This is a paper I wrote while pursuing my masters.

1. Decision Techniques for Managers
Term Project
Relationship of Underwriting Techniques
Between Credit Managers during the Month
Of November 2001
DRAFT
By: Mike Baker
463 North Elm Street
West Bridgewater, MA 02379

2. Background Summary
MFG Mortgage Company (“MFGMC”)* is a subsidiary of a Miami based investment-banking firm
serving the mortgage industry for the past twenty-two years.
As a direct portfolio lender originating, funding and servicing both residential and
commercial mortgages on a wholesale level, we specialize in non-conforming properties and
borrowers. Our programs are tailored for real estate investors, the self-employed,
the credit impaired borrower and the people who don't fit the traditional, or
conventional lending practices of "Fannie Mae" or “Freddie Mac” as is the case with
most lenders.
Our goal is to provide financing products that allow our clientele, the Mortgage Broker or
Lender, to service their customers as well as the convenience and confidence that other lenders
can not deliver.
Our products and programs are a unique blend of simplified underwriting and creative loan
structuring. Coupled with our devotion to customer service and understanding of the challenges
faced by our clientele in this demanding market.
Underwriting
MFGMC offers two types of programs: residential and commercial. What makes these products
unique is that they come with a stated income I stated asset feature that is very attractive to the
self-employed borrower, who often times finds it difficult to verify, or fully document, the income
they earn due to the type of business they are in. Also unique to this program is the fact that we
will accept borrowers with blemished credit histories. We understand that these loans are
inherently risky due to the diminished amount of diligence performed before consummation.
Credit Managers perform an analysis that follows what is known in the industry as “The Five
Principals of Credit or the Five C's”. They are Capacity, Capital, Character, Condition and
Credit.
Capacity - represents .the borrower ability to repay the loan request. Since our programs are
stated income this presents somewhat of a challenge in that the income presented on the loan
application is not verified. However, by reviewing the data presented against the entire
mortgage package it is possible to ascertain whether or not the income presented is realistic in
light of the mortgage file. For example, we would question the validity of the borrower’s income
if their savings balance did not support the amount listed. In other words, someone who made
$10,000 a month but only had $250.00 in a saving account and showed little debt to offset the
income would lead a Credit manager to question the validity of the income listed.
Capital - in relationship to the borrower is defined as the ability to exercise appropriate financial
controls over their business as well as the ability to build up liquidity and net worth. Again, our
product is, in addition to stated income, stated assets. However taken against the data
presented in the entire mortgage file we are able to discern the validity of the assets presented.
* Not the company’s real name.

3. Character -· this particular area is divided into two groups; 1) the borrower(s) personal credit
history, and 2) the borrower(s) personal employment history. The borrower(s) credit report will
give us a full picture of the borrowers past and present performance and will present a picture of
the borrower’s willingness to repay the debt they currently had, have and that which they are
applying for. Ability and willingness to repay a debt is at the core of any credit decision.
Condition - this relates primarily to the business environment in which the borrower operates.
While of interest it is not too much of a concern since we are underwriting the borrower and the
subject property as part of our exist strategy.
Collateral - this is paramount in making our credit decision. The subject property is that which
secures the loan. When we look at the property, through the eyes of the Appraiser, we see the
type of property we are securing not only in terms of its condition and worth but also as a means
to escape should the borrower, for whatever reason, default on the loan. We want assurance
that should we have to initiate foreclosure proceedings that we will be able to recover our
investment and whatever expense we incurred to recoup our investment.
Workflow
Loan applications arrive at MFGMC through the origination efforts of our in-house sales staff (as
well as from two outside sales reps). Sales have a history of promoting the ease of doing
business with MFGMC (it has also been a standard mantra when we were known as and as
such, it is not unusual for an application to arrive - via fax - incomplete. Our sales staff will write
up a summary sheet (what is known as the Gold Sheet) and submit the loan to a person who
will enter specific parts into our Loan Origination System. Upon completion the file will be
submitted to Underwriting for review and decision. The Credit Manager has the ability to either
approved, reject or suspend the loan. Suspension implies that there is not enough information
present to render a decision.
Issue
Since the addition of a new Credit Manager to the staff and a changeover of computerized
origination systems the Sales Department has expressed their concern at both the type of
commitment letter sent to the Mortgage Broker I Lender and what they perceive as a document
containing too many conditions prior to the final consummation of the loan. They have gone so
far as to suggest that the performance of the new Credit Manager is not in line with what they
perceive as the appropriate way to do business. For purposes of this paper we will focus on the
number of conditions that are applied to the loans prior to consummation as well as they Loan
-to - value and credit grade.
Problem Statement
By examining the data we want to address several questions I statements. The first of which:
~ Loans underwritten by new Credit Manager will generally contain more conditions than
if the loan were underwritten by the other.
~ Loans with high credit grades will not benefit from a reduction of the number of
conditions than those loans with lesser credit grades, and,

4. ~ Loans underwritten with a low loan-to-values will receive as many conditions as those
with higher loan-to-values.
The significance of these statements is that regardless of the level of risk involved the borrower
will receive the same level of scrutiny from the credit manager.
Definition of Variables Involved
As part of our analysis we are asked to define, by variable type, the sampling data presented.
The following definitions will help in determining what to apply to the data
sets:
Quantitative Variable - a quantitative variable is naturally measured as a number for which
meaningful arithmetic operations make sense. Examples: Height, age, crop yield, GPA, salary,
temperature, area, air pollution index (measured in parts per million), etc.
Qualitative Variable - a qualitative variable, sometimes known as a categorical variable, is data
that has a non-numerical value, or an alternate meaning. The Credit Managers are seen as a
qualitative variable since 1) they have an alternate meaning and 2) there are a limited number of
alternative values.
There are several variables involved in determining whether the problem statement hold true.
They are:
~ Credit Manager - for purposes of this paper, they were given a numerical designation -
1 and 2. This is a qualitative or categorical variable in that it is a representation of the
Credit managers that are involved in the study.
~ Credit Grade - the grading system employed at MFGMC consists of 5 levels; AA, A, B,
C and D. I have given them numerical weights from 1 through 5 (AA - D). This is a bit of
a toss up since, technically the numbers are artificial - thus qualitative, but quantitative in
that we are giving them numerical value.
~ Loan-to-value - this is stated as a percentage and represents the amount of the loan in
comparison to the appraised value, or in the case of a purchase, the sales price of the
property. This data represents a quantitative variable.
~ Number of Conditions - this is the total number of conditions that appear on the pre-
approval from the Credit Manager. This data represents a quantitative variable.
Type of Analysis to be performed.
Analysis will be performed on several different levels. First, we will explore the overall
population in order to determine what type of loan is being pre-approved by the Credit
Managers. Charts will be prepared that will compare credit grade to the number of conditions
applied as well as loan-to-value to the number of conditions applied. What we will be looking for
is a reduction of conditions based upon the higher credit grade and/or lower loan-to-value, for
each represents positive factors that would reduce our level of risk on a particular deal. If there
were a relationship then the data would suggest that we are more lenient when risk is lower and
more stringent when risk is higher. This would be consistent with good risk analysis and
inconsistent with the position of the Sales Manager. If the opposite were to be found, in that no

5. relationship exists between the credit grade, loan-to-value and the number of conditions applied
then the position of the Sales Manager would be validated.
Population
The population will be made up of the loans that were submitted, to the Credit Managers, for a
credit decision during the month of November. From this data we will eliminated those loans
which were either withdrawn because of factors outside of the control of the Credit Manager (i.e.
interest rate, loan limit or program disqualification), were rejected by the Credit Manager or
those that were suspended pending additional information from the Mortgage Broker I Lender.
Suspended loans are loans whereby the Credit Manager could not render a decision to either
approve or reject a loan application. Thus, the population is comprised of those loans where a
pre-approval was issued to the Mortgage Broker I Lender.
Preliminary data shows that 236 loans, representing $44,087,125.00 in new business was
originated in the month of November. Once all the filters have been put into effect the sample
population was reduced to 174, or a loss of 26.3%. A breakdown of the sample shows that the
net population is broken down into two separate sub-groups - "Gamma' and 'Sigma'. Each sub-
group representing a particular Credit Manager. The data set, Gamma, contains 85 loans,
whereas the data set, Sigma, contains 89. Since the populations is of manageable size that
allows us to work with there will be no need to apply data sampling techniques that were
discussed in the textbook and in class.
The population can also be broken down by examining the relationship between named variable
as compare against an established base. For purposes of this paper, we have established that
base the number of conditions applied to the loan population. An example, which will be
discussed in detail later, is the relationship between Loan-to-Value and the Number of
Conditions applied to the population.
Characteristics of the Population as a Whole
As stated earlier the population, which is made up of all loans that were preapproved, consists
of 174 loans. The graph presented below illustrates the population based upon credit score
distribution.
As you will note the majority of the loans underwritten and pre-approved fall into the 'A' credit
category. This tells us that the borrower, from a credit standpoint has demonstrated their
capacity for maintaining the ability to manage their finances from an historical perspective.

6. Fig. 1
Now lets' break the population down into sub-set by Credit Manager.
Fig.2

7. Since we have broken the population down by Credit Manger, we are able to note that the
general shape and slope appear to be consistent with the population as a whole, but with some
subtle differences. For instance, we can see the Underwriter Sigma has underwritten more 'A+'
and 'A" loans than Gamma. The same could be said for loans with a credit Grade of 'B'. Why is
this? Current practice is to place the loans in a central bin that is located between to their two
offices and standard operating procedure calls for the selection of mortgage applications based
on the FIFO method (first in - first out). That would lead the observer to conclude either that the
distribution of the population was subject to random error or that the distribution was skewed by
outside influences. Without a control feature in place there is no way to determine the cause of
each Credit Managers population distribution. This will require further analysis and is outside the
realm of this
paper.
The Effect of Loan-to-Value on the Number of Conditions Placed on Pre-Approved
Loans
Loan-to-value, or the amount of our loan as it compares against the value of the property is
essential in that it determines the level of overall risk associated with the loan. MFGMC
underwrites the loan with an eye towards having an acceptable exit plan should the borrower
default on the mortgage. MFGMC, through foreclosure, wants to ensure that it will be able to
recover I recoup any exposure it may suffer as a result of either a voluntary forfeiture of the
property or a formal foreclosure process.
Logic would dictate that if our overall risk is minimal due to a loan level that is low in comparison
the market value of the property, as determined through our review appraisal process, than from
an underwriting standpoint we could be more liberal with the borrower in our determination as to
whether or not our exposure is acceptable. Logic dictates that a borrower will not walk away
from a property that they have a large stake in without extreme circumstances coming into play.
On the graph presented below you will note that we have measured the effect loan-to-value
has on the overall population.
Fig. 3

8. As one can see from the graph presented above the y-axis represents the loan-to-value of the
whole population. Specifically, the population contains loans with a loan-to-value of 33.33% to
92.31% and as one is drawn to the trend line, you can see that the population has been sorted
according to LTV. As we examine the relationship between LTV and the number of conditions
on the loans, we see that there is no appreciable difference in the number of conditions applied.
In fact, upon closer examination of the right side of the graph beginning with loan number 141
and ending with loan number 176 (representing the loans with the highest LTV and thus the
greater risk) we see that the range of conditions applied is actually smaller than the range in
other portions of the graph. This is evidenced by the minimal deviation from the lower trend line.
Taken as a whole we can conclude that no discern able relationship exists between loan-to-
value and the number of conditions applied.
Now the question is if there is a discernable relationship based upon the performance of a
particular Credit Manager. We will begin by presenting a similar graph based upon the efforts of
Credit Manager: Gamma

9. Fig.4
Again, we can see that the performance of Credit Manager: Gamma is similar to that of the
population as a whole. Note the minimal deviations from the condition trend line as the loan-to-
value exceeds the 60% mark. It is also interesting to note that a smaller series of minimal
deviations occur at the 50% LTV level. As we can see from the descriptive analysis presented
on the next page the range of conditions presented within this sub-set is from 9 to 18 with the 15
representing the mean.
Descriptive Summary - Number of Conditions Applied.
Fig. 5

10. The mean, or the measure of central tendency, represents the number of times a value has
appeared in a sample set. In the case of Credit Manager: Gamma the number of conditions that
appear most frequently within the population is 15. This is a strong number in that it is supported
by the median, which is insensitive to the extreme scores (which in our sample would be 9 and
18). The numbers of loans within the sub-set, which contain 15 conditions, are 14 or 16.5%.
Since the descriptive summary was based on 95% we can say that 95% of the population lies
within 2 standard deviation units from the mean.
Again, if we note the range between the minimum amount of conditions presented (9) and the
maximum amount (18) you will see that the total dispersion within the group is 9 (18 - 9). As a
measure of variability, we can say that the standard deviation from the mean is 1.82. Alone this
information does not hold much weight but later we will compare it to the performance of Credit
Manager: Sigma.
Credit Manager: Sigma had approximately the same size in terms of units that qualified to be
included in the sample set. On the next page, we will examine a graph, similar to the one
presented earlier.
Fig. 6

11. Here we can see that the condition trend line has a slightly more distinct downward dip
as the loan-to-value increases. This would lead the review to conclude that this Credit
Manager actually places fewer conditions on a file in relation to the higher level of
exposure. Thus, one could initially conclude that the higher the LTV the likelihood is that
the loan would have fewer conditions.
In, and of itself, this would constitute an interesting finding; however product matrix calls
for higher credit grades as the LTV increases. Therefore, it is necessary for us to
examine the relationship between Credit Grade and LTV versus the No. of Conditions
applied for those loans with the best credit grade and lowest LTV against those loans
with the worst credit grade and the highest LTV.
Fig. 7

12. Above is a graphical representation of Credit Managers: Gamma and Sigma
performance as applied to loans that represent the least risk in terms of credit quality
and collateral exposure. As you will note the base sort was on credit score with the
sample set being made up of only 'A+' and 'A' loans that had loan-to-values of 50% or
less. Again, we can see that the No. of Conditions Applied trend line dips downward as
credit score decreases. We would expect to see the opposite since the 'A+', with the
lowest LTV would represent the least risk in terms of default. Looking at the third loan in
our sample set (see arrows) highlights this. This loan has an LTV of 33.33%, a credit
score of A+ and was given 19 conditions as part of the pre-approval.
Fig. 8

13. As we examine the above description summary of the sample set we can see that the mean of
the Credit Score is 1.71, with a mean LTV of 44.09% and conditions at 15.09. An interesting
feature of this sample is that the range of dispersion in the number of conditions applied, in this
case 7, is not too far from the overall population of 9.
Now we turn our attention to those loans, which represent the greatest risk in terms of exposure
and default. The graph on the next page represents loans with a credit grade of 'C' or worse and
has an LTV of greater than 50%.
Fig. 9

14. As we see from the above graph the trend line for the number of conditions applied increases in
relation to the credit grade of the loans within the sample set.
Fig. 10
Relationship between Credit Grade and the Number of Conditions Applied Up until now we have
focused our attention on the relationship between Loan-to-Value and the number of conditions
applied to the loans that make up the population. To a lesser degree, we have inserted the
effect Credit Grade has on the amount of conditions applied. Now we focus on Credit Grade as
the dominant factor.
In figures 11 through 13 we will see a graphical representation of the relationship between the
credit grade and the number of conditions applied in the overall population as well as to the
populations of the individual Credit Managers.
Fig. 11

15. Fig. 12

16. Fig. 13
Fig.14
Fig. 15

17. As you can see there from Figure 11 through 13 there appears to be little in the way of a
relationship between the credit score and the number of conditions applied. In order to prove
this it is necessary to perform a Correlational Analysis.
Correlational Analysis
Whenever we measure objects, or data, that contain more than one variable we want to assess
if an association exist between the variables, for if it does then we will be able to determine, or
assess, a cause and effect relationship. If it does not then we can say, that no relationship
exists. In our case we would be looking for a number either far to the right of zero - thus
providing evidence that there is a positive relationship, or in other words the greater the credit
grade (0 being the greatest) the higher the number of conditions. A negative relationship would
be where we would see an inverse relationship whereas the lower the credit grade (A) would
result in a higher the number of conditions. In this case, we would see a large negative number.
In the case where no relationship exists, we would see a number at, or near zero.
Fig.16
As with the overall population and that of Gamma we can see a slight negative relationship -
0.04 and 0.01 respectively. From a practical perspective, we can say no relationship exists
between the credit grade and the number of conditional applied. For Credit Manager: Sigma,
there was a slight positive relationship noted - 0.02. Again, from a practical perspective this is
also insignificant.
If we apply correlational analysis on the relationship between the loan-to-value and the number
of conditions applied, we should see the same results that were presented in the earlier figures.
Fig.17

18. As with the correlational analysis performed on the credit grade we can see here that the
relationship, both positive and negative is insignificant and from a practical sense, we can state
that no relationship exists.
At this point, it is prudent to restate our problem statement and see how the data either supports
or refutes the statement.
~ Loans underwritten by one Credit Manager will generally contain more
conditions than if the other underwrote the loan.
Technically, we can agree with this statement. In figure 5 we see that the mean for Credit
Manager Sigma is one point higher then that of Credit Manager Gamma and of the overall
population. What we need to do is assign a level of importance to the one point. We know,
through a review of figures 12 and 13, that there is little, if any correlation between the level of
conditions applied and the credit grade of the borrowers. We also know that the same lack of
correlation exist when LTV is the dominate factor (see figure 17).
~ Loans with high credit grades will not benefit from a reduction of the number
of conditions, than those loans with lesser credit grades.
This statement is true. In figures 11, 12 and 13 we did not see an appreciable decrease in the
number of conditions applied to their respective populations. We did note that for Credit
Manager Sigma the number of conditions varied to a greater degree than that of Credit Manager
Gamma.
~ Loans underwritten with a low loan-to-values will receive as many conditions
as those with higher loan-to-values.
This statement is true. In figure six we saw that there was no appreciable difference in the
amount of conditions applied based on the loan-to-value of the property.
Further when we examine Figure 7 which incorporate the three variables as they relate to loans
that are considered the lowest risk to _ we find that the amount of conditions applied is higher
that loans with great risk or exposure. In Figure 9, which represents loans that are considered
high risk we can actually see a slight increase in the amount of conditions applied where we
would expect a greater variance.
So where does this leave us?
As we said in the beginning of this paper, all lenders must focus on the principle known as “The
5 C's” - Capacity, Capital, Character, Condition and Collateral, whenever they assess the
potential risk associated with a mortgage transaction.

19. This assessment is heightened in light of the type of product we offer where it is often difficult to
evaluate the Capital and Capacity portions of the borrower and where Character may be
questionable. In reviewing many of the pre-approval forms that have been issued we note that,
for a vast majority of the cases they are requests for information.
These 'requests' fall into three categories; 1) to rectify deficiencies in the information presented,
2) to increase the quality of the loan request, and 3) to add strength - or justify- the decision to
approve the loan. It would stand to reason that if item number 1 were satisfied prior to review by
the Credit Manager then item number 2 would be minimized or eliminated and item number 3
would decrease. This is important in that where we were looking at the number of conditions
applied being a cause with the effect being a greater difficulty of consummating a loan we can
now look at the number of conditions applied as the effect with the cause being the deficient
loan submissions.
Whereas a relationship could not be established to a high degree between the credit grade,
loan-to-value and the conditions applied we may be able to find that a relationship exists
between the quality of the loan application and the conditions applied.
I recommend that a controlled experiment be done over a one month time period. This
experiment will monitor the amount of condition applied to loans submitted by a loan officer who
submits a quality application for review by the Credit Managers, if it can be shown that said loan
officer receive less conditions then the rest of the sales department then we can validate the
finding that initial quality is a dominate factor in the amount of conditions applied.