
and other debt prove overwhelming, bankruptcy is one option you should explore
and better understand.

Bankruptcy is usually used to eliminate miscellaneous unsecured revolving debts
(like credit cards) so you have more money with which to make mortgage pay-
ments and keep the home.

The biggest challenge with considering bankruptcy is finding unbiased sources of
information and advice. Some supposed counselors won’t discuss or recommend
it to you; others, such as bankruptcy attorneys, often have a bias at the other end
of the spectrum. Truly independent or the HUD-approved counselors recom-
mended earlier in this chapter are a good starting point.

Be careful if the financial counselor or advisor is affiliated with a “credit repair”
service or a “bankruptcy mill” because his solutions are almost guaranteed to be
the products offered by his own or an affiliated company.

Consider the Future Impact
to Your Credit Report

Folks who make little if any effort to find a solution to their housing debt woes and
who choose to walk away from a property that’s proven to be a loss from an
investment perspective often suffer consequences down the road. Before taking
this step, think for a moment about the long-term consequences. If you were a
lender, how motivated would you be to lend money to someone who threw in the
towel without working to find a solution? And if you did lend such a person money,
would you give him or her the best loan rates and terms that you give folks with
excellent credit histories?

CHAPTER 14 Ten-Plus “Must-Knows” About Foreclosure 227

Roll up your sleeves and work with your lender and talk with counselors to find a
solution that will enable you to keep your property. Remember that the lender is
best served by having the property occupied by an owner who will continue to prop-
erly maintain the home. Use that argument to your advantage because the lender
will very likely suffer added costs and expenses (such as insurance) if the home is
unoccupied for an extended period of time. Many municipalities are well aware that
these vacant homes aren’t being properly maintained and are an invitation to squat-
ters and crime. They have passed new laws allowing for significant fines and penal-
ties if these homes become a blight on the neighborhood. Your credit report may still
suffer damage, but you can minimize the fallout both now and in the future.

Two of the biggest questions after a foreclosure, short sale, or bankruptcy are

» How long before my credit recovers?
» How long before I can get a mortgage again?

Regarding the first question, most lenders don’t want you to know that it only
takes two to three years (of effort) to rebuild your credit scores to a level worthy
of a new home loan. As far as credit cards, auto loans, and other loans? You can get
those very quickly after problems on your credit report.

As for getting a loan to buy another house, different types of loans require differ-
ent waiting periods before you’re eligible to apply for a new mortgage. But the
waiting periods are often much less than most people expect. Check out Table 14-1
to understand the various waiting periods.

TABLE 14-1 Required Wait Times before Applying for a New Mortgage

Program Foreclosure Bankruptcy Short Sale

Conventional 7 years from
completion

Chapter 7: 4 years from
discharge/dismissal
Chapter 13: 2 years
from discharge/4 years
from dismissal

4 years from completion

FHA 3 years from
completion
date

Chapter 7: 2 years from
discharge
Chapter 13: 1 year of
payment period must
have elapsed with
satisfactory payment
performance and
permission
from the court

No waiting period if
* Borrower made all mortgage/installment
payments within the month due for 12 months
before the short sale, and
* Made all mortgage/installment payments
within the month due for the 12-month period
before the date of the loan application for the
new mortgage
3 years of waiting from completion date
required if borrower was in default at time of sale

(continued)

228 PART 5 The Part of Tens

Understand the Realities of Investing
in Foreclosed Property

You may be considering purchasing a property that’s in some stage of foreclosure.
Although earning handsome returns on investing in foreclosed property is an
option, make absolutely sure that you know what you’re getting into. Doing so
isn’t as easy as some real estate investing cheerleaders may lead you to think.

Often, property that ends up in foreclosure has physical problems. So if you rush
to buy without thoroughly inspecting a property inside and out, you could end up
with more trouble and costs than you expected.

Although a proven way for savvy real estate entrepreneurs to build their empire,
investing in foreclosures is for sophisticated, experienced investors only. Finding
and buying a good property at an attractive price (including the realistic or actual
costs for repairs, renovations, upgrades, plus holding and marketing costs) takes
a lot of homework and patience. See the latest edition of our book Real Estate
Investing For Dummies (Wiley) for more details.

Program Foreclosure Bankruptcy Short Sale

VA 2 years from
foreclosure
date

Chapter 7: 2 years from
discharge
Chapter 13: 1 year of
payment period must
have elapsed with
satisfactory payment
performance and
permission
from the court

2 years from completion

USDA 3 years from
completion

Chapter 7: 3 years from
discharge
Chapter 13: 1 year of
payment period must
have elapsed with
satisfactory payment
performance and
permission
from the court

TABLE 14-1 (continued)

Real Estate Finance in the New Economy, First Edition

Piyush Tiwari and Michael White.
© 2014 John Wiley & Sons, Ltd. Published 2014 by John Wiley & Sons, Ltd.

Introduction

The purpose of this chapter is to discuss how funds flow to property. Who
are the investors? Who are the intermediaries, and what mechanisms do
they use to channelise funds from investors into property?

These mechanisms have evolved within the financial systems as a way to
allocate risk associated with financing property to those who can assume
them for returns in commensuration with the risk. Figure 3.1 shows the
flow of funds to property, though it may be flagged here that property is only
a small component of overall investment space. In a simplified scenario,
domestic or foreign economic agents such as households, firms and
government with surplus financial resources in present time (in terms of
savings) can invest in those domestic or foreign opportunities (including
property) that require these resources to carry out economic activities and
earn risk-adjusted return on their investment in the future. These economic
agents could invest directly in these opportunities (such as housing and
office buildings for own use purposes) or channelise their savings into
various opportunities through primary capital markets or through secondary
financial sectors such as banks, pension funds and insurance companies for
investment in income-generating properties.

The purpose of the financial system and financing mechanisms is to
reduce impediments and create opportunities for the flow of funds from

Financial Systems, Flow of Funds
to Property and

Innovations

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64 Real Estate Finance in the New Economy

investors to investment opportunities. Property has its own specific charac-
teristics. An objective of this chapter is to discuss how the financial system
(and financing mechanisms) have evolved in response to the characteristics
of property asset class and to what extent various mechanisms have been
able to address the specificities of property.

Bank-based and market-based financial systems

The mechanisms for financing assets that develop in a country depend on
the regulatory and institutional environment within which financial system
operates. There are two types of systems: (i) market-based and (ii) bank-
based financing system. This does not mean that the mechanisms that
would evolve in a country would conform only to one system or the other.
It means that one of these two systems would have predominant influence
in the evolution of financial mechanisms through which resources would be
mobilised and investments will take place. Mechanisms conforming to the
other system will exist in a meaningful but to a lesser extent. The differences
between the two financial systems arise from the way savings are mobilised;
investments are identified, made and monitored; and risks are managed.

The other difference is from the legal perspective. In a bank-based econ-
omy (Germany and Japan), laws governing financial systems are enacted and
implemented by the government. These are based mainly on the civil law
rather than the common law. Market-based financial systems are found
most often in countries (the United States and the United Kingdom) that
employ a common law legal system. Common law is less defined and can
vary from case to case. Instead of government enacting and implementing
the laws governing financial system, common law-based regulation is
implemented through courts.

Primary financial
sectors

Households

Firms

Government

Secondary financial
sectors such as banks,
pension funds,
insurance

companies

Primary securities
market

Property
Core
Residential
Commercial
Industrial

Non-core
Hotels
Warehousing
Healthcare
Others

Savings Intermediaries Investment

Figure 3.1 Flow of funds to property.

White, M., & Tiwari, P. (2014). Real estate finance in the new economy : Real estate finance in the new economy. Retrieved from http://ebookcentral.proquest.com
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Financial Systems, Flow of Funds to Property and Innovations 6

In a market-based system, primary securities markets play the dominant
role. Banks in such a system are less dependent upon interest from loans and
gain much of their revenue through fee-based services. In contrast, in a
bank-based financial system, banks play a major role in channelising
financial flows to investment opportunities through loans. Interests earned
on loans form the major part of their income. In a market-based system, a
number of non-banking sources for investments exist. Investments by
private systems and government often compete with those of the bank.

In a pure bank-based system, banks mobilise capital, identify good
projects, monitor managers and manage risk. The risk management, infor-
mation dissemination, corporate control and capital allocation are all left to
market forces in a market-based system. In a well-developed market, any
information that is available is revealed quickly in the public markets,
which reduces the transaction costs. Some view this as a shortcoming of
market-based system as the incentives for individual investors to acquire
information decline (Stiglitz, 1985). Standardisation becomes the key, and
in this context, there may not be enough incentives to identify innovative
investment opportunities. In a bank-based system, banks form long-run
relationships with borrowers (firms), information is private, and investments
are custom made.

There are other concerns which proponents of the bank-based system
identify with the market-based system. Liquid markets create a myopic
investor climate where investors have fewer incentives to exert rigorous
corporate control (Bhide, 1993). However, powerful banks with close rela-
tionships with firms can more effectively obtain information about firms
and manage their loans/investments to these firms than markets. The view
against bank-based system is that powerful banks can stifle innovation by
extracting informational rents and protecting established firms with close
bank–firm ties from competition (Rajan, 1992). Moreover, in the absence of
appropriate regulatory restrictions, they may collude with firm managers
against other creditors and impede efficient corporate governance (Wenger
and Kaserer, 1998). Market-based systems will reduce these inherent
inefficiencies associated with bank-based systems.

Levine (2001) minimises the importance of the bank-based versus market-
based debate. He argues that financial arrangements comprising contracts,
markets and intermediaries arise to ameliorate market imperfections
and provide financial services. Financial arrangements emerge to assess
potential investment opportunities, exert corporate control, facilitate risk
management, enhance liquidity and ease savings mobilisation (Levine
2001). Finance is a set of contracts. These contracts are defined and made
effective by legal rights and enforcement mechanisms. From this perspec-
tive, a well-functioning legal system facilitates the operation of both
market- and bank-based systems. While focusing on legal systems, it is not

66 Real Estate Finance in the New Economy

inconsistent with banks or markets playing an important role in the econ-
omy. With financial innovations and their export that has accompanied
globalisation, the coexistence of bank-based and market-based system has
further got reinforced.

The mechanisms that develop for financing or investment in an asset take
root in the efficiency of the market. Though a detailed discussion on market
efficiency is out of the scope of the present book, it is important to mention
the key elements of an efficient market. According to Fama (1970), an
efficient financial market is one in which security prices always fully reflect
the available information. This hypothesis rests on three arguments which
progressively rely on weaker assumptions. First, investors are assumed to be
rational and hence value securities rationally for its fundamental value. Any
new information is quickly factored in the price. As a result, all available
information is captured in the price. Second, to the extent that some
investors are not rational, their trades are random and therefore cancel each
other out without affecting prices. Third, to the extent that investors are
irrational in similar ways, they are met in the market by arbitrageurs who
eliminate their influence on prices (Shleifer, 2000).

Given the aforementioned definition of the efficient markets, property
markets are not efficient. Trading on this asset is infrequent (one property
does only few times during its life), transactions take time to materialise,
and the transaction costs are high. Information on transaction price, rental
and lease terms are highly private, and hence, third party valuation plays a
key role in guiding buyers’ and sellers’ decisions. Participants in the market
are few and most deals are negotiated deals. Property being local in nature,
laws and local planning regulations play a very important role in determin-
ing the value of the property. Importance to understand these local norms
for the participants makes the market thin.

The role of financial system is to evolve mechanisms that can take into
account characteristics of property asset class and create opportunities for
investors to invest in this asset class. Hence, the question to ask in case of
property investment is what bank-based and market-based mechanisms
have evolved for channelising savings from the real economy for financing
of property development and investment in property as an asset class. As
would be discussed later, to a large extent, property is financed through debt
instruments, mainly debt from commercial banks. In the last two to two
and half decades, innovations in financial mechanisms have taken place to
enhance the role of the market-based financing in the sector. Figure 3.2
presents the flow of funds to the property.

Though various mechanisms would be discussed in detail in the next sec-
tion, it is important to observe here that both bank-based and market-based
systems are operative in any economy. The extent to which an economy is
able to use these mechanisms depends on the depth and maturity of its

Financial Systems, Flow of Funds to Property and Innovations 6

banking system and primary markets as well as regulatory environment
within which intermediaries and investors operate. It is also important to
observe from the aforementioned figure that in a fully developed system,
bank-based and market-based systems interact and innovate to provide
mechanisms to fund property assets and new developments.

Property investors and intermediaries

There are four major types of investors: institutional investors, unregulated
investors, households and proprietors and corporations. Motives for invest-
ment in property differ for different investors. Even for investors in the same
type, as described earlier, motives could differ. There are those who invest
in property for financial reasons, that is, they are looking for a return on
their investment, and there are those whose intention is to invest in prop-
erty for occupation purposes. Figure 3.3 presents the further breakdown of
investors in each type.

Following are the two distinct and mutually exclusive investment objec-
tives (Geltner et al. 2007): (i) the growth (savings) objective, which implies a
relatively long time horizon with no immediate or likely intermediate need
to use the cash being invested, and (ii) the income (current cash flow) objec-
tive, which implies that the investor has short-term and ongoing cash
requirements from his investments. There are other considerations that
affect investors in the property markets. Risk is an important factor in the

Households
Firms
Government

Primary securities
market

Primary sectors

Secondary sectors

Commercial banks
Mutual funds

Savings institutions

Insurance companies
Pension

funds

Government

Equity
instruments

Debt
instruments

Equity

Property asset
market

Existing properties

Office
Retail
Commercial
Residential
Industrial
Hotels
Warehouses
Others

New property
Development

Loans

Figure 3.2 Typical flow of funds to property.

68 Real Estate Finance in the New Economy

decision for investment in real estate. This arises from the concern that
future investment performance may vary over time in a manner that is not
entirely predictable at the time of investment. Time horizon, for which
investor wants to stay invested, itself is a factor. Liquidity, that is, the ease
with which the asset could be bought or sold at full value without much
affecting the price of the asset, is another consideration. Investor expertise
and management burden that investment in property pose determines the
ability and desire of investor to invest in property. Minimum size of invest-
ment required also determines the willingness and ability of an investor to
invest in property.

Considering that investor space is heterogeneous, an elaborate invest-
ment system with a number of intermediaries and mechanisms through
which funds flow to property has emerged. While the investment mecha-
nisms will be discussed in the next section, it is interesting to see the range
of intermediaries that operates in the property investment markets
(Figure 3.4). These intermediaries are the conduits between investors and
investment, and the investment mechanisms are the products through
which investment is made.

Investment mechanisms

Figure 3.5 presents a highly simplified version of real estate investment sys-
tem with main mechanisms depicted there. The figure does not cover
investment mechanisms exhaustively, as given the range of possibilities
that exists, it will be highly ambitious to attempt here.

Institutional
Banks
Savings and loan
corporations
Pension funds
Insurance
companies
Finance and
securities firms

REITs

Personal trust
Government

Unregulated
investors

Religious
organizations
Private and
personal trusts
Non-profit
organizations

Households,
Proprietors

Persons
Families
Limited
partnerships
Sole
proprietorship

Corporations
Publicly traded
companies
Private
companies

Property investment

Figure 3.3 Commercial real estate investors.

Financial Systems, Flow of Funds to Property and Innovations 6

Private investment mechanisms

Debt The private investment mechanisms (Figure 3.5) for debt and equity
are the traditional ways of financing property during its development phase
and later when it is an income-generating asset. These two together form
the major share of investment flow in property. The finance for development
is short term and takes the form of a loan or an overdraft facility. Since
during the time the construction is on, there is no cash flow to repay the
debt, the repayment is a bullet payment of accumulated interest and
principal at the time of when the project development ends. In the United

Mutual funds,
real estate funds

Commercial banks,
specialized mortgage

institutions
Securities dealers

REIT companies,
listed property

funds

Asset backed
securities

Savings institutions

Insurance
companies, pension

companies

Private equity,
venture capitalists

Governments,
sovereign funds

Figure 3.4 Intermediaries in the property investment market.

Property in physical form (e.g. offices, retail, industrial, hotels)

Limited partnerships/
property funds/private
REITs/sovereign wealth
funds: own equity in properties:
LP shares privately held and
privately traded

Direct
investors
High net worth
individuals,
developers,
individuals

Bank loans for
development/
commercial mortgages/
mortgage debentures:
Senior (debt) claims – privately
held and traded

REITs
Issue publicly traded shares,
may own underlying assets,
LP units by UPREITs,
mortgages, CMBS

CMBS
Publicly traded securities
based on a pool of
mortgages

Initial listing, rights issue,
stock split

Property
derivatives

Private
investment
mechanisms

Traditional
Public
investment
mechanisms

Innovative
Public
investment
mechanisms

Bonds/commercial
papers issued by
property companies

Figure 3.5 Investment mechanisms.

70 Real Estate Finance in the New Economy

Kingdom, 30 years ago, the term of the loan coincided with the length of the
construction period, but later, lenders have started to look at the loan period
up to the first rent review (usually after 5 years of completion of project) as
the expectation is that the property would have stabilised and a better value
for refinancing or sale could be achieved for owners at this stage. The loan
for development is provided by one bank or a syndicate of banks.

In the United Kingdom, which is a market-based system, during the prop-
erty boom period of 1980s, property companies were able to assemble a
panel of banks with the help of an underwriter who would compete on inter-
est rates for an opportunity to lend. Property companies had the opportunity
to raise debt finance on very competitive rates from different lenders up to
an agreed limit (Lizieri et al., 2001). However, during the 1990s, when the
property downturn began, the lending market became very conservative in
their lending (Lizieri et al., 2001). These loans from banks are secured on
company assets or development project assets and/or other fixed and float-
ing charges that borrowers could provide. During 1970s, the interest rates
used to be fixed but the interest rate risk for lenders was too high to assume,
and hence, these were replaced by floating rates based on London Interbank
Borrowing Rate (LIBOR) or some other floating rates plus a margin that
banks charged to assume risks associated with the development and the bor-
rower (Lizieri et al., 2001). In that sense from the risk perspective, each
development project and borrower is different and the interest rates charged
to them is different. This is where complication arises, and the importance
of a bank-based system assumes importance as banks with relation with
borrowers are supposedly able to assess their risks better.

Commercial mortgages represent the senior claim on any cash flow (called
senior debt) that is generated by stabilised property (i.e. development is over
and property has been let out and is receiving cash flows in the form of rent and
other incomes) and provide lenders/investors (typically banks or insurance
companies) with fixed tenured, contractually fixed cash flow streams.
Traditionally, the mortgage is a long-term loan (typically 15–25 years tenure)
kept as a ‘whole loan’ on the balance sheet of investors to maturity, meaning
that it is not broken into small homogeneous shares or units such as corporate
bonds that are traded on stock exchanges. However, over the last three–four
decades, a number of variants of mortgages have also emerged first in response
to the low loan-to-value (LTV) ratio for property lending and the low valuation
accorded to property for lending purposes (the practice is that the property is
valued on the basis of ‘vacant possession’) and the second the comfort that
banks have gained with property investment. As per the Basel Accord,
commercial property lending carries full risk weighting, and hence, other types
of mortgage instruments have emerged that combine the balance-sheet and
off-balance-sheet lending. These mortgage instruments, more prevalent in the
United States, take the form of profit sharing mortgages such as participating

Financial Systems, Flow of Funds to Property and Innovations 71

or convertible mortgages. These mortgages are usually more prevalent
during weaker markets to unlock more capital for borrowers.

Participation loans, also called equity participation loans, are a combina-
tion of loan and equity that a lender take in a project. Though it is called
equity participation, but the lender does not acquire ownership interest in
the project. Lender’s interest is only limited to the participation in the cash
flows, and this participation kicks in only after property has starting gener-
ating cash flows (Brueggeman and Fisher, 2008). Lenders receive a percent-
age of potential gross income or net operating income or cash flow after
regular debt service, as may be agreed for their participation. In return for
receiving participation, lenders charge lower interest rates on their loans.
Participations are highly negotiable between lenders and borrowers, and
there is no standard way of structuring them (Brueggeman and Fisher, 2008).

A convertible mortgage gives lenders an option to purchase full or partial
interest in the property at the end of some specified time period. This pur-
chase option allows lenders to convert their mortgage to equity ownership.
Lenders may view this as a combination of mortgage loan and purchase of a
call option, which gives them an option to acquire full or partial equity
interest for a predetermined price on the option’s expiration date (Brueggeman
and Fisher, 2008). Here again, lenders accept a lower interest rate in exchange
for the conversion option.

Equity The source of equity capital for property development and asset
investment is provided by two types of investors: those who want to actively
involve in management and operation of underlying property asset such as
property companies (e.g. developers, institutions and high net worth
individuals) and others who want to invest in property to diversify their
investment portfolios but do not want hands on involvement. A number of
mechanisms have emerged to meet the needs of these investors with the
needs for equity for property investment. Examples of these passive equity
investment mechanisms are units in real estate equity funds such as real
estate limited partnerships (RELPs), commingled real estate funds (CREFs)
and private Real Estate Investment Trusts (REITs – whose units are not
publicly traded); companies; and mutual funds, though passive equity
investment mechanisms provide investors with an ownership interests in
the underlying asset but give limited governance authority over the assets
and are not traded in the liquid public exchanges.

The greater part of the pan-European market is concentrated in non-
REIT form including limited partnerships, companies and mutual fund
vehicles. REITs are quite popular in the United States. Partnerships are
very popular as they allow even the most complex arrangements to be
structured through a partnership agreement rather than under a company
law (Brown, 2003).

72 Real Estate Finance in the New Economy

The fund sponsor for a real estate equity fund is usually an affiliate of a
developer or professional investment organisation. The investors generally
provide most, if not all, of the investment. The structure of the fund has at
least one general partner and any number of limited partners. The general
partners, who are the fund sponsors, provide ‘sweat equity’ and have the
responsibility for management of partnership assets. They may have a small
equity contribution. Limited partners are very restricted in management of
a joint venture, and their personal liability is limited. However, limited
partners insist on having approval rights over a variety of major decisions
ranging from refinancings, sales, leases and budgets to insurance, service
contracts and litigations (Larkin et al., 2003). Some funds also have invest-
ment committees that include limited partners with role to provide advice
to general partners on issues such as conflicts of interest and valuation of
fund assets (Larkin et al., 2003).

The legal form of the fund is largely determined by the tax efficiency of
the form chosen and familiarity of the fund structure to prospective inves-
tors (Larkin et al., 2003). These funds though structured to be tax efficient,
are not generally tax shelters, in that they do not attempt to accelerate tax
deductions to shelter unrelated incomes (Larkin et al., 2003). The investment
objectives of real estate private equity funds can be either capital apprecia-
tion or income. The investment cycle varies depending on the strategy of
the fund, target assets and needs of the investors. For example, while real
estate opportunity funds typically have 2–3-year investment period, 1–2-year
monitoring period, a fund organised for acquisition of a typical asset may
just have duration of 2–3 years. These funds will typically leverage each real
estate investment by borrowing money to finance a portion of the purchase
price and securing the debt by granting direct lien on the property assets
owned by the fund.

RELPs were popular in the United States prior to 1986 due to tax advan-
tages associated with them. Passive investors could offset their other
incomes with property (tax) loss from investment in limited partnerships.
With these advantages gone with the Tax Reform Act of 1986, their popularity
has declined. Moreover, many of the funds in the United States were organ-
ised under the laws of the State of Delaware or tax heavens like Cayman
Islands. In Europe, though, there is no one jurisdiction of choice for real
estate private equity funds, and the choice of jurisdiction depends on the
nature of investors, tax residency of the investor and the likely jurisdiction
of the property assets (Larkin et al., 2003).

Another important source of equity capital, in recent times, is the
Sovereign Wealth Funds (SWFs). These funds are wholly owned government
entities that invest nation’s surplus wealth in broad array of investments
overseas. A number of governments, such as Abu Dhabi, Qatar, Kuwait,
Norway, Singapore, Australia and China, have floated these funds. SWFs are

Financial Systems, Flow of Funds to Property and Innovations 73

commonly active investors or passive asset managers. Though the asset
holding of SWFs is not easily available, their property holdings are about
5–10%. A number of SWFs have taken controlling positions in large property
assets (Langford et al., 2009).

Public investment mechanisms

Property companies and developers access markets by issuing bonds and
commercial paper for debt and through initial public offer (IPO) or rights
issue or stock split for equity funding. The problem, however, is that given
the risk perceived by the market (arising from concerns about true value of
underlying property and cyclical nature of property markets) for investment
in property, very few companies are able to issue unsecured debt paper that
would obtain the highest ratings. The same problem is faced with the equity
offers of property companies. The view is that the property companies are
valued on the basis of discounted net asset value. Markets trade property
securities at a heavy discount to net asset value (NAV) (due to reasons
such as contingent capital gains tax liability, valuation uncertainty, hidden
management cost, illiquidity of the underlying asset), which averaged 25%
in the long run in the United Kingdom (Lizieri et al., 2001). Property stocks
are usually considered as value stock and are also affected by the cyclicity of
general stock markets. During the stock market downturn of the late 1990s
in the United Kingdom, property stocks performed badly even compared to
other value stocks, which led some public property companies (such as
MEPC) to delist their stocks and others to buy back shares (Lizieri et al.,
2001). A similar trend is observed in India, where large property company
stocks (such as DLF, Unitech) are being traded at a heavy discount since
2007, which has led to the postponement of some property companies’
decision to raise equity capital from public markets.

other mechanisms

There are some other mechanisms as well through which property is
financed. One of them, sale and leaseback, is common and takes many
forms. Many non-property companies, whose core business is not property,
have owner-occupied properties for their use, enter in a sale and leaseback
type of arrangement with property companies. Non-property companies sell
the ownership interest in the property to a property company and take long-
term lease on it. The reason for entering in such an arrangement is that it
allows them to free up financial resources locked in property for their core
business, trim down their balance sheet by reducing asset size and also gain
from tax shield as lease payments are expenses for tax purposes. From the
perspective of the market as well, non-property companies that hold too

74 Real Estate Finance in the New Economy

much of property assets face problems. The asset value of their properties
may not be fully reflected in company’s market capitalisation. For a number
of major retailers, a big problem is that their property assets are valued far
in excess of the retailers’ market capitalisation, posing a threat of takeover
and asset stripping. The problem is further complicated for companies
whose required rate of return is higher than what property can (notionally)
generate. These would motivate companies to unlock the capital from
property assets.

Another form of sale and leaseback is where the property company sells
the completed development to an institution and takes long-term lease. The
company expects to gain from profit rent from subletting. The risk and profit
sharing structure between the institution and the property company varies
from a fixed rent to an agreed sharing of any rental uplift (Lizieri et al.,
2001). Another type of financing arrangement that exists is the sale and
leaseback of land. These are done to increase the proportion of financing in
a development project. The land is sold to an investor and is leased back.
Here, the developer/investor owns the building and leases back the land
from another investor. By doing this, 100% financing of land is obtained and
the building is financing on usual lender’s property finance loan terms.

Innovations

The major innovations have been around the development of public invest-
ment vehicles for investment in property asset class. These have taken the
form of asset-backed securities (ABS), commercial mortgage-backed securi-
ties (CMBS), REITs and property derivatives. While ABS and CMBS are debt
vehicles, REITs are equity investment vehicles.

In ABS or CMBS, the issuer, usually a special purpose vehicle, offers bonds
or commercial paper to the capital markets. Cash flows from a single or a
pool of property assets (such as rental income or loan repayments) is used to
pay the coupon and capital redemption payment on the bonds or commercial
paper. Some charge on underlying property assets provides security against
default. The mechanism of CMBS is briefly discussed in Appendix 4.1.

There are several advantages to a lender from CMBS and to a firm from
ABS. By securitising loans (in case of CMBS) or rental income and value
streams (in case of ABS), the exposure to specific risk associated with prop-
erty reduces. For lenders, the securitised loan portfolio moves off their
balance sheet, thereby improving their capital adequacy and solvency
ratios. For firms, ABS allows them to raise capital secured on value and
income stream of their property assets while retaining the ownership.
Issuing ABS or CMBS on public markets allows firms and lenders to tap a
new source of capital. In case of CMBS, there are also opportunities to earn

Financial Systems, Flow of Funds to Property and Innovations 75

profits from arrangement and service fee and from the spread between the
interest on loans and coupon payable on bond or commercial papers. For
firms, ABS is a cheaper source of borrowing. The cost of these borrowing
could further reduce as in case of tenanted properties, the rating of the
bond depends on the tenant covenants rather than the rating of the firm
issuing securities.

There are a number of advantages to investors too. They are able to gain
exposure to a market that faces huge entry barriers and high transaction costs.
Investment in securities allows them to benefit from liquid and marketable
nature of this asset. Moreover, since the underlying asset is a pool of loans or
pool of properties, the specific risk to an investor reduces. It is also claimed
that for ABS or CMBS, the transaction, monitoring and management costs for
an investor are lower than those associated with investing in direct property.

These advantages, however, come at a cost. ABS or CMBS reduce the flex-
ibility for the issuer in managing their securitised portfolio. There are also
costs associated with arrangement, underwriting, rating and credit enhance-
ment of a bond or commercial paper.

Lizieri et al. (2001) argue that asset-backed securitisation offers further
potential for innovation, which could be explored. Underlying any lease,
there are three sources of cash flows. The first is base rent, which given
strong tenant covenants is a secure stream of income. The second is the
possibility of a rental uplift at the time of rent review (in the United Kingdom
where lease agreements specify upward-only rent revisions at the time of
rent reviews, the possibility of rental uplift are far more secure compared to
the United States where rent revisions are linked to the prevailing market
conditions). This stream of income would appeal to certain investors who
have larger appetite for risk but would require a higher return. The third
class of cash flows arises from the fact that property has a residual value at
the end of the lease which is still more speculative investment but would
appeal to certain investors. These income streams could be sold to investors
as income strips. The first, which is based on base rent, would attract
institutional investors. There is a role for brokerage in the development of
this market. Initial costs may be high due to costs involved in structuring
these products and legal fees.

REITs, discussed briefly in Appendix 4.1, provide tax-efficient vehicles
for equity investment in property. These allow subdivision of ownership of
single property or developments.

The development in property derivatives has been slow partly due to lack
of transparency and infrequent data. In the United Kingdom, over the last few
years, property derivatives based on IPD income return and capital growth
have emerged. These derivatives, one called Property Index Certificates and
the other called Property Index Forwards, are synthetic investment vehicles
offering opportunities for low-cost exposure to property market.

76 Real Estate Finance in the New Economy

Economic assessment of investment mechanisms

The focus of this section is on the role that private and public innovative
investment mechanisms such as sales and leaseback, asset and mortgage-
backed securitisation and REITs have played in financing property. Chapter 4
shows that the flow of funds to property through public markets using
securitisation and REITs has increased substantially over the last 20 years.
A number of these innovations, though originated in the United States,
have been exported to other economies. However, how well have these
instruments been able to overcome the limitations of property asset class
(namely, lumpy investment, inelastic supply in the space market, infrequent
transactions, huge transaction cost, information asymmetry, a somewhat
rigid valuation system that does not allow innovations such as in lease
terms, huge specific risk associated with investment in direct property)
that lead to inefficiencies in the market and create opportunities for
investors and property companies and strike a balance between the risk and
return for investors with the relatively lower-cost funding for property
companies compared to traditional secured debt and equity needs to be
examined. The efficient market hypothesis would suggest that if markets
are efficient, then there are no opportunities for any abnormal gains. In that
context, innovative mechanisms serve to rebalance the risk and return in
any transaction or help in addressing sources of inefficiencies in the market,
should they exist.

Lease financing

The growth in lease financing instead of financing property ownership by
companies has been phenomenal over the last four decades, and this has
largely been due to tax shield that lease financing provides. Three arguments
that are often put forward in favour of lease financing are that (i) it provides
100% financing of space, (ii) it ‘finances’ property asset off balance sheet and
(iii) it is beneficial from the taxation point of view. Sale and leasebacks are
special case of lease financing. With these oft-cited benefits, it is expected
that lease financing allows companies to unlock value from property assets.
However, this may not always be possible.

The argument that the lease financing provides 100% financing for space
required by the company is flawed. While it is true that the capital required
for lease transaction is lower than required for ownership, the two
transactions are not comparable. Rental payments are fixed only in the short
term and at all time have higher claim on the cash flows of the company
than some other expenses. This poses a significant risk if the lessee becomes
financially constrained. Thus, the liability for rental payment for lessee is

Financial Systems, Flow of Funds to Property and Innovations 77

equivalent to liability on a bond or debt (Lizieri et al., 2001). Moreover,
when an investor buys a property, she acquires working space and an
investment in performance of property market relative to her business
performance. Besides claim on the future stream of rents, other sources of
value accrue to the investor. These additional sources of value arise because
property has reversionary value after the expiry of lease term and also has
the option to redevelop before completion of lease attached to it (Lizieri
et al., 2001). These options have value if the rents and yields are more
variable, and these options would rise or fall in value depending on the
relative performance of property market relative to tenant’s own business
performance. However, given the risk of owning a building and its
management, non-property occupiers may be less keen to finance the
property than specialist property investors.

The second argument that leasing finances property assets off balance
sheet needs more investigation. It is right that leasing does not appear on
the balance sheet of a company as loan does, but does it have an impact
on the value of the company is not clear. From the perspective of financial
risk for a company, the higher the gearing (debt), the more is the equity risk.
However, if the company replaces debt (for purchase of property) by another
expense that is not on the balance sheet and appears only on the profit and
loss account (lease payments) but has a higher claim on cash flows, would it
lower the risk of equity? A stream of research (see, e.g. Dhaliwal, 1986; Ely,
1995; quoted in Lizieri et al., 2001) has argued it otherwise. These operational
leases are sticky and have the same effect as debt on equity risk. In fact, the
equity risk is better estimated after taking into account the effect of operating
leases, and the underlying market ‘true’ value of a company does not depend
on whether or not the cost of space is disclosed in balance sheet or profit and
loss accounts.

The tax advantage associated with leases is only marginally more than
debt as the interest payment on debt is also an expense from tax point of
view in the profit and loss statement. The level of gearing possible in a pur-
chase of a building would, however, determine the extent to which interest
can be claimed as an expense. This advantage loses its value if the company
is a tax-exempt entity. There is though a demand side perspective. There are
many institutional investors (such as pension funds, insurance companies)
who seek asset with long duration of maturities and are constrained from
investing in equities or other volatile securities (Lizieri et al., 2001). These
investors would constitute the demand side for assets which could be long
leased to companies.

If lease financing does not add value per se, then any value add from such
deals would have to come from mispricing, which then would mean that
some would gain and some would lose.

78 Real Estate Finance in the New Economy

Asset-backed securities

The argument in favour of ABS is that these mechanisms, which are secu-
red by income or value of the underlying portfolio of property assets, can
(i) reduce the cost of borrowing for the company issuing these securities
compared to secured debt because the rating of securities is on the strength
of tenants occupying the property and not the corporate owning the assets,
(ii) increase the leverage on property assets than the LTV that lenders would
offer, (iii) allow investors to diversify into property without the need for
taking full ownership of the property, (iv) offer better returns than an
equivalent-rated corporate bond and (v) limit the exposure to single asset or
borrower for an investor.

The clear advantage of securitisation is that it permits the risk to be spread
over a range of investors, and the relatively low cost of each security permits
investors to take exposure to a portfolio of property assets and hold it in
their portfolio. Investors who would have faced market barriers in investing
in property can gain exposure to property lending market. Since these inves-
tors do not face specific risk associated with property, the required return
from their investment in these securities is lower. This may put downward
pressure on interest rates on ABS.

The advantage to a company that securitisation reduces the weighted
average cost of capital as the coupon payment on securities is lower than
the interest on a loan is debatable. The aforementioned claim is based on
the argument that these securities receive ratings on the strength of ten-
ants which are better than company’s itself and hence are rated higher.
This reduces the coupon demanded by investors. However, the impact of
the price of securities on company’s valuation is not that straightforward.
Since these securities are traded in the secondary market, their price at
any given point in time depends on the risk–return profile perceived by
the market for holding these securities, which continually changes. From
this perspective, issuing securities exposes the company far more to the
scrutiny by the market, which has its own advantages and disadvantages.
Moreover, the debt that was secured on the company as a whole is assessed
on the basis of the quality of the overall rental cash flow to cover interest
payments and on the value of underlying property assets as security in the
event of default. However, the creation of security ring fences certain
good properties through mortgages and subsidiary structures for asset-
backed securitisation, which undermines the quality of remaining cash
flow for the company (Lizieri et al., 2001). Hence, the benefits for the
company from claimed reduced cost of borrowings are not that
straightforward.

On one hand, asset-backed securitisation may lead to financing flexibil-
ity; on the other hand, it could lead to inflexibility in occupiers market.

Financial Systems, Flow of Funds to Property and Innovations 79

Long leases, upward-only rent reviews as in the United Kingdom, senior
claim on company’s assets, have has led to superior ratings for a number of
issues but this has hampered the development of flexible occupier market
with leases that are shorter and have break clauses and innovative rent fix-
ing arrangements. There is a counter view too. In the United States, leases
are short, there is flexibility in rent fixation, and leases are tenant favouring,
but the growth of asset-based securitisation market is no less. In the United
States, it is argued that investors also focus on the quality of buildings
besides the strength of the lease.

While securitisation allows property companies to increase their gearing,
it comes at a cost. With high leverage, equity investors demand higher
returns as their risk increases. Moreover, securitisation poses constraints on
company’s operations as securitised assets can be disposed off or their uses
are changed easily.

Mortgage-backed securitisation

Mortgage-backed securitisation allows originating banks to access new
sources of capital. The risks which in traditional debt structure are borne
by one lender are spread over a large number of investors. Another source
of risk diversification happens due to the pooling of a large number of
property loans in a CMBS structure, which reduces the specific risk
associated with one property for investors. Investors can gain access to
property markets which were previously subject to entry barriers, lack of
information, illiquidity and high transaction costs. The rating agencies
play the role of market makers as they rate mortgage-backed securities
on the same risk parameters as they would for a corporate bond. The
behaviour of rating agencies has, however, been criticised heavily after the
subprime loan crisis in 2007 discussed in Chapter 6. A number of authors
argue that rating agencies did not price the risk appropriately due to moral
hazard problems, and others argue that risk was difficult to price as
sufficient time series of data covering more than one property cycle was
not available.

Given that mortgage-backed securities allow risk diversification, the cou-
pon demanded by investors on these securities is lower than the interest
charged on individual loans. Lenders being aware of securitisation would
reduce the interest charged to borrowers. Kolari et al. (1998) estimate that
for every 10% increase in securitisation as a proportion of origination, the
spread on home loans declines by 0.2%. In case of CMBS, the spread over
risk-free rate had contracted phenomenally until 2007. However, after the
2007 subprime crisis, the spread on CMBS rose sharply and delinquencies
have increased substantially (Figure 3.6).

80 Real Estate Finance in the New Economy

REITs

REITs have been able to channelise retail funds into property equity (see
Appendix 4.1 for a brief description of REITs). The growth and acceptance of
REITs worldwide indicates the potential of this mechanism in channelising
funds for property investment. Since REITs are traded on public markets,
any market information gets quickly reflected in the price of the security.
The problem, however, is that the NAV or the underlying portfolio is deter-
mined through conventional valuation in the direct property market.

1
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Figure 3.6 US CMBS 2004–2008 vintage delinquencies.
Source: Reproduced by permission of RREEF (2010).

1996 1998

Premium/discount to NAV

2000 2002 2004 2006 2008 2010*1990
–60

–

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Figure 3.7 REIT premium or discount to NAV in the United States. * indicates
estimate.
Source: Reproduced by permission of RREEF (2010).

Financial Systems, Flow of Funds to Property and Innovations 81

A number of studies indicate that the indirect property market (securities
traded on the public markets) leads the direct property market. The result is
that REIT securities are traded either at a premium or discount (Figure 3.7).
The disagreement on value between the direct and indirect market also
offers opportunities for arbitrage.

Impact of new investment mechanisms on business
culture and practices

Arms length (market) versus relationship (bank based)

One of the major criticisms of public investment mechanisms is that they
are arms-length deals. They shift the responsibility of monitoring and
management of asset cash flows to the market. The financing is done by
the market, and banks are merely conduits between investors and inves-
tees. Investors in the public market have rather short-term perspective
than those who invest in direct property. In public markets, credit rating
agencies play a crucial role. However, the experience of 2007 subprime
crisis indicates that failure could happen on part of credit rating agencies
(Chapter 6).

One major risk with property is the large lot size of most commercial
property assets, which, along with property heterogeneity, exposes lenders
to high level of specific risk. Loan syndication though allows for sharing of
risks among lenders, reduced monitoring and due diligence costs but also
exposes them to another risk, counterparty risk. This risk arises because of
the reduced level of due diligence that banks in a syndicate undertake, each
assuming that others would have done detailed loan appraisal and risk anal-
ysis. During strong markets, the tendency to do detailed risk analysis by
participating banks in a syndicate is far more less.

Caution in lending has led to cyclical trends in property lending. During
strong markets, competition among banks leads to compression in interest
rates, high LTVs on already inflated valuations and lax due diligence.
Precisely, the reverse happens during weak markets.

Cultural impediments The new market-based mechanisms for investment
in property may conflict with the business culture. For example, the first
asset securitisation in Japan was in 1994. Three years after, in an attempt to
encourage securitisation, regulators in Japan amended trust and special
purpose corporation laws that addressed tax and commercial code hurdles.
These changes cleared the way for establishment of privately placed bonds
backed by property and for debt collateralised by property (Feder and Kim,
2002). Despite these changes, the development of securitisation market in

82 Real Estate Finance in the New Economy

Japan has been slow. A major reason for slow development of securitisation
market is the cultural impediments that prevent financial institutions from
fully embracing securitisation. Japanese law requires that obligators be
notified of loan assignments (Feder and Kim, 2002). However, financial
institutions are unwilling to notify debtors that their loans have been
assigned since they believe that this would embarrass their clients and
would also damage the banking relationships.

The due diligence process that goes with new mechanisms also faces cul-
tural barriers. Loans and customers verification on phone or face-to-face
meetings as in the United States is seen as an invasion of privacy in Japan.
Bankruptcy and dealing with it is also viewed differently in Asia (Feder and
Kim, 2002). Culturally, Asians view bankruptcy as a personal failure, which
causes problems in taking corrective actions. Instead of letting the bankrupt
entity fail, governments and courts in Asia have tried to keep them alive,
often by asking banks to restructure loans rather than foreclose, at the cost
of economic recovery.

Another cultural impediment that comes in the way of mechanisms
such as sale and leaseback, particularly in Asia, is that companies are reluc-
tant to sell their property assets as this is viewed as a sign of weakness or
even failure.

Valuation practices Valuation practices prevalent in different markets
cause impediments for the development and adoption of new mechanisms.
As has been discussed earlier, valuation practices have hindered innovations
in lease structures. There are differences across markets as well. For
example, while valuers in Western countries value assets on the basis of
discounted cash flow method, that is, the return rate generated from
properties, the valuation approach used in many Asian markets is very
different. In Japan, there is a practice of government notifying official land
values, which is used in valuation by banks and landlords (Feder and Kim,
2002). This leads to substantial differences in the valuation arrived at by
using discounted cash flow methods and alternate practices used in some
markets.

Conclusions

To summarise, the chapter presented a nontechnical overview of the
mechanisms through which property is financed. The evolution of these
mechanisms, either bank based or market based, depends on the inves-
tor requirements as well as the institutional and regulatory framework
within which financial systems operate. In any economy, both bank-based

Financial Systems, Flow of Funds to Property and Innovations 83

and market-based mechanisms are available to channelise funds from
investors to property asset class. A range of investors (households,
institutional investors, corporations, unregulated investors) through
intermediaries (mutual funds, security dealers, banks, ABS, REITs, private
equity, sovereign funds) and various investment mechanisms invest in
property.

The innovation in property investment has been in the lease financing
and development of public markets for property asset through ABS, CMBS
and REITs. In addition, property derivatives have also come up recently. The
discussion in the chapter suggests that some of the oft-cited advantages of
these mechanisms are unfounded. The contribution of these mechanisms
though has been in widening the space of investment in property. Advantages
to investors associated with innovative mechanisms depend on their
individual circumstances.

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The Determinants of Subprime Mortgage Performance
Following a Loan Modification

Maximilian D. Schmeiser & Matthew B. Gross

Published online: 24 February 2015
# Springer Science+Business Media New York (outside the USA) 2015

Abstract We examine the evolution of mortgage modification terms obtained by
distressed subprime borrowers during the recent housing crisis and the effect of the
various types of modifications on the subsequent loan performance. Using the
CoreLogic Loan Performance dataset that contains detailed loan level information on
mortgages, modification terms, second liens, and home values, we estimate a discrete
time proportional hazard model with competing risks to examine the determinants of
post-modification mortgage outcomes. We find that principal reductions are particularly
effective at improving loan outcomes, as high loan-to-value ratios are the single greatest
contributor to re-default and foreclosure. However, any modification that reduces total
payment and interest (P&I) reduces the likelihood of subsequent re-default and fore-
closure. Modifications that increase the loan principal—primarily through capitalized
interest and fees—are more likely to fail, even while controlling for changes in P&I.

Keywords Mortgagemodification . Subprime .Mortgage default . Foreclosure . HAMP

JEL Classification D12 . G21 . R20 . R28

Introduction

Following the exuberant housing market of the mid-2000s, a national housing price
collapse that began in 2007 resulted in many borrowers owing more on their mortgages
than their homes were worth. This inability to pay off a mortgage with the proceeds
from a home’s sale, combined with widespread unemployment and declines in income,
made many mortgages unsustainable for borrowers (Mayer et al. 2009). In response to
the resulting millions of homeowners who defaulted on their mortgages and faced

J Real Estate Finan Econ (2016) 52:1–27
DOI 10.1007/s11146-015-9500-9

The views expressed are solely those of the authors and do not represent the views of the Federal Reserve
Board, the Federal Reserve System, or their staff members.

M. D. Schmeiser (*)
Federal Reserve Board, Washington, DC, USA
e-mail: max.schmeiser@frb.gov

M. B. Gross
University of Michigan, Ann Arbor, MI, USA

foreclosure, mortgage modifications were actively pursued by policymakers, consumer
advocates, and, to a lesser extent, investors and mortgage servicers as a means of
keeping borrowers in their homes.

Mortgage modifications, whereby the terms of the loan are altered in order to
promote repayment by a distressed borrower, were relatively rare prior to the recent
housing crisis. The vast majority of defaults self-cured, and foreclosure proceedings
offered the lender or servicer a high recovery rate for the remaining loans (Ambrose and
Capone 1996; Capone 1996; Adelino et al. 2009). This dynamic was altered during the
housing crisis when mortgage default rates rose dramatically and the share of self-
curing delinquent mortgages plummeted, particularly among subprime mortgages
(Agarwal et al. 2011; Sherlund 2008). This increase in defaults, combined with
plunging home values, changed the relative costs and benefits of providing alternatives
to foreclosure, including mortgage modifications (Cutts and Merrill 2008).

Early in the housing crisis, the parameters of mortgage modifications, including
when they were even offered, varied widely depending on the mortgage servicer
(Agarwal et al. 2011). Moreover, the mortgage modifications made in 2008 often
failed to lower monthly payments for the borrower, with approximately half of all
modifications in the subprime and alt-a market yielding payment increases (White
2009). Similarly, data from the Office of the Comptroller of the Currency’s (OCC)
mortgage metrics report, which includes prime loans and covers approximately
two-thirds of all first-lien mortgages outstanding in the United States, show that,
in 2008, 32 % of modified loans resulted in an increase in monthly payments and
42 % in a decrease in the monthly payments (Office of the Comptroller of the
Currency 2009). As these early mortgage modifications rarely improved the
affordability of the mortgage payment, the loans were highly likely to re-default
following the modification: Over 60 % of mortgages modified in 2008 had re-
defaulted within 12 months (Goodman et al. 2011).

As part of the policy response to the financial crisis, the federal government allocated
billions of dollars to programs aimed at assisting homeowners in distress. These
programs included the Home Affordable Modification Program (HAMP), introduced
in March 2009, which provided incentive payments to mortgage lenders, servicers,
borrowers, and investors for modifying loans to conform to the HAMP guidelines. The
primary requirement was that the first lien mortgage payment be reduced to 31 % of the
borrower’s income; however, the terms of the loan that are modified in order to achieve
the reduction in payment varied from borrower to borrower. The intent of the HAMP
payment reduction requirement was to improve the affordability of the mortgage for
distressed borrowers and thus improve their chances of remaining in their homes.

Following the introduction of HAMP, an increasing share of modified loans received
payment decreases, regardless of whether or not they qualified as HAMP modifica-
tions. In the first quarter of 2009, 53 % of modifications involved a payment reduction;
by the second quarter of 2009, 78 % of modifications involved a payment reduction.
Thereafter, the percent of modifications involving a reduction in the monthly payment
continued to increase, reaching approximately 93 % by the fourth quarter of 2012
(Office of the Comptroller of the Currency 2013). While many mortgage modifications
since the implementation of HAMP are not classified as resulting directly from the
program, the standard terms offered on these proprietary modifications changed fol-
lowing HAMP’s implementation (Goodman et al. 2011).

2 M. D. Schmeiser, M. B. Gross

The number of mortgage modifications increased substantially beginning in 2009
and peaked at over 250,000 in the second quarter of 2010 (Goodman et al. 2012; Office
of the Comptroller of the Currency 2011). While the number of modifications each
quarter has generally decreased since mid-2010, as of the second quarter of 2014,
2.49 % of residential mortgages were still at some stage of the foreclosure process and
6.04 % were at least one payment past due but not in foreclosure (Mortgage Bankers’
Association 2014). Thus, mortgage modifications continue to play an important role in
the recovery of the housing market, and it is therefore important to understand what
aspects of modifications are most successful at allowing the borrower to avoid default
and foreclosure.

Despite the important role that mortgage modifications have played in the response
to the housing crisis, relatively little research examines which types of mortgage
modifications are the most successful at avoiding subsequent re-default and foreclo-
sure. While a handful of studies examined post-modification loan performance, that
research has either tended to focus on narrow geographic areas (Voicu et al. 2012a) or
only pre-HAMP loan modifications (Quercia and Ding 2009; Haughwout et al. 2009;
Agarwal et al. 2011). This study augments the existing literature by examining post-
modification loan performance for a national sample of subprime loans using a rich
dataset that includes information on junior liens, current property valuations, and
detailed information on the parameters of loan modifications. Specifically, we examine
whether reductions in principal, interest rate, or P&I, are most effective at reducing
subsequent re-default and foreclosure. Using loan-level data from CoreLogic’s Loan
Performance Asset Backed Securities (ABS) data on privately securitized subprime
mortgages originated from 2000 through 2007, we find that principal reductions are the
most effective type of modification, as they generally lower the borrower’s monthly
payment and reduce the loan-to-value (LTV) ratio in addition to having an independent
effect on re-default. However, any modification that improves the affordability of the
mortgage, such as a reduction in the monthly P&I, reduces the probability of subse-
quent re-default and foreclosure. Our results provide insights to loan servicers, mort-
gage investors, and policymakers as to the relative effectiveness of the various types of
loan modifications, allowing them to more accurately assess the cost of a modification
relative to the cost of a foreclosure.

Previous Literature

A large body of literature exists on the determinants of mortgage default for prime
mortgages (Deng et al. 2000; Phillips and VanderHoff 2004; Quercia and Stegman
1992; Ambrose et al. 1997) and subprime mortgages (Kau et al. 2011; deRitis et al.
2010; Danis and Pennington-Cross 2008) prior to the housing crisis. However, from the
1990s through the mid-2000s, mortgage underwriting standards declined substantially,
resulting in an unprecedented national wave of defaults and foreclosures when house
prices subsequently fell and economic conditions deteriorated (Demyanyk and Van
Hemert 2011; Haughwout et al. 2008; Mian and Sufi 2009).

With this wave of mortgage defaults, researchers turned their attention to analyzing
mortgage outcomes for borrowers in default, with an emphasis on whether the loan
terminated in foreclosure or received a modification. These studies identified a wide

Subprime Mortgage Performance Post-Modification 3

range of factors that affect mortgage outcomes, with state laws governing foreclosure,
the amount of home equity, credit scores at origination, and the presence of junior liens
among the most significant (Voicu et al. 2012b; Chan et al. 2014; Gerardi et al. 2013b).
Interventions, such as mortgage default counseling, were also shown to substantially
increase the probability that a borrower receives a loan modification and reduces the
probability of foreclosure (Collins and Schmeiser 2013; Collins et al. 2013).

While the literature on outcomes for loans in default following the housing crisis has
provided significant insight into the determinants of receiving a loan modification, a
much smaller body of literature has examined the parameters of mortgage modifica-
tions and how they affect subsequent loan performance. Among the earliest studies of
post-modification loan performance was Quercia and Ding (2009), who used a national
sample of subprime and alt-a securitized mortgages drawn from the Columbia Collat-
eral File that were modified in 2008. They found that the greater the reduction in the
monthly payment, the lower the likelihood that the mortgage would re-default by
December 2008. Payment reductions achieved through a combination of rate and
principal reductions were most effective at reducing re-default, followed by rate
reductions alone.

Many of the subsequent studies focused on analyzing the performance of pre-
HAMP loan modifications. For example, Haughwout et al. (2009) used the CoreLogic
Loan Performance data on subprime and alt-a securitized loans to analyze the deter-
minants of post-mortgage modification re-default prior to the implementation of
HAMP. Using a proportional hazard framework, they find that the greater the reduction
in the monthly payment, the lower the likelihood that the mortgage re-defaults. Further,
they find that having a negative equity position substantially increases the probability
of re-default.

Agarwal et al. (2011) also focused on mortgage modifications that occurred prior to
the introduction of HAMP. Using the OCC Mortgage-Metrics database, they estimate
the probability that a loan re-defaults (60 plus days delinquent) within six months of a
modification, and find that the probability of re-default declines the more monthly
payments are reduced, and that re-default rates increase as LTV increases. They also
find that the servicer of the mortgage has a significant effect on the ultimate success of
the modifications, even after controlling for the terms of the modification.

One of the only studies to examine mortgage modifications both pre- and post-
HAMP was done by Voicu et al. (2012a). Focusing only on the New York City area,
they find that modifications where the interest rate or principal are reduced are less
likely to re-default. Further, they find that HAMP modifications perform better than
proprietary modifications, although they are unable to determine what aspects of
HAMP yield better loan performance.

Our research expands on this existing literature in several ways. First, we use a
sample of subprime and alt-a mortgages drawn from across the United States rather
than one specific geographic area. Second, we examine both HAMP and proprietary
mortgage modifications from 2008 through 2013 and follow their performance through
the fourth quarter of 2013. Finally, using a discrete time proportional hazard frame-
work, we control for the full range of information CoreLogic collects on the loans,
including the presence and amount of any junior liens, a current property value
generated using an automated valuation model (AVM), and detailed terms for the
mortgage modifications.

4 M. D. Schmeiser, M. B. Gross

Data

The data for this study come from CoreLogic’s Loan Performance ABS data on
privately securitized mortgages. The CoreLogic ABS data include information on
subprime and alt-a loans but do not include information on agency-backed securities
or loans held in portfolio.1 As of 2010, these data contained monthly performance
history for about 20 million individual loans. The CoreLogic data used in this paper are
only representative of privately securitized subprime and alt-a loans, not the entire U.S.
mortgage market. While the coverage of these data may limit the generalizability of our
findings, these loans are of particular interest to investors and policymakers given the
high incidence of default, foreclosure, and modification in this population.

The CoreLogic data contain detailed static and dynamic information on the loans
and their performance. The static data include information from origination such as date
of origination, the zip code where the property is located, the borrower’s FICO score,
origination balance, interest rate, P&I amount, and servicer. The dynamic data are
updated monthly and include information on the current interest rate, mortgage balance,
payment amount, and loan performance.

CoreLogic also provides two supplemental files that are used in our analysis. The
first contains detailed information on whether a borrower received a loan modification,
as well as the parameters of the modification (for example, reduction in principal,
reduction in interest rate, or change in amortization term). While CoreLogic does not
explicitly identify a loan as being a HAMP modification, we infer whether or not the
loan was modified under HAMP by whether the characteristics of the modification
follow the HAMP program waterfall for reducing the monthly payment, such as
reducing the interest rate to 2 % and then extending the term of the loan once the
2 % floor is reached. The second file is the CoreLogic TrueLTV Data, which matches
the loans in the CoreLogic Loan Performance data to public records to obtain infor-
mation on subsequent liens taken out on the property. These data also contain a
monthly estimate of the property’s value from their AVM. The combination of monthly
data on the value of all liens on the property with the monthly estimate of the property’s
value from the AVM allows for the computation of a current combined loan-to-value
(CLTV) ratio.

The ability to include a current estimate of CLTV based on the inclusion of junior
liens in the loan amount and a value estimated specifically for that property represents a
major improvement over previous studies. Past research has largely excluded junior
liens from the loan amount and has been limited to the inclusion of metropolitan
statistical area (MSA) level price indices or adjusting the appraised value at origination
by some price index to capture current property value.

Given the number of loans in the CoreLogic ABS data, we select a 5 % random
sample from the universe of first-lien mortgages. Our data on modifications and loan
performance cover the period from January 2008 through December 2013. We restrict
our data to loans originated no earlier than January 2000 and modifications occurring
after January 2008. To provide economic context for the loan performance, we merge
in monthly state-level unemployment rates obtained from the Bureau of Labor

1 CoreLogic also has a separate database on privately securitized prime and/or jumbo loans; however we
restrict our analysis sample to the subprime and alt-a loan data.

Subprime Mortgage Performance Post-Modification 5

Statistics. Finally, in order to proxy for local housing market conditions and borrowers’
expectations for future house price changes, we include the year-on-year percent
change in the property ZIP code’s House Price Index (HPI) from CoreLogic.

After we merge our 5 % random sample of the CoreLogic ABS data with the
supplemental loan modification file and drop all observations for loan identification
(ID) numbers that have no modifications over the course of our study period, we have
approximately 2.3 million loan month observations from approximately 64,000 indi-
vidual loans. After dropping observations with missing data, we are left with 37,027
unique loans. Figure 1 plots the number of mortgage modifications occurring each
month in our sample over the period from January 2008 to December 2013. The
number of monthly modifications peaked in early 2009, just prior to the enactment
of HAMP, before plummeting.2 The number of modifications increased sharply again
in early 2010, and since then it has largely declined.

Figure 2 plots the terminal outcomes for all of the modified loans in our data over
time. The graph shows that real estate owned (REO) is the most likely terminal
outcome for a modified loan in our sample, except for two short periods in 2012 and
2013. The peak of foreclosure occurred at the end of 2011 and has fluctuated below that
peak in the time since. Short sales and foreclosure sales increased to a peak at the end of
2012 and appear to have declined in the months after, while payoffs have remained
relatively flat over the sample period.

Figure 3 shows a survival graph for the share of loans that remain current or 30 days
delinquent in the months following a modification. The survival rates to 60 plus days
delinquent are plotted separately by the year in which the mortgage received its first
modification to illustrate the substantial variation in subsequent loan performance. The
rate at which loans become 60 plus days delinquent following a modification declines
substantially in each successive year from 2008 to 2012. For loans first modified in
2008, over 60 % had re-defaulted within 12 months of the modification. In contrast, for
loans first modified in 2012, the 12-month re-default rate had declined to only 20 %.

The top panel of Fig. 4 shows the percentage of modified loans receiving either a
principal increase or decrease over the sample period. From 2008 until 2012, a loan
modification was far more likely to result in an increase in the mortgage principal
balance than to result in a decrease in mortgage principal, as fees and accrued interest
were often rolled into the modified principal amount. From 2009 through 2010,
approximately 80 % of modifications resulted in the mortgage principal increasing,
thereafter declining until reaching less than 40 % in late 2012. The share of modifica-
tions resulting in principal decreases rose steadily throughout the sample period, and by
mid-2012 actually exceeded the share of loans with principal increases. Since mid-
2012, the share of modified loans in our sample involving a principal reduction has
consistently exceeded 40 %.

The bottom panel of Fig. 4 shows the percentage ofmodified loans that yield either an
increase or decrease in the monthly payment amount. Throughout the study period, the
majority of modifications have resulted in a reduction in monthly borrower payments.
The share of borrowers whose monthly payment was lowered has increased over time,
rising from around 50 % in January 2008 to just below 80 % in October 2013.

2 This drop in modifications may be partially attributable to mortgages qualifying for HAMP modification and
entering their three-month trial period, as HAMPmodifications are not counted until they are made permanent.

6 M. D. Schmeiser, M. B. Gross

As found in the previous literature, the typical modification received by borrowers
varies substantially over time, with the launch of the HAMP program corresponding to
a change in the terms of modifications. Prior to the implementation of HAMP in March
2009, 21 % of modifications resulted in a P&I increase and 73 % a P&I decrease, and
79 % resulted in an increase in the principal balance, while only 4 % resulted in a
reduction in principal. For those whose P&I was reduced, the average reduction was
17 % of the pre-mod P&I. Post-HAMP, the share of modifications that resulted in a P&I
increase fell to 11. While 69 % of modifications still resulted in an increase in the
principal balance following the introduction of HAMP, the share where the principal

Fig 1 Number of mortgage modifications per month for the sample

Fig 2 Number of mortgage terminations per month by termination type, for the sample

Subprime Mortgage Performance Post-Modification 7

was reduced increased to 22 %. Moreover, among those receiving a principal reduction,
the average amount went from $17,253 pre-HAMP to $65,633 post-HAMP. The share
of loans that involved a reduction in the interest rate increase only slightly from pre- to
post-HAMP, going from 82 % to 87 % of modifications.

Table 1 presents descriptive statistics for the first modification experienced by each
mortgage in our analysis sample. We further present summary statistics separately for non-
HAMP andHAMPmodifications. The subprime nature of our sample is apparent from the
average characteristics at the time of origination: 50 % had low or no documentation and
the average FICO score was 638. Nearly three-fourths of the mortgages were originated in
either 2005 or 2006, and 62 % were refinancings. The majority of first modifications were
performed from 2008 to 2010, with only 29 % occurring in 2011 through 2013. Almost
23 % of the first modifications in our sample are classified as HAMP modifications.

On average, 15 % of first modifications resulted in a P&I increase, 80 % resulted in a
P&I decrease, and the remaining 5 % experienced no change in P&I. For those loans
where the P&I was reduced, the average decrease was $949. The reduction in P&I was
largely driven by a reduction in the interest rate on the loan, with an average rate
reduction of 3.9 percentage points. Nearly three-fourths of the first modifications in our
sample result in an increase in principal balance, consistent with Fig. 4 and the OCC
Mortgage Metrics reports. Almost 16 % of modifications, and 36 % of HAMP modi-
fications, resulted in a principal reduction, with an average reduction of $76,000 and
$83,000, respectively, among those loans where the principal was reduced. The average
principal balance post-modification was $265,000, and 45 % of the sample had a junior
lien at the time of modification. Overall, the average CLTV barely changed before and
after the modification, remaining at 115 %, meaning that even after a modification the
average homeowner was underwater on his mortgage. Moreover, almost 16 % of those
receiving a modification had a CLTV greater than 150 % after their modification.

Fig 3 Kaplan-Meier survival graph for mortgage performance, by year of modification Notes: Failure is
defined as the mortgage reaching 60 plus days delinquent post-modification. Analysis time begins at the
month of modification.

8 M. D. Schmeiser, M. B. Gross

Empirical Model

We begin our analysis of how the various types of loan modifications affect subsequent
loan performance by using a simple probit model to estimate the probability that a loan
reaches 60 plus days delinquent within 12 months following a loan modification. Our
probit model takes the form:

Pr Y izs ¼ 1ð Þ ¼ f αþ βX i þ γModi þ δCLTV i þ π HPIz þ θStates þ εisð Þð1Þ
where Y is an indicator for whether or not the loan becomes 60 plus days delinquent
within 12 months and X is a vector of loan characteristics from origination, including
an indicator for whether the loan was used for a home purchase, categories for the

Fig 4 Share of sample loan modifications where borrower has their principal increased/decreased (top) and
payment increased/decreased (bottom), by date of modification

Subprime Mortgage Performance Post-Modification 9

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Ju
di
ci
al
fo
re
cl
os
ur
e
st
at
e

0.
37
86

0.
48
51

0.
37
65

0.
48
45

0.
38
59

0.
48
68

St
at
e
w
ith

re
de
m
pt
io
n
la
w

0.
46
24

0.
49
86

0.
44
57

0.
49
70

0.
51
99

0.
49
96

N
on
-r
ec
ou
rs
e
st
at
e

0.
58
00

0.
49
36

0.
56
47

0.
49
58

0.
63
21

0.
48
23

C
ur
re
nt

at
tim

e
of

m
od

0.
26
46

0.
44
11

0.
28
39

0.
45
09

0.
19
87

0.
39
90

30
to

60
da
ys

de
lin
qu
en
t
at
m
od

0.
13
20

0.
33
85

0.
14
91

0.
35
62

0.
07
34

0.
26
09

90
da
ys

de
lin
qu
en
t
at
m
od

0.
49
49

0.
50
00

0.
46
58

0.
49
88

0.
59
44

0.
49
10

L
is
pe
nd
en
s
at
m
od

0.
10
85

0.
31
10

0.
10
12

0.
30
16

0.
13
35

0.
34
01

O
ri
gi
na
te
d
20
04

0.
08
31

0.
27
61

0.
08
88

0.
28
45

0.
06
36

0.
24
41

O
ri
gi
na
te
d
20
05

0.
28
34

0.
45
06

0.
29
37

0.
45
55

0.
24
80

0.
43
19

O
ri
gi
na
te
d
20
06

0.
45
75

0.
49
82

0.
45
31

0.
49
78

0.
47
25

0.
49
93

O
ri
gi
na
te
d
20
07

0.
14
11

0.
34
81

0.
12
72

0.
33
32

0.
18
85

0.
39
12

Fi
rs
t
m
od
if
ie
d
in

20
08

0.
25
22

0.
43
43

0.
32
60

0.
46
87

0.
00
00

0.
00
00
Fi
rs
t
m
od
if
ie
d
in

20
09

0.
21
66

0.
41
19

0.
25
64

0.
43
66

0.
08
05

0.
27
20

Fi
rs
t
m
od
if
ie
d
in

20
10

0.
24
17

0.
42
81

0.
20
18

0.
40
14

0.
37
83

0.
48
50

Fi
rs
t
m
od
if
ie
d
in

20
11

0.
13
36

0.
34
02

0.
10
25

0.
30
33

0.
23
98

0.
42
70

Fi
rs
t
m
od
if
ie
d
in

20
12

0.
09
29

0.
29
03

0.
06
52

0.
24
69

0.
18
75

0.
39
03

Fi
rs
t
m
od
if
ie
d
in

20
13

0.
06
30

0.
24
30

0.
04
81

0.
21
40

0.
11
40

0.
31
79

10 M. D. Schmeiser, M. B. Gross

T
ab
le
1

(c
on
tin

ue
d)

Fu
ll
sa
m
pl
e
N
on
-H
A
M
P
H
A
M
P
M
ea
n
SD
M
ea
n
SD
M
ea
n
SD

FI
C
O

at
or
ig
in
at
io
n

63
8.
13

64
.9
9

63
2.
50

64
.

0
0

65
7.
37

64
.6
5

H
A
M
P
m
od
if
ic
at
io

n
in

di
ca
to
r

0.
22
62

0.
41
84

0
0

1
0

M
on
th
ly

P&
I

pr
e-
m

od
if
ic
at
io
n

($
)

1,
68
4.
68

1,
66
9.
09

1,
64
4.
52

1,
01
5.
80

1,
82
2.
08

2,
96
0.
24

R
ed
uc
tio

n
in

P&
I
($
)

−9
49
.7
1

64
2.
40

−7
33
.3
8

56
2.
82

−1
08
7.
37

1.
98

Pe
rc
en
t
in
cr
ea
se

in
P&

12
.6
51
3

15
.1
13
0

12
.6
51
3
15
.1
13
0
0
0

Pe
rc
en
t
re
du
ct
io
n
in

P&
I

51
.0
82
9

18
.1
56
7

40
.1
09
2

16
.9
40
9

58
.0
65
5

15
.2
15
1

In
te
re
st
ra
te

pr
e-
m
od
if
ic
at
io
n

7.
33
24

2.
06
02

7.
62
68

1.
94
75

6.
32
54

2.
11
66

R
ed
uc
tio
n
in

in
te
re
st
ra
te

−3
.8
50
7

1.
98
35

−2
.8
79
9

1.
67
38

−4
.4
68
5

1.
91
69

Pe
rc
en
t
re
du
ct
io
n
in
in
te
re
st
ra
te

54
.1
99
0

21
.0
55
4

36
.9
97
6

18
.5
38
0

65
.1
44
3

14
.0
96
6

Pr
in
ci
pa
l
ba
la
nc
e
pr
e-
m
od
if
ic
at
io
n
($
)

26
4,
57
0

17
3,
88
4

25
0,
52
1

16
7,
99
9

31
2,
63
4

18
4,
76
2

Pr
in
ci
pa
l
ba
la
nc
e

po
st
-m

od
if
ic
at
io
n
($
)

26
5,
11
6

18
4,
15
6

25
5,
89
9

18
2,
76
6

29
6,
64
8

18
5,
41
2

R
ed
uc
tio
n
in

pr
in
ci
pa
l

($
)

−7
6,
17
9

74
,1
04

−6
5,
65
9

68
,8
39

−8
2,
87
2

76
,5
29

Pr
in
ci
pa
l
de
cr
ea
se

in
di
ca
to
r

0.
15
79

0.
36
46

0.
09
9

0.
29
87

0.
35
93

0.
47
98

Pe
rc
en
t
re
du
ct
io
n
in
pr
in
ci
pa
l

25
.4
18
3

16
.4
98
9

23
.9
37
6

15
.6
32
4

26
.3
60
4

16
.9
62
8

Pr
in
ci
pa
l
in
cr
ea
se

in
di
ca
to
r

0.
72
66

0.
44
57

0.
77
39

0.
41
83

0.
56
48

0.
49
58

L
oa
n
to

va
lu
e
ra
tio

pr
e-
m
od
if
ic
at
io
n

11
5.
09

38
.3
4

11
1.
75

35
.9
7

12
6.
52

43
.6
2

L
oa
n
to
va
lu
e
ra
tio
po
st
-m
od
if
ic
at
io
n

11
5.
30

70
.1
9

11
3.
61

75
.5
1

12
1.
09

47
.2
7

C
LT

V
15
0
%

an
d
ab
ov
e

0.
15
55

0.
36
24

0.
12
76

0.
33
37

0.
25
09

0.
43
35

Y
ea
r
on

ye
ar

ch
an
ge

in
H
PI

(%
)

−6
.1
4

10
.0
7

−7
.5
9

10
.1
6

−1
.1
7

7.
95

U
ne
m
pl
oy
m
en
t
ra
te
at
m
od
if
ic
at
io
n

8.
83

2.
42

8.
50

2.
44

9.
99

1.
98

N
um

be
r
of

lo
an
s

37
,0
27

28
,6
52

8,
37
5

Subprime Mortgage Performance Post-Modification 11

borrower’s FICO score, whether the home was owner-occupied, whether the loan had
low or no documentation, and indicators for the origination year. Mod is a vector of
loan characteristics at the time of modification, including loan servicer fixed-effects,
indicators for the payment status of the loan with current as the omitted category, an
indicator for whether the property has a junior lien, and modification year indicators.
Mod further includes the key characteristics of the mortgage modification of interest for
our analysis: whether it is a HAMP modification, the percent reduction in principal, an
indicator for having an increase in principal, the percent reduction in P&I, the percent
increase in P&I, and the percent reduction in the interest rate. We further include the
CLTV ratio at the time of modification in categories with less than 80 % used as the
omitted category. HPI is the year-on-year percent change in the CoreLogic HPI for the
property ZIP code at the time of modification. Finally, State is a vector of indicators for
whether the property is in a judicial foreclosure, redemption law, or non-recourse state.
The probit model is run as a cross-sectional analysis using only the covariate values
from the time of the modification. Moreover, state fixed-effects are omitted so as to
allow the inclusion of the various state-specific mortgage laws.

We supplement this probit analysis with a discrete time proportional hazard frame-
work with competing risks analysis of how the various modification parameters affect
mortgage performance and mortgage outcomes over the entire post-modification peri-
od. This strategy also allows us to take advantage of time variation in variables such as
the CLTV and the state unemployment rate.

Once a borrower receives a modification, he should be current on his payments in
the month following the modification, creating a good origination point to compare the
outcomes of modified loans. Since we focus only on modified loans, we drop all
mortgages that do not receive modifications over the course of our sample period. The
status of a mortgage in our sample in a given month can take on one of a number of
different discrete states. We categorize the set of possible states into six options: current
or 30 days delinquent, 60 plus days delinquent, foreclosure filing (lis pendens), REO/
sale out of foreclosure, short payoff, or re-modification. REO and sale out of foreclo-
sure are combined into one outcome since they are equivalent from the borrower’s
perspective, both resulting in the loss of the home. Since so few borrowers who
received a modification prepaid their mortgage in full, we simply dropped them from
the sample.

We structure our data in event history format so as to estimate our proportional
hazard model using a standard multinomial logit. Months since modification is then
included as a covariate to allow for time dependence of the hazard rate. We include as
covariates the same loan characteristics from origination used in the probit analysis: an
indicator for whether the loan was used for a home purchase, categories for the
borrower’s FICO score, whether the home was owner-occupied, whether the loan
had low or no documentation, and indicators for the origination year. The loan-level
covariates from the time of modification again include loan servicer fixed-effects,
indicators for the payment status of the loan at modification, an indicator for whether
the property has a junior lien, modification year indicators, and indicators for whether
the property is in a judicial foreclosure, redemption law, or non-recourse state. We then
include the characteristics of the mortgage modifications: being a HAMP modification,
the percent reduction in principal, an indicator for having an increase in principal, the
percent reduction in P&I, the percent increase in P&I, and the percent reduction in the

12 M. D. Schmeiser, M. B. Gross

interest rate. However, in the proportional hazard framework we allow the CLTV ratio
to vary over time as the house price and loan balances change. We further include the
monthly state unemployment rate to capture changes in the economic conditions faced
by the borrower, and the ZIP code level percent change in HPI over the past 12 months.

To evaluate whether or not the performance of HAMP modifications differs from
that of proprietary modifications, we re-estimate our proportional hazard model on the
sample limited to non-HAMP and then HAMP modifications. When the sample is
limited to non-HAMP modifications, the covariates included in the analysis remain
identical to those for the full sample, with the exception of the removal of the HAMP
indicator. When the sample is limited to HAMP modifications, we also drop the
indicator for first modification occurring in 2009 and use that as the reference category,
and drop the percent increase in P&I, as none of the HAMP modifications resulted in a
higher P&I for the borrower. Moreover, the sample period now begins in April 2009, as
no HAMP modifications occurred prior to that date.

Results

Table 2 presents the results of our probit analysis of the effect of the various modifi-
cation parameters on the 12-month probability of re-default. Of the key mortgage
modification parameters of interest, the coefficients on reduction in the P&I and the
interest rate have the greatest magnitude and are statistically significant. A 1 %
reduction in P&I is estimated to reduce the probability of re-default by 0.23 percentage
point, while a 1% reduction in the interest rate reduces the probability of re-default by
0.17 percentage point. From Table 1, the average P&I reduction was 51 % and the
average interest rate reduction was 54 % among those who received a reduction.
Overall, 39 % of the loans in our estimation sample become 60 days or more delinquent
within only 12 months of receiving a loan modification, suggesting that the average
reduction in P&I reduced re-default by 30 %, and the average interest rate reduction
reduce re-default by 23 %. The magnitude of coefficient on the percent principal
reduction is somewhat less than that for P&I and interest rate, with that a 1 % reduction
in the principal balance estimated to reduce the 12-month re-default rate by 0.14
percentage points. With an average reduction in principal of 25 % for those who
received a reduction, this translates into a 9 % lower probability of re-default. This is
in addition to the effect of principal reductions that operates through a reduction in the
P&I and the CLTV. While HAMPmods appear to be somewhat less likely to re-default,
the coefficient is only marginally significant.

The probit results also suggest what types of modifications are particularly prone to
failure. In particular, a modification that increases the mortgage principal increases the
probability of re-default within 12 months by 1.9 percentage points, or 5 %, while a 1
percentage point increases in the P&I increases the probability of re-default within
12 months by 0.13 percentage point, or 4 % given an average increase in P&I of 13 %.
Loans that are severely delinquent or in foreclosure at the time of modification are,
unsurprisingly, the most prone to re-default, with a loan modified when 90 plus days
delinquent being 11 percentage points more likely to re-default within 12 months, and a
loan modified when in the foreclosure process being 15 percentage points more likely
to re-default.

Subprime Mortgage Performance Post-Modification 13

Table 2 Determinants of mortgage default post modification

All modifications 60+ days delinquent within
12 months

Junior lien 0.0280***

(5.3485)

Loan used for purchase 0.0427***

(8.2310)

FICO at origination 580 to 649 −0.0462***
(−7.0780)

FICO at origination 650 to 719 −0.1347***
(−17.8363)

FICO at origination 720 and above −0.2253***
(−22.4313)

Not owner occupied 0.0353***

(3.1789)

Low or no documentation 0.0046

(0.9146)

Judicial foreclosure state 0.0256***

(4.8530)

State with redemption law 0.0170***

(3.0925)

Non-recourse state −0.0026
(−0.5052)

30 to 60 days delinquent at modification −0.0170**
(−2.1800)

90 days delinquent at modification 0.1060***

(18.1344)

Lis pendens at modification 0.1476***

(17.9199)

HAMP modification indicator −0.0079
(−0.9975)

Percent reduction in principal −0.0014***
(−4.5463)

Principal increase indicator 0.0194***

(2.9093)

Percent reduction in interest rate −0.0017***
(−10.7336)

Percent reduction in P&I −0.0023***
(−12.3182)

Percent Increase in P&I 0.0013***

(4.0230)

Originated 2004 0.0095

(0.6456)

Originated 2005 0.0165

14 M. D. Schmeiser, M. B. Gross

Table 2 (continued)

All modifications 60+ days delinquent within
12 months

(1.2427)

Originated 2006 0.0321**

(2.4315)

Originated 2007 0.0489***

(3.4342)

First modified in 2009 −0.0737***
(−8.7590)

First modified in 2010 −0.1280***
(−12.5243)

First modified in 2011 −0.1811***
(−17.6658)

First modified in 2012 −0.2013***
(−17.2642)

First modified in 2013 −0.3572***
(−23.5345)

CLTV 80 to 89 % 0.0038

(0.4244)

CLTV 90 to 94 % 0.0005

(0.0472)

CLTV 95 to 99 % 0.0076

(0.6955)

CLTV 100 to 124 % 0.0219***

(2.7271)

CLTV 125 to 149 % 0.0396***

(4.2387)

CLTV 150 % and Above 0.0518***

(5.1732)

Year on year change in HPI −0.0033***
(−10.0126)

Unemployment rate −0.0144***
(−8.7692)

Log-likelihood 20863.829

Chi-Sq 7740.34

Borrowers 37,027

Probit model for 12 month re-default. Coefficients are average marginal effects. Z-statistics in parentheses.
Sample is CoreLogic Loan Performance data on subprime and alt-a mortgages originated from January 1,
2000 to January 1, 2008 and modified after January 1, 2008. Mortgage servicer fixed-effects are included in
the model, but coefficients are omitted due to data license agreement

*p<0.10, **p<0.05, ***p<0.01

Subprime Mortgage Performance Post-Modification 15

Other characteristics that appear to contribute to the probability of re-default within
12 months of a modification include the FICO score at loan origination, the year of
origination and the year of modification, and the CLTV. FICO scores from origination,
which may have been years in the past, do a remarkable job of predicting loan
performance, as those with FICO scores above 720 at origination are 23 percentage
points, or 59 %, less likely to re-default within 12 months of a modification. CLTV is
also strongly predictive of re-default, with those having a CLTV in excess of 150 %
being 5 percentage points, or 17 %, more likely to re-default than those with a CLTV
below 80 %.

Turning now to our proportional hazard model, we examine a wider range of post-
modification loan outcomes. We begin our analysis of post-modification performance
with the full sample of modified loans. Table 3 presents the results of the multinomial
logit model, where the coefficients are reported as relative risk ratios with the reference
outcome being current or 30 days delinquent. In the first column, we report estimates
for the outcome being 60 plus days delinquent, followed by the loan being re-modified,
entering foreclosure, ending in a foreclosure sale or REO, and short payoff in the final
column.

Having a junior lien on the property at the time of modification is among the largest
contributors to the mortgage experiencing an adverse outcome. Loans with junior liens
have a 27 % higher relative risk of being 60 plus days delinquent, entering foreclosure,
or ending in a foreclosure sale or REO. They are also 11 % more likely to require a re-
modification, 27 % more likely to enter foreclosure, and 35 % more likely to terminate
in a foreclosure sale or REO.

Even after a loan modification, the borrower’s FICO score at the time of mortgage
origination remains a strong predictor of subsequent mortgage outcomes. Borrowers
with FICO scores below 580 at origination are used as the reference category in the
model. The higher the origination FICO score, the less likely the loan is to re-default,
enter foreclosure, require re-modification, or enter one of the terminal outcomes. For
example, borrowers with FICO scores between 580 and 649 have a 22 % lower relative
risk of being 60 plus days delinquent, followed by those with a FICO between 650 and
719, who have a 46 % lower relative risk, and then those with a FICO over 720, who
have a nearly 70 % lower relative risk of being delinquent. The effect is similarly
pronounced for the foreclosure filing or foreclosure sale/REO outcomes—borrowers
with FICO scores of 720 or above at origination have a 57 % lower relatively risk of
foreclosure filing and a 43 % lower relative risk of foreclosure sale/REO.

State-level mortgage laws also play a role in determining re-default following a
modification, as loans in states with judicial foreclosure have a 4 % higher relative risk
of being 60 plus days delinquent than states without judicial foreclosure. Judicial
foreclosure states also have a 158 % higher risk of being in the foreclosure process,
but only a 16 % higher risk of foreclosure sale/REO. Conversely, mortgages made in
states with redemption laws, or where mortgages are non-recourse loans, have a 5 %
and 15 % lower relative risk of being 60 plus days delinquent, respectively. Borrowers
in non-recourse states are substantially less likely to experience a foreclosure sale/REO,
with a 26 % lower relative risk.

The modification terms affect the post-modification chances of re-default in ways
that are consistent with our expectations. Reductions in principal balance, interest rate,
and P&I all lower the risk of being 60 plus days delinquent, with a 1 % reduction in any

16 M. D. Schmeiser, M. B. Gross

Table 3 Determinants of mortgage default post modification

All modifications 60+ days delinquent

Modification Foreclosure
filing

REO/
foreclosure
Sale

Short pay
off

Junior lien 1.2733*** 1.1150*** 1.2723*** 1.3459*** 1.1281*

(44.9547) (9.5839) (34.6548) (6.1347) (1.8369)

Loan used for purchase 1.2159*** 1.0438*** 1.3071*** 1.4688*** 1.4738***

(36.8474) (3.7812) (39.2300) (8.0780) (5.9692)

FICO at origination 580
to 649

0.7848*** 0.8934*** 0.8385*** 0.8622** 0.8373**

(−38.1109) (−7.9138) (−21.0595) (−2.4673) (−2.0873)
FICO at origination 650

to 719
0.5410*** 0.7836*** 0.6537*** 0.7441*** 0.8378*

(−78.6858) (−14.6813) (−42.1766) (−4.1235) (−1.8095)
FICO at origination 720

and above
0.3378*** 0.7152*** 0.4266*** 0.5712*** 0.8112*

(−94.0195) (−15.4874) (−57.7733) (−5.5750) (−1.6845)
Not owner occupied 1.0106 0.9582* 1.3679*** 2.1275*** 1.8103***

(0.8250) (−1.6727) (21.3709) (8.9369) (5.1218)
Low or no

documentation
0.9878** 1.0224** 1.2000*** 1.0175 0.8296***

(−2.3411) (2.0058) (27.1400) (0.3623) (−2.8198)
Judicial foreclosure

state
1.0422*** 1.0557*** 2.5766*** 1.1558*** 0.8627**

(7.6312) (4.6364) (133.7266) (2.9102) (−2.0377)
State with redemption

Law
0.9459*** 1.0852*** 0.9335*** 1.0720 1.2980***

(−10.1165) (6.8926) (−9.2896) (1.3807) (3.7563)
Non-recourse state 0.8496*** 0.9907 1.0331*** 0.7376*** 0.7393***

(−31.7401) (−0.8406) (4.7742) (−6.4792) (−4.6375)
30 to 60 days delinquent

at mod
0.8506*** 1.0264 0.6662*** 0.7091*** 0.8609*

(−20.4122) (1.5383) (−37.9351) (−4.6998) (−1.6587)
90 days delinquent at

mod
1.5013*** 1.1951*** 1.3060*** 1.1806*** 1.0781

(67.1580) (13.8839) (34.0016) (3.0398) (1.0346)

Lis pendens at
modification

1.5621*** 1.2345*** 2.1958*** 1.6905*** 0.9957

(50.5895) (11.5045) (77.4103) (7.3833) (−0.0376)
HAMP modification

Indicator
0.8341*** 0.9409*** 0.7914*** 0.7818*** 0.6662***

(−20.3934) (−3.6840) (−19.6933) (−2.7566) (−3.4606)
Percent reduction in

principal
0.9907*** 1.0109*** 0.9852*** 0.9872*** 0.9945

(−25.5084) (18.8404) (−27.8904) (−2.8212) (−1.2255)
Principal increase

indicator
1.2094*** 0.7299*** 1.2489*** 1.1880*** 0.9078

(28.1732) (−23.2988) (25.2877) (2.7894) (−1.2770)
Percent reduction in

interest rate
0.9916*** 0.9950*** 0.9929*** 0.9937*** 0.9916***

(−56.1733) (−15.1709) (−37.6971) (−4.8288) (−4.8137)
Percent reduction in

P&I
0.9914*** 0.9994 0.9862*** 0.9823*** 0.9862***

(−44.1244) (−1.5710) (−55.5205) (−10.0025) (−5.8604)
Percent increase in P&I 1.0045*** 1.0088*** 1.0020*** 0.9957 1.0025

(14.3399) (13.6317) (5.0461) (−1.5278) (0.7809)
Originated 2004 1.1481*** 1.0119 0.9762 0.7048** 1.2120

(8.8870) (0.3647) (−1.1664) (−2.4506) (0.8113)

Subprime Mortgage Performance Post-Modification 17

Table 3 (continued)

All modifications 60+ days delinquent Modification Foreclosure
filing
REO/
foreclosure
Sale
Short pay
off

Originated 2005 1.1088*** 0.9463* 0.9831 0.7445** 1.3131

(7.2592) (−1.8733) (−0.9170) (−2.3776) (1.2376)
Originated 2006 1.1784*** 0.9635 1.0621*** 0.7274*** 1.2920

(11.6467) (−1.2746) (3.2825) (−2.5834) (1.1682)
Originated 2007 1.3672*** 1.0123 1.1482*** 0.6287*** 1.3245

(20.5445) (0.3895) (6.9617) (−3.3706) (1.2147)
First modified in 2009 0.6957*** 0.9107*** 0.7605*** 0.7376*** 0.7112***

(−59.5842) (−5.9199) (−34.9966) (−5.5318) (−4.3944)
First modified in 2010 0.5140*** 0.7017*** 0.6536*** 0.6877*** 0.7741**

(−87.8916) (−20.2591) (−42.3184) (−5.1038) (−2.5694)
First modified in 2011 0.4580*** 0.5377*** 0.7023*** 0.6894*** 0.9344

(−68.9384) (−30.5340) (−22.7338) (−3.1181) (−0.4648)
First modified in 2012 0.4157*** 0.4299*** 0.5889*** 0.4662*** 0.7683

(−47.9201) (−32.1937) (−18.3581) (−2.9972) (−1.0258)
First modified in 2013 0.2637*** 0.5833*** 0.1885*** 0.8902

(−34.7610) (−16.9116) (−17.7689) (−0.2651)
CLTV 80 to 89% 1.0997*** 1.2090*** 1.1745*** 0.9662 0.8112

(8.9568) (9.1192) (10.1971) (−0.2344) (−1.2821)
CLTV 90 to 94% 1.1333*** 1.2079*** 1.3592*** 1.5842*** 0.8945

(10.2082) (7.6177) (17.4063) (3.0610) (−0.5876)
CLTV 95 to 99% 1.1525*** 1.2532*** 1.4145*** 1.7309*** 0.7382

(11.6426) (9.0919) (19.9955) (3.7243) (−1.4955)
CLTV 100 to 124% 1.3217*** 1.4081*** 1.6865*** 2.7290*** 1.2835*

(30.7907) (18.6866) (39.8238) (8.9984) (1.9305)

CLTV 125 to 149% 1.5117*** 1.6120*** 2.1482*** 4.0728*** 2.2934***

(41.3086) (22.9354) (54.4275) (12.0104) (6.1622)

CLTV 150% and above 1.7614*** 1.8950*** 3.1539*** 9.9549*** 5.6810***

(54.6232) (29.2492) (80.3222) (19.8489) (13.1472)

Year on year change
in HPI

0.9940*** 1.0028*** 0.9813*** 0.9874*** 0.9981

(−17.7814) (4.0056) (−41.4944) (−4.1502) (−0.4622)
Unemployment rate 1.0414*** 0.9170*** 1.0731*** 0.9642** 0.9561**

(26.8725) (−28.1434) (32.6121) (−2.4733) (−2.2265)
Log-likelihood −1137702.7
Chi-Sq 316459.25

Observations 1,353,338

Borrowers 37,027

Competing risk models with relative risk ratios reported. t-statistics in parentheses. Sample is CoreLogic Loan
Performance data on subprime and alt-a mortgages originated from January 1, 2000 to January 1, 2008 and
modified after January 1, 2008. Mortgage servicer fixed-effects are included in the model, but coefficients are
omitted due to data license agreement.

* p<0.10, ** p<0.05, *** p<0.01

18 M. D. Schmeiser, M. B. Gross

Table 4 Determinants of mortgage default post modification

Non-HAMP
modifications

60+ days
delinquent

Modification Foreclosure
filing

REO/
Foreclosure
sale

Short pay
off

Junior lien 1.2722*** 1.1124*** 1.2594*** 1.3133*** 1.1135

(41.9659) (8.2996) (31.3031) (5.3759) (1.5593)

Loan used for purchase 1.1960*** 1.0274** 1.2926*** 1.4716*** 1.4356***

(31.7350) (2.1326) (35.5934) (7.7811) (5.3158)

FICO at origination 580
to 649

0.7919*** 0.9109*** 0.8509*** 0.8597** 0.8680

(−35.0749) (−6.0823) (−18.5257) (−2.4449) (−1.6128)
FICO at origination 650

to 719
0.5519*** 0.8046*** 0.6617*** 0.7540*** 0.8471

(−71.7569) (−11.8361) (−38.8341) (−3.8002) (−1.6242)
FICO at origination 720

and above
0.3519*** 0.7207*** 0.4393*** 0.5788*** 0.9019

(−83.4536) (−13.0672) (−51.9767) (−5.1397) (−0.7968)
Not owner occupied 0.9966 0.9461** 1.3513*** 2.0542*** 1.8247***

(−0.2575) (−2.0132) (19.9094) (8.2309) (5.0931)
Low or no

documentation
1.0096* 1.0296** 1.2471*** 1.0498 0.8347***

(1.7061) (2.3728) (31.2218) (0.9749) (−2.6029)
Judicial foreclosure state 1.0415*** 1.0668*** 2.6010*** 1.1973*** 0.8630*

(7.1112) (4.9975) (128.8797) (3.4955) (−1.9599)
State with redemption

law
0.9459*** 1.1487*** 0.9313*** 1.0607 1.3017***

(−9.5693) (10.5981) (−9.1480) (1.1293) (3.6618)
Non-recourse state 0.8667*** 1.0327*** 1.0501*** 0.7434*** 0.7480***

(−26.3498) (2.5861) (6.8252) (−6.0771) (−4.2868)
30 to 60 days delinquent

at mod
0.8711*** 1.0511*** 0.6906*** 0.7253*** 0.8510*

(−16.9756) (2.7606) (−33.8067) (−4.3171) (−1.7504)
90 days delinquent at

mod
1.5439*** 1.2553*** 1.3614*** 1.2212*** 1.0677

(68.2650) (15.9497) (37.4361) (3.5274) (0.8675)

Lis pendens at
modification

1.5786*** 1.3016*** 2.3141*** 1.6851*** 1.0146

(47.8949) (12.6952) (77.2253) (6.9095) (0.1184)

Percent reduction in principal 0.9873*** 1.0159*** 0.9807*** 0.9800*** 0.9936

(−30.8919) (22.8105) (−31.9079) (−3.7905) (−1.3504)
Principal increase indicator 1.1997*** 0.7220*** 1.2490*** 1.1681** 0.8977

(25.4108) (−21.4975) (23.9162) (2.4235) (−1.3746)
Percent reduction in interest

rate
0.9899*** 0.9986*** 0.9909*** 0.9916*** 0.9887***

(−62.8272) (−3.8173) (−44.9936) (−6.0057) (−6.0427)
Percent reduction in

P&I
0.9919*** 0.9989** 0.9860*** 0.9825*** 0.9865***

(−38.1220) (−2.3086) (−51.6676) (−9.1789) (−5.3259)
Percent increase in P&I 1.0041*** 1.0096*** 1.0010** 0.9953 1.0011

(12.4262) (13.6109) (2.4406) (−1.5856) (0.3116)
Originated 2004 1.1733*** 1.0379 0.9912 0.7577* 1.2189

(9.8864) (1.0516) (−0.4093) (−1.8624) (0.8163)
Originated 2005 1.1373*** 0.9968 1.0230 0.8101 1.3033

(8.6818) (−0.0993) (1.1770) (−1.6131) (1.1748)

Subprime Mortgage Performance Post-Modification 19

Table 4 (continued)

Non-HAMP
modifications
60+ days
delinquent
Modification Foreclosure
filing
REO/
Foreclosure
sale
Short pay
off

Originated 2006 1.2277*** 1.0137 1.1121*** 0.7825* 1.3787

(13.9607) (0.4298) (5.5522) (−1.8893) (1.4303)
Originated 2007 1.4490*** 1.0794** 1.2039*** 0.6611*** 1.3900

(23.1838) (2.2166) (8.9034) (−2.8319) (1.3828)
First modified in 2009 0.7002*** 0.9450*** 0.7722*** 0.7438*** 0.7067***

(−57.4827) (−3.5209) (−32.5178) (−5.2972) (−4.4100)
First modified in 2010 0.4941*** 0.7252*** 0.6274*** 0.6777*** 0.7519***

(−87.9564) (−17.3779) (−43.9487) (−5.0708) (−2.7404)
First modified in 2011 0.4602*** 0.5192*** 0.7342*** 0.7994* 1.0223

(−61.2545) (−28.1316) (−18.0242) (−1.7643) (0.1437)
First modified in 2012 0.4078*** 0.3794*** 0.5582*** 0.3994*** 0.8639

(−42.1570) (−30.4024) (−17.1404) (−2.8973) (−0.5365)
First modified in 2013 0.2430*** 0.5534*** 0.1767*** 0.9228

(−31.3391) (−15.5741) (−15.9837) (−0.1686)
CLTV 80 to 89% 1.0913*** 1.1076*** 1.1803*** 0.9633 0.8307

(7.8294) (4.5274) (10.0180) (−0.2428) (−1.1082)
CLTV 90 to 94% 1.1200*** 1.0574** 1.3528*** 1.6300*** 0.9426

(8.7923) (2.0562) (16.3322) (3.1282) (−0.3079)
CLTV 95 to 99% 1.1391*** 1.1070*** 1.4202*** 1.6756*** 0.7082

(10.1602) (3.7557) (19.3251) (3.3213) (−1.6280)
CLTV 100 to 124% 1.3057*** 1.2198*** 1.6880*** 2.7884*** 1.2639*

(27.8860) (9.9254) (37.8945) (8.7554) (1.7466)

CLTV 125 to 149% 1.4722*** 1.3880*** 2.1086*** 4.0417*** 2.3198***

(36.4910) (14.2415) (50.3475) (11.3669) (6.0441)

CLTV 150% and above 1.6945*** 1.6195*** 3.0614*** 9.9146*** 5.3354***

(47.8820) (19.6891) (74.0466) (18.8599) (12.1807)

Year on year change
in HPI

0.9943*** 1.0014* 0.9811*** 0.9860*** 0.9953

(−15.8587) (1.8069) (−39.7647) (−4.4260) (−1.1104)
Unemployment rate 1.0544*** 0.8889*** 1.0879*** 0.9818 0.9665*

(33.6480) (−35.8583) (37.5526) (−1.2106) (−1.6489)
Log-likelihood −1256731.9
Chi-Sq 277739.38

Observations 1,383,936

Borrowers 28,652

Competing risk models with relative risk ratios reported. t-statistics in parentheses. Sample is CoreLogic Loan
Performance data on subprime and alt-a mortgages originated from January 1, 2000 to January 1, 2008 and
receiving a proprietary modification after January 1, 2008. Mortgage servicer fixed-effects are included in the
model, but coefficients are omitted due to data license agreement

* p<0.10, ** p<0.05, *** p<0.01

20 M. D. Schmeiser, M. B. Gross

of these terms reducing the risk of being delinquent by 0.9, 0.8, and 0.9 %, respectively.
Conditional on receiving a principal reduction, the average mortgage balance reduction
was 25 % (Table 1), suggesting that a typical loan that received a principal reduction
was 23 % less likely to be 60 plus days delinquent. As our model controls for any
changes in the CLTV resulting from the principal reduction, this estimate captures only
part of the total effect of a principal reduction on subsequent loan performance. For
those who received an interest rate reduction, the average change was 54 %, while for
those receiving a P&I reduction the average was 51 %; thus, our coefficient estimates
imply typical reductions in the relative risk of being 60 plus days delinquent of 45 %
and 44 %, respectively.

Turning to the effect of mortgage modification terms on the subsequent risk of
entering foreclosure or terminating in a foreclosure sale/REO, the effect of principal
reductions and P&I reductions becomes even more pronounced. A 1 % reduction in
principal is estimated to reduce the likelihood of receiving a foreclosure filing by 1.5 %
and terminating in a foreclosure sale/REO by 1.3 %. A 1 % reduction in P&I yields a
1.4 % reduction in the likelihood of receiving a foreclosure filing, a 1.8 % reduction in
the likelihood of terminating in a foreclosure sale/REO, and a 1.4 % reduction in the
likelihood of a short sale. Following Chen et al. (2014) we disaggregated the percent
reduction in P&I into categories for the ranges 10 to 30 %, 31 to 40 %, and 40 plus
percent to examine whether reductions in P&I have a non-linear effect on mortgage
performance, and in particular whether loans receiving substantial P&I reductions are
actually more likely to default. In results not shown, we find no evidence that P&I
reductions of 40 % or more are associated with an increase in re-default and foreclo-
sure. In fact, the benefits of P&I reductions in terms of lower re-default and foreclosure
continue to increase in proportion to the size of the P&I reduction.

As previously mentioned, many of the early mortgage modifications actually result-
ed in increases in principal balances and the monthly P&I, as accumulated interest and
fees were capitalized. The ineffectiveness of this approach is clearly demonstrated in
our results, as modifications that resulted in any principal balance increase were 21 %
more likely to be 60 plus days delinquent, 25 % more likely to result in a foreclosure
filing, and 19 % more likely to terminate in a foreclosure sale/REO. Modifications that
included an increase in P&I were 0.5 % more likely to be 60 plus days delinquent for
each 1 % increase in P&I, and 0.2 % more likely to result in a foreclosure filing for each
1 % increase in P&I. They were also far more likely to end up requiring a subsequent
re-modification, with a 0.9 % higher relative risk for each 1 % increase in P&I. For
those loans that had an increase in P&I, the average was 13 %, implying that the
average modification that increased P&I was 6.5 % more likely to be 60 plus days
delinquent, 2.6 % more likely to be in foreclosure, and 12 % more likely to require re-
modification.

Even conditional on the actual modification terms, modifications done through the
HAMP program appear to be particularly effective. HAMP modifications have a 17 %
lower relative risk of being 60 plus days delinquent, a 21 % lower relative risk of
experiencing a foreclosure filing, a 22 % lower relative risk of terminating in a
foreclosure sale/REO, and a 34 % lower relative risk of ending in a short sale. This
finding is consistent with Agarwal et al. (2012) who find HAMP modifications to be
more aggressive than non-HAMPmodifications and result in better mortgage outcomes
for the borrowers.

Subprime Mortgage Performance Post-Modification 21

The CLTV following a modification is by far the largest determinant of subsequent
mortgage outcomes, with the likelihood of default, foreclosure, and REO increasing
substantially as the CLTV increases. The CLTV is constructed by dividing the current
total value of the first lien mortgage and any junior liens by CoreLogic’s AVM estimate
for the property value. Relative to the reference category of a CLTV below 80 %, a
borrower with a CLTV ratio between 80 and 89 % has a 10 % higher relative risk of
being 60 plus days delinquent, a 21 % higher risk of requiring a re-modification, and a
17 % higher relative risk of entering foreclosure. However, for borrowers with a CLTV
between 80 and 89 % there is no significant difference in the likelihood of the mortgage
terminating in a foreclosure sale/REO or short sale compared with those borrowers with
a CLTV below 80 %.

The likelihood of an adverse outcome consistently increases with each higher
category of CLTV included in the model, with a notable jump in coefficient magnitude
once CLTV enters the 90 to 94 % category, and again once CLTV enters the 100 to
124 % category. Borrowers with a CLTV of between 100 and 124 % are 32 % more
likely to be 60 plus days delinquent, 69 % more likely to enter foreclosure, 173 % more
likely to terminate in a foreclosure sale/REO, and 28 % more likely to terminate in a
short sale. They are also 41%more likely to require a re-modification of their mortgage.
Finally, at the extreme, borrowers with a CLTVof 150% ormore are 76%more likely to
be 60 plus days delinquent, 215%more likely to enter foreclosure, 895%more likely to
terminate in a foreclosure sale/REO, and 468 % more likely to terminate in a short sale.

While these values for the effect of CLTVon adverse outcomes may appear extreme,
they are consistent with Bhutta et al. (2010) who find that borrowers with a CLTV of
150 % or more are over 10 times more likely to default than borrowers with a CLTVof
around 100 %. This result is driven in part by their finding that when home equity falls
below negative 62 % (CLTV greater than 162 %) borrowers are far more likely to
strategically default on their loans (stop paying the mortgage even if they are able to
make the monthly payment). Guiso et al. (2013) also find that borrowers are substan-
tially more likely to report a willingness to strategically default when the absolute value
of negative equity is $100,000 or greater. However, Gerardi et al. (2013a) suggest that
strategic default is less prevalent than previously estimated, as only 14 % of borrowers
with negative equity who default in the Panel Study of Income Dynamics (PSID) have
sufficient assets to make 1 month’s mortgage payment. Borrowers who have substantial
negative equity may also be those who experience the most severe (unobserved)
negative financial shocks.

As mentioned above, the aggregate effect of a principal reduction on subsequent
loan performance is a combination of the share of principal reduced and the change in
the CLTV. A principal reduction also mechanically reduces the P&I, assuming the loan
amortization period isn’t shortened. Given the large magnitude of the coefficients found
for CLTV, as well as the magnitude of the coefficients on both the principal reduction
and P&I variables, principal reductions would appear to be an extremely effective
modification strategy, particular for those borrowers with CLTVs in excess of 100 %.

As we find that modifications done under the HAMP are significantly more effective
than proprietary mortgage modifications, we next split our sample into non-HAMP and
HAMP modifications and re-run our analysis. The results for the non-HAMP loans are
presented in Table 4, while those for the HAMP loans are presented in Table 5. For the
non-HAMP loans, we see little change in any of the coefficient magnitudes or levels of

22 M. D. Schmeiser, M. B. Gross

Table 5 Determinants of mortgage default post modification

HAMP modifications 60+ Days
delinquent

Modification Foreclosure
filing
REO/
Foreclosure
sale
Short pay
off

Junior lien 1.2769*** 1.0242 1.4534*** 1.9502*** 1.1773

(14.8055) (0.8521) (16.3867) (3.8747) (0.7110)

Loan used for purchase 1.3223*** 1.1606*** 1.3555*** 1.3244 1.6920**

(16.7001) (5.1026) (13.1568) (1.5854) (2.2257)

FICO at origination 580
to 649

0.7298*** 0.7737*** 0.7103*** 0.9153 0.4686**

(−13.7789) (−5.8839) (−10.8122) (−0.3267) (−2.0791)
FICO at origination 650

to 719
0.4973*** 0.6020*** 0.6437*** 0.8023 0.6760

(−27.6850) (−10.8470) (−12.8334) (−0.7562) (−1.0786)
FICO at origination 720

and above
0.2973*** 0.5416*** 0.4008*** 0.6435 0.3876**

(−36.3609) (−11.2618) (−19.9360) (−1.2523) (−2.0673)
Not owner occupied 1.0812 1.0448 1.2044*** 2.5018** 1.0964

(1.4584) (0.5465) (2.6301) (2.4583) (0.1265)

Low or no
documentation

0.9013*** 0.8925*** 0.9316*** 0.8320 0.9162

(−6.3275) (−3.9006) (−3.1042) (−1.0222) (−0.3608)
Judicial foreclosure state 0.9977 1.0397 2.1104*** 0.7697 0.8402

(−0.1295) (1.2411) (29.2578) (−1.2912) (−0.5791)
State with redemption

law
0.9731 0.9947 1.0135 1.4994* 1.0943

(−1.4761) (−0.1616) (0.5063) (1.9547) (0.3245)
Non-recourse state 0.7295*** 0.8826*** 0.8681*** 0.6747** 0.7933

(−18.7155) (−4.1199) (−6.0095) (−2.1106) (−0.8999)
30 to 60 days delinquent

at mod
0.5009*** 0.8321*** 0.3032*** 0.3327** 1.0002

(−15.6834) (−3.2659) (−17.4861) (−2.2604) (0.0005)
90 days delinquent at

mod
1.2457*** 0.9642 0.9435** 0.8234 1.2067

(10.1226) (−1.0616) (−2.0399) (−0.8828) (0.5803)
Lis pendens at

modification
1.5941*** 0.8803*** 1.5821*** 1.7137** 1.0171

(17.5472) (−2.7671) (13.9688) (2.2060) (0.0395)
Percent reduction in

principal
1.0097*** 1.0077*** 1.0052*** 1.0177* 1.0147

(11.0946) (6.4157) (4.0510) (1.6524) (0.9976)

Principal increase
indicator

1.2142*** 0.9707 1.0514* 1.1770 1.1096

(8.6353) (−0.8403) (1.6519) (0.6555) (0.3132)
Percent reduction in

interest rate
1.0087*** 0.9598*** 1.0119*** 1.0123** 1.0250***

(16.9568) (−40.3141) (16.7990) (2.5421) (3.0605)
Percent reduction in

P&I
0.9849*** 1.0011 0.9852*** 0.9779*** 0.9796***

(−28.0106) (1.1856) (−20.0788) (−4.0891) (−2.7613)
Originated 2004 0.8561*** 0.7692*** 0.9018 0.2825** 1.0584

(−2.7036) (−2.7963) (−1.3250) (−2.1101) (0.0485)
Originated 2005 0.8320*** 0.6713*** 0.6598*** 0.2800*** 1.4304

(−3.5959) (−4.7178) (−6.0229) (−2.8840) (0.3442)
Originated 2006 0.8159*** 0.7572*** 0.7442*** 0.3788** 0.7107

(−4.0284) (−3.3410) (−4.3409) (−2.2817) (−0.3273)

Subprime Mortgage Performance Post-Modification 23

statistical significance from the full sample, which is relatively unsurprising given that
the HAMP loans make up only 22 % of the full sample.

In contrast, the results for the HAMP loans reported in Table 5 are notably different
than those for the full sample. Here, reductions in principal and the interest rate are

Table 5 (continued)

HAMP modifications 60+ Days
delinquent
Modification Foreclosure
filing
REO/
Foreclosure
sale
Short pay
off

Originated 2007 0.8401*** 0.8437** 0.8083*** 0.3576** 0.8860

(−3.3311) (−1.9668) (−3.0302) (−2.2789) (−0.1146)
First modified in 2010 0.9903 0.8569*** 1.2433*** 0.8930 1.3761

(−0.4296) (−3.1601) (6.8587) (−0.5029) (0.9907)
First modified in 2011 0.9206*** 0.6803*** 1.2960*** 0.6111 1.6019

(−2.7779) (−7.3475) (5.9507) (−1.3932) (0.9976)
First modified in 2012 0.8792*** 0.6438*** 1.3522*** 0.7962 0.5658

(−2.9541) (−7.1034) (4.4854) (−0.4231) (−0.4917)
First modified in 2013 0.7286*** 0.6224*** 0.5815*** 4.2347

(−3.9772) (−6.0387) (−2.8068) (1.1606)
CLTV 80 to 89% 1.2800*** 1.5546*** 1.1718*** 1.3032 0.2791

(6.8232) (7.5289) (2.8266) (0.5221) (−1.1701)
CLTV 90 to 94% 1.3592*** 1.9505*** 1.5196*** 0.9390 0.0000

(7.3005) (9.9623) (6.8122) (−0.0936) (.)
CLTV 95 to 99% 1.4107*** 1.8112*** 1.4033*** 3.0099** 1.3795

(8.2372) (8.6379) (5.3437) (2.2921) (0.4395)

CLTV 100 to 124% 1.5661*** 2.2271*** 1.7789*** 2.1400* 1.3855

(14.7574) (16.2408) (12.8357) (1.8777) (0.6100)

CLTV 125 to 149 % 2.0071*** 2.4952*** 2.7280*** 4.7492*** 1.8312

(21.2951) (16.8353) (21.2819) (3.8129) (1.0619)

CLTV 150 % and above 2.6373*** 2.6027*** 4.6177*** 11.2310*** 9.3693***

(28.8969) (17.2630) (32.0381) (5.9585) (4.2273)

Year on year change
in HPI

0.9879*** 0.9939*** 0.9791*** 0.9994 1.0076

(−11.0115) (−2.7886) (−13.5891) (−0.0537) (0.5034)
Unemployment rate 0.9497*** 0.9220*** 0.9651*** 0.7572*** 0.9993

(−8.3987) (−7.9595) (−3.7764) (−4.1921) (−0.0073)
Log-Likelihood −127453.67
Chi-Sq 53445.08

Observations 239,626

Borrowers 8,375

Competing risk models with relative risk ratios reported. t-statistics in parentheses. Sample is CoreLogic Loan
Performance data on subprime and alt-a mortgages originated from January 1, 2000 to January 1, 2008 and
receiving a HAMP modification after March 2009. Mortgage servicer fixed-effects are included in the model,
but coefficients are omitted due to data license agreement

*p<0.10, ** p<0.05, *** p<0.01

24 M. D. Schmeiser, M. B. Gross

estimated to modestly increase the relative risk of being 60 plus days delinquent,
entering foreclosure, or terminating with a foreclosure sale/REO. This result is likely
driven by the waterfall nature of HAMP modifications, where larger reductions in the
interest rate or principal balance are indicative of a particularly distressed borrower.
However, the coefficient on the percent reduction in P&I remains consistent with what
we would expect, as a 1 % reduction in P&I is estimated to reduce the relative risk of
being 60 plus days delinquent by 1.5 %, entering foreclosure by 1.5 %, terminating in a
foreclosure sale/REO by 2.3 %, and terminating in a short sale by 2 %.

For the sample of HAMP loans, the magnitude of the coefficients on the CLTV
categories increases substantially relative to the non-HAMP loans, particularly at the
upper-end of the CLTV distribution. At every level of CLTV in excess of 80 %, we see
substantial increases in the relative risk of the adverse outcomes. Again, there is a
particularly pronounced jump in the effect of CLTVs in excess of 100 %: Those with a
CLTVof 100 to 124 % are now 57 % more likely to be 60 plus days delinquent, 78 %
more likely to enter foreclosure, and 114 % more likely to terminate in a foreclosure
sale/REO. At a CLTVof 150 % or more, the mortgage is 164 % more likely to be 60
plus days delinquent, 362 % more likely to enter foreclosure, 1000 % more likely to
terminate in a foreclosure sale/REO, and 837 % more likely to terminate in a short sale.
Overall, these results suggest that HAMP modifications that reduce principal would be
most effective at reducing subsequent default and foreclosure.

Conclusion

We use both a probit model and a discrete time proportional hazard framework with
competing risks to analyze how the parameters of mortgage modifications affect the
post-modification loan performance. Using a rich dataset that provides information on
modification parameters, second liens, and current property values our estimates
suggest the completely intuitive conclusion that modifications that improve the terms
of the loan for the borrower—such as reductions in the interest rate, the monthly P&I,
or the loan’s principal balance—reduce the likelihood that the borrower re-defaults and
enters foreclosure. Conversely, modifications that capitalize accrued interest and fees,
resulting in an increase in the mortgage balance, or that increase the monthly P&I are
particularly prone to re-default and end in foreclosure. Principal reductions are partic-
ularly effective, as they appear to independently affect subsequent mortgage perfor-
mance, as well as affect subsequent performance through a reduction in the LTV ratio
and the P&I. HAMPmodifications also appear to perform substantially better than non-
HAMP modifications, independent of the terms of the actual modification.

Mortgages with CLTV ratios in excess of 100 % following a modification, and
especially those with CLTV ratios above 150 %, are far more likely to re-default than
those with some equity. Thus, when implementing a principal reduction, it would appear
reasonable for servicers to target reductions in the total of outstanding loan balances on
the property below one of these key CLTV thresholds. However, a broad-based policy of
principal reduction may introduce moral hazard into the mortgage market, resulting in
borrowers strategically defaulting to obtain a modification and reduce their principal
balance (Foote et al. 2008). Applying the learnings from the previous literature on
negative equity and strategic default to target only borrowers experiencing both high

Subprime Mortgage Performance Post-Modification 25

negative equity and an income shock may reduce moral hazard and yield more cost-
effective principal reductions (Bhutta et al. 2010; Foote et al. 2008).

While principal reductions are clearly the most effective type of mortgage modifi-
cation, as measured by subsequent loan performance, they may not necessarily be the
most cost-effective for the investor on a net present value basis. Further analysis of the
costs to investors of the various types of modifications relative to their effect on loan
performance is necessary to make the final determination. However, our results provide
an important input into the calculation of modification costs versus benefits.

Acknowledgements The authors are grateful for the comments provided by Lisa Dettling, Alice Henriques,
Joanne Hsu, Steven Laufer, Dean Lillard, Kevin Moore, Michael Palumbo, John Sabelhaus, Jeffrey Thomp-
son, and Carly Urban, as well as seminar participants at The Ohio State University and the Federal Reserve
Board. Further, the authors would like to thank an anonymous referee for invaluable feedback and guidance.

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Subprime Mortgage Performance Post-Modification 27

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c.11146_2015_Article_9500

The Determinants of Subprime Mortgage Performance Following a Loan Modification
Abstract
Introduction
Previous Literature
Data
Empirical Model
Results
Conclusion
References

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