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Working Paper Series
The Role of Non-Owner-Occupied
Homes in the Current Housing and
Foreclosure Cycle
WP 10-11
Breck L. Robinson
University of Delaware and
Federal Reserve Bank of Richmond
Richard M. Todd
Federal Reserve Bank of Minneapolis
This paper can be downloaded without charge from:
http://www.richmondfed.org/publications/
The Role of Non-Owner-Occupied Homes
in the Current Housing and Foreclosure Cycle
Breck L. Robinson and Richard M. Todd*†
Working Paper No. 10-11
May 2010
Abstract
Non-occupant homeowners differ from owner occupants in that they tend to have lower-risk credit
characteristics, such as higher credit scores, but may also have weaker incentives to maintain
mortgage payments when housing values fall. During the recent housing boom, the share of
mortgage borrowing by non-occupant owners was relatively high in states where home values
appreciated relatively rapidly. After the housing boom, foreclosures on non-occupant mortgages in
several Midwestern and Northeastern states reflected primarily a high rate of foreclosure per
mortgage, not a high volume of mortgages to non-occupants. The reverse held true in some coastal
and mountain states. Nevada and Florida have experienced the greatest impact overall, because
they have both a high volume of mortgages to non-occupant owners and a high rate of foreclosure
on those mortgages.
Key words: Non-owner occupants, foreclosures
JEL Classification: D10, G01
*
Breck Robinson is an Associate Professor in the School of Urban Affairs and Social Policy at the University of
Delaware and a Visiting Scholar in Banking Supervision and Research at the Federal Reserve Bank of Richmond.
Richard Todd is a Vice President at the Federal Reserve Bank of Minneapolis.
†
For their advice and assistance, we thank Larry Cordell, Anne Davlin, Ron Feldman, Fred Furlong, Michael Grover,
Crystal Myslajek, Eli Popuch, Ned Prescott, Dan Rozycki, Michael Schramm, Laura Smith, and Niel Willardson.
p. 1 of 69
I. Introduction
The past decade’s business cycle was accompanied by a dramatic boom and bust in the housing
market. As illustrated in Figure 1, the housing cycle, in turn, was marked by a disproportionate rise
and fall in home buying and mortgage borrowing by non-occupant owners, by which we mean
individuals who own housing units other than their primary residence. Examples include investors
in residential property and owners of vacation homes. As the housing prices declined, foreclosures
on properties owned by non-occupants reached high levels in some areas too. In this study, we
provide background information on the role that non-occupant homeowners played in the recent
housing cycle and foreclosure crisis. We show that the foreclosure rate has been similar for both
owner-occupants and non-occupant owners. Nonetheless, non-occupant owners make up a distinct
market segment whose activity was associated with regional differences in housing appreciation and
foreclosure impacts.
We also find that the factors underlying the impact of non-occupant foreclosures vary among states.
For example, the high incidence of foreclosures on non-occupant mortgages in Michigan, Indiana
and some other states in the Midwest and Northeast primarily reflects a high rate of foreclosure per
non-occupant mortgage, not a high volume of mortgages to non-occupants. By contrast, in Hawaii,
Idaho, and some other coastal and mountain states, foreclosures on non-owner-occupied homes
have been prominent even though the foreclosure rate on non-occupant mortgages has been
relatively low. In those states, unlike Michigan or Indiana, the importance of foreclosures on nonowner-occupied properties stems from the high volume of non-owner-occupied mortgages issued.
Nevada and Florida stand out because both states experienced a very high volume of mortgages on
non-owner-occupied properties and a very high foreclosure rate.
II. What Do We Mean by Non-Occupant Owners, and Why Do We Care?
Policymakers and journalists have asked whether ―investors‖—those who buy houses not to live in
but primarily for expected profits from income and capital gains—have played a disproportionate
role in the current housing cycle. However, because it is often difficult to measure investor activity
in the housing and mortgage market, we focus instead on the broader concept of non-occupant
owners of single-family homes. A single family home is defined as a detached structure with one to
four residents or a townhouse and a condominium contained in a larger building but available for
sale separately. For the purposes of this study, we will ignore investments in multifamily rental
housing, defined here as structures with five or more housing units intended for renters.1
Decisions about how to analyze non-occupant owners are often driven by data limitations. For
example, data collected under the Home Mortgage Disclosure Act (HMDA) distinguish between
mortgages on owner-occupied and non-owner-occupied properties but provide no additional
information as to whether a non-owner-occupied property is a vacation home, an investment
property, or something else. However, data obtained from Lender Processing Services (LPS) allow
the non-owner-occupied category to be segmented into two groups; second homes and other (or
1
Multifamily housing played a relatively small role in the recent housing cycle. Between 2000 and 2005, the number of
multifamily rental housing starts increased by 4 percent, far less than the 38 percent rise in starts of one- to four-unit
homes. Between 2005 and 2008, multifamily starts fell by 15 percent, compared to the 64 percent drop in starts of oneto four-unit homes.
p. 2 of 69
―investor‖ properties).2 Second home is defined as a non-primary residence purchased mostly for
recreational use or as an occasional or seasonal residence. Although the accuracy of this additional
information is unclear, we make some use of it to shed additional light on the role of non-occupant
owners in the recent cycle.3
Why should we care about the role non-occupant homeowners played in the current mortgage
crisis? One reason is to have a better understanding of housing cycles in general and the recent
foreclosure crisis in particular. Given the still very high rate of mortgage defaults in 2010, a better
understanding of the role of non-owner-occupied homeowners might also help direct policymakers
in their efforts to reduce foreclosures. Current foreclosure mitigation efforts have been used to
address the needs of owner-occupiers rather than other owners, but cities struggling to contain the
negative spillover effects of vacant properties might benefit from a better understanding of the local
prevalence and implications of non-owner-occupant foreclosures. Finally, efforts to improve the
future supervision and regulation of housing and mortgage markets will require an accurate
assessment of what led to the current crisis.
Although much of the current interest in non-owner-occupied homes focuses on the problems that
profit-oriented investors may have caused in bidding up home prices or triggering foreclosures, it is
worth noting that many non-occupant owners play a beneficial role in the single-family housing
market. According to a Harvard University Joint Center for Housing Studies analysis of the 2003
American Housing Survey, 35 percent of American renters live in single-unit properties and another
21 percent live in two- to four-unit structures.4 That is, non-occupant owners of single-family
housing provided about half of the rental units in the U.S. in 2003.
III. Non-Occupant Owners Are a Distinct Market Segment
Non-occupant owners are likely to have different motives, characteristics, and behaviors than
owner-occupants. Below, we discuss these differences conceptually and validate the differences in
characteristics and behaviors using data from mortgage and foreclosure records.
Motives. Homeowners derive two distinct benefits from owning a home, the value associated with
consumption and the potential financial benefits that we will call profit. Homeowners derive
consumption value by occupying the home and enjoying the direct benefits from its use.
Homeowners can also gain financially if their home appreciates or if they rent out all or a portion of
the home. Depending on how the homeowner plans to use the home will determine how much
value and the type of value that homeowner will derive from the home. At one extreme, owneroccupants would put a relatively high value on the consumption value of home ownership since
2
We use LPS’s mortgage data for much of the analysis in this paper, partly because they provide the only information
we have on mortgage performance. In Appendix 2, we consider how the LPS data compare to alternative national and
local data sources on mortgages. The results indicate that the LPS data cover only about 60 percent as many mortgages
as the HMDA data include and may undercount the ratio of mortgages to non-occupants slightly when compared with
the HMDA data. Nonetheless, we also find that the LPS data provide fairly reliable measures of useful ratios and
relationships, such as foreclosure rates and rankings of regional outcomes.
3
In Appendix 3, we use the LPS data to examine the differences between residential mortgages to second-home and
investor borrowers. In most states, investor borrowers outnumber second-home borrowers by three-to-one or more, but
second-home mortgages become more significant in a few high-amenity states like Hawaii. Both HMDA and LPS
depend on homeowners to self-report the occupancy status of the home, and LPS also relies on self-reporting to
distinguish second homes from investment properties. Because of differences in underwriting based on the occupancy
and usage of the home, borrowers have an incentive to misreport their true intentions.
4
We calculated these percentages from the more detailed data presented in Figure 8-1 of Apgar and Narasimhan (2008).
Their figures also include information on single-unit homes including manufactured housing units.
p. 3 of 69
they are able to use and modify the home to meet their needs. At the other extreme are owners who
do not reside in the home and therefore consume very little of the non-monetary benefits of
homeownership. These owners place a higher value of the current and future cash flows that may
be generated from the home in the form of rental income and capital gains, net of costs. Owners of
vacation homes or rental properties who occupy them only occasionally would fall somewhere in
between these two extremes.
Characteristics and Risk Factors. It makes sense that non-occupant owners would have different
financial characteristics than owner-occupants, given the capital and credit needed to purchase an
investment or vacation property.5 As shown in Figures 2, 3, and 4, data from mortgage applications
validate this supposition. Using HMDA data, Figure 2 shows that non-occupants tend to have
noticeably higher incomes than owner-occupants. In the typical state, the median income of nonoccupant home purchasers rose from just over $100,000 in 2004 to close to $125,000 in 2007. By
contrast, in the typical state the mean income of owner-occupant borrowers was about $60,000 to
$65,000. The incomes of borrowers who refinanced exhibited similar patterns by occupancy status.
Using LPS data, figures 3a to 3c show that, by common underwriting standards, non-occupant
borrowers have a lower risk profile than owner-occupants. Figure 3a shows that non-occupant
homeowners have higher credit scores when compared to owner-occupants. This is especially true
of vacation-home buyers, whose average FICO score in a typical state is about 740, compared with
about 720–730 for investors and 700 for owner-occupants.
Another measure of credit risk is the loan-to-value (LTV) ratio.6 Figure 3b shows that LTV ratios
on investor-owned mortgages tended to be comparable to or somewhat lower than LTV ratios on
loans to owner-occupants. The lowest LTV ratios were on mortgages on vacation homes. Overall,
then, non-occupant mortgage holders have less risky LTV ratios.
Mortgage applicants’ total debt relative to their income is another common risk measure. Figure 3c
shows that the debt-to-income (DTI) ratio for non-occupants tends to be on par with or lower than
the DTI ratio for owner-occupants. Compared with owner-occupants, investors had lower DTI
ratios on mortgages originated in 2004-2005 and comparable ratios in 2006-2007. DTI ratios on
second-home mortgages were lower in all four years shown. Overall, compared with owneroccupants over 2004-2007, non-occupants tended to have higher credit scores and comparable or
lower LTV and DTI ratios.
Although the higher incomes and superior credit scores of non-occupant borrowers could give them
access to larger amounts of mortgage credit, Figure 4a shows that non-occupant mortgages are
slightly smaller in size. When combined with non-occupants’ substantially higher incomes, these
lower loan amounts imply significantly lower loan-to-income ratios for non-occupants. The HMDA
and LPS data confirm that non-occupants do indeed have lower loan-to-income ratios, another
5
We also looked at the racial, ethnic, and gender differences between owner-occupants and non-occupant owners, using
HMDA data for the primary loan applicant. In most states the primary applicant on non-occupant mortgages was more
likely to be male. Racial and ethnic differences between occupant and non-occupant primary applicants were generally
small with the largest difference between Latino owner-occupants and non-owner occupants in states like Arizona,
California, Florida, New Mexico, Nevada, and Texas.
6
The data for loan-to-value is derived at the time the loan is originated. However, the LTV figure maybe different from
the original amount if the loan amount or appraisal is updated while the loan is being serviced. As a result, it is possible
that some unknown percentage of the mortgages in the sample may have appraisal values that are provided after the
loan was originated. As a result, if the non-occupant property values have fallen more (less) rapidly than the value of
owner-occupant properties, then data would exaggerate (underestimate) the relative ex ante risk investors.
p. 4 of 69
indicator of lower credit risk.7 In short, standard criteria used to underwrite mortgages indicate that
non-occupants should be better credit risks than owner-occupants.
Behavior. Despite their apparently superior risk credentials, non-occupant owners are often no
more likely than owner-occupants to make their mortgage payments. In deciding whether to pay,
both non-occupant owners and owner-occupants consider the foreclosure option, whereby their
mortgage debt is extinguished and the property’s title is conveyed to the lender. However, nonoccupant owners may be freer to take advantage of the profit opportunities this option creates. Both
know that the foreclosure option limits their direct financial loss to the equity they have invested in
the home, primarily their down payment.8 Because the homeowner has limited liability, the value
of the foreclosure option rises as the owner’s equity (the difference between the value of the home
and the amount owed on the mortgage) declines or turns negative. The relationship between the
foreclosure option and equity is particularly strong for non-occupants, because non-occupants place
a particularly high value on the profit opportunities that may occur associated with homeownership.
In addition, foreclosure imposes greater noninvestment losses on owner-occupants, such as moving
costs, credit impairment effects, and emotional stress. As a result, owner-occupants are less likely
to exploit the mortgage foreclosure option. Not facing these same constraints, non-occupants are
freer to exercise their foreclosure option when expected profits are low.9 For this reason, nonoccupant homeowners are considered ―ruthless‖ borrowers.10
The theory behind the mortgage foreclosure option suggests that, other things being equal,
foreclosure rates will be higher for mortgages on non-owner-occupied homes. For this reason,
mortgage lenders often compensate for the ruthlessness of non-occupant borrowers by imposing
stricter terms on their mortgages. A Federal Reserve staff member experienced in banking
supervision and policy analysis states that most mortgage lenders charge an interest rate premium
on mortgages to non-occupants ranging from 25 to 100 basis points, other things being equal.
Lenders may also seek additional risk controls, such as higher down payments on loans to nonoccupants.11
The higher default risk associated with non-occupants is also known among investors in mortgage
products. These investors often place restrictions on the percentage of loans originated to nonoccupants in their mortgage portfolios. According to Doug Duncan, the chief economist for the
Mortgage Bankers Association, investors expect that ―people who don't live in homes are more
7
However, we cannot tell from the HMDA and LPS data if non-occupants support multiple mortgages with the same
reported income.
8
Many states allow creditors to seek additional compensation from the borrower when sale of the property fails to cover
the unpaid mortgage balance, but in practice these deficiency judgments are infrequently pursued and even less
frequently collected.
9
See Gerardi, Shapiro, and Willen (2008). Non-occupants also have some disadvantages. As noted above, public
assistance to delinquent borrowers usually targets owner-occupants and seeks to exclude non-occupants. In addition,
whereas owner-occupants in effect rent to themselves, non-occupants must either forgo rental income or seek tenants.
The latter involves both administrative costs and risks, such as the tenant damaging the property or failing to pay the
rent. When expected capital gains are small, these disadvantages are relatively large and can significantly limit nonoccupant demand for single-family homes.
10
In Appendix 1, these basic motives are presented more formally in a simple adaptation of the mortgage payment
model of Gerardi, Shapiro, and Willen (2008).
11
For example, a 1998 filing with the Securities and Exchange Commission (SEC) by Green Point Financial
Corporation notes that first-lien adjustable rate mortgages to non-occupants require at least 25 percent down, an
elevated interest rate, and possibly a prepayment penalty. See sec.edgar-online.com/1998/03/26/09/0001047469-98011577/Section2.asp.
p. 5 of 69
likely to walk away when mortgage rates reset higher or the property loses value.‖12 His assessment
is corroborated by filings with the Securities and Exchange Commission that caution investors in
mortgage backed securities about the presence of mortgages to non-occupants.13
Previous studies find that default and loss rates are higher on mortgages to non-occupant owners
than on mortgages to owner-occupants, at least after controlling for credit scores and other standard
underwriting criteria. Cowan and Cowan (2004) use 1995-2001 data provided by a large subprime
mortgage lender, and they state that ―The likelihood of default, whether measured by foreclosure or
delinquency, is much greater for properties where the owner is not the occupant.‖14 Immergluck
and Smith (2004) find that, in the Chicago area, a higher volume of subprime lending to nonoccupant owners is associated with higher foreclosure rates, even when several risk factors are
accounted for.15 Avery, Canner, and Brevoort (2007) find that, other factors held constant, nonoccupants’ share of a county’s mortgages in 2004 was significantly associated with the county’s
increase in mortgage delinquencies between 2004 and 2007.16 In a detailed analysis of public
records in Massachusetts, Gerardi, Shapiro, and Willen (2008) use mortgage data on condominiums
and multi-family dwellings to proxy for loans to non-occupants.17 After controlling for other risk
factors, they state that ―owners of condominiums and multi-family homes are estimated,
respectively, to have 42 percent and 57 percent higher conditional default probabilities than owners
of single-family homes.‖ Using data from over 100,000 subprime and Alt-A mortgages that were
originated between 1992 and 2007,18 Bajari, Chu, and Park (2008) find that, other things being
equal, non-occupants are more likely to exercise their foreclosure option. Haughwout, Peach, and
Tracy (2008) compare owner-occupants, investors, and second-home buyers with regard to rates of
early (within the first year) default on mortgages originated from 2001 to 2007. After controlling
for observed risk factors, these authors find that ―investors are more likely to default in the first
year‖ and that investors’ early default behavior is more sensitive to housing depreciation and having
negative equity in the property. They also find that owners of second homes behave differently
from investors and are not more likely than owner-occupants to default early.19 Lenders may also
face higher rates of loss given foreclosure when the property is non-owner-occupied.20
We have seen that non-occupant owners tend to have stronger financial characteristics than owneroccupants but less incentive to make mortgage payments when times are tough. Nationally, these
opposing effects have apparently offset each other in recent years, resulting in only small
differences in foreclosure rates on mortgages to non-occupant owners and owner-occupants. Our
LPS data on home-purchase and refinance mortgages originated in 2004 show that the national
foreclosure rate on non-occupant mortgages was 4.5 percent, which is slightly under the 5.0 percent
12
Investors own about one-fifth of Bay Area homes in foreclosure. See McCormick and Said (2007).
For example, the S-3/A SEC Filing by J. P. Morgan Acceptance Corp I on February 8, 2006, states ―The mortgaged
properties in the trust fund may not be owner occupied. Rates of delinquencies, foreclosures and losses on mortgage
loans secured by non-owner occupied properties may be higher than mortgage loans secured by a primary residence.‖
See http://sec.edgar-online.com/2006/02/08/0001162318-06-000172/Section14.asp
14
See Cowan and Cowan (2004). The reported quote is especially unfavorable to investors because it appears to be
unconditional; that is, they do not include controls for risk factors such as the borrower’s income or credit score. The
authors report a smaller difference in performance between mortgages on vacation homes and owner-occupied homes,
in part because in their data (as in our LPS data) owners of second homes tend to be above average in credit quality.
15
See Immergluck and Smith (2004).
16
See Avery, Brevoort, and Canner (July 2008).
17
See Gerardi, Shapiro, and Willen (2008).
18
Personal communication from Sean Chu of the Board of Governors of the Federal Reserve System, based on
supplemental calculations related to research reported in Bajari, Chu, and Park (2008).
19
See Haughwout, Peach, and Tracy (2008).
20
See Qi and Yang (2007).
p. 6 of 69
13
rate on owner-occupants’ mortgages.21 As defaults escalated for both groups in subsequent years,
the foreclosure rate for non-occupants grew somewhat higher but remained close to the foreclosure
rate for owner-occupants. By year of mortgage origination, the foreclosure rates for non-occupants
and owner-occupants were the following: 8.5 and 8.1 in 2005, 12.6 and 12.0 percent in 2006, and
7.6 and 6.8 percent in 2007.
An examination of state foreclosure rates confirms the general alignment of non-occupant and
owner-occupant foreclosure rates but also identifies places where they diverge. Figure 5 displays
the relative foreclosure rates on non-owner-occupied and owner-occupied mortgages by year of
origination for 2004-2007 by state. The 45-degree line in the figure represents equality between the
foreclosure rate for non-occupants and occupants. The symbol for most states lies near this line, in
keeping with the similarity on non-occupant and owner-occupant foreclosure rates nationally. There
are exceptions, however. The symbols for some of the Midwestern states (for example, Michigan,
Indiana and Ohio) are well above the 45-degree line, which means that in these states non-occupant
borrowers are distinctly more likely than owner-occupants to be in foreclosure. The symbols for
some of the rural or Sunbelt states that may have a high percentage of vacation homes relative to the
national average are well below the 45-degree line, at least in some years. This means that nonoccupant owners are less likely to enter into foreclosure than owner-occupants. Despite these
exceptions, Figure 5 indicates that foreclosure rates between non-occupants and owner-occupants
are highly correlated across the 50 states and the District of Columbia.22
We noted above that non-occupant owners may focus more exclusively on the expected profits of
home ownership, when compared to owner-occupants. For this reason alone, non-occupants are
likely to respond more quickly to changes in economic conditions or demographic trends that would
generate profitable opportunities in acquiring single-family housing. In addition, investors and even
vacation home buyers can own multiple properties in multiple areas, which is impossible (by
definition) for owner-occupants. For these reasons, we expect non-occupants’ buying and mortgage
borrowing activity to be more procyclical than for owner-occupants.
Using data from the National Association of Realtors (NAR), Figure 6 supports the relative
procyclicality of non-occupant home buying activity. As the housing boom rose to its peak in 2005,
home buying by non-occupants grew faster than that of owner-occupants. For example, purchases
by investors increased almost 50 percent between 2003 and 2005. Over the same time period,
investors’ share of the overall increase in home sales is about 61 percent. If you add in the 20
percent rise in vacation-home purchases, non-occupants account for 75 percent of the growth in
sales in the final two years (2004–2005) of the boom. In the subsequent housing downturn, the
NAR data show that non-occupants accounted for 65 percent of the decline in home sales. Over the
same period, the cyclical swing in owner-occupant purchases is much milder. For example, sales to
owner-occupants rose just 6.4 percent from 2003 to 2005 and then fell 13.5 percent during the
housing downturn.
The HMDA data also show that non-occupant owners were relatively active in the recent housing
cycle. We focus here on originations of home-purchase mortgages, rather than mortgages used for
home improvements or refinancing of an existing mortgage. As reflected in Figures 1 and 7, nonoccupants’ share of home-purchase mortgages had been rising since the early 1990s but accelerated
after 2001. As the housing boom peaked, home-purchase mortgage originations to non-occupant
21
Our data include all foreclosures from the date of origination through July 2009.
Cross-sectionally, for the 50 states and District of Columbia, the correlations between owner-occupied and nonoccupant foreclosure rates are 0.87 in 2004, 0.90 in 2005, 0.89 in 2006, and 0.92 in 2007.
p. 7 of 69
22
owners rose 84 percent between 2003 and 2005. Over the same period, home-purchase mortgage
originations to owner-occupants rose much less, 36 percent. As a result, non-occupant borrowers
accounted for 26 percent of the increase in home-purchase mortgage borrowing between 2003 and
2005, even though their share of this market was only about half that size.23
Both the NAR and HMDA data report flows, for either new purchases or new mortgages. Neither
directly addresses what happened to the stock of housing units. For example, a large flow of nonoccupants buying homes could simply reflect the churning of properties among non-occupant
owners, with no net increase in the number of non-owner-occupied properties. A rise in the number
of non-occupants who buy properties from developers in order to ultimately resell them to owneroccupants (sometimes referred to as flipping) could also cause a boost in non-occupant buying with
little net change in non-occupants’ share of ownership.24
In fact, data from the American Housing Survey (AHS) show that non-occupants’ share of
ownership did not parallel their share of home purchasing and borrowing. Although the flow data
show a big rise in the share of buying and borrowing by non-occupants in 2003-2005, the AHS data
show the stock of owner-occupied single-family homes growing faster than other types of housing
units.25 Some of this divergence could be explained by considerable churning or flipping in the
stock of non-owner-occupied housing for the period 2003-2005.26
After 2005, the data on single-family housing stock are difficult to interpret, due to a big increase in
vacant properties that may reflect foreclosures. Again, the stock data appear to be at odds with the
flow data for purchases and mortgages. For example, the AHS data indicate that the stock of nonowner-occupied single-family housing grew faster than the stock of owner-occupied housing in the
23
The NAR and HMDA data disagree over the magnitude of non-owner occupants’ role in the housing boom and bust,
although both sources confirm that home purchasing was more procyclical for non-occupant owners than for owneroccupants during the recent housing cycle. We have not looked into the reasons for the differences between the NAR
figures and the HMDA data, but there are some obvious possibilities. NAR’s figures come from a survey, which is
subject to sampling error and, possibly, response bias. However, HMDA data have limitations as well. HMDA only
captures home purchases that involve a new mortgage (thereby omitting cash purchases). In addition, HMDA’s
coverage under-represents mortgages by small lenders in rural areas, which could lead to an undercount of vacationhome mortgages, for example. The HMDA data can also exaggerate changes in home purchases, when two or more
mortgages are associated with a single home purchase. For example, combinations of first and second mortgages to
finance a single home purchase increased substantially in 2004–2006, according to Avery, Brevoort, and Canner (2007).
24
The term ―flipping‖ generally has a negative connotation but can also provide needed financing for housing
development in rapidly growing markets.
25
From the second quarter of 2003 to second quarter of 2005, the total number of owner-occupied single-family
housing units grew by 3.8 percent, faster than the 2.9 percent increase in total single-family housing units. Thus,
owner-occupied single-family units grew faster than the total of all other kinds of single-family units. (We defined a
single-family unit as a unit in a structure with 4 or fewer total units.) A 2.2 percent drop in the number of single-family
rental units that were occupied or for rent (as opposed to for sale) restrained the overall growth in single-family units
between 2003 and 2005. However, rapid growth in certain sub-categories of the non-owner-occupied single-family
housing stock from 2003 to 2005 was more aligned with the data on non-occupant buying and borrowing. For example,
the stock of seasonal (vacation) single-family properties grew 14 percent over this period, and the stock of vacant
single-family properties for sale rose 16 percent.
26
See Benjamin, Chinloy, Hardin III, and Wu (2008) for a model that incorporates residential real estate churning and
flipping, with an application to the Miami, Florida, real estate market for the time period 2004-2006 . They argue that
apartment buildings were converted to condominiums because investors seeking to convert them were able to outbid
investors who were planning to maintain apartments as rental units. They find (pp. 631-632) that there is ―a higher
price when the volume of sales accounted for by converters rises‖ and that the ―entry of condo converters to the MiamiFort Lauderdale apartment market in 2004 causes a bubble in prices, which eventually dissipates.‖
p. 8 of 69
years 2005-2007. However, during this same time period, non-occupants accounted for a
disproportionate share of the drop in the flow of home purchases and home-purchase borrowing.27
Although the NAR and HMDA flow data show different patterns of change than the AHS housing
stock data, they mostly agree on a key point—that the behavior of non-occupant buyers and
borrowers is in many ways distinct from that of owner-occupants. In this section, we have presented
the case that investors and other non-occupant home owners have motives, characteristics, and
behavior patterns that, on average, differ from those of owner-occupants. Having established that
they are different, we now look across space (regions of the U.S.) and time to examine how home
buying and mortgage borrowing by investors and other non-occupant borrowers were related to
housing prices and foreclosures in recent years.
IV. Non-Occupant Owners and Housing Prices
We argue above that non-occupant home buyers respond primarily to profit opportunities and can
do so more quickly and extensively than owner-occupants. This suggests that non-occupant owners
may be attracted to areas where single-family housing is expected to appreciate more rapidly. In
other words, causality might run from expectations of rapidly rising prices to above-average buying
activity by non-occupants. Causality may flow in the other direction too, such as when a growing
population of affluent middle-aged families seeks to buy vacation properties, boosting demand and
increasing prices for homes in recreational areas. The direction of causality in these relationships is
often difficult to determine, and we do not attempt to do so here. However, we can document
growing correlation between housing appreciation and non-occupant mortgage borrowing near the
end of the housing boom.
We wish to observe if non-occupant borrowers are relatively active in areas where housing prices
appreciate rapidly. To do so, we spatially correlate the level or percentage change in the share of
home-purchase mortgage originations to non-occupants with the percent change in housing prices,
across the 50 states and the District of Columbia.28 In Figure 8, we take the percentage change for a
ten-year period, for the share of home-purchase mortgage borrowing by non-occupants and housing
prices.29 The figure shows that a number of states lie fairly close to the 45-degree line, which
would symbolize a one-to-one relationship between the percentage change in non-occupant share
and the percentage change in housing prices. However, several states lie far from the 45-degree
line. Among these outliers, the more common case is for non-occupants’ share of home purchase
mortgages to grow considerably faster than home prices, as in Delaware, Georgia, Idaho, Indiana,
Michigan, Minnesota, Montana, New Mexico, Pennsylvania, Tennessee, or Texas. The clearest
case of prices rising faster than non-occupants’ share is California, but the District of Columbia,
Florida, Massachusetts, and Rhode Island also share this pattern. With so many points lying well
off the 45-degree line in multiple directions, the overall statistical correlation is low.
27
From the second quarter of 2005 to second quarter of 2007, the total number of owner-occupied single-family
housing units grew by 2.8 percent, which is slower than the 3.4 percent increase in total single-family housing units.
Thus non-occupants’ share in the ownership of single-family housing units increased during the housing bust. A 5.9
percent rise in the number of single-family rental units that were occupied or for rent contributed to the overall growth
in single-family units between 2003 and 2005 as well as to the rise in non-occupants’ share of ownership. The stock of
seasonal (vacation) single-family properties and vacant single-family properties for sale also continued to grow rapidly
over this period (by 14 and 40 percent, respectively).
28
The mortgage data come from HMDA. Repeat-sales data for housing from the Federal Housing Finance Authority
(FHFA, formerly the Office of Federal Housing Enterprise Oversight) are used to measure housing appreciation.
29
The correlation was just 0.09, which is not significantly different from zero.
p. 9 of 69
Although home prices and the share of non-occupant home-purchase mortgage borrowing had only
a slight tendency to move together over the 1996–2006 time period as a whole, their relationship
tended to be stronger near the end of the period. This is illustrated in Figure 9, which uses the state
data mentioned above to compute cross-sectional correlations between the one-year percentage
change in home-prices and the share of mortgage originations for home-purchase to non-occupant
borrowers.30 The left-most (green) bar for each year displays the contemporaneous (no lags)
correlation between the annual percentage change in home prices and non-occupants’ share of
home-purchase mortgages. The middle bar (yellow) displays correlations in which non-occupants’
share of mortgages is lagged one year, and the right-most (maroon) bar displays correlations in
which the price change is lagged one year. Values above 0.25 or below -0.25 are statistically
significant at a 10 percent level or better. Using 10 percent as the standard, the contemporaneous
correlations are insignificant or negative until 2003, but then the correlations become mostly
statistically significant and positive for the final three years of the boom.31 As a result, we find that
as the housing boom progressed, the spatial correlation of home price appreciation and nonoccupants’ share of home purchase mortgage borrowing seemed to increase from weak or negative
to moderately positive and statistically significant.32
V. The Spatial Pattern of Non-Occupant Mortgage Borrowing and Foreclosures
As noted earlier, foreclosure rates for non-occupant owners and owner-occupants have been similar,
both nationally and at the state level, during the housing bust. However, significant variation in the
extent of non-occupant borrowing and non-occupant foreclosure rates has made the impact of
foreclosures on properties owned by non-occupants far from uniform across the United States. In
this section, we document and analyze these regional variations.33
One way to measure the extent of non-occupant foreclosures is to compute the percentage of
foreclosures that involve properties owned by non-occupants. Figure 10 uses LPS data for
mortgages originated in 2006 to show that this percentage varies significantly across states. For
example, the share of foreclosures involving non-occupant owners averages 11 percent nationwide
but equals or exceeds 14 percent in two Midwestern states, four Southeastern states, and three
Western states.
The share of foreclosures involving non-owner-occupied properties may be useful for some
purposes, such as determining which types of foreclosure mitigation programs are needed in a given
area. Nonetheless, we do not emphasize it in this study because it may distort the relative
significance of non-owner-occupant foreclosures across regions. For example, in states with
relatively few foreclosures overall, the incidence of foreclosures on non-owner-occupied properties
could be low in an absolute sense and yet account for a high share of the state’s few foreclosures.
Conversely, in states with many foreclosures, non-owner-occupied properties could account for a
30
Here we use non-occupants’ share, not the change in non-occupants’ share, to focus on the relationship between the
level of non-occupant activity and housing appreciation.
31
During the boom years, the highest correlations in Figure 9 result when neither variable is lagged. The lowest
correlations result when the variable for housing price appreciation is lagged, but even these are significantly positive in
2005-2006. There is also a slight asymmetry between the two correlations with lagged variables. This asymmetry is
somewhat similar to Wheaton and Lee’s findings on lead-lag relationships between sales and prices for total home
purchases. See Wheaton and Lee (2008).
32
See Wheaton and Nechayev (2008) for a related finding. They find that the share of non-occupant mortgage
borrowing is positively correlated with errors in forecasting housing prices.
33
A key question is whether the LPS data we use accurately capture the regional variation in non-occupant borrowing
and foreclosure. Appendix 2 presents evidence that the LPS data are reliable for this purpose.
p. 10 of 69
relatively low share of overall foreclosures and yet be much more common and significant than in
low-foreclosure states. This is not just a hypothetical issue, in our view, for some of the nonoccupant foreclosure shares in Figure 10 seem to have been materially influenced by the relatively
good or bad performance of owner-occupied mortgages in certain states. For example, the map
shows that the non-occupant share of foreclosures was 13 percent in New Mexico and 12 percent or
less in Arizona. However, foreclosure rates on owner-occupant mortgages originated in 2006 were
about three times as high in Arizona as in New Mexico. As a result, using the share of foreclosures
to measure the impact of non-occupant borrowing artificially dilutes the scale of the non-occupant
foreclosure problem in Arizona and, relatively speaking, exaggerates it in New Mexico.
Accordingly, we prefer to use a measure that is not affected by the extent of foreclosures among
owner-occupants and reflects only the prevalence and performance of non-occupant mortgages. By
prevalence, we mean, for a given year of origination, the number of non-occupant mortgages
divided by the total number of housing units.34 By performance, we mean the foreclosure rate (the
number of foreclosures on non-occupant mortgages divided by the total number of non-occupant
mortgages), again for a given year of origination. The product of these two factors is what we call
the impact of non-occupant foreclosures, or the number of foreclosures on non-occupant mortgages
divided by the total number of housing units. In other words,
Impact = Prevalence x Performance.
We make use of this relationship to show that states vary significantly not only in the overall impact
of non-occupant foreclosures but also in the two factors, prevalence and performance, that
determine the overall impact.
Using LPS data on annual cohorts of home-purchase and refinance mortgages, Figure 11a shows
how the prevalence and performance of non-occupant mortgages evolved nationally for mortgages
originated each year between 2004 and 2007. In 2004, for example, the data include 404 mortgages
originated to non-occupant borrowers for every 100,000 housing units in the U.S. Of the mortgages
originated to non-occupants in 2004, 4.5 percent had been foreclosed on or were in foreclosure by
July 2009, the last month for which we have data. Together, the prevalence and performance of
non-occupant mortgages originated in 2004 imply that there were about 18 foreclosures on nonoccupant mortgages originated for every 100,000 housing units. Compared with 2004, the impact
of foreclosures on non-occupant mortgages increased for loans originated in 2005 and 2006. This is
partly because non-occupant mortgages became more prevalent (533 and 471 per 100,000 housing
units, respectively) during this time period and also because their performance deteriorated (to
foreclosure rates of 8.5 and 12.7 percent, respectively). As a result, our overall measure of impact
(non-occupant foreclosures per 100,000 housing units) rose to 45.1 in 2005 and then to 59.6 in
2006, well above the overall 2004-2007 average of 37.6 foreclosures per 100,000 housing units.35
34
We analyze first-lien, home-purchases plus refinanced mortgages on single-family homes, but we omit homeimprovement loans. The data on state housing units come from the American Community Surveys for the time period
2004–2007. In Appendix 5, we present an alternative measure of mortgage prevalence based on non-occupant
mortgage originations as a percentage of total first-lien home-purchases plus refinanced mortgage originations in the
same calendar year. Results based on this ―per mortgage‖ measure of the prevalence of non-occupant mortgages are
similar to the ―per housing unit‖ results presented in the body of this paper, but the details of state rankings change. We
focus on the per housing unit measure because it captures the broadest base for measuring the impact of non-occupant
foreclosures and is not affected by state-to-state differences in the intensity of mortgage lending, as noted in a paper
written by Mayer and Pence (2008).
35
The full impact of non-owner occupied mortgages is higher than the numbers reported in this study, because the LPS
data do not cover the entire mortgage market and may underestimate the share of mortgages to non-occupants. The
p. 11 of 69
This can be seen in Figure 11a by noting that 2004 lies below the curved line while 2005 and 2006
lie above, for the line shows all the combinations of prevalence and performance that equate to the
average national impact for non-occupant foreclosures during the time period, 2004-2007. The U.S.
foreclosure impact for non-occupant mortgages originated in 2007 is 27.4 per 100,000 housing
units, which is below the 2004-2007 national average. This was partly due to the housing bust,
which reduced the prevalence of non-occupant mortgages sharply to 358 per 100,000 housing
units.36 Table 1 summarizes the data in Figure 11a and provides an overview of the impact of nonoccupant foreclosures in 2004-07.
Table 1: Prevalence, Performance and Foreclosure Impact
of Non-Occupant Mortgages in our LPS Data, 2004-07
Year of Mortgage
Origination
2004
2005
2006
Performance
(Percent of NonOccupant Mortgages
Foreclosed)
4.5
8.5
12.7
Prevalence
(Number of NonOccupant Mortgages per
100,000 Housing Units)
404
533
471
Impact
(Number of Non-Occupant
Mortgage Foreclosures per
100,000 Housing Units)
18.1
45.1
59.6
2007
2004-2007
7.6
8.5
358
441
27.4
37.6
Figures 11b to 11e show how the relative impact of non-occupant foreclosures varied across U.S.
states for the years 2004-2007, as well as how the impact outcomes were driven by the underlying
prevalence and performance factors. In these figures, prevalence, performance, and impact are
measured relative to national norms.37 This means that the point 1.0 on the horizontal axis stands
for a level of prevalence equal to the 2004-07 U.S. average for prevalence. Similarly, the point 1.0
on the vertical axis stands for a performance level equal to the 2004-2007 U.S. average for
performance. The highest point in Figure 11b is labeled IN for Indiana, and it has an x-axis value of
about 0.64. This means that Indiana’s prevalence measure, non-occupant home-purchase and
refinance mortgages originated in Indiana in 2004 as a fraction of total housing units in Indiana in
2004, is about 0.64 times the corresponding 2004-07 U.S. average for the prevalence of nonoccupant mortgages. On the y-axis, the value for Indiana is almost 2. This means that Indiana has a
foreclosure rate for non-occupant mortgages originated in 2004 that is almost 2 times higher than
the corresponding 2004-2007 U.S. average. The product of these two factors for Indiana is about
1.26. This degree of relative impact means that the number of foreclosures on non-occupant
degree to which the data underestimate non-occupant foreclosures is discussed in Appendix 2, where we show that the
LPS data capture 50 to 60 percent as many mortgage originations as the HMDA data and also imply a share of nonoccupant mortgages in 2006 that is about 27 percent lower than in the HMDA data (10.5 percent versus 14.4 percent).
In Appendix 2, we adjust our data to take account of the LPS data’s limited coverage of originations. This raises our
impact measures significantly. For example, the peak impact (in 2006) rises to 103.2 non-occupant foreclosures per
100,000 housing units. However, we show in the appendix that the adjusted data and the unadjusted data imply similar
relative impacts across the states, and on that basis we use the unadjusted data in the body of this paper. We do not
adjust for the difference in non-occupants’ share of mortgage originations between the LPS and HMDA data, because
we don’t know which is more accurate. However, if we were to assume that the higher share implied by HMDA is
correct, our impact figures would rise to 37 percent in 2006. (This increase is derived by dividing 14.4 by 10.5.) Along
with our coverage adjustment, this would imply a non-occupant foreclosure impact of 141.5 per 100.000 housing units
for mortgages originated in 2006.
36
Another factor is that loans originated in 2007 have had less time to enter foreclosure than loans in the earlier years.
37
The data for these figures appear in Table A4-1 of Appendix 4.
p. 12 of 69
mortgages originated in Indiana in 2004, as a percentage of housing units in Indiana in 2004, was 26
percent higher than the corresponding national average for the period 2004-07.
When relative prevalence and relative performance are both 1.0, they multiply to create a relative
impact measure of 1.0 as well. A relative impact measure of 1.0 means that non-occupant
foreclosures equal the 2004-07 national average of 37.6 per 100,000 housing units. The middle of
the three curves sweeping from the upper left toward the lower right in Figures 11b-11e illustrates
all points for which the relative impact measure is 1.0. For any state whose dot lies above this
curve, the impact of non-occupant foreclosures exceeds the 2004-07 national average impact, and
vice versa for points below the curve. The lower-most and upper-most curves in the graph illustrate
points where non-occupant foreclosures per housing unit (impact) are, respectively, half of and
three times the 2004-07 national average. For mortgages originated in 2004, both Indiana and
Nevada have impact measures that clearly exceed the 2004-07 national average. However, in most
states, the impact of non-occupant mortgages originated in 2004 was at or below the 2004-07
average.
Figure 11b also tells us something about why foreclosures on non-occupant mortgages originated in
2004 are relatively important (or not) in each state. Note that Indiana suffered an above-average
impact from non-occupant mortgage foreclosures because of performance problems, not an
unusually high prevalence of non-occupant mortgages. On the other extreme, Nevada experienced
a relatively low foreclosure rate on mortgages to non-occupant owners but had an above-average
impact because non-occupant mortgages were unusually prevalent.
Figures 11c, 11d, and 11e present the same analysis but for non-occupant mortgages originated in
2005, 2006, and 2007. From 2004 to 2005, the whole distribution of impact measures shift toward
the northeast, as non-occupant mortgages surged to a peak level of prevalence while also
performing more poorly. In three states—Nevada, Florida, and Arizona—the impact of nonoccupant foreclosures from 2005 originations reached or exceeded 3 times the 2004-07 national
average. This was driven by high prevalence in Arizona and a combination of high prevalence and
poor performance in Nevada and Florida. For a cluster of Midwestern states (Indiana, Michigan,
Ohio, Minnesota, and Missouri), the foreclosure impact for 2005 non-occupant originations reached
or exceeded the 2004-07 national average, even though this type of mortgage was not especially
prevalent. However, these states have above-average impact measures due to below-average
performance of non-occupant mortgages.
Very poor performance caused the foreclosure impact from non-occupant mortgages to peak in
2006 even though prevalence declined that year. Visually this shows up as a shift toward the
Northwest in the distribution of impact measures. Several Midwestern states continued to
experience very poor performance combined with unexceptional prevalence. Deteriorating
performance combined with still high prevalence kept the impact numbers very high in Nevada,
Florida, and Arizona, while additional states began to experience impact outcomes that were above
the 2004-2007 national average. In the case of Hawaii, Utah, and Idaho, high impact outcomes
were driven by high prevalence, while poor performance was the problem in states like Indiana,
Michigan, and Ohio.
In most states, a combination of lower prevalence and better performance reduced the foreclosure
impact of non-occupant mortgages originated in 2007. This is seen in a shift toward the Southwest
in the distribution of impacts in Figure 11e, as compared to Figure 11d. However, the level of
impact remained quite high in Nevada, Florida, and Arizona.
p. 13 of 69
To provide a clearer view of the geographic patterns in non-occupant foreclosure impact and its
underlying factors, Figures 12, 13, and 14 use maps to show the prevalence, performance, and
impact for 2006 mortgage originations in the 50 states and the District of Columbia. Figure 12
shows that in 2006, non-occupant mortgages were relatively prevalent in the West (including
Hawaii); along the mid- to lower-East Coast from Florida to New Jersey; and in Vermont. Since we
have already seen that in several of these states the impact of non-occupant mortgages was not
especially high, it’s not surprising that Figure 13 shows relatively low non-occupant foreclosure
rates in many Western and mid- and lower-East Coast states, the big exceptions being California,
Nevada, and Arizona in the West and Florida and Georgia on the lower East Coast. Figure 13 also
shows that foreclosure rates for non-occupant mortgages were well above-average in much of the
Midwest and parts of the Northeast. Figure 14 presents the effect of prevalence and performance on
the relative impact of non-occupant mortgages. States that rank high in both Figures 12 and 13, like
Florida and Nevada, are also high (in red) in Figure 14. The figures printed in black indicate that
the impact of non-occupant foreclosures was 752 percent of the national average in Nevada and 645
percent of the national average in Florida. The eight states with the highest impacts are all in the
West or Southeast. Michigan, Indiana, Rhode Island, Minnesota, Ohio, Illinois, Missouri, and
Connecticut are all above average in Figure 14 due to poor performance, or high foreclosure rates
for their non-occupant mortgages.38
VI. Conclusion
Non-occupant home buyers make up a distinct and significant segment of the U.S. housing and
mortgage markets. We show that compared to owner-occupants, non-occupant borrowers tend to
have higher incomes, higher credit scores, smaller loans, and generally a lower overall risk profile.
Nonetheless, the national rate of foreclosure on mortgages to non-occupants is comparable to the
foreclosure rate on mortgages to owner-occupants, presumably due to non-occupants’ greater
willingness to use their foreclosure option when the value of housing falls. We also show that, the
share of non-occupants borrowing to buy homes was positively correlated with increases in housing
prices during the final years of the housing boom.
Lastly, we document that the prevalence and performance of non-occupant mortgages varies
significantly by state, leading to significant differences in the overall impact of non-occupant
foreclosures in local housing markets. In Michigan, Indiana, and some other Midwestern and
Northeastern states, the overall incidence of non-occupant foreclosures exceeds the national average
mainly due to the poor performance of non-occupant mortgages. By contrast, in Idaho and some
other Western states, the relatively high incidence of non-occupant foreclosures is primarily driven
by the relatively high prevalence of non-occupant mortgages. The states that experienced the
highest impact from foreclosures on properties owned by non-occupants (Florida, Nevada and
Arizona) exhibit both relatively poor performance and relatively high prevalence of non-occupant
mortgages.
38
Maps covering mortgages originated in 2004, 2005, and 2007 are presented in Appendices Four.
p. 14 of 69
Figure 1
Two Facets of the Housing Cycle
18
12
16
10
14
8
12
6
10
4
8
2
6
0
4
-2
2
-4
0
-6
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Non-Owner-Occupant Mortgage Share (left axis)
p. 15 of 69
FHFA Home Price Index (right axis)
Percentage Change, April-to-April
Percent of First-Lien Home-Purchase Mortgages,
by Value (HMDA)
(Housing Prices and Mortgages to Non-Occupants)
Figure 2: Distributions Across States of the Median Income of Owner-Occupant and Non-OwnerOccupant Mortgage Borrowers, by Loan Purpose and Year of Origination (HMDA)
Median Borrower Income by Occupancy (Home Purchase)
150
100
CA
CA
DC
CA
HA
CA
50
$ Thousands
200
(Box-Whisker Distribution over U.S. States, 2004-07)
2004
2005
owner_occupant
2006
2007
non_occupant_owner
(Each box covers the 25th to 75th percentile; the line in the box is the median. )
Median Borrower Income by Occupancy (Refinance)
120
100
CA
80
CA
60
$ Thousands
140
160
(Box-Whisker Distribution over U.S. States, 2004-07)
2004
2005
owner_occupant
2006
2007
non_occupant_owner
(From HMDA data; each box covers the 25th to 75th percentile; the line in the box is the median. )
[Note: The data are median applicant income for each state and the District of Columbia, segregated by occupancy. The
line in each box is the median of the distribution of median incomes across the 50 states and the District of Columbia.
Each box covers the inter-quartile range (25th percentile to the 75th percentile) of the distribution. The ―whiskers‖
extend beyond the box either to the end of the distribution or to a length of 1.5 times the inter-quartile range, whichever
comes first. Dots beyond the whiskers identify extreme outlier jurisdictions.]
p. 16 of 69
Figure 3a: Distribution Across States of the Mean FICO Score of Owner-Occupant and NonOwner-Occupant Mortgage Borrowers, by Year of Origination (LPS)
FICO Score Distributions by Type of Owner
760
(Box-Whisker Distribution over U.S. States, 2004-07)
720
LA
AK
680
700
LA
MS
MS
MS
MS
660
FICO
740
CO
2004
2005
owner_occupant
second_home
2006
2007
investor
(From LPS data. Each box covers the 25th to 75th percentile; the line in the box is the median. )
[Note: The data are mean FICO scores for each state and the District of Columbia, segregated by occupancy. Loans
whose loan-to-value ratio exceeded 400 were dropped.]
p. 17 of 69
Figure 3b: Distribution Across States of Loan-to-Value Ratios for Owner-Occupant and NonOwner-Occupant Mortgage Borrowers, by Year of Origination (LPS)
Loan-to-Value Distributions by Type of Owner
80
(Box-Whisker Distribution over U.S. States, 2004-07)
65
70
75
AK
CA
HA
60
CA
RI
MA
2004
2005
owner_occupant
second_home
2006
2007
investor
(From LPS data. Each box covers the 25th to 75th percentile; the line in the box is the median. )
Note: Loan-to-value ratios here are computed as the original principal of the mortgage divided by the most recently
available appraised value of the mortgaged property. Loans whose loan-to-value ratio exceeded 400 were dropped.
p. 18 of 69
Figure 3c: Distribution Across States of Debt-to-Income Ratios of Owner-Occupant and NonOwner-Occupant Mortgage Borrowers, by Year of Origination (LPS)
Debt to Income Distributions by Type of Owner
SD
SD
ND
NE
AR
ND
NE
SD
VT
40
ND
OH
30
DTI
50
60
(Box-Whisker Distribution over U.S. States, 2004-07)
AR
NY
NY
NY
20
NY
DC
2004
2005
owner_occupant
second_home
2006
2007
investor
(From LPS data. Each box covers the 25th to 75th percentile; the line in the box is the median. )
Note: Loans whose loan-to-value ratio exceeded 400 were dropped.
p. 19 of 69
Figure 4: Distribution Across States of the Median Amount of Owner-Occupant and Non-OwnerOccupant Mortgages, by Loan Purpose and Year of Origination (HMDA)
Median Mortgage Amount by Occupancy (Home Purchase)
400
(Box-Whisker Distribution over U.S. States, 2004-07)
300
HA
200
CA
HA
0
100
$ Thousands
CA
HA
CA
HA
CA
HA
DC
2004
2005
2006
owner_occupant
2007
non_occupant_owner
(From HMDA data; each box covers the 25th to 75th percentile; the line in the box is the median. )
Median Mortgage Amount by Occupancy (Refinance)
400
(Box-Whisker Distribution over U.S. States, 2004-07)
CA
HA
200
0
100
$ Thousands
300
CA
HA
2004
2005
owner_occupant
2006
2007
non_occupant_owner
(From HMDA data; each box covers the 25th to 75th percentile; the line in the box is the median. )
[Note: The data are median mortgage amount for each state and the District of Columbia, segregated by occupancy.]
p. 20 of 69
Figure 5: A Cross-State Comparison of Foreclosure Rates on Non-Owner-Occupied and OwnerOccupied Mortgages, by Year of Origination (2004-2007)
Home Purchase and Refinance Originations, LPS
FL
MI
MN
10
MS
GA
MOKY
IL
WV AZ
RI
AL TN
NY
CT OK
NJ WI
LAMATX
IA
NHSC CO
PA
DCHA MD KSNEME
ND AKSD NCAR VA
ID
DE
UTNM
OR
WA
VT
MT
WY
MO
MN TN KY
CO
5
Owner-Occupied Foreclosure Rate (%)
10
0
2006 Foreclosure Rates by Occupancy
20
15
RI
15
FL
IN
OH
MN
GA
5
10
Owner-Occupied Foreclosure Rate (%)
NV
MI
MI
NV
AZ
CA
AZ
10
Non-Owner-Occupied Foreclosure Rate (%)
25
20
15
5
CT
NJ MS
MO
NY
MD
TN
AL
KY
DC WI
OK
WVVA
MA
PA TXHA
ID
SC
NHLA
AK KSAR
DE
IACO
NE
UT
OR
NCWA ME
SDNM
VT
MT
MN
OHIN GA
MS IL
MD NJ
UT
MO
ID
WI
CT TN
ALSC
NHNY
DCLAWV
OR
VA KY
NMHA
WA
PAMA
AR
CO
ME
OK
TX
KS
DE
IA
AK NC
NEVT
MT
SD
WY
ND
0
10
5
0
5
CA
Home Purchase and Refinance Originations, LPS
FL
IL
0
GA
MS
2007 Foreclosure Rates by Occupancy
Home Purchase and Refinance Originations, LPS
WY
ND
NV
0
5
0
OH
15
OH
IL
IAAL
NE OK
NY
NV
LA
TX
RI
FLKS
WI
WV
SDPANH
NJCT
NC
MA
SC
NDCA
AKUT ME AR
DCMD
AZ
WY
ID
NM
VT DE
WAMT
HA VA
OR
0
MI
IN
IN
5
10
15
Non-Owner-Occupied Foreclosure Rate (%)
20
2005 Foreclosure Rates by Occupancy
Home Purchase and Refinance Originations, LPS
20
2004 Foreclosure Rates by Occupancy
10
15
20
Owner-Occupied Foreclosure Rate (%)
25
p. 21 of 69
0
CA
RI
5
10
Owner-Occupied Foreclosure Rate (%)
15
Figure 6
Home Sales by Use, 2003-07
(from the National Assn. of Realtors)
9
8
7
Million Units
6
5
4
3
2
1
0
2003
2004
2005
Primary Residence
p. 22 of 69
Vacation
2006
Investment
2007
Figure 7
Home-Purchase Mortgage Originations by Occupancy
(HMDA data on number of originations)
8
7
6
Millions
5
4
3
2
1
0
1996
1997
1998
1999
2000
Owner-Occupied
p. 23 of 69
2001
2002
2003
Non-Owner-Occupied
2004
2005
2006
Figure 8
Relative Change in House Prices and Non-Occupant Share of Mortgages
350
HMDA Purchase Mortgages and FHFA Housing Price Index, 1996-2006
GA
MN
ID
300
NM
DE
250
PAMT
200
TX
MI
150
OH
WY
TN
UT
AL SCIL
NC
WVMOAK
CO
KSND
LA
IN
IAAR
KY
OK
MS WI
SD
VT
DC
NY MD
CT HAVA
NJ
WA
NH
ME
AZ
NV
OR
MA
FL
RI
CA
100
NE
100
150
200
250
300
% Change in Housing Prices 1996-2006
p. 24 of 69
350
Figure 9
0.60
Cross-State Correlations Between Non-Occupants' Share of HomePurchase Mortgage Originations and Annual Percentage Changes in
Housing Prices, 1997-2006
0.50
0.40
0.30
0.20
0.10
0.00
-0.10
-0.20
-0.30
-0.40
1997
1998
No Lags
1999
2000
2001
Mortgage Share Lagged 1 Year
p. 25 of 69
2002
2003
2004
2005
Price Change Lagged 1 Year
2006
Figure 10: Share of Foreclosure That Involve Non-Owner-Occupied Properties (LPS data for
mortgages originated in 2006)
p. 26 of 69
Figure 11a: National Prevalence and Performance of Non-Occupant Mortgages,
by Year of Origination, 2004-2007
U.S. Non-Occupant Mortgage and Foreclosure Impact
2004-07 Home Purchase and Refi Originations (LPS)
10
12
2006
8
2005
6
2007
4
2004
300
400
500
Prevalence (Non-Occupant Mortgages per 100,000 Housing Units)
600
2004-07 Natl. Avg.= 37.6 foreclosures per 100,000 housing units
p. 27 of 69
Figures 11b, c, d, and e: The Relative Impact (per Housing Unit) of Non-Occupant Foreclosures, by
States and Underlying Factors, for Mortgages Originated in 2004, 2005, 2006, and 2007
Relative Non-Occupant Foreclosure Impact per Housing Unit
2
3
2004 Home Purchase and Refinance Originations (LPS)
IN
OH
MI
MS
NV
HA
0
1
GA
MO
KY TN MN
IL
IA
AL
NE
NY
OK
KS
TX
RI
FL
WVLA
PA
SDWI
CO
CT MA
NJ
NCNH
SC
AR
UT
ND
AK ME MD
CA
DC ID
WY
NM VT
VA
MT
WA
DE AZ
OR
0
1
2
3
Relative Non-Occupant Mortgage Prevalence (U.S.=1)
4
Relative Non-Occupant Foreclosure Impact per Housing Unit
3
2005 Home Purchase and Refinance Originations (LPS)
2
MI
IN
OH
NV
FL
AZ
DE
UT
ID
HA
0
1
GA
MN
MS
MO
KY
IL
WV
CA
RI
AL
TN
NY
CT
OKWI
NJ
IA LA
TX
MANH
CO SC
NE KS PA
ME VA MD
DC
NDSD
AK
NC
AR
NM
OR
VT WA
MT
WY
0
1
2
3
Relative Non-Occupant Mortgage Prevalence (U.S.=1)
p. 28 of 69
4
Relative Non-Occupant Foreclosure Impact per Housing Unit
3
2006 Home Purchase and Refinance Originations (LPS)
FL
NV
MI
2
IN
OH
MN
RI
GA
AZ
CA
ID
HA
UT
0
1
IL
CT
MS
NJ
MO
NY
MD
TN
AL
KY
WI
DC
VA
WVLA OKPA
MA
SC
TX NH
CO
AK
DE
AR
IA
NE KS
OR
ME NC WA
NM
SD
VT
MT
WY
ND
0
1
2
3
Relative Non-Occupant Mortgage Prevalence (U.S.=1)
4
Relative Non-Occupant Foreclosure Impact per Housing Unit
3
2007 Home Purchase and Refinance Originations (LPS)
2
FL
MI
NV
AZ
0
1
MN
OH
GA
IN
CA
MS IL NJ
MD
WI RIMO
TN NH
WVLA
AL
NY CT
SC DC
KY
OR
VA
NM
WA
MA
PA
AR
CO
OK TX MENC
KS
DE
IA
AK
VT
NE
MT
SD
WY
ND
0
ID UT
HA
1
2
3
Relative Non-Occupant Mortgage Prevalence (U.S.=1)
4
Note: Middle line represents an impact equal to the LPS data 2004-07 national average of 37.6
non-occupant foreclosures per 100,000 housing units. The lower and upper lines represent
impacts of, respectively, half and three times that national average impact.
p. 29 of 69
Figure 12: Non-Owner-Occupied Mortgage Prevalence per Housing Unit, Relative to 2004-2007
U.S. Average (LPS data for mortgages originated in 2006)
p. 30 of 69
Figure 13: Non-Owner-Occupied Mortgage Foreclosure Rates, Relative to 2004-2007 U.S. Average
(LPS data for mortgages originated in 2006)
p. 31 of 69
Figure 14: Non-Owner-Occupied Mortgage Foreclosure Impact per Housing Unit, Relative to U.S.
2004-2007 Average (LPS data for mortgages originated in 2006)
p. 32 of 69
References
Apgar, William C., and Shekar Narasimhan (2008). ―Capital for Small Rental Properties:
Preserving a Vital Housing Resource.‖ Chapter 8 in: Revisiting Rental Housing: Policies,
Programs, and Priorities, ed. Nicolas P. Retsinas and Eric S. Belsky. Brookings Institution Press,
Washington, DC.
Avery, Robert B., Kenneth P. Brevoort, and Glenn B. Canner (2007). ―The 2006 HMDA Data.‖ The
Federal Reserve Bulletin 93(Dec.): A73–A109.
—— (2009). ―Geographic Patterns in Mortgage Loan Performance.‖ Unpublished working paper,
Board of Governors of the Federal Reserve System.
Bajari, Patrick, Chenghuan Sean Chu, and Minjung Park (2008). ―An Empirical Model of Subprime
Mortgage Default from 2000 to 2007.‖ NBER Working Paper 14625. National Bureau of Economic
Research.
Benjamin, John D., Peter Chinloy, William G. Hardin III, and Zhonghua Wu (2008). ―Clientele
Effects and Condo Conversions.‖ Real Estate Economics 36(3): 611–634.
Coulton, Claudia, Tsui Chan, Michael Schramm, and Kristen Mikelbank (2008). Pathways to
Foreclosure: A Longitudinal Study of Mortgage Loans, Cleveland and Cuyahoga County, 2005–
2008. Report of the Center on Urban Poverty and Community Development, Mandel School of
Applied Social Sciences (June). Case Western Reserve University, Cleveland, OH.
Cowan, Adrian M., and Charles D. Cowan (2004). ―Default Correlation: An Empirical Investigation
of a Subprime Lender.‖ Journal of Banking and Finance 28: 753–771.
Gerardi, Kristopher, Adam Shapiro, and Paul Willen (2008). ―Subprime Outcomes: Risky
Mortgages, Homeownership Experiences, and Foreclosures.‖ Working Paper 07-15 (May 4, 2008,
version). Federal Reserve Bank of Boston.
Haughwout, Andrew, Richard Peach, and Joseph Tracy (2008). ―Juvenile Delinquent Mortgages:
Bad Credit or Bad Economy?‖ Staff Report 341 (August). Federal Reserve Bank of New York.
Immergluck, Dan, and Geoff Smith (2004). Risky Business—An Econometric Analysis of the
Relationship Between Subprime Lending and Neighborhood Foreclosures. Woodstock Institute,
Chicago.
Mayer, Chris, and Karen Pence (2008). ―Subprime Mortgages: What, Where, and to Whom?‖
Finance and Economics Discussion Series paper 2008-29. Board of Governors of the Federal
Reserve System.
McCormick, Erin, and Carolyn Said (2007). ―Investors Own About One-Fifth of Bay Area Homes
in Foreclosure.‖ San Francisco Chronicle (Dec. 16).
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Qi, Min, and Xialong Yang (2007). ―Loss Given Default of High Loan-to-Value Residential
Mortgages.‖ Economics and Policy Analysis Working Paper 2007-4 (August). Office of the
Comptroller of the Currency, U.S. Treasury.
Todd, R.M. (2010). ―Foreclosures on Non-Owner-Occupied Properties in Ohio’s Cuyahoga County:
Evidence from Mortgages Originated in 2005-2006.‖ Community Affairs Research Report 2010-2.
Federal Reserve Bank of Minneapolis.
Wheaton, William C., and Nai Jia Lee (2008). ―Do Housing Sales Drive Prices or the Converse?‖
Working Paper 08-01 (Jan. 28). Department of Economics, Massachusetts Institute of Technology,
Cambridge.
p. 34 of 69
Appendix 1: A simple theory of investor mortgage default
The mortgage payment decision for both non-occupants and owner-occupants is affected by the
option to default on the mortgage. We modify the model presented in Gerardi, Shapiro, and Willen
(2008) by assuming that non-occupants (―investors‖) are purely profit-maximizers. Our model is a
simple, two-period analysis. In period zero, the non-occupant owner owns the property and has
financed it with an interest-only mortgage.39 In period one, the mortgage is due. Refinancing the
mortgage is not an available option in the model. As a result, the owner in period zero has the
option of making the mortgage payment and keeping the property, selling the property to pay off the
mortgage, or defaulting on the mortgage. The payoff structure under each scenario can be written
as follows:
Option 1: Pay the mortgage in period 0; sell the property in period 1.
π0 = ρ - rMU
π1 = (H1 – U)+,
where π0 is profit in period zero, π1 is profit in period one, ρ is net rental income, rM is the
mortgage interest rate, U is the unpaid balance on the investor’s interest-only mortgage, H1
is the price of the house in period one, and (H1 – U)+ means either (H1 – U) or zero,
whichever is greater.40 This last term reflects the fact that, in period one, the investor will
sell the house and pay off the mortgage if H1 ≥ U but will simply default if not.
Option 2: Sell the property in period 0.
π0 = H0 – U
π1 = 0,
where H0 is the price of the house in period zero.
Option 3: Default on the mortgage in period 0.
π0 = π1 = 0.
Options 2 and 3 can be combined into a single ―Don’t keep the property‖ option in which π0 = (H0 –
U)+ and π1 = 0. Then the entire investor decision in this simple framework boils down to keeping
the property (making the period one mortgage payment) if
ρ + {[1/(1+r)](H1 – U)+ - (H0 – U)+} ≥ rMU,
39
Before purchasing the property, and in particular before making the down payment, the investor would analyze
capital gains by comparing future property values to the purchase price (albeit while also recognizing that potential
losses are limited to the down payment). Thereafter, as in the analysis presented here, the mortgage default option leads
the investor to analyze capital gains by comparing the future value of the property to the unpaid balance on the
mortgage instead of the current value of the property.
40
We parallel Gerardi, Shapiro, and Willen (2008) also in assuming that housing prices in both periods are known in
period zero. As they note, allowing the period one housing price to be unknown as of period zero would mean that both
the expected value and the variance of the period one housing price would affect the investor’s decision.
p. 35 of 69
where r is the interest rate at which the investor discounts period one profits. The first term on the
left is period-zero net rental income. The bracketed term on the left is the present value of expected
capital gains from future sale, taking into account the value of the option to default today or
tomorrow. The term on the right is the period-zero interest payment on the mortgage.
p. 36 of 69
Appendix 2: Comparing the LPS Mortgage Data to Some Alternatives
We rely heavily on LPS’s data that are reported by a group of large mortgages servicers. Here we
assess and adjust for how the LPS data differ from other national and local data on mortgages,
focusing on first-lien mortgages originated in 2004–2007 for the purposes of either home purchase
or mortgage refinance.41
Compared to the HMDA data, the LPS data cover about 50 percent of the mortgages originated in
2004 and about 60 percent of those originated in 2005-07.42 This coverage difference implies that
our prevalence and foreclosure impact measures for non-occupant mortgages underestimate the
actual levels of prevalence and impact. We provide adjustments that scale up our LPS measures to
more realistic levels, and we show that these adjustments do not greatly alter the relative regional
distributions of prevalence, performance, and impact discussed in the body of the paper. Similarly,
we find that the share of mortgages made to non-occupant owners is lower in the LPS data than in
the HMDA data. We show that this difference is fairly uniform across states and should have a
minor effect on the relative regional patterns discussed in the body of the paper. Finally, we
benchmark the LPS data for selected ZIP-code areas against local records on non-owner-occupied
mortgages and foreclosures and again find a high spatial correlation. We conclude that the LPS
data are likely to undercount the extent of non-owner-occupant home buying, mortgage borrowing,
and mortgage foreclosures, but this undercounting appears to be relatively uniform across the
country. As a result, we assert that the LPS data correctly identifies significant regional patterns in
non-occupant mortgage prevalence, performance, and impact.
Figure A2-1 documents that the LPS data cover fewer mortgages than the HMDA data. The figure
compares the total number of mortgage originations43 in the LPS data with the total number of
mortgage originations reported under HMDA for each state by year of origination for the period
2004-2007. LPS’s coverage is highest for the most recent year, probably in part because the market
share of the servicers reporting to LPS has increased. For 2004, Figure A2-1 shows that mortgage
originations in the LPS database are nearly half of the total number of HMDA originations in a
typical state, where the fraction of mortgage originations falls within the range of 0.30 to 0.62.44
For home-purchase mortgages originated in 2005-07, LPS’s coverage rises to about 60 percent of
the HMDA originations in a typical state, but the fraction falls into the high 30s in some states,
while rising to about 80 percent in others.
LPS’s incomplete coverage of the mortgage market depresses our estimates of the prevalence and
foreclosure impact of non-occupant mortgages. In this appendix, we created adjusted estimates by
inflating each state’s annual LPS data by the state’s ratio of HMDA to LPS originations for that
year. For example, in Alabama in 2006, the number of mortgage originations reported by LPS was
41
We also limit our comparisons to mortgages for which LPS reports that the borrower is an owner-occupant, a secondhome owner, or an investor. We omit LPS mortgages for which the occupancy status of the borrower is not known to
be one of these three types.
42
Some of the LPS-HMDA gap arises because LPS obtains its data from mortgage servicers whose collective market
share is less than 100 percent of the total market for mortgage originations. It also reflects our omission of LPS
mortgages with unknown owner occupancy type.
43
In the comparison of the LPS and HMDA data in this appendix, we use ―mortgage‖ to refer to loans made to purchase
a house or refinance an existing mortgage but exclude home improvement loans.
44
Differences among the states can arise because of regional variations in the speed with which mortgages are paid off
or are foreclosed as well as regional variations in the coverage of both the LPS and HMDA data. The collective market
share of the mortgage servicers that provide data to LPS may vary regionally, and HMDA’s coverage can be spotty to
varying degrees in rural areas, for example.
p. 37 of 69
about 56.1 percent of the corresponding figure from HMDA. Thus, for our adjusted estimates, we
scale the LPS mortgage data for Alabama for 2006 by 1.78 (the inverse of 0.561) and make
corresponding adjustments for the other states and years. These adjustments significantly raise the
level of all of our prevalence-per-housing-unit and impact-per-housing-unit measures.45 For
example, the peak national non-occupant foreclosure impact based on just the LPS data was 59.6
non-occupant foreclosures per 100,000 housing units, for mortgages originated in 2006 (as
discussed in conjunction with Figure 11 above.). After scaling up the LPS data, we compute that
the impact measure is 73 percent higher, or 103.2 non-occupant foreclosures per 100,000 housing
units for mortgages originated in 2006.
It is obvious that scaling up the LPS mortgage data to match HMDA’s state-by-state total of
mortgage originations significantly raises the level of our per-housing-unit prevalence and
foreclosure impact measures. However, we find that it does not greatly change the cross-state
spatial distribution of prevalence and impact that was discussed above in conjunction with Figures
11, 12, and 14. For example, across the 50 states and the District of Columbia in the year of peak
impact (2006), the correlation coefficient for the unadjusted and adjusted impact measures is 0.99.
The general similarity in regional patterns can be seen by comparing the adjusted data shown in
Figures A2-2a through A2-2d with the unadjusted data shown in Figures 11b through 11e. Finally,
Table A2-1 provides a complete listing of our adjusted measures for 2004-2007 originations, for
comparison with the unadjusted data presented in Table A4-1 below. Although the two tables differ
in detail, states that rank high (low) in the unadjusted data also rank high (low) in the adjusted data.
The LPS data also show a lower share of mortgages to non-occupants, compared to the HMDA
data. In the LPS data, the percentage of mortgages to non-occupants was about 10 percent in 20042006, or about three to four percentage points less than the corresponding shares computed from
HMDA originations.46 It is not clear whether the LPS or HMDA fraction is more accurate, as both
are subject to misreporting and less than complete coverage. For that reason, we make no detailed
adjustments for this difference. However, if we were to assume that the HMDA figures are correct,
then the low share of non-occupants in the LPS data would imply about a 25 percent underestimate
of the prevalence and foreclosure impact of mortgages to non-occupant owners.
Although the share of mortgages to non-occupants could be biased downward in the LPS data, it
seems to vary among the states much like the corresponding HMDA shares. Figures A2-3a to A23d compare LPS and HMDA data for 2004-07 across states with regard to the share of mortgages
(home-purchase and refinance) to non-occupant owners. The percentages are generally lower in the
LPS data, as discussed above and as indicated by the fact that most of the points in these figures are
on or near the 45-degree line, which depicts equal shares between the two data sets. Nonetheless,
the LPS and HMDA shares generally move together, so that states that are relatively high by one
measure are also relatively high according to the other. This is confirmed by high statistical
correlations—0.87 for 2004, 0.91 for 2005, 0.88 for 2006, and 0.83 for 2007—between the two
measures of the share of mortgages to non-owner occupants. Overall, we find that the relative
regional patterns for non-occupant mortgages and foreclosures are similar in the LPS and HMDA
data.
45
All of our performance measures and the per-mortgage measures of prevalence and impact that are found in
Appendix 5 are only slightly affected, because the adjustment affects the numerators and denominators by similar
amounts.
46
More precisely, the LPS data put the non-occupant share of home-purchase plus refinance originations at 8.7 percent
in 2004, 10.2 percent in 2005, 10.5 percent in 2006, and 10.3 percent in 2007. The corresponding shares based on
HMDA originations are 11.9, 13.5, 14.4, and 14.1 percent, respectively.
p. 38 of 69
We have also compared the LPS data at the ZIP-code level with data from local property records in
Ohio and Minnesota. Our most extensive ZIP-level comparison involves 49 ZIP Code areas in
Cuyahoga County, Ohio, where we obtained local data on mortgages originated in 2005 and 2006
from Michael Schramm of Case Western Reserve University.47 The Case Western data are
estimated to cover about 68 percent of all local originations. LPS’s coverage is again lower; the
number of mortgages in the LPS data equals about 55 percent of the number of mortgages in the
Case Western data for 2005 and about 61 percent for 2006. Despite this difference in coverage, the
share of mortgages to non-occupants in the LPS data for these ZIP Codes matches fairly well with
the share computed from the Case Western data. Among mortgages originated in 2005 in the 49
ZIP Code areas, the LPS data show an 11.5 percent non-occupant share, compared to a 13.2 percent
share in the Case Western data. For 2006, the share computed from the LPS data is 14.5 percent,
slightly lower than the 14.8 percent share computed from the Case Western data.
As shown in Table A2-2, the Case Western data imply somewhat lower foreclosure rates than what
we find in the LPS data. These differences partly reflect the time periods involved. The Case
Western data tabulate whether a mortgage has had any foreclosure notice between its origination
date and April 2008. The LPS data tabulate whether a mortgage has been in the foreclosure process
(―pre-sale‖ status) between origination and July 2009, or 15 months longer than the period tracked
by Case Western. Note that both data sources imply that the rate of foreclosure on non-owneroccupied mortgages was about 1.7 to over 3 times higher than on owner-occupied mortgages.
Despite their different levels of foreclosure, the two sources agree on how lending and foreclosure
rates varied among the 49 ZIP Codes, as shown in Table A2-3.48 This reinforces the conclusion that
the LPS data provides reliable information about the spatial pattern of non-occupant borrowing and
foreclosure.
We also compared the LPS data to local records for two ZIP Codes in Ramsey County, Minnesota,
which includes the city of St. Paul. Minnesota provides special property tax relief to owneroccupants. To receive the credit, owner-occupants must file for the state’s homestead credit.
County records show which properties are on file as owner-occupied homesteads. Through a laborintensive process, staff at the Federal Reserve Bank of Minneapolis pulled mortgage records for
samples of both homesteaded and non-homesteaded single-family residences in ZIP Code areas
55106 and 55109.49 As a result, we can estimate the number of single-family homes in these ZIP
Codes that had active mortgages in early 2008. Furthermore, we can estimate the percentage of
these mortgaged single-family homes that were non-homesteaded, which serves as an estimate of
the percentage of mortgaged single-family homes owned by non-occupants.
47
As before, these mortgage data are for loans made to purchase a house or refinance an existing mortgage and exclude
home improvement loans. Collection of the Cuyahoga County data is described in Coulton, Chan, Schramm, and
Mikelbank (2008). Using the same data for the years 2005-2006, Todd (2010) shows a pattern of relatively high
prevalence, poor performance, and high foreclosure impact associated with mortgages by non-local banks to non-owner
occupants in low-income, minority neighborhoods with low housing values.
48
In both 2005 and 2006, one ZIP Code had no non-owner-occupied mortgages and was thus omitted when the
correlation for foreclosure rates was computed. For 2005, the LPS data had no non-owner-occupied mortgages in ZIP
Code area 44114, and in 2006 the Case Western data had none in ZIP Code area 44115.
49
ZIP Code area 55106 covers much of St. Paul’s ―East Side‖, an inner-city low- to moderate-income area that has
recently experienced a high level of foreclosure. ZIP Code area 55109 covers some moderate- to medium-income
inner-ring suburbs on the northern and eastern edges of St. Paul.
p. 39 of 69
Using these estimates, we find that the share of mortgages to non-occupant owners computed from
Ramsey county property records is close to the share computed from the LPS data for our two ZIP
Code areas. We estimate that 80 percent of the single-family homes in the ZIP Code area 55106
had active mortgages in early 2008 and that 10.2 percent of them were not homesteaded. Given that
we sampled 10 percent of the single-family property records for this ZIP Code, a 95 percent
confidence interval would state that the percentage of active mortgages to non-occupants falls in the
range of 6.2 to 14.2 percent. The LPS data on 2004-2006 originations imply that 13.1 percent of
active mortgages in ZIP Code area 55106 were to non-occupant owners as of March 2008. This
percentage is higher than what the property records imply but within the 95 percent confidence
interval. In ZIP Code area 55109, about 76 percent of the single-family properties we sampled had
an active mortgage in early 2008, and just 3.6 percent of these mortgages were on non-homesteaded
properties. In the LPS data, 4.2 percent of the active mortgages in ZIP Code area 55109 are to nonoccupants, a figure which is again higher than what we estimate from property records but within
the 95 percent confidence interval. These two Minnesota ZIP Code areas provide a bit more
evidence that our LPS data reflect actual regional differences in lending to non-owner-occupants as
well as, perhaps, the actual share of this form of lending.
p. 40 of 69
Table A2-1: Adjusted Indices of Non-Occupant Mortgage Performance, Prevalence, and Impact
Adjusted Indices of the Relative Performance, Prevalence, and Impact of Non-Owner-Occupied (NOO) Mortgages and Foreclosures, by State and
Year Mortgage Was Originated (based on LPS data scaled up to each state's number of HMDA originations)
Performance Index*
Prevalence Index
Impact Index
(NOO
(Foreclosures per mortgage, for NOO
(Mortgages per housing unit, for NOO
foreclosures per housing unit, by year of
mortgages, by year of origination, as a
mortgages, by year of origination, as a
origination, as a percentage of the 2004-07
percentage of the 2004-07 national average) percentage of the 2004-07 national average)
national average)
State
2004
2005
2006
2007
2004
2005
2006
2007
2004
2005
2006
2007
88
103
61
68
74
63
60
77
39
AL
68
61
42
AK
30
48
74
32
66
67
56
48
20
32
41
15
AZ
24
99
170
136
243
334
221
149
57
331
376
203
AR
33
44
70
44
48
55
54
50
16
24
38
22
CA
28
100
166
94
153
155
108
83
43
156
179
77
CO
42
62
74
42
148
147
132
124
63
91
98
52
CT
42
81
136
65
73
77
61
46
30
62
83
30
DE
18
40
74
34
192
209
155
123
34
83
114
42
DC
27
54
98
59
140
156
134
92
37
84
131
54
FL
52
160
289
210
230
309
234
136
120
494
677
285
GA
108
136
166
103
95
119
118
86
102
161
195
88
HA
15
54
94
52
298
308
210
143
46
165
197
73
ID
22
41
88
73
182
271
209
146
41
112
184
107
IL
75
107
145
88
62
75
73
59
46
80
106
51
IN
197
207
226
100
74
76
74
54
145
157
168
54
IA
69
68
70
34
37
37
34
31
25
25
23
10
KS
56
57
68
39
58
60
53
46
32
35
36
18
KY
88
111
103
54
50
53
50
42
44
59
52
22
LA
55
69
87
57
54
56
51
48
29
38
44
27
ME
30
55
60
42
115
112
92
72
35
61
55
30
MD
27
56
109
83
129
148
120
83
34
83
131
69
MA
38
65
89
47
91
93
69
57
35
61
62
27
MI
153
210
250
168
72
74
68
54
110
154
169
91
MN
86
128
173
116
73
76
63
48
63
98
110
55
MS
118
120
129
89
47
50
53
55
55
60
68
49
MO
96
115
118
78
80
87
85
72
76
100
101
56
MT
19
13
38
27
89
96
89
82
17
13
33
22
NE
62
56
69
27
36
35
41
37
22
20
28
10
NV
55
170
251
164
379
417
281
162
210
708
703
266
NH
45
60
82
59
105
96
79
62
47
58
65
37
NJ
42
76
125
86
99
114
88
63
42
86
110
55
NM
21
33
57
51
103
138
129
91
22
46
73
46
NY
58
84
115
61
45
50
44
35
26
42
50
21
NC
41
46
61
37
113
136
133
107
46
63
81
39
ND
31
49
27
6
34
34
30
27
11
16
8
2
OH
170
188
216
105
68
76
70
53
117
143
151
55
OK
58
78
96
40
50
55
54
45
29
43
52
18
OR
14
30
66
54
131
161
127
102
19
49
84
55
PA
49
60
89
45
70
80
71
53
35
48
63
24
RI
53
89
159
74
141
128
86
62
74
114
137
46
SC
33
62
85
60
160
209
176
130
53
130
149
78
SD
47
46
55
20
44
47
47
50
21
21
26
10
TN
83
88
108
64
63
78
84
74
53
69
91
47
TX
55
66
84
39
62
70
78
63
34
46
66
24
UT
32
35
67
78
139
187
193
152
44
66
128
119
VT
20
23
44
32
161
146
115
95
32
33
51
30
VA
20
52
91
53
115
131
101
75
23
68
92
40
WA
18
28
61
48
102
120
111
98
18
33
68
48
WV
51
100
91
61
50
54
45
38
26
54
41
24
WI
51
73
100
73
88
89
78
65
45
66
78
48
WY
22
12
29
11
84
78
75
65
19
9
22
7
National
825 per 955 per 799 per 598 per 37.7 per 82.9 per 103.2 per 46.9 per
Average
4.57%
8.68% 12.91%
7.84% 100,000 100,000 100,000 100,000 100,000 100,000 100,000 100,000
* Not affected by the adjustment
p. 41 of 69
Table A2-2: Case Western and LPS Foreclosure Rates in Cuyahoga County (percent)
Owner-Occupied
Non-Owner-Occupied Combined
2005 Case Western
11
31
13
2005 LPS
15
25
16
2006 Case Western
8
25
10
2006 LPS
17
33
19
Table A2-3: Correlations between Selected Case Western and LPS Variables
2005
Number of Owner-Occupied Mortgages
0.93
Number of Non-Owner-Occupied Mortgages
0.93
Number of Foreclosures on Owner-Occupied Mortgages
0.96
Number of Foreclosures on Non-Owner-Occupied Mortgages
0.95
Percent of Mortgages Not Owner-Occupied
0.82
Foreclosure Rate on Non-Owner-Occupied Mortgages
0.89
2006
0.91
0.98
0.96
0.97
0.92
0.94
Figure A2-1
LPS vs. HMDA Originations, Home Purchase plus Refi
.3
.4
.5
.6
.7
.8
(Box-Whisker Distribution over U.S. States, 2004-07)
_2004
_2006
_2005
_2007
LPS data as of Nov. 2009. Boxes cover 25th to 75th percentile; the line in the box is the median.
p. 42 of 69
Figure A2-2a (2004, adjusted LPS data)
Figure A2-2b (2005, adjusted LPS data)
MI
IN
2
2
3
2005 Home Purchase and Refinance Originations (Adjusted LPS)
Relative Non-Occupant Foreclosure Rate (U.S.=1)
Relative Non-Occupant Foreclosure Impact per Housing Unit
2004 Home Purchase and Refinance Originations (Adjusted LPS)
3
Relative Non-Occupant Foreclosure Impact per Housing Unit
IN
OH
OH
MI
0
0
GA
MN
MS
KY IL MO
WV
CA
RI
ALTN
NY
OK CTWI NJ
IA LA TX MA
SC
MDDC
NE KS PA NH ME VA CO
NDSDARAK
NC
DE
OR UT
WA NM
VT
WY MT
1
1
GA
MO
KYTNMN
IL
IA AL
NENY
OK
KS
LA
TXPA WI
RI
WV
SD
CO
CT MANJNHNC
NDAR AK
MEMDUT
CASC
DC
WY
ID
NMVA
MTWA
OR VT DE
NV
FL
AZ
HA
AZ
ID
HA
0
MS
NV
FL
1
2
3
Relative Non-Occupant Mortgage Prevalence (U.S.=1)
4
Figure A2-2c (2006, adjusted LPS data)
0
1
2
3
Relative Non-Occupant Mortgage Prevalence (U.S.=1)
4
Figure A2-2d (2007, adjusted LPS data)
FL
MI
NV
IN
OH
MN
1
2
3
Relative Non-Occupant Mortgage Prevalence (U.S.=1)
MI
NV
AZ
1
MN
OH
GA
IN
MS
ILNJ CA
MD
RI
IDUT
WIMO
CT NH
TN
WV
NY
LA AL VA DCOR SCHA
KY
NM
MA ME WA CO
PA
AR
OK
TX
NC DE
IA KS
NEAK
MTVT
SD
WY
ND
0
0
1
AZ
CAGA
RI
IL
CT
MS NJ
MO
NY
MD
TN
KY AL
WI
DC
OK
HA
WV
PATX VA
LA MA
SC ID
NH
CO
AK
DE
AR
IANEKS
UT
OR
NC
ME WA NM
SD
VT
MT
WY
ND
0
FL
2
2
3
2007 Home Purchase and Refinance Originations (Adjusted LPS)
Relative Non-Occupant Foreclosure Rate (U.S.=1)
Relative Non-Occupant Foreclosure Impact per Housing Unit
2006 Home Purchase and Refinance Originations (Adjusted LPS)
3
Relative Non-Occupant Foreclosure Impact per Housing Unit
4
p. 43 of 69
0
1
2
3
Relative Non-Occupant Mortgage Prevalence (U.S.=1)
4
Figure A2-3a
Figure A2-3b
Non-Occupants' Share of Mortgage Originations
Non-Occupants' Share of Mortgage Originations
HMDA versus LPS, Home Purchase plus Refinance, 2005
IDAZ
DE
SC
.2
VT
SC
NV FL
ME ORMT
UTDC
CO
GA
WYWV AL
RI
TX
PA
NJVA
WA LATNMOOK MS
AR
IN
OH
WI
AK
MD
NHCA
MA
NY
KS
KY
MN MI SD
CTND
IL
IA NE
DENM
NCAZ
ID FL
NV
0
0
.1
MENM NC
DC
ORMT
WY
UT
CO
RI
WV
AL GA
AK
NHVA
NJ
TX
MD
PA
CA
WA
WI
IN
LA
AR
MS
MO
TN OK
MA
KS
NDMI OH
SD KY
MNNY
CT
IL
NE
IA
HA
HA
.1
VT
Non-Occupants' Share, LPS
.2
.3
.3
HMDA versus LPS, Home Purchase plus Refinance, 2004
0
.1
.2
Non-Occupants' Share, HMDA
0
.3
.1
.2
Non-Occupants' Share, HMDA
.3
HMDA=LPS
HMDA=LPS
Figure A2-3c
Figure A2-3d
Non-Occupants' Share of Mortgage Originations
Non-Occupants' Share of Mortgage Originations
HMDA versus LPS, Home Purchase plus Refinance, 2006
VT
HA
SC
.2
Non-Occupants' Share, LPS
.2
.3
.3
HMDA versus LPS, Home Purchase plus Refinance, 2007
.1
VT
CT
HA
NV
FL SC
ID
AZ
DE
CO NC
MEMT
NM
OR
UT
DC
GA
TXALTN MS
OH
MI
MO
WA
WY
CA
SD
NH
AR
RI
VA
PA
OK
INWVWI LA
MD
MA
NY
NJ
KY
AK
MN
KS
ND ILNE
IA
0
0
.1
ID FL
NV
NC
DENM
ME UT
AZMT
DC
OR
CO GA
TX
AL
TN
WY
IN
MO MSAR
PA OH
WA
WI
RINH
VA MIWV
KY OK
NJ
MD
CA
NY
AK SD
LA
MA
KS
NDMN
CT IL NE
IA
0
.1
.2
Non-Occupants' Share, HMDA
.3
HMDA=LPS
0
.1
.2
Non-Occupants' Share, HMDA
HMDA=LPS
p. 44 of 69
.3
Appendix 3: Investor and Second-Home Foreclosures Considered Separately
In most of our analysis we have dealt with non-occupant owners as a whole, without distinguishing
between second-home owners (owners of non-primary residences intended mainly for the owners’
occasional occupancy) and investors (owners of non-primary residences primarily meant to be
rented and/or sold for gain). As discussed in the body of the paper, we have not stressed this
distinction in part because key data sources such as HMDA do not make this distinction. However,
the LPS data include a data field that distinguishes between the two sub-groups that make up the
non-occupant category. In this appendix we examine separately the prevalence of these two types
of non-occupant mortgages as well as their performance (foreclosure rate) and foreclosure impact
(per housing unit). We find that mortgages to investors account for over three-fourths of the
foreclosures among non-occupant mortgages, partly because they are more prevalent (accounting
for about two-thirds of non-occupant mortgages), but also because they perform more poorly.
However, the relative importance of second-home mortgages is above the national norm in only a
few states, and Hawaii is the only state that has a foreclosure rate that is consistently higher for
second homes than for investor-owned properties.
Tables A3-1 to A3-4 provide information on how investors and second-home owners contributed to
the overall performance, prevalence, and foreclosure impact associated with non-occupant
mortgages originated in the years 2004-2007. For example, in Alabama in 2004, there were 236.35
new mortgages made to non-occupants for every 100,000 housing units, and 58.9 percent of them
were originated by investors. The rate of foreclosure on Alabama’s 2004 non-occupant mortgages
was 5.84 percent overall, but the performance of investor-owned mortgages was much worse, with
8.13 percent entering foreclosure by July 2009. The resulting foreclosure impact from mortgages
originated in 2004 in Alabama was 13.80 foreclosures for every 100,000 housing units, with
investors accounting for 82 percent of the foreclosure impact. The last two rows in each of the
tables give the U.S. annual average for the year and for the entire 2004-2007 period, respectively.
As shown above in Figure 11, the prevalence of mortgages to non-occupant owners rose to a peak
in 2005 before subsiding in 2006 and again in 2007. However, investors’ share of non-occupant
mortgages stayed near two-thirds nationally over the whole period, as shown in the second column
of Tables A3-1 to A3-4. Investors’ share of non-occupant mortgage originations varies from state
to state and year to year but only falls below 50 percent in a small number of states, most notably
Vermont.
Columns 5 to 7 of the tables show the foreclosure rates on all non-occupant mortgages as well as on
mortgages to investors and second-home owners. Nationally, the foreclosure rate on mortgages to
investors is significantly higher than the foreclosure rate on mortgages for second homes. The same
is true in most states for all four years. This general pattern is consistent with the evidence on credit
qualifications in Figure 3 (above) and the theoretical perspective on ruthlessness discussed in
Appendix 1. Since investor mortgages are more prevalent, their relatively high rate of foreclosure
tends to heavily influence the overall rate of foreclosure on non-occupant mortgages. Thus, the
typical pattern is for the foreclosure rate on all non-occupant mortgages to be 10 to 20 percent lower
than the foreclosure rate on mortgages to investors and for the foreclosure rate on second homes to
be at least 25 percent lower than the overall non-occupant rate. The gap between investor and
second-home foreclosure rates was widest in 2004 and narrowed somewhat thereafter. However,
the performance gap between investor and second-home mortgages was narrower in a minority of
states, and in a few cases (Hawaii in all four years and Arkansas, North Dakota, Wyoming, and
p. 45 of 69
Utah one year each) second-home mortgages were foreclosed at a higher rate than investor
mortgages.
Columns 8 to 10 show the impact measure—the number of foreclosures per 100,000 housing
units—for all non-occupant mortgages as well as the share of the overall impact attributed to
investor versus second-home mortgages. Because mortgages to investors were more common and
performed more poorly than second-home mortgages, at least 75 percent of the foreclosure impact
from non-occupant mortgages nationwide can be attributed to investor mortgages. Investors’ share
in the overall impact from non-occupant mortgages was especially high in a number of Midwestern
and Northeastern states.
Second homes played a prominent role in non-occupant foreclosures in some states, however. The
leading example is Hawaii, where second-home mortgages consistently accounted for at least 55
percent of all non-occupant foreclosures. Second-home foreclosures accounted for about half of the
foreclosures on non-owner-occupied properties in some years in Montana, Vermont, and Wyoming,
too, and their share was above the national average in other rural states with attractive recreational
amenities. Second homes also accounted for a moderately high proportion of non-occupant
foreclosures in some large states that have experienced unusually high impacts from non-occupant
foreclosures. For example, 30 to 40 percent of the non-occupant foreclosures in the hardest hit
states, Nevada and Florida, have been on second homes, and the share in hard-hit Arizona is almost
as high.
Figures A3-1 to A3-8, in the format of Figure 11 above, show how the prevalence and performance
of investor and second-home mortgages contributed to their respective foreclosure impacts by state.
In these figures, the two types of non-occupant mortgages are benchmarked against their own 20042007 national averages. For example, in Figure A3-1, the point for Hawaii (HA) has a prevalence
index (or x-axis value) of almost 2. This means that for mortgage originations in 2004, the
prevalence of investor mortgages in Hawaii (per housing unit) was almost twice the 2004-2007
national average prevalence measure for investor mortgages. On the y-axis, which indexes
foreclosure rates (performance), Indiana’s value of nearly 2 means that Indiana’s rate of foreclosure
on investor mortgages originated in 2004 was about twice as high as the 2004-2007 national
average for investor mortgages (as also shown in Table A3-1). Figures A3-5 to A3-8 are similar
except that the national averages are for the prevalence and performance of second-home mortgages
originated in 2004-2007. As in Figure 11, the middle of the three curves in each graph shows all
points where the impact of investor (Figures A3-1 to A3-4) or second home (Figures A3-5 to A3-8)
foreclosures equals the respective national average impact for 2004-2007. As before, the upper
curve shows all points where the impact was three times the national average, and the lower curve
shows all points where the impact was half the national average.
The first four figures show that in 2005-2007, Nevada, Florida, and Arizona had the highest
foreclosure impact for investor-owned mortgages. The high impact from 2005-2007 originations in
these three states is a result of their above-average prevalence and high foreclosure rate on investor
mortgages. States such as Georgia, Indiana, Michigan, Minnesota, Missouri, and Ohio have near to
moderately above-average impacts from investor mortgages in most years that are almost entirely
due to a high rate of foreclosure on these mortgages. By contrast, areas such as California, the
District of Columbia, Colorado, Hawaii, Idaho, and Utah experience a similar degree of impact
primarily due to an above-average prevalence of mortgages to investors.
p. 46 of 69
Nevada, Florida, and Arizona are joined by Hawaii as the states with the highest relative impact
from foreclosures on second homes (Figures A3-5 toA3-8). The above-average prevalence of
second-home mortgages in these states was the primary cause initially, among the loans originated
in 2004. Starting in 2005, the relatively poor performance of second-home mortgages in Nevada,
Florida, and Arizona (and to a lesser extent Hawaii) added to their overall foreclosure impact.
Several other states also have an above-average impact from second-home foreclosures in some
years, but these states tend to have a high prevalence of second homes. Among these other states,
Georgia is notable for an above-average rate of foreclosure on second homes, and Idaho, South
Carolina, and Utah exemplify states where the prevalence of mortgages on second homes was
relatively high.
p. 47 of 69
Table A3-1
Foreclosure Impact Decomposition by Type of Non-Occupant Owner (NOO), 2004 (LPS data)
Performance (NOO foreclosure rate)
Prevalence
Impact
All NOO
(NOO
mortgages
per 100000
housing
units)
AL
AK
AZ
AR
CA
CO
CT
DE
DC
FL
GA
HA
ID
IL
IN
IA
KS
KY
LA
ME
MD
MA
MI
MN
MS
MO
MT
NE
NV
NH
NJ
NM
NY
NC
ND
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VT
VA
WA
WV
WI
WY
U.S. 2004
U.S. 20042007
236.35
299.88
861.93
183.26
637.13
635.54
314.05
791.95
649.36
810.36
381.49
1216.89
727.24
249.07
281.73
134.62
223.95
171.46
184.32
400.20
498.58
385.60
293.82
318.99
137.59
305.60
435.70
155.32
1300.49
462.45
463.55
461.75
196.80
401.66
150.60
210.90
221.15
567.92
256.51
501.67
565.01
194.45
232.07
260.58
619.41
534.37
438.89
453.30
119.51
252.79
405.50
404.26
441.35
All NOO
(NOO
foreclosures
Investor
2nd Home
Investor
2nd Home
per 100000
Share
Share
All NOO
Investors 2nd-Home housing units) Share
Share
58.9%
41.1%
5.84%
8.13%
2.55%
13.80
82.0%
18.0%
74.4%
25.6%
2.56%
3.11%
0.95%
7.67
90.5%
9.5%
55.9%
44.1%
2.01%
2.31%
1.64%
17.36
64.1%
35.9%
62.5%
37.5%
2.83%
4.11%
0.71%
5.19
90.6%
9.4%
75.9%
24.1%
2.41%
2.71%
1.49%
15.38
85.1%
14.9%
61.3%
38.7%
3.62%
5.17%
1.15%
22.98
87.7%
12.3%
80.0%
20.0%
3.56%
4.11%
1.35%
11.17
92.4%
7.6%
41.8%
58.2%
1.51%
3.04%
0.41%
11.97
84.1%
15.9%
83.8%
16.2%
2.27%
2.38%
1.71%
14.73
87.8%
12.2%
47.9%
52.1%
4.47%
5.66%
3.38%
36.24
60.6%
39.4%
71.5%
28.5%
9.22%
11.43%
3.66%
35.17
88.7%
11.3%
46.8%
53.2%
1.31%
0.98%
1.60%
15.95
35.1%
64.9%
63.3%
36.7%
1.90%
2.03%
1.68%
13.82
67.5%
32.5%
80.9%
19.1%
6.38%
7.38%
2.18%
15.90
93.5%
6.5%
83.1%
16.9%
16.87%
19.49%
3.98%
47.54
96.0%
4.0%
75.7%
24.3%
5.90%
7.03%
2.38%
7.94
90.2%
9.8%
86.8%
13.2%
4.75%
5.08%
2.57%
10.63
92.9%
7.1%
78.2%
21.8%
7.53%
8.66%
3.48%
12.91
89.9%
10.1%
78.3%
21.7%
4.69%
5.48%
1.83%
8.65
91.6%
8.4%
44.8%
55.2%
2.58%
3.54%
1.81%
10.34
61.4%
38.6%
71.0%
29.0%
2.27%
2.74%
1.14%
11.33
85.5%
14.5%
69.9%
30.1%
3.24%
4.22%
0.97%
12.51
91.0%
9.0%
64.0%
36.0%
13.04%
18.50%
3.31%
38.30
90.9%
9.1%
65.2%
34.8%
7.34%
10.09%
2.19%
23.41
89.6%
10.4%
63.9%
36.1%
10.12%
13.69%
3.80%
13.92
86.5%
13.5%
72.6%
27.4%
8.19%
10.57%
1.86%
25.02
93.8%
6.2%
51.0%
49.0%
1.63%
2.13%
1.11%
7.09
66.7%
33.3%
79.9%
20.1%
5.27%
6.06%
2.11%
8.18
91.9%
8.1%
60.0%
40.0%
4.74%
5.10%
4.19%
61.64
64.6%
35.4%
54.0%
46.0%
3.83%
5.42%
1.96%
17.72
76.5%
23.5%
61.6%
38.4%
3.57%
4.89%
1.45%
16.54
84.4%
15.6%
61.8%
38.2%
1.81%
1.83%
1.79%
8.37
62.3%
37.7%
65.7%
34.3%
4.97%
6.61%
1.82%
9.78
87.5%
12.5%
51.3%
48.7%
3.50%
5.44%
1.46%
14.06
79.7%
20.3%
74.4%
25.6%
2.65%
2.67%
2.59%
3.99
75.0%
25.0%
85.1%
14.9%
14.55%
16.44%
3.77%
30.68
96.1%
3.9%
78.6%
21.4%
4.97%
5.71%
2.28%
11.00
90.2%
9.8%
68.4%
31.6%
1.22%
1.36%
0.91%
6.90
76.4%
23.6%
75.6%
24.4%
4.22%
4.85%
2.28%
10.82
86.8%
13.2%
74.1%
25.9%
4.51%
5.67%
1.20%
22.63
93.1%
6.9%
39.8%
60.2%
2.85%
4.44%
1.80%
16.12
62.0%
38.0%
64.0%
36.0%
4.05%
5.63%
1.25%
7.88
88.9%
11.1%
67.5%
32.5%
7.11%
9.27%
2.61%
16.49
88.1%
11.9%
72.3%
27.7%
4.71%
5.76%
1.96%
12.27
88.5%
11.5%
62.5%
37.5%
2.72%
3.68%
1.12%
16.83
84.6%
15.4%
27.0%
73.0%
1.72%
3.64%
1.01%
9.20
57.1%
42.9%
72.6%
27.4%
1.70%
1.91%
1.15%
7.47
81.5%
18.5%
75.1%
24.9%
1.52%
1.67%
1.09%
6.90
82.2%
17.8%
51.8%
48.2%
4.34%
6.89%
1.60%
5.19
82.2%
17.8%
57.6%
42.4%
4.35%
6.33%
1.67%
11.00
83.8%
16.2%
63.9%
36.1%
1.91%
1.99%
1.76%
7.74
66.7%
33.3%
65.4%
34.6%
4.48%
5.71%
2.16%
18.10
83.3%
16.7%
66.8%
33.2%
8.50%
9.95%
p. 48 of 69
5.67%
37.6
77.9%
22.1%
Table A3-2
Foreclosure Impact Decomposition by Type of Non-Occupant Owner (NOO), 2005 (LPS data)
Performance (NOO foreclosure rate)
Prevalence
Impact
All NOO
(NOO
mortgages
per 100000
housing
units)
AL
AK
AZ
AR
CA
CO
CT
DE
DC
FL
GA
HA
ID
IL
IN
IA
KS
KY
LA
ME
MD
MA
MI
MN
MS
MO
MT
NE
NV
NH
NJ
NM
NY
NC
ND
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VT
VA
WA
WV
WI
WY
U.S. 2004
U.S. 20042007
295.09
354.77
1331.47
243.16
796.37
735.63
380.02
962.47
808.80
1223.76
495.74
1544.99
1247.90
345.92
311.74
159.08
270.32
199.81
207.69
461.11
656.18
441.76
330.46
379.73
173.89
398.76
589.12
165.58
1751.33
488.29
583.62
688.03
241.94
522.63
165.55
248.96
266.92
809.00
343.37
568.32
821.15
221.60
321.28
321.90
933.12
581.44
565.89
640.07
164.43
317.01
420.26
533.16
441.35
All NOO
Investor
2nd Home
Share
Share
All NOO Investors 2nd-Home
61.3%
38.7%
7.54%
9.59%
4.29%
67.7%
32.3%
4.07%
4.36%
3.46%
62.2%
37.8%
8.47%
9.27%
7.15%
63.6%
36.4%
3.79%
4.61%
2.36%
76.7%
23.3%
8.55%
9.34%
5.96%
61.5%
38.5%
5.26%
7.10%
2.34%
80.1%
19.9%
6.90%
7.90%
2.88%
50.1%
49.9%
3.41%
5.25%
1.56%
82.9%
17.1%
4.59%
5.11%
2.07%
55.1%
44.9%
13.66%
16.10%
10.67%
68.6%
31.4%
11.60%
13.73%
6.95%
47.0%
53.0%
4.57%
3.73%
5.32%
67.9%
32.1%
3.53%
3.75%
3.06%
79.6%
20.4%
9.16%
10.37%
4.41%
83.1%
16.9%
17.67%
20.48%
3.84%
75.3%
24.7%
5.81%
7.07%
1.96%
84.8%
15.2%
4.89%
5.39%
2.04%
77.8%
22.2%
9.52%
11.44%
2.78%
76.4%
23.6%
5.86%
6.76%
2.94%
44.9%
55.1%
4.66%
5.86%
3.68%
75.7%
24.3%
4.83%
5.41%
3.01%
68.7%
31.3%
5.59%
7.33%
1.77%
66.5%
33.5%
17.91%
24.29%
5.28%
65.9%
34.1%
10.95%
15.17%
2.80%
66.5%
33.5%
10.24%
13.44%
3.89%
73.6%
26.4%
9.80%
12.10%
3.37%
47.4%
52.6%
1.15%
1.17%
1.13%
78.7%
21.3%
4.80%
5.30%
2.96%
60.6%
39.4%
14.52%
15.57%
12.90%
54.5%
45.5%
5.16%
6.96%
3.01%
67.3%
32.7%
6.45%
8.09%
3.08%
61.6%
38.4%
2.83%
2.96%
2.62%
68.1%
31.9%
7.20%
9.28%
2.77%
51.3%
48.7%
3.94%
5.31%
2.50%
73.4%
26.6%
4.17%
4.86%
2.24%
85.9%
14.1%
16.07%
17.57%
6.89%
78.1%
21.9%
6.67%
7.45%
3.88%
70.8%
29.2%
2.58%
2.73%
2.20%
79.6%
20.4%
5.13%
5.86%
2.31%
73.3%
26.7%
7.62%
8.90%
4.12%
45.7%
54.3%
5.33%
6.77%
4.11%
62.5%
37.5%
3.89%
5.19%
1.73%
67.2%
32.8%
7.49%
9.42%
3.53%
71.9%
28.1%
5.66%
6.39%
3.79%
62.0%
38.0%
3.03%
3.23%
2.71%
29.7%
70.3%
1.96%
3.01%
1.51%
75.6%
24.4%
4.44%
5.15%
2.23%
75.6%
24.4%
2.35%
2.53%
1.80%
56.3%
43.7%
8.58%
13.38%
2.39%
59.5%
40.5%
6.27%
8.76%
2.62%
61.9%
38.1%
1.01%
1.47%
0.27%
67.1%
32.9%
8.46%
9.83%
5.66%
66.8%
33.2%
8.50%
9.95%
p. 49 of 69
5.67%
(NOO
foreclosures
per 100000
housing units)
22.24
14.44
112.73
9.21
68.12
38.73
26.21
32.81
37.14
167.13
57.53
70.66
43.99
31.68
55.09
9.24
13.21
19.02
12.16
21.50
31.67
24.70
59.19
41.57
17.81
39.07
6.77
7.95
254.21
25.20
37.67
19.47
17.43
20.58
6.90
40.00
17.81
20.85
17.63
43.32
43.74
8.62
24.07
18.22
28.26
11.39
25.11
15.05
14.10
19.89
4.25
45.10
37.6
Investor
2nd Home
Share
Share
78.0%
22.0%
72.5%
27.5%
68.1%
31.9%
77.4%
22.6%
83.8%
16.2%
82.9%
17.1%
91.7%
8.3%
77.2%
22.8%
92.3%
7.7%
64.9%
35.1%
81.2%
18.8%
38.3%
61.7%
72.1%
27.9%
90.2%
9.8%
96.3%
3.7%
91.7%
8.3%
93.7%
6.3%
93.5%
6.5%
88.1%
11.9%
56.5%
43.5%
84.9%
15.1%
90.1%
9.9%
90.1%
9.9%
91.3%
8.7%
87.3%
12.7%
90.9%
9.1%
48.3%
51.7%
86.9%
13.1%
65.0%
35.0%
73.5%
26.5%
84.4%
15.6%
64.4%
35.6%
87.8%
12.2%
69.1%
30.9%
85.7%
14.3%
94.0%
6.0%
87.3%
12.7%
75.1%
24.9%
90.8%
9.2%
85.6%
14.4%
58.1%
41.9%
83.3%
16.7%
84.6%
15.4%
81.2%
18.8%
66.0%
34.0%
45.7%
54.3%
87.7%
12.3%
81.3%
18.8%
87.8%
12.2%
83.1%
16.9%
90.0%
10.0%
78.0%
22.0%
77.9%
22.1%
Table A3-3
Foreclosure Impact Decomposition by Type of Non-Occupant Owner (NOO), 2006 (LPS data)
Performance (NOO foreclosure rate)
Prevalence
Impact
All NOO
(NOO
mortgages
per 100000
housing
units)
AL
AK
AZ
AR
CA
CO
CT
DE
DC
FL
GA
HA
ID
IL
IN
IA
KS
KY
LA
ME
MD
MA
MI
MN
MS
MO
MT
NE
NV
NH
NJ
NM
NY
NC
ND
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VT
VA
WA
WV
WI
WY
U.S. 2004
U.S. 20042007
331.42
293.86
954.57
260.66
607.33
697.42
328.02
756.48
729.25
983.32
476.27
1188.12
1087.29
357.77
307.60
158.33
249.73
199.20
196.21
427.28
571.60
354.80
311.72
341.80
199.72
424.20
580.81
170.24
1321.68
419.51
461.49
646.46
229.29
534.50
154.34
250.14
271.47
702.29
322.52
420.17
698.37
216.79
351.83
357.83
1011.44
480.70
455.58
618.44
130.79
307.48
402.78
470.81
441.35
All NOO
(NOO
foreclosures
Investor
2nd Home
Investor
2nd Home
per 100000
Share
Share
All NOO Investors 2nd-Home housing units) Share
Share
65.0%
35.0%
8.82%
10.84%
5.07%
29.25
79.9%
20.1%
69.9%
30.1%
6.32%
7.13%
4.44%
18.57
78.8%
21.2%
63.0%
37.0%
14.51%
16.53%
11.07%
138.53
71.8%
28.2%
66.0%
34.0%
6.00%
7.49%
3.10%
15.63
82.4%
17.6%
76.7%
23.3%
14.15%
15.17%
10.79%
85.93
82.2%
17.8%
63.7%
36.3%
6.34%
8.24%
3.00%
44.22
82.8%
17.2%
79.1%
20.9%
11.60%
13.65%
3.87%
38.05
93.0%
7.0%
51.9%
48.1%
6.28%
9.32%
3.01%
47.54
76.9%
23.1%
83.4%
16.6%
8.39%
9.24%
4.09%
61.15
91.9%
8.1%
55.4%
44.6%
24.70%
28.50%
19.99%
242.86
63.9%
36.1%
66.4%
33.6%
14.15%
16.69%
9.13%
67.37
78.3%
21.7%
48.4%
51.6%
8.00%
7.33%
8.62%
94.99
44.4%
55.6%
70.9%
29.1%
7.56%
8.01%
6.46%
82.20
75.1%
24.9%
80.5%
19.5%
12.43%
14.10%
5.54%
44.47
91.3%
8.7%
83.0%
17.0%
19.34%
22.34%
4.72%
59.50
95.9%
4.1%
70.8%
29.2%
5.97%
7.07%
3.30%
9.45
83.9%
16.1%
82.8%
17.2%
5.84%
6.45%
2.89%
14.58
91.5%
8.5%
78.5%
21.5%
8.77%
10.16%
3.71%
17.47
90.9%
9.1%
76.6%
23.4%
7.44%
8.69%
3.36%
14.61
89.4%
10.6%
42.5%
57.5%
5.11%
8.69%
2.47%
21.85
72.2%
27.8%
79.1%
20.9%
9.29%
10.44%
4.95%
53.09
88.9%
11.1%
67.4%
32.6%
7.60%
9.69%
3.28%
26.95
85.9%
14.1%
71.0%
29.0%
21.37%
27.61%
6.09%
66.63
91.7%
8.3%
65.7%
34.3%
14.81%
19.80%
5.27%
50.63
87.8%
12.2%
69.1%
30.9%
11.03%
12.84%
6.99%
22.04
80.4%
19.6%
73.7%
26.3%
10.12%
12.33%
3.94%
42.95
89.8%
10.2%
50.4%
49.6%
3.23%
3.48%
2.97%
18.75
54.3%
45.7%
79.0%
21.0%
5.91%
7.01%
1.81%
10.07
93.6%
6.4%
62.2%
37.8%
21.42%
22.04%
20.40%
283.09
64.0%
36.0%
50.4%
49.6%
6.99%
10.18%
3.75%
29.34
73.4%
26.6%
68.6%
31.4%
10.66%
13.14%
5.22%
49.18
84.6%
15.4%
59.4%
40.6%
4.84%
5.13%
4.43%
31.32
62.9%
37.1%
67.2%
32.8%
9.85%
12.87%
3.68%
22.59
87.7%
12.3%
54.1%
45.9%
5.23%
6.62%
3.59%
27.94
68.4%
31.6%
71.8%
28.2%
2.32%
2.93%
0.75%
3.57
90.9%
9.1%
87.6%
12.4%
18.48%
20.28%
5.76%
46.23
96.1%
3.9%
79.9%
20.1%
8.23%
9.29%
3.99%
22.33
90.3%
9.7%
72.6%
27.4%
5.64%
5.89%
4.98%
39.62
75.8%
24.2%
79.8%
20.2%
7.57%
8.55%
3.69%
24.41
90.2%
9.8%
70.9%
29.1%
13.55%
17.55%
3.82%
56.94
91.8%
8.2%
46.6%
53.4%
7.25%
9.64%
5.16%
50.61
61.9%
38.1%
61.3%
38.7%
4.71%
6.82%
1.35%
10.20
88.9%
11.1%
66.5%
33.5%
9.23%
11.51%
4.71%
32.47
82.9%
17.1%
70.4%
29.6%
7.20%
7.99%
5.31%
25.76
78.2%
21.8%
65.1%
34.9%
5.69%
5.31%
6.39%
57.50
60.8%
39.2%
33.0%
67.0%
3.76%
6.72%
2.31%
18.09
58.9%
41.1%
75.5%
24.5%
7.76%
9.03%
3.85%
35.35
87.8%
12.2%
75.4%
24.6%
5.22%
5.49%
4.42%
32.30
79.2%
20.8%
55.7%
44.3%
7.75%
10.31%
4.53%
10.14
74.2%
25.8%
59.2%
40.8%
8.55%
11.64%
4.06%
26.29
80.6%
19.4%
63.4%
36.6%
2.49%
2.95%
1.70%
10.04
75.0%
25.0%
67.8%
32.2%
12.66%
14.4%
9.1%
59.59
76.9%
23.1%
66.8%
33.2%
8.50%
9.95%
p. 50 of 69
5.67%
37.6
77.9%
22.1%
Table A3-4
Foreclosure Impact Decomposition by Type of Non-Occupant Owner (NOO), 2007 (LPS data)
Performance (NOO foreclosure rate)
Prevalence
Impact
All NOO
(NOO
mortgages
per 100000
housing
units)
AL
AK
AZ
AR
CA
CO
CT
DE
DC
FL
GA
HA
ID
IL
IN
IA
KS
KY
LA
ME
MD
MA
MI
MN
MS
MO
MT
NE
NV
NH
NJ
NM
NY
NC
ND
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VT
VA
WA
WV
WI
WY
U.S. 2004
277.40
258.30
650.07
217.81
471.98
625.62
260.07
638.17
574.90
567.10
369.77
891.39
765.72
285.06
218.22
150.36
216.26
164.79
169.14
387.44
437.71
314.95
238.60
262.81
186.87
359.18
517.27
142.67
757.73
358.72
356.79
493.10
189.32
440.89
125.26
190.97
226.87
559.25
248.69
328.02
500.71
209.65
295.85
293.76
820.55
412.93
359.28
538.58
107.86
256.51
363.03
358.45
U.S. 20042007
441.35
All NOO
Investor
2nd Home
Share
Share
All NOO
Investors 2nd-Home
63.2%
36.8%
5.25%
6.54%
3.03%
65.4%
34.6%
2.74%
2.31%
3.57%
61.9%
38.1%
11.60%
14.42%
7.02%
65.3%
34.7%
3.78%
5.14%
1.23%
75.1%
24.9%
7.99%
9.00%
4.96%
64.6%
35.4%
3.59%
4.43%
2.06%
76.5%
23.5%
5.56%
6.43%
2.73%
48.0%
52.0%
2.94%
4.29%
1.71%
81.0%
19.0%
5.02%
5.89%
1.29%
50.5%
49.5%
17.93%
22.42%
13.35%
67.1%
32.9%
8.78%
9.74%
6.82%
47.6%
52.4%
4.41%
4.00%
4.77%
67.8%
32.2%
6.27%
7.08%
4.57%
79.8%
20.2%
7.49%
8.47%
3.60%
80.5%
19.5%
8.58%
9.92%
3.04%
73.1%
26.9%
2.87%
3.37%
1.50%
84.6%
15.4%
3.30%
3.50%
2.21%
76.2%
23.8%
4.58%
5.18%
2.68%
77.3%
22.7%
4.86%
5.51%
2.66%
34.9%
65.1%
3.56%
7.01%
1.71%
76.1%
23.9%
7.06%
8.08%
3.83%
64.7%
35.3%
4.05%
5.34%
1.68%
67.2%
32.8%
14.36%
19.36%
4.12%
63.1%
36.9%
9.88%
13.71%
3.34%
72.7%
27.3%
7.63%
8.44%
5.49%
72.6%
27.4%
6.66%
8.23%
2.49%
49.6%
50.4%
2.26%
2.42%
2.12%
72.0%
28.0%
2.33%
2.74%
1.28%
60.3%
39.7%
14.01%
15.67%
11.49%
45.8%
54.2%
5.07%
7.38%
3.11%
61.5%
38.5%
7.35%
9.63%
3.72%
60.6%
39.4%
4.32%
4.69%
3.75%
63.5%
36.5%
5.23%
6.99%
2.15%
55.4%
44.6%
3.14%
3.59%
2.58%
64.3%
35.7%
0.51%
0.40%
0.72%
86.6%
13.4%
8.95%
9.80%
3.47%
76.4%
23.6%
3.45%
3.63%
2.88%
70.9%
29.1%
4.58%
5.17%
3.17%
77.5%
22.5%
3.86%
4.34%
2.22%
63.9%
36.1%
6.36%
9.10%
1.50%
43.3%
56.7%
5.12%
6.93%
3.73%
62.3%
37.7%
1.74%
2.14%
1.06%
66.9%
33.1%
5.50%
5.69%
5.10%
72.3%
27.7%
3.32%
3.53%
2.77%
69.3%
30.7%
6.69%
7.03%
5.93%
37.6%
62.4%
2.72%
3.11%
2.49%
73.3%
26.7%
4.50%
5.21%
2.55%
76.0%
24.0%
4.14%
4.48%
3.09%
51.2%
48.8%
5.25%
8.42%
1.94%
57.2%
42.8%
6.25%
8.28%
3.54%
60.6%
39.4%
0.91%
0.56%
1.44%
66.6%
33.4%
7.65%
8.79%
5.36%
66.8%
33.2%
8.50%
9.95%
p. 51 of 69
5.67%
(NOO
foreclosures
per 100000
housing units)
14.55
7.09
75.38
8.23
37.72
22.47
14.46
18.78
28.85
101.67
32.46
39.27
48.02
21.35
18.72
4.31
7.14
7.55
8.23
13.78
30.92
12.77
34.27
25.97
14.26
23.91
11.71
3.33
106.13
18.18
26.23
21.29
9.89
13.84
0.64
17.10
7.83
25.63
9.60
20.85
25.62
3.64
16.26
9.76
54.91
11.24
16.16
22.32
5.66
16.04
3.30
27.41
37.6
Investor
2nd Home
Share
Share
78.8%
21.2%
55.0%
45.0%
76.9%
23.1%
88.7%
11.3%
84.5%
15.5%
79.7%
20.3%
88.5%
11.5%
69.9%
30.1%
95.1%
4.9%
63.1%
36.9%
74.4%
25.6%
43.2%
56.8%
76.6%
23.4%
90.3%
9.7%
93.1%
6.9%
86.0%
14.0%
89.7%
10.3%
86.1%
13.9%
87.6%
12.4%
68.8%
31.3%
87.0%
13.0%
85.3%
14.7%
90.6%
9.4%
87.5%
12.5%
80.3%
19.7%
89.7%
10.3%
52.9%
47.1%
84.6%
15.4%
67.4%
32.6%
66.7%
33.3%
80.5%
19.5%
65.8%
34.2%
85.0%
15.0%
63.4%
36.6%
50.0%
50.0%
94.8%
5.2%
80.3%
19.7%
79.9%
20.1%
87.1%
12.9%
91.5%
8.5%
58.7%
41.3%
76.9%
23.1%
69.3%
30.7%
76.8%
23.2%
72.8%
27.2%
42.9%
57.1%
84.9%
15.1%
82.1%
17.9%
82.0%
18.0%
75.8%
24.2%
37.5%
62.5%
76.5%
23.5%
77.9%
22.1%
Figure A3-1
Relative Investor Foreclosure Impact per Housing Unit
2
3
2004 Investor Purchase and Refinance Originations (LPS)
IN
MI
OH
MS
0
1
GA
MO
MN
TN
KY
AL
IL
WVIA NY
WI TX
NE
SD
RIFL
LAOK
NC NHNJ
CO
KS
PA
MA
ARVTME SCCT
UT
AK
DE
MD
ND
MTWY
NMVA
WA OR
0
NV
CA
IDAZ DC
HA
1
2
3
Relative Investor Mortgage Prevalence (2004-07 Nat'l Avg.=1)
4
Figure A3-2
Relative Investor Foreclosure Impact per Housing Unit
3
2005 Investor Purchase and Refinance Originations (LPS)
MI
2
IN
OH
FL
MN
MS
WV
1
KY
NV
GA
MO
IL
RI
NJ
SC CO
MD
VA DE
NM WA
CA
AZ
DC
UT
OR
HA
ID
0
ALTN
NY
WI
CT
IA LA OK NHMA
TX PA
ME
KSNC
NE
SD
ND
AR
AK
VT
WY
MT
0
1
2
3
Relative Investor Mortgage Prevalence (2004-07 Nat'l Avg.=1)
p. 52 of 69
4
Figure A3-3
Relative Investor Foreclosure Impact per Housing Unit
3
2006 Investor Purchase and Refinance Originations (LPS)
FL
MI
IN
NV
2
OH
MN
RI
GA
AZ
CA
1
CTIL NJ
NY
MS
MO
WI TN
AL
MD
WV KY NH
MA
SC DE
DC
VA
LAMEOKPA
CO
ARAK TX
HA
IASD
NE
VT KS
NC
OR
UT
NM WA
ID
WYMT
0
ND
0
1
2
3
Relative Investor Mortgage Prevalence (2004-07 Nat'l Avg.=1)
4
Figure A3-4
Relative Investor Foreclosure Impact per Housing Unit
3
2007 Investor Purchase and Refinance Originations (LPS)
2
FL
MI
AZ
1
MN
WV
IN NJGA
OH
RI
CA
IL MO MD
MS
WI
ID UT
MENH
NY
SC
ALCT
DC
LA
MA VA
KY
OR
AR TN
NM
WA
CO
PA
DE
HA
KSTXNC
IA OK
NE VT
MT
SDAK
0
ND
0
NV
WY
1
2
3
Relative Investor Mortgage Prevalence (2004-07 Nat'l Avg.=1)
p. 53 of 69
4
Figure A3-5
Relative 2nd-Home Foreclosure Impact per Housing Unit
1
2
3
4
2004 2nd-Home Purchase and Refinance Originations (LPS)
0
IN
MS GA
OH
KY
MI
ND
TNAL
KS
IAOK
MN
ILPA
NE
TX
NH
MO
LA
NY
ME ID
DC
WI WY
WV
CANM
NJNC
CT
SD
RIMD
VA
CO
UT
MA
AK WA
OR MT
AR
0
NV
FL
SC AZ
VT
HA
DE
2
4
Relative 2nd-Home Mortgage Prevalence (2004-07 Nat'l Avg.=1)
6
Figure A3-6
Relative 2nd-Home Foreclosure Impact per Housing Unit
3
4
2005 2nd-Home Purchase and Refinance Originations (LPS)
2
NV
FL
1
OH
AZ
GA
CA
0
MI
IL AL RI
SC
MS TX
OK
IN
TN
AK MDNJ NH ME
MO
ID
NE
LA CT
KY
MN
NY
UT
WI
NM
NC
WV
NDPAAR VA
OR CO
KS
IA
WA
MA
SD DC
DE
VT
MT
WY
0
2
4
Relative 2nd-Home Mortgage Prevalence (2004-07 Nat'l Avg.=1)
p. 54 of 69
HA
6
Figure A3-7
Relative 2nd-Home Foreclosure Impact per Housing Unit
4
2006 2nd-Home Purchase and Refinance Originations (LPS)
NV
2
3
FL
AZ
CA
GA
HA
0
1
MS
ID UT
OH IL MITX
MN
NJ OR
SC
AL
MD
IN
TN
WVAK DC
NM
WA
WI
OK
MO
CT
VA
RI
NH
KY
NY MA
NC
LA
IAPA
AR
CO MT
DE
KS
ME
VT
NE
WY
SD
ND
0
2
4
Relative 2nd-Home Mortgage Prevalence (2004-07 Nat'l Avg.=1)
6
Figure A3-8
Relative 2nd-Home Foreclosure Impact per Housing Unit
3
4
2007 2nd-Home Purchase and Refinance Originations (LPS)
FL
2
NV
1
GA
AZ
UT
ID
0
MS TN
CA
MI MD NJ
SC
IL AK
WIWAOR NM
OH
MN
NH
INOK
AL
CTTX
KY
LA
NC
VA
MO
VT
PA
KSWV
NY
MT
COME
DE
MA
IA AR
RI WY
NE
DC
ND SD
0
HA
2
4
Relative 2nd-Home Mortgage Prevalence (2004-07 Nat'l Avg.=1)
p. 55 of 69
6
Appendix 4: The Prevalence, Performance and Impact (per Housing Unit) of Non-Occupant
Mortgages Originated in 2004, 2005, and 2007
The results here are for mortgages originated in 2004, 2005, and 2007. The maps have the same
format as Figures 12-14, which was explained in the body of the paper. Table A4-1 summarizes the
data for the maps in this appendix as well as for Figures 11-14.
Table A4-1
Indices of the Relative Performance, Prevalence, and Impact of Non-Owner-Occupied (NOO) Mortgages and Foreclosures, by
State and Year Mortgage Was Originated
State
AL
AK
AZ
AR
CA
CO
CT
DE
DC
FL
GA
HA
ID
IL
IN
IA
KS
KY
LA
ME
MD
MA
MI
MN
MS
MO
MT
NE
NV
NH
NJ
NM
NY
NC
ND
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VT
VA
WA
WV
WI
WY
Performance Index
(Foreclosures per mortgage, for
NOO mortgages, by year of
origination, as a percentage of the
2004-07 national average)
2004
2005
2006
2007
88
103
62
68
30
48
74
32
24
99
170
136
33
44
70
44
28
100
166
94
42
62
74
42
42
81
136
65
18
40
74
35
27
54
98
59
52
160
290
210
108
136
166
103
15
54
94
52
22
41
89
74
75
107
146
88
198
207
227
101
69
68
70
34
56
57
68
39
88
112
103
54
55
69
87
57
30
55
60
42
27
57
109
83
38
66
89
48
153
210
251
168
86
128
174
116
119
120
129
90
96
115
119
78
19
13
38
27
62
56
69
27
56
170
251
164
45
61
82
59
42
76
125
86
21
33
57
51
58
84
116
61
41
46
61
37
31
49
27
6
171
188
217
105
58
78
96
40
14
30
66
54
49
60
89
45
53
89
159
75
33
62
85
60
48
46
55
20
83
88
108
64
55
66
84
39
32
36
67
78
20
23
44
32
20
52
91
53
18
28
61
49
51
101
91
62
51
74
100
73
22
12
29
11
Prevalence Index
(Mortgages per housing unit, for
NOO mortgages, by year of
origination, as a percentage of the
2004-07 national average)
2004
2005
2006
2007
67
75
63
54
68
80
67
59
195
302
216
147
42
55
59
49
144
180
138
107
144
167
158
142
71
86
74
59
179
218
171
145
147
183
165
130
184
277
223
128
86
112
108
84
276
350
269
202
165
283
246
173
56
78
81
65
64
71
70
49
31
36
36
34
51
61
57
49
39
45
45
37
42
47
44
38
91
104
97
88
113
149
130
99
87
100
80
71
67
75
71
54
72
86
77
60
31
39
45
42
69
90
96
81
99
133
132
117
35
38
39
32
295
397
299
172
105
111
95
81
105
132
105
81
105
156
146
112
45
55
52
43
91
118
121
100
34
38
35
28
48
56
57
43
50
60
62
51
129
183
159
127
58
78
73
56
114
129
95
74
128
186
158
113
44
50
49
48
53
73
80
67
59
73
81
67
140
211
229
186
121
132
109
94
99
128
103
81
103
145
140
122
27
37
30
24
57
72
70
58
92
95
91
82
p. 56 of 69
Impact Index
(NOO foreclosures per housing unit, by
year of origination, as a percentage of
the 2004-07 national average)
2004
2005
2006
2007
59
78
39
37
20
38
49
19
46
300
368
200
14
24
42
22
41
181
228
100
61
103
118
60
30
70
101
38
32
87
126
50
39
99
162
77
96
444
645
270
93
153
179
86
42
188
252
104
37
117
218
128
42
84
118
57
126
146
158
50
21
25
25
11
28
35
39
19
34
51
46
20
23
32
39
22
27
57
58
37
30
84
141
82
33
66
72
34
102
157
177
91
62
110
135
69
37
47
59
38
66
104
114
64
19
18
50
31
22
21
27
9
164
675
752
282
47
67
78
48
44
100
131
70
22
52
83
57
26
46
60
26
37
55
74
37
11
18
9
2
82
106
123
45
29
47
59
21
18
55
105
68
29
47
65
26
60
115
151
55
43
116
134
68
21
23
27
10
44
64
86
43
33
48
68
26
45
75
153
146
24
30
48
30
20
67
94
43
18
40
86
59
14
37
27
15
29
53
70
43
21
11
27
9
Figure A4-1: Non-Owner-Occupied Mortgage Prevalence per Housing Unit, Relative to 2004-2007
U.S. Average (LPS data for mortgages originated in 2004)
Figure A4-2: Non-Owner-Occupied Mortgage Foreclosure Rates, Relative to 2004-2007 U.S.
Average (LPS data for mortgages originated in 2004)
p. 57 of 69
Figure A4-3: Non-Owner-Occupied Mortgage Foreclosure Impact per Housing Unit, Relative to
U.S. 2004-2007 Average (LPS data for mortgages originated in 2004)
Figure A4-4: Non-Owner-Occupied Mortgage Prevalence per Housing Unit, Relative to 2004-2007
U.S. Average (LPS data for mortgages originated in 2005)
p. 58 of 69
Figure A4-5: Non-Owner-Occupied Mortgage Foreclosure Rates, Relative to 2004-2007 U.S.
Average (LPS data for mortgages originated in 2005)
Figure A4-6: Non-Owner-Occupied Mortgage Foreclosure Impact per Housing Unit, Relative to
U.S. 2004-2007 Average (LPS data for mortgages originated in 2005)
p. 59 of 69
Figure A4-7: Non-Owner-Occupied Mortgage Prevalence per Housing Unit, Relative to 2004-2007
U.S. Average (LPS data for mortgages originated in 2007)
Figure A4-8: Non-Owner-Occupied Mortgage Foreclosure Rates, Relative to 2004-2007 U.S.
Average (LPS data for mortgages originated in 2007)
p. 60 of 69
Figure A4-9: Non-Owner-Occupied Mortgage Foreclosure Impact per Housing Unit, Relative to
U.S. 2004-2007 Average (LPS data for mortgages originated in 2007)
p. 61 of 69
Appendix 5: Non-Occupant Mortgage Prevalence and Foreclosure Impact Measured per Mortgage
Originated Instead of Per Housing Unit, 2004-2006
This paper presents measures of non-occupant mortgage prevalence and non-occupant foreclosure
impact based on a state’s volume of non-occupant mortgage originations and non-occupant
foreclosures relative to the total number of housing units in the state. An alternative measure is to
express these volumes relative to the total volume of mortgage originations.50 These alternative
measurements are presented here for mortgages originated in 2004-2007, using formats similar to
those of Figures 11, 12, and 14 but with total mortgage originations in the denominator instead of
total housing units.51
Compared to the per housing unit measures in Figures 11, 12, and 14, the per mortgage measures
presented here tend to show a higher impact in Midwestern states like Ohio and Indiana, where
mortgage originations per housing unit in 2004-2007 were lower than in Western states like Arizona
and Idaho. The fact that 2004-2007 mortgage originations per housing unit were higher in Arizona,
than in Indiana and Ohio may be related to the faster rate of growth in population and new housing
construction in recent years in Arizona.
2004
Figure A5-1
Relative Non-Occupant Foreclosure Impact per Mortgage
2
3
2004 Home Purchase and Refinance Originations (LPS)
IN
OH
MI
HA
0
1
MS
GA
MO
KY
MNTN
IL
IA
AL
NE NY
OK
KS
NV
TX
LA
RI
FL
WI
WV
PA
SD NJ
NH
CO
CT
NC
MA
SC
UT
NDAR
ME
AK
CA
MD
DC NM
AZ
WY
ID
VT
VA
MT
WA
DE
OR
0
1
2
3
Relative Non-Occupant Mortgage Prevalence (U.S.=1)
4
Note: Middle line represents an impact equal to the LPS data 2004-07 national average of 8.5
non-occupant foreclosures per 1000 home mortgages (owner-occupied and non-owner
occupied) . The lower and upper lines represent impacts of, respectively, half and three times
that national average impact.
50
In all cases, the mortgages considered are first-lien housing purchase and refinance mortgages on single-family
housing units.
51
Figure 13 is not affected by the choice of total mortgages as the denominator for defining prevalence and impact.
p. 62 of 69
Figure A5-2
Figure A5-3
p. 63 of 69
2005
Figure A5-4
Relative Non-Occupant Foreclosure Impact per Mortgage
3
2005 Home Purchase and Refinance Originations (LPS)
2
MI IN
OH
NV
FL
SC
HA
0
1
GA
MN
MS
MO
IL KY
WV
CA
AZ
RI
AL
TN
CTNY
OK
NJ
WI
LATX
IA
MA
NH
PA CO ME
NE KS
MD
DC
VA
ND
NC
SDAK
AR
ID
DE
UT
NM
OR
WA
VT
MT
WY
0
1
2
3
Relative Non-Occupant Mortgage Prevalence (U.S.=1)
Note: Middle line represents an impact equal to the LPS data 2004-07 national average of 8.5
non-occupant foreclosures per 1000 home mortgages (owner-occupied and non-owner
occupied) . The lower and upper lines represent impacts of, respectively, half and three times
that national average impact.
p. 64 of 69
4
Figure A5-5
Figure A5-6
p. 65 of 69
2006
Figure A5-7
Relative Non-Occupant Foreclosure Impact per Mortgage
3
2006 Home Purchase and Refinance Originations (LPS)
FL
NV
MI
2
IN
OH
0
1
MN
CA
GA AZ
RI
IL
CT
NJMS
NY MO
MD
AL DC
KYWITN
OK
VA
WV
MA
PA TX
ID SCHA
LA NH
AK AR CO
DE
IA NEKS
UT
OR
WA
NC
ME
NM
SD
VT
MT
WY
ND
0
1
2
3
Relative Non-Occupant Mortgage Prevalence (U.S.=1)
Note: Middle line represents an impact equal to the LPS data 2004-07 national average of 8.5
non-occupant foreclosures per 1000 home mortgages (owner-occupied and non-owner
occupied) . The lower and upper lines represent impacts of, respectively, half and three times
that national average impact.
p. 66 of 69
4
Figure A5-8
Figure A5-9
p. 67 of 69
2007
Figure A5-10
Relative Non-Occupant Foreclosure Impact per Mortgage
3
2007 Home Purchase and Refinance Originations (LPS)
2
FL
MI
NV
AZ
0
1
MN
IN OHGA
CA MS
IL NJ
MDMO UT
RI
WI
ID
CT NY
TN
WV
AL
SC
NH
DC
LA
KY
VAWA ORNM
HA
MA
PA
AR TX
CO
ME
OK
KS
NC
DE VT
IA
NEAK
MT
SD
WY
ND
0
1
2
3
Relative Non-Occupant Mortgage Prevalence (U.S.=1)
Note: Middle line represents an impact equal to the LPS data 2004-07 national average of 8.5
non-occupant foreclosures per 1000 home mortgages (owner-occupied and non-owner
occupied) . The lower and upper lines represent impacts of, respectively, half and three times
that national average impact.
p. 68 of 69
4
Figure A5-11
Figure A5-12
p. 69 of 69
@FedFRASER