For efficient Mortgage Servicing it is important to identify risk
as a continuous process throughout the life cycle of the loan.
Analytical tools using behavioral models together with macro
and micro economic parameters to project interest rates and housing
prices need to be used to calculate scores and identify problem loans.
Mortgage credit risk
management: no more a
back office function
“Banks’ ability to change are being
outstripped by both the sheer
volume of troubled loans and
the rates of change of rules and
regulations” – Ernst and Young.
Mortgage Servicing is the
most challenged entity within
financial services in the wake of
the economic downturn. The
fundamental underlying assumption
of referring to mortgages as
‘secured loans’ itself has become
questionable. Loans are being
approved and sanctioned based on
the assessing credit worthiness of a
borrower and the property price at
the time of approval. Once the loan
is approved, no one has any control
over those parameters.
Mortgage Servicers are under
heavy pressure due to the dramatic
change in the nature of estimation
of a customer’s credit risk and the
volatility of property prices. These
completely change the traditional
relationship between default, risk,
and profitability. The underlying
data requirements, staffing mix,
everything is changed. Hence, it
has become more important to
identify risk as a continuous process
throughout the life cycle of the
loan rather than as a stage in the
workflow. Mortgage credit risk
management is no more a back
office function!
Every time a property goes into
foreclosure, the lender spends an
average of US $50,000 to liquidate
the property through a bank sale.
They key costs include:
- Legal costs for handling the foreclosure
- Administrative fees
- Preparation for sales
- Costs of restoring the property to saleable condition
- Real estate commissions
- Loss on unpaid principal balance
- Mortgage Insurance limits on reimbursements
As of November 2011, in the
US alone, the 2.1 million homes
in foreclosure or with seriously
delinquent mortgages is expected
to take more than eight years on
average to clear because of slowmoving
foreclosure sales in half the
states. Real estate prices in many
markets are likely to be impacted
for years to come as a result of the
sluggish pace.
With the primary focus being
on home retention, lenders and
borrowers are focusing on loan
modifications. The U.S. Homeowner
Affordability and Stability Plan
is expected to result in three to
four million loan modifications.
However, less than 35% of the
modifications are resulting in
reduced monthly payments for the
borrower.
Financial Institutions are looking
for a better way to identify and deal
with problem loans.
Lenders need ways to costefficiently
handle the foreclosure
process, and for that they need
analytics.
Analytics has traditionally played a
major role in the decision making
process of the mortgage industry.
Now it is time to leverage analytics
to identify the cash flow impact
and model loan modifications more
effectively.
Towards better risk
prediction in mort gage
servicing
Many lenders have started realizing
the benefits of investing for
analytics in servicing to help identify
risk and prevent defaults. A FICO Study claims their clients benefit
significantly as follows:
- Conventional Conforming Loans-loss avoidance of over US $2 million
- Option ARM Loans-loss avoidance of US $31 million
- Non-Agency Loans-loss avoidance of US $6.7 million
Far from a temporary fix, these
institutions are rebuilding servicing
with a new, long-term market
perspective. The analytical tools
calculate scores using many
behavioral models. They also use macro and micro economic
parameters to project the interest
rates and housing prices. MIAC,
LPS, SAP Cognilytics are some
of the few leading platforms in
the market that provide Mortgage
Analytics.
Use of additional data in
Analytics
There are huge untapped, unused
data resources in the mortgage
market such as geo risk, property tax
delinquencies, data on borrower’s
other liens, etc. The picture here
provides a view of how lenders
use analytical tools and data for
scoring. Many of the untapped
data resources have a wealth of
information that can help scoring
with greater accuracy.
In addition to using the tools, the process of scoring should be continuous at all stages of the loan life cycle. The
picture here emphasizes the need for continuous monitoring.
A Gartner study shows that
mortgage analytics solutions can
easily bring a range of potential
savings including:
- 10-20% increase in the net present value
- An average of US $10M per year
With the new ways of collecting
and using data, and with continuous
monitoring and tracing processes,
lenders can better understand risk as
well as the opportunity and ensure
better guidance in their investment
decisions. |
