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EFFECTS OF HOUSEHOLD CREDITWORTHINESS ON
MORTGAGE REFINANCINGS
S. Peristiani, P. Bennett, G. Monsen, R. Peach, and J. Raiff
Federal Reserve Bank of New York
Research Paper No. 9622
August 1996
This paper is being circulated for purposes of discussion and comment only.
The contents should be regarded as preliminary and not for citation or quotation without
permission of the author. The views expressed are those of the author and do not necessarily
reflect those of the Federal Reserve Bank of New York or the Federal Reserve System.
Single copies are available on request to:
Public Information Department
Federal Reserve Bank of New York
New York, NY 10045
EFFECTS OF HOUSEHOLD CREDITWORTHINESS
ON MORTGAGE REFINANCINGS
August 7, 1996
by S. Peristiani, P. Bennett, G. Monsen, R. Peach, J. Raiff1
Abstract: Using a unique loan level data set that links individual
household credit ratings with property and loan characteristics,
we test the extent to which homeowners' equity and credit ratings
affect the likelihood that mortgage loans will be refinanced as
interest rates fall. The lo git model estimates strongly support the
importance of both the equity and credit variables. These results
are interesting both from the viewpoint of investors in mortgage
products (since prepayments are directly affected) andfrom the
perspective of monetary policy (since refinancings are one channel
by which lower interest rates normally help reliquify households).
I.
Introduction
Homeowners typically have the option of prepaying all or part of the outstanding balance
of their mortgage loans at any time, usually without penalty. However, unless they have
sufficient wealth to pay off the balance, exercising this option requires obtaining a new loan.
1
Stavros Peristiani, Paul Bennett, and Richard Peach are economists at the Federal Reserve Bank
o(New York. Gordon Monsen and Jonathan Raiff are principals of the Mortgage Research Group of
Jersey City, New Jersey. The authors wish to thank Elizabeth Reynolds for outstanding technical support
on this project.
The views expressed in this paper are those of the authors and do not necessarily reflect the
position of the Federal Reserve Bank of New York or the Federal Reserve System.
Evidence is accumulating that variation in homeowners' ability to qualify for new mortgage
credit and in the cost of that credit accounts for a significant part of the observed variation in
refinancing behavior. It follows that individual homeowner and property characteristics, such as
personal credit ratings and changes in home equity, must be considered systematically, along
with changes in mortgage interest rates, in the analysis and prediction of mortgage prepayments.
Initial research into the factors influencing prepayments focused almost exclusively on
the difference between the interest rate on a homeowner' s existing mortgage and rates available
.on new loans. This was due in large part to the fact that the data sets used to investigate this
issue were aggregate data on the pools of mortgages serving as the underlying collateral for
mortgage backed securities. More recent research has relied upon loan level data sets that permit
inclusion of individual property, loan, and borrower characteristics into the analysis. This paper
represents a significant advance of the literature on mortgage prepayments as it is the first study
to introduce quantitative measures of individual homeowner credit histories into a loan level
analysis of the factors influencing the probability that a homeowner will refinance. In addition,
we use county-level repeat sales home price indexes to estimate changes in individual
homeowner' s equity over time.
Our findings strongly support the hypothesis that, other things
equal, the worse the credit rating the lower the probability that a loan will be refinanced. In
addition, we provide further reinforcement' of the finding that changes in home equity also
strongly influence that probability.
These findings are important from an investment risk management perspective since they
confirm that the responsiveness of mortgage cash flows to changes in interest rates will be
significantly influenced by credit and equity conditions of individual borrowers. The evidence is
overwhelming that these conditions are subject to dramatic changes. As shown in Chart 1,
personal bankruptcies have risen quite sharply since the mid- l 980's. While this partly reflects
changes in laws and attitudes, it nonetheless suggests that credit histories for a growing segment
of the population are deteriorating. Furthermore, as shown in Chart 2, home price movements,
the key determinant of changes in homeowners' equity, have been considerably different both
over time and in different regions of the country. For example, home price appreciation was
quite rapid in the Middle Atlantic and Pacific states in the mid to late 1980s, but then prices
actually declined during much of the first half of the 1990s. In contrast, rates of home price
appreciation in the East North Central and South Atlantic states were significantly less volatile
over this same period. As mortgage rates fell during the first half of the 1990s, many households
likely found it difficult, if not impossible, to refinance existing mortgages due to poor credit
ratings or erosion of home equity. Consequently, the prepayment experience of otherwise
similar pools of mortgage loans may be vastly different depending on their proportions of creditand/or collateral-constrained borrowers.
The findings in this paper also contribute to the understanding of how constraints on
credit availability affect the transmission of monetary policy to the economy (e.g., see Bemanke
(1993)). A number of studies have confirmed Fazzari, Hubbard, and Petersen's (1988) finding
that investment expenditures by credit-constrained businesses are especially closely tied to those
firms' cashflows and relatively insensitive to changes in interest rates, reflecting constraints on
their ability to obtain credit. Analogously, credit- and/or equity-constrained homeowners may
be less sensitive to changes in interest rates because of limited access to new credit, thereby
short-circuiting an important channel by which lower interest rates improve household cashflows
and stimulate the 9conomy.
2.
Previous Lpan Level Research on Mortgage Prepayments
'
Recognitiop that, in addition to changes in interest rates, individual loan, property, and
borrower characte~stics play a key role in determining the likelihood that a mortgage loan will
prepay has spawn~d a relatively new branch of research on mortgage prepayments based on loan
'
level data sets. T~is research has focused on the three major underwriting criteria mortgage
lenders use in deci~ing whether to extend credit--equity, income, and credit history. Past studies
have investigated ~he effect of changes in homeowners' equity and income on their ability to
exercise the optio~ to prepay.
'
For ex amp/e, Cunningham and Capone ( 1990), using a sample of loans secured by
properties in the ~ouston, Texas area, estimated post-origination loan-to-value ratios (LTVs) and
.
'
. post-origination ptyment-to-income ratios based on changes in regional home prices and
incomes. They copcluded that post-origination equity was a key determinant of the termination
•
experience of thos~ loans (inverse relationship for defaults, positive relationship for
I
prepayments) whereas post-origination income was insignificant. Caplin, Freeman, and Tracy
I
(I 993), using a sa1flple of loans secured by properties in six states, also find evidence of the
I
. importance of ho~e equity in influencing the likelihood of mortgage prepayment. They assess
the effect of post-~rigination equity by dividing their sample into states with stable or weak
i
property markets ~based on transaction-based home price indices for specific SMSAs) and
i
according to wheter the loans had high or low original LTVs. They found that in states with
weak property m~kets prepayment activity was less responsive to declines in mortgage interest
.i
rates than was the case in states with stable property markets, consistent with the hypothesis that
changes in home equity play an important role.
Archer, Ling, and McGill (1995) found that home equity had an important effect on the
probability that a loan would be refinanced and provide evidence that changes in borrower
income also play a significant role. The authors matched records from the 1985 and 1987
national samples of the American Housing Survey (AHS) to derive a subsample of non-moving
owner-occupant households with fixed-rate primary mortgages, some of whom had refinanced
since the interest rate on their loan in 1987 was different from that reported in 1985. Their
estimate of post-origination home equity was derived from the sum of the book value of all
mortgage debt divided by the owner's assessment of the current value of his or her own
property. 2 In addition, a post-origination mortgage payment-to-income ratio, derived from the
homeowner' s recollection of total household income, was included as an explanatory variable.
The authors found that, along with changes in interest rates, post-origination home equity and
income were significant and of the hypothesized signs.
This paper advances this literature in several important respects. This is the first study to
systematically investigate the effect of the third underwriting criteria-- homeowners' credit
histories. In addition, we introduce an innovation in the estimation of post-origination equity by
using county-level repeat sales home price·indices.3 We employ a unique loan level data set
2
Homeowners assessments of the current market values of their properties may be biased,
particularly around turning points. See for example, DiPasquale and Sommerville (1995) and Goodman
and Ittner (1992).
'The authors would like to thank the firm of Case Shiller Weiss, Inc.(CSW) of Cambridge,
Massachusetts for providing these home price indices.
that, in addition to providing the information on credit histories, allows large samples for major
population centers as well as the nation as a whole and permits identification of the reason for
prepayment--refinance, sale, or default. Finally, the richness of this data set allows us to look at
borrower behavior over numerous time intervals, which should enhance the robustness of our
results.
3.
Our Data
The data was provided by the Mortgage Research Group (MRG) of Jersey City, New
Jersey, which has entered into a strategic alliance with TRW to provide data for research on
mortgage finance issues. MRG maintains a database covering roughly 42 million residential
properties located in 396 counties in 36 states. The database is arranged into "tables". The
primary table is the transaction table, which is based on the TRW Redi Property Data data base.
This table is organized by properties, with a detailed listing of the major characteristics of all
transactions pertaining to each property. For ihe roughly 42 million properties covered,
information is provided for between 150 and 200 million transactions. For example, if the
property is purchased, a purchase transaction code is entered along with key characteristics such
as date of closing, purchase price, original mortgage loan balance, and maturity and type of
mortgage (fixed rate, adjustable rate, balloon, etc). The characteristics of any subsequent
transactions are also recorded, such as a refinancing of the original mortgage, another purchase of
the same property, and, for some counties, a default. The primary sources of this information are
the records of county recorders and tax assessors offices, which are surveyed on a regular basis
to keep the transaction data up-to-date.
Multiple other tables providing information such as the physical characteristics of the
property, the demographic characteristics of the population that lives in the vicinity of the
property, and periodic snapshots of the credit histories of the occupants of those properties can be
linked to the transaction table on the basis of property identification numbers. The data on credit
histories is derived from TRW Information Services, the consumer credit information side of the
organization.
The sample used in this study was constructed in several stages: First, we selected
groups of counties representing the four major regions of the country. In the East, we chose four
counties surrounding New York City ( Orange county in New York State, and Essex, Bergen,
and Monmouth Counties in New Jersey). In the South, we chose six counties in central Florida
(Citrus, Clay, Escambia, Hernando, Manatee, and Marion). In the Midwest, we chose Cook and
five surrounding counties in Illinois (Dekalb, DuPage, Kane, McHenry, and Ogle). Finally, in
the West we selected Los Angeles, Ventura, and Riverside counties in California. Selecting
these four diverse areas assures that our statistical findings are general rather than specific to a
particular housing market. Furthermore, over the past decade the behavior of home prices in
these four regions has been quite different.
In these counties, we identified the most recent purchase transaction for each property.
The mortgages on some of these properties were subsequently refinanced, in some cases more
than once, while the others had no further transactions recorded through the end of our sample
period, December 1994. This established the zero-one, refinance-no refinance dependent
variable we then try to explain. (For those loans refinanced, the new Joan could be greater than,
equal to, or Jess than the remaining balance on the old Joan.) We then limited the sample to
j_
properties originally financed with fixed-rate loans outstanding for a year or more. In the final
step, the snapshots of credit histories were linked to a random sample of these properties by
MRG. The resulting sample consists of 12,855 observations, of which slightly under one-third
were refinanced.
An interesting feature of this sample is its varying time dimension. For example, the
most recent purchase transaction can range from as recent as one year back from the sample cutoff date of December 1994 to as far back as 10 years ( 1984). Refinancings, the precise date of
.which are known, occur at varying intervals after the original purchase. Furthermore, the sample
includes refinancings that occurred in the 1986-early 1987 "refi wave" as well as from the 1993early 1994 wave, although most are from that latter period.
4.
Our Model
To refinance a mortgage is to exercise the call option imbedded in the standard residential
mortgage contract. In theory, a borrower will exercise this option when it is "in the money",
meaning that refinancing would reduce the current market value of his liabilities by an amount
equal to or greater than the costs of carrying out the transaction. In fact, many borrowers with
apparently in the money options either fail to exercise them, or exercise them only after interest
rates have fallen quite far below the rate on their existing mortgage, while others exercise the
option when it apparently is not in the money. This heterogeneity of behavior appears to be due
in large part to homeowners' ability to secure replacement finant:ing. If the individual cannot
qualify for a new mortgage, or can only qualify at an interest rate much higher than that available
to the best credit risks, then refinancing may not be possible or worthwhile even though at first
glance the option appears to be in the money. Our hypothesis is that, in addition to a decline in
equity resulting from a decline in the property value, refinancing may not be possible or
worthwhile because the borrowers' personal credit history has deteriorated.
In short, in our model the dependent variable is a discrete binary indicator that assumes
the value of one when the homeowner refinances and the value of zero otherwise. We use logit
analysis to estimate the effect of various explanatory variables on the probability that a loan is
refinanced. The explanatory variables may be categorized as (1) market interest rates and other
factors in the lending environment affecting the cost, both financial and nonfinancial, of carrying
out a refinancing transaction; (2) the credit history of the homeowner; and (3) the current loan-tovalue ratio (as opposed the that prevailing at the time of purchase), typically referred to in the
literature as the post-origination LTV. In addition, as in most prepayment models, we include
the number of months since origination or the "age" of the mortgage t_o capture age-correlated
effects not stemming from equity, credit, or the other eKplanatory variables. More details about
the definitions and specification of these variables follows while Table I presents summary
statistics.
Interest Rates and Lending Environment
Fundamentally, the determination of the degree to which the call option is in the money,
or alternatively the strength of the incentive to refinance, is made by comparing the contract rate
on the existing mortgage with the rate that could be obtained on a new mortgage. This
comparison must also take into account transaction costs such as discount points and assorted
closing costs, the opportunity cost of the time spent shopping for and qualifying for a new loan,
and the borrower 's marginal tax rate, since mortgage interest is an allowed itemized deduction.
As will be discussed more fully below, there are numerous ways in which the strength of the
incentive can be measured. The simplest, which we label as SPREAD , is the contract rate on the
existing mortgage minus the interest rate that could be obtained on a new mortgage. For all loans
in the sample, the contract rate on the existing mortgage is measured as the Freddie Mac national
average commitm ent (contract) rate on fixed rate loans for the month the loan was closed. This
is the so-called A paper rate or the rate available to the best credit risks. Likewise, the rate
obtained on a new mortgage for those who did refinance was the same national average A paper
4
contract rate in the month the new loan closed. By basing SPREAD on the A paper rate, we
explicitly exclude from this variable the influences that individual borrower equity and credit
conditions may have on the actual spread faced by a particular borrower.
Assigning a spread to those homeowners that did not refinance is problematic and has
been handled differently by different authors.' In tackling this problem, we noted that, as
depicted in Chart 3, those who did refinance rarely did so at the highest spread (lowest market
rate) that occurred over the period from their original purchase to the date they refinanced. If all
the spreads observed over that period were ranked from highest to lowest, on average those who
did refinance did so at about the 75th percentile. Accordingly, we assigned non-refinancers the
Strictly speaking, there is typically a 30 to 60 day lag between the date of application for a
the
mortgage and the date of closing, although borrowers typically have the option of locking in the rate at
We
closing.
of
date
the
to
up
way
the
all
cases
time of application or letting the rate float, in some
experimented with lagging the national average rate by one and then two months and found that in neither
case were the results significantly different from using the average rate for the month the loan closed.
4
For example, Archer, Ling, and McGill (1995) assign to those observations that did not refinance
the lowest monthly average Freddie Mac commitment rate on 30-year FRMs over the two year time
interval of their study.
5
•
75th percentile of spreads observed over the period from date of original purchase to the end of
our sample period (December 1994).6 In addition to SPREAD, we also include as an
explanatory variable the historical standard deviation (HSD) of market rates during the relevant
time interval, i.e. purchase to refinance or purchase to end of sample period. HSD is measured as
the standard deviation of the JO-year Treasury bond rate. We expect this variable to be directly
related to the probability that a loan is refinanced. That is, for a given value of SPREAD, if
during the relevant time interval market rates were relatively volatile, a homeowner would have
been more likely to observe an opportunity to refinance than if rates were relatively stable.7
A third, related explanatory variable is Lending Environment (LE), defined as the change
in the average level of points and fees (expressed as a percent of the loan amount) on
conventional fixed rate loans closed over the time period from original purchase to either
refinancing or the end of the sample period. This variable is intended to capture the fact that, as
noted by many industry experts, over the period from the late 1980s into the 1990s the mortgage
lending industry became much more competitive in general and much more aggressive with
regard to soliciting refinancings. Over this period mortgage servicers began contacting
customers with spreads greater than some threshold, often as low as 50 basis points, and
encouraged them to refinance. Transactions costs declined as competition reduced points and
6
As with those who did refinance, the spread assigned to non-refinancers was based on A paper
interest rates. Any decline in incentive to refinance due to a homeowner' s inability to qualify for the A
paper rate, in which case actual spread would be less than that assigned, will be captured through the home
equity and credit variables.
7
1n contrast to HSD, which is a backward-looking measure of rate variability, theory predicts that
rational debtors expecting higher future volatility would ceteris paribus delay refinancing due to the
increased value of their repayment option. HSD is premised on a distinction between expected forwardlooking and historical backward-looking volatility.
11
fees: (See Chart 4.) Indeed, many lenders began offering loans with no out-of-pocket costs.
Psychic transaction costs were also reduced as lenders introduced "no doc" (documentation) and
"low doc" loan programs and drastically shortened the period from application to approval and
then from approval to closing. This change in lending environment likely increased the
probability of a refinancing, all else equal.
It is important to note that the particular combination of SPREAD, HSD, and LE that we
employ to capture the effect of changes in interest rates and the lending environment, while quite
reasonable, represents only one approach. For the current study, a key issue is whether the
estimated effects of the credit history and/or home equity variables are significantly altered by
how the interest rate variables are specified. Therefore, to test the robustness of the credit and
equity variables, we experimented with several alternative specifications of the interest rate
variables. As will be discussed below, the effects of our personal creditworthiness and home
equity measures are insensitive to that specification.
Personal Creditworthiness
As mentioned above, for the purposes of this study complete TRW credit reports were
matched to the individual records of the property transaction table that make up our sample of
loans. This matching was based on record-identification numbers; any information that would
enable an individual or a property to be identified was masked. The full credit report provides a
wealth of information on individuals' credit histories, ranging from summary measures to
detailed delinquency information on numerous categories of sources of credit. Our hypothesis is
that, other things equal, the worse the credit rating the lower t)le probability that a loan will be
12
refinanced, either because the homeowner is unable to qualify for a new loan or because the
interest rate at which he or she is able to qualify is too high to make it worthwhile to exercise the
option. To test this hypothesis, we experimented with numerous alternative measures of
creditworthiness, all of which strongly support it. However, we could find little empirical basis .
for concluding that one measure performed better than the others.
The most general, summary measure of creditworthiness is the total number of
derogatories, which we have labeled TDEROG. Four distinct events result in a derogatory. The
first is a charge off, meaning that after making a reasonable attempt to collect a debt a lender has
deemed it to be uncollectible and so has elected to declare it as a bad debt loss for tax purposes.
There are not any hard and fast rules about when a lender can elect to charge off a debt or what
represents a reasonable effort to collect. A charge off may be the result of a bankruptcy but most
often is not. A second, related event resulting in a derogatory is a collection, meaning a lender
has enlisted the services of a collection agency in an effort to collect the debt. The remaining
two events resulting in a derogatory are liens and judgements, both of which are labeled public
derogatories because they are effected through the courts and are a matter of public record.
Somewhat more specific credit indicators of credit history are the summary measures of
worst now (WRSTNOW) and worst ever (WRSTEVER) across all credit lines. As the names
imply, these variables capture an individual's worst payment performance across all sources of
credit as of some moment in time (now) and over the individuals entire credit history (ever).
Both variables can take on values of I (all credit lines current), 30 (scheduled payment on one or
more credit lines 30 days late), 60 (scheduled payment on one or more credit lines 60 days late),
13
90, 120, or 400 (a debt has been charged off, as described above). 8 Note that a worst ever of 400
constitutes a derogatory, whereas some lesser indicator of credit deterioration, such as a 90 or
120, for example, does not.
Chart 5 presents a hypothetical example of worst now and worst ever. An individual has
three credit lines, a home mortgage, a credit card, and an ·auto loan, which at the beginning of this
individuals credit history (t-11) are all current. At that point in time both worst now and worst
ever have values of 1. For some reason, perhaps loss of employment, illness, or divorce, this
individual begins to experience some difficulty meeting scheduled payments on a timely basis.
The credit card reaches 120 days late, at which point that lender elects to charge off the debt and
both worst now and worst ever take on the value of 400. Eventually, this individual is able to get
all credit lines current again, bringing worst now back down to I by period t-1. But worst ever
remains at 400 due to the charge off of the credit card debt in period t-6. 9
Table 2 presents a cross tabulation of the worst now and worst ever readings for the
individuals in our sample. For worst now, 85.5% of the sample has a value of I while 8.0% have
a value of 400. Values from 30 to 120 represent just 6.5% of the total. In contrast, for worst ever
18.4% of the sample have a value of 400 while just 52.9% have a value of I. Thus, while at any
point in time nearly nine of every 10 individuals has a perfect credit rating (worst now= I), at
some point in their credit history roughly half the population experienced something less than a
8
In fact, both variables can take on more values than those listed. For example, a value of 34
indicates that an individual is persistently 30 days late. For the purposes of this study, we have constrained
worst now and worst ever to take on the values listed here.
Legally, worst ever is supposed to look back just seven years. In practice, the worst ever reading
.
might look back more than seven years unless disputed by the individual in question.
9
14
perfect credit rating (worst ever> I). In fact, 8.0% have a worst now of I but a worst ever of 400.
As is discussed more fully below, when entered individually into our model worst now and worst
ever perform similarly; there is no statistical basis for choosing one over the other. On the other
hand, improvement in one's credit rating appears to matter. We find some weak evidence that
for those with a worst ever of 400, the probability that a homeowner will refinance is somewhat
higher if worst now was better than 400.
Finally, in addition to the summary measures of worst now and worst ever, identical
measures for separate categories of credit lines are available, such as for loans secured by real
estate, bank credit cards, retail charge accounts, finance company loans, etc. On the theory that
mortgage lenders give the performance on one type of credit line more weight than others, we
also experimented with worst ever for bank credit cards (WRSTCRD). As before, this credit
measure turns out to be highly significant, but the explanatory power of the model is essentially
the same as when the alternative measures are used. 10
Post-Origination Home Equity
In addition to a poor credit history, another event that could prevent a homeowner from
refinancing, regardless of how far current interest rates fall, is a decline in property value which
significantly erodes that owner's equity in the property. For example, if the owner originally
made a 20 percent downpayment (origination LTV=80 percent), a 15 percent decline in property
10
To an increasing extent, mortgage lenders are relying on credit scores as a single measure
summarizing the vast amount of information on the credit report. For an overview of this issue see Avery,
Bostic, Calem, and Canner (1996). An extension of the research on the effect of credit histories on
mortgage refinancings, credit scores could also be tested as an alternative measure of credit worthiness.
value following the date of purchase would push the post-origination LTV to nearly 95 percent,
typically the maximum possible with conventional financing. In addition to the fact that loan
underwriters would likely be leery of the recent trend in property values and so may be reluctant
to approve a loan, an LTV in excess of 80 percent would typically require some form of
mortgage insurance, which would increase transaction costs and reduce the effective interest rate
spread by as much as 25 to 50 basis points. If the original LTV was greater than 80 percent,
much smaller declines in property values would have similar effects. In contrast, increases in
property values would likely increase the probability of refinancing. Greater equity simply
makes it easier to qualify for a loan while it may also increase the incentive to refinance in order
to take equity out of the property ("cash out" refinancing). Furthermore, if price appreciation
substantially lowers the post-origination LTV, through refinancing a borrower may be able to
lower or eliminate the cost of mortgage insurance, thereby increasing the effective interest rate
spread.
To capture the effect of changes in home equity on the probability of refinancing, we
•
enter an estimate of post-origination LTV as an explanatory variable. The numerator is the
amortized balance of the original first mortgage on the property using standard amortization
formulas for fixed rate mortgages and the interest rate assigned to that loan, as discussed above.
11
The denominator is the original purchase price indexed using the Case Shiller Weiss repeat sales
The presence of second mortgages and home equity loans (HELs) introduces additional
considerations into the issue of refinancings. On the one hand they would tend to reduce a homeowner' s
equity. On the other hand, since they typically have interest rates well above the rates on first mortgage
Joans, the spread based on the homeowners' weighted-average cost of credit would likely be higher. While
the MRG data base indicates the presence and amount of second mortgages and HELs taken out since the
original purchase, we did not investigate their effect on refinance probabilities. This is an area for future
research.
·
11
16
home price index for the county in which the property is located. While they are not completely
free of bias, repeat sales home price indexes are generally regarded as the best indicator, of the
options currently available, of movements in home prices over time. This approach allows the
computation of a post-origination LTV for each month from date of purchase to either the date of
refinance or the end of the sample period. For loans that did refinance, the post-origination LTV
used is the estimate for the month in which the refinance loan was closed. For loans that did not
refinance, the post-origination LTV is the average over the entire period from date of purchase to
the end of the sample period.
It should be noted that virtually all of the movement in LTV is the result of changes in the
denominator. The amount of amortization of the original balance of a mortgage is relatively
modest over the typical life of the mortgages in our sample. In contrast, over the time period
represented by this sample home price movements have been quite dramatic in some regions.
For example, based on the Case Shiller Weiss repeat sales indices, home prices in the California
counties included in our sample declined by roughly 30 percent from 1990 to 1995.
Age or "Burn-out"
The actual prepayment experience of pools of mortgages typically exhibits an increase in
the conditional prepayment rate (CPR) during roughly the first 50 to 60 months, at which point
loans are described as being "seasoned" and the prepayment rate then begins to decline. As the
aging process continues, the remaining loans in a pool become quite resistant to further
prepayments even with strong incentives, a phenomenon known as "bum out". To capture this
effect, most prepayment studies include the age of the loan or number of months since
17
origination as an explanatory variable.
One explanation of bum out is that homeowners who are not constrained in any way are
relatively quick to refinance when their option goes in the money. In contrast, homeowners who
are credit-, equity-, and/or income-constrained are prevented from exercising their option and
become a greater proportion of the remaining loans in a pool over time. To the extent that our
equity and credit variables capture this effect, the age of the loan per se should be less important
than would be the case in a model that did not include those variables. However, recognizing
that credit and equity may not capture all age-correlated effects, we also include AGE as an
explanatory variable. Since the effect of aging may not be a simple linear one, we also included
age squared (AGESQ). Chart 6 compares the frequency distribution of AGE, broken out
separately between homeowners that refinanced and those that did not. The general shape of
these distributions is similar, although as one would expect, the proportion of higher AGE values
is greater for non-refinancers than for refinancers. 12
5.
Empirical findings
Basic Model
The logit estimation results of our basic model, shown in Table 3, clearly demonstrate
the significance of the creditworthiness and the home equity measures for refinancing activity.
The results are presented for the four regions (California, Florida, Illinois, and New York/New
Jersey) and for all regions combined. The basic model includes SPREAD as the measure of the
12
As noted earlier, the sample excludes observations with AGE less than 12 months. The cells in
Chart 6 refer to the lower value in each AGE range (i.e. "I year" means between I and 2 years of age, and
so forth).
18
strength of the incentive to refinance and WRSTNOW as the measure of credit history.
As expected, the coefficients of the variable SPREAD are uniformly significant and
positive. In addition, the coefficients on HSD are positive as expected, also consistent with the
importance of interest rate effects. As noted, high values of HSD indicate more opportunities
during the measurement interval for the homeowner to have observed and locked in a lower rate.
Lending environment (LE) is also significant with the predicted sign, suggesting that increased
lender aggressiveness has boosted the probability that a loan will be refinanced. The estimated
coefficients of WRSTNOW are negative and significant, providing strong support for the
hypothesis that a poor credit history lessens a homeowner' s ability to refinance. Changes in
home equity also have an important influence on the probability of refinancing, as evidenced by
the negative sign and high level of significance of LTV.
Chart 7 demonstrates the estimated
effect of changes in house price by graphing simulated values of the probability of prepayment
for different levels of the post-origination house price as a percent of the original purchase price.
Finally, while it is not true for each region, for all regions combined AGE and AGESQ are
significant with negative signs, indicating that credit and equity do not explain all of the decline
in probability of refinancing as a mortgage ages.
Sensitivity Analysis -- Alternative Measures of Incentive to Refinance
As noted earlier, a test of the robustness ofthe significance of the credit and equity
variables is to see how they perform under alternative specifications of the incentive to refinance.
Table 4 presents the estimation results for our model for all four regions combined using four
alternative measures of the strength of the incentive to refinance. In the first column are the
estimated coefficients when using SPREAD, as defined above. The next three columns
introduce the measures PROBIN, INMONEY, and PVALUE, respectively, all of which are
explained in detail in the appendix. All four measures are highly significant while the overall
explanatory power of the model is essentially the same in all cases. Most important for the
central hypothesis of this study, however, is the fact that, irrespective of the measure of incentive
to refinance, the estimated effects ofWRSTNOW and LTV are qualitatively similar. Moreover,
the values of HSD and LE are also significant and of the expected sign in all cases.
Sensitivity Analysis -- Alternative Measures of Credit History
As a further test of the finding that credit history is an important determinant of the
probability of refinancing, Table 5 presents logit estimations of the basic model for all regions
combined after having replaced the credit history variable WRSTNOW with three alternatives-WRSTEVER, WRSTCRD, and TDEROG, all of which are described in Section 4 above. All
four alternatives give similar results, so there is no basis for preferring one over the others. (The
coefficient value on TDEROG differs due to the difference in measurement scales.) On the
other hand, regardless of the credit history measure used, credit history is highly significant and
the overall model retains its explanatory power.
Interactions Between Credit and Interest Rate Effects
As argued above, for an individual (or firm) without access to external financing,
variations in market interest rates may have little effect on his decision making. In this case, the
argument would be that variations in market interest rates relative to the contract rate on a
20
homeowners' existing mortgage would have greater effect on the refinancing probability of a
borrower with a perfect credit history than for a homeowner with serious credit difficulties, such
as a 400 for either WRSTNOW or WRSTEVER. To test this hypothesis, we created two
subsamples, one of individuals with values of WRSTNOW equal to 1 ("good credits") and the
other with values equal to 400 ("bad credits"). We then estimated our basic model for each of
these subsamples while dropping the credit history variable.
The results of this exercise, shown in Table 6, confirm the presence of key interactions
between credit history and SPREAD. The size of the coefficient of SPREAD among "good
credits" is approximately twice as high as it is among "bad credits", with a corresponding sizable
drop in statistical significance in the latter case. This result further emphasizes the dependence
of estimates of interest rate sensitivities on credit factors. Pools of mortgages with relatively
high proportions of borrowers with poor credit histories will experience significantly slower
prepayment rates, all else equal. Therefore, investors in mortgage-backed securities are affected
by the credit conditions of the households represented in the underlying pools of mortgages even
though they may be insulated against homeowner default per se.
Improvement in Credit Report
As noted in the discussion in Section 4 above, there are cases in our sample where an
individual's credit history has improved in the sense that WRSTNOW has a lower value than
WRSTEVER. In fact, as shown in Table 2, 8.0 percent of the sample had a WRSTEVE R of 400
(the worst credit classification) and a WRSTNOW of 1 (the best credit classification). We
investigated the extent to which improvement in a homeowner's credit history may increase his
21
or her access to credit. To do this, we estimated the model over a subsample that had a value
of
400 for WRSTEVER. We then added a variable labeled IMPROVE defined as the differen
ce
between WRSTEVER and WRSTNOW. IfWRST EVER was 400 and WRSTN OW was
I, for
example, IMPROVE would equal 399. In contrast, if WRSTEVER was 400 and WRSTN
OW
was 400, improve would equal zero. Thus, a positive sign on the estimated coefficient of
this
variable indicates that the improvement in credit rating does help an individual gain access
to
credit for refinancing, other factors equal. As seen in the results, presented in Table 7, the
sign of
the estimated coefficient of IMPROVE is indeed positive, providing some support for the
idea
that improvement in credit history helps. Simulations based on this equation suggest that
a value
of 399 for IMPROVE raises the likelihood of refinancing by about 5 percent. However, that
coefficient is not statistically significant at the 95 percent level of confidence.
Effect of Lending Environment
As a final sensitivity test, we investigated the effect of the variable intended to capture
changes in the lending environment (LE). Table 8 presents estimation results for our model
for
all four regions combined using the four alternative measures of the strength of the incentiv
e to
refinance (the same as Table 4) but excluding the variable LE. Without LE, the overall
explanatory power of the model is diminished somewhat. While they remain highly significa
nt,
the magnitudes of the coefficients on the alternative measures of incentive to refinance, on
AGE,
and on HSD are changed. Of particular interest, the variables WRSTNOW and LTV remain
highly significant and the estimated coefficients are relatively stable. These results suggest
that
LE is capturing an important influence on the probability that a loan is refinanced. Also,
the
22
results confirm that inclusion of LE in the model is not influencing the finding of the significance
of the credit and equity variables.
6.
Effect of Credit History and Home Equity on the Probability of Refinancing
Using the separately estimated equations for the WRSTNO W=l and WRSTNOW=400
subsamples presented in Table 6, it is possible to simulate values for the probability of
refinancing for hypothetical individuals with different credit histories and different values of the
post-origination LTV. Table 9 summarizes these results. The four columns of this table
represent alternative combinations of the variables WRSTNOW and LTV. Moving down each
column, the variable SPREAD increases from 0 to 300 basis points, which should normally
motivate refinancing. The first column, with WRSTNOW= I and post-origination LTV= 60%,
shows how an individual who is neither equity- nor credit-constrained would react to an increase
in SPREAD. Note that with SPREAD= 0, the probability of refinancing is 0.29, suggesting that
refinancings motivated by the desire to extract equity from the property is fairly high among this
group.
As SPREAD rises to 300 basis points, the probability of refinancing essentially doubles
to nearly 60 percent. Moving to the second column, where LTV=IO0%, the probabilities drop
quite sharply; at SPREAD=0 the probability is just . I while at SPREAD=300 the probability is
0.32, about half that when LTV=60%.
In contrast, the third and fourth columns depict an individual who is severely creditconstrained (WRSTNOW=400). As suggested above, having substantial equity can overcome
many of the problems associated with a poor credit history. With LTV=60%, probabilities of
refinancing are essentially the same at SPREAD =0 and 100 as in the WRSTNOW = I case.
However, without substantial equity (LTV=l00%) the probability of refinancing is not only low
but also unresponsive to increases in SPREAD.
CONCLUSIONS
The foregoing analysis provides compelling evidence that poor credit histories
significantly reduce the probability that a homeowner will refinance a mortgage, even when the
financial incentive from doing so appears to be quite strong. Moreover, consistent with previous
work, we have found that refinancing probabilities are quite sensitive to the amount of equity a
homeowner has in his or her property. Poor credit histories and low equity positions make it
very difficult for homeowners to meet lenders' underwriting criteria, and so they are blocked
from obtaining the replacement financing necessary for them to exercise the option to prepay
their existing mortgage.
On one level, this research contributes to the evidence that households' financial
conditions can have significant consequences on the channels through which declines in interest
rates affect the overall economy. From the broadest viewpoint, mortgage refinancings can be
viewed as redistributions of cash flows among households or investment intermediaries. For
those households able to reduce financing costs by locking in a lower interest rate on their
mortgage, it is likely that there would be a wealth or permanent income effect that might boost
overall consumption spending. Conversely, to the extent that households are unable to obtain
replacement financing at lower interest rates due to deteriorated credit histories and/or erosion of
equity, the stimulative effect on consumption would likely be less than otherwise would be the
case.
The other side of the coin is the effect on the investors in the various cash flows
generated by a pool of mortgages. When homeowners refinance, those investors lose abovemarket-rate income streams and so are keenly interested in any factors which may have a
significant bearing on the probability that a homeowner will refinance. This analysis
demonstrates quite clearly that in addition to changes in interest rates and home prices, those
investors should also be concerned with both the credit histories of the homeowners represented
in a particular pool of mortgages as well as trends in those credit histories over time.
Notwithstanding guarantees against credit risk, the relative proportions of credit-constrained
households represented in pools of mortgages will have a significant impact on the prepayment
experiences of those pools under various interest rate and home price scenarios.
Appendix
Alternative Measures of the Incentive to Refinance
Theory suggests that homeowners will refinance if the benefits of doing so, in terms of
lower after-tax mortgage interest payments over the expected life of the loan, exceed the costs of
obtaining a new loan. There are numerous alternative measures that can be used to capture the
strength of the incentive to refinance, four of which were employed in this study. Those
alternatives, described below, fall into the general categories of discrete and cumulative.
Discrete Measures
The simplest measure of the net benefit provided by refinancing is the spread between the
contract rate on the existing loan (C) and the prevailing market rate (R), that is,
SPREAD,
=
C- R,,
(1)
where (t) represents the time period. While not explicitly treated in this measure, one could
imagine that, due to transaction costs, there is an implicit critical threshold of SPREAD, say I 00
to 150 basis points, that must be exceeded in order to trigger a refinancing. That threshold would
likely vary across borrowers and over time. 13
Another drawback of this simple measure is that it does not take into account the fact that
the financial benefit of refinancing is a function of the expected life of the new loan. Richard
13
See Follain, Scott, and Yang (1992) and Follain and Tzang (1988).
26
and Roll (1989) propose an alternative measure that accounts for this expected life by comparing
the present values of the existing mortgage over its remaining maturity evaluated at the
competing interest rates C and R. More specifically, define
PV/C)
=
PV,(R)
=
[ l -( 1 +C)t-360]
(3)
C
[l-(l +R)t-360]
(2)
R
The variables PV(R) and PV(C) represent the per dollar annuity of monthly payments at interest
rates Rand Cover the remainder of the original 30 year maturity. The ratio of PV(R) and PV(C)
offers a simple criterion for refinancing:
PVALUE,
C
=
(-
R
1-( 1 +R)t-360
) (------'------'-)
=
1-( 1 +C)t-360
C
(-)xy.
R
(4)
Again ignoring transaction costs, we would expect that the incentive to refinance strengthens the
more PVALUE exceeds I.
An important issue that arises when using discrete measures such as SPREAD and
PVALUE in cross-sectional analysis is the fact that they take on unique value for different time
periods or intervals and it is not clear what value should be used. For example, in a study such
as this one homeowners can be thought of a purchasing their home at period t=O. From that
point in time elapses a window of T periods, where T is either the time period in which the loan
27
is refinanced or, in the case of nonrefinancers, the end of the time period of the sample. For
individuals that refinance it seems clear that the appropriate value of SPREAD or PVALOE to
use in the cross sectional analysis is the value at or near the time of refinancing. For individuals
that do not refinance, however, it is unclear what value is appropriate. An infinite number of
possibilities exist and there is a certain amount of arbitrariness in selecting any particular one. In
our analysis of the data we noted that, on average, those homeowners who did refinance did so
not at the maximum values of SPREAD or PV ALUE but rather at around the 75-th percentile of
the values that occur between the date of purchase and date of refinancing. Therefore, we
assigned individuals that did not refinanced the 75-th percentile of the values of SPREAD and
PV ALUE that existed from their date of purchase to the end of the sample period (December
1994).
Cumulative Measures
An alternative approach to measuring the strength of the incentive to refinance is to
construct a measure that cumulates the individual values of each period in the relevant time
interval. An advantage of these cumulative measures is that the issue of what values to assign to
those who did not refinance is much more clear cut.
We employed two cumulative measures, the first of which we labeled INMONEY, which
is defined as the proportion of time periods since the date of purchase that the homeowner's
option has been in the money .. More specifically, the option is defined as being in the money
when the following condition holds:
28
PV,(R) - PV/C)
> TC,,
(5)
where the present value terms are defined by equations (2) and (3) above and TC is a measure of
transaction costs.
14
For simplicity, let y, be a binary 0-1 variable measuring when the
homeowner is in the money,
Yt
=
1
if
Yt
=
0
if
otherwise.
(6)
INMONEY is then defined as,
INMONEY
=
(7)
The variable INMONEY requires that the present value difference (PV(R)-PV(C)) exceed
the transaction costs (TC) limit in order to be in the money (get a I). In some instances,
however, we observe refinancings when the present value difference is less than our measure of
transactions costs. At the same time, INMONEY does not gauge by how much the present value
difference exceeds transactions costs. To overcome these deficiencies, a second, more
complicated cumulative measure was constructed which assigns a probability to the likelihood
of refinancing at each time period since the date of purchase. In particular,
14
As a measure of transaction costs we used the average initial fees and charges for fixed rate
loans closed as published by the Federal Housing Finance Board.
PROBJN
(8)
_!_ ~T P[d >0],
TL-
'
where d,=PV(R)-PV(C)-TC,. PROBIN measures the average probability that a homeowner is in
the money. To estimate PROBIN, we.simply use the empirical moments of d, and assume that
the probability distribution P[.] is normal.
REFERENCES
Wayne Archer, David Ling, and Gary McGill, "The Effect of Income and Collateral Constraints
on Residential Mortgage Terminations", NBER Working Paper No. 5180, July 1995.
Robert V. Avery, Raphael W. Bostic, Paul S. Calem, and Glenn B. Canner, "Credit Risk, Credit
Scoring, and the Performance of Home Mortgages", Federal Reserve Bulletin, July 1996, pp.
621-648.
Ben Bernanke, "Credit in the Macroeconomy," Federal Reserve Bank of New York Quarterly
Review, Spring 1993, pp 50-70.
Andrew Caplin, Charles Freeman, and Joseph Tracy, "Collateral Damage: How Refinancing
Constraints Exacerbate Regional Recessions," NBER Working Paper No. 4531, November 1993.
Sewin Chan, "Spatial Lock-in: Do Falling House Prices Constrain Residential Mobility?"
mimeo, Rutgers University, November 1995.
Donald F. Cunningham and Charles A. Capone, Jr., "The Relative Termination Experience of
Adjustable to Fixed-Rate Mortgages", Journal of Finance, Vol. XLV, No. 5, December 1990.
Denise DiPasquale and C. Tsuriel Somerville, "Do Housing Price Indexes Based on Tranacting
Units Represent the Entire Stock? Evidence from the American Housing Survey," Journal of
Housing Economics 4(3), 1995, pp 195-229.
Arturo Estrella, "Measures of Fit with Dichotomous Dependent Variables: Critical Review and a
New Proposal," Unpublished paper, Federal Reserve Bank of New York, 1996.
Steven M. Fazzari, R. Glenn Hubbard, and Bruce C. Peterson, "Financing Constraints and
Corporate Investment," Brookings Papers on Economic Activity, 1988, No. I, pp 141-195.
James R. Follain, James 0. Scott, TL Tyler Yang, "Microfoundations of a Mortgage Prepayment
Function," Journal of Real Estate and Economics, vol. 5, 1992, pp. 197-217.
James R. Follain and Dah-Nein Tzang, "Interest Rate Differential and Refinancing a Home
Mortgage," The Appraisal Journal, vol. LVI, no. 2, April 1988.
John L. Goodman and John B. Ittner, "The Accuracy of Home Owners' Estimates of Value,"
Journal o[Housing Economics 2(4), 1992, pp 339-357.
Lynn Paquette, "Estimating Household Debt Service Payments," Federal Reserve Bank of New
York Quarterly Review, Summer 1986, pp 12-23.
Scott F. Richard and Richard Roll, "Prepayments on Fixed-Rate Mortgage-backed Securities,"
The Journal o(Portfolio Management, Spring 1989, pp 73-82.
Ronald W. Spahr and Mark A Sunderman, "The Effect of Prepayment Modeling in Pricing
Mortgage-Backed Securities," Journal o(Housing Research, Volume 3, Issue 2, pp 381-400,
31
1992.
Charles N. Schorin, "Modeling and Projecting MBS Prepayments," in Frank J. Fabbozi, ed.,
Handbook of Mortgage Backed Securities, Probus Publishing Company, 1992, U.S.A., pp 221262.
32
Chart 1
Total Personal Bankruptcies
1,000,000
800,000
600,000
400,000
200,000
0
L....L---l..---'--'---'--'-....__.,__...__._____.___.____.____._-'---'--'--'--.,__...__._____.___._---'----'--'---'--'-....__.,__...__L........L___._---l..---'---J
1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995
Sources: U.S. courts and Federal Reserve Board of Governors
Chart 2
Fannie Mae - Freddie Mac Repeat Sales Housing Price Index
U.S. and Selected Regions
25
25
.,
Middle Atlantic , ' ,
20
'
15
.... .... ,
10
,
'
15
-
'
~
5
0
.•.,
20
•
-
,-;\
\
,,
....•
•••
...
East North Central
•
\ I
_./
\··
'
\
.....····....:-/~·:·:·:··.
·::;
.. •
-~
. ~\ .
I I
··-..!
.-···..•..
:
\ .. •··..
I ,. ., v'J ; , ,,.J" -;
~
10
I
;___]\......
;-~
'\
~
-.
......
I
5
I
0
,
V
'
South Atlantic
-5
'"-;~--'-'-'-~-; :;~-L-~~~_j_ J....J....~~~'-- '-J....J.......L. J.....L....J-L-J ....J.......L.J.. ...L....J_j_J... .J......C...C... .L.J.--'-'--'-J..
1981.1
1983.1
1985.1
1987.1
Source: Office of Federal Housing Enterprise Oversight
1989.1
1991.1
..J.......L.J.... .L....J......C... .L.J....J
1993.1
1995.1
-5
Chart 3
Spread Assigned to Those Who Did Not Refinance
Basis Point Spread (C-R)
300
1--······················------··------------- ..··
75th Percentile
-/-(__ _ _ _ _ _ _ __
200
100
o""'----------------;?L---------------1----l
-100
-200
Date of Purchase
Date of Refinancing
Time
Chart4
Initial fees and charges
Percent
3
2.5
2
1.5
I
0.5
I
l11111111111l1111111111rl11111111111l11111111111l1111111111rl11111111111l11111111111l11111111111l11111111111l11111111111l11111111111l11111111111l11111111111l111
Jan-83 Jan-84 Jan-85 Jan-86 Jan-87 Jan-88 Jan-89 Jan-90 Jan-91 Jan-92 Jan-93 Jan-94 Jan-95 Jan-96
Source: Federal Housing Finance Board
I
Chart 5
Example of Worst Now/Worst Ever Credit Histories
Worst Ever
1
30
60
90
120
400
400
400
400
400
400
Worst Now
1
30
60
90
120
400
90
60
30
30
1
I
A
(mortga ge)
1
1
1
30
1
30
1
1
B
(credit card)
1
30
60
90
120
C
(auto loan)
1
1
30
60
t-10
t-9
t-8
400
Credit Lines
t-11
30
30
30
1
400
--
--
--
--
30
60
90
60
30
30
1
1
t-7
t-6
t-5
t-4
t-3
t-2
t-1
t
TIME
--
--
Chart 6
Age Distribution of Mortgage Loans
Frequency
Percent
30
Refinancings
25
Non-Refinancings
20
15
10
5,~
0
1
2
3
4
5
6
7
8
9
10
1
Age (in years)
2
3
4
5
6
7
8
9
10
Chart 7
Effect of change in house price on probability of refinancing
Probability of refinancing
0.6
r------~-------,r--------------,
Original sale price
0.5
/
0.4
0.3
0.2
0.1
0
L....l..-==:l:::::::::r..._---1._L__j____j__j_____L_-----1_..L__j__L_..J......_j__L__j___l_j___J_
10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200
House Price as a Percent of Original Purchase Price
Table 1
Summary Statistics for Variables
Explanatory
Variable
Description
WRSTN OW
Worst current delinquency (l=good credit,
30, 60, 90, 120, 150, 180, 400=default)
26.5
42.5
WRSTE VER
Worst delinquency ever (l=good credit, 30,
60, 90, 120, 150, 180, 400=default)
64.9
101
WRSTCRD
Worst delinquency ever on credit cards
(l=good credit, 30, 60, 90, 120, 150, 180,
400=default)
23.1
35.4
TDERO G
Total number of derogatories
0.34
0.58
SPREAD
Coupon rate minus prevailing market rate
(percent)
1.66
1.30
PROBIN
Probability of being in-the-money (decimal)
0.50
0.39
INMON EY
Proportion in-the-money (decimal)
0.18
0.12
PVALUE
Present value ratio (decimal)
1.17
1.08
LTV
Current loan-to-value (percent)
67.6
74.3
HSD
Historical standard deviation (percent)
0.11
0.11
AGE
Loan maturity (years)
4.90
5.44
LE
Lending environment measured by change in
transactions costs (percent)
0.24
0.13
Sale price of house (thousands of dollars)
150
129
Original loan balance (thousands of dollars)
104
103
Monthly payment on first mortgage (dollars)
1,150
948
Balance-to-limit on all credit lines (percent)
76.8
77.3
Refinancing
Mean
No
Refinancing
Other Related Variables
Notes: The interest rate measures SPREAD, PR OBIN, INMONEY, and PV ALUE are describe
d
more extensively in the Appendix.
Table 2
Crosstab of Worst Now by Worst Ever
Worst Now
Worst
Ever
1
30
60
90
120
400
Total
1
52.9%
0.0%
0.0%
0.0%
0.0%
0.0%
52.9%
30
15.2%
1.2%
0.0%
0.0%
0.0%
0.0%
16.4%
60
5.9%
0.7%
0.5%
0.0%
0.0%
0.0%
7.1%
90
1.7%
0.2%
0.2%
0.3%
0.0%
0.0%
2.4%
120
1.8%
0.1%
0.2%
0.1%
0.6%
0.0%
2.9%
400
8.0%
0.8%
0.4%
0.5%
0.7%
8.0%
18.4%
Total
85.5%
3.0%
1.3%
0.9%
1.3%
8.0%
100.0%
Table 3
Logit Estimation of Basic Model
Explanatory Variable
California
Florida
Illinois
NY&NJ
All regions
CONSTANT
2.605***
(52.85)
1.715***
(40.40)
9.746***
(192.16)
-0.804
(2.12)
1.420***
(90.87)
SPREAD
0.272***
(11.42)
0.752***
(136.72)
0.907***
(39.61)
0.629***
(77.59)
0.573***
(251.79)
WRSTNOW
-0.00069
(2.01)
-0.00068**
(4.28)
-0.00204**
(4.76)
-0.00169***
(11.38)
-0.00115***
(26.05)
LTV
-0.0346***
(123.49)
-0.0227***
(94.34)
-0.119***
(254.70)
-0.0359***
(223.10)
AGE
-0.317***
(9.43)
-1.100***
(102.69)
-0.858***
(23.26)
1.156***
(13.31)
-0.214***
(17 .80)
AGESQ
-0.0617***
(45.27)
0.0738***
(42.09)
-0.0713***
(21.47)
-0.214***
(24.13)
-0.0561 ***
(143.01)
HSD
4.530***
(27.80)
3.608***
(29.14)
6.444***
(19.89)
2.744***
(7.28)
4.355***
(112.23)
LE
5.718***
(180.57)
4.171***
(192.12)
3.887***
(29.78)
3.125***
(53.74)
4.428***
(530.82)
-0.0339***
· (601.55)
DUM_IL
-0.474***
(33.18)
DUM_FL
0.201 ***
(12.59)
DUM_CA
0.479***
(49.09)
# of Refinancings
879
1510
362
1166
3917
# of Non-refinancings
1543
3396
1686
2313
8938
PseudoR2
0.280
0.224
0.462
0.278
0.249
Chi-Square of Model
703.08
1126.04
926.55
1000.65
3279.10
Concordant Ratio
80.5%
78.1%
92.4%
80.0%
79.3%
Notes: Explanatory variables are defined more extensively in Table 2. Pseudo R2 is defmed in
Estrella (1996). The symbols (***,**, *) indicate statistical significance at the 1, 5, and 10
percent levels.
Table 4
Logit Estimation using Alternative Measures of Incentive to Refinance
(All regions)
Variable
SPREAD
PROBIN
INMON EY
PVALU E
CONST ANT
1.420***
(90.87)
0.845***
(32.42)
1.365***
(84.16)
-8.651 ***
(671.88)
Alternative Incentive
Measure
0.573***
(251.79)
3.200***
(337.53)
1.564***
(70.68)
11.292***
(1066.78)
WRSTN OW
-0.00115***
(26.05)
-0.00130***
(33.26)
-0.00125***
(31.48)
-0.00105***
(20.43)
LTV
-0.0339***
(601.55)
-0.0350***
(630.38)
-0.0338***
(608.04)
-0.0354***
(578.99)
LE
4.428***
(530.82)
4.421 ***
(555.62)
5.460***
(927.54)
2.803***
(213.32)
AGE
-0.214***
(17.80)
-0.280***
(30.69)
-0.0218
(0.197)
-0.566***
(121.78)
AGESQ
-0.0561 ***
(143.01)
-0.0451***
(90.35)
-0.0707***
(229.05)
-0.0232***
(24.25)
HSD
4.355***
(112.23)
5.022***
(151.06)
4.249***
(108.16)
5.105***
(144.72)
DUM_I L
-0.474***
(33. I 8)
-0.495***
(36.05)
-0.485***
(35.12)
-1.390***
(224.94)
DUM_F L
0.201 ***
(12.59)
0.254***
(19.46)
0.139**
(6.12)
-0.644***
(92.10)
DUM_C A
0.479***
(49.09)
0.564***
(66.05)
0.391 ***
(32.63)
-0.263***
(12.59)
# of Refinancings
3917
3917
3917
3917
# of Non-refinancings
8938
8938
8938
8938
Pseudo R2
0.249
0.255
0.235
0.323
Chi-Square of Model
3279.10
3365.69
3088.80
4302.84
Concordant Ratio
79.3%
80.0%
78.6%
82.2%
Table 5
Logit Estimation using Alternative Measures of Credit (All regions)
Variable
WRSTNOW
WRSTEVER
WRSTCRD
TDERO G
CONST ANT
1.420***
(90.87)
1.404***
(88.79)
1.431***
(92.40)
1.407***
(89.06)
SPREAD
0.573***
(251.79)
0.566***
(245.42)
0.574***
(252.81)
0.571 ***
(249.92)
Alternative Credit
Measure
-0.00115***
(26.05)
-0.00102***
(40.17)
-0.00100***
(13.24)
-0.0663***
(18.3 I)
LTV
-0.0339***
(601.55)
-0.0333***
(579.40)
-0.0340***
(607.25)
-0.0339***
(599.62)
AGE
-0.214***
(17.80)
-0.205***
(16.32)
-0.217***
(18.40)
-0.213***
(17.68)
AGESQ
-0.0561 ***
(143.01)
-0.0564***
(144.34)
-0.0558***
(141.98)
-0.0561 ***
(143.64)
HSD
4.355***
(112.23)
4.347***
(111.83)
4.358***
(112.74)
4.362* **
(112.83)
LE
4.428***
(530.82)
4.427***
(530.01)
4.426***
(530.89)
4.438* **
(533.09)
DUM_ IL
-0.474***
(33.18)
-0.451 ***
(29.85)
-0.479***
(33.98)
-0.472***
(32.88)
DUM_FL
0.201***
(12.59)
0.208***
(13.46)
0.199***
(12.27)
0.204***
(12.96)
DUM_ CA
0.479***
(49.09)
0.467***
(46.64)
0.470***
(47.26)
0.478***
(48.81)
# of Refinancings
3917
3917
3917
3917
# of Non-refinancings
8938
8938
8938
8938
Pseudo R 2
0.249
0.250
0.248
0.248
Chi-Square of Model
3279.10
3293.12
3265.53
3271.48
Concor dant Ratio
79.3%
79.4%
79.3%
79.3%
Table 6
Logit Estimation by Credit Category (All regions)
Explanatory Variable
WRST NOW =l
WRSTNOW=400
CONSTANT
1.187***
2.245***
(56.29)
(12.99)
0.585***
0.266*
(233.60)
(3.30)
-0.0317***
-0.0439***
(470.89)
(58.26)
-0.172***
-0.273
(10.18)
(1.77)
-0.0593***
-0.0533***
(140.52)
(7.76)
4.273***
3.983**
(94.51)
(5.28)
4.445***
4.798***
(472.25)
(38.39)
-0.387***
-1.039***
(19.65)
(7.04)
0.147**
0.496**
(5.99)
(4.11)
0.417***
0.694**
(33.49)
(5.67)
# of Refinancings
3522
218
# of Non-refinancings
7488
802
Pseudo R2
0.248
0.244
Chi-Square of Model
2805.72
250.31
Concordant Ratio
79.2%
80.5%
SPREAD
LTV
AGE
AGESQ
HSD
LE
DUM_ IL
DUM_FL
DUM_CA
Table 7
Evaluating the effect of credit rating improvements
Explanatory Variable
WRSTEVER=400
CONSTANT
2.572***
(35. 13)
IMPROVE
0.000471
(2.24)
SPREAD
0.431 ***
(18.70)
LTV
-0.0487***
(148.43)
HSD
4.823***
(17.29)
AGE
-0.420***
(9.14)
AGESQ
-0.0470***
(13.53)
LE
4.824***
(84.00)
DUM_IL
-0.839***
(12.02)
DUM_FL
0.616***
(13.84)
DUM_CA
1.057***
(26.79)
# of Refinancings
494
# ofNon-refinancings
1839
Pseudo R2
0.257
Chi-Square of Model
603.18
Concordant Ratio
81.3%
IMPROVE = WRSTEVER - WRSTNOW.
1 P-value for coefficie
nt is 0.13.
Table 8
Logit Estimation using Alternative Measures of Incentive
to Refinance: Without
Lending Environment (All regions)
Variable
SPRE AD
PRO BIN
INM ONE Y
PVA LUE
CON STAN T
0.851 ***
(34.97)
-0.144
(1.06)
0.543***
(14.89)
-11.195***
(1460.73)
Alternative Incentive
Measure
1.018***
(1047.10)
5.225***
(1130.42)
3.820***
(499.92)
13.655***
(1873.37)
WRS TNO W
-0.00103***
(22.14)
-0.00129***
(34.21)
-0.00119***
(30.70)
-0.00099***
(18.67)
LTV
-0.0355***
(682.88)
-0.0368***
(723.05)
-0.0361 ***
(751.58)
-0.0363***
(616.40)
AGE
-0.0687
( 1.88)
-0.123**
(6.06)
0.337***
(50.13)
-0.446***
(77.04)
AGE SQ
-0.0454***
(96.67)
-0.0305***
(42.91)
-0.0660***
(204.46)
-0.0167***
(12.61)
HSD
3.972***
(100.42)
5.119***
(171.43)
4.055***
(112.16)
4.891 ***
(137.79)
DUM _IL
-0.323***
(16.13)
-0.352***
(19.26)
-0.259***
(10.82)
-1.509***
(265.85)
DUM _FL
0.390***
(50.27)
0.461 ***
(68.43)
0.377***
(49.42)
-0.745***
(121.69)
DUM _CA
0.769***
(138.55)
0.885***
(178.35)
0.755***
(137.96)
-0.302***
(16.60)
# of Refinancings
3917
3917
3917
3917
# ofNon-refinancings
8938
8938
8938
8938
0.206
0.211
0.155
0.307
Chi-Square of Model
2703.57
2767.28
2027.51
4081.45
Concordant Ratio
77.0%
77.6%
73.7%
81.3%
Pseudo R
2
Table 9
Probability of Refinancing: Simulated Values
SPREAD
WRST NOW= 1
WRSTNOW = 400
LTV= 60
LTV= 100
LTV= 60
LTV= 100
0
0.29
0.11
0.34
0.11
100
0.38
0.16
0.36
0.12
200
0.48
0.23
0.37
0.13
300
0.58
0.32
0.39
0.14
Notes: The simulated probabilities were obtained using models summarized in
Table 6.
"
@FedFRASER