Thanks for your work on this assignment. The biggest challenge i.docx

Thanks for your work on this assignment. The biggest challenge
in your paper is that you used many words to try and make a
particular point, but in doing so, your message got lost. I would
like to see you be much more explicit in your writing to help the
reader understand your main ideas. Please see comments and
example for guidance.
Dr. Guevara
( 1.52 / 2.00) Writes a Reflective Mentoring Philosophy Which
is At Least 300 Words
Basic - Writes a limited reflective mentoring philosophy that is
between 200 and 250 words. The philosophy is underdeveloped.
Comments:
While your philosophy meets the 300-word requirement, you
still needed to address a wider variety of the recommended
aspects of mentoring.
( 2.28 / 3.00) Responds to the Required Questions Regarding the
Documentation Form Using the Text as Support
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the documentation form, minimally using the text as support.
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You suggest some interesting ideas, but more details were
needed. Additionally, you did not use the text to support your
thinking.
( 0.84 / 1.10) Applied Ethics: Ethical Self-Awareness
Basic - Defines both core beliefs and the origins of the core
beliefs.
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The paper tends to be generic and overgeneralizes situations
without recognizing ethical complexities.
( 0.84 / 1.10) Creative Thinking: Connecting, Synthesizing, and
Transforming
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You did not synthesize information in an organized way, which
prevents the reader from gaining a clear sense of analysis.
( 0.18 / 0.20) Written Communication: Control of Syntax and
Mechanics
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correctly within the body of the assignment.
Overall Score: 6.24 / 8.00
Overall Grade: 6.24
J R E R u V o l . 3 9 u N o . 2 – 2 0 1 7
W h a t C a n W e L e a r n f r o m P a s t M i s t a k e s ?
L e s s o n s f r o m D a t a M i n i n g t h e F a n n i e
M a e M o r t g a g e P o r t f o l i o

A u t h o r s S t a n i s l a v M a m o n o v a n d R a q u e l
B e n b u n a n - F i c h
A b s t r a c t Fannie Mae has been widely criticized for its role
in the recent
financial crisis, yet no detailed analysis of the systematic
patterns
of the mortgage defaults that occurred has been published. To
address this knowledge gap, we perform data mining on the
Fannie Mae mortgage portfolio of the fourth quarter of 2007,
which includes 340,537 mortgages with a total principal value
of $69.8 billion. This portfolio had the highest delinquency rate
in the agency’s history: 19.4% versus the historical average of
1.7%. We find that although a number of information variables
that were available at the time of mortgage acquisition are
correlated with the subsequent delinquencies, building an
accurate model proves challenging. Identification of the
majority
of delinquencies in the historical data comes at a cost of low
precision.
The financial crisis of 2007–2009 is considered the worst since
the Great
Depression of the 1930s (Financial Crisis Inquiry Commission,
2011). The crisis
was precipitated by the rapid decline in housing prices in the
United States. The
decline triggered a complex web of events leading to the
insolvency of a number
of financial institutions and consequent freezing in the credit
markets, which had
broad effects across the economy (Financial Crisis Inquiry
Commission, 2011).
The U.S. GDP contracted 0.3% in 2008 and another 3.1% in
2009 (Young, 2013).

The financial crisis affected many people. Nearly $11 trillion in
household wealth
vanished, and four million families lost their homes to
foreclosure (Young, 2013).
Unemployment reached 10% in the fall of 2009 (Bureau of
Labor Statistics, 2015)
and the effects of the crisis persist eight years later (Andriotis,
2015).
The rapid decline in housing prices starting in 2007 has been
attributed to the
period of irrational exuberance in the preceding years that was
fueled by easy
credit available for home financing (Shiller, 2015).
Approximately 70% of real
estate purchases in the U.S. are financed (Financial Crisis
Inquiry Commission,
2011), meaning that the buyers borrow at least a part of the
home value to facilitate
2 3 6 u M a m o n o v a n d B e n b u n a n - F i c h
the purchase. Government-sponsored enterprises (GSEs), Fannie
Mae and Freddie
Mac, were established in 1938 and 1970 respectively, to make it
easier for
individual homebuyers to afford a home (Peterson, 2008). The
GSEs buy
mortgages from banks and financial intermediaries, offering
liquidity in the
mortgage markets and making it easier for individual
homebuyers to acquire
financing.

The GSEs back nearly 60% of all individual real estate
mortgages in the U.S.
(Kan and Robotti, 2007). The agencies securitize mortgages for
sale to investors
and they also hold a substantial portfolio of mortgages on their
balance sheets.
Fannie Mae is the larger of the two agencies. In 2007, the
Fannie Mae mortgage
portfolio was valued at $403 billion, while the Freddie Mac
portfolio was valued
at $75 billion (Fannie Mae, 2008; Freddie Mac, 2008). Both
agencies were highly
leveraged and the decline in the real estate values associated
with the financial
crisis triggered a wave of mortgage defaults that effectively
bankrupted both
agencies, leading the Federal Housing Finance Agency to place
both in
conservatorship in 2008 (Financial Crisis Inquiry Commission,
2011).
The GSEs have been frequently criticized for loose underwriting
standards in the
period preceding the crisis (Wallison and Calomiris, 2009).
However, to the best
of our knowledge, no systematic analysis of the agencies’
mortgage portfolios has
been published to substantiate the criticism, and, even more
importantly, to extract
lessons for the future. We take the first steps in this task here.
We review the
Fannie Mae prime 30-year fixed-rate mortgage (FRM) portfolio
delinquencies in
the 2000–2014 period and we identify the fourth quarter of 2007
as the mortgage
portfolio with the highest rate of severe delinquencies. We

perform extensive data
mining on this portfolio to understand the extent to which data
mining techniques
can be used to build predictive models based on the
identification of systematic
patterns and salient predictors. The answers to these questions
shed light on the
systematic relations between the information variables that were
available prior to
mortgage origination and mortgage delinquencies during the
financial crisis to
inform practice and policy decisions. In the sections that
follow, we provide an
overview of the studies on mortgage delinquencies associated
with the recent
financial crisis, as well as a summary of prior work on applying
data mining
techniques to predict credit defaults. We describe the dataset in
our study and
present the data mining results. We conclude with a discussion
of our findings
and their implications for practice and policy.
u B a c k g r o u n d
Government estimates for the fourth quarter of 2015 show that
there is
approximately $13.795 trillion in outstanding mortgage
obligations in the U.S.
(Federal Reserve, 2016). Mortgage lending is an important area
of practice and
the recent financial crisis stimulated new research aimed at
understanding the
causes of the mortgage defaults (Demyanyk and Van Hemert,
2011), as well as
the evaluation of the effectiveness of the government programs
aimed at alleviating

W h a t C a n W e L e a r n f r o m P a s t M i s t a k e s ? u 2 3
7
J R E R u V o l . 3 9 u N o . 2 – 2 0 1 7
borrower hardship brought about by the financial crisis
(Schmeiser and Gross,
2016). The research on mortgage defaults is commonly
grounded in the competing
hazards model developed by Deng (1997) and Clapp, Deng, and
An (2006) who
apply econometric models to investigate the individual and
structural factors that
affect mortgage default decisions. Much of the published
research in this stream
relies on the information that was available after the mortgages
had been issued
(ex post stage). The research done by Smith (2011) exemplifies
the use of the ex
post data for analysis. Using loan-level and credit data to
evaluate mortgage
performance after origination, the author shows that declining
credit scores are
associated with the higher probability of a default, whereas
increasing credit scores
are associated with refinancing.
There has been much less recent work examining the effects of
the salient factors
that are available prior to mortgage origination (ex ante stage).
This is likely due
to the transition of many financial institutions to automated
underwriting systems

that in effect codified existing practices (Lacour-Little, Park,
and Green, 2012).
For example, the maximum loan-to-value (LTV) ratio of 80% is
required by GSEs
(Consumer Financial Protection Bureau, 2013), and it has
become a widely
accepted industry standard for qualified mortgages. Borrowers
are generally
required to obtain mortgage insurance if they borrow more than
80% of the value
of a home, thus significantly raising the cost of obtaining a non-
qualified
mortgage. Similarly, the industry has established norms for the
qualified mortgage
borrower credit score, LTV, and debt-to-income (DTI) as other
critical factors
(Consumer Financial Protection Bureau, 2014). To the best of
our knowledge,
there has not been a published systematic examination of the
critical values in
these factors that may influence mortgage defaults, particularly
in the environment
of falling real estate prices and increasing unemployment.
Further, there is some
disagreement on the critical role of these factors in lending
decisions. For example,
Archer, Elmer, Harrison, and Ling (2002) show that LTV had
little predictive value
in multifamily properties.
In this study, we examine the data that were available to Fannie
Mae prior to
mortgage origination (ex ante) and we apply data mining
techniques to explore
the systematic relations that were present at the mortgage
origination stage that

may yield clues to mortgage risks. We also examine the effects
of specific critical
values of the borrower credit score, LTV, and DTI on mortgage
defaults in the
Fannie Mae portfolio.
R e s e a r c h o n t h e M o r t g a g e D e f a u l t s A s s o c i a
t e d w i t h t h e
R e c e n t F i n a n c i a l C r i s i s
The competing hazards model, developed by Deng (1997),
Ciochetti, Deng, Gao,
and Yao (2002), Clapp, Deng, and An (2006), An and Qi (2012),
and Jiang,
Nelson, and Vytlacil (2014), is the dominant theoretical
perspective in the recent
ex post mortgage default research. In the competing hazards
model, lenders face
two interdependent risks. The first risk is that the borrower will
repay the mortgage
ahead of term, thus precluding the lender from earning interest
over the full term
of the mortgage. The second risk is that the borrower will
default on the mortgage.
Clearly, the mortgage default risk poses a greater threat because
it affects both
the unpaid principal and the future interest payments. The two
risks are
interdependent, because an early mortgage prepayment
eliminates the default risk

and a mortgage default markedly reduces the likelihood of an
early prepayment.
In the wake of the recent financial crisis, there have been
investigations into the
factors that affect mortgage default decisions that revealed
some unexpected
findings. Initial studies suggested that the decline in housing
prices produced
negative equity (outstanding mortgage balance being higher
than the value of a
home) for many borrowers. The negative equity was proposed as
the motive
underlying the rising rate of defaults (Bajari, Chu, and Park,
2008; Foote, Gerardi,
and Willen, 2008). Subsequent research on the mortgage
defaults in 2008 showed
that negative equity was not an immediate trigger for a
mortgage default, as may
be expected from a completely rational real estate investor, but
rather most
homeowners with negative equity did not default until the
negative equity reached
40% of the value of the home (Campbell and Cocco, 2011). Elul
et al. (2010)
further elucidated the relation between negative equity and the
defaults by showing
that liquidity shocks (loss of income) play a greater role in
explaining mortgage
defaults than negative equity. To add a further nuance to the
complexity of the
individual decisions underlying mortgage defaults, a survey of
mortgage borrowers
showed that individual numerical ability is negatively correlated
with mortgage
defaults after controlling for general cognitive ability, as well

as demographic and
financial variables (Gerardi, Goette, and Meier, 2010). While
the studies focusing
on the ex post default decisions offer insights on the underlying
causes of
mortgage defaults, the ex post data (e.g., a borrower’s
employment prospects
during an economic downturn) are difficult to gauge accurately
at the time of
mortgage origination and therefore, these results are difficult to
translate into
underwriting decisions.
D a t a M i n i n g S t u d i e s o f C r e d i t D e f a u l t s
Data mining, also often called ‘‘knowledge discovery in
databases’’ (KDD) refers
to algorithmic discovery of patterns in data (Fayyad, Piatetsky-
Shapiro, and
Smyth, 1996). We should note that data mining is different from
the commonly
employed econometric models that are concerned with
parameter estimation in
the context of specified models. Data mining techniques do not
specify a model
a priori, but rather focus on evaluation of how different types of
data mining
models can capture patterns of covariation in the data.
Published data mining
studies of mortgage defaults have been primarily done using
datasets originating
from outside the U.S. using different modeling techniques
(support-vector
machine, artificial neural network, decision tree, and random
forest). A seminal
study that evaluated the efficacy of different modeling

techniques using eight
credit scoring datasets from the United Kingdom and Benelux
suggest that support
vector machines and artificial neural network algorithms could
deliver the best
9
J R E R u V o l . 3 9 u N o . 2 – 2 0 1 7
results (Baesens et al., 2003). A study of mortgage defaults in
Israel found that
the decision tree algorithm offered the best accuracy in
predicting defaults
(Feldman and Gross, 2005), and a study of a synthetic German
credit dataset
based on real-world data showed that random forest algorithm
outperforms other
techniques in predicting loan defaults (Ghatasheh, 2014). In
summary, data mining
studies with international credit datasets did not produce
conclusive findings
regarding the best way to model credit defaults. This has been
confirmed in recent
work that showed that different algorithms offer better
performance across
different international credit-related datasets (Zurada, Kunene,
and Guan, 2014).
u M e t h o d
D a t a S o u r c e

Following the financial crisis, GSEs are required to make their
mortgage
origination and mortgage performance data public. We obtained
the Fannie Mae
mortgage origination and mortgage performance data covering
the period between
the first quarter of 2000 and the first quarter of 2014 directly
from the agency.
The mortgage origination dataset contains information that was
available to the
agency at the time of mortgage acquisition. These data include
individual borrower
characteristics (e.g., personal credit score), as well as
information about the
property (number of units) and the financial details of the
transaction (e.g., LTV
ratio). The complete data dictionary is provided in the
Appendix. The full dataset
includes 21.7 million FRMs with the combined principal value
of $4.186 trillion
acquired by Fannie Mae between January 2000 and March 2014.
The mortgage performance dataset contains information about
how the specific
loans performed over time after acquisition by Fannie Mae on a
monthly basis.
The dataset contains over 917 million records pertaining to 21.7
million individual
mortgages. Each record in the mortgage performance dataset
contains the Loan
Identifier field that is related to the Loan Identifiers specified
in the mortgage
origination dataset. This correspondence allowed us to relate the
mortgage
origination data to the mortgage performance data.

Industry practice shows that mortgage payers who fall behind
by three months
nearly invariantly end up in default on the mortgage obligation
(Sun, 2013). The
three-month period of delinquency is often referred to as
‘‘technical default’’ in
the banking industry (Quercia and Stegman, 1992). However, to
avoid confusion
with the actual mortgage default that requires the transfer of
legal rights to the
property and is often delayed in relation to the technical default
(Allen, Peristiani,
and Tang, 2013), we refer to a three-month delinquency as a
‘‘severe delinquency.’’
To develop the dataset for our analysis, we combined the
information containing
the predictor variables from the mortgage origination dataset
with the subsequent
delinquency status of the individual mortgages from the
mortgage performance
E x h i b i t 1 u Severe Delinquency Rates in the Fannie Mae
Portfolios in 2000:Q1–2014:Q1
dataset. We created a binary dependent variable, Severe
Delinquency, which we
assigned the value of 1 if a loan became delinquent for three or
more months and
0 otherwise.
E x p l o r a t o r y A n a l y s i s

In the first step of our analysis, we examined the historical
delinquency rates for
mortgages acquired by Fannie Mae over the period from
2000:Q1 to 2014:1. In
Exhibit 1, we summarize the severe delinquency rates for the
FRMs acquired by
Fannie Mae over this period of time. As can be seen, the
delinquency rate rose
dramatically for mortgages acquired by the agency through the
end of 2007. The
portfolio of mortgages acquired by Fannie Mae in 2007:Q4 had
the highest default
rate over the history of the agency at 19.4%. The historical
mortgage default rates
in the Fannie Mae portfolio averaged 1.7% (Peterson, 2009).
The Fannie Mae
2007:Q4 prime mortgage portfolio includes 340,537 mortgages,
with a total
principal value of more than $69.8 billion.
In the next step, we examined the historical variability of the
key factors known
to affect mortgage defaults: borrower credit scores, LTV, and
DTI (Demyanyk and
Van Hemert, 2011). The plot of the results for mortgages
acquired by Fannie Mae
in 2000–2014 presented in Exhibit 2 does not reveal any drastic
changes in the
average borrower characteristics (credit scores) or the loan
characteristics (LTV
1

J R E R u V o l . 3 9 u N o . 2 – 2 0 1 7
E x h i b i t 2 u Average Borrower Credit Score, LTV, and DTI
in the Fannie Mae Portfolios in
2000:Q1–2014:Q1
or DTI) over the period preceding 2007:Q4. There is a
significant rise in the
average borrower credit score following the financial crisis,
reflecting credit
tightening that occurred in its aftermath (Shenn, 2012), but no
obvious
deterioration in the borrower credit scores, financial leverage
(DTI) or increasing
amount of borrowing vis-a-vis the value of the properties (LTV)
are evident in
the period prior to 2007:Q4.
The exploratory analysis did not produce any obvious insights
into the potential
causes of the significant rise in the delinquency rates in the
Fannie Mae portfolio
of mortgages in 2007–2008. This raises the question of whether
there are
systematic patterns of delinquencies in the Fannie Mae portfolio
that can shed
light on the underlying causes of delinquencies and help prevent
similar events in
the future. To address this question, we performed data mining
on the dataset of
mortgages acquired by Fannie Mae in 2007:Q4, seeking to build
predictive models
able to capture patterns in the mortgage delinquencies that
occurred. Our rationale

for choosing to focus on this dataset stems from the fact that
this mortgage
portfolio had highest delinquency rate in the agency history at
19.4%, versus the
historical average of 1.7%. This dataset bears witness to the
course of mortgage
delinquencies that followed the financial crisis. Analysis of this
dataset may yield
E x h i b i t 3 u Summary Statistics
Variable Summary Statistics
Original interest rate Mean 5 6.51%, Std. dev. 5 0.37%
Original balance Mean 5 $205,327, Std. dev. 5 $100,687
Loan-to-value (LT V) ratio Mean 5 73.73, Std. dev. 5 15.73%
Combined loan-to-value (CLT V) ratio Mean 5 75.37, Std. dev.
5 16.07%
Number of borrowers Mean 5 1.52, Std. dev. 5 0.52
Debt-to-income (DTI) ratio Mean 5 39.30, Std. dev. 5 12.28
Borrower credit score Mean 5 719, Std. dev. 5 61
Co-borrower credit score Mean 5 727, Std. dev. 5 60
First time borrower Yes 5 11.1%, No 5 88.8%, Unknown 5 0.1%

Loan purpose Purchase 5 41%, Cash-out refinance 5 39.4%,
Refinance 5 19.6%
Property type Single-family homes 5 73.4%, Planned unit
development 5 15.8%, Condo 5 9.5%, Multi-
family homes 5 0.7%, Co-op 5 0.6%
Number of units 1 5 96.7%, 2 or more 5 3.3%
Occupancy Principal 5 85.6%, Investment 5 9.6%, Second
home 5 4.8%
insights to the value of information variables available prior to
mortgage
origination that can affect subsequent mortgage delinquencies.
Close inspection of the dataset revealed that the data provided
by Fannie Mae is
generally of good quality with few aberrant records. We
removed 530 records that
were missing the primary borrower credit score and 99 records
that had $0 original
balance (these records were a subset of the records missing the
credit score). After
cleaning, the dataset contains 340,007 records, with a combined
principal value
at origination of $69.8 billion. Exhibit 3 below provides the
summary statistics
for the individual variables in the data.
P r e d i c t i o n M o d e l s
Loan delinquency prediction is a binary classification problem.
Prior research has
shown that different data mining algorithms perform better on
different loan

datasets (Zurada, Kunene, and Guan, 2014). We evaluated six
data mining
algorithms for their ability to predict loan delinquencies in our
sample: logistic
regression, decision tree, random forest, boosted trees, support-
vector machines,
and artificial neural networks. We briefly discuss each of the
modeling techniques,
3
J R E R u V o l . 3 9 u N o . 2 – 2 0 1 7
as well as their merits and weaknesses. In the discussion of
merits and weaknesses,
we specifically focus on the interpretability of individual
models, because
mortgage delinquency prediction may expose the agencies to
legal challenges
requiring the agencies to justify their decision to accept or
reject a specific
mortgage. For this reason, an ideal model would provide full
transparency into
the mortgage acceptance / rejection decision.
Logistic regression is a generalization of the linear regression
models. This
modeling technique relates the predictor variables to the log of
odds of an event,
and it estimates the parameters using the maximum likelihood
approach for the
log of odds function:

Log(Odds) 5 b 1 b x 1 b x 1 b x ,0 1 i 2 2 i i
where xi [i 5 1, 2, . . . , n] are variables that influence the odds
of the outcome
of interest.
In a binary classification problem with a balanced sample, odds
greater than 50%
would mean that the event will occur, and odds below 50%
would mean that the
event would not occur. Logistic regression is a popular
modeling technique
frequently used in practice (Hosmer and Lemeshow, 2004),
although the method
is not without limitations. For example, a linear relation is
assumed between the
predictors and the log of odds of an event occurrence in a linear
regression.
Logistic regression provides visibility into the significance of
individual predictors
in the model and the sign (positive or negative) associated with
each; however,
the specific correlation coefficients may be difficult to
interpret. Further, the
logistic regression model is sensitive to missing values. Data
imputation is
required to retain cases with missing values and this can lead to
biased parameter
estimates, particularly where the missing values are non-random
(Allison, 2000).
Decision trees is a classification algorithm that recursively
separates observations
in branches to build a tree for the purpose of improving
prediction accuracy
(Safavian and Landgrebe, 1991). For classification problems,

the branching points
are based on the improvement in one of the commonly used
information gain
metrics (Entropy or Gini index), which capture the improvement
in homogeneity
of each subset of data after the split. The branching points
identify the variables
and the corresponding thresholds that are used for the data split.
The decision tree
models are transparent and easy to interpret; however, the
decision tree algorithm
is greedy and therefore it may not capture the optimal global
partitioning of the
data (Safavian and Landgrebe, 1991). While the decision tree
algorithm is a
powerful modeling technique, it has a known weakness in
potentially over-fitting
the training data (Bradford et al., 1998). A number of decision
tree-based
modeling techniques have been developed that leverage the
decision tree ability
to capture non-linear relations in the data and also safeguard
against over-fitting.
Random forest is an example of an ensemble modeling
technique that combines
the predictions of multiple decision trees to achieve better
overall performance
(Breiman, 2001). The random forest algorithm builds multiple
tree models by
randomly selecting a subset of predictor variables and a subset
of data to build

each tree. The algorithm sets aside an ‘‘out-of-bag’’ subsample
of data, continually
evaluates the incremental improvement in the prediction
accuracy with the addition
of each new tree, and it only retains the trees that to the overall
model accuracy.
Boosted trees is another tree-based ensemble modeling
technique (Bauer and
Kohavi, 1999). Similarly to the random forest models, the
boosted trees algorithm
involves the construction of multiple tree models and
aggregating the predictions
across the collection of models. The distinction of the boosted
trees approach to
modeling is in improving the prediction accuracy by increasing
the weights of
misclassified cases in each modeling round. By focusing on the
misclassified
cases, the boosted trees algorithm can develop better overall
results. By virtue of
being ensemble techniques, both the random forest and boosted
trees models offer
limited visibility into how individual predictors affect the
dependent variable, but
the models can be used to identify the relative importance of the
individual
predictors to the overall model quality.
In addition to the models discussed above, which provide at
least a degree of
transparency into the effects of individual factors, we also
include two ‘‘black
box’’ modeling techniques in our analysis: support vector
machine (SVM) and
artificial neural networks (ANN). The SVM modeling technique

applies
mathematical (kernel) functions to transform the input feature
space to identify a
boundary that can help separate the two classes of outcomes for
a binary variable
(Amari and Wu, 1999). SVMs can utilize different kernel
functions. Following
the evaluation of different kernels, we found that the Laplacian
transform (Qi,
Tian, and Shi, 2012) produced the best results for the SVM
family of models with
our dataset and this is the kernel function for which we report
the SVM results.
The ANN is another modeling algorithm that we utilized. ANNs
are an advanced
modeling technique that evolved from research aiming to model
the function of
biological neural networks (Yegnanarayana, 2009). ANNs are
typically comprised
of several layers of interconnected nodes. The input layer nodes
correspond to the
individual predictor variables. The input nodes are connected to
the inner layer
nodes, which can be programmed to perform different types of
non-linear
transformations (e.g., a logistic function), and which, in a
binary classification
problem, ultimately connect to a single output node. The
parameters affecting the
individual connections between the nodes in the neural networks
are ‘‘learned’’
through training by utilizing a back-propagation function, which
captures the
errors on the output node and back-propagates the parameter
adjustment
throughout the network to achieve better fit over the training

rounds.
M o d e l P e r f o r m a n c e E v a l u a t i o n
The performance of the binary classification algorithms is
commonly assessed by
splitting the data, using a part of the data (training set) to build
the models and
5
J R E R u V o l . 3 9 u N o . 2 – 2 0 1 7
E x h i b i t 4 u Classification Matrix
Predicted
False True
Actual
False True negatives (TN) False positives (FP)
True False negatives (FN) True positives (TP)
then assessing the model performance on the remaining data
(test set) using the
classification matrix (Exhibit 4) and the derived metrics. In this
study, we relied
on three model performance measures: true positive rate,
positive predictive value
rate, and accuracy.

True positive rate 5 TP / (TP 1 FN )
Positive predictive value 5 TP / (TP 1 FP )
Accuracy 5 (TP 1 TN ) / (TP 1 TN 1 FP 1 FN )
where TP denotes true positives, TN denotes true negatives, FP
denotes false
positives, and FN denotes false negatives.
The true positive rate reflects the model’s ability to correctly
identify severe
delinquencies that occurred vis-à-vis false negative errors,
which indicate
mortgages that are predicted to not fall into delinquency, but
did. A model with
the higher positive predictive value will make fewer false
positive errors. The
positive predictive value reflects the model’s ability to correctly
predict true
positives, which indicate mortgages that become delinquent,
vis-à-vis false
positives. The false positive signal from a model would imply
that a mortgage is
likely to become delinquent and such errors would likely lead to
denial of credit
to misclassified borrowers.
It is important to note that the true positive rate of the
individual models is a
critical measure of the model’s performance for predicting loan
delinquencies. A
false negative, a mortgage delinquency that is not predicted
accurately at
origination, exposes the underwriter to the potential loss of a
part of the principal.

The average value of a loan in the 2007:Q4 portfolio was
approximately $205,000.
Prior research on actual losses by mortgage underwriters in case
of a default (loss
given default, LGD) suggests that the LGD can reach 50% on
residential
mortgages (Park and Bang, 2014). Most of the severe
delinquencies in our dataset
occurred within 18 months of the mortgage origination when
less than 5% of the
principal had been repaid. A conservative estimate of 25% LGD
would imply that
every false negative would carry a cost of at least $50,000
without accounting for
the costs associated with the asset recovery. In recognition of
the true positive rate
as the key metric for the evaluation of model performance, we
optimized our
models for the maximum true positive rate, while setting the
positive predictive
value threshold to 30%.
We followed the recommended practice of k-fold cross-
validation (Breiman,
Friedman, Olshen, and Stone, 1984) for the evaluation of the
individual algorithm
performance. The k-fold cross-validation consists of three steps:
1. Dividing the dataset into k disjoint subsets. Subsets are
stratified for the
dependent variable to reduce model accuracy estimation bias

(Kohavi,
1995).
2. Training the algorithm on k 5 1 subsets while withholding
one of the
subsets for model performance evaluation. The process is
repeated
withholding each of the subsets.
3. The cross-validation model performance results are averaged
to produce
an estimate of the classifier accuracy.
Research suggests that 10-fold cross-validation is generally
sufficient to establish
model performance estimate (Breiman, Friedman, Olshen, and
Stone, 1984;
Kohavi, 1995).
We used R (64-bit, version 3.1.3) software to build and evaluate
the data mining
models (Anon, 2015). R includes an implementation of the
general linear models
(glm) in the default distribution. We used this implementation
for the logistic
model in our analysis. We used the following packages to build
the respective
models: rpart (decision tree), randomForest, ada (boosted trees),
kernlab (SVM),
and nnet (neural networks).
u R e s u l t s
All models had difficulty with the accurate prediction of
mortgage delinquencies.
The ANN neural network algorithm showed the best results,
accurately predicting

83.4% of delinquencies on average. The random forest model
performed the worst,
accurately predicting just 58.9% of the delinquencies. The
results of 10-fold cross-
validation for each of the modeling techniques are given in
Exhibit 5.
It is also important to note that while we optimized the models
for the true positive
rate, we sacrificed the positive predictive value, a.k.a. the
precision of the models.
The average precision of the ANN is only 36.7%. This means
that roughly two
out of three predicted severe delinquencies will be false alarms.
These (false
positive) errors would imply opportunity costs (missed
opportunity to earn
interest) and they would also potentially affect availability and
cost of credit to
the population of misclassified borrowers.
7
J R E R u V o l . 3 9 u N o . 2 – 2 0 1 7
E x h i b i t 5 u Model Performance
True Positive Rate Positive Predictive Value Accuracy
Logistic regression 82.7% 5 0.8% 36.9% 5 2.4% 59.9% 5 0.6%
Decision tree 70.3% 5 2.7% 32.9% 5 2.9% 66.5% 5 0.7%

Random forest 58.9% 5 0.5% 30.9% 5 0.9% 66.5% 5 0.1%
Boosted trees 64.6% 5 1.5% 42.8% 5 2.0% 69.2% 5 0.7%
SVM 82.8% 5 3.2% 36.5% 5 2.6% 59.3% 5 0.5%
Neural network 83.4% 5 2.0% 36.7% 5 2.9% 59.4% 1 0.5%
E x h i b i t 6 u Feature Importance for the Model’s True
Positive Rate
Neural Network SVM Logistic Regression
Feature Score Feature Score Feature Score
Co-Borrower Credit Score 0.160 Credit Score 0.078 Co-
Borrower Credit Score 0.106
Credit Score 0.117 Loan Purpose 0.034 Credit Score 0.073
DTI 0.063 DTI 0.029 CLT V 0.063
LT V 0.043 LT V 0.028 Mortgage Insurance 0.041
Original Balance 0.032 Seller 0.025 DTI 0.036
Seller 0.024 CLT V 0.025 LT V 0.027
Original IR 0.021 Num Borrowers 0.022 Original Balance 0.014
Mortgage Insurance 0.009 Channel 0.016 Original IR 0.012
Property Type 0.008 Original IR 0.016 Seller 0.005
CLT V 0.007 Original Balance 0.016 First Time Borrower 0.001

Although all the models performed poorly, it is still possible to
gain insights on
the influence of individual predictor variables on the model
accuracy. In the next
step, we examined the effects of individual variables on the
accuracy of the models
using the feature permutation-based method (Altmann, Toloşi,
Sander, and
Lengauer, 2010). This method relies on withholding individual
predictors and
iteratively examining the effect of withholding the information
on the model
positive predictive rate using the test data. The feature
importance scores were
estimated over several trials. The scores did not change
significantly over the trials
and the representative scores are provided because the focus is
on the relative
feature importance as opposed to the specific scores associated
with the individual
features. Exhibit 6 provides the results.
E x h i b i t 7 u Number of Records and Default Propensity vs.
Credit Score
The borrower and co-borrower credit scores, LTV, CLTV, and
DTI are well known
predictors of mortgage defaults (Demyanyk and Van Hemert,
2011). To further
explore the relation between these variables and subsequent
delinquencies, we
binned the records and visualized the number of records

corresponding to each
bin (the height of the bars in Exhibits 6–9) and the default
propensity. Exhibits
10–12 reflect the data underlying the visualizations.
The visualizations reveal that the relations between the
borrower credit score, LTV,
and DTI are not linear. For example, the LTV visualization
reveals that the default
rate generally rises with the increasing LTV; however, the
mortgages with LTVs
of 75%–80% actually had lower delinquency rates (16.88%)
versus mortgages
with LTVs of 71%–75% (20.13%). The difference is significant
at p , 0.001 (Z
score 5 13.996). The default rates rise dramatically for the
mortgages with LTVs
above 80% (30.8%). This difference is also statistically
significant (Z 5 42.2,
p , 0.001).
To identify the critical values of these factors in influencing
mortgage
delinquencies in the Fannie Mae dataset, we constructed a
decision tree model
focusing on these variables. The resultant decision tree is shown
in Exhibit 13.
9
J R E R u V o l . 3 9 u N o . 2 – 2 0 1 7

LTV
The decision rules emergent from the CART decision tree
algorithm reveal that
the credit score is the key information factor that is predictive
of severe
delinquencies. While the overall dataset has a 19.4% severe
delinquency rate,
FRMs issued to borrowers with credit scores greater or equal to
704 had only
11% severe delinquency, compared to 32% severe delinquency
rate for borrowers
with credit scores below 704. The severe delinquency rate
increases to 38% for
borrowers with credit scores below 666. The decision tree also
reveals the layering
of risks. LTV reflects the borrowed amount in relation to the
value of the property;
for borrowers with credit scores between 666 and 704, LTV is
the key factor that
is correlated with severe delinquencies. Borrowers with credit
scores between 666
and 704, who borrowed more than 84% of the value of the
property, exhibited
the severe delinquency rate of 34%. For borrowers with credit
scores below 666,
DTI becomes a significant predictor of mortgage delinquencies;
41% of borrowers
with credit scores below 666 and DTI greater than 34 ended up
in severe
delinquency.
Exhibits 14 and 15 provide further evidence of risk layering
between the individual
borrower credit score, individual level of indebtedness reflected
in DTI and the

equity held in the property at origination (LTV).
DTI
u D i s c u s s i o n
In this study, we examined whether data mining techniques can
capture systematic
patterns of mortgage defaults in the Fannie Mae FRM portfolio.
We focused on
the mortgages acquired by the agency in the fourth quarter of
2007. We found
that the borrower credit score, LTV, and DTI were the most
significant factors
correlated with the subsequent severe delinquencies. The list of
factors identified
as significant predictors of mortgage delinquencies in our
models suggests that
mortgage underwriters, including Fannie Mae, are collecting
information that can
be useful in predicting future delinquencies. We found that
certain threshold values
of credit scores, LTV, and DTI are associated with significantly
higher delinquency
rates in the Fannie Mae portfolio from 2007:Q4. Borrower
credit scores below
704 were a strong predictor of serious delinquency. About a
third (32%) of
borrowers with credit scores below 704 were in technical
default on their
mortgages in our dataset, compared with less than 11.5% of
borrowers with credit

scores above 704. The size of the mortgage in relation to the
property value (LTV)
was also a significant predictor, but the relation between LTV
and severe
1
J R E R u V o l . 3 9 u N o . 2 – 2 0 1 7
E x h i b i t 10 u Number of Records and Delinquency Rate by
Credit Score
Credit Score Avg. Delinquency # of Records
460 58.3% 24
480 56.8% 37
500 58.0% 143
520 57.9% 330
540 51.2% 772
560 42.8% 1,440
580 41.7% 6,467
600 40.1% 10,935
620 38.9% 19,167
640 34.9% 25,952

660 29.7% 30,853
680 24.3% 32,528
700 20.5% 33,985
720 16.9% 31,168
740 13.1% 34,759
760 8.9% 41,448
780 5.8% 44,034
800 3.8% 25,441
820 2.2% 502
840 0.0% 4
delinquency is not linear. Borrowers putting down payments of
less than 16%
were much more likely to become severely delinquent on their
obligations (29%
severe delinquency rate) than borrowers with 75 , LTV # 80, for
whom the
severe delinquency rate was 16.9%. However, the delinquency
rate was also higher
(20.1%) for borrowers with 70 , LTV # 75, indicating non-linear
relations
between the predictors and the target. We also found evidence
of layered risks.
Overleveraged borrowers with relatively low credit scores
(,666), for whom the
combined monthly debt obligations exceeded 34% of their gross

monthly incomes,
were also much more likely to fall behind on their mortgage
payments (41%
severe delinquency rate). Spotty prior credit history, limited
personal financial
investment in the property, and excessive borrowing against
income make perfect
sense as predictors of mortgage delinquency. Following the
financial crisis, Fannie
Mae announced tighter credit requirements for qualified
mortgages (Reuters, 2008;
Shenn, 2012); the agency raised the minimum required credit
score from 580 to
640 and raised the minimum required down payment to 20%.
LTV
LT V Delinquency Rate Number of Records
0 0 9
5 16.0% 169
10 4.0% 428
15 4.9% 956
20 5.2% 1,741
25 5.1% 2,786

30 5.5% 3,815
35 6.7% 5,182
40 7.5% 6,931
45 10.0% 8,919
50 11.5% 11,975
55 13.9% 13,850
60 16.4% 17,987
65 18.8% 23,560
70 22.6% 31,756
75 20.1% 39,748
80 16.9% 95,214
85 30.8% 16,650
90 28.7% 35,520
95 28.3% 22,811
The surprising finding from our analysis was that it is very
challenging to build
an accurate prediction model for mortgage delinquencies using
the data from the
Fannie Mae portfolio. Model optimization for the true positive
rate was only
possible at a significant cost to the precision of the model. Our
best model, a

neural network, had an 83.4% average true positive rate;
however, the average
precision of the model was only 36.7%. Operationalizing the
model would carry
significant opportunity costs for the agency because it would
likely lead to refusal
to underwrite a significant number of mortgages. The false
positive errors would
also imply a societal cost as the rejected applications would
likely preclude an
opportunity to own a home.
This brings up the next question: How can we improve the
quality of the data
mining models to predict severe mortgage delinquency? One
possible reason for
the challenges that we encountered in building the models could
be information
insufficiency. We may be missing key information that could
help us build better
3
J R E R u V o l . 3 9 u N o . 2 – 2 0 1 7
DTI
DTI Delinquency Rate Number of Records
5 12.5% 1,506
10 8.9% 4,977

15 8.9% 12,054
20 10.4% 22,501
25 12.1% 33,340
30 15.1% 42,256
35 18.7% 47,355
40 21.8% 48,611
45 24.2% 44,192
50 26.3% 33,464
55 25.5% 22,082
60 26.8% 17,017
models. Collection of additional information at the time of
mortgage origination
would offer a possible solution. Prior research offers some
support for this
proposal. Credit default analysis on a dataset from Israel, for
example, identified
the level of education and the type of professional employment
as the key
predictors of credit defaults (Feldman and Gross, 2005).
Therefore, collection of
additional information at the time of mortgage origination,
including the education
level and professional employment, may help improve the
quality of the models.

An alternative and more likely explanation for the challenges
that we encountered
in building an accurate prediction model using the Fannie Mae
mortgage portfolio
dataset is that the financial crisis served as an exogenous cause
of mortgage
defaults. In this scenario, the information that was available at
the time of
mortgage origination simply would not be helpful in accurately
predicting the
consequences of a crisis for the portfolio. The exogenous cause
explanation would
imply that there was an external shock to the system that
affected the base rate
of mortgage delinquencies, as well as the nature of the
deterministic and
probabilistic relations among the data available at origination.
Exogenous causes
are often mentioned in discussions of macroeconomic models
(e.g., models of
unemployment) (Zivot and Andrews, 2002). The Great
Depression and the oil
crisis of the 1980s are classic examples of exogenous events
that caused
disruptions of linkages among macroeconomic factors and make
it difficult to
build accurate econometric models spanning these periods of
history. The recent
financial crisis had its origins in the rising defaults among the
subprime borrowers
that quickly spread to the prime mortgage borrowers and were
amplified through

E x h i b i t 13 u Decision Tree Model Showing Critical Credit
Score, LT V, and DTI Levels
the broader economic downturn (Financial Crisis Inquiry
Commission, 2011). This
exogenous shock reshaped the relations between the information
used for borrower
risk evaluation at the mortgage underwriting stage and
subsequent defaults.
The recent financial crisis had a number of causes. The issuance
of 5 / 1, 3 / 1, and
2 / 1 adjustable-rate mortgages (ARMs) and their securitization
were among them
(Financial Crisis Inquiry Commission, 2011). ARMs that carry a
low introductory
interest rate, which resets after the initial 2-, 3-, or 5-year
period, gained in
popularity in 2005–2006. Many of the ARMs were issued to
subprime borrowers.
The problems of subprime lending were also exacerbated by so-
called ‘‘liar’’ loans
(LaCour-Little and Yang, 2013) and corporate governance
issues (Peni, Smith, and
Vähämaa, 2013). As the interest rates on these mortgages began
to reset in 2007,
the mortgage payments for the borrowers grew drastically,
triggering defaults
(Mayer, Pence, and Sherlund, 2009). Although ARMs
constituted a relatively
5

J R E R u V o l . 3 9 u N o . 2 – 2 0 1 7
E x h i b i t 14 u Severe Delinquencies vs. Credit Score and
LTV
small part of the overall mortgage market in 2007, the defaults
on these mortgages
produced a domino effect (Sherlund, 2010). As the properties
bought with ARMs
went into foreclosure they triggered rapid general declines in
property values, as
well as a series of events that affected all sectors of the
economy, including prime
mortgage borrowers. The economic downturn led to many
people losing their jobs
and the loss of steady income triggered many delinquencies on
the traditional
fixed-rate mortgages that were a part of the Fannie Mae
portfolio.
The exogenous cause explanation for the failure of data mining
techniques to
accurately capture the patterns of defaults in the dataset imply
that economic
shocks will drastically increase mortgage default rates even
among well-qualified
borrowers. We examined the default rates among the best-
qualified borrowers,
those with credit scores above 760 for whom the historical
delinquency rate across
different types of credit (consumer loans, credit cards,
mortgages, etc.) is less than
2% (Fair Issac Corporation, 2015). We find that the delinquency
rate for this group
was 5.5%–6.4% for the mortgages acquired by Fannie Mae in

2007. The 3X
increase in the severe delinquency rate among the best qualified
borrowers
E x h i b i t 15 u Severe Delinquencies vs. Credit Score and DTI
provides supporting evidence for the role of the financial crisis
as an exogenous
shock.
The current economic climate has largely pushed the concerns
about the stability
of the housing market to the back of everyone’s mind and the
GSEs have resumed
some of the practices that contributed to the financial crisis. For
example, the
agencies now approve high LTV mortgages that require the
borrowers to put just
3% down (Fannie Mae, 2015). The practical implication of the
financial crisis
being an exogenous shock is that even if Fannie Mae restricted
mortgage
purchases to the most qualified borrowers, the agency would
have still faced
bankruptcy. In this scenario, a significant reduction in the
financial leverage of the
agency would be necessary for the agency to weather the next
financial crisis. The
recent financial crisis brought the Dodd-Frank reform to the
banking sector,
effectively reducing financial average among the largest banks
from 30:1 before

the crisis to less than 10:1 after the reform (Acharya, Engle, and
Richardson,
2012). A similar reform would be required to safeguard GSEs
from bankruptcy
going forward.
u C o n c l u s i o n
In this study, focusing on the data available prior to mortgage
origination, we
examined the predictive value of several data mining techniques
using the Fannie
7
J R E R u V o l . 3 9 u N o . 2 – 2 0 1 7
Mae mortgage dataset from the fourth quarter of 2007, which
had the highest
delinquency rate in the agency’s history. Our data mining
efforts revealed that the
borrower credit score, and loan-to-value and debt-to-income
ratios were the most
important predictors of mortgage delinquencies. The ANN was
the best
performing model in our analysis. However, the ANN model
identified the
majority of severe delinquencies that occurred only at the
expense of a high rate
of false positives. The most likely reason for the predictive
model shortcomings
is that the financial crisis served as an exogenous shock, the
effects of which
cannot be accurately modeled using the data available at

mortgage acquisition.
This result suggests that Fannie Mae’s current efforts to reduce
future
delinquencies by tightening mortgage qualification requirements
may prove
insufficient. Analysis of the Fannie Mae mortgage portfolio
from 2007:Q4 shows
that 16.7% of mortgages issued to borrowers with credit scores
above 640 were
severely delinquent in our dataset compared to the 1.7%
historical delinquency
rate. These results provide empirical support for the calls to
reform the housing
GSEs (Spahr and Sunderman, 2014).
u A p p e n d i x
uu D a t a D i c t i o n a r y f o r t h e F a n n i e M a e M o r t g
a g e
O r i g i n a t i o n D a t a s e t
Loan Identifier Unique ID Assigned to Each Mortgage
Channel The variable specifies the mortgage origination
channel. The
mortgages were either originated directly by retail banks,
through
a broker, or acquired from a different origination party after the
mortgage was issued.
Seller name The entity that delivered the mortgage loan to
Fannie Mae. For
legal reasons, we excluded this variable from our models.
Original interest rate The original interest rate on a mortgage
loan as identified in the

original mortgage loan documents.
Original unpaid principal
balance
The original amount of the mortgage loan as indicated by the
mortgage documents. This is the amount of money being
borrowed by the homebuyer to finance the purchase of the
home.
Original loan term The number of months in which regularly
scheduled borrower
payments are due.
Origination date The date of the loan.
First payment date The date of the first scheduled mortgage
loan payment to be
made by the borrower under the terms of the mortgage loan
documents. The first payment date is typically 30–45 days after
the mortgage origination date. This variable was not used in the
models as it closely mirrors the Origination date.
Loan Identifier Unique ID Assigned to Each Mortgage
Original loan-to-value (LT V) A ratio calculated at the time of
origination for a mortgage loan.
The original LT V reflects the loan-to-value ratio of the loan
amount secured by a mortgaged property on the origination date
of the underlying mortgage loan. A higher LT V reflects that the
homebuyer is borrowing a higher percentage of the property
value.

Original combined loan-to-
value (CLT V)
A ratio calculated at the time of origination for a mortgage loan.
The CLT V reflects the loan-to-value ratio inclusive of all loans
secured by a mortgaged property on the origination date. CLT V
accounts for any secondary mortgages that the property owner
may take out using the property as the collateral.
Number of borrowers The number of individuals obligated to
repay the mortgage loan.
Debt-to-income ratio A ratio calculated at origination derived
by dividing the
borrower’s total monthly obligations (including housing
expense)
by his or her stable monthly income.
Borrower credit score A numeric value used by financial
services industry to evaluate
the quality of borrower credit. The score in the Fannie Mae
portfolio is based on the ‘‘classic’’ FICO score developed by
Fair
Isaac Corporation.
First-time home buyer
indicator
The indicator denotes whether or not a borrower or co-borrower
qualifies as a first-time homebuyer. An individual is considered
as
a first-time homebuyer if he / she 1) is purchasing the property;
2) will reside in the property; 3) had no ownership interest in a
residential property during three-year period preceding the date
of the purchase of the property.

Loan purpose An indicator that denotes if a mortgage is used for
either property
purchase, refinancing or refinancing with a cash-out option.
Property type The field denotes whether the property is a
cooperative share,
condominium, planned urban development, single-family home
or
a manufactured home.
Number of units The number of units comprising the related
mortgaged property.
Occupancy status The indicator denotes how the borrower used
the mortgaged
property at the origination date of the mortgage (principal
residence, second home or investment property).
Property state A two-letter abbreviation indicating the state
within which the
property securing the mortgage loan is located.
ZIP (3-digit) The code designated by the U.S. Postal Service
where the subject
property is located.
Mortgage insurance
percentage
The percentage of mortgage insurance coverage obtained for an
insured conventional mortgage loan and used in the event of
default to calculate the insurance benefit.
Co-borrower credit score A numerical value used by the
financial services industry to

evaluate the quality of borrower credit. The score in the dataset
refers to the ‘‘classic’’ FICO score developed by Fair Isaac
Corporation.
9
J R E R u V o l . 3 9 u N o . 2 – 2 0 1 7
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199Cityscape: A Journal of Policy Development and Research •
Volume 19 Number 2 • 2017
U.S. Department of Housing and Urban Development • Office of
Policy Development and Research
Cityscape
Commentary: What Can We Learn
From Government Attempts To
Modify the Allocation of Mortgage
and Consumer Credit in the
United States?
Anthony Yezer
George Washington University

Introduction
This commentary considers the Community Reinvestment Act
(CRA) in historical context. CRA
reflects one of many government attempts to influence the
allocation of mortgage and consumer
credit, but many of these interventions have had adverse
outcomes. This commentary is written
in the hope that those who are aware of history will stop
repeating it. Specifically, I argue that
lawmakers have been too quick to succumb to political
pressures and have failed to follow basic
economic principles when creating mortgage market policies.
As a result, expanding access to
credit has been prioritized over the safety and soundness of the
housing and mortgage markets.
The Legacy of Past Housing Policies
Until the 1990s, restrictions on banks limited their geographical
expansion. The policy of not al-
lowing interstate branching was formalized in the McFadden
Act1 of 1927 and strengthened by the
Bank Holding Company Act2 of 1956. Although these
restrictions seem absurd today, they had im-
portant implications for mortgage finance. Because they could
not branch across state lines, banks
held local mortgages in their portfolios and were forced to take
substantial geographic risk that
could not easily be diversified away. The lack of portfolio
diversification was magnified because
deposits were also local. As a result, a downturn in the local
economy could result in bank failure,
because customers would withdraw deposits and loan
performance would deteriorate. Banks could
not market the poorly performing local loans, and liquidity
problems would turn into insolvency.

1 Pub. L. 69–639.
2 Pub. L. 84–511, 70 Stat. 133.
200
Yezer
The CRA Turns 40
Given the political unpopularity of branching, the answer to
geographic risk diversification was
to get mortgages out of the portfolios of the depository
institutions that underwrote and endorsed
them. The National Housing Act3 of 1934, which established
the Federal Housing Administration
(FHA) and introduced mortgage insurance to make mortgages
more marketable, accomplished this
goal. In the beginning, insurable mortgages had a maximum
term to maturity of 20 years and a
maximum loan-to-value (LTV) ratio of 80 percent, based on
strict appraisals and required property
inspections. The founding of the Federal National Mortgage
Association (Fannie Mae) in 1938 to
purchase both FHA-guaranteed and conventional mortgages was
the second answer to the problem
of diversifying geographic risk. Fannie Mae enabled housing to
be financed by ultimate lenders who
held a well-diversified portfolio. In many cases, banks, which
could not diversify geographically due
to statutory limits, purchased the mortgage-backed securities
back from Fannie Mae.
The prohibition against branching provided a justification for
federal involvement to diversify

geographic risk, but it introduced other problems. Initially, FHA
Section 203(b) mutual mortgage
insurance was seen as a success. FHA was designed to protect
homebuyers and taxpayers, but
the limits on both maturity and LTV ratio crept upward as house
prices rose and memories of the
Great Depression faded. Redlining—which was designed to
manage FHA’s risk by avoiding neigh-
borhoods where house prices were likely to decline—came
under attack for discriminating against
minority neighborhoods.
Yet another policy was added in response: Section 235 of the
Fair Housing Act4 of 1968. It relaxed
lending criteria, reduced property inspection requirements
(increasing the risk that mortgages
were made on flawed units), and provided interest rate
subsidies. The next 5 years were marked
by scandal; more than 240,000 units went into default, resulting
in a foreclosure rate five times
that of FHA insurance. The effects of dilapidated and
abandoned structures on neighborhoods
turned residents against FHA and raised demands that the
private sector become more involved in
financing higher-risk loans. In my opinion, the primary impetus
for passage of the Home Mortgage
Disclosure Act5 (HMDA) of 1975 and the Community
Reinvestment Act6 (CRA) of 1977 was the
complete failure of Section 235, which was in turn a reaction to
deficiencies in FHA Section 203(b)
mutual mortgage insurance.
Given the failures of these FHA programs, public policy turned
to the thrift industry to provide
mortgage credit to lower-income borrowers and, again, set up
policy conditions that worked

against sound economic principles. Thrifts were given valuable
competitive advantages. First,
Regulation Q interest-rate ceilings were set to keep the cost of
capital for thrifts artificially low but
high enough to give them an advantage over commercial banks
in attracting small savers. Second,
restrictions on branching, particularly convenience and
advantage regulations, gave thrifts some
degree of local market power. However, a combination of rising
interest rates and financial innova-
tion that provided small savers access to market returns through
Money Market Mutual Funds
prompted disintermediation and destroyed the thrift business
model. Economists had forecast
these effects, but regulators ignored them.
3 Pub. L. 73–479.
4 Pub. L. 90–284, 82 Stat. 73.
5 Pub. L. 94–200, 89 Stat. 1124.
6 Pub. L. 95–128, 91 Stat. 1147, Title VIII.
Commentary: What Can We Learn From Government Attempts
To Modify
the Allocation of Mortgage and Consumer Credit in the United
States?
201Cityscape
The thrift crisis of the 1980s gave the banking system a reprieve
from the regulatory effects of
HMDA and CRA, as the government’s problem was not how to
finance more housing, but how to
dispose of all the mortgages and properties acquired in the
financial crisis. Eventually, about 750

insolvent institutions with assets of $800 billion (in 2016
dollars) closed. The Resolution Trust
Corporation, established under the Financial Institutions
Reform, Recovery, and Enforcement Act7
of 1989, was involved in disposing of defaulted housing assets
not unlike that which followed the
demise of the Section 235 program. Once again, history
repeated itself.
The Post-1990 Public Policy Record
Since 1990, a steady technological transformation of mortgage
and consumer credit markets has
taken place. Brick-and-mortar branches are closing. Lending is
accomplished on the internet.
Property appraisals and tax assessments are automated. When
HMDA passed in 1975, property
records were recorded on paper and filed in local courthouses.
Now, property-transfer records
are available on the web, easily scraped, and matched with
HMDA records, so that today there is
virtually no privacy in HMDA data. Indeed, the publication of
HMDA data is inconsistent with U.S.
Census Bureau standards for preserving privacy.8 CRA has also
failed to respond to technological
change. CRA is based on the presumption that deposit insurance
is so valuable to banks with
brick-and-mortar branches that they will assume substantial
examination and compliance costs
and will adjust lending, investment, service practices, or a
combination of the three to achieve an
“outstanding” or “satisfactory” CRA rating. That presumption,
however, is technologically obsolete
and financially unsound for both borrower and lender. Equally
troubling is that economists have
been unsuccessful in determining that having institutions with
high CRA ratings makes a signifi-

cant difference in overall community economic performance.
Given that 97 percent of institutions
examined achieve high ratings, the opportunity to study the
effects of unsatisfactory performance
on local economies is scarce.9 Paradoxically, it may be that
CRA has actually discouraged branching
that could expand the definition of market area. Given that
branches have been closing rapidly,
perhaps having CRA impose extra burdens on banks that branch
is not a good idea.10
Despite the fact that the financial landscape had changed in a
way that made CRA’s original
premises invalid, the 1990s instead saw a move to combine
CRA with the now revitalized and
recapitalized government-sponsored enterprises (GSEs; that is,
Fannie Mae and the Federal Home
Loan Mortgage Corporation, or Freddie Mac) to provide high-
risk mortgage credit. The Federal
Housing Enterprise Safety and Soundness Act11 of 1992
enabled HUD to set mortgage purchase
7 Pub. L. 101–73, 103 Stat. 183.
8 For example, Gerardi and Willen (2008) were able to identify
more than 70 percent of HMDA respondents by matching
census tract, lender, and loan amount with readily available
property records purchased from a commercial firm. This
finding contrasts sharply with CFPB’s (2016) assurance to the
public regarding privacy of their HMDA data, which stated,
“This provides enough information about the location to be
useful, but still provides protections for individual privacy.”
9 For an excellent discussion of the nature of CRA examinations
and attempts to find economic effects, see Getter (2015).
10 For example, Bank of America’s annual reports indicate that
it has 4,600 branches today compared with 6,100 in 2009.
Of course, institutions can earn CRA points by selective

branching, so the net effect of CRA on branching is difficult to
determine. The intent of CRA is a complete reversal of previous
public policy that discouraged branching. Indeed, under
convenience and advantage regulation, banks were allowed to
branch only into fast-growing, higher-income areas, because
the concern was to preserve the safety and soundness of the
banking system.
11 Pub. L. 102–550, 106 Stat. 3672, Title XIII.
202
Yezer
The CRA Turns 40
goals for the GSEs and established the Office of Federal
Housing Enterprise Oversight (OFHEO)
to monitor their safety and soundness. Again, sound economic
principles were ignored. Initially,
OFHEO calibrated a stress test using GSE mortgages acquired
between 1979 and 1997 and was
required to publish the results.12 However, OFHEO never
updated it. Frame, Gerardi, and Willen
(2015) found that, if the stress test had been updated, by 2004 it
would have been apparent that
the GSEs had a capital adequacy problem. Yet, in 2004, HUD
raised the GSE affordable housing
goals at precisely the time when the GSEs should have been
contracting. In mid-2007, the GSEs
reported $65.5 billion in book value of equity against $1.7
trillion in assets (3.9-percent ratio). In
June 2008, they reported $54 billion in equity supporting $1.8
trillion in assets. On September 7,
2008, they were put into receivership. OFHEO and the Federal

Reserve had allowed them to
expand, rather than contract, based on faulty modeling and
political pressure. Impartial economic
analysis would have curtailed their operations years earlier, but
political forces always triumph over
economic analysis in mortgage market policy.
During the 1990s, another episode occurred in which political
pressure caused large FHA losses
in a policy initiative at least as flawed as the Section 235
program: the seller-funded downpayment
program. Originally designed to expand access to
homeownership, the seller-funded downpayment
program enabled sellers to “voluntarily” contribute the
downpayment for FHA-insured mortgages
to an approved nonprofit organization, which used part of the
contribution to help finance a
downpayment. The Housing and Economic Recovery Act13 of
2008 finally terminated the program
due to high default rates. Hard experience demonstrated the
economic unsoundness of assuming
that sellers would voluntarily contribute funds to a third party
to pay the downpayment without
raising the asking price by the amount of the contribution.
Simple economic analysis would have
demonstrated the fallacy of the program’s expectations and
prevented the high rates of default and
foreclosure.
Since the housing crisis of the mid-2000s, the gap between
economic analysis and public policy
toward credit markets has only grown wider. For example, the
Dodd-Frank Wall Street Reform and
Consumer Protection Act14 of 2010 (Dodd-Frank Act) limits the
fees that mortgage brokers can
charge to take, underwrite, and endorse mortgages. Fees are

now expected to be uniform; no yield
spread premium can be paid, regardless of whether the applicant
applies online and has perfect
financial records, a large downpayment, high credit score, and
ample income or whether the
applicant is unable to use a computer, keeps financial records in
a shoe box, and has a low credit
score.15 Discovering the lack of creditworthiness of individuals
whose financial records are in a
shoe box can be very expensive. The response to regulation of
fees for brokerage services has been
a predictable decline in mortgage brokers serving low-income,
less-educated borrowers.
12 The requirement to publish a stress test is problematic and
shows a profound misunderstanding of the problem of bank
regulation because the test, once published, invites institutions
to engage in regulatory capital arbitrage. The process is
like giving students the questions on the final examination at
the start of a course. They are likely to learn the answers and
nothing else.
13 Pub. L. 110–289, 122 Stat. 2654.
14 Pub. L. 111–203, 124 Stat. 1376.
15 The Federal Reserve proposed the original broker
compensation rule in July 2009. It is now part of the Dodd-
Frank Act. Section
129 B of the Truth in Lending Act was modified, adding section
(c), which prohibits a mortgage originator from receiving and
no person from paying compensation based on the terms of the
loan except for the amount of a residential mortgage loan.
To Modify

States?
203Cityscape
GSE regulation also continues to be problematic, for example,
when political pressure does not
allow for GSEs to price mortgages based on geographic risk. In
2008, the GSEs asked Congress
to be allowed to require more equity in declining housing
markets, a logical step to protect them
against default risk. As Hurst et al. (forthcoming) explained—
The declining market policy was announced in December of
2007 and was implemented
in mid-January of 2008. After receiving large amounts of
backlash from a varied set of
constituents, the policy was abruptly abandoned in May of
2008. Consumer advocacy
groups rallied against the policy, arguing that it was a form of
space-based discrimination.
Real estate trade organizations used their political clout to
protest the policy because it
was hurting business. For example, the Wall Street Journal
summarized the GSEs’ aban-
doning the declining market policy by saying, “The change
comes in response to protests
from vital political allies of the government sponsored provider
of funding for mortgages,
including the National Association of Realtors, the National
Association of Home
Builders, and organizations that promote affordable housing for
low-income people.”
The Washington Post reported, “Critics, including the National
Association of Realtors
and consumer advocacy groups, had charged that Fannie Mae’s
policy further served to

depress sales and real estate values in the areas tainted as
declining.
A further attempt in 2014 by the GSEs to add a 25-basis-point
origination fee that would help
cover additional losses in five states where foreclosure delays
were long was also defeated by the
same political forces.
Recently, Luan (2017) demonstrated that the spread between
interest rates on jumbo mortgages
(loans above the GSE and FHA ceiling) and conventional (GSE
and FHA) rates can be used to pre-
dict future changes in house prices. This important finding
shows that the jumbo market responds
effectively to differences in local risk. In contrast, these risks
are ignored in pricing conforming
mortgages. The result is that the normal role of interest rates in
attenuating housing price bubbles
is short-circuited by the GSE and FHA pricing policy. How
important was this in the housing
bubble that precipitated the mortgage crisis? That is difficult to
determine, but Tian reported that
a 1-standard-deviation change in the jumbo- and conforming-
mortgage spread is associated with
a 2.5-percent lower rate of subsequent house price appreciation.
Clearly, current federal housing
policy accommodates local housing bubbles, with CRA
contributing to the dynamic by encourag-
ing banks to make loans even in areas where rising house prices
threaten housing affordability.
Contrasting an Economic Perspective and Current
Regulatory Policy
The fundamental basis for disagreement between economic
analysis and current regulatory policy

is that economists believe in using prices—that is, interest
rates—to ration credit among alternative
uses and users. Economists believe that raising interest rates is
the proper response to a local hous-
ing bubble, but public policy views higher interest rates as a
bad thing because they make housing
less affordable just as prices of housing units are rising.
204
Yezer
The CRA Turns 40
CRA is based on the notion that the banking system is
responsible for making housing affordable,
even when market prices are rising. Economists believe that, if
housing is less affordable, that is
a housing market issue. If housing costs $300 or more per
square foot, the banking system is not
responsible for making it affordable for low- or moderate-
income households. Economists say that
lawmakers should examine policies influencing the supply
(zoning, building codes, transportation
access, and so on) and demand (tax treatment of mortgage
interest and property taxes and the
exclusion from capital gains taxation) for housing rather than
the local bank branch.
In addition to insisting on geographic uniformity of mortgage
interest rates, public policy tends to
oppose variation in rates across individuals. Borrowers are
urged to shop for credit, presumably
in the hope of lowering cost. However, current examination

practices penalize lenders if interest
rates are determined to be correlated with borrower
demographic characteristics. Given that demo-
graphic characteristics are correlated with financial and
numerical literacy, the correlation of rates
with demographic characteristics is inevitable if borrowers earn
positive returns from shopping in
credit markets, something that an economist would see as a
positive.
Also, attitudes toward applicant competence differ between the
regulatory environment and
economics. Applicants face two challenges: (1) finding
investment opportunities that are attractive,
and (2) determining the best debt instrument to use in financing
the opportunity. Most economists
would say that the first challenge is greater and that many
borrowers make bad investment choices.
Indeed, one function of responsible lending is to stop borrowers
from making bad decisions
by denying them credit or making the credit so expensive that
the problematic nature of the
investment decision is clear. CRA does not credit lenders who
prevent financial failure by denying
applications or offering credit only at high rates. Loan denials
are not seen as a valuable deterrent
to overly optimistic loan applicants.
Another problem concerns the economic view of appropriate
mortgage instruments. Advocates
who focus on expanding access to credit often discount the risks
and costs of making mortgages;
some expect the financial system to provide homebuyers with
30-year, self-amortizing mortgages
with no prepayment penalties, no deficiency judgments, debt-to-
income ratios of 0.43, and LTV

ratios of 0.95 or more. Credit scores of 620 (even 600) should
be adequate, and an interest rate
within 200 basis points of the 10-year treasury rate is expected.
These mortgages may be economi-
cally sound as long as nominal housing prices are rising, but if
house prices fall, as happens
periodically, this mortgage product could result in substantial
losses. Accordingly, the financial
system is generally not willing to hold such mortgages as an
asset in significant quantity unless the
government offers some form of guarantee that causes losses to
fall on taxpayers or to be passed on
to future homebuyers in the form of higher guarantee fees.
Finally, the premise of CRA is inconsistent with modern
economic thought. The idea that local
depository institutions should reinvest local deposits by holding
local liabilities is completely
inconsistent with modern portfolio theory and sound banking
practice. Modern technology has
broken the link between local deposit taking and lending. The
great mystery about CRA is how
such a flawed view of banking has survived so long in a country
that leads the world in both
internet technology and financial economics.
To Modify
States?
205Cityscape
Acknowledgments

The author acknowledges suggestions and encouragement
provided by Carolina Reid, Mark Shro-
der, Edgar Olsen, and Steve Malpezzi.
Author
Anthony Yezer is a professor of economics in at George
Washington University.
References
Consumer Financial Protection Bureau (CFPB). 2016. “About
HMDA.” Transcript of a video on the
Home Mortgage Disclosure Act.
http://www.consumerfinance.gov/data-research/hmda/learn-
more.
Frame, W. Scott, Kristopher Gerardi, and Paul S. Willen. 2015.
The Failure of Supervisory Stress
Testing: Fannie Mae, Freddie Mac, and OFHEO. Working Paper
15-4. Boston: Federal Reserve
Bank of Boston.
Gerardi, Kristopher S., and Paul S. Willen. 2008. Subprime
Mortgages, Foreclosures, and Urban
Neighborhoods. Working Paper 08-6. Boston: Federal Reserve
Bank of Boston.
Getter, Darryl E. 2015. The Effectiveness of the Community
Reinvestment Act. Congressional Research
Report 7-5700. Washington, DC: Congressional Research
Service.
Hurst, Erik, Benjamin J. Keys, Amit Seru, and Joseph S. Vara.
Forthcoming. Regional Redistribu-

tion Through the U.S. Mortgage Market, American Economic
Review.
Luan, Tian. 2017. Did Investors Price the Housing Bubble?
Evidence From the Jumbo/Conforming
Rate Spread. Working paper. Washington, DC: George
Washington University, Institute for Interna-
tional Economic Policy.
http://www.consumerfinance.gov/data-research/hmda/learn-
more
Reproduced with permission of copyright
owner. Further reproduction prohibited
without permission.
Thanks for your work on this assignment. The biggest challenge
in your paper is that you used many words to try and make a
particular point, but in doing so, your message got lost. I would
like to see you be much more explicit in your writing to help the
reader understand your main ideas. Please see comments and
example for guidance.
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is At Least 300 Words
Basic - Writes a limited reflective mentoring philosophy that is
between 200 and 250 words. The philosophy is underdeveloped.
Comments:
While your philosophy meets the 300-word requirement, you
still needed to address a wider variety of the recommended
aspects of mentoring.

( 2.28 / 3.00) Responds to the Required Questions Regarding the
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the documentation form, minimally using the text as support.
Relevant details are missing.
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You suggest some interesting ideas, but more details were
needed. Additionally, you did not use the text to support your
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Basic - Defines both core beliefs and the origins of the core
beliefs.
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The paper tends to be generic and overgeneralizes situations
without recognizing ethical complexities.
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( 0.18 / 0.20) Written Communication: Control of Syntax and
Mechanics
Proficient - Displays comprehension and organization of syntax
and mechanics, such as spelling and grammar. Written work
contains only a few minor errors and is mostly easy to
understand.
Comments:
Good job! Correct conventions facilitate the reading of the text.
( 0.18 / 0.20) Written Communication: APA Formatting
Proficient - Exhibits APA formatting throughout the paper.
However, layout contains a few minor errors.
( 0.20 / 0.20) Written Communication: Page Requirement
Distinguished - The length of the paper is equivalent to the
required number of correctly formatted pages.
( 0.20 / 0.20) Written Communication: Resource Requirement

Distinguished - Uses more than the required number of
scholarly sources, providing compelling evidence to support
ideas. All sources on the reference page are used and cited
correctly within the body of the assignment.
Overall Score: 6.24 / 8.00
Overall Grade: 6.24
Running head: Mentoring Philosophy and Document Analysis 1
Mentoring Philosophy and Document Analysis
Annette Williams
ECE 672 Personnal Management & Staff Development for Early
Childhood Administrators
April 6, 2020
Dr. Guevara
- 1 -
1
1. April
date goes last [Frank
Guevara]

2
Acting as a leader and a mentor especially in the field of early
childhood education, it is
essential to have effective planning for the activities that will
be involved in mentoring as well as
documentation of progress, expectations as well as outcomes of
the learners. The appropriate
aspect that will aid in the above undertaking is having an
effective individual philosophy of
mentoring and also acting as a leader, and this will also be
important for the manner that a person
approaches the professional associations with the early
childhood teachers and also the staff. The
approach will also be essential for supporting the professional
developmental motives of these
people. As far as this situation is concerned, my mentoring
philosophy is creating choices as well
as building skills for the mentees and also instilling a sense of
confidence and integrity to all that
are involved, i.e. the childhood teachers and the other staff
members that are involved.
Regarding the above individual philosophy, adult learners’
motivators, goals and even

styles of communication exist. The notable motivators of the
adult learners include creating a
useful and also relevant experience of learning, facilitating
explorations, focusing on practical
knowledge and skills as well as accommodation group
associations, (Borras, 2016). The other
motivator is building society via social technologies. The
essential goals include developing the
knowledge of truth and attainment of the goodness of this field.
The essential styles of
communication that will be involved in this philosophy will
entail face-to-face communication,
the use of technology to communicate between the various
parties and the use of written styles of
communication, (Adubato, 2006). Therefore, the above styles of
communication will be
important and useful.
The creation of an interactive and environment for the inquiry
will also be important. The
situation will be done by considering the opinions and views of
all individuals. A safe
environment for interaction will also be created so that growth
and change will be achieved.

- 2 -
1
2
1. i.e.
use a parenthesis when you
have (i.e.,) [Frank Guevara]
2. motivator is building
society
it's unclear how we're building
society? [Frank Guevara]
3
The environment will be created by involving people from all
corners of lives and situations, and
this is known as diversity. It will also be important to challenge
the staff and the early childhood
educators, especially by delegating minor tasks to them so that
they may carry them and by
doing so, they will be growing and developing.

Developing and implementing an individual’s mentoring
philosophy is a complex and
long-term undertaking as it involves various undertakings. One
of the key undertakings involved
in this case is locating, reviewing as well as revising the
prevailing forms and at the same time,
discussing the numerous importance of tailoring the forms so
that they may meet the specific
needs of the mentoring philosophy. The prevailing forms will
also be important for the creation
of a portfolio of mentoring for the professional work either in
the current working environment
or in a future and a potential working area. The forms will also
be important for creating
essential relationships when it comes to mentoring
relationships, and by reviewing such forms,
important and significant questions will be answered.
Regarding the above situation, a certain form is important, and
this is a form that would
aid in answering the essential and relevant questions that may
aid in the development of the
mentoring philosophy. The form is entitled to record the notes
of a coaching process for every
visit that has been made to the teacher. As far as this form is

concerned, I find it to be not only
useful but also important during every visit and also after every
visit with a teacher that is meant
to guide the development of the mentoring philosophy. The
reason for claiming that this form is
useful and important is that it offers a place where a person may
describe the general needs and
interests as well as the goals of the teacher or in other words,
the individual entrusted with the
work of mentoring. Under this space, various needs and
interests and also the goals of the
mentoring philosophy will be developed and written down.
Therefore, during and after every
- 3 -
1
2
3
1. environment will be
created by involving people
from all corners of lives and
situations, and this is

known as diversity.
but what if your community
doesn't have much diversity?
[Frank Guevara]
2. delegating minor
building ownership is an
effective strategy [Frank
Guevara]
3. the mentoring
philosophy.
any forms used for mentoring
will not explicitly state one's
mentoring philosophy; that's
the point of part 1 of this
assignment...for you to
articulate your philosophy
[Frank Guevara]

4
visit, such goals will be developed further, and there is also a
likelihood that any goal, interest or
need that does not fit in the mentoring philosophy and process
may be eliminated. The form also
offers an opportunity for evaluating whether the goal, need, or
interest is short or long-term.
After this identification, it may also be examined whether or not
the goals, interests, and needs
were negotiated and suggested by the teacher as a mentor. After
a realization that such aspects
were never negotiated, then the teacher may provide a further
direction for the situation.
The other reason for claiming that the form is useful and
important is that it provides an
opportunity for evaluating the manner that the teacher will
evaluate whether or not the goals,
needs, and interests of the mentoring philosophy and process
have been attained. In this case, the
mentoring process and philosophy are an undertaking that needs
to be achieved, and as a result, a
need for evaluating the attainment of such goals and interests
has to prevail. The aspects also

need to be covered in the form, and since this form provides
such an opportunity, then it would
be important to claim that this form is useful and important.
Despite that this form is useful and important, several and
important revisions may be
done to the form, and these would aid in enhancing the
efficiency of the form. One of the notable
revisions that would be made on this form is aligning an
individual’s mentoring philosophy to
the questions that are asked in the form. As an example,
mentoring philosophies between the
individuals differ, and it would be important to consider the
philosophy of the concerned person
in the evaluation form (Searby, 2016). Therefore, this is an
important revision that would be
made on this form, and it would aid in further enhancing the
form as it will allow the questions
asked in the form to be aligned to the mentoring philosophy of
an individual. The mentoring
philosophy and process is an undertaking that needs to be done
systematically and within a
certain time frame so that each undertaking will be attained or
accomplished within the specified
- 4 -

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