If you’ve gone house hunting, these thoughts are familiar as you enter another property: Is this the one? Should I keep looking? What if the perfect home is just around the corner?
This is a familiar dilemma for house hunting and other major personal finance decisions.
It also belongs to a class of mathematical problems known as the theory of “optimal stopping,” which is the problem of choosing a time to take a given action.
Fortunately, there’s an answer to maximise your chances of getting the best outcome.
Thirty-seven percent.
This article was written by a Financial Horse Contributor.
The 37% Rule
The 37% Rule defines a simple series of steps for solving this problem. It’s a simple yet powerful tool for better decision-making.
Here’s how it works.
Imagine you’re searching for a new property. You’ve gone through listings and lined up viewings.
Assume listings are so popular you can’t compare options. You can either buy the property immediately, giving up all others, or walk away and never return. (This may not be so far-fetched for property buyers for the last few years.)
Spending 37% of your property hunt noncommittally exploring options gives you the highest chance of getting the best property.
If you’ve given yourself three months to find the perfect apartment, spend the first 37% (33 days) exploring without committing. View many properties, gather information, and establish a baseline of your wants and what’s available. After that, go for the next best property you’ve seen.
This solution is described in “Algorithms to Live By” by Brian Christian. He writes that modern computers face similar problems. For instance, when a search engine is looking for the most relevant results, it needs to decide when to stop searching and return the best results found.
Computer scientists have developed algorithms to tackle these problems — and they can also help us solve real-world problems.
The classic optimal stopping example is the “secretary problem.”
In this scenario, you’re tasked with hiring the best secretary from 100 applicants. You interview candidates one by one in random order, comparing them only relative to each other.
After each interview, you must decide whether to hire that candidate, ending the process, or continue searching. If you pass on a candidate, they’re gone forever and you can’t make an offer later.
This setup creates a dilemma: how long to continue interviewing to gather information, and when to decide? The goal is to determine the optimal strategy to maximise your chances of hiring the best overall candidate, balancing the risk of passing on a strong candidate against finding a better one later.
The most optimal strategy the Look-Then-Leap Rule. Christian writes:
“You set a predetermined amount of time for “looking”—that is, exploring your options, gathering data—in which you categorically don’t choose anyone, no matter how impressive. After that point, you enter the “leap” phase, prepared to instantly commit to anyone who outshines the best applicant you saw in the look phase.”
You’d interview the first 37 candidates without making a decision. After that, choose the next candidate better than all previous ones. This approach gives you the best balance of information (defining the “best” candidate) and agency (the ability to make an offer to the best applicant).
It’s easy to see the applications to personal finance, whether it’s for property hunting, considering the next job opportunity, or evaluating interior design firms for home renovation.
The Threshold Rule: When to Accept an Offer
The property hunting scenario assumes we have no preexisting sense of what makes a good or bad home. This simplifies the problem but is vastly different from reality.
Let’s flip the scenario and now imagine you are now looking to sell your property.
As each offer arrives, you must decide whether to accept or reject it. But there are two new elements to this problem.
First, when selling a property, you often have more information. You can estimate the range of offers you’re likely to receive and evaluate each offer objectively. It would be foolish to give up the first offer you receive if the offers falls in the 95th-percentile of expected offers.
Second, turning down the offer will always incur a cost to waiting. This doesn’t just mean a financial cost. It might be time, effort, or even missing out on other opportunities, such as if you need to sell your current property before you can purchase a new home.
In these situations, the Threshold Rule can be more effective than the 37% Rule.
The Threshold Rule suggests accepting an offer immediately if it exceeds a certain percentile of your expected range. This approach considers the cost of waiting for better offers and the potential gains from holding out.
Let’s break it down with a practical example:
Imagine you’re selling your home, and you expect offers between $400,000 and $500,000. The Threshold Rule considers the cost of waiting for each new offer:
- If waiting costs are negligible (say, $1), be extremely selective. In this case, you should wait for an offer of $499,552.79 or higher.
- If each wait costs $2,000 (perhaps in marketing or holding costs), you should accept any offer of $480,000 or above.
- In a slow market where each wait costs $10,000, lower your threshold to $455,279.
- If waiting costs half or more of your expected offer range ($50,000 in this case), accept the very first offer you receive.
This rule balances the potential for a better offer against the real costs of waiting. In other words, a seller’s market, you can afford to be picky. In a buyer’s market, beggars can’t be choosers.
Conclusion
We often need to make decisions while dealing with uncertainty, time constraints, partial information, and a rapidly changing world.
The 37% Rule offers a simple algorithm for decision-making strategies for major personal finance.
Its flexibility allows it to be applied to any situation where you need to make the best choice when options only present themselves one by one.
This is your best bet to prevent analysis paralysis and letting the best opportunities go by.