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January 2013, EB13-01

Economic Brief
All Mortgages Are Not Created Equal
By Karl Rhodes and Breck L. Robinson

Housing experts have studied the relative performance of different types of
mortgages during the housing crisis. But foreclosure analysis often overlooks
distinctions between mortgages issued to occupant owners and those issued
to non-occupant owners. This Economic Brief highlights the impact of nonoccupant-owner mortgages on the housing crisis.
From 2002 through 2005, the peak of the housing boom, first-lien, home-purchase mortgages
issued to non-occupant owners increased each
year, both in raw numbers and as a percentage
of the home-mortgage market. Non-occupant
owners’ annual share of this market increased
from about 8 percent in 2000 to nearly 16 percent in 2005.1
Policymakers have asked whether speculation
on investment homes played a disproportionate
role in the housing crisis. This question is worth
exploring for at least three reasons. First, as
noted above, mortgages issued to non-occupant
owners represented a fast-growing segment of
the home-mortgage market in the years leading
up to the housing crisis. Second, previous studies
suggest that non-occupant owners (including
investors) are more likely than occupant owners
to default on mortgages, even after controlling
for credit scores and other risk characteristics.2
And third, there was a positive correlation between the disproportionate growth in non-occupant-owner mortgages and rapid home-price
appreciation during the housing boom. This
correlation brings up a causality question that
this Economic Brief does not attempt to answer
definitively, but it seems reasonable to suggest
that causality could have run both ways. Investors may have gravitated to areas where they

EB13-01 - The Federal Reserve Bank of Richmond

observed or expected rapid home-price appreciation, and the increased demand they generated
in those areas may have driven prices up further.
One of the co-authors of this Economic Brief
(Robinson) has measured the impact of nonoccupant-owner mortgages using data obtained
through Lender Processing Services (LPS) and
the Home Mortgage Disclosure Act (HMDA).3
He analyzed the prevalence and performance
of non-occupant-owner mortgages on second
homes, vacation homes, and investment homes,
including rental properties with one to four
units.4 The HMDA data are more comprehensive,
but the LPS data on non-occupant-owner mortgages can be subdivided into “second homes”
and “other homes.” The former category includes
vacation homes, while the latter category includes rental properties and other homes owned
primarily for investment purposes.
Foreclosure Theory
Two dominant theories attempt to explain why
homeowners end up in foreclosure—triggerevent theory and option theory. Trigger-event
theory refers to life-changing events, such as
divorces or job losses, that significantly impair
homeowners’ ability to make timely mortgage
payments. Option theory deals with foreclosures
that occur when homeowners decide to stop

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making payments even though they still have sufficient funds to remain current on their mortgages.5
Trigger-event theory and option theory are not
mutually exclusive. Quite often trigger events—such
as dramatic decreases in property values—cause
homeowners to consider exercising their foreclosure
options. (Option theory primarily comes into play in
non-recourse states, where mortgage lenders cannot take actions beyond foreclosure to recoup their
investments.) In a non-recourse state, when a home
has negative equity—that is, when the outstanding
balance of its mortgage exceeds the home’s market
value—and when the amount of negative equity
exceeds the observable transaction costs associated
with default, many homeowners will at least consider
exercising their foreclosure option, especially if they
expect the market value of their homes to remain
flat or decline further.6
Under option theory, the option strike price for any
given homeowner is unknown because some transaction costs, such as loss of self-esteem, are unobservable. But generally an owner occupant would be
less likely to exercise his default option because his
transaction costs—both observable and unobservable—would tend to be higher than those of the
non-occupant owner. The occupant owner would
need to find another home, and he would incur the
relocation expense and emotional trauma associated with leaving his primary residence. In addition,

it could be more difficult for the owner occupant to
purchase or rent a new home because the recent
foreclosure would impair his credit. In sharp contrast,
a non-occupant owner, especially an investor, would
tend to have lower observable transaction costs and
fewer sentimental attachments to his property. He
likely would be more “ruthless”—that is, more willing
to exercise his foreclosure option to optimize his
financial results.7
In theory, the home-mortgage market would compensate for this greater risk by applying higher
underwriting standards and/or higher interest rates
to mortgages issued to non-occupant owners.8 This
theory is supported by both the HMDA data and the
LPS data, which show that non-occupant owners
have higher median incomes, higher FICO scores,
lower debt-to-income ratios, and lower loan-to-value
ratios compared to owner occupants.9
In a theoretical market where both lenders and borrowers have perfect information about mortgage
default probabilities, higher underwriting standards
would keep foreclosure rates for non-occupant
owners roughly in line with foreclosure rates for
occupant owners. But in the real world, when the
housing market began to deteriorate, foreclosure
rates grew faster for non-occupant owners. For loans
originated in the years 2005 through 2007, foreclosure rates for non-occupant owners were 12.8

Figure 1: Percent of Foreclosures Involving Non-Occupant-Owner Mortgages

18.4

17.7

17.8

16.7

15.1

Less than 8
8 to 10
10 to 12
12 to 14
More than 14

18.9
DC - 15.5
16.0

14.8
21.5

18.9
14.6
19.1

Sources: Lender Processing Services Applied Analytics data for mortgages
orginated in 2006 and Robinson’s calculations

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percent, 20 percent, and 17.4 percent, respectively.
During the same years, foreclosure rates for occupant owners were 12.3 percent, 18.7 percent, and
15.1 percent, respectively.
State-by-State Analysis
At the national level, occupant-owner mortgages
outperformed non-occupant-owner mortgages
three years in a row. These differences were statistically significant, and in some states, these differences
were dramatic, especially in markets where housing
prices appreciated the most in the years leading up
to the housing crisis.

foreclosures that involved non-occupant owners was
about 12 percent in both Arizona and Alabama. But
both types of mortgages performed much worse in
Arizona than in Alabama, and in raw numbers, there
were many more non-occupant-owner foreclosures
in Arizona than in Alabama. So attempting to use
non-occupant owners’ share of foreclosures to measure non-occupant owners’ impact on the housing
crisis would greatly understate their role in Arizona
and greatly overstate their role in Alabama.

Robinson’s state-by-state analysis of the LPS data
looks first at the share of foreclosures involving nonoccupant-owner mortgages that were originated in
2006, a percentage that varies widely across states.10
(See Figure 1.) Given media accounts of mass foreclosures on second homes and investment properties in California, it is surprising that only 7 percent
of foreclosures in the Golden State involved nonoccupant owners—a much lower share than in the
Southeastern states of Florida (19.1 percent), South
Carolina (18.9 percent), North Carolina (16 percent),
and Georgia (14.6 percent).

To overcome this problem, Robinson developed an
impact score composed of two factors, the prevalence of non-occupant-owner mortgages and the
performance of non-occupant-owner mortgages.
He defined prevalence as the number of non-occupant-owner mortgages divided by the total number
of housing units in the year the mortgages were
originated.11 He defined performance as the number
of foreclosures on non-occupant-owner mortgages
originated in a given year divided by the total number of non-occupant-owner mortgages originated
during that year. Multiplying the prevalence and performance factors produces a score that indicates the
overall impact of non-occupant-owner mortgages
on the housing crisis.

The problem with Figure 1, however, is that it tells
only part of the story. For example, the share of

Nationally, the impact of non-owner-occupant mortgages nearly tripled from 2004 (before the crisis) to

Figure 2: Performance — Non-Occupant-Owner Mortgage Foreclosure Rates Relative to the U.S. Average

137.4

200.6
253.5

RI - 133.7

156.4
138.7 173.1

153.0

0 to 75
76 to 100
101 to 125
More than 125

184.7
152.1

160.3
FL - 253.3

Note: The U.S. average, measured from 2004 through 2007, equals 100.
Sources: Lender Processing Services Applied Analytics data for mortgages
orginated in 2006 and Robinson’s calculations

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Figure 3: Prevalence — Non-Occupant-Owner Mortgages Per Housing Units Relative to the U.S. Average
132.4

151.6
231.4

292.9

DE - 157.7
MD - 126.4
DC - 159.3

214.7
150.0
140.8

0 to 75
76 to 100
101 to 125
More than 125

215.2

136.9

145.9

248.4
FL - 229.4

Note: The U.S. average, measured from 2004 through 2007, equals 100.
Sources: Lender Processing Services Applied Analytics data for mortgages
orginated in 2006 and Robinson’s calculations

Figure 4: Impact — Non-Occupant-Owner Mortgage Impact Scores Relative to the U.S. Average

242.4
136.8

RI - 128.1

747.6
170.7

0 to 75
76 to 100
101 to 125
125 to 300
More than 300

136.2 137.1

MD - 132.6
DC - 165.3

217.0
400.0
253.8

130.8
169.2
FL - 584.9

Note: The U.S. average, measured from 2004 through 2007, equals 100.
Sources: Lender Processing Services Applied Analytics data for mortgages
orginated in 2006 and Robinson’s calculations

2006 (the first year of the crisis).12 The growing impact was driven primarily by declining performance.
The performance factor increased 190 percent from
2004 to 2006, while the prevalence factor increased
only 18 percent.
At the state level, however, the relative importance
of the two contributing factors varied substantially.
Indiana, Ohio, and Michigan suffered more from
poor performance while experiencing relatively low

prevalence. (Compare Figures 2 and 3.). Conversely,
Hawaii, Idaho, and Utah experienced high prevalence while suffering only average or slightly below
average performance. (Compare Figures 2 and 3.)
The highest overall impact scores, expressed as
percentages of the national average (100), were in
Nevada (747.6), Florida (584.9), and Arizona (400)—
states plagued by both high prevalence and poor
performance. (See Figure 4.)
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Policy Implications
High foreclosure rates persist in many American
housing markets. The crisis is not over, and policymakers have been mostly unsuccessful in their
attempts to mitigate the damage.13

4

Robinson’s analysis excludes mortgages on rental properties
with five or more units because multifamily housing played
a relatively small role in the housing crisis.

5

For a more complete explanation, see Vandell, Kerry D., “How
Ruthless Is Mortgage Default? A Review and Synthesis of the
Evidence,” Journal of Housing Research, 1995, vol. 6, no. 2,
pp. 245–264.

Foreclosure prevention programs historically have
focused on occupant owners, perhaps because
policymakers assume that assisting non-occupant
owners would amount to making transfer payments
to wealthy individuals. But foreclosures on mortgages
held by non-occupant owners may harm lowerincome people who are renting those properties.
There also could be a contagion effect if foreclosures
on non-occupant-owned homes depress property
values enough to place neighboring homeowners
in positions of negative equity.

6

Most homeowners with negative equity remain current on
their mortgages, so it is difficult for lenders and policymakers
to determine which borrowers need help to prevent foreclosure. See Foote, Christopher L., Kristopher Gerardi, and
Paul S. Willen, “Negative Equity and Foreclosure: Theory and
Evidence,” Journal of Urban Economics, September 2008, vol.
64, no. 2, pp. 234–245.

7

For a more detailed discussion, see Kau, James B., Donald C.
Keenan, and Taewon Kim, “Default Probabilities for Mortgages,”
Journal of Urban Economics, May 1994, vol. 35, no. 3,
pp. 278–296.

8

For more on using option theory to price mortgages, see Kau,
James B., and Donald C. Keenan, “An Overview of the OptionTheoretic Pricing of Mortgages,” Journal of Housing Research,
1995, vol. 6, no. 2, pp. 217–244.

9

Another possible explanation is that borrowers who can afford second homes are more likely to have stronger financial
characteristics.

Mitigation efforts might be more effective if they included non-occupant owners nationally or in states
where the impact of non-occupant-owner mortgages is particularly high.
Karl Rhodes is a managing editor in the Research
Department of the Federal Reserve Bank of Richmond.
Breck L. Robinson is an associate professor at the
University of Delaware and a visiting scholar in the
Supervision, Regulation, and Credit Department of
the Federal Reserve Bank of Richmond.

10

Robinson tracked the performance of these mortgages
through June 2011.

11

Non-occupant-owner mortgages are defined as first-lien,
home-purchase loans, including refinancing of single-family
homes but excluding home-improvement loans. The data on
housing units by state comes from the American Community
Surveys for 2004–07.

12

The impact of non-occupant-owner mortgages may be
higher than the numbers reported here indicate because
the LPS data do not cover the entire mortgage market and
may underestimate the share of mortgages issued to nonoccupant owners.

13

Foreclosure prevention programs initiated during the housing
crisis include Hope for Homeowners, Home Affordable
Mortgage Program, and Loan Mod in a Box, among others.

Endnotes
1

Data obtained through the Home Mortgage Disclosure Act
show 2005 as the peak year for mortgage originations in
the United States both for owner occupants and non-owner
occupants.

2

For example, see Cowan, Adrian M., and Charles D. Cowan,
“Default Correlation: An Empirical Investigation of a Subprime
Lender,” Journal of Banking & Finance, April 2004, vol. 28, no. 4,
pp. 753–771; also Immergluck, Dan, and Geoff Smith, “Risky
Business—An Econometric Analysis of the Relationship Between Subprime Lending and Neighborhood Foreclosures,”
Manuscript, Woodstock Institute, March 2004.

3

See Robinson, Breck L., “The Performance of Non-OwnerOccupied Mortgages during the Housing Crisis,” Federal
Reserve Bank of Richmond Economic Quarterly, Second
Quarter 2012, vol. 98, no. 2, pp. 111–138.

This article may be photocopied or reprinted in its
entirety. Please credit the authors, source, and the
Federal Reserve Bank of Richmond and include the
italicized statement below.
Views expressed in this article are those of the authors
and not necessarily those of the Federal Reserve Bank of
Richmond or the Federal Reserve System.

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