The home advantage bias and attribution bias during football refereeing in the German Bundesliga and the English Premier League.

The home advantage bias and attribution bias during football refereeing in the German Bundesliga and the English Premier League.

Andrew had always dreamt of a career in professional sports, unlike most people that want to work in sports he wasn’t interested in the fame and money. Andrew had spent the last 10 years working up to where he is. He had refereed for many small clubs and matches in the lower leagues. Now Andrew had been selected to referee for the top leagues. During his first big match Andrew was the top referee, he found himself in a stadium with 60,000 cheering and yelling fans. One of the star strikers went down after being tackled, he was holding is leg in pain. Andrew had a decision to make, was this a foul? He felt the pressure as the crowds began to yell. His decision would influence the outcome of the whole match.

Referees, judges and umpires all have an important role to play when it comes to sporting events. In a lot of sports the decisions of these officials can have a big impact on the position of a team in the league tables. The position of a team in the league table can furthermore affect the finances of a team, it’s investors and in some cases, it’s supporters.

One well known phenomena in sporting events is the home team advantage (Garicano et al., 2005; Rickman and Witt, 2008). The home team advantage phenomena states that a team playing in their home stadium will have an advantage over the opposing team simply because they are in their home stadium. The home team advantage has been shown in almost all sports. In football, researchers have investigated the home team advantage in the Italian Serie A league (Scoppa, 2008), the US leagues (Garicano et al., 2005), German Bundesliga (Kocher et al., 2004) and the English Premier League (Rickman and Witt, 2008). The decisions that referees make play a major role in the home team advantage.

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In 2007 Perrersson-Lidbom and colleagues at the Universities of Stockholm and Munich had an opportunity to study the home team advantage in 24 matches after a serious of problems in the Italian Serie A and Serie B leagues. On February the 2nd 2007 supporters of the Italian football clubs Calcio Catania and Palermo Calcio clashed with each other and police in a violent and serious act of hooliganism. Filippo Raciti, a police officer in Catonia was killed during the rioting and around a hundred people were injured. Following the rioting police forced some of the clubs to play their home games without spectators. The researchers found that the presence of the supporters had an influence on the decisions of referees. When matches were played in front of spectators referees would punish away team players more harshly and home team players more lightly for the same offences (e.g., fouls). The home team advantage disappeared when there were no spectators. This reveals that although referees are expected to be unbiased factors such as cheering spectators can influence their decisions.

Another study of the home team advantage phenomena was conducted at the California Polytechnic State University by Richard Pollard in 2002. Between 1987 and 2001 there was a building boom for new sporting stadiums in United States. Pollard gathered data from 37 teams as they moved to new stadiums. If the home team advantage was in part to do with the stadium itself then the advantage should temporarily disappear when teams move stadium. Pollard found that 24% of the advantage of playing at a team’s home stadium is lost in the months after moving stadium. A large bit of the home team advantage remains which may be in part due to the referee’s decision-making.

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One influence on a referee’s decision-making that many readers may find surprising are seemingly unimportant perceptual cues such at the height of a footballer (Quaquebeke and Giessner, 2010). Perceptual cues such as height can influence a referee’s decisions through a cognitive bias called the attribution bias. The attribution bias states that an attribute that does not implicitly seem important to the referee can influence a decision, when prompted the referee would not acknowledge that the attribute was an important factor in their decision. Quaquebeke and Giessner (2010) investigated what factors can influence the decision of a referee to punish a foul. They found that since height can sometimes be associated with the concepts of strength and aggression height can have an influence on decision-making. When an ambiguous tackle is seen by a referee the referee is more likely to attribute the wrongdoing to the taller of two players. Field data of decision-making by referees in seven UEFA Champion Leagues, three FIFA World Cups and the German Bundesliga league all support the attribution bias in refereeing.

So, although many of us are unlikely to find ourselves in the position of Andrew presiding over important decisions in a football match it is important to remember that we cannot always be unbiased in our decision-making. When Andrew finds himself deciding which of two players were in the wrong when going into a tackle he should bear-in-mind that factors that he is not aware of (e.g., height and crowd noise) play a role in his decision-making.

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The framing effect in bonobos, chimpanzees and capuchin monkeys.

The framing effect in bonobos, chimpanzees and capuchin monkeys.

Noam Chimpsky had had a long day playing with the other chimpanzees. Noam was starting to feel hungry so headed towards the eating area of his enclosure. He had previously stored some fruit on a branch of a large tree. On the way to collect his fruit Noam noticed that one of the other chimpanzees had a new fruit that he had never seen before. Noam was determined to try some of the new fruit. He collected some of his fruit from his stash of fruit and went towards the other chimpanzee who was still carrying the new fruit. Noam wanted to trade some of his fruit for the new fruit. He stopped in front of the other chimpanzee, placed his fruit on the ground and tried to trade. The chimpanzee with the new fruit was happy to trade but wanted more fruit then Noam was willing to give. Noam decided that trading was a bad idea if he had give away too much fruit so he went off to his tree, with his fruit in hand and settled down to eat – afterall, Noam could try some of the new fruit another day.

Noam’s choice was to trade a small amount of food for the new fruit or to trade a lot of his fruit for the new fruit. In the end, Noam decided that the new fruit was not worth a lot of his fruit. Like humans Noam is susceptible to the framing effect. For Noam, his dilemma was to accept a negatively framed trade were he would lose out by giving away too much of his fruit or to broker a positively framed trade were he would give away a small amount of fruit for the new fruit.

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The framing effect states that the manner in which options are presented (or framed) can influence how we evaluate choices. We evaluate the options relative to a reference point (i.e., the amount of fruit the Noam had to start with). Changes that seem to worsen the status quo (i.e., Noam giving away a lot fruit for the new fruit) are treated differently to changes that improve the status quo (i.e., Noam gaining the new fruit after trading for a small amount of fruit). The way in which we (and Noam) perceive the choice is important because we are more willing to invest in a choice that is positively framed, rather than negatively framed. The framing effect has been documented extensively in human decision-making in areas such as financial trading (Seo et al., 2010) and medical decision-making (Bornstein et a., 2001). However, as we have seen in the case of Noam other animals, other than humans also exhibit the framing effect. According to molecular-clock estimates our genus split with other primates around 23 million years ago (Schneider et al., 2001), which means that we share a common ancestor with other primates. We share some of our decision-making processes (e.g., the framing effect) with the other animals.

One study the sought to investigate the framing effect in other primates used 40 bonobos (Pan paniscus) and chimpanzees (Pan trogladytes) (Krupenye et al., 2015). The apes were required to make choices between a positively framed option that provided a preferred food item (fruit) and a negatively framed option with a different food item (peanuts). The apes completed 5 sessions of 12 trials on separate days. Both the bonobos and the chimpanzees choose the positively framed option more than the negatively framed option demonstrating that they were susceptible to the framing effect. Furthermore, male apes were more susceptible then female apes to the framing effect.

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Capuchin monkeys (Cebus appella) have also shown the framing effect in several different studies (Chen et al., 2017; Lakshminarayanan et al., 2011). In their natural environments capuchins live in complex environments, they are socially sophisticated primates whose native environment requires careful management of their scarce resources. The study by Lakshminarayanan et al., (2011) found that capuchins are able to learn how to trade tokens for food. When trading tokens they are susceptible to framing effect for positively and negatively framed choices.

The results of the capuchin, bonobo and chimpanzee studies suggest that the mechanisms that drive the framing effect is evolutionarily ancient. Some of our ‘human’ economic biases are shared by Noam and the other primates throughout the primate order. These studies highlight the importance of comparative research in understanding the origins of cognitive biases and individual differences in human decision-making. To understand the human brain and decision-making we should complement of research by looking towards our distant relatives.

The sunk cost effect in customer loyalty schemes.

The sunk cost effect in customer loyalty schemes.

Gary was bored at work sat at his desk. He was wasting time before a meeting by browsing some of the popular shopping websites looking for a new book to read for his commute to work. Like many of us, Gary has several membership subscriptions for websites that he shops on a lot. The subscriptions that he buys for with a small annual cost guarantees Gary quick delivery, priority ordering and discounts on a large range of products. Since he was bored at work Gary thought that he’d take advantage of his subscription and order another book – afterall he pays for the service so why not make the most of it?

Almost all large companies have subscription membership schemes that offer their customers exclusive benefits. Mobile phone (cell phone), internet, utility (e.g., gas and electricity), insurance, and travel providers all offer their main services alongside additional schemes with small membership charges for exclusive offers. These schemes are everywhere. Subscription-based membership schemes are very profitable for most companies, and rely on customers opting for easy, lazy decision-making over well thought through decision-making. The historical cost of purchasing the subscription for a small fee is an important aspect of this easy decision-making.

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Traditional economic theories explain that when making decisions historical costs should be irrelevant, they should not factor into present decisions (Kramer, 2017). One of the most prominent cognitive biases were historical costs are an important influencer of decision-making is known at the sunk cost fallacy. Kelly (2004) summarized the sunk cost fallacy into two claims: i) individuals give weight to sunk costs in their decision-making (i.e., subscription costs), and ii) it is irrational for them to do so.

Two popular examples of subscription-based membership schemes that benefit from the sunk cost fallacy are the Bahn Card 50 and Amazon Prime. The Bahn Card 50 is the original customer loyalty scheme for Deutsche Bahn, the main German railway operator. The Bahn Card 50 was introduced in 1992, as of 2014 it had 5 million subscribers. The fee for the card is EUR 255 per year which gives the owner of the card a 50% discount on train fares. Like all of the other subscription-based membership schemes the owner of the Bahn Card 50 regards the subscription cost as a sunk cost (historical cost), in doing so they try to make the most of the scheme (Tacke & Firmer, 1992).

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Amazon’s subscription-based membership scheme is called Amazon Prime. Prime was introduced 2004. For a fee of $99 (as of November 2014) subscribers get faster delivery and priority to access to products on Amazon’s shopping website. Prime has become an important part of Amazon’s business model. After the introduction of Prime Amazon recorded a 150% increase in purchases by Prime members in the year immediately after joining the scheme. Prime is responsible for 20% of Amazon’ overall sales in the United States. For a company that makes billion in profits every year exploiting the sunk cost fallacy has proven to be very profitable.

The sunk cost fallacy is just one of many cognitive biases that influence decision-making. As we have seen above for people like Gary, and of course most of us, the sunk cost bias can affect the way in which we make decisions through subscription-based membership schemes. It is easier to think that since we have already paid a membership fee that gives us a discount we should take advantage of this and use it. If you sit back and think about the situation not exploiting the discount actually saves us more money than the discount would because we are not spending anything more. Big companies such as Amazon have learnt to use these cognitive biases, thereby making huge profits from our quick, easy and effortless decision-making.

The gambler’s fallacy, framing, anchoring and hindsight bias in judicial decision-making.

The gambler’s fallacy, framing, anchoring and hindsight bias in judicial decision-making.

Months before William had moved into a new house with his wife, Sarah. They were excited about their move to their new home. William and Sarah unlocked the door to their new house and waited for the furniture removal staff to arrive at the end of their driveway. As the removal van drove up the driveway the van struck the side of William and Sarah’s car, writing the car off. Several months later William and Sarah headed to court to claim for the damages of their car against the removal company. Over many years their lawyer had learnt to be careful in the way that she words her cases in court. Their lawyer asked the judges “How much would you award the plaintiff in compensatory damages?” rather than “We are claiming ‘x’ amount from the defendant.” William and Sarah’s lawyer had learnt that framing a case in such away could exploit the framing bias.

The judicial system is one of the most important systems in all countries with a court of law. Judges play a significant role in society deciding on the outcome of many cases and setting precedencies for future cases. The public expect judges to decide on the outcome of each case fairly, without systematic errors or bias. However, like any instance where a person must make a judgement or decision judges are subject to systematic biases.

One of the most important aspects of law for many people is immigration law. Asylum judges must make decisions that can determine the fate of the individual in court. The United States offers asylum to foreign national who can (i) prove that they have a well-founded fear of persecution in their own countries, and (ii) that their race, religion, nationality, political opinions, or membership in a particular social group is one central reason for the threatened persecution. In 2016 a study by Harvard academics investigated the use of heuristics and biases in the asylum courts of the United States (Chen et al., 2016). Chen and colleagues accessed data through a Freedom of Information Act request of 699 decisions that were made by 357 judges in 45 courts from 1985 to 2013. All cases in the US are handled on a first-in-first-out basis with no quotas as to how many individuals are granted, or not granted asylum. On analysis of the data Chen et al found that judges were subject to the gambler’s fallacy. Judges were more likely to grant (or deny) asylum after denying (or granting) asylum to a previous applicant. The judges presiding over the fate of the asylum applications believes in a representation of randomness, as such, they were more likely to alternate between granting and denying asylum.

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There are many other heuristics and biases that play an important role in the judicial decisions of judges in the US these include framing (as demonstrated by William and Sarah’s lawyer), anchoring, and the hindsight bias. In a joint study with Cornell Law School researchers at a major conference issued questionnaires to 167 federal magistrates. The questionnaires contained examples of cases that a court judge would preside over. From the responses to the questionnaires Guthrie et al (2002) found that judges were subject to anchoring in personal injury claims, framing in copyright action cases, and the hindsight bias in cases of medical negligence.

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A retrospective study by Rachlinski et al (2000) examined the effect of the hindsight bias on decisions in real court cases. The hindsight bias is a mental shortcut that states that we have an inclination, after an event has occurred, to see that the event was predictable. The researchers found that judges failed to appreciate the problems associated with judging an event from hindsight. One example (Chase v. Pevear) from the Supreme Judicial Court of Massachusetts decided that the trustees of two high risk investments should have known the outcome of the investments with a nearly omniscient ability. In the case of First Alabama Bank v. Martin the Alabama Supreme Court held that another group of high risk, high yield equities were speculative, as shown by the fact that trustees lost money by selling ‘at the bottom of the market.’ The courts in both cases assumed that the investors should have known that the price of the equities were recovering, and hence the trustees should not have sold them. One court even held a trustee liable for failing to predict the stock market crash of 1929.

The public expects our judges to make well balanced judgements and decisions without falling for cognitive biases. As humans tasked with make important decisions the judicial system is not isolated from any of the shortcuts that we use to make decisions. For William and Sarah seeking their compensation for the damage to their car the experience that their lawyer had with the framing bias came in useful. In many of other cases, such as with the example of the gambler’s fallacy in the asylum courts these cognitive biases and mental shortcuts can become problematic. We have seen how the framing bias, anchoring heuristic and hindsight bias are all important when making decisions, even at the highest levels of decision-making in society.

 

 

 

Heuristics in political voting.

Heuristics in political voting.

On the way to vote in the general election John and Sarah were discussing which candidate and political party they were going to vote for. There were two main candidates up for consideration in the general election. The first candidate was seeking a second term in office whilst the second candidate was opposed to the first on policies but had higher approval ratings. Sarah mentioned to John that she was going to vote for the first candidate because the economy had done well under his administration. John said that he was going to vote for the second candidate because she had higher approval ratings, as such, was more popular than the first candidate. They both went on to cast their votes for their favoured candidates and continued on in the day as normal.

In the example of John and Sarah above we see a typical quick discussion with some reasons given for choosing one candidate over another in a general election. Like many voters, John and Sarah think about the state of the economy and approval ratings when trying to decide on who to endorse for public office. Voting in this way is not necessarily the most rational way to vote, however, this demonstrates some of the cognitive biases that influence our decisions during the voting process. When pressed to give a reason why we have voted in the way that we did many of us would say that we weighed up the policies of each candidate (and political party). We like to think of ourselves as rational decision makers however this is not so. Cognitive biases influence our decisions far more than would like to think so.

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General elections are some of the most important decisions that we can make. Although general elections in the United Kingdom are every four years the political party that goes on to win an election can influence our lives in almost every way. The incumbent Prime Minister takes charge of the direction of economic, health, judicial, educational and defence policy. If you have been saving up for the deposit for that nice first home and you are planning to take out the mortgage on the home in the next couple of years the housing policies of the Prime Minister are important, some ministers may help first time buyers whilst others may not.

When putting the ‘X’ next to the preferred candidate’s name and political party at the ballot box we all have a preference for some policies over another (e.g., conservative or liberal policies). John may like the idea of the nationalisation of railways, as such he decides to vote for the party that has historically been in favour of nationalising the railway network. By voting in this way John has made an affective (i.e. emotional) response towards a political party and voted by using the likability heuristic rather than thinking through all of the policies of each political party (Brady & Sniderman, 1985). The likeability heuristic has help John make a quick decision about who to vote without the need for agonising over the decision.

Another rule of thumb (or heuristic) is to evaluate the incumbent Prime Minister on the basis of the economy’s current performance – if the economy is doing well than the Prime Minister is a good candidate (Popkin et al., 1992; Schneider et al., 1985). One problem with this ‘economic performance heuristic’ is that it makes the assumption that the Prime Minister controls the economy, for the most part this is incorrect. The Prime Minister can decide on policies, but they do not control the economy. This ‘economic performance heuristic’ merely rewards good economic luck whilst punishing poor economic luck. For the Prime Minister’s cabinet, this rules of thumb provides incentives to sacrifice long-term economic growth for small boosts in activity, particularly, in election years (Fiorina et al., 1981).

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A third heuristic that many of us use when deciding on who to vote for in a general election, or even local elections, is the ‘approval rating heuristic’. One cleverly designed study in 1993 by Jeffery Mondak at the University of Pittsburgh, Pennsylvania showed that participants used Ronald Reagan’s approval ratings as a cue to performance. When Reagan had high approval ratings, participants said that they would be more willing to vote for him, compared to when he had low approval ratings, Reagan’s policies did not differ with popularity. The study by Mondak demonstrates one important heuristic, that is that, we are more likely to vote for a candidate with high approval ratings than low approval ratings simply because they are more popular than the other candidates.

Heuristics aren’t only important when making decisions in elections they also play a role in the outcome of referendums. Under the direct democracy system of Switzerland, the Swiss have more referendums than any other country. In 2016 alone the Swiss had 13 referendums on a variety of subjects such as highway construction, wine production and economic policy (see Table 1 for an example). At first thought the idea of having a lot of referendums seems like a good idea because the public get their say on all government policies, however this system has its problems. When voting in a referendum we are expected to become informed about a diverse and highly complex serious of issues in our spare-time. One study by Gruner and Hertig (1983) collected data from the regular referendum voters in Switzerland after each vote from 1977 to 1983, they found that only 20% of the voters were actually well informed about the issues at stake, many of the voters only knew the name of the referendum.

(Table 1)

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So, how do we make these decisions about the referendums that are crucial for the direction of government policy? Two of the heuristics that are important decision-making tools for referendum voting are the status quo bias and the likeability heuristic (Passy, 1993; Clarke et al., 2012). The status quo bias suggests that we favour the known over unknown, and reject the new and untested in favour of the familiar. As we can see in the table above many voters choose to stick with the familiar and reject the new without any clear reason. One example of this is the Swiss referendum on joining the European Economic Area (EEA) in December 1992. Following the voting academics asked the voters for their reasons and how they voted (Passy, 1993). On average 30% of the voters could not give any reason for how they voted (despite voting), 50% were able to give one reason and 20% gave two reasons, we can take this as an indication of the level of knowledge that each voter had. Many of the voters voted to reject joining the EEA by using the status quo heuristic.

The second of the heuristics that is involved in decision-making in referendums was demonstrated in Britain’s Alternative Vote referendum on the 5th of May 2011. This referendum is unique because there were some strong political personalities involved in campaigning for and against the alternative ballot. By the time of the referendum Nick Clegg, leader of the Liberal Democrat party had become decidedly unpopular, he was part of the coalition government and went back on many of his campaign policies. Nick Clegg campaigned for the Alternative Ballot. Following the voting many voters stated that they voted against the alternative vote because they no longer liked Nick Clegg (Clarke et al., 2012). Voters used the likeability heuristic rather than weighing up the consequences of voting for or against the referendum.

Like John and Sarah if you are agonising about who to vote for in a general election of which way to vote during a referendum bear-in-mind that there are many cognitive biases (heuristics) can help you make the decision. For better or for worse even when making highly important decisions cognitive biases such as the likeability heuristic, approval rating heuristic, the status quo bias and economic performance rule of thumb influence our decisions. To avoid these cognitive biases, we can start by thinking about the reasons why are considering voting the way we are.

The anchoring and adjustment heuristic in real estate transactions.

The anchoring and adjustment heuristic in real estate transactions.

Jack and Sally were looking forward to buying their first house. Like many of us they browsed real estate websites and searched through the real estate agent shop windows looking for that ideal first home. As Jack and Sally browsed the potential properties they kept in mind their budget. Jack made note of a few potential properties with the correct number of bedrooms, bathrooms, reception rooms and garden size whilst noting the seller’s asking price. Eventually after visiting a few properties for viewings they decided to put an offer in for one of the properties. Jack noticed the asking price saying that it was above their budget and for a moment began to lose hope. After discussing with Sally as to how much to offer for the house Sally reminded Jack that although the house was on offer for £500,000 they could in fact put in an offer below the asking price. Jack and Sally went on to offer £450,000 and after some consideration the seller agreed to the price. Jack and Sally won the property that they wanted to buy and in the process demonstrated one of the most prevalent cognitive biases in human decision-making, namely the anchoring effect.

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The anchoring effect (also called the anchoring and adjustment bias or anchoring and adjustment heuristic) is one of the cognitive biases that occurs most often when making a judgement about the quality, value or worth of an item. The effect works because when you are given a number (e.g., 1200 meters) that relates to a property of an item (the quality etc) with a question about that property you are likely to, whether knowingly or not, anchor your judgement by using the number as a reference point (i.e. the anchor). Imagine a boat tethered to a lowered anchor, the boat cannot move far from the anchor and remains within range of the length of the tether. We typically do not deviate a lot from the anchor. The anchoring effect has been observed to influence factors such as charitable giving, price valuations, fairness judgements, loyalty judgements, judgements of guilt, and prosocial motives among many other factors (Soule & Madrigal, 2015). The anchoring effect is just one of many cognitive biases that influences our judgements in a systematic and predictable fashion. It is simply easier to accept the anchor (e.g., the house seller’s asking price) and adjust closely to the anchor (e.g., £50,000 less rather than £150,000) than to make an entirely new judgement about something (e.g., the value of the house).

In the case of Jack and Sally there is a clear anchoring effect (albeit to no negative effect to the seller). Jack and Sally see the initial asking price (the anchor) and consider making offers that are close to this, they are anchored by the seller’s asking price. Of course, in the case of buying a house deviating from the asking price by too much (toward a lower price) will result in a rejected offer.

It is important to understand the anchoring effect because prices are by nature simple numeric information that can act as the anchor or reference point. Since the majority of the most agonising judgements that we must make in life revolve around pricing (e.g., buying that first house, holiday home, car or a big holiday) understanding the role of cognitive biases in decision-making is important. To avoid falling into the trap of the cognitive biases we should make ourselves aware of these biases. We are not the rational decision makers that we think we are.

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One large study investigating the anchoring effect in residential home sales recorded data from 14,000 separate transactions (Bucchioneri & Minson, 2013). The researchers noted that the literature on housing economics, negotiations and auctions converge on the notion that home prices are an objective function of the property’s neighbourhood and characteristics (e.g., number of rooms, size of the house and characteristics). However, as we have seen above the judgement and decision-making literature on the anchoring effect suggest that there is a positive relationship between the listing price (asking price) and the sale price. The analysis of the 14,000 transactions found that higher asking prices are associated with higher sales prices independent of the property’s features, which is consistent with the anchoring effect. For the average property in the study overpricing by 10 to 20% lead to an increase in sales price because buyers were anchored by this higher sales price. So, whether a property has 5 large bedrooms in a desirable area of the countryside or 2 small bedrooms in a noisy part of a city asking for higher price for the 2-bedroom property (compared to the 5-bedroom property) could result in a higher sales price than the 5-bedroom property despite the larger property being initially more desirable than the smaller.

In the domain of property rentals the same study by Bucchianeri and Minson found that by adopting the same strategy of overpricing the asking rental price by 10 to 20% there was an increase in rental value of $117 to $163.

The data from transactions on house sales and rental pricing suggest that although we tend to believe that it is the characteristics of a property that determines the value of the property this is not the case. Pricing strategies are the major determiner of the value of the property. So, whether you are buying you first starter home, buying your dream family home, renting your house or looking to rent a house being aware of the anchoring effect will save you or make you more money. If you are selling your house to take full advantage of anchoring set your asking price 10-20% higher than the valuation, after all, if your property does not sell you can simply reduce the price at a later point. If like Jack and Sally you are buying your first home, to take advantage of the anchoring effect you can start by being aware that the asking price is not absolute, you can put in a lower offer if this is reject simply increase the offer by a small amount.

The finance room