Writing a 2 pages report related to finance

Housing Financial Market Early Warning System Prototype Project Charter

Fall 2017

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Project Charter

1. Business Background For more than fifty years, the U.S. government has sought to increase home ownership through policy

(e.g., mortgage interest deductions), government-sponsored enterprises (e.g., Federal Home Loan

Mortgage Corporation (FHLMC), aka Freddie Mac, and Federal National Mortgage Association, aka

Fannie Mae), and related subsidies. The Department of Housing and Urban Development (HUD) justifies

this strategy as home ownership is, “… a great way to create wealth and pass it on to your family… to

build a nest egg for college or retirement… and to protect against life’s setbacks.” HUD’s mission is to

provide vital public services through its nationally administered programs. Through the Office of

Housing, HUD oversees the Federal Housing Administration (FHA), the largest mortgage insurer in the

world, as well as regulates the housing industry business. The mission of the Office of Housing is to:

• Contribute to building and preserving healthy neighborhoods and communities

• Maintain and expand homeownership, rental housing and healthcare opportunities

• Stabilize credit markets in times of economic disruption

• Operate with a high degree of public and fiscal accountability

• Recognize and value its customers, staff, constituents, and partners

Addressing the public interest to provide affordable housing while ensuring the stability of credit

markets and operating with fiscal responsibility may require trade-offs, create friction between these

values and impede appropriate fiscal policy decision-making in times of high volatility or financial crisis.

The financial crisis which prompted the passage of the Emergency Economic Stabilization Act of 2008

and resulting Troubled Asset Relief Program (TARP) required hundreds of billions of dollars in order to

stabilize the credit markets.

The specific causes of the financial crisis are numerous and their individual contributions hard to

disentangle. Artificially low interest rates, inaccurate mortgage ratings, complex financial instruments,

and the poor assignment of risk to behavior among both home owners and lenders all played a part in

the crisis.

Both the government and private enterprise seek to avoid experiencing such a collapse again. The

development of an early warning detection system for government housing lending may allow

regulatory or programmatic changes to offset or reduce disruptions and improve fiscal accountability of

these programs and the agencies that administer them.

2. Project Objective & Scope Your team has been assigned the development of an early warning system prototype that will serve to

assist in the recognition of conditions that may lead to financial troubles. The team must first select a set

of criteria relevant to indicating the health of the mortgage lending industry. The system should store

the supporting data for the selected criteria, process the data (e.g., descriptive statistics and analytics),

and visually present the results via a dashboard (using multiple charts such as bar charts, line charts, text

tables). The dashboard should include:

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• Current values, indicating which are out of normal range

• An overall status given the values of multiple indicators

• Allow the testing of what-if scenarios (e.g., what if economic growth was 6% instead of 4%)

The specific criteria to use are at the discretion of the teams, thus the team members will need to spend

some time becoming familiar with the mortgage lending domain. Examples of criteria may include

existing conditions (e.g. current production volumes of lending existing and emerging lender types

(banks, non-banks), current capacity of the market to service these homeowners) and user-defined set

of criteria (e.g. economic scenario factors cost of production or servicing projected home ownership

growth/reduction, available credit market capacity).

Appendix B provides helpful links describing the many causes of the financial collapse. The indicators

selected must exist in an accessible data set or allow for the creation of a unique, proprietary data set by

the team. Appendix A provides links to relevant data sources; many others are available.

3. Project Approach Teams will follow an agile approach with three sprints targeted for September, October, and November

(see Appendix D for dates). For the first sprint, the teams should include (at a minimum) mortgage debt

outstanding as provided by www.federalreserve.gov/econresdata/releases/mortoutstand/current.htm

as a line chart on a dashboard.

The scope of the second and third sprint to be determined through the course of the semester. As new

metrics are added, in most cases the database will need to be modified to maintain referential integrity.

Teams will be provided a workplan template with some items/tasks for the first sprint completed.

The tools used to build the prototype are at the discretion of the students (though the final selection of

the tools should be approved by the instructor). Students will use their own laptops for construction

which will require the team to think through coordination and how to manage back-ups.

The students will meet every week with the faculty advisor to report on progress and issues.

Business domain questions should be sent to the instructor who will forward the questions on to the

sponsors and then post the answers for all teams.

4. Constraints and Risks Students are expected on average to spend six to eight hours a week each working on the project. The

project dates necessarily coincide with the academic semester given that the students are receiving

academic credit for the class. The schedule is provided in Appendix D. The students may not be familiar

with the tools and may need to dedicate part of their time assigned to the project in self-study.

5. Deliverables Teams will provide to the instructor at the end of the semester:

• The prototype consisting of a database, a processing layer, and a presentation layer (via a zip

file)

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• User manual explaining the use and options available in the prototype

• An architecture design document (what tools have been used and how do they interact)

• Related design documents

o Use cases

o ERD for database

o Pseudocode for processing

o Mock dashboard

• Testing documents

The deliverables will be evaluated based upon the criteria provided in the rubric provided in Appendix C.

6. Appendix

Appendix A

The list below consists of web sites that provide mortgage-related economic data. Some sources may

require registration. Some source provided individual records within the web page and may require web

scrap data scraping; some data sources may allow a real-time call via an API (usually with a key provided

through registration). Consider too proprietary data sets available through the UT Dallas library web site.

catalog.data.gov/dataset

fred.stlouisfed.org/categories/97

www.huduser.gov/portal/datasets/fmr.html

www.fhfa.gov/DataTools

data.oecd.org/united-states.htm#profile-economy

www.consumerfinance.gov/data-research/consumer-credit-trends/mortgages/

www.ginniemae.gov/data_and_reports/disclosure_data/Pages/disclosure_history.aspx

www.fanniemae.com/portal/index.html

www.freddiemac.com/

www.mls.com/

www.zillow.com/

www.jpmorganchase.com/corporate/institute/institute.htm

Appendix B

FactCheck.org (http://www.factcheck.org/2008/10/who-caused-the-economic-crisis/) provides a set of explanations for the financial collapse of 2008, each with a link. Not listed below but worth considering is the contribution of the ratings agencies such as Standard & Poors and Moodys.

▪ The Federal Reserve, which slashed interest rates after the dot-com bubble burst, making credit cheap. ▪ Home buyers, who took advantage of easy credit to bid up the prices of homes excessively. ▪ Congress, which continues to support a mortgage tax deduction that gives consumers a tax incentive to

buy more expensive houses. ▪ Real estate agents, most of whom work for the sellers rather than the buyers and who earned higher

commissions from selling more expensive homes.

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▪ The Clinton administration, which pushed for less stringent credit and down payment requirements for working- and middle-class families.

▪ Mortgage brokers, who offered less-credit-worthy home buyers subprime, adjustable rate loans with low initial payments, but exploding interest rates.

▪ Former Federal Reserve chairman Alan Greenspan, who in 2004, near the peak of the housing bubble, encouraged Americans to take out adjustable rate mortgages.

▪ Wall Street firms, who paid too little attention to the quality of the risky loans that they bundled into Mortgage Backed Securities (MBS), and issued bonds using those securities as collateral.

▪ The Bush administration, which failed to provide needed government oversight of the increasingly dicey mortgage-backed securities market.

▪ An obscure accounting rule called mark-to-market, which can have the paradoxical result of making assets be worth less on paper than they are in reality during times of panic.

▪ Collective delusion, or a belief on the part of all parties that home prices would keep rising forever, no matter how high or how fast they had already gone up.

Appendix C

The rubric below will be used to evaluate the deliverables described in Section 5.

Below Expectations Meets Expectations Exceeds Expectations Points

Data Superficial or no use of

secondary data

The database consists of

multiple tables each for a

data source with no

references or normalization

Extensive use of secondary

data with some integration

Database is normalized

(assuming a SQL-like

database)

Extensive use of secondary

data integrated with primary

data set

API to automatically update

dataset (or feed straight to

graphs)

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Analysis Primarily descriptive statistics

with limited insight as to how

the measures “matter”

Combination of descriptive

statistics and analytic

methods (e.g., clustering)

Identification of ranges that

are safe, present issues, or

are dangerous

Combination of descriptive

statistics and analytic

methods (e.g., clustering)

Use of composite analytics

(e.g., combination of several

criteria to project second

order effects)

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Presentation/

Functionality

Rudimentary or no what if

Static graphs each presenting

information in isolation

What if analysis on a metric

by metric basis

Graphs provide some form of

interactivity (via form

changes or clicks)

What if analysis allows

combination of factors

What if allows entry of

general economic projections

which then impacts the

primary measures

Changes in one graph

affect/update the other

graphs

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Deliverables Incomplete documentation

(e.g., no test plan),

rudimentary design

documents (e.g., would be

difficult to begin construction

from design documents)

Limited justification for

selection of economic criteria

in user manual

Executed test plan

Reasonable justification for

selection of economic criteria

(as documented in user

manual)

Analytics explained (in user

manual)

Executed, comprehensive (all

functionality) test plan

Coherent user manual that is

helpful for someone who had

no prior knowledge of the

prototype

Rigorous justification for

selection of economic criteria

in user manual

Analytics thoroughly

explained (in user manual)

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Appendix D

Assignments & Academic Calendar

Date Description Due

August 22nd Introduction

August 29th Meeting with Sponsor

September 5th Team Scrum Meetings with Instructor Update deck/meeting minutes, Workplan

September 12th Team Scrum Meetings with Instructor Update deck/meeting minutes

September 19th Team Scrum Meetings with Instructor Update deck/meeting minutes

September 26th Class Presentations (Sprint 1) Presentation deck/meeting minutes

October 3rd Team Scrum Meetings with Instructor Update deck/meeting minutes

October 10th Team Scrum Meetings with Instructor Update deck/meeting minutes

October 17th Team Scrum Meetings with Instructor Update deck/meeting minutes

October 24th Class Presentations (Sprint 2) Presentation deck/meeting minutes

October 31st Team Scrum Meetings with Instructor Update deck/meeting minutes

November 7th Team Scrum Meetings with Instructor Update deck/meeting minutes

November 14th Mock Presentations Draft presentation deck/meeting minutes

November 21st Off

November 28th Presentations to Sponsor (Sprint 3) Presentation deck

December 5th No Meeting Project deliverables