Loss Aversion in the Norwegian Housing Market

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Loss Aversion in the Norwegian Housing Market

Andreas Eidspjeld Eriksen

Master of Philosophy in Economics 30 credits

Department of Economics Faculty of Social Science

May 2020

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Abstract

This thesis investigates the role of loss aversion in list price decisions, experimenting with three different reference points and two substitutes for expected market price. By using transaction data from Oslo and Stavanger, I find evidence that list prices are increased when sellers are facing prospective nominal losses. The effect of facing a loss is stronger if the seller paid above expected market price when buying the property. In addition, the results are evaluated based on reduced forms of the specifications, which among other things, supports the notion that the Norwegian appraised value is a strong indicator for expected market value, which could solve the identification problem in previous studies. The results does not directly imply identification of loss aversion because loan-to-value is not controlled for, but the result suggests reference dependence.

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Preface

I am grateful to my two supervisors Andr´e K. Anundsen and Erling Røed Larsen at Housing Lab, Oslo Metropolitan University, for introducing me to the topic, and for great supervision and creative input. Your guidance and knowledge have been invaluable. I would like to thank all the people at Housing Lab for fun and interesting discussions in lunch breaks. The great work environment has made me feel like a part of Housing Lab, and hopefully I will see you sooner rather than later. I would also like to thank Anders Lund and Eiendomsverdi AS for providing the data used in this thesis. Without these unique data this thesis would not have existed, and I really hope you find the results interesting. In addition, I would like to thank Kjell Arne Brekke for allowing me to sign him on as the project leader in the notification form at NSD. Finally, I would like to thank my wife Karoline for support and understanding throughout the years of studying at the University of Oslo, not to mention in the hundreds of hours invested in writing my thesis. Any remaining errors are my own.

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1 Introduction

Houses are expensive relative to most households’ income. Because of their high value, they account for the largest fraction of total household assets. Flavin and Yamashita (2002) find that homes are likely to be a major component in asset portfolios, and play a major role in consumption bundles. Hence, selling a property can be stressful; choices made in the selling process could potentially have large impacts on the household budget and future consumption.

When selling properties, the high stakes could give sellers incentives to affect the final selling price. Moreover, risks associated with selling could induce cognitive biases in the decision making process (Kahneman & Tversky, 1979; Tversky & Kahneman, 1974). In housing markets, people could potentially lose money as a result of realizing a negative price development. In such situations, the cognitive bias of loss aversion can manifest in decisions about list prices. Loss aversion is the tendency that people get more pain from losing an amount of money compared to the satisfaction from gaining the same amount. In other words, when people make decisions concerning losses and gains under uncertainty, “losses loom larger than gains” (Kahneman & Tversky, 1979, p. 279). Housing markets are characterized by risks and uncertainties, and some sellers face prospective losses. Hence, loss aversion should affect list prices in housing markets due to relatively high psychological costs of losses in these markets.

However, it is not straight forward to identify cognitive biases in housing markets. There are attempts of identifying loss aversion and anchoring, and the most notable is the seminal paper of Genesove and Mayer (2001). In this thesis, I apply and extend on the Genesove-Mayer framework for identifying loss aversion to find out how prospective losses affect list prices.

Understanding how list prices are affected is important to better understand underlying market mechanisms in housing markets, and could be essential for policy makers in designing efficient regulation. In a broader sense, empirical evidence of behavioral biases can give incentives to implement behavioral concerns into economic analysis. In addition, the potential large implications for the individual household budget and portfolio gives motivation for studying how list prices are decided upon. The following anecdote presents a directly related situation that sellers could experience:

Suppose Paul has decided to sell his home. The first step in the selling process is to hire a real estate agent to help with selling the property. The agent gives an estimate of the market price and strategic advice for deciding upon a list price, and Paul expects that the list price may affect the selling price. Further, suppose that when he bought his home, Paul paid a higher price than what he could expect to get today. Thus, Paul faces a prospective loss, which can create unintended psychological effects that alters his decision regarding the list price. According to behavioral theory, Paul is expected to increase the list price compared to a situation with no prospective loss.

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The situation suggested by this anecdote presents the fundamental situation which is examined in this thesis, namely whether sellers in housing markets are loss averse. As suggested in the anecdote, prospective losses and gains are considered from a reference point, which could be determined by the previous selling price. In the paper of Genesove and Mayer (2001), information on repeated sales in the Boston condominium market is used to identify prospective losses, using the seller’s purchase price as a reference point. If there are no behavioral effects, the expected market price should alone determine the list price, hence no backward-looking behavior. The anecdote also suggests that list prices affect selling prices. It is expected that an efficient market will correct any list prices deviating from the expected market prices, but previous studies have found evidence of list prices affecting selling prices (see e.g. Anenberg, 2011; Anundsen et al., 2020; Bucchianeri and Minson, 2013; Genesove and Mayer, 2001; Han and Strange, 2016; Haurin et al., 2013).

I start with replicating Genesove and Mayer (2001). More specifically, I estimate the effect of facing a prospective loss on list price, while also including expected market price and a control variable of prospective gains, the latter as suggested by Bokhari and Geltner (2011).

The prospective gains control variable makes it simpler to identify biases in the coefficient on prospective loss, evaluated by the reduced form of the specification. Adding to previous research, I introduce two additional values that could serve as reference points. First, hedonic prices at the time of the previous sale, meaning fitted values from estimation of selling prices using inherent property attributes and time-effects, a method introduced by Rosen (1974).

Second, the appraised value at the time of the previous sale. Both these additional hypothetical reference points suggest that the seller has the expected market price as the reference point in the process of deciding on a list price. In total, three hypothetical reference points are investigated.

The appraised value is applied to greater lengths in this thesis. TheNorwegian appraised value is obtained as part of the selling process from a surveyor. The strength of using the appraised value is that it is found to be a good indicator for the list price, and it has the advantage of including information about qualities that are unobserved by the econometrician.

In fact, by performing a simple linear regression of log(list price) on log(appraised value) the R squared becomes 0.9872.¹ In this thesis, the appraised value is also used as an alternative to the hedonic prices. Hence, two substitutes of the expected market price are used in the estimations. In addition to the specifications with the three hypothesized reference points and the two substitutes for expected market value, I investigate whether the effects of facing a prospective loss depends on whether the seller paid above, at, or below the previous expected market price. This relationship is estimated with all combinations of reference points and market price substitutes.

The data used in this thesis are housing transactions provided by Eiendomsverdi AS, containing observations of transactions in Oslo and Stavanger over the period 2003–

1Anundsen et al. (2020) argues that appraised value is an unbiased predictor of the actual selling price.

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2019. The initial number of observations is 310,343, and each observed transaction has transaction- and property-specific variables. Estimations are done in two stages for Oslo and Stavanger separately. Over the period, Oslo has experienced a continuous increase in prices, which can be characterized as a booming market, while Stavanger was in a boom until 2013. I obtain predicted prices using a hedonic time dummy model, and estimations of the relationship between prospective loss and list price are done using non-parametric bootstrapping. Significance is evaluated based on the basic method of Davison and Hinkley (1997).

The main results suggest a positive and significant effect on list prices. The estimated effect suggests that if a seller faces an increase of 10 percent prospective loss, this is associated with the seller increasing the list price by somewhere between 3.0 and 6.6 percent. Estimations using the appraised value as expected market price indicate that the effect is in the upper part of this range. The estimate when the seller considers the actual price paid for the property, while using appraised value as expected market price, is 5.7 percent. Thus, if emphasizing the actual nominal break-even point as the reference point and the advantages of using appraised value as expected market price, then this estimate should be preferred. In addition, the effect of facing a prospective loss is stronger for sellers who paid above expected market price.

This thesis adds to the previous literature of loss aversion, anchoring, and reference dependence in housing markets (e.g. Anenberg, 2011; Bokhari and Geltner, 2011; Eini¨o et al., 2008; Engelhardt, 2003; Genesove and Mayer, 2001; Haurin et al., 2013), by investigating the additional reference points, using the unique Norwegian appraised value, and investigating the effect of previous over- or underpayment. Further, I evaluate reduced forms of all specifications, and find that using the appraised value as a substitute for expected market value in some cases give identification of the relations of interest, given some specific assumptions.² In addition, I discuss an implication of the reduced form result of Bokhari and Geltner (2011) and how this could give a downward bias in the estimate of the effect facing a prospective loss on list price.

The thesis is structured as follows: section two gives an introduction to prospect theory, loss aversion, anchoring and related housing studies. The data are described in section three, which includes a description of the data tidying process and a subsection on institutional background.

In section four, empirical models are discussed, which includes discussions of the hedonic modelling, a general review of the model framework of Genesove and Mayer (2001), and presentations and discussions of all specification combinations. Here, I also evaluate estimation biases based on reduced forms of the specifications. This is followed by the estimation results in section five, which also includes the description and results using the over- and underpayment specification. Finally, the thesis is concluded in section six.³

2Identification has been a major issue in previous studies of loss aversion in housing markets, see Genesove and Mayer (2001).

3All data tidying and estimations are done in RStudio.

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2 Literature Review

2.1 Behavioral economics

2.1.1 Prospect theory

Prospect theory was introduced as an alternative to expected utility theory, which relates to how rational choices are made under risk or uncertainty (Kahneman & Tversky, 1979). The paper has over the years gained much attention, and Kahneman was awarded with the Nobel prize in economics in 2002 particularly for this work. A reason for the award was that Kahneman and Tversky integrated psychology into economic theory, which started the field of behavioral economics.

Expected utility theory is widely applied in the field of economics for analysis of economic agents’ decision making, and the axioms which create the foundation of its application was presented in the book of Von Neumann and Morgenstern (1947). Expected utility theory in it’s simplest form can be illustrated by the following example: consider a risk neutral agent that has two choices, either gain $100 for sure, or gain $50 with 50% probability and gain

$150 with 50% probability. This problem can be presented as the choice between($100,1)and ($50,0.5; $150,0.5).⁴ Because the agent is risk neutral, the expected utility of this gamble is equal to the certainty choice, namely u($100) =0.5u($50) +0.5u($150), making the agent indifferent between the two choices. If the agent was risk averse, which is the common assumption in economics, then the certainty-choice would be preferred to the gamble, hence the utility function of the agent is concave. And if the agent is risk seeking, the gamble is preferred over the certainty-choice.

Kahneman and Tversky (1979) criticized expected utility theory by presenting multiple empirical results which violate the axioms of expected utility theory. They presented three effects that give violations of expected utility theory, which they labelled the certainty effect, the reflection effect, and the isolation effect. In short, the certainty effect is the effect of over- weighting the certain outcomes, for instance the certainty choice ($100,1) in the example above, which can lead to reversion of preferences if restating the outcomes with the same relative probabilities. The reflection effect is when the certainty effect manifests for positive prospects, then for the same outcomes and probabilities but with negative prospects, the order of preferences is reversed. An example of this isproblem 3of Kahneman and Tversky (1979):

in the choice between (4,000,0.8)and(3,000,1), 80% of subjects chose the certainty-choice of receiving 3,000, thus the preference is (3,000,1)(4,000,0.8) implying risk aversion.

However, when the choice is stated for negative prospects, the preference is reversed giving (−3,000,1)≺(−4,000,0.8)implying risk seeking preferences.⁵ The isolation effect is related

4The general notation is(x,p), where x is the outcome and p is the respective probability of that outcome.

5This problem and others in Kahneman and Tversky (1979) are in fact based on problems in the French paper

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to theelimination-by-aspectsprocess discussed in Tversky (1972), suggesting that when people evaluate choices, they eliminate shared aspects and focus on the differences. Following the problem above of the choice between(3,000,1)and(4,000,0.8), but as the second stage of a game that starts with the probability of 75% of the game ending immediately. This first stage probability is shared by both choices thus eliminated, making people reduce the choice to the one that over-weights the “certain” choice. However, if framed in a reduced one-stage game of the choice between(3,000,0.25)and(4,000,0.20)the certainty effect does not kick in, and the majority of subjects preferred(4,000,0.20).⁶

As a result of the violations of expected utility theory these three effects induce, the alternative prospect theory was introduced. The differences in components between expected utility theory and prospect theory are the value function v(x) and the decision weights π(p).

In general, a prospect has the total value which equals the value of each possible outcome multiplied by their respective weighted probabilities, hence V(x₁,p₁;x₂,p₂) =π(p₁)v(x₁) + π(p₂)v(x₂)for the choice (x₁,p₁;x₂,p₂).⁷ The sum of the decision weights does not have to equal 1, as a result of the psychological weighting of the different probabilities. Further, the weighting function is non-linear and was initially vaguely defined, but this was improved upon by introduction of cumulative prospect theory (Tversky & Kahneman, 1992).⁸

The value function differs in some aspects from the utility function. A feature of prospect theory, which is arguably the main feature, is that “the carriers of value are changes in wealth or welfare, rather than final states” (Kahneman & Tversky, 1979, p. 277). Thus, the value of a prospect is defined by the changes the prospect constitute, meaning that there must be a status-quo level of wealth that the changes are evaluated from. In this context, the status-quo level is called the reference point. Tversky and Kahneman (1991) summarizes the three main aspects of the value function: first the reference point of which gains and losses are evaluated from, implying reference dependence. Second, the value function has diminishing sensitivity of gains and losses, meaning decreasing marginal value constituted by the value function’s concavity for gains and convexity for losses. Third, the value function is steeper for losses than for gains, giving a distinctive kink at the reference point. Kahneman and Tversky (1984) labelled the difference in steepness loss aversion, which implies that losing $100 hurts more than the satisfaction of gaining $100. In absolute terms, the relation for the prospect of $100 becomes|v($100)|<|v(−$100)|. These three aspects are summarized by the hypothetical value function in figure 1.

of Allais (1953), which also highlights some problems of expected utility theory. The problem is an example of the Allais paradox, and the paper was later directly translated to English in 1979. Also, in the Kahneman and Tversky version, outcomes was presented in Israeli pounds.

6The two-stage game is problem 10and the reduced-form framing isproblem 4in Kahneman and Tversky (1979).

7This is the regular expression if the outcomes are not strictly positive or negative.

8One aspect of the initial weight function was that, due to the non-linearity, it could violate stochastic dominance, meaning that it is possible that an agent could choose a dominated gamble (Barberis, 2013).

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Figure 1: An early example of a hypothetical value function

A hypothetical value function. From “Prospect Theory: An Analysis of Decision under Risk”, by Kahneman and Tversky, 1979.

Studies proceeding Kahneman and Tversky (1979) have investigated different aspect of prospect theory and tried to make it more tangible. Some studies have tried to estimate the value function and the weighting function using different function specifications on different experimental data (see e.g. Camerer and Ho, 1994; Lattimore et al., 1992; Tversky and Kahneman, 1992).⁹ And directly related to loss aversion is the disposition effect, which is the “general disposition to sell winners too early and hold loser too long” (Shefrin & Statman, 1985, p. 777), implying that investors are reluctant to realizepaper losswhile prone to realize paper gain.¹⁰ In Kahneman and Tversky (1979) the reference point is defined merely vague as being neutral and a zero-point. As a result, some studies have tried to clarify some of the uncertainties regarding prospect theory, where the papers of K˝oszegi and Rabin (2006, 2007, 2009) are arguably the most notable. An important insight from the K˝oszegi-Rabin papers, as pointed out by Barberis (2013), is their argument that gains and losses could be evaluated from a reference point determined by agents’ expectations. Further, some studies propose other alternatives to expected utility theory such as Loomes and Sugden (1982) who develop a theory with regret explaining rational decisions under uncertainty, and Gul (1991) who develops a theory of disappointment aversion to explain decisions under uncertainty.

9For those interested, Stott (2006, p. 106) gives an overview of the different proposed functional forms of the value function and the weighting function.

10In their paper, Shefrin and Statman (1985) discuss the close relation tomental accounting, which is the mental process of how people keep track of their money and how they spend it by creating accounts in their minds (Thaler, 1985).

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2.1.2 Anchoring and loss aversion

While prospect theory proclaim that choices under uncertainty are evaluated based on prospective values weighted by a non-linear transformation of probability of each possible outcome, the focus of this thesis is the loss aversion aspect, which must be considered in conjunction with the reference dependence aspect. Preceding the introduction of loss aversion and reference dependence as aspects of prospect theory, Tversky and Kahneman (1974) discussed three central heuristics in judgement under uncertainty and how these lead to biased judgements; the representativeness heuristic, the availiability heuristic, and the anchoring and adjustment heuristic. To clarify, the term heuristic means a process “which reduce the complex tasks of assessing probabilities and predicting values to simpler judgemental operations” (Tversky & Kahneman, 1974, p. 1124).¹¹ Thus, people use shortcuts in assessing probabilities when facing uncertainty. The representativeness heuristic is used when people are assigning someone or something into a specific category or class. This label assignment can for instance be how the description of a person can create the association of the person’s line of work. The availability heuristic is used when people are evaluating frequencies of categories or classes, by associating the problem with similar known cases. An example of this is how the probability of heart attack for certain people is assessed by using the known heart attack incidents among friends and family members (Tversky & Kahneman, 1974, p. 1127).

Although the first two heuristics are not directly related to the research question in this thesis, the important heuristic is anchoring and adjustment.¹² This heuristic is the process of estimating values; a person is given some initial value, and then the person makes some adjustments from this initial value. An example of anchoring and adjustment, which also relates to the real estate perspective of this thesis, comes from the study of Northcraft and Neale (1987).

They conducted two experiments where students and real estate agents were asked to estimate the value of a property. Subjects were given a booklet containing information of the property and then toured it; the only difference in the booklets were the stated list prices which deviated either above or below the true liar price. The findings of this study were that the subjective estimations deviated consistently with the given list price, meaning they found evidence of an anchoring strategy among participants. To summarize, anchoring is the effect of being presented with a value, which then serves as a starting-point for evaluations of the value in question. After the anchoring, the person adjust the value estimate beginning at the anchored value.

11The models of heuristics and biases to understand judgement under uncertainty has been criticized. One of the most persistent critics is the German psychologist Gerd Gigerenzer, who argues that the cognitive illusions or errors which occur due to the “heuristics” does not really exist. See e.g. Gigerenzer (1991, 1994) for critique, and Kahneman and Tversky (1996) for a reply.

12The anchoring and adjustment heuristic originates from Slovic and Lichtenstein (1971, pp. 712–713). They discuss the strategy ofstarting-point and adjustment, and give the insight that the adjustment is often inefficient.

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2.2 Loss aversion and anchoring in housing studies

Loss aversion and anchoring are subjects of interest in real estate economics. The two terms are to some degree interchanged, but a common rule is that studies dealing with anchoring could be investigating seller or buyer behavior, while loss aversion studies mostly investigate seller behavior.

To see how the terms are interchanged, and for clarification in this thesis, I deem it necessary to define how the terms are connected. Anchoring is the effect an initial value has on evaluations of values, gains and losses are evaluated from a reference point which is called reference dependence, and loss aversion is the difference in slopes of valuations of changes from this reference point. By the additional insight from K˝oszegi and Rabin (2006), the reference point can be determined by expectations. Thus, it could be argued that the seller is anchoring to the initial price associated with the property, at the time when the seller bought the property, and value estimation is based in this starting-point. Moreover, the anchor can serve as the reference point when evaluating gains and losses when selling the property, making the anchor the mental break-even value. Hence, loss aversion in this context is not exclusively the extensive margin adjustment; the question of whether or not sell the property.¹³ Loss aversion could be related to the behavior the seller exerts when faced with a prospective loss; if a seller is loss averse, this is revealed by a higher reluctance to sell below, relative to the additional inclination to sell above, the reference point price, giving the kink at the reference point. A seller is more reluctant to accept the loss, and will try to minimize the loss, hence anintensive marginadjustment.

The original study of loss aversion in housing markets is the study of Genesove and Mayer (2001). They investigate loss aversion in the Boston condominium market by applying econometric methods on transaction data between 1990 and 1997, finding a significant effect of loss aversion on list prices and selling prices, although the results on selling prices could be interpreted as insignificant because the lower-bound effect is found to be insignificant.¹⁴ Given that the upper-bound significance on selling prices alone indicate causality, the effect of loss aversion on selling price suggests that the market does not fully correct deviations from the expected market price, implying some inefficiency in the housing market. If the market was fully efficient list prices should have no effect on selling prices, because deviation from expected market price would have been corrected by the market, coercing sellers to correct listing prices (Genesove & Mayer, 2001).

More recent studies have also investigated loss aversion in the housing market: Engelhardt (2003) investigates how loss aversion affect household mobility, meaning the probability of

13The extensive margin adjustment in a choice of gambling is whether or not to enter the game, or accept the gamble.

14Two things worth mentioning: first, an additional interesting result is that loss aversion gives longer time on market. Second, the study proceeded the study Genesove and Mayer (1997), investigating how loan-to-value ratio affects list prices, meaning liquidity constraints. It is indirectly admitted that loss aversion was an omitted variable in the 1997-study (Genesove & Mayer, 2001, p. 1236).

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moving. He found that loss aversion gave a reduced probability of moving; a result which could be interpreted as extensive margin adjustments resulting from loss aversion. Bokhari and Geltner (2011) investigate how loss aversion affect list prices and how behavioral effects affect selling prices in the commercial real estate market between 2001 and 2009. By using the loss aversion model framework of Genesove and Mayer (2001) as a foundation to study anchoring and loss aversion effects on selling prices, they found that loss aversion is a strong effect even for experienced traders on list prices and that anchoring and loss aversion have significantly positive effect on selling prices.¹⁵ Anenberg (2011) finds positive effects of loss aversion and equity constraints on selling prices in San Francisco between 1988 and 2005. Haurin et al.

(2013) study the U.K. housing market between 2002 and 2009, finding that loss aversion is a reason for the relatively high list to sale price ratio in a bust. In addition, other studies of housing markets also find significant effects of anchoring and loss aversion, for instance Andersen et al.

(2019), Bracke and Tenreyro (2016), Bucchianeri and Minson (2013), Clapp et al. (2019b), Eini¨o et al. (2008), Stephens and Tyran (2012).¹⁶ Also, Bao and Meng (2017) provide a review of studies of loss aversion in housing markets.

In general, studies of anchoring and loss aversion often focus on effects on selling prices.

Yet, the selling price is often determined in an auction or as a result of a bargaining process between the buyer and the seller. The list price, on the other hand, comes before the sale and is decided upon by the seller. Han and Strange (2016) study strategic list prices both theoretically and empirically, and find evidence that lowering the list price could attract more potential buyers to a certain point. Here, more potential bidders are associated with higher selling price as a results of increased probability of inducing a bidding war. Repetto and Solis (2019) study how the behavioral left-digit bias in list prices affects selling prices in Sweden, and find, among other things, that when list prices are strategically decreased to induce the bias, this is associated with more bidders and more bids. The increase in bids is also part of the findings of Anundsen et al.

(2020), however, they also find that the anchoring effect of strategically lowering the list price counters thisherding effect, resulting in lower selling price.¹⁷ The impact of strategic list pricing on selling prices is also investigated by Bucchianeri and Minson (2013), finding evidence of a positive relationship between list and selling prices, implying an anchoring effect.

There are also some alternative studies to the anchoring and loss aversion studies above with a theoretical approach (e.g. Buisson, 2016; Yavas and Yang, 1995). Yavas and Yang (1995) derive a model for list prices which, among other factors, depends on a signalling function.

The study focuses mainly on how list price affect broker effort, and how this affects time on

15In this context, the anchor is the list price. Thus, if a seller faces a prospective loss, a higher list price will be set, and the property is sold at a higher price. This effect is in addition to the seller’s loss aversion effect on selling price.

16Note that Bracke and Tenreyro (2016) find anchoring effects but no loss aversion, which they ascribe the limited losses during the period covered in their data.

17In the real estate context, the herding effect is the effect of lowering the list price which attracts more potential buyers.

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market. It is argued that “the main role of the list price is that it sends a signal to the buyer about the lowest acceptable price for the seller” (Yavas & Yang, 1995, p. 351). As pointed out by Haurin et al. (2013), theoretical models often argues that the list price has two roles, namely as a signal of acceptable price and a signal of quality, which both are implemented by Yavas and Yang (1995). Therefore, theoretical models often assumes that list prices are determined by rationality rather than the non-rationality inherent in behavioral economics. On the other hand, Buisson (2016) derives theoretical models for list prices with behavioral “prospect theory”

effects, finding results opposite of Genesove and Mayer (2001). Here, the argument is that the Genesove-Mayer loss aversion results could be due to liquidity constraints even with their control variable of loan-to-value.

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3 Data

3.1 The data

The data used in this thesis are housing transactions provided by Eiendomsverdi AS. The sample only consists of dwellings. Eiendomsverdi AS is a firm owned by the largest Norwegian banks, which collects data from real estate agents, official records such as the Norwegian Land Registry and advertisement agencies. The data contain observations from Oslo and Stavanger of listings that are registered as either sold or unsold in the period between January 2, 2003, and December 31, 2019. In addition, there are incidents of properties listed before the sample start, a total of 987, which are included in the sample because the observations are registered as either sold or unsold within the sample period. The data covers approximately 70 percent of transactions after 2007, where the remaining 30 percent includes sales not listed on the market, bequests and other types of transactions.¹⁸ All data tidying and estimations are done in RStudio.¹⁹

The transaction related variables included are date of listing, date of sale, date of registration in the Land Registry, list price, selling price, common debt, and appraised value done by a surveyor. The property related variables are unit ID, observation ID, name of municipality, name of city district, zip code, estate type, size of living area, size of utility floor space, floor, number of bedrooms, build year, lot size, and type of ownership.

The initial data contain 310,343 observations, with 268,330 observations from Oslo and 42,013 observations from Stavanger. Because many properties are sold more than once, the total number of registered properties is 167,944. A total of 90,545 properties are only registered once, meaning that the remaining 219,798 observations consist of properties registered multiple times. As mentioned above, the data consist of both sold and unsold dwellings. A property is identified as unsold if the date of sale and date of registration in the Land Registry are both missing. The initial data contain a total of 8,776 observations which can be characterized as unsold.

A main concern of identification in this thesis, which will be further addressed in subsection 5, is the fact that the data does not contain information of sellers’ loan-to-value ratio, meaning the share of the price paid at the time of purchase that was financed by loans. Previous studies of loss aversion (see e.g. Anenberg, 2011; Engelhardt, 2003; Genesove and Mayer, 2001) use loan-to-value as a control variable, because the effect of reference dependence could to some degree capture the effect high loan-to-value has on list price. Acquiring mortgage data in Norway is not a simple task, and access is given through a separate data management system

18Examples of sales that are not listed are: agreement of sale directly between neighbors, friends, or family.

Other examples are direct agreements between two contractors, or between a homeowner and a contractor. Also, bequests implies, for instance, transferring the ownership from a deceased husband to his wife, or from a deceased parent to the children.

19The following packages are used: tidyverse, lubridate, broom, scales, fastDummies, sandwich, lmtest, boot.

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with limited flexibility. Thus, I have decided that acquisition of loan-to-value is beyond the scope of this thesis.

3.2 Method of trimming

The data is not flawlessly registered, and the data contain multiple problematic features that I deal with to be able to perform regressions. One problem is that there are randomly missing values for many observations. Other problems are that there are some cases of double- registrations, in some cases multiple properties are registered with the same unit ID, and some observations have information which is simply impossible. These problems and others are dealt with, and how this is done is described in this subsection.

A major aspect of the research question is the fact that I will perform regressions with repeated sales, identifying sellers’ behavior based on information about previous transactions.

This implies an extra challenge in the data cleaning procedure, namely that I must take into account that breaking a sequence of observations for a property will mess up the alleged identification of a seller. For example, if a property has three observations, then I cannot remove the second observation and claim that the seller of the last observation is the buyer in the previous observation. One idea to deal with this is to simply remove all observations that are registered more than two times, but this will result in losing much information that could have been used in the hedonic regression described in subsection 4.1.1. Doing this will also remove the opportunity of for instance picking out two observations from a multi-observations sequence to obtain a repeated sales pair. Hence, to ensure that as much information as possible is kept, I have conducted a comprehensive data cleaning procedure.

3.2.1 Removing observations

The first stage of cleaning the data is removing observations that I deem problematic. The steps done and the number of observations removed are given in table 1. As seen in the first step, 18,696 observations are removed because properties with cooperative-ownership have sequences of observations listed before 2007. Before 2006, cooperatives was not registered in the Land Registry, giving highly inaccurate registrations of transaction- and property-specific values.²⁰ Therefore, these cooperative registrations before 2007 are removed.²¹

The second step is to restrict the data to properties sold less than five times. The sample contains information of a total of 17 years. If a property is sold six times, this is associated with a turnover rate of 2.83, which seems like a short time for owning a property. Also, due to the

20The Norwegian Land Registry (Grunnboken in Norwegian) is the official register of properties with the ownership types owner-occupier and cooperative. If a property is a owner-occupier, this could for instance be a detached house or an apartment.

21In fact, mandatory registration of cooperatives was implemented July 2006 (Burettslagslova, 2003, §14-1).

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problem of multiple properties registered with the same unit ID, the length of sequences ranges from 1 to 804. Thus, all properties sold more than five times are removed, resulting in dropping 28,208 observations.

The third step is to remove properties with other ownership types than owner-occupier and cooperative. The majority of properties in Norway are either owner-occupier or cooperative.

In larger cities cooperatives are more common than in rural areas. Other ownership types also exist, but these only constitute a minority of the total dwelling inventory, and they are associated with special agreements and contracts which are unobserved. The remainder of these properties are removed, giving only a reduction in the sample of 271.

Table 1: Removing observations

Step Data size Obs removed Description

Initial 310,343

1 291,647 18,696 Properties of CO before 2007 2 263,439 28,208 Properties sold more than 5 times

3 263,168 271 Properties with ownership other than OO and CO 4 255,136 8,032 Properties within 13 weeks between listing dates 5 254,538 598 Observations with missing price assumption 6 251,215 3,323 Properties with more than one estate type 7 250,822 393 Properties containing double-registrations 8 246,815 4,007 Sequences containing different properties

Notes: CO is cooperative ownership, and OO is owner-occupier ownership. The description column gives information of the criteria for removal. In step 5observationsare removed, implying observations part of a property sequence. Whenpropertiesare removed, this implies whole property sequences are removed.

Also note that step 7 was performed after the reported repairing in 3.2.2 was done.

The fourth step is to remove properties having 13 weeks or less between registered listing dates. This takes care of some of the properties that are listed on the market, then withdrawn temporarily and tried in the market again some time later. Also, this takes care of properties with some unknown aspects which makes people sell short time after buying, for instance investors or people with drastic change in budget or preferences. Such observations could account for unnecessary noise in the data, and 8,032 observations are removed.

The fifth step is to remove missing values of the most crucial variable to the research question, the list price. List price is the dependent variable in the second-stage models and it is therefore necessary to remove all observations that are missing list price. Missing list price is a typical example of randomly missing values, and measures are taken to extract repeated sales from sequences containing such observations. Thus, a total of 598 observations are removed.²² The last steps removes properties to make sure that problems with mistakes in value-

22Another strong contender for this treatment is selling price, but I do not remove these for now. At this stage, the observations with missing selling price was marked with an indicator variable. Not including the observations of unsold observations, there was 1,054 observations with missing selling price.

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registration, double-registrations, and the remaining multiple-properties unit IDs. Properties with more than one estate type in the sequence were dropped, for instance apartment and detached house, dropping a total of 3,323 observations. Then properties which contained double-registrations were dropped. The first criterion for qualifying as double-registration was that number of days between lagged date of sale and date of sale had to be 30 days or less. The second criterion was non-negative difference in month between lagged date of sale and registration date. These criteria gave a reduction of 393 observations. Finally, sequences containing different properties were removed based upon multiple combinations of criteria:

different zip code and floor number, different number of bedrooms and floor number, different zip code and number of bedroom, different living area and floor number, and different zip code, build year and lot size.²³ In total, these criteria gave a reduction in observations of 4,007, resulting in 246,815 remaining observations in the sample.

3.2.2 Dealing with problematic values

The second stage of cleaning the data is repairing randomly missing values. This is done as a measure to include as much information as possible. Repairing an observation implies using information in the sequence of registrations, for the property in question, to replace the problematic value. For example, if a property is registered more than once it is possible that living area of one of the observations is missing. Removing the observation could disrupt a repeated sales sequence, and removing the whole property will lead to unnecessary loss in information needed in the hedonic regression. Hence, to keep information I chose repairing the observations. The procedure of repairing was done before step 7 in table 1, because the procedure of removing observations is based on criteria which involves multiple observations within property sequences. If imposing criteria that are too strict, this could lead to high loss of information. To avoid this problem, I chose to do the repairing procedure before step 7.

Table 2 reports the number of repaired observations and how many I was unable to repair. An observation qualified for repair if there was missing values or if the registered value was deemed not credible. The criteria for all variables subject to this treatment are listed in the table notes.

To repair problematic values, the requirement was that there must be at least two observations for the property, and at least one of the observations did not have a problematic value of the variable in question. Therefore, not repaired observations as share of total candidates are larger than 0.3 for all variables.²⁴ When a property qualified for this treatment, the value replacing the problematic value was either the mean of the non-problematic values within the property sequence, or the last observed non-problematic value if integers was needed. The latter method

23This procedure with combinations of different values was chosen as a result of small deviations in e.g. living area or number of bedrooms. This is discussed in subsection 3.2.3.

24Imputation was considered for the remainders, and I actually imputed problematic utility floor space. But utility floor space will not be used later on, thus it is not included in the table.

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Table 2: Repairing missing values

Variable Obs repaired Not repaired Share not repaired

LivingArea (LA) 104 899 0.896

Apartment floor 5,138 9,188 0.641

Bedrooms 2,218 10,479 0.825

BuildYear 684 344 0.335

LotSize 695 3,451 0.832

Notes: The reported numbers are from before stage 1 step 7, thus total number of observations containing repaired observations can be smaller after step 8. If observations were not repaired, then the values are set to NA (missing value), because these values are considered to be either outliers or not credible. The criteria for repairs follows:LA=0,LA=1 andNA replaced with within mean of living area. Floor<1,>19 andNAreplaced with Floor of the last within property observation. Bedrooms>7 andNAreplaced with Bedroom of the last within property observation.BuildYear=0,BuildYear=1 andNAreplaced with BuildYear of the last within property observation.LotSize<100m²andNAreplaced with within mean of LotSize.

was chosen for integers, such as build year, instead of a rounded mean because registrations done in later years can be considered to be more accurate than earlier registrations. And the first method, which is chosen when the variable does not need to be an integer, is used because there could be small variations in area variables. This is discussed more in 3.2.3.

3.2.3 Aspects of the trimmed data

As mentioned above, there exist occurrences of small variations in area related variables in some property sequences. Also, in many variables there exist occurrences of values that are not credible. These problems are typical measurement errors giving error-in-variables (EIV) problems.²⁵ Examples of this are living areas reported to be one square meter and floor reported to be minus 20. These non-credible outliers are now dealt with, but what remains are the small variations. An example of typical small variations is an apartment that has one observation with living area of 50 square meters and one with 51 square meters. Such variations seems to be randomly occurring, making it probable that the measurement error are uncorrelated with other variables. Given the assumption of random measurement errors, such variations is considered as classical measurement errors. A well known result of such errors is that they give inconsistency and attenuation in estimators. However, even if the variations are random, they are also minuscule which on the other hand limits the magnitude of the problem. Hence, I regard the EIV problem related to measurement errors in the data to be dealt with.

As presented in table 1, I have also taken care of multi-property sequences. The criterion for removal was that more than one of the variables mentioned had different values. The reason

25See e.g. Stock and Watson (2015) for a discussion on sources of EIV.

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for not dropping observations when one variable had different values is related to the discussion about EIV. If there were small variations in area related variables, then all these would have been removed and much information would have been lost. Yet, a reliable source to identify different properties within a sequence is deviation in area. In addition, there could be uncertainties regarding exact build year of a property, especially older buildings. Thus a combination of variables was used to identify multi-property sequences.

There are still some problems regarding some variables which needs to be dealt with. First, it is unclear how the floor variable is defined: for an apartment it is obvious that the floor is referring to the floor number which the apartment is situated. However, some apartments extend to multiple floors. For example, an apartment situated in both second and third floor could potentially be registered as either floor 2 or 3, which makes the floor variable ambiguous.

And for non-apartment properties such as detached houses, if the floor variable has value 2 this could be regarded as a two story house, making the floor variable even more ambiguous. Hence, I create a new dummy variablefirst-floor-apartmentwhich takes value 1 for apartments situated in the first floor, and else value 0.

Many observations have deviations in build year due to uncertainties regarding the actual build year. To deal with this problem, I follow Anundsen and Røed Larsen (2018) in creating dummies for the build year eras before 1950, between 1950 and 1979, between 1980 and 1999, and after 1999. I also deal with variation in lot size by making a new dummy variable which takes value 1 if the lot size is over 1,000 square meters and else 0. In addition, number of bedrooms are separated into dummies for the range[0,4], meaning that if all these dummies are taking value 0, there are more than 4 bedrooms.

In Norway, some properties have shared debt, often occurring when they are part of joint ownership. This variable is denoted common debt, and it is considered a part of the purchasing price for properties. As a result, list price and selling price must be considered with inclusion of common debt, and new variables taking common debt into account are therefore made to be used in the estimations.

3.2.4 Description of the final data

The last thing to do before regressions can be done is to trim the data. First, I split the data into two subsets, one for each of the municipalities included, Oslo and Stavanger. Second, I find the lower 1 percentile and the upper 99 percentile of the variables living area, list price including common debt, and list price per square meter of living area.²⁶ Then I trim each subset on these percentile values by removing whole property sequences.

Starting at a total of 246,815 observations in total, the subset for Oslo containing 205,444 is trimmed down to 193,292, and the subset for Stavanger containing 38,271 is trimmed down

26List price including common debt is referred to aslist pricefrom this point.

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to 35,509. The total remaining observations are 228,801, and a total of 18,014 observations are dropped. After the trimming is done, list price ranges from 75,000 NOK to 13.5 MNOK in Oslo and 75,000 NOK to 8.95 MNOK in Stavanger. The living area ranges from 23 square meters to 259 square meters in Oslo, and 30 square meters to 288 square meters in Stavanger.

In figure 2 the annual averages of list price and annual averages of list price per square meter are plotted. It should be noted that list price per square meter are increasing at a higher rate than the list price in Oslo, implying that the average property sold in recent years are smaller than before. Also, it should be noted that the Oslo market was in a boom throughout the sample period, while Stavanger was in a boom until 2013.

Figure 2: Annual averages of list price and list price per square meter

Oslo Stavanger

2003 2005 2007 2009 2011 2013 2015 2017 2019 2003 2005 2007 2009 2011 2013 2015 2017 2019 2,000,000

3,000,000 4,000,000 5,000,000 6,000,000 7,000,000

20,000 30,000 40,000 50,000 60,000 70,000

Year of listing

Average list price Average list price per square meter

Variables

Average list price Average list price/m2

Notes: Left y-axis shows average list price in NOK (Norwegian krone) and right y-axis shows average list price per square meter. Here, square meter is given by living area. Stavanger is the “oil captial” of Norway where many people are employed in the oil industry. The 2014 oil crisis lead to high local unemployment rate in Stavanger, and the effect on listing prices can be seen at the point where the increase of the price curve is declining from 2014.

Table 3 reports some descriptive shares, for instance how many properties in each subset has the different ownership types. There is a higher degree of cooperative properties in Oslo, the Norwegian capital, which could be due to the larger share of apartments in Oslo than in Stavanger, respectively 0.87 and 0.48. It also reports the number of repeated sales and single sales and their respective shares, which are close to each other at 0.354 in Oslo and 0.392 in Stavanger. This implies that about the same proportion of single registrations are kept in each subset only to add information to the hedonic regressions.

Table 4 and 5 reports a more thorough overview of descriptive statistics for the two subsets

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Table 3: Some descriptive shares

Oslo Stavanger

Variables Share Obs Share Obs

Ownership: Owner-occupier 0.608 117,549 0.778 27,629 Ownership: Cooperative 0.392 75,743 0.222 7,880

Apartments 0.870 168,120 0.480 17,050

First floor apartment 0.193 32,524 0.280 4,777

Unsold observations 0.0153 2956 0.0222 787

Build year: pre 1950 0.351 67,787 0.219 7,765 Build year: 1950-1979 0.337 65,011 0.315 11,173 Build year: 1980-1999 0.147 28,433 0.223 7,915 Build year: post 1999 0.165 31,767 0.243 8,636

Single sale obs 0.354 68,489 0.392 13,933

Repeated sale obs 0.646 124,803 0.608 21,576

Notes: First floor apartments are reported as share of total apartments. Build year has 294 missing values in Oslo subset and 20 in Stavanger subset, which is the reason why the numbers does not add up to the total in each subset. Single sale observations are properties registered only once, and repeated sale observations are properties registered more than once.

Oslo and Stavanger. The table reports two new variables, time-on-market (TOM) and months since last sale. TOM is calculated as the interval in days between the date of listing and the date of sale, and it gives a measure of how long a property is listed before it is sold. And months since last sale is calculated as the interval in months between lagged date of sale and the date of listing, giving a measure of how long sellers have owned their properties. Further, figure 3 reports the annual number of sold observations for the different estate types, serving as a companion to the summary tables.

All specifications in this thesis are estimated in logarithmic terms. Hence, list price, selling price and appraised value, all including common debt, are transformed into logarithms. Area variables such as living area are also transformed into logarithms.

3.3 Institutional background

In Norway, the majority of sales of dwellings are done through real estate agents. An agent is hired by a seller to do the job of selling and make sure that everything related to the transaction, including paperwork, is done according to the current legislation. Because there are strict rules and regulations regarding real estate transactions and brokerage, in order to acquire a certification to act as a real estate agent one need to finish a three year higher level education.²⁷ After finishing the degree, additional two years of practice at a real estate

27The education gives a Norwegian bachelor degree. It contains many of the same subjects as a Norwegian bachelor of business administration.

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Table 4: Data summary Oslo

Apartment Non-apartment

Variable Obs Mean Median SD Obs Mean Median SD

List price 168,120 3,103k 2,700k 2,611k 25,172 5,674k 5,000k 2,611k Selling price 164,820 3,221k 2,850k 1,679k 24,832 5,844k 5,300k 2,662k

Common debt 119,008 176k 86k 322k 4,252 184k 89k 373k

LivingArea 167,882 67.1 64 25.6 24,975 146 141 42.5

UtilityFloorSpace 167,882 68.6 65 26.6 24,975 162 154 53.3

Floor 160,624 3.03 3 1.84 NA NA NA NA

Bedrooms 162,043 1.81 2 0.77 24,501 3.37 3 0.85

Build year 167,861 1955 1958 38.3 25,137 1970 1973 28.9

LotSize 166,711 12.3k 3.1k 19.3k 24,438 5.8k 0.9k 14.2k

LP/m² 167,882 49.4k 46.3k 18.1k 24,975 39.5k 36.7k 15.4k SP/m² 164,589 51.5k 48.5k 18.9k 24,640 40.8k 37.9k 16.0k

TOM 158,186 23.5 11 42.8 23,497 28.6 11 50.8

MSLS 62,873 50.0 44 30.8 5,467 57.6 51 36.9

Notes: The total number of observations is 193,292. Due to missing values the total number of observations is lower for the different variables. Prices and LotSize are reported in 1,000 denoted by k. Price variables are in NOK and area variables are in square meters. The abbreviations in the table are as follows: LP is list price, SP is selling price, TOM is time on market, MSLS is months since last sale. For instance, LP/M² means list price per square meter. Floor gives statistics for apartments, as floor for non-apartments could be misleading. Missing utility floor space (UFS) is imputed by predicting fitted values of the specification:

U FSi =a+b1LivingAreai+b2dummy.apartment_i+b3(LivingArea_i×dummy.apartment_i) +ei, estimated by OLS. This was done after therepairingwas done in the same fashion as described for LivingArea in table (2), implying that imputation was done for the properties that were only observed once or for those which had all UFS missing. The result is that the total number of missing values for LivingArea is equal for UFS.

brokerage firm is needed to obtain a licence (Eiendomsmeglingsloven, 2007, §4-2). Real estate brokerage is only allowed to be conducted when affiliated to a brokerage firm with a permit, and this permit is given by the Financial Supervisory Authority of Norway. Compared to some countries, for instance the U.S., buyers in Norway does not hire real estate agents.

The strict legislation of real estate brokerage takes this into account; the agent must act in an impartial manner, and all relevant information about the property and the transaction process, including advises about the sale process and the auction, must be given to both the seller and the buyer (Eiendomsmeglingsloven, 2007, §1-1 & §6-3). Therefore, the real estate agent acts as a mediator in real estate transactions, hired and paid by the seller.

Another feature of the real estate market in Norway is that until 2016, many sellers hired a professional surveyor to evaluate the value of their property, as part of the procedure of selling the property through a real estate agent. A reason for using appraised value was to provide an unbiased external valuation, which in turn was a part of the surveyors main job of evaluating the technical qualities of the property. The appraised value can also be called appraisal value, assessed value or value evaluation, and must not be confused with the American property tax

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Table 5: Data summary Stavanger

Apartment Non-apartment

Variable Obs Mean Median SD Obs Mean Median SD

List price 17,050 2,531k 2,400k 999k 18,459 3,665k 3,400k 1,545k Selling price 16,458 2,528k 2,400k 980k 18,182 3,687k 3,425k 1,535k

Common debt 6,731 445k 198k 639k 1,578 332k 150k 603k

LivingArea 16,966 70.9 68 24.4 18,310 139 129 50.6

UtilityFloorSpace 16,966 75.2 70 27.1 18,310 156 143 61.2

Floor 15,988 2.61 2 1.93 NA NA NA NA

Bedrooms 16,725 1.79 2 0.708 18,088 3.16 3 1.05

Build year 17,038 1979 1989 34.2 18,451 1959 1965 37.0

LotSize 16,859 5.8k 2.6k 9.1k 17,592 1.7k 0.4k 7.1k

LP/m2 16,966 39.7k 39.6k 10.8k 18,310 27.7k 27.8k 8.9k

SP/m2 16,376 39,579 39.3k 10.8k 18,036 27.9k 27.8k 8.9k

TOM 15,759 43.5 14 75.4 17,205 37.4 13 64.4

MSLS 6,183 48.2 41 30.6 5,327 54.9 48 34.7

Notes: The total number of observations is 35,509. Due to missing values the total number of observations is lower for the different variables. Prices and LotSize are reported in 1,000 denoted by k. Price variables are in NOK and area variables are in square meters. The abbreviations in the table are as follows: LP is list price, SP is selling price, TOM is time on market, MSLS is months since last sale. For instance, LP/M² means list price per square meter. Floor gives statistics for apartments, as floor for non-apartments could be misleading. Missing utility floor space (UFS) is imputed by predicting fitted values of the specification:

U FSi=a+b1LivingAreai+b2dummy.apartment_i+b3(LivingArea_i×dummy.apartment_i) +ei, estimated by OLS. This was done after therepairingwas done in the same fashion as described for LivingArea in table (2), implying that imputation was done for the properties that were only observed once or for those which had all UFS missing. The result is that the total number of missing values for LivingArea is equal for UFS.

related assessed value. I will continue using the term appraised value.²⁸

The process of selling a property through a real estate agent includes several steps. First and most important for this thesis, deciding the list price. This is done as a part of hiring the real estate agent, and is a decision mostly made by the seller. However, the list price is decided upon after the real estate agent presents the agent’s market price prediction. The prediction is presented together with contract proposal which includes prices and length of the contractual relationship. The prediction done by the agent is not binding, and the seller is free to set some other list price, but it could be argued that the market price prediction can have an anchoring effect on the seller. Because the seller probably has less knowledge about market prices, it should be no surprise that a price prediction from the agent is more reliable than the seller’s expectation. But it should be noted that the agent could have incentives to present a higher prediction because the typical Norwegian real estate agent fee is set to a percentage of the final selling price (see e.g. Anundsen et al., 2020; Han and Strange, 2016 for studies of strategic

28In 2016, a new valuation tool substituting appraised value was introduced called E-takst (Strømnes, 2016). It was introduced by the provider of the data used in this thesis, Eiendomsverdi AS, and it has become an industry norm among real estate agents to use E-takst. Using E-takst makes it possible for real estate agents to make better market value predictions by themselves.

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Figure 3: Number of annual sales, by estate type

257457 701 3,082

295571 789 3,857

351 686 842 4,791

409 758 930 5,248

696 726 893 9,199

541 711 858 6,631

552 767 955 7,830

763 773 999 10,954

866 865 1,117 12,890

856 900 1,060 13,566

951 867 1,098 13,319

926 874 1,069 13,523

872 841 1,056 14,373

788 689 977 13,615

841 727 923 13,614

871 785 990 13,960

966 879 1,038 14,997

4,497 5,512

6,670 7,345

11,514

8,741 10,104

13,489

15,73816,382 16,235 16,392 17,142

16,069 16,10516,606 17,880

0 2,500 5,000 7,500 10,000 12,500 15,000 17,500

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Year of sale

Number of observations

Estate type Apartment Detached Semi−detached Row

Notes: The table reports a total of 216,421 observation whereby 12,380 are removed due to missing date of sale.

In addition, recalling the first step in table 1, all properties reported as cooperative ownership before 2007 are removed. The majority of properties with cooperative ownership are apartments, which makes the numbers in years 2003-2007 less representative to the real-world numbers, especially for apartments.

list pricing, and Anglin and Arnott, 1991 for a study on the real estate related principal-agent problem).²⁹

Second, after the seller has agreed upon a contract to hire the real estate agent, a professional surveyor is hired to assert the technical qualities of the property and provide an appraised valuation, given that this happened before 2016. This additional value assertion could be viewed as a better predictor of the market price. If the contract was agreed upon after 2016, the agent would have included a “better” prediction of the market price when presenting the contract proposal.

Third, preparing the property for advertising, followed by advertising the property on the internet, and some times also in local newspapers. Internet advertisement is most often done through the online marketplace Finn.no. Fourth, the showings of the property are conducted, giving potential buyers the opportunity to have a look at the property. At this stage, the agent collects contact information on those who still are interested in buying. Fifth, the auctioning

29All real estate agents must include a proposal of commission that is based upon per-hour rates (Eien- domsmeglingsloven, 2007, §7-2). However, the proposed percentage fee is usually put to a level that makes the per-hour rates seem expensive. This insight is based upon my own experience of working in the real estate brokerage sector.

Loss Aversion in the Norwegian Housing Market