1. Introduction
The COVID-19 pandemic since early 2020 has greatly affected various financial products and caused predictions of economic growth to be revised. Global economic growth was forecast by the International Monetary Fund in October 2020 to decrease by 4.4%. Additionally, a rising unemployment rate has resulted in effects on incomes. Just as other products and commercial sectors do, the real estate industry should evolve under the effect of such economic variables [1]. However, the transaction volume in Taiwan’s real estate market during this pandemic period increased rather than declined; according to data from the Ministry of the Interior, ownership transfers and transactions across Taiwan in the first quarter of 2021 totaled 80,831, which was 16.54% higher than that in the same quarter of 2020 (69,361 transactions).
Taiwan is a small island nation located in East Asia with a population of over 23 million people. The country has a highly developed economy and a bustling real estate industry, with property prices in major cities such as Taipei among the highest in Asia. In recent years, Taiwan’s government has implemented measures to cool the housing market and curb speculation, such as increasing property taxes and tightening mortgage lending standards. Despite these efforts, housing affordability remains a major concern for many Taiwanese, especially younger generations.
Real estate purchases involve large amounts of money, and collecting related data is necessary to support decision-making. Because of the rapid development of information technology, people can now obtain considerable and complex information. Real estate purchase decisions have undoubtedly improved because of an increase in the quality and availability of information. However, such information abundance may affect people’s processing and interpretation of information, resulting in potential buyers resorting to intuition or rules of thumb to aid their decision-making when facing a limit in their information-processing skills—using “heuristics” in cognitive processing [2].
In the face of uncertainty, cognitive biases are mainly induced by the framing effect or anchoring effect, and such biases can cause overreactions and underreactions during investment behaviors and decisions. Considering the anchoring effect of earnings forecasts, overreactions or underreactions occur when a person faces positive or negative information because analysts tend to defend their viewpoints and are likely to mostly persist with their initial forecasts when making revisions [3]. The framing effect refers to the change in the relative attractiveness of a prospect to a person when the prospect is stated using a frame different from the person’s original frame, whereas the anchoring effect relates to people’s tendency to rely on familiar reference values, which are taken as anchor points when making assessments.
Given that a real estate purchase involves an overall assessment, joint information framing includes the presentation of both framing and anchoring information; a positive or negative description of the real estate market is provided along with price information, and the real estate price can be a reference anchor point for homebuyers. Since homebuyers receive all the elements of information as a whole rather than independently, this study argues that various information frames not only separately but also jointly affect real estate purchase decisions.
When the market price sufficiently and immediately reflects all information, investors in the market are rational, and their investment analyses are independent and non-interferential [4]. However, this is not the reality. Investors often overreact or underreact to information, and irrational phenomena abound. The prospect theory explains irrational phenomena in the financial market based on personality traits [5]. People are susceptible to interference by psychological factors and external information when they are faced with uncertainty, and this interference prevents them from behaving rationally and leads to their overconfidence and cognitive bias.
Personality traits are significantly correlated with investment risk management [6]. Personality traits can explain the causes of individual behavior, and a person’s work-related behavior is affected by various personality traits [7]. Numerous studies have reported that personality traits are the sum of an individual’s patterns of thoughts, feelings, and behaviors; these are characteristics with which one person can be distinguished from another [8]. Personality is defined as individuality in the manifestations of people’s bodies, minds, and souls, both alone and with other people and things in their life; such individuality varies depending on genetic factors and the growth environment [9]. Thus, people with different personalities have unique insights when making investments and performing financial management, decision-making, and planning. Consequently, the personality traits of homebuyers can influence the effect of joint information framing on their real estate purchase decisions. Psychologists divide personality traits into those indicating optimistic and pessimistic psychological states; optimists generally have high expectations, whereas pessimists do not usually have any expectations and believe that their suffering is likely to exceed their enjoyment [10]. Different people may interpret received information differently and hence react differently due to their personality traits.
The effects of the pandemic on the real estate market remain an issue for in-depth discussion. Since real estate purchase information involves real estate market trend forecasts and price information, the effect of joint information framing on real estate purchase decisions is a crucial issue of interest. This study explored the effect of joint information framing on the real estate market with the COVID-19 pandemic as the research background. The effect of joint information framing on real estate purchase decisions was understood through a consideration of behavioral finance, and whether the effect of such framing on real estate purchase decisions was altered under different personality traits (levels of optimism) was investigated. This study explored the growth, rather than decline, of the transaction volume in Taiwan’s real estate market during the global pandemic.
This study explored the impact of framing and anchoring effects on purchase attitudes and intentions, with a focus on the role of optimism levels. Results showed that negative framing had a stronger effect on highly optimistic respondents, leading to significant negative correlations with purchase attitudes and intentions. Additionally, the anchoring effect was more pronounced for less optimistic participants, with high-price anchoring having a stronger negative impact on their purchase intentions. Conversely, low-price anchoring had a stronger positive effect on the purchase intentions of highly optimistic respondents. Further analysis of joint information framing revealed that negative framing was positively correlated with a relatively low price in high-price anchoring for participants with low levels of optimism. However, for low levels of optimism and low-price anchoring, negative framing was positively correlated with willingness to pay. These findings have practical implications for real estate investors, who can use this information to make informed purchase decisions. In addition, the creation of inclusive, safe, and sustainable human settlements can be informed by these results. Overall, this study sheds light on the complex interplay between cognitive biases and individual differences in decision-making processes.
This article has five sections. The following section is “Literature Review,” which discusses research on the framing effect, the anchoring effect, information framing, personality traits, and real estate purchase decisions. The third section is “Research Methods,” which explains the research framework, measurement tools, and variable definitions. The fourth section is “Empirical Results,” which examines the influence of the framing effect, the anchoring effect, and joint information framing on real estate purchase decisions. It also investigates whether the strength of these effects varies among individuals based on their level of optimism. The last section is “Conclusion and Suggestions”.
2. Literature Review
In the face of uncertainty, people primarily use their intuition to make judgments [11], and this tendency is even stronger among those lacking experience. Compared with the buying of general products, real estate purchase requires more abundant and complex information. To simplify the information, people generally resort to rules of thumb or intuition to make decisions, but this often leads to hasty generalizations. The following subsections introduce theories based on biases induced by how people interpret information.
2.1. Framing Effect
When faced with information, positive or negative phrasings lead to different risk preferences, and these preferences affect people’s final decisions; this phenomenon is called the framing effect [12]. The function of framing is to drive action by organized experience [13]. The framing essentially involves the functions of selection and highlighting; a frame is selected by emphasizing certain parts of reality, with the target individual then recognizing a specific definition of the problem, interpreting the problem in a certain way, coming to an ethical evaluation, and making appropriate suggestions [14]. The framing effect is not likely to vary between the sexes, and accounting for proper equivalency in the amount of framing effects is likely to enhance the interpretations made regarding moral reasoning and judgment [15]. Different problems produce varying levels of framing effects within the exact decisional domains [16]. One reason for the framing effect varies by the domain that might affect the emotional response caused by the decisional problems. The consequences of framing effects for different real-life decisions should be discussed. Numerous studies have discovered that people are generally affected by framing effects. Negative framing had a stronger effect on participants than positive framing, with the potential amounts to be gained and lost and the probability of success as the main influencing factors [17].
The effect of attribute framing has been found in the context of a ground meat product and the discovery that participants’ reviews of the meat’s quality were more favorable when the product’s attributes were framed positively [18]. However, the effect of positive and negative information framing varies depending on how motivated a participant is to process information [19]. The frames may affect consumers’ upgrading objectives and judgments as a result of their different assessments of emotional costs [20]. From a personal economic perspective, one study found that when the framing showed the attractiveness of green apartments, people were more likely to choose green housing. [21]. The framing effect was measured by comparing the results, which varied based on how attracted the participants were to single items; thus, when different experimental designs and types of expression and presentation regarding questions lead participants to adopt different options, the participants are affected by the framing effect.
The presented literature review indicates that people are susceptible to both positive and negative framing effects. Under this premise, the present study explored the influences of positive and negative attribute framing on real estate purchase decisions in the context of the COVID-19 pandemic. The following research hypotheses were proposed:
Positive framing affects real estate purchase decisions.
Negative framing affects real estate purchase decisions.
2.2. Anchoring Effect
The anchoring effect refers to the effect of predetermined impressions in a person’s mind on their decision-making, leading to unexpected behaviors. A person is “anchored” when going through the decision-making process [7]. Individual investment decisions are influenced by anchoring behavior, and decisions are influenced by their past experience to illustrate the anchoring effect [22].
The intensity of the anchoring effect depends on the decision maker’s familiarity with the target object and a predetermined numerical criterion [23]. The anchoring effect occurs only when a person has cognitive biases; investors make decisions based on the most easily accessible reference values despite the potential irrelevance of such values [24].
Some irrational investors are affected by the anchoring effect, which means that stock prices do not completely reflect the available information [25]. Therefore, efficiency is unattainable in a market affected by the anchoring effect. Real estate price judgments are made by people lacking complete real estate purchase-related knowledge and are affected by the reference prices people have learned [7]. Behaviors related to information asymmetry are common in the real estate investment market; thus, investors are prone to the anchoring effect. For example, buyers often use the reference price provided by the seller as an anchor or follow the standards for real estate prices in the market. Non-local homebuyers may have less knowledge about the local real estate market and may have to pay higher search costs. Non-locals may also be anchored by real estate prices in their previous residential areas [26].
Although decision-making is often affected by the anchoring effect, this effect can be negative or insignificant. The anchoring effect is affected when the anchor point and the target event pertain to the same category, whereas if they have different units, such as monetary and temporal units, the anchoring effect is insignificant [27]. Homebuyers hold various beliefs regarding real estate price distribution, and these beliefs are altered or adjusted under the anchoring effect. Studies also indicated that cross-region homebuyers are often susceptible to information asymmetry and thus pay a premium; in this group, the reference point and anchoring effect are insignificant [28].
A lower initial anchor point results in a lower final selected value and a higher initial anchor point results in a higher final selected value. Decision-making is affected unless a proper adjustment is made, so the actual value often diverges from the anchor point [29]. Consumers believe that expensive goods are always higher quality than low-priced goods, and they have a more positive attitude toward and stronger intention to purchase high-priced goods; therefore, high-price anchoring affects them more strongly than low-price anchoring [30]. The high-price anchoring effect is significantly stronger than the low-price anchoring effect for both male and female homebuyers facing systematic risks [7].
Consumers are likely to expect a higher-quality product when they receive positive product messages and regard a product’s quality as poor when receiving negative messages; positive information is always accompanied by high-price anchoring, making consumers willing to pay higher prices to purchase the product [31]. For instance, tourism operators often attract customers through advertisements featuring a low price and this featured price then results in an anchoring effect on customers; the price they are willing to pay is reduced, and their final decisions are affected by the interaction between framing and anchoring caused by the advertisement message [32]. In another example, consumers’ attitudes, purchase intentions, and judgments were significantly affected by positive or negative descriptions of organic foods and the presented anchor price, but the interaction between the framing and anchoring effects was insignificant [33].
Regarding the measurement of the anchoring effect, a reference value (anchor) is generally employed as the starting point, based on which judgments or decisions are made through anchoring and adjustment. The reference value is then combined with other reference information to make further adjustments to attain the final estimation of value [34].
The aforementioned literature reveals that the anchoring effect is indeed common in daily life, such as when making any purchases, and even in decision-making regarding investments and real estate purchases; high-price anchoring and low-price anchoring have differing effects on the actual value a person is willing to pay. The present study employed a questionnaire to examine whether participants’ real estate purchase decisions were influenced by the anchoring effect and compared high-price anchoring with low-price anchoring. Based on the literature reviewed in this section, the following research hypotheses were proposed:
High-price anchoring affects real estate purchase decisions.
Low-price anchoring affects real estate purchase decisions.
2.3. Joint Information Framing
This study termed the combined reaction to the framing and anchoring effects as “joint information framing”, which refers to positive and negative framing and information involving reference points for anchoring that induce various decision-making behaviors in investors [31].
Consumers’ willingness to pay is strengthened when the framing and anchoring effects are combined, such as when a positive message (e.g., those communicating excellent product quality and great value for money) is accompanied by a high-price anchor point; conversely, when consumers’ reviews of and expectations of product quality are lowered under the influence of negative messages, they are less willing to pay when the anchor price point is higher. This indicates that the framing-effect–anchoring-effect combination (joint information framing) strongly influences consumers’ product attitude, purchase intention, and willingness to pay. A decision is anchored in a better direction when information is framed positively and, conversely, in an inferior direction when information is framed negatively [35,36].
Thus, the literature indicates the great importance of joint information framing, which makes people susceptible to the positivity or negativity of information and the high or low price of anchoring. The present study explored the effect of joint information framing on real estate purchase decisions. Because joint information framing can have positive (high price) or negative (low price) effects, the following research hypothesis was proposed:
Joint information framing affects real estate purchase decisions.
2.4. Level of Optimism and Real Estate Purchase Decision
Consumers’ housing purchase decisions can be influenced by housing market sentiment and personality traits [37]. Different scholars and classifications divide personality traits into different types. This study considered personality to be of two types, namely optimism and pessimism. The outcome expectancy was grounded in these two trait types and argued that when faced with risks and uncertainty, optimists are likely to expect positive outcomes and that situations will evolve in ways that are beneficial to them, whereas pessimists believe that good rarely happens to them and expect situations to evolve in ways that are unfavorable to them [38]. In the Taiwan setting, optimism has been interpreted as “confidence for positive outcomes and perseverance to pursue one’s goal,” whereas pessimism has been interpreted as “negative future expectation, self-doubt, and easily giving up halfway” [39].
Other scholars have proposed a theory of explanatory style; people with an optimistic explanatory style usually attribute negative events to other people or the external environment rather than themselves and regard these events as only temporary, not affecting aspects of their life, while people with a pessimistic explanatory style attribute negative events to themselves and regard these events as happening continually and involving various aspects of their life [39]. In addition, scholars have suggested that optimism and pessimism are cognitive biases; optimists underestimate the outcomes of negative events and always believe that the misfortunes of others will not happen to them, which is a common optimism bias [40].
Different reactions emerge when people adopt the perspective of viewing things optimistically or pessimistically. Psychologists preliminarily divide optimism and pessimism into three types, namely trait, attributional, and defensive. The trait type is inherent and tenacious, essentially framing a person’s overall expectations for the future. The attributional type consists of an explanation after an event; optimists tend to seek external reasons and make positive attributions, whereas pessimists adopt internal and negative attributions. The defensive type of pessimism differs from the two aforementioned types; before an event, defensive pessimists imagine the worst possible to prepare themselves to cope, thus reducing the probability of failure.
Based on a corporate model, one study found that overly optimistic managers distorted their policy in corporate decision-making [41]. This model presumed that in a situation of information equality and sufficient internal funds, optimistic managers would be more likely to overinvest than pessimistic managers, whereas, in a situation of insufficient internal funds, optimistic managers would believe that their company is undervalued by the market and, compared with pessimistic managers, would be more likely to use internal funds first rather than invest through financing, leading to underinvestment. The study also revealed that when forecasting future income, investment decisions, and cash flow, optimistic managers generally overestimated the value of their business or the success of the decision while underestimating their tendency for risk-induced overinvestment; this phenomenon was more significant for people with higher optimism and even over-optimism.
The aforementioned studies have revealed that when facing various situations and in different types of decision-making, individuals may react differently due to their personality traits. Therefore, the magnitude of the effect of various information framings—namely the framing effect, the anchoring effect, and joint information framing—may vary. This study employed the level of optimism as a major moderator and analyzed the respective magnitude of the effect of each of the aforementioned types of information framing on real estate purchase decisions depending on a person’s level of optimism. The following research hypotheses were formulated accordingly:
Level of optimism moderates the influence of framing on real estate purchase decisions.
Level of optimism moderates the influence of anchoring on real estate purchase decisions.
Level of optimism moderates the influence of joint information framing on real estate purchase decisions.
3. Research Methods
This study explored the effect of joint information framing (the framing effect and anchoring effect) on real estate purchase decisions. A 3 (positive framing vs. negative framing vs. control group) × 3 (high price vs. low price vs. control group) between-subjects experimental design was employed. The participants were randomly assigned to one of the nine conditions, and their level of optimism was determined. This study analyzed the moderation influences of the level of optimism on the relationship of the framing effect, anchoring effect, and joint information framing effect with real estate purchase decisions.
3.1. Research Framework
The framing effect and anchoring effect were the independent variables manipulated in the experiment, the real estate purchase decision was the dependent variable, and the level of optimism was the moderator. The framing, anchoring, and joint-information-framing effects were assumed to be correlated with real estate purchase decisions (H1–H3), and the level of optimism was postulated to moderate the magnitude of the framing, anchoring, and joint-information-framing effects on real estate purchase decision (H4a–H4c). The conceptual framework is presented in Figure 1.
3.2. Experimental Design and Measurement Instruments
Before the formal experiment, a small-scale pilot test was conducted to determine the adequacy of the experimental design, the time required for the experiment, and whether the questions were framed. The results obtained from the pilot study were analyzed to understand the research content.
In the formal experiment, participants were randomly assigned to nine groups. The participants in each group were requested to read a fictional report, which is detailed in Table 1. The dependent variable was real estate purchase decision (purchase intention, purchase attitude, and willingness to pay) and was measured using seven items. Items 1–3 concerned purchase intention, whereas items 4–6 concerned purchase attitude. Items 1–6 were scored using 7-point Likert scales; for the purchase intention items, 1 indicated “strongly disagree,” and 7 indicated “strongly agree”; for the purchase attitude items, 1 indicated “very low,” “not at all,” and “very weak” for items 4–6, respectively, whereas 7 indicated “very high,” “very much,” and “very strong.” Item 7 was an open-ended question about willingness to pay.
In addition, the participants’ optimism/pessimism was measured using the Revised Life Orientation [38] (Table 2). The test contains 10 items; items 1, 4, and 10 are positive items with positively framed content; items 3, 7, and 9 are negative items with pessimistically framed content; and the remaining items are supplementary. A translation-back translation process was carried out [42] (Van de Vijver and Leung 1997) to maintain consistency between the Chinese and English versions.A 6-point Likert scale was used to score each participant’s opinion and perception regarding these items. The reverse-coded items (negative items) were reverse scored before the total score was obtained; a higher total score indicated a higher level of optimism. The participants completed the questionnaire online and had to provide their online data (email, Facebook, or Line information). Each participant’s URL was used to assign them randomly to a group.
3.3. Variable Definition
This study combined the framing and anchoring effects and explored their influence on real estate purchase decisions. The survey was conducted using social media application software. The framing effect and anchoring effect were the independent variables, real estate purchase decision (purchase intention, purchase attitude, and willingness to pay) was the dependent variable, and level of optimism was the moderator. Age; sex; income; having studied real estate and finance or taken a related course; and experience in buying, selling, or investing in real estate were included as control variables. The definition of these variables is presented in Table 3.
4. Empirical Results
In this section, the research data and the results are examined. Descriptive statistics were first employed to determine the sampling distribution and demographic profile of the participants. Analyses of variance and hierarchical regression were then conducted to determine the existence of significant effects among the variables and explain their correlations. Based on the empirical results, this study determined whether the hypotheses were supported.
4.1. Descriptive Statistics
This study surveyed 522 participants, primarily aged 30–50, with 50.38% being men. The most common monthly disposable income was TWD 25,001–50,000, with only 6.90% having an income under TWD 5000. About a quarter of the participants had studied real estate and finance, and over 40% had experience in buying, selling, or investing in real estate. The service industry was the most common industry of employment, followed by manufacturing, while students made up the smallest proportion. More than two-fifths of the participants lived in northern Taiwan, with other locations consisting of southern, central, and eastern Taiwan, as well as the outlying islands (Table 4).
4.2. Descriptive Statistics of the Model Variables
Table 5 shows the descriptive statistical analysis of the model variables, which includes the framing effect, anchoring effect, purchase intention, willingness to pay, and purchase attitude. The scores for framing effect and anchoring effect ranged from 1 to 3, with mean scores of 2.07 and 1.99, respectively. The scores for purchase intention and purchase attitude ranged from 1 to 7, with mean scores of 4.31 and 4.47, respectively. Participants were willing to pay anywhere from TWD 1 million to TWD 18 million, with a mean amount of TWD 6.8038 million. Further details are presented in Table 5.
4.3. Reliability and Validity Analysis
This study analyzed the reliability and validity of the questionnaire using SPSS. The Cronbach’s α values for the optimism and pessimism items were calculated and found to be 0.685 and 0.636, respectively, indicating acceptable reliability [43]. Validity was analyzed through two personality trait dimensions, optimism and pessimism, with the CR and AVE values exceeding the acceptable thresholds of 0.70 and 0.50, respectively, indicating the validity of the measurement in this study [44]. These results are presented in Table 6.
4.4. Analysis of Variance
The effect of information framing on real estate purchase decisions was examined (Table 7). No significant difference was found among positive/negative framing and joint information framing for purchase intention (F = 0.122, n.s. and F = 0.568, n.s., respectively). A significant difference was found between high- and low-price anchoring effects on purchase intention (F = 6.348, p < 0.01). For real estate purchase attitude, a 1% significant difference existed between positive and negative framing (F = 4.712, p < 0.01) and a 5% significant difference existed between high- and low-price anchoring (F = 3.794, p < 0.05) but not in joint information framing. A significant difference (1%) was observed only between high- and low-price anchoring for willingness to pay (F = 19.550, p < 0.001).
4.5. Hierarchical Regression Analysis
Hierarchical regression was conducted to explore the correlation of real estate purchase decisions with the framing effect, the anchoring effect, and joint information framing.
4.5.1. Framing Effect
In Model 1, income was positively correlated with real estate purchase decision (β = 0.139, p < 0.01; β = 0.148, p < 0.01; and β = 0.153, p < 0.01), and men were willing to pay more than women (β = 0.108, p < 0.05). Model 2 showed that optimism positively correlated with purchase intention (β = 0.296, p < 0.001) and purchase attitude (β = 0.263, p < 0.001), while negative framing negatively correlated with purchase attitude (β = −0.096, p < 0.05). In Model 3, negative framing × optimism negatively correlated with purchase intention and attitude (β = −0.118, p < 0.05; β = −0.096, p < 0.05), with highly optimistic participants showing stronger effects of negative framing (presented in Table 8). The interaction analysis [45] results in Figure 2 and Figure 3 show that the highly optimistic group had significantly negative correlations between purchase attitude and intention with negative framing (β = −0.328, p = 0.051; β = −0.440, p = 0.004, respectively).
4.5.2. Anchoring Effect
In the anchoring effect analysis, Model 1 examined the impact of high-price anchoring, low-price anchoring, and level of optimism on real estate purchase decisions. High-price anchoring had a significant negative effect on purchase intention (β = −0.108, p < 0.05), while the level of optimism was significantly positively correlated with purchase intention and purchase attitude. Low-price anchoring had a negative effect on willingness to pay (β = −0.212, p < 0.001). Model 2 revealed that the interaction variables had a significant positive effect on purchase intention for highly optimistic participants, with high-price anchoring × level of optimism having a 5% significant effect (β = 0.116, p < 0.05) and low-price anchoring × level of optimism having a 1% significant effect (β = 0.130, p < 0.01). The interaction analysis showed that the negative effect of high-price anchoring on purchase intention was stronger for participants with low levels of optimism (β = −0.578, p < 0.001), while the positive effect of low-price anchoring on purchase intention was stronger for participants with high levels of optimism (β = 0.459, p < 0.01) (Figure 4 and Figure 5) (Table 9).
4.5.3. Joint Information Framing
In Model 2, this study examined if the participants’ level of optimism influenced their reactions to joint information framing. Positive framing, negative framing with high-price anchoring, and negative framing with low-price anchoring were all found to have a significant positive effect on willingness to pay (Table 10). An interaction analysis revealed that negative framing was positively correlated with willingness to pay for a relatively low price in high-price anchoring (β = 124.729, p < 0.05) and negatively correlated with willingness to pay for a relatively high price in high-price anchoring (β = −165.001, p < 0.1) for the low-optimism group (Figure 6). Similarly, negative framing was positively correlated with willingness to pay for a relatively low price in low-price anchoring (β = 112.158, p < 0.1) but not for a relatively high price (β = −143.389, n.s.) for the low-optimism group (Figure 7). Conversely, negative framing was negatively correlated with willingness to pay with relatively high-price anchoring information, and the negative effect of negative framing on willingness to pay increased.
5. Conclusions
Real estate markets involve information asymmetry and non-transparency, which can lead to irrational behavior and cognitive biases among buyers. To understand the relationship between these factors and real estate purchase decisions, an experimental study collected 522 valid responses through an online questionnaire. This study evaluated the strength of framing, anchoring, and joint-information-framing effects on real estate purchase decisions, with the level of optimism as the major moderator. During the COVID-19 pandemic, this study found that negative reactions to negative framing were stronger among highly optimistic participants, resulting in more negative real estate purchase decisions. Conversely, the participants with low levels of optimism were relatively reserved regarding real estate purchases. This study also revealed that the low-optimism group reacted overly pessimistically, whereas the high-optimism group reacted overly optimistically when faced with price information. Regarding the correlation between negative framing and willingness to pay among the low-optimism group, this study found that such framing was positively correlated with a relatively low price in high-price anchoring and negatively correlated with a relatively high price in high-price anchoring. Such framing was also positively correlated with a relatively low price in low-price anchoring. This study inferred that the low-price real estate market has been less severely affected by the COVID-19 pandemic than the high-price real estate market, with registration data revealing a considerable effect on the luxury and high-priced real estate market.
In terms of management practice, this study provides suggestions for the real estate industry, such as increasing information transparency and decreasing trading costs to promote sustainable development in the real estate market. This study also highlights the importance of understanding investors’ psychological factors and personality traits when analyzing real estate purchase decisions.
While this study provides valuable insights into the effects of framing and anchoring on real estate purchase decisions, it is limited by the focus on only one type of framing effect (attribute framing) and the use of an online questionnaire. Future studies could explore other types of framing effects and use more varied data collection methods, such as in-person interviews or focus groups. Furthermore, as the COVID-19 pandemic continues to impact real estate markets worldwide, it is crucial to monitor its effects and adjust marketing strategies accordingly. Real estate professionals should consider this study’s findings when designing marketing materials and messaging for different types of properties and target audiences. Overall, this study highlights the importance of understanding how cognitive biases and personality traits can affect real estate purchase decisions. By recognizing and addressing these factors, real estate professionals can better serve their clients and promote a more efficient and sustainable market.
Conceptualization, J.-Y.C. and M.-H.W.; methodology, J.-Y.C.; software, J.-Y.C. and M.-H.W.; validation, J.-Y.C. and M.-H.W.; formal analysis, J.-Y.C.; investigation, J.-Y.C.; resources, J.-Y.C.; data curation, J.-Y.C. and M.-H.W.; writing—original draft preparation, J.-Y.C.; writing—review and editing, M.-H.W.; visualization, J.-Y.C. and M.-H.W.; supervision, J.-Y.C.; project administration, J.-Y.C.; funding acquisition, J.-Y.C. and M.-H.W. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Informed consent was obtained from all subjects involved in the study.
Data are available upon special request from the corresponding author.
The authors declare no conflict of interest.
Footnotes
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Enlarge this image.Figure 2. Negative framing × level of optimism interaction (in purchase intention).
Enlarge this image.Figure 3. Negative framing × level of optimism interaction (in purchase attitude).
Enlarge this image.Figure 6. Negative framing × high-price anchoring × level of optimism interaction.
Enlarge this image.Figure 7. Negative framing × low-price anchoring × level of optimism interaction.
Situations designed for the experimental questionnaire.
| Situational Dimension | Status | Description of the Situation Designed for the Questionnaire |
|---|---|---|
| Framing effect | Positive framing | The world is currently affected by COVID-19, which has greatly influenced the economy of various countries. The Central Bank of the country is following the international trend of monetary easing, which is giving rise to unprecedentedly low interest rates. Mortgage interest rates are thus low. The public believes that the probability of housing prices rising is 60%. |
| Negative framing | The world is currently affected by COVID-19, which has greatly influenced the economy of various countries. Companies and households are encountering operational issues and sharp decreases in income, respectively, and these problems are causing the unemployment rate to rise and economic capacities to decline. The public believes that the probability of housing prices dropping is 40%. | |
| Control group | The world is currently affected by COVID-19, and various countries have hastily invested in vaccine research and development. The Central Epidemic Command Center has announced that Taiwan has signed a supply contract with the American company ANT for 6 million doses of the COVID-19 vaccine, and the doses are expected to be delivered soon. | |
| Anchoring effect | High price | In this part, participants were presented with an advertisement for real estate and manipulated at a high price, as detailed in |
| Low price | In this part, participants were presented with an advertisement for real estate and manipulated at a low price, as detailed in |
|
| Control group | In this part, participants were presented with an advertisement for real estate without price disclosure, as detailed in |
|
| Dependent variable | Q1: I think this property is worth buying. |
|
The personality traits scale.
| 1. I tend to have a positive outlook during uncertain times. | 6. I feel the need to stay occupied. |
| 2. Relaxation comes easily to me. | 7. I rarely anticipate things going in my favor. |
| 3. Murphy’s Law seems to always apply to me. | 8. I am not prone to getting easily upset. |
| 4. I have a consistently positive outlook for my future. | 9. Good fortune seldom comes my way. |
| 5. My friends bring me great joy. | 10. In general, I expect good things to outweigh the bad in my life. |
Source: Revised by [
Definition and explanation of variables.
| Variable | Definition | Explanation |
|---|---|---|
| Framing effect (FE) | 1 = positive; 2 = negative; 3 = control | Nominal variables |
| Anchoring effect (AE) | 1 = positive; 2 = negative; 3 = control | Nominal variables |
| Level of optimism (LOP) | Measured using a 6-point Likert scale | Continuous variable |
| Purchase intention (PI) | Measured using a 6-point Likert scale | Continuous variable |
| Purchase attitude (PA) | Measured using a 6-point Likert scale | Continuous variable |
| Willingness to pay (WTP) | Value (in units of TWD 10,000) written by the participant | Continuous variable |
| Age (AGE) | Obtained by converting the year of birth |
Continuous variable |
| Sex (SEX) | 0 = female; 1 = male | Nominal variable |
| Income (INC) | 1 = TWD 5000 or lower; 2 = TWD 5001–10,000; 3 = TWD 10,001–25,000; 4 = TWD 25,001–50,000; 5 = TWD 50,001 or higher | Divided into five levels |
| Having studied real estate and finance or taken a related course (COU) | 0 = no; 1 = yes | Nominal variable |
| Experience in buying, selling, or investing in real estate (EXP) | 0 = no; 1 = yes | Nominal variable |
Descriptive statistics for demographical variables.
| Demographical Variable | Category | Sample Size | (%) |
|---|---|---|---|
| AGE | 30–35 years | 184 | 35.25 |
| 36–40 years | 142 | 27.20 | |
| 41–45 years | 128 | 24.52 | |
| 46–50 years | 68 | 13.03 | |
| SEX | Female | 259 | 49.62 |
| Male | 263 | 50.38 | |
| INC | TWD 5000 or lower | 36 | 6.90 |
| TWD 5001–10,000 | 58 | 11.11 | |
| TWD 10,001–25,000 | 133 | 25.48 | |
| TWD 25,001–50,000 | 204 | 39.08 | |
| TWD 50,001 or higher | 91 | 17.43 | |
| COU | No | 396 | 75.86 |
| Yes | 126 | 24.14 | |
| EXP | No | 307 | 58.81 |
| Yes | 215 | 41.19 | |
| Occupation | Soldier, public servant, or educator | 43 | 8.24 |
| Information industry | 45 | 8.62 | |
| Financial industry | 23 | 4.41 | |
| Service industry | 151 | 28.93 | |
| Manufacturing | 139 | 26.63 | |
| Mass communication or advertising | 8 | 1.53 | |
| Freelancer | 34 | 6.51 | |
| Student | 7 | 1.34 | |
| Housekeeper | 30 | 5.75 | |
| Other | 42 | 8.05 | |
| Location | Northern Taiwan (i.e., Taipei, New Taipei, Keelung, Taoyuan, Hsinchu, or Yilan) | 223 | 42.72 |
| Central Taiwan (i.e., Miaoli, Taichung, Changhua, Nantou, or Yunlin) | 140 | 26.82 | |
| Southern Taiwan (i.e., Chiayi, Tainan, Kaohsiung, or Pingtung) | 149 | 28.54 | |
| Eastern Taiwan (i.e., Hualien or Taitung) | 7 | 1.34 | |
| Outlying islands (i.e., Kinmen, Lianjiang, or Penghu) | 3 | 0.57 |
Descriptive statistics.
| Entire sample | Variable | Sample Size | Minimum | Maximum | Mean | Standard Deviation | ||
| FE | 522 | 1 | 3 | 2.07 | 0.82 | |||
| AE | 522 | 1 | 3 | 1.99 | 0.82 | |||
| PI | 522 | 1 | 7 | 4.31 | 1.18 | |||
| PA | 522 | 1 | 7 | 4.47 | 1.07 | |||
| WTP | 522 | 100 | 1800 | 680.38 | 329.79 | |||
| Positive | High price | PI | 55 | 1 | 7 | 4.17 | 1.36 | |
| PA | 55 | 1 | 7 | 4.61 | 1.05 | |||
| WTP | 55 | 100 | 1500 | 803.62 | 287.83 | |||
| Low price | PI | 52 | 1.67 | 7 | 4.34 | 1.18 | ||
| PA | 52 | 2.67 | 6.67 | 4.62 | 0.89 | |||
| WTP | 52 | 100 | 1500 | 572.31 | 321.06 | |||
| Control | PI | 50 | 1 | 6.67 | 4.42 | 1.27 | ||
| PA | 50 | 1 | 7 | 4.63 | 1.27 | |||
| WTP | 50 | 100 | 1500 | 713.92 | 343.61 | |||
| Negative | High price | PI | 57 | 1 | 6 | 4.03 | 1.04 | |
| PA | 57 | 1.67 | 5.67 | 4.06 | 0.76 | |||
| WTP | 57 | 100 | 1800 | 736.49 | 340.54 | |||
| Low price | PI | 59 | 2 | 7 | 4.49 | 1.10 | ||
| PA | 59 | 1.67 | 7 | 4.27 | 1.17 | |||
| WTP | 59 | 100 | 1599 | 546.25 | 294.22 | |||
| Control | PI | 58 | 1 | 6.33 | 4.30 | 1.15 | ||
| PA | 58 | 1 | 7 | 4.50 | 1.21 | |||
| WTP | 58 | 100 | 1500 | 710.34 | 346 | |||
| Control | High price | PI | 65 | 1 | 7 | 3.99 | 1.04 | |
| PA | 65 | 2.33 | 6.33 | 4.30 | 0.83 | |||
| WTP | 65 | 100 | 1600 | 765.69 | 315.57 | |||
| Low price | PI | 64 | 1.67 | 7 | 4.59 | 1.30 | ||
| PA | 64 | 1 | 7 | 4.54 | 1.27 | |||
| WTP | 64 | 150 | 1500 | 568.27 | 291.71 | |||
| Control | PI | 62 | 1.33 | 6.33 | 4.43 | 1.07 | ||
| PA | 62 | 2 | 7 | 4.76 | 0.97 | |||
| WTP | 62 | 100 | 1500 | 708.96 | 337.68 | |||
Note: WTP (units of TWD 10,000).
Reliability and validity analysis.
| Item | Mean | Standard Deviation | Cronbach’s α | Factor Loading | CR | AVE |
|---|---|---|---|---|---|---|
| In uncertain times, I usually expect the best. | 3.74 | 1.19 | 0.69 | 0.82 | 0.81 | 0.60 |
| I’m always optimistic about my future. | 3.93 | 1.14 | 0.84 | |||
| Overall, I expect more good things to happen to me than bad. | 3.99 | 1.08 | 0.64 | |||
| If something can go wrong for me, it will. | 3.38 | 1.03 | 0.64 | 0.67 | 0.79 | 0.55 |
| I hardly ever expect things to go my way. | 3.44 | 1.15 | 0.77 | |||
| I rarely count on good things happening to me. | 2.92 | 1.32 | 0.78 |
Note: the composite reliability (CR) and average variance extracted (AVE) were determined to verify the ability of the instrument to measure the problem of interest, ensuring the accuracy of the measurement.
Analysis of variance.
| Variable | N | PI | PA | WTP | |||
|---|---|---|---|---|---|---|---|
| Mean | F Value | Mean | F Value | Mean | F Value | ||
| PFE | 157 | 4.306 | 0.122 | 4.620 | 4.712 ** | 698.44 | 0.469 |
| NFE | 174 | 4.274 | 4.167 | 663.27 | |||
| FCG | 191 | 4.335 | 4.531 | 681.12 | |||
| HAE | 177 | 4.058 | 6.348 ** | 4.318 | 3.794 * | 768.07 | 19.550 *** |
| LAE | 175 | 4.482 | 4.474 | 562.05 | |||
| ACG | 170 | 4.306 | 4.633 | 710.89 | |||
| FE × AE | 522 | 4.306 | 0.568 | 4.473 | 0.794 | 680.38 | 0.151 |
Note: PFE means positive framing, NFE means negative framing, FCG means control group in framing, HAE means high-price anchoring, LAE means low-price anchoring, ACG means control group in anchoring, and FE × AE means framing × anchoring. *** = p < 0.001; ** = p < 0.01; * = p < 0.05.
Hierarchical regression analysis of the framing effect.
| Variable | PI | PA | WTP | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Standardized Coefficient β | Standardized Coefficient β | Standardized Coefficient β | |||||||
| Model 1 | Model 2 | Model 3 | Model 1 | Model 2 | Model 3 | Model 1 | Model 2 | Model 3 | |
| AGE | −0.017 | −0.041 | −0.041 | 0.004 | −0.02 | −0.019 | 0.042 | 0.045 | 0.045 |
| SEX | 0.041 | 0.058 | 0.058 | 0.036 | 0.052 | 0.052 | 0.108 * | 0.105 * | 0.105 * |
| INC | 0.139 ** | 0.092 * | 0.097 * | 0.148 ** | 0.098 * | 0.100 * | 0.153 ** | 0.158 ** | 0.159 ** |
| COU | 0.002 | 0.01 | 0.013 | −0.007 | 0.002 | 0.004 | −0.03 | −0.03 | −0.03 |
| EXP | 0.044 | 0.015 | 0.012 | 0.01 | −0.012 | −0.014 | −0.008 | −0.004 | −0.005 |
| PFE | −0.019 | −0.022 | 0.034 | 0.031 | 0.026 | 0.025 | |||
| NFE | −0.012 | −0.015 | −0.096 * | −0.099 * | −0.013 | −0.014 | |||
| LOP | 0.296 *** | 0.301 *** | 0.263 *** | 0.268 *** | −0.039 | −0.037 | |||
| PFE × LOP | 0.044 | 0.024 | 0.009 | ||||||
| NFE × LOP | −0.118 * | −0.096 * | −0.035 | ||||||
| Cox & Snell R2 | 0.027 | 0.111 | 0.131 | 0.025 | 0.107 | 0.118 | 0.041 | 0.044 | 0.045 |
| Nagelkerke R2 | 0.018 | 0.097 | 0.114 | 0.016 | 0.093 | 0.101 | 0.032 | 0.029 | 0.026 |
Note: PFE means positive framing, NFE means negative framing, and LOP means level of optimism. *** = p < 0.001; ** = p < 0.01; * = p < 0.05.
Hierarchical regression analysis of the anchoring effect.
| Variable | PI | PA | WTP | |||
|---|---|---|---|---|---|---|
| Standardized Coefficient β | Standardized Coefficient β | Standardized Coefficient β | ||||
| Model 1 | Model 2 | Model 1 | Model 2 | Model 1 | Model 2 | |
| AGE | −0.039 | −0.044 | −0.016 | −0.017 | 0.042 | 0.043 |
| SEX | 0.067 | 0.062 | 0.058 | 0.057 | 0.095 * | 0.097 * |
| INC | 0.098 * | 0.099 * | 0.107 * | 0.107 * | 0.149 ** | 0.150 ** |
| COU | −0.004 | −0.005 | 0.000 | 0.000 | 0.001 | 0.001 |
| EXP | 0.007 | 0.007 | −0.023 | −0.023 | 0.002 | 0.003 |
| HAE | −0.108 * | −0.115 * | −0.121 * | −0.123 * | 0.073 | 0.074 |
| LAE | 0.063 | 0.055 | −0.049 | −0.051 | −0.212 *** | −0.211 *** |
| LOP | 0.291 *** | 0.291 *** | 0.260 *** | 0.260 *** | −0.038 | −0.038 |
| HAE × LOP | 0.116 * | 0.023 | −0.006 | |||
| LAE × LOP | 0.130 ** | 0.034 | −0.024 | |||
| R 2 | 0.132 | 0.147 | 0.104 | 0.105 | 0.107 | 0.108 |
| Adjusted R2 | 0.119 | 0.131 | 0.09 | 0.087 | 0.093 | 0.09 |
Note: HAE means high-price anchoring, LAE means low-price anchoring, and LOP means level of optimism. *** = p < 0.001; ** = p < 0.01; * = p < 0.05.
Hierarchical regression analysis of the joint information framing.
| Variable | PI | PA | WTP | |||
|---|---|---|---|---|---|---|
| Standardized |
Standardized |
Standardized |
||||
| Model 1 | Model 2 | Model 1 | Model 2 | Model 1 | Model 2 | |
| AGE | −0.041 | −0.036 | −0.02 | −0.016 | 0.042 | 0.051 |
| SEX | 0.062 | 0.063 | 0.058 | 0.061 | 0.099 * | 0.093 * |
| INC | 0.103 * | 0.109 * | 0.103 * | 0.108 * | 0.150 ** | 0.150 ** |
| COU | −0.001 | −0.002 | −0.002 | −0.006 | 0.001 | 0.001 |
| EXP | 0.004 | −0.004 | −0.016 | −0.022 | 0.007 | 0.004 |
| PFE | −0.019 | −0.022 | 0.033 | 0.029 | 0.022 | 0.026 |
| NFE | −0.013 | −0.008 | −0.101 * | −0.100 * | −0.016 | −0.002 |
| HAE | −0.120 * | −0.119 * | −0.129 ** | −0.130 ** | 0.073 | 0.071 |
| LAE | 0.055 | 0.057 | −0.05 | −0.049 | −0.212 *** | −0.211 *** |
| LOP | 0.294 *** | 0.295 *** | 0.257 *** | 0.255 *** | −0.04 | −0.039 |
| PFE × HAE | 0.014 | 0.02 | 0.078 | 0.081 | 0.015 | 0.016 |
| PFE × LAE | −0.029 | −0.026 | 0.057 | 0.059 | −0.008 | −0.013 |
| NFE × HAE | 0.006 | 0.007 | −0.017 | −0.019 | −0.046 | −0.044 |
| NFE × LAE | −0.007 | −0.008 | −0.013 | −0.011 | −0.042 | −0.049 |
| PFE × LOP | 0.053 | 0.056 | 0.034 | 0.035 | −0.01 | −0.012 |
| NFE × LOP | −0.108 * | −0.104 * | −0.097 * | −0.098 * | −0.047 | −0.041 |
| HAE × LOP | 0.099 * | 0.093 | 0.001 | −0.003 | −0.013 | −0.027 |
| LAE × LOP | 0.124 ** | 0.115 * | 0.028 | 0.022 | −0.027 | −0.04 |
| PFE × HAE × LOP | −0.023 | −0.019 | 0.097 | |||
| PFE × LAE × LOP | −0.066 | −0.061 | 0.027 | |||
| NFE × HAE × LOP | 0.037 | −0.013 | 0.151 ** | |||
| NFE × LAE × LOP | 0.028 | 0.009 | 0.127 ** | |||
| R 2 | 0.169 | 0.174 | 0.137 | 0.141 | 0.113 | 0.13 |
| Adjusted R2 | 0.139 | 0.138 | 0.106 | 0.103 | 0.081 | 0.091 |
Note: PFE means positive framing, NFE means negative framing, HAE means high-price anchoring, LAE means low-price anchoring, and LOP means level of optimism. *** = p < 0.001; ** = p < 0.01; * = p < 0.05.
Appendix A
Enlarge this image.Figure A1. Participants are presented with an advertisement for real estate and manipulated at a high price.
Appendix B
Enlarge this image.Figure A2. Participants are presented with an advertisement for real estate and manipulated at a low price.
Appendix C
Enlarge this image.Figure A3. Participants are presented with an advertisement for real estate without price disclosure.
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