Big Short

Context

The US housing boom had the conditions for a perfect storm. After seeing the constant increase in housing prices, regular people started investing in the housing market. Given the surge in demand for mortgages, lending institutions reduced their criteria to approve more loans. To increase eligibility, lending institutions designed mortgages that had an interest rate that increased every year. Investors saw this as an opportunity to buy a house, let its price increase, and sell it with a profit.

However, in 2005, foreclosures started to increase. People with mortgages with increasing interest rates started to default on their payments. In 2005, over 885,000 foreclosure notices were filed. A year later, this number rose to 1.25 million, a 41 percent increase.

The mortgage foreclosures created a systemic failure. First, they reduced the demand for housing purchases, which in turn, lowered the price of houses. Second, this decrease in housing value made it harder for people to refinance existing mortgages. Third, people who could no longer flip or afford their houses started defaulting more. Finally, the lending institutions, which had borrowed a significant amount of money to multiply their returns (leverage), faced unprecedented losses causing a global financial crisis.

Sources: Wikipedia, History, Federal Reserve History.

Overview

DannyMay, a mortgage lender, wants to develop a better system to assess the risk of its applicants. The company barely survived the 2008 financial crisis, and after a change of CEOs, it wants to reduce its exposure to bad loans. DannyMay investors will unlock the next round of financing based on the progress made in the upcoming quarter. Therefore, this initiative is the CEO’s top priority.

To fulfill this request, the Risk Analysis Department has tasked you with assessing the risk of hundreds of applicants. This, they expect, will let them have a preliminary model that will improve DannyMay’s balance sheet. You’ll be presenting your findings to the Head of the Risk Analysis Department.

Vocabulary

Mortgage: a loan used to acquire real estate.
Risk Analysis: when related to the lending industry, this is an assessment of the likelihood that a person will pay back its loan.
Financial Crisis: an event that causes a steep decline in asset prices preventing consumers and businesses to pay their debt.
Asset: a resource with economic value that someone owns with the expectation that it will increase in value.
Leverage: use of borrowed capital to invest. This could exponentially multiply the gains and losses of the investors.

Learning Goals

At the end of this project, students will be able to:

Requirements

For full installation instruction and technical requirements click here.

Submission

You will submit your findings via a repository on Github. The repository will be named <first name>-<last name>-big-short. The repository should contain your Jupyter notebook and SQL file.

Rubric

The following rubric is divided in technical and professional skills. In order to complete this project successfully, students need to achieve 18 points.

Technical Skills

Completion

4: The student was able to exceed the technical requirements and deliver additional insights based on the provided data set.
3: The student was able to complete the technical requirements for this project.
2: The student was able to complete the technical requirements except for one or two missing features.
1: The student was not able to complete the technical requirements or does not have a working solution.

Organization

4: The student organized his/her notebook, queries, and insights in a clear and logical structure, and included references where needed.
3: The student organized his/her notebook, queries, and insigths in a logical structure.
2: The student organized his/her notebook and queries in a mostly logical structure.
1: The student’s project was not clear nor organized.

Style

4: The student followed SQL and Python conventions, including proper naming and composition. Student wrote his/her notebook in a clear and concise way.
3: The student followed SQL and Python conventions, including proper naming and composition.
2: The student mostly followed SQL and Python conventions bar some omissions.
1: The student writing was inconsistent and unclear, and his/her code didn’t follow conventions.

Professional Skills

Communication

4: The student communicated with his/her instructors in a professional manner. The student proactively provided updates, and responded to instructors’ requests in time. The student also communicated any setbacks in time and requested help when necessary.
3: The student communicated with his/her instructors in a professional manner and responded to instructors’ requests in time.
2: The student communicated with his/her instructors in a professional manner.
1: The student did not communicated with his/her instructors or did so in a unprofessional way.

Time Management

4: The student delivered all the technical requirements and additional insights in time.
3: The student delivered all technical requirements in time.
2: The student delivered most of technical requirements in time.
1: The student did not delivered his technical requirements in time.

Problem Solving

4: The student was able to solve his/her own problems by reviewing documentation, obtaining help from peers and mentors, and reaching out to instructors as a last resort.
3: The student was able to solve his/her own problems by obtaining help from peers and mentors, and reaching out to instructors.
2: The student was able to solve his/her own problems by only reaching out to instructors.
1: The student was not able to solve his/her own problems even with additional help.