The Giant Pool of Money

Group case study - The Giant Pool of Money

Nikita Abhyankar, Trey Ngo, Henry Nguyen, Robb Provenzano, and Prutha Shah

Bus 210

Introduction

The financial crisis of 2008 sets the stage for the radio program, This American Life, episode 355: The Giant Pool of Money. The program focusses on the main cause of the financial fallout: the housing market collapse. As described, the initial bubble within the market was steadily growing due to poor predictions and practices by investors. Many investors believed in the historical data at their disposal and stood by the profits that they would earn by making risky loans to nearly anyone who applied. However, throughout their reasoning they were falling victim to the pull of heuristics. These heuristics and biases clouded their judgment; rather than analyze the true nature of the situation, they followed the trends that were forming. These trends ‒ loosening loan requirements and restrictions ‒ were developed under the concepts of the confirmation trap and social proof. Each of these decision heuristics ignored true analysis and based decision making on either the information one wants to believe is true or on the actions of others. As a result, investors failed to question if their predictions of profit could be confirmed and followed other investors in their dangerous lending pursuits.

Heuristics

The confirmation trap is explained by Bazerman and Moore (2012) as, “when we encounter information that is consistent with our beliefs, we usually accept it with an open mind and a glad heart” (p. 47). In other words, one considers information that sustains their initial assessment instead of seeking to disprove this assessment and verify their assumption. This was shown by the investors who reviewed historical data on default rates of existing home loans. They perceived an average default rate of less than two percent, but the data was for more traditional loans. The new no income no assets (NINA) loans, were untested and had no historical data for analysis. Additionally, the investors were eager to turn any profit and believed the false data to confirm they were investing in successful mortgage backed securities. The investors wanted to focus primarily on the positives of their investments. Secondly, the confirmation trap was also experienced by individual home buyers. Home buyers saw housing prices increased rapidly with those who purchased a house for one year and sold that house for a profit. Prospective home buyers followed suit in hopes of making the same profit. They never reviewed why house prices were increasing and did not question how they were getting approved on home loans.

One consequence of confirmation trap is, people searched and believed in the information that favors their decision. In this case, the home buyers were attracted to the increasing housing prices and their lump sum future profits. Instead of considering mainly the profit, they should have also looked at the other factors. For instance, the home buyers should have not neglected the fact that with the increasing housing prices, their income remained constant, making them incapable to pay future debts. Secondly, they should have also critically evaluated how people were getting loans. They should have been more informed of NINA loans. This insight would have helped home buyers look for other loans that were less risky. Thirdly lenders did not clearly mention the consequences of the adjustable mortgage rates. This made the buyers face higher rate than expected. If the lenders were more transparent, home buyers would have a clearer picture of what they were getting into.

An important method to avoid confirmation trap is by questioning the available data, to analyze the credibility. Investors were constantly reviewing historical data that were irrelevant to the current loan situations. Instead of confidently relying on the false data, their analysis software needed data from NINA loans. Additionally, confirmation trap bias could have been avoided by the credit rating agencies, who rated the BBB tranches as AAA. They should have done their part ethically without falling prey to the profits. They could have been firmer with their rating of loans in the tranches. Those involved in confirmation trap could have looked at all the information to get an unbiased perspective.

Social proof was another heuristic that heavily influenced the decision making of brokers, bankers, and Wall Street. Social proof is a bias that states “one means we use to determine what is correct is to find out what other people think is correct” (Cialdini, 2006, p.88). The individual focused in this example is Mike Garner, whose job was to sell NINA loans and afterwards sell hundreds of these loans to Wall Street. While Mike Garner was not the individual changing the mortgage loan requirements, he pushed individuals to apply for these loans, knowing that they could not pay back the large sums of money. Social proof influenced Mike to sell NINA loans because other mortgage companies were also selling NINA loans. This collective decision to sell risky NINA loans was further enhanced by shifting liabilities from selling NINA loans to Wall Street in a few months’ time. In short, social proof incentivized Mike Garner to sell risky loans because if everybody was doing it, then that makes it acceptable. There were uncertainties whether NINA loans were profitable, but social proof states that when dealing with uncertainty, “we are most likely to look to and accept the actions of others as correct” (Cialdini, 2006, p.98). Mike Garner did not contemplate what was morally right or sustainable long-term because if everybody was selling NINA loans, it was more appropriate and high profits were earned.

Social proof also influenced a firm’s decision to buy mortgages from mortgage companies. At first, large firms (e.g. Merrill Lynch and Goldman Sachs) refused to buy mortgages because they were too risky. Eventually, Bear Stearns agreed to buy these mortgages and Mike Garner explains, “once one person buys them, usually all the rest follow suit” (Thisamericanlife.org, 2008). Furthermore, social proof demonstrates the similarity of one group have towards another will also create bias. Once Bear Stearns agreed to buy mortgages, other small firms began buying. Afterwards, larger firms started buying and eventually, other large firms followed pursuit due to the bias of similarity. Ultimately, firms nationwide bought unorthodox mortgages due to the influence of social proof bias of abundance and resemblance.

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