Three Decision-Making Biases That Cloud Your Judgement

In a recent article, I explored how the human mind is wired to make emotional, snap decisions, even when the decision is of utmost importance.

When reflecting on your own life, did you utilize a process or emotional instinct? For example, how did you decide where to go to college? Where to live? Who to spend your life with? What profession? Did you use a data-driven process to make these decisions, or were they made more by a feeling? I moved to Chicago for work after college, but left the derivative trading industry in 2002. Did I run a process to evaluate where to live next? No. Chicago is a great city, but living there for 25 years has been quite arbitrary. My guess is that you have a similar experience with one of the questions I pose above.

I recognize that it can be difficult to run a data-driven process when making monumental life decisions. However, being disciplined and process-oriented is very possible with investing – and extremely important. For example, Warren Buffett spent 15 years studying every annual report and meeting with the company’s management team of each company he was considering investing in before deciding to invest. Buffett knew that his patient study would combat the human inclination to make snap decisions. In real estate investing, in particular, it is essential to use a process to make decisions because it is essential to get asset selection and design correct. It is very expensive to move or redesign a building! 

With Origin Investments, my partner Michael Episcope and I have created a company designed to optimize decision making. But, first, we looked at three biases that cloud judgement in order to develop a system that could optimize our decision making. Here they are:

Recency Bias is the human mind’s tendency to value what has happened in the near past over other data, even when other data is more significant. If investors in the stock market were asked how they felt about the market on Feb 9, 2018, the recency bias would make them focus on the volatility of the previous two weeks, rather than the eight-year bull market. 

In real estate investing this can be problematic. For investors who are selecting managers, it is insufficient to look only at recent returns. Longer track records and larger data sets provide much stronger indications of competitive advantages and sound judgment. It also is helpful to benchmark a manager to specific asset classes and risk to determine how they are performing versus their peers. If a manager is describing a few recent deals that have gone well, it is a prudent decision to wait to see more data before investing. 

Loss aversion is the human tendency to feel more pain in losses than joy in gains. This manifest itself in investing when we hold on to our losing stocks too long, since selling would lock in the losses. Holding on to losing investments is irrational, as at every point there is an opportunity cost of holding the stock --- using the money to buy another stock, which may be a better investment.

In real estate investing, loss aversion has been magnified since the 2008 market crash. Real estate values shrunk and investors lost significant sums of capital. As a result, the same investors swore off the asset class and refused to invest in real estate ever again. This is an emotional, snap decision when you consider that no asset class has outperformed real estate over the past few decades. A process would have uncovered this, allowing the investors to participate in the robust recovery that has generated outsized returns.

Group Think Bias is when a group of people strive for unanimity, overriding their motivation to consider independent and alternative views that may result in a better decision. Individual investors may not have the training or access to data to build their own models to evaluate deals. This means the people whose judgment they value most in their networks are likely influencing their investment decisions, which is not a sound methodology.

But this challenge also exists at investment firms and, therefore, the best firms design systems that are created to combat group think. At Origin, we use a data-driven process, create checks and balances through different teams, analyze assets separately, and compare the subjective model output to models built using only historical data based on the risk/return characteristics of the asset. 

Next week I will go further into the process we use at Origin to combat our group think biases.