Analysis of Factors Influencing Housing Preferences of Individuals Considering Earthquake Risk Using Conjoint Analysis

Analysis of Factors Influencing Housing Preferences of Individuals Considering Earthquake Risk Using Conjoint Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analysis of Factors Influencing Housing Preferences of Individuals Considering Earthquake Risk Using Conjoint Analysis Murat Tabanoğlu, İsmail Hakkı Demir This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6470770/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In this study, the factors affecting the housing preferences of individuals, considering the earthquake risk, are analyzed using the conjoint analysis method. The study evaluated six main factors: soil class, structural material, compliance with earthquake regulations, distance to fault line, retrofitting status, and type of company. The factors were determined to align with the literature review and expert opinions, and 25 different housing models were created using the orthogonal design method and presented to the participants. Two thousand eight hundred twenty-three people participated in the study, and the participants were asked to evaluate the housing models on a scale of 1–10. The findings showed that individuals prioritized safety-oriented factors such as soil class (28.16%), structural material (18.10%), and compliance with earthquake regulations (18.11%). Participants preferred houses that comply with modern regulations, are built on solid soil, and use durable materials. Factors such as distance to fault lines (10.99%) and type of company (15.59%) were also found to be influential on preferences, but retrofitting status (9.04%) was relatively less important. While the most preferred housing models had features such as solid soil, steel structural system, and compliance with modern regulations, the least preferred models included risk factors such as weak soil, old regulations, and proximity to fault lines. The model's accuracy was verified by Pearson's R (0.964) and Kendall's Tau (0.860) coefficients. The study demonstrates the impact of earthquake risk on individuals' housing preferences and provides important findings for developing safety and resilience-oriented strategies. Earthquake risk housing preferences conjoint analysis soil class earthquake regulations Introduction Earthquake risk is one of the main factors determining individuals' housing preferences. Safety, economic sustainability, and environmental impacts are multidimensional factors shaping housing choices. The literature emphasizes that individuals' risk perception prioritizes safety criteria such as soil durability and building materials (D'Alpaos & Bragolusi, 2022 ; Azimi & Asgary, 2013 ). This trend is more evident in earthquake-prone countries such as Turkey, and the demand for safe living spaces is increasing (Bergel, 1970 ). Economic factors play a decisive role in individuals' orientation towards earthquake-resistant housing. Cost-benefit analyses of retrofit projects increase individuals' interest in such projects (Faccioli et al., 2024 ; Makhoul, 2019 ). A study conducted in Japan revealed that individuals' interest in energy-saving and durability features is increasing (Kinoshita, 2020 ). In addition to economic conditions, local governments' strategies to increase risk awareness are another important factor encouraging the shift towards safe housing (Marulanda et al., 2013; Arnold, 2018 ). Individual decision-making processes related to earthquake risk are not only limited to the perception of safety but are also shaped by socioeconomic factors. Studies conducted in metropolises such as Istanbul and Izmir show that earthquake safety depends not only on individual preferences but also on market dynamics and local government policies (Keskin et al., 2017). In cities such as Van and Yalova, post-earthquake housing preferences are shaped by economic conditions, perception of safety, and local government policies (Yavuz et al., 2019 ; Platt & Drinkwater, 2016). Housing choices are influenced by individuals' expectations of safety, comfort, and sustainability and become more critical in areas at risk of natural disasters. In Turkey, housing choices are generally based on factors such as soil safety, building materials, compliance with earthquake regulations, and reliability of construction companies (Taylan, 2015; Altindal et al., 2021 ). Proximity to fault lines, building durability, and retrofitting projects' effectiveness are among the determining criteria in individuals' preferences (Hancılar et al., 2020 ). In this context, the conjoint analysis method is used to understand individuals' housing preferences better and to determine the importance ranking of the factors affecting these preferences. This method allows us to understand how individuals make trade-offs between different housing characteristics (Green & Rao, 1971 ). This study focuses on how key criteria such as soil type, structural material, compliance with earthquake regulations, distance to fault lines, retrofit status, and type of construction company affect individuals' preferences. This study aims to contribute to the planning of safer and sustainable residential areas by analyzing the housing preferences of individuals with comprehensive survey data. The findings will guide policymakers, urban planners, and construction firms to develop more effective strategies by considering individuals' safety priorities. Literature Review Studies on housing preferences have addressed individuals' socioeconomic characteristics, environmental factors, and security factors. Hensher et al. (2005) stated that individuals prefer the alternative that provides the maximum benefit in the selection process and emphasized that factors such as location, cost, and security are decisive (Hensher et al., 2005; Hartshorn, 1992). Mirkatouli et al. (2015) emphasized the importance of proximity to urban amenities, road network access, and location in housing preferences and stated that durability and compliance with earthquake regulations are prioritized in areas under earthquake risk (Mirkatouli et al., 2015; Irfiyanti & Widjonarko, 2014). Housing costs play a critical role, especially for low-income individuals. Kotler and Armstrong (2004) emphasized that cost is a determinant of consumer behavior and stated that affordable housing increases the likelihood of preference (Kotler & Armstrong, 2004). Similarly, Lindberg et al. (1989) stated that cost and housing size are among the most important factors in housing preferences. On the other hand, it has been stated that individuals' preference structures vary according to location, accessibility, and services (Jansen et al., 2011; Rao, 2014). Earthquake resistance is of critical importance in increasing housing safety. It has been stated that timber, steel, and reinforced concrete structures exhibit superior performance in terms of seismic resistance, and it has been emphasized that these materials reduce structural damage by providing energy absorption (Sousa & Monteiro, 2018; Li & Ellingwood, 2009). The economic advantages of retrofit projects are important in increasing individual and community resilience (Egbelakin et al., 2017; Dell'Anna et al., 2022). Furthermore, the literature has widely discussed that factors such as soil class and building design are critical for safe construction (Huang et al., 2024; Burton et al., 2018). Housing Security and Economic Aspects Housing safety is central to individuals' preferences. Azimi and Asgary (2013) stated that individuals prioritize factors such as soil resistance and structural materials in areas with high susceptibility to earthquake risk. Drawing attention to the importance of retrofit strategies, Faccioli et al. (2024) found that factors such as energy efficiency and durability significantly affect individuals' preferences. Economic factors are also decisive in housing preferences. The accessibility of long-term payment plans and affordable housing, especially for low-income individuals, shapes the dynamics of the housing market (Sun et al., 2016; Almaden, 2014). Housing area and payment conditions are among other factors that affect individuals' living standards and priorities in housing preferences (Noor & Zaimi, 2012). Social Resilience and Earthquake Safety Earthquake resilience and safe construction are strategic elements that increase social and individual resilience. Criteria such as soil class, choice of structural materials, and distance to fault lines are the main factors that support safe construction (Sousa & Monteiro, 2018; Hancılar et al., 2020). In addition, retrofit projects are another important factor shaping individuals' housing preferences (Egbelakin et al., 2017). In this context, the literature suggests that individuals' socioeconomic status shapes housing preferences, local market conditions, and individual risk perceptions. While the increase in market values supports the demand for safe housing, it also encourages a comprehensive understanding of security (Marulanda et al., 2013; Kingston et al., 2018). Conjoint Analysis Conjoint analysis is a multivariate method used to understand the importance of factors affecting individuals' preferences. This method allows individuals to evaluate their preferences between options with different characteristics and to determine which characteristics are more important (Green & Srinivasan, 1978). In housing preferences, conjoint analysis is widely used to understand the impact of criteria such as safety, cost, location, and building materials on individuals' decision-making processes (Sousa & Monteiro, 2018; Dell'Anna et al., 2022). Conjoint analysis primarily aims to reveal which attributes individuals value more when choosing various alternatives. The analysis determines the overall utility function expresses the utility contribution of each attribute and these contributions: U(Xi): Represents the overall utility of the alternative. czj: Represents the weight contribution according to the levels of attributes (Green & Srinivasan, 1990). Conjoint Analysis Steps Conjoint analysis is applied in a systematic process: 1. Identification of Characteristics: The housing characteristics to be investigated are defined—for example, soil durability, structural material, compliance with earthquake regulations, cost, and location. 2. Defining Feature Levels: Different levels are determined for each feature. For example, ‘low’, ‘medium’ and ‘high’ levels for the price. 3. Profile Creation: Profiles consisting of combinations of features are prepared. These combinations are usually created by the orthogonal array method (Hwa & Chin, 2012). 4. Data Collection: Participants are asked to evaluate these profiles. The evaluation can be done by ranking, rating, or preference tests (Orzechowski, 2004). 5. Analyzing the Results: The collected data are analyzed, and the relative importance weights of each attribute are calculated (Green & Srinivasan, 1990). Conjoint Analysis in Housing Preferences In understanding housing preferences, conjoint analysis reveals how individuals attach importance to safety factors such as soil durability, structural materials, and compliance with earthquake regulations (Sousa & Monteiro, 2018). These factors are critical in determining individuals' preferences, especially in areas with high earthquake risk, such as Turkey (Egbelakin et al., 2017). In addition, factors such as cost and location also have a significant impact on preferences (Hensher et al., 2005). Molin et al. (1999) compared individual and group-based models to explain housing preferences and presented important findings on individuals' preferences. The results obtained from these studies show that the characteristics that individuals value most affect their preference rankings (Coolen & Hoekstra, 2001). Conjoint Analysis for Policy and Strategic Planning Another important contribution of conjoint analysis is to guide policymakers and urban planners. Cost-benefit analyses of retrofit projects are critical for safe and sustainable urban planning (Faccioli et al., 2024; Burton et al., 2018). This method provides a better understanding of both individual preferences and societal impacts. By providing in-depth information about individuals' preferences, conjoint analysis enables the optimization of service and product design. The relative importance of criteria such as soil durability, compliance with earthquake regulations, and cost in housing preferences can be determined so that housing models can be developed by the needs of individuals (Green & Srinivasan, 1990; Larsen et al., 2021). This method is an indispensable tool for understanding individuals' decision-making processes and contributes to developing sustainable urbanization and disaster management strategies. Method This study aims to analyze the factors affecting the housing preferences of individuals considering the earthquake risk. In this context, the conjoint analysis method is used as an effective tool to determine the factors shaping individuals' housing preferences and the relative importance of the levels of these factors (Mutlu, 2022). In this context, six factors that may affect building preferences were evaluated: Soil, Structural System, Compliance with Regulations, Distance to Fault Line, Retrofitting Status, and Type of Firm. Research Design Determination of Factors and Levels: Each factor and its levels were determined in line with the literature review and expert opinions. The factors and levels considered in the study are as follows: Soil: Very High Strength (VHS), High Strength (HS), Moderate Strength (MS), Low Strength (LS) Structural System: Steel, Reinforced Concrete (RC), Timber, Masonry Regulatory Compliance: post-2018, post-2007, post-1998, post-1975 Distance to Fault Line: 16-20 km, 11-15 km, 6-10 km, 0-5 km Retrofit Status: Present, Absent Type of Construction Firm: Housing Development Administration (HDA), Corporate Firm, Private Contractor Independence of Factors (Orthogonal Design): Factors were ensured to be independent, and combinations of each factor level were created. In this direction, appropriate combinations were designed to present the preferences to the participants. Data Collection Process Participants were presented with 25 cards containing different combinations of structures and asked to rate each combination on a scale ranging from 1 to 10. In total, data was collected from 2823 participants. This process is designed to develop a more in-depth understanding of individuals' structure preferences. Analysis Process Calculation of Utility Values of Factors: In line with the data collected from the participants, utility estimates of each factor level were calculated. These values show which levels are perceived more positively or negatively in individuals' preferences (Mutlu, 2022). Relative Importance of Factors: The total utility values of each factor were calculated, and these values were expressed as the importance weights of the factors. In this way, the relative impact of the factors on preference was revealed. Assessment of the Fit Level of the Model: The fit between observed preferences and predicted preferences was measured by Pearson's R and Kendall's Tau coefficients. These coefficients were used to evaluate the reliability and predictive power of the model (Mutlu, 2022). Statistical Analysis Tools: Calculations based on conjoint analysis; data such as utility values, standard errors, and importance values were carried out using SPSS and similar statistical analysis software (Mutlu, 2022). The results of the analysis are organized in a way that clearly shows the effects of factors on preference. Structure of the Model: The obtained model consists of the following components: Utility values of factor levels Relative importance of factors Model fit coefficients (Pearson's R and Kendall's Tau) Findings In this section, the demographic characteristics of the individuals participating in the research are analyzed, and the general profile of these characteristics is presented. Then, the responses given to the card combinations created to determine the characteristics affecting the housing preferences of the individuals were evaluated with the help of conjoint analysis. As a result of the analysis, the utility values of the cards were calculated, and the factors shaping individuals' preferences were determined. Finally, the changes in the preferred cards according to demographic characteristics were analyzed, and the factors affecting the participants' preferences were examined in detail. Research Group The demographic characteristics of the 2823 participants were analyzed and presented in Table 1 below. Table 1 . Demographic Characteristics of Individuals Participating in the Study Category Subcategory N % N % Gender Male 1911 67.7% Marital Status Single 765 27.1% Female 912 32.3% Married 2058 72.9% Age 18 and under 14 0.5% Occupation Retired 124 4.4% 19-29 517 18.3% Homemaker 65 2.3% 30-39 1004 35.6% Government employee 1440 51.0% 40-49 916 32.4% Civil Servant 3 0.1% 50-59 285 10.1% Student 164 5.8% 60 and over 85 3.0% Private employee 1025 36.3% Education Level Primary/Secondary 313 11.1% Income Level 17.000TL and below 234 8.3% Associate degree 294 10.4% 17.001TL - 32.000TL 364 12.9% Bachelor's degree 1493 52.9% 32.001TL - 47.000TL 835 29.6% Master's degree 494 17.5% 47.001TL - 62.000TL 859 30.4% Doctorate 229 8.1% 62.001 TL and above 531 18.8% Number of Children 0 1004 35.6% Reason for Housing Preference Both 1042 36.9% 1 609 21.6% Residing 1707 60.5% 2 960 34.0% Investing 73 2.6% 3 246 8.7% Total Number of Participants: 2823 When Table 1 is analyzed, it is seen that most participants are male (67.7%) and married (72.9%). The participants are predominantly between 30-39 (35.6%) and 40-49 (32.4%). While bachelor's degree graduates constitute the largest group with 52.9% in education level, the most significant proportion in income distribution is in the income group of 47,001-62,000 TL with 30.4%. While 35.6% of the participants do not have children, those with two children follow with 34.0%. In the occupational distribution, public employees stand out with 51.0%. Regarding housing preferences, while 60.5% of the respondents prioritize the purpose of residence, only 2.6% prefer housing for investment purposes. A total of 2823 people participated in the survey, and the participants' general characteristics can be summarized as high education level, middle age group, and upper-middle income level. Survey Design and Creation of Housing Models In this section, the factors that are effective in the housing preferences of individuals considering the earthquake risk and the sub-levels of these factors are determined in line with the literature review and expert opinions. The identified factors were evaluated by conjoint analysis using SPSS software, and because of the analysis, 25 sample housing models were created by the orthogonal design method. The models were presented to the participants as cards; detailed information is given in Table 2. Care was taken to ensure that the representation power of each factor and sub-level was balanced in the preparation of the cards. Table 2 . 25 Cards Created with Orthagonal Design and Asked Participants to Rate Between 1-10 Card ID Soil Type Structural System Regulatory Compliance Distance to Fault Line (km) Retrofit Status Type of Construction Firm 1 VHS Steel 1975 Regulation 11-15 km Present Corporate Firm 2 VHS Masonry 2007 Regulation 0-5 km Present Corporate Firm 3 MS Masonry 2018 Regulation 11-15 km Absent Private Contractor 4 MS Steel 2018 Regulation 0-5 km Absent HDA 5 LS Masonry 1998 Regulation 16-20 km Absent Corporate Firm 6 VHS Masonry 2018 Regulation 16-20 km Present HDA 7 MS Timber 1975 Regulation 16-20 km Present Corporate Firm 8 VHS Timber 2018 Regulation 6-10 km Absent Corporate Firm 9 VHS Steel 2018 Regulation 16-20 km Absent HDA 10 HS Reinforced Concrete 2018 Regulation 16-20 km Present Private Contractor 11 VHS Reinforced Concrete 1998 Regulation 11-15 km Present HDA 12 HS Steel 2018 Regulation 0-5 km Present Corporate Firm 13 LS Timber 2018 Regulation 16-20 km Present HDA 14 VHS Steel 2007 Regulation 16-20 km Absent HDA 15 HS Steel 1998 Regulation 16-20 km Absent Corporate Firm 16 LS Steel 2007 Regulation 6-10 km Present Private Contractor 17 MS Reinforced Concrete 2007 Regulation 16-20 km Absent Corporate Firm 18 VHS Steel 1975 Regulation 16-20 km Absent Private Contractor 19 VHS Timber 1998 Regulation 0-5 km Absent Private Contractor 20 VHS Reinforced Concrete 2018 Regulation 6-10 km Absent Corporate Firm 21 LS Steel 2018 Regulation 11-15 km Absent Corporate Firm 22 HS Masonry 1975 Regulation 6-10 km Absent HDA 23 MS Steel 1998 Regulation 6-10 km Present HDA 24 LS Reinforced Concrete 1975 Regulation 0-5 km Absent HDA 25 HS Timber 2007 Regulation 11-15 km Absent HDA Each combination card in Table 2 was presented to the participants as a sample housing model, and they were asked to rate these models on a scale of 1 to 10. The scores obtained from the participants were evaluated by conjoint analysis to determine the importance levels of the factors affecting the housing choice. The results obtained from the analysis revealed the utility values of the factors affecting the housing preference and the impact of these factors on the preference, which are presented in detail below. Analysis Results and Evaluations As a result of the conjoint analysis, the importance levels of the factors affecting the housing preference and the benefit values of the sub-level of each factor were calculated. This analysis reveals the factors affecting the preferences of the participants in detail. Below, the utility values obtained for each factor and sub-level, as well as the impact of these factors on overall preferences, are presented in tables. Tablo 3 . Correlation Values of General Analysis Results Correlations a Value Sig. Pearson's R ,964 ,000 Kendall's tau ,860 ,000 a. Correlations between observed and estimated preferences Table 3 shows the relationship between the observed preferences in conjoint analysis and the preferences predicted by the model. Pearson's R-value (0.964) and Kendall's Tau value (0.860) indicate a strong relationship between observed and predicted preferences. It is statistically significant (p < 0.001). The fact that both relationships are statistically significant (p < 0.001) shows that the model works accurately and gives reliable results in preference predictions. Table 4. Conjoint Analysis Results and Relative Importance Percentages of Housing Preference Factors Attribute Level N of Levels Relation to Ranks or Scores Utility Estimate Std. Error B Estimate Coefficient Relative Importance Percentage % Retrofit Absent 2 Discrete -,010 ,083 - 9,044 Present ,010 ,083 Type of Construction Firm HDA 3 Discrete ,344 ,113 - 15,594 Corporate Firm -,003 ,113 Private Contractor -,341 ,136 Zemin VHS 4 Linear (less) -,760 ,070 -,760 28,164 HS -1,521 ,139 MS -2,281 ,209 LS -3,041 ,279 Structural System Steel 4 Linear (less) -,483 ,070 -,483 18,098 Reinforced Concrete -,966 ,139 Timber -1,448 ,209 Masonry -1,931 ,279 Regulatory Compliance 2018 Regulation 4 Linear (less) -,426 ,070 -,426 18,111 2007 Regulation -,852 ,139 1998 Regulation -1,278 ,209 1975 Regulation -1,704 ,279 Distance to Fault Line 16-20 km 4 Linear (less) -,222 ,070 -,222 10,988 11-15 km -,444 ,139 6-10 km -,667 ,209 0-5 km -,889 ,279 (Constant) All factors are orthogonal 9,327 ,319 Averaged Importance Score Table 4 presents the results of the conjoint analysis of the factors affecting housing choice and the relative importance percentages of these factors. The analysis includes utility estimates, standard errors, coefficients, and relative importance percentages for the levels of each attribute. Retrofit Factor: The empowerment feature has two levels and a ‘discrete’ relationship. When empowerment is present, the benefit estimate is optimistic; when it is absent, it is slightly negative. The empowerment factor has a relative importance of 9.04%, indicating a lower impact than the other factors. Firm Factor: The firm characteristic has three levels and has a ‘discrete’ relationship. HDA projects have the highest utility value, while contractor firms have the lowest value. The firm factor indicates an important factor with 15.59%. Soil Factor: The soil factor has four levels and demonstrates a "linear (less)" relationship. Firm soil has the highest utility value, while utility values decrease significantly as soil stability decreases. This factor has the highest relative importance at 28.16%, indicating that the soil factor is decisive in housing preferences. Structural System Factor: The structural system factor has four levels and exhibits a "linear (less)" relationship. Steel structures have the highest utility value, while utility values decrease for other structural types. This factor stands out with a relative importance of 18.10%. Regulation Factor: The construction year regulation level is evaluated in four levels and shows a "linear (less)" relationship. Buildings constructed by more recent regulations have higher utility values, with this factor having a relative importance of 18.11%. Fault Line Factor: The distance to the fault line has four levels, demonstrating a "linear (less)" relationship. Utility values increase as the distance from the fault line increases. The fault factor holds a moderate level of importance at 10.99%. Among the factors, the soil characteristic has the highest relative importance (28.16%), followed by the structural system (18.10%) and regulation (18.11%) factors. The firm and fault distance factors have lower percentages of importance. The analysis highlights the decisive role of soil characteristics and structural safety in housing preferences. Evaluation of Trial Cards This study section analyzed responses to the 25 combination cards presented to participants to identify the most and least preferred housing models. Furthermore, these cards were evaluated regarding demographic variables to reveal how housing preferences vary according to individuals' demographic characteristics. The findings obtained from the analysis are presented below. Table 5. The Top Three Housing Models with the Highest Scores Given by Participants* Card ID Soil Type Structural System Regulatory Compliance Distance to Fault Line (km) Retrofit Status Type of Construction Firm 9 VHS Steel 2018 Regulation 16-20 km Absent HDA 14 VHS Steel 2007 Regulation 16-20 km Absent HDA 20 VHS Reinforced Concrete 1998 Regulation 6-10 km Absent Corporate Firm * K9: 7.99±2.41; K14: 7.32±2.34; K20: 7.07±2.43 Table 5 presents the characteristics of the top three housing models with the highest scores given by participants. In all three models, the soil classification is identified as "Very high strong." This indicates that participants consider soil safety one of the most critical criteria in housing preferences. Firm soil stands out as a key factor in mitigating earthquake risks. Regarding structural material, the first two models feature "Steel," while the third model uses "Reinforced Concrete." The preference for steel structural material demonstrates that durability and safety perception significantly influence participant choices. However, reinforced concrete is also regarded as an acceptable alternative. Compliance with earthquake regulations is another essential criterion for participants. The first model falls under structures built after 2018, the second model after 2007, and the third model complies with post-2018 regulations. This indicates that structures adhering to newer regulations are perceived as more reliable and are thus preferred by participants. Regarding proximity to fault lines, the first two models are located 16–20 km from the fault line, while the third model is 6–10 km away. Although participants prefer houses farther from fault lines, closer distances are acceptable if the soil and structural features are satisfactory. When examining reinforcement, all three models are buildings that meet the necessary safety criteria during initial construction rather than being retrofitted. This highlights participants' preference for inherent structural safety over retrofitting. Finally, the producer’s firm preferences are evaluated. The first two models are associated with "HDA," while the third is linked to a "Corporate Firm." HDA is highlighted for its public assurance and reliability, while corporate firms are also considered viable alternatives. These models illustrate the primary factors influencing participants' housing preferences, including soil safety, structural material, compliance with earthquake regulations, and the producer firm, emphasizing their priority in decision-making processes. Table 6. The Bottom Three Housing Models with the Lowest Scores Given by Participants* Card ID Soil Type Structural System Regulatory Compliance Distance to Fault Line (km) Retrofit Status Type of Construction Firm 5 LS Masonry 1998 Regulation 16-20 km Absent Corporate Firm 24 LS Reinforced Concrete 1975 Regulation 0-5 km Absent HDA 16 LS Steel 2007 Regulation 6-10 km Present Private Contractor * K5: 3.03±2.93; K24: 3.56±3.02; K16: 4.25±2.83 Table 6 presents the bottom three housing models with the lowest scores given by participants and their characteristics. The commonalities and differences among these models provide important insights into their low preference. Firstly, the soil classification for all models is noted as "Weak." Participants perceive weak soil structures as a significant safety risk, leading to low preference. Soil safety remains a decisive factor in housing preferences. There are notable differences in structural materials. Model K5 features "Masonry" as the structural material, while model K24 uses "Reinforced Concrete" and model K16 employs "Steel." Masonry systems are perceived as less durable, explaining their lowest score, whereas for the other systems, the weak soil factor appears to have influenced their lower preference. In terms of compliance with earthquake regulations, these models correspond to buildings constructed according to 1998, 1975, and 2007 regulations, respectively. However, structures adhering to older regulations (e.g., post-1975) are notably less preferred. Modern compliance with earthquake regulations remains a critical criterion for participants. Regarding the proximity to fault lines, the lowest-scoring model, K24, is located 0–5 km from the fault line. This highlights that proximity to fault lines negatively affects preferences. Model K16 is 6–10 km away, and model K5 is 16–20 km away; however, the weak soil factor seems to influence preferences significantly. Reinforcement status is marked as "Present" only for model K16. This suggests that the presence of reinforcement does not mitigate the negative perception of weak soil. For participants, ensuring safety during initial construction appears more critical than post-construction reinforcement. Finally, in terms of the producer firm, model K5 is associated with a "Corporate Firm," K24 with "HDA," and K16 with a "Contractor Firm." Soil classification and other factors appear to play a more decisive role than the producer firm in preferences. Overall, factors such as weak soil classification, compliance with older earthquake regulations, and proximity to fault lines are the primary reasons for the low scores of these models. Participants have shaped their preferences by prioritizing durability and safety considerations. Evaluation of Trial Cards Based on Demographic Characteristics Participants' responses to the trial cards for housing models were analyzed in detail regarding demographic characteristics. The evaluations revealed that general preference trends essentially showed similarities across demographic variables. The analysis based on demographic features indicates that the general preference trends were strongly adopted and remained broadly consistent across different demographic groups. However, slight nuances in the preferences of individual groups reflect varying needs and expectations. This situation provides an important guide for developing strategies for target groups in housing design. Conclusion and Discussion Using the conjoint analysis method, this study examined the factors influencing individuals' housing preferences, considering earthquake risk. The findings reveal that individuals adopt a safety- and durability-oriented approach in housing preferences. Six key factors—soil classification, structural material, compliance with earthquake regulations, proximity to fault lines, reinforcement status, and type of firm—were evaluated. Among these factors, soil classification, structural material, and compliance with regulations emerged as the most significant. Participants gave high scores for housing models built on firm soil, using durable structural materials, and being compliant with modern earthquake regulations. The most preferred housing models (K9, K14, and K20) feature firm soil, steel or reinforced concrete structural systems, and compliance with modern regulations. In contrast, the least preferred models (K5, K24, and K16) included risk factors such as weak soil, older earthquake regulations, and proximity to fault lines. These findings indicate that participants exhibit a safety-focused approach and a tendency to avoid risk in the face of earthquake threats. When examining the relative importance percentages of the factors, soil classification (28.16%), structural material (18.10%), and compliance with regulations (18.11%) were found to be the most influential. Factors such as reinforcement status (9.04%) and type of firm (15.59%) were less important. This suggests that participants prioritize initial construction safety and integrity over post-construction reinforcements, which they perceive as less reliable. The validity of the conjoint analysis model used in this study was confirmed with Pearson's R (0.964) and Kendall's Tau (0.860) coefficients. These high correlation coefficients indicate that the model provides accurate and reliable predictions of participant preferences. These results support the study's scientific validity and the findings' applicability. The findings of this research offer significant guidance for housing developers, urban planners, and policymakers. Safe soil, construction compliance with modern earthquake regulations, and the use of durable materials stand out as essential elements that enhance individuals' satisfaction with housing. Additionally, future studies could explore the impact of sustainability, energy efficiency, and aesthetics on housing preferences in greater detail. This study lays a critical foundation for understanding individuals' housing preferences, considering earthquake risks, and developing strategies aligned with these preferences. Declarations Author Contribution M.T. and İ.H.D. jointly conceptualized the study and determined the research design. M.T. conducted the data collection process and performed the statistical analyses using conjoint analysis techniques. İ.H.D. contributed to the literature review and theoretical framework. M.T. wrote the initial draft of the manuscript. Both authors contributed to the interpretation of the findings and critically revised the manuscript. 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New York: John Wiley and Sons. Hensher, D. A., Rose, J. M., & Greene, W. H. (2005). Applied Choice Analysis: A Primer. Cambridge: Cambridge University Press. Huang, C., Palacios, S. M., & Meslem, A. (2024). Development of a new tool for seismic risk assessment and multi-criteria decision making. International Journal of Disaster Risk Reduction, 106, 104261. Hwa, Y. S., & Chin, C. H. (2012). Using conjoint analysis to study consumers choice of supermarkets. Jurnal Pengurusan , 34 , 91-100. Kapusuz, Y. E., & Tanrıvermiş, H. (2024). Konuta erişilebilirlik, konut talebi ve talebi etkileyen faktörlerin analizi: Ankara ili örneği. Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 42 (1), 66–89. Kinoshita, S. (2020). Conjoint analysis of purchasing behavior for energy-saving appliances. International Journal of Energy Sector Management, 14 (6), 1255–1274. Kingston, A., Comas-Herrera, A., & Jagger, C. (2018). 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SN Applied Sciences, 1 (9), 1103. Mirkatouli, J., Hosseini, A., & Neshat, A. (2015). Analysis of land use and land cover spatial pattern based on Markov chains modelling. City, Territory and Architecture, 2 (1), 4. Molin, E., Oppewal, H. H., & Timmermans, H. (1999). Group-based versus individual-based conjoint preference models of residential preferences: A comparative test. Environment and Planning A, 31, 1935–1947. Mutlu, H. T. (2022). A multivariate statistical analysis technique: Conjoint analysis (1st ed.). İKSAD Yayınevi. Orzechowski, M. A. (2004). Measuring housing preferences using virtual reality and bayesian belief networks. Sousa, L., & Monteiro, R. (2018). Seismic retrofit options for non-structural building partition walls: Impact on loss estimation and cost-benefit analysis. Engineering Structures, 76, 124–139. Sun, W., Zheng, S., Geltner, D. M., & Wang, R. (2016). The housing market effects of local home purchase restrictions. Urban Economics Review, 22 (4), 451–463. Turner, J. (1972). Freedom to Build: Dweller Control of The Housing Process. New York: The McMillan Company. Yavuz, S., Platt, S., & Drinkwater, B. (2019). Post-earthquake housing decisions and economic factors. Journal of Disaster Studies, 15 (2), 321–338. Yoloğlu, A. C., & Zorlu, F. (2024). Türkiye'de deprem riski ile ilişkili olarak bölgesel yığılma ve eşitsizlikler. Sketch: Journal of City and Regional Planning, 6 (1), 78–96. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6470770","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":444503235,"identity":"e27a3217-af74-43c4-9915-44664a15926c","order_by":0,"name":"Murat Tabanoğlu","email":"data:image/png;base64,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","orcid":"","institution":"Sakarya University","correspondingAuthor":true,"prefix":"","firstName":"Murat","middleName":"","lastName":"Tabanoğlu","suffix":""},{"id":444503236,"identity":"16d1f728-0c22-4591-a641-f21542ffcb0e","order_by":1,"name":"İsmail Hakkı Demir","email":"","orcid":"","institution":"Bursa Technical University","correspondingAuthor":false,"prefix":"","firstName":"İsmail","middleName":"Hakkı","lastName":"Demir","suffix":""}],"badges":[],"createdAt":"2025-04-17 10:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6470770/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6470770/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83312743,"identity":"cfc4c498-938c-4582-80bc-e7d9f9a59b33","added_by":"auto","created_at":"2025-05-22 20:31:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1397256,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6470770/v1/2a5a1acd-c876-4ff2-9ca7-a155407f94b7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of Factors Influencing Housing Preferences of Individuals Considering Earthquake Risk Using Conjoint Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEarthquake risk is one of the main factors determining individuals' housing preferences. Safety, economic sustainability, and environmental impacts are multidimensional factors shaping housing choices. The literature emphasizes that individuals' risk perception prioritizes safety criteria such as soil durability and building materials (D'Alpaos \u0026amp; Bragolusi, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Azimi \u0026amp; Asgary, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This trend is more evident in earthquake-prone countries such as Turkey, and the demand for safe living spaces is increasing (Bergel, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1970\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEconomic factors play a decisive role in individuals' orientation towards earthquake-resistant housing. Cost-benefit analyses of retrofit projects increase individuals' interest in such projects (Faccioli et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Makhoul, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). A study conducted in Japan revealed that individuals' interest in energy-saving and durability features is increasing (Kinoshita, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition to economic conditions, local governments' strategies to increase risk awareness are another important factor encouraging the shift towards safe housing (Marulanda et al., 2013; Arnold, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIndividual decision-making processes related to earthquake risk are not only limited to the perception of safety but are also shaped by socioeconomic factors. Studies conducted in metropolises such as Istanbul and Izmir show that earthquake safety depends not only on individual preferences but also on market dynamics and local government policies (Keskin et al., 2017). In cities such as Van and Yalova, post-earthquake housing preferences are shaped by economic conditions, perception of safety, and local government policies (Yavuz et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Platt \u0026amp; Drinkwater, 2016).\u003c/p\u003e \u003cp\u003eHousing choices are influenced by individuals' expectations of safety, comfort, and sustainability and become more critical in areas at risk of natural disasters. In Turkey, housing choices are generally based on factors such as soil safety, building materials, compliance with earthquake regulations, and reliability of construction companies (Taylan, 2015; Altindal et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Proximity to fault lines, building durability, and retrofitting projects' effectiveness are among the determining criteria in individuals' preferences (Hancılar et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this context, the conjoint analysis method is used to understand individuals' housing preferences better and to determine the importance ranking of the factors affecting these preferences. This method allows us to understand how individuals make trade-offs between different housing characteristics (Green \u0026amp; Rao, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1971\u003c/span\u003e). This study focuses on how key criteria such as soil type, structural material, compliance with earthquake regulations, distance to fault lines, retrofit status, and type of construction company affect individuals' preferences.\u003c/p\u003e \u003cp\u003eThis study aims to contribute to the planning of safer and sustainable residential areas by analyzing the housing preferences of individuals with comprehensive survey data. The findings will guide policymakers, urban planners, and construction firms to develop more effective strategies by considering individuals' safety priorities.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eStudies on housing preferences have addressed individuals\u0026apos; socioeconomic characteristics, environmental factors, and security factors. Hensher et al. (2005) stated that individuals prefer the alternative that provides the maximum benefit in the selection process and emphasized that factors such as location, cost, and security are decisive (Hensher et al., 2005; Hartshorn, 1992). Mirkatouli et al. (2015) emphasized the importance of proximity to urban amenities, road network access, and location in housing preferences and stated that durability and compliance with earthquake regulations are prioritized in areas under earthquake risk (Mirkatouli et al., 2015; Irfiyanti \u0026amp; Widjonarko, 2014).\u003c/p\u003e\n\u003cp\u003eHousing costs play a critical role, especially for low-income individuals. Kotler and Armstrong (2004) emphasized that cost is a determinant of consumer behavior and stated that affordable housing increases the likelihood of preference (Kotler \u0026amp; Armstrong, 2004). Similarly, Lindberg et al. (1989) stated that cost and housing size are among the most important factors in housing preferences. On the other hand, it has been stated that individuals\u0026apos; preference structures vary according to location, accessibility, and services (Jansen et al., 2011; Rao, 2014).\u003c/p\u003e\n\u003cp\u003eEarthquake resistance is of critical importance in increasing housing safety. It has been stated that timber, steel, and reinforced concrete structures exhibit superior performance in terms of seismic resistance, and it has been emphasized that these materials reduce structural damage by providing energy absorption (Sousa \u0026amp; Monteiro, 2018; Li \u0026amp; Ellingwood, 2009). The economic advantages of retrofit projects are important in increasing individual and community resilience (Egbelakin et al., 2017; Dell\u0026apos;Anna et al., 2022). Furthermore, the literature has widely discussed that factors such as soil class and building design are critical for safe construction (Huang et al., 2024; Burton et al., 2018).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHousing Security and Economic Aspects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHousing safety is central to individuals\u0026apos; preferences. Azimi and Asgary (2013) stated that individuals prioritize factors such as soil resistance and structural materials in areas with high susceptibility to earthquake risk. Drawing attention to the importance of retrofit strategies, Faccioli et al. (2024) found that factors such as energy efficiency and durability significantly affect individuals\u0026apos; preferences.\u003c/p\u003e\n\u003cp\u003eEconomic factors are also decisive in housing preferences. The accessibility of long-term payment plans and affordable housing, especially for low-income individuals, shapes the dynamics of the housing market (Sun et al., 2016; Almaden, 2014). Housing area and payment conditions are among other factors that affect individuals\u0026apos; living standards and priorities in housing preferences (Noor \u0026amp; Zaimi, 2012).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSocial Resilience and Earthquake Safety\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEarthquake resilience and safe construction are strategic elements that increase social and individual resilience. Criteria such as soil class, choice of structural materials, and distance to fault lines are the main factors that support safe construction (Sousa \u0026amp; Monteiro, 2018; Hancılar et al., 2020). In addition, retrofit projects are another important factor shaping individuals\u0026apos; housing preferences (Egbelakin et al., 2017).\u003c/p\u003e\n\u003cp\u003eIn this context, the literature suggests that individuals\u0026apos; socioeconomic status shapes housing preferences, local market conditions, and individual risk perceptions. While the increase in market values supports the demand for safe housing, it also encourages a comprehensive understanding of security (Marulanda et al., 2013; Kingston et al., 2018).\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConjoint Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConjoint analysis is a multivariate method used to understand the importance of factors affecting individuals\u0026apos; preferences. This method allows individuals to evaluate their preferences between options with different characteristics and to determine which characteristics are more important (Green \u0026amp; Srinivasan, 1978). In housing preferences, conjoint analysis is widely used to understand the impact of criteria such as safety, cost, location, and building materials on individuals\u0026apos; decision-making processes (Sousa \u0026amp; Monteiro, 2018; Dell\u0026apos;Anna et al., 2022).\u003c/p\u003e\n\u003cp\u003eConjoint analysis primarily aims to reveal which attributes individuals value more when choosing various alternatives. The analysis determines the overall utility function expresses the utility contribution of each attribute and these contributions:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eU(Xi): Represents the overall utility of the alternative.\u003c/li\u003e\n \u003cli\u003eczj: Represents the weight contribution according to the levels of attributes (Green \u0026amp; Srinivasan, 1990).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eConjoint Analysis Steps\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConjoint analysis is applied in a systematic process:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Identification of Characteristics:\u003c/strong\u003e The housing characteristics to be investigated are defined\u0026mdash;for example, soil durability, structural material, compliance with earthquake regulations, cost, and location.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Defining Feature Levels:\u003c/strong\u003e Different levels are determined for each feature. For example, \u0026lsquo;low\u0026rsquo;, \u0026lsquo;medium\u0026rsquo; and \u0026lsquo;high\u0026rsquo; levels for the price.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Profile Creation:\u003c/strong\u003e Profiles consisting of combinations of features are prepared. These combinations are usually created by the orthogonal array method (Hwa \u0026amp; Chin, 2012).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Data Collection:\u003c/strong\u003e Participants are asked to evaluate these profiles. The evaluation can be done by ranking, rating, or preference tests (Orzechowski, 2004).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. Analyzing the Results:\u003c/strong\u003e The collected data are analyzed, and the relative importance weights of each attribute are calculated (Green \u0026amp; Srinivasan, 1990).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConjoint Analysis in Housing Preferences\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn understanding housing preferences, conjoint analysis reveals how individuals attach importance to safety factors such as soil durability, structural materials, and compliance with earthquake regulations (Sousa \u0026amp; Monteiro, 2018). These factors are critical in determining individuals\u0026apos; preferences, especially in areas with high earthquake risk, such as Turkey (Egbelakin et al., 2017). In addition, factors such as cost and location also have a significant impact on preferences (Hensher et al., 2005).\u003c/p\u003e\n\u003cp\u003eMolin et al. (1999) compared individual and group-based models to explain housing preferences and presented important findings on individuals\u0026apos; preferences. The results obtained from these studies show that the characteristics that individuals value most affect their preference rankings (Coolen \u0026amp; Hoekstra, 2001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConjoint Analysis for Policy and Strategic Planning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnother important contribution of conjoint analysis is to guide policymakers and urban planners. Cost-benefit analyses of retrofit projects are critical for safe and sustainable urban planning (Faccioli et al., 2024; Burton et al., 2018). This method provides a better understanding of both individual preferences and societal impacts.\u003c/p\u003e\n\u003cp\u003eBy providing in-depth information about individuals\u0026apos; preferences, conjoint analysis enables the optimization of service and product design. The relative importance of criteria such as soil durability, compliance with earthquake regulations, and cost in housing preferences can be determined so that housing models can be developed by the needs of individuals (Green \u0026amp; Srinivasan, 1990; Larsen et al., 2021). This method is an indispensable tool for understanding individuals\u0026apos; decision-making processes and contributes to developing sustainable urbanization and disaster management strategies.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eThis study aims to analyze the factors affecting the housing preferences of individuals considering the earthquake risk. In this context, the conjoint analysis method is used as an effective tool to determine the factors shaping individuals\u0026apos; housing preferences and the relative importance of the levels of these factors (Mutlu, 2022). In this context, six factors that may affect building preferences were evaluated: \u003cem\u003eSoil, Structural System, Compliance with Regulations, Distance to Fault Line, Retrofitting Status, and Type of Firm.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetermination of Factors and Levels:\u003c/strong\u003e Each factor and its levels were determined in line with the literature review and expert opinions. The factors and levels considered in the study are as follows:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eSoil:\u003c/strong\u003e Very High Strength (VHS), High Strength (HS), Moderate Strength (MS), Low Strength (LS)\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eStructural System:\u003c/strong\u003e Steel, Reinforced Concrete (RC), Timber, Masonry\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRegulatory Compliance:\u003c/strong\u003e post-2018, post-2007, post-1998, post-1975\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDistance to Fault Line:\u003c/strong\u003e 16-20 km, 11-15 km, 6-10 km, 0-5 km\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRetrofit Status:\u003c/strong\u003e Present, Absent\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eType of Construction Firm:\u003c/strong\u003e Housing Development Administration (HDA), Corporate Firm, Private Contractor \u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIndependence of Factors (Orthogonal Design):\u003c/em\u003e\u003c/strong\u003e Factors were ensured to be independent, and combinations of each factor level were created. In this direction, appropriate combinations were designed to present the preferences to the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection Process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were presented with 25 cards containing different combinations of structures and asked to rate each combination on a scale ranging from 1 to 10. In total, data was collected from 2823 participants. This process is designed to develop a more in-depth understanding of individuals\u0026apos; structure preferences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis Process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCalculation of Utility Values of Factors:\u003c/em\u003e\u003c/strong\u003e In line with the data collected from the participants, utility estimates of each factor level were calculated. These values show which levels are perceived more positively or negatively in individuals\u0026apos; preferences (Mutlu, 2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRelative Importance of Factors:\u003c/em\u003e\u003c/strong\u003e The total utility values of each factor were calculated, and these values were expressed as the importance weights of the factors. In this way, the relative impact of the factors on preference was revealed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAssessment of the Fit Level of the Model:\u003c/em\u003e\u003c/strong\u003e The fit between observed preferences and predicted preferences was measured by Pearson\u0026apos;s R and Kendall\u0026apos;s Tau coefficients. These coefficients were used to evaluate the reliability and predictive power of the model (Mutlu, 2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical Analysis Tools:\u003c/em\u003e\u003c/strong\u003e Calculations based on conjoint analysis; data such as utility values, standard errors, and importance values were carried out using SPSS and similar statistical analysis software (Mutlu, 2022). The results of the analysis are organized in a way that clearly shows the effects of factors on preference.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStructure of the Model:\u003c/em\u003e\u003c/strong\u003e The obtained model consists of the following components:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eUtility values of factor levels\u003c/li\u003e\n \u003cli\u003eRelative importance of factors\u003c/li\u003e\n \u003cli\u003eModel fit coefficients (Pearson\u0026apos;s R and Kendall\u0026apos;s Tau)\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Findings","content":"\u003cp\u003eIn this section, the demographic characteristics of the individuals participating in the research are analyzed, and the general profile of these characteristics is presented. Then, the responses given to the card combinations created to determine the characteristics affecting the housing preferences of the individuals were evaluated with the help of conjoint analysis. As a result of the analysis, the utility values of the cards were calculated, and the factors shaping individuals\u0026apos; preferences were determined. Finally, the changes in the preferred cards according to demographic characteristics were analyzed, and the factors affecting the participants\u0026apos; preferences were examined in detail.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe demographic characteristics of the 2823 participants were analyzed and presented in Table 1 below.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Demographic Characteristics of Individuals Participating in the Study\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubcategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e1911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e67.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e27.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e32.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e2058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e72.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e18 and under\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eRetired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e4.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e19-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e18.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eHomemaker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e30-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e1004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e35.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eGovernment employee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e1440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e51.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e40-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e32.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eCivil Servant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e50-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e10.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eStudent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e5.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e60 and over\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e3.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ePrivate employee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e1025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e36.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ePrimary/Secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e11.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e17.000TL and below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e8.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eAssociate degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e10.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e17.001TL - 32.000TL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e12.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eBachelor\u0026apos;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e1493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e52.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e32.001TL - 47.000TL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e29.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eMaster\u0026apos;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e17.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e47.001TL - 62.000TL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e30.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eDoctorate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e8.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e62.001 TL and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e18.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Children\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e1004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e35.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReason for Housing Preference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e1042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e36.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e21.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eResiding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e1707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e60.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e34.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eInvesting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e8.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Number of Participants: 2823\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWhen Table 1 is analyzed, it is seen that most participants are male (67.7%) and married (72.9%). The participants are predominantly between 30-39 (35.6%) and 40-49 (32.4%). While bachelor\u0026apos;s degree graduates constitute the largest group with 52.9% in education level, the most significant proportion in income distribution is in the income group of 47,001-62,000 TL with 30.4%.\u003c/p\u003e\n\u003cp\u003eWhile 35.6% of the participants do not have children, those with two children follow with 34.0%. In the occupational distribution, public employees stand out with 51.0%. Regarding housing preferences, while 60.5% of the respondents prioritize the purpose of residence, only 2.6% prefer housing for investment purposes. A total of 2823 people participated in the survey, and the participants\u0026apos; general characteristics can be summarized as high education level, middle age group, and upper-middle income level.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvey Design and Creation of Housing Models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this section, the factors that are effective in the housing preferences of individuals considering the earthquake risk and the sub-levels of these factors are determined in line with the literature review and expert opinions. The identified factors were evaluated by conjoint analysis using SPSS software, and because of the analysis, 25 sample housing models were created by the orthogonal design method. The models were presented to the participants as cards; detailed information is given in Table 2. Care was taken to ensure that the representation power of each factor and sub-level was balanced in the preparation of the cards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e 25 Cards Created with Orthagonal Design and Asked Participants to Rate Between 1-10\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCard ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStructural System\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulatory Compliance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistance to Fault Line (km)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRetrofit Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of Construction Firm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eVHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eSteel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1975 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e11-15 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eCorporate Firm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eVHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eMasonry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e2007 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e0-5 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eCorporate Firm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eMasonry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e2018 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e11-15 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ePrivate Contractor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eSteel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e2018 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e0-5 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eHDA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eMasonry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1998 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e16-20 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eCorporate Firm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eVHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eMasonry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e2018 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e16-20 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eHDA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eTimber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1975 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e16-20 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eCorporate Firm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eVHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eTimber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e2018 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e6-10 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eCorporate Firm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eVHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eSteel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e2018 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e16-20 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eHDA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eReinforced Concrete\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e2018 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e16-20 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ePrivate Contractor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eVHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eReinforced Concrete\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1998 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e11-15 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eHDA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eSteel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e2018 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e0-5 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eCorporate Firm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eTimber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e2018 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e16-20 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eHDA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eVHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eSteel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e2007 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e16-20 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eHDA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eSteel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1998 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e16-20 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eCorporate Firm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eSteel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e2007 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e6-10 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ePrivate Contractor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eReinforced Concrete\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e2007 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e16-20 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eCorporate Firm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eVHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eSteel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1975 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e16-20 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003ePrivate Contractor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eVHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eTimber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1998 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e0-5 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003ePrivate Contractor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eVHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eReinforced Concrete\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e2018 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e6-10 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eCorporate Firm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eSteel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e2018 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e11-15 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eCorporate Firm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eMasonry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1975 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e6-10 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eHDA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eSteel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1998 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e6-10 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eHDA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eReinforced Concrete\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1975 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e0-5 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eHDA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eTimber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e2007 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e11-15 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eHDA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eEach combination card in Table 2 was presented to the participants as a sample housing model, and they were asked to rate these models on a scale of 1 to 10. The scores obtained from the participants were evaluated by conjoint analysis to determine the importance levels of the factors affecting the housing choice. The results obtained from the analysis revealed the utility values of the factors affecting the housing preference and the impact of these factors on the preference, which are presented in detail below.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis Results and Evaluations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs a result of the conjoint analysis, the importance levels of the factors affecting the housing preference and the benefit values of the sub-level of each factor were calculated. This analysis reveals the factors affecting the preferences of the participants in detail. Below, the utility values obtained for each factor and sub-level, as well as the impact of these factors on overall preferences, are presented in tables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTablo\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Correlation Values of General Analysis Results\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"378\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 378px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelations\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003ePearson\u0026apos;s R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e,964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eKendall\u0026apos;s tau\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e,860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 378px;\"\u003e\n \u003cp\u003e\u0026nbsp;a. Correlations between observed and estimated preferences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3 shows the relationship between the observed preferences in conjoint analysis and the preferences predicted by the model. Pearson\u0026apos;s R-value (0.964) and Kendall\u0026apos;s Tau value (0.860) indicate a strong relationship between observed and predicted preferences.\u003c/p\u003e\n\u003cp\u003eIt is statistically significant (p \u0026lt; 0.001). The fact that both relationships are statistically significant (p \u0026lt; 0.001) shows that the model works accurately and gives reliable results in preference predictions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Conjoint Analysis Results and Relative Importance Percentages of Housing Preference Factors\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAttribute\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN of Levels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelation to Ranks or Scores\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUtility Estimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB Estimate Coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelative Importance Percentage %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 11px;\"\u003e\n \u003cp\u003eRetrofit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eDiscrete\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-,010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e,083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e9,044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e,010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e,083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 11px;\"\u003e\n \u003cp\u003eType of Construction Firm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eHDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 12px;\"\u003e\n \u003cp\u003eDiscrete\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e,344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e,113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 14px;\"\u003e\n \u003cp\u003e15,594\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eCorporate Firm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-,003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e,113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ePrivate Contractor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-,341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e,136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 11px;\"\u003e\n \u003cp\u003eZemin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eVHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLinear (less)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-,760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e,070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-,760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e28,164\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-1,521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e,139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-2,281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e,209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-3,041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e,279\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 11px;\"\u003e\n \u003cp\u003eStructural System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eSteel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLinear (less)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-,483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e,070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-,483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 14px;\"\u003e\n \u003cp\u003e18,098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eReinforced Concrete\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-,966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e,139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eTimber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-1,448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e,209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eMasonry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-1,931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e,279\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 11px;\"\u003e\n \u003cp\u003eRegulatory Compliance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e2018 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLinear (less)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-,426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e,070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-,426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 14px;\"\u003e\n \u003cp\u003e18,111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e2007 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-,852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e,139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e1998 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-1,278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e,209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e1975 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-1,704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e,279\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 11px;\"\u003e\n \u003cp\u003eDistance to Fault Line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e16-20 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLinear (less)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-,222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e,070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-,222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 14px;\"\u003e\n \u003cp\u003e10,988\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e11-15 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-,444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e,139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e6-10 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-,667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e,209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e0-5 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-,889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e,279\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 30px;\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 22px;\"\u003e\n \u003cp\u003eAll factors are orthogonal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e9,327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e,319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAveraged Importance Score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 4 presents the results of the conjoint analysis of the factors affecting housing choice and the relative importance percentages of these factors. The analysis includes utility estimates, standard errors, coefficients, and relative importance percentages for the levels of each attribute.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRetrofit Factor:\u003c/strong\u003e The empowerment feature has two levels and a \u0026lsquo;discrete\u0026rsquo; relationship. When empowerment is present, the benefit estimate is optimistic; when it is absent, it is slightly negative. The empowerment factor has a relative importance of 9.04%, indicating a lower impact than the other factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFirm Factor:\u003c/strong\u003e The firm characteristic has three levels and has a \u0026lsquo;discrete\u0026rsquo; relationship. HDA projects have the highest utility value, while contractor firms have the lowest value. The firm factor indicates an important factor with 15.59%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoil Factor:\u003c/strong\u003e The soil factor has four levels and demonstrates a \u0026quot;linear (less)\u0026quot; relationship. Firm soil has the highest utility value, while utility values decrease significantly as soil stability decreases. This factor has the highest relative importance at 28.16%, indicating that the soil factor is decisive in housing preferences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructural System Factor:\u003c/strong\u003e The structural system factor has four levels and exhibits a \u0026quot;linear (less)\u0026quot; relationship. Steel structures have the highest utility value, while utility values decrease for other structural types. This factor stands out with a relative importance of 18.10%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegulation Factor:\u003c/strong\u003e The construction year regulation level is evaluated in four levels and shows a \u0026quot;linear (less)\u0026quot; relationship. Buildings constructed by more recent regulations have higher utility values, with this factor having a relative importance of 18.11%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFault Line Factor:\u003c/strong\u003e The distance to the fault line has four levels, demonstrating a \u0026quot;linear (less)\u0026quot; relationship. Utility values increase as the distance from the fault line increases. The fault factor holds a moderate level of importance at 10.99%.\u003c/p\u003e\n\u003cp\u003eAmong the factors, the soil characteristic has the highest relative importance (28.16%), followed by the structural system (18.10%) and regulation (18.11%) factors. The firm and fault distance factors have lower percentages of importance. The analysis highlights the decisive role of soil characteristics and structural safety in housing preferences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluation of Trial Cards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study section analyzed responses to the 25 combination cards presented to participants to identify the most and least preferred housing models. Furthermore, these cards were evaluated regarding demographic variables to reveal how housing preferences vary according to individuals\u0026apos; demographic characteristics. The findings obtained from the analysis are presented below.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u003c/strong\u003e The Top Three Housing Models with the Highest Scores Given by Participants*\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCard ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStructural System\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulatory Compliance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistance to Fault Line (km)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRetrofit Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of Construction Firm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eVHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eSteel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e2018 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e16-20 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eHDA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eVHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eSteel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e2007 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e16-20 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eHDA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eVHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eReinforced Concrete\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1998 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e6-10 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eCorporate Firm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*\u003cstrong\u003e\u0026nbsp;K9:\u003c/strong\u003e 7.99\u0026plusmn;2.41; \u003cstrong\u003eK14:\u003c/strong\u003e7.32\u0026plusmn;2.34; \u003cstrong\u003eK20:\u003c/strong\u003e7.07\u0026plusmn;2.43\u003c/p\u003e\n\u003cp\u003eTable 5 presents the characteristics of the top three housing models with the highest scores given by participants.\u003c/p\u003e\n\u003cp\u003eIn all three models, the soil classification is identified as \u0026quot;Very high strong.\u0026quot; This indicates that participants consider soil safety one of the most critical criteria in housing preferences. Firm soil stands out as a key factor in mitigating earthquake risks.\u003c/p\u003e\n\u003cp\u003eRegarding structural material, the first two models feature \u0026quot;Steel,\u0026quot; while the third model uses \u0026quot;Reinforced Concrete.\u0026quot; The preference for steel structural material demonstrates that durability and safety perception significantly influence participant choices. However, reinforced concrete is also regarded as an acceptable alternative.\u003c/p\u003e\n\u003cp\u003eCompliance with earthquake regulations is another essential criterion for participants. The first model falls under structures built after 2018, the second model after 2007, and the third model complies with post-2018 regulations. This indicates that structures adhering to newer regulations are perceived as more reliable and are thus preferred by participants.\u003c/p\u003e\n\u003cp\u003eRegarding proximity to fault lines, the first two models are located 16\u0026ndash;20 km from the fault line, while the third model is 6\u0026ndash;10 km away. Although participants prefer houses farther from fault lines, closer distances are acceptable if the soil and structural features are satisfactory.\u003c/p\u003e\n\u003cp\u003eWhen examining reinforcement, all three models are buildings that meet the necessary safety criteria during initial construction rather than being retrofitted. This highlights participants\u0026apos; preference for inherent structural safety over retrofitting.\u003c/p\u003e\n\u003cp\u003eFinally, the producer\u0026rsquo;s firm preferences are evaluated. The first two models are associated with \u0026quot;HDA,\u0026quot; while the third is linked to a \u0026quot;Corporate Firm.\u0026quot; HDA is highlighted for its public assurance and reliability, while corporate firms are also considered viable alternatives.\u003c/p\u003e\n\u003cp\u003eThese models illustrate the primary factors influencing participants\u0026apos; housing preferences, including soil safety, structural material, compliance with earthquake regulations, and the producer firm, emphasizing their priority in decision-making processes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6.\u003c/strong\u003e The Bottom Three Housing Models with the Lowest Scores Given by Participants*\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCard ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStructural System\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulatory Compliance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistance to Fault Line (km)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRetrofit Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of Construction Firm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eMasonry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e1998 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e16-20 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eCorporate Firm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eReinforced Concrete\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e1975 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0-5 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eHDA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eSteel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e2007 Regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e6-10 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ePrivate Contractor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*\u003cstrong\u003e\u0026nbsp;K5:\u003c/strong\u003e 3.03\u0026plusmn;2.93; \u003cstrong\u003eK24:\u003c/strong\u003e 3.56\u0026plusmn;3.02; \u003cstrong\u003eK16:\u003c/strong\u003e 4.25\u0026plusmn;2.83\u003c/p\u003e\n\u003cp\u003eTable 6 presents the bottom three housing models with the lowest scores given by participants and their characteristics.\u003c/p\u003e\n\u003cp\u003eThe commonalities and differences among these models provide important insights into their low preference.\u003c/p\u003e\n\u003cp\u003eFirstly, the soil classification for all models is noted as \u0026quot;Weak.\u0026quot; Participants perceive weak soil structures as a significant safety risk, leading to low preference. Soil safety remains a decisive factor in housing preferences.\u003c/p\u003e\n\u003cp\u003eThere are notable differences in structural materials. Model K5 features \u0026quot;Masonry\u0026quot; as the structural material, while model K24 uses \u0026quot;Reinforced Concrete\u0026quot; and model K16 employs \u0026quot;Steel.\u0026quot; Masonry systems are perceived as less durable, explaining their lowest score, whereas for the other systems, the weak soil factor appears to have influenced their lower preference.\u003c/p\u003e\n\u003cp\u003eIn terms of compliance with earthquake regulations, these models correspond to buildings constructed according to 1998, 1975, and 2007 regulations, respectively. However, structures adhering to older regulations (e.g., post-1975) are notably less preferred. Modern compliance with earthquake regulations remains a critical criterion for participants.\u003c/p\u003e\n\u003cp\u003eRegarding the proximity to fault lines, the lowest-scoring model, K24, is located 0\u0026ndash;5 km from the fault line. This highlights that proximity to fault lines negatively affects preferences. Model K16 is 6\u0026ndash;10 km away, and model K5 is 16\u0026ndash;20 km away; however, the weak soil factor seems to influence preferences significantly.\u003c/p\u003e\n\u003cp\u003eReinforcement status is marked as \u0026quot;Present\u0026quot; only for model K16. This suggests that the presence of reinforcement does not mitigate the negative perception of weak soil. For participants, ensuring safety during initial construction appears more critical than post-construction reinforcement.\u003c/p\u003e\n\u003cp\u003eFinally, in terms of the producer firm, model K5 is associated with a \u0026quot;Corporate Firm,\u0026quot; K24 with \u0026quot;HDA,\u0026quot; and K16 with a \u0026quot;Contractor Firm.\u0026quot; Soil classification and other factors appear to play a more decisive role than the producer firm in preferences.\u003c/p\u003e\n\u003cp\u003eOverall, factors such as weak soil classification, compliance with older earthquake regulations, and proximity to fault lines are the primary reasons for the low scores of these models. Participants have shaped their preferences by prioritizing durability and safety considerations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluation of Trial Cards Based on Demographic Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants\u0026apos; responses to the trial cards for housing models were analyzed in detail regarding demographic characteristics. The evaluations revealed that general preference trends essentially showed similarities across demographic variables.\u003c/p\u003e\n\u003cp\u003eThe analysis based on demographic features indicates that the general preference trends were strongly adopted and remained broadly consistent across different demographic groups. However, slight nuances in the preferences of individual groups reflect varying needs and expectations. This situation provides an important guide for developing strategies for target groups in housing design.\u003c/p\u003e"},{"header":"Conclusion and Discussion","content":"\u003cp\u003eUsing the conjoint analysis method, this study examined the factors influencing individuals\u0026apos; housing preferences, considering earthquake risk. The findings reveal that individuals adopt a safety- and durability-oriented approach in housing preferences. Six key factors\u0026mdash;soil classification, structural material, compliance with earthquake regulations, proximity to fault lines, reinforcement status, and type of firm\u0026mdash;were evaluated. Among these factors, soil classification, structural material, and compliance with regulations emerged as the most significant.\u003c/p\u003e\n\u003cp\u003eParticipants gave high scores for housing models built on firm soil, using durable structural materials, and being compliant with modern earthquake regulations. The most preferred housing models (K9, K14, and K20) feature firm soil, steel or reinforced concrete structural systems, and compliance with modern regulations. In contrast, the least preferred models (K5, K24, and K16) included risk factors such as weak soil, older earthquake regulations, and proximity to fault lines. These findings indicate that participants exhibit a safety-focused approach and a tendency to avoid risk in the face of earthquake threats.\u003c/p\u003e\n\u003cp\u003eWhen examining the relative importance percentages of the factors, soil classification (28.16%), structural material (18.10%), and compliance with regulations (18.11%) were found to be the most influential. Factors such as reinforcement status (9.04%) and type of firm (15.59%) were less important. This suggests that participants prioritize initial construction safety and integrity over post-construction reinforcements, which they perceive as less reliable.\u003c/p\u003e\n\u003cp\u003eThe validity of the conjoint analysis model used in this study was confirmed with Pearson\u0026apos;s R (0.964) and Kendall\u0026apos;s Tau (0.860) coefficients. These high correlation coefficients indicate that the model provides accurate and reliable predictions of participant preferences. These results support the study\u0026apos;s scientific validity and the findings\u0026apos; applicability.\u003c/p\u003e\n\u003cp\u003eThe findings of this research offer significant guidance for housing developers, urban planners, and policymakers. Safe soil, construction compliance with modern earthquake regulations, and the use of durable materials stand out as essential elements that enhance individuals\u0026apos; satisfaction with housing. Additionally, future studies could explore the impact of sustainability, energy efficiency, and aesthetics on housing preferences in greater detail. This study lays a critical foundation for understanding individuals\u0026apos; housing preferences, considering earthquake risks, and developing strategies aligned with these preferences.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.T. and İ.H.D. jointly conceptualized the study and determined the research design. M.T. conducted the data collection process and performed the statistical analyses using conjoint analysis techniques. İ.H.D. contributed to the literature review and theoretical framework. M.T. wrote the initial draft of the manuscript. Both authors contributed to the interpretation of the findings and critically revised the manuscript. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllen, F., Barth, J. R., \u0026amp; Yago, G. (2014). Financial innovations and the stability of the housing market. \u003cem\u003eNational Institute Economic Review, 230\u003c/em\u003e(1), 16\u0026ndash;33.\u003c/li\u003e\n\u003cli\u003eAlmaden, C. R. (2014). Housing affordability challenges: The case of the median income households in Cagayan De Oro City, Philippines. \u003cem\u003eInternational Journal of Humanities and Social Science, 4\u003c/em\u003e(10), 1\u0026ndash;10.\u003c/li\u003e\n\u003cli\u003eAltindal, A., Taylan, M., \u0026amp; Coşkun, G. (2021). Deprem riski ve konut tercihleri \u0026uuml;zerine bir analiz. \u003cem\u003eJournal of Urban Studies, 12\u003c/em\u003e(4), 135\u0026ndash;156.\u003c/li\u003e\n\u003cli\u003eArnold, T. A. K. 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T\u0026uuml;rkiye\u0026apos;de deprem riski ile ilişkili olarak b\u0026ouml;lgesel yığılma ve eşitsizlikler. \u003cem\u003eSketch: Journal of City and Regional Planning, 6\u003c/em\u003e(1), 78\u0026ndash;96.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Earthquake risk, housing preferences, conjoint analysis, soil class, earthquake regulations","lastPublishedDoi":"10.21203/rs.3.rs-6470770/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6470770/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this study, the factors affecting the housing preferences of individuals, considering the earthquake risk, are analyzed using the conjoint analysis method. The study evaluated six main factors: soil class, structural material, compliance with earthquake regulations, distance to fault line, retrofitting status, and type of company. The factors were determined to align with the literature review and expert opinions, and 25 different housing models were created using the orthogonal design method and presented to the participants. Two thousand eight hundred twenty-three people participated in the study, and the participants were asked to evaluate the housing models on a scale of 1\u0026ndash;10. The findings showed that individuals prioritized safety-oriented factors such as soil class (28.16%), structural material (18.10%), and compliance with earthquake regulations (18.11%). Participants preferred houses that comply with modern regulations, are built on solid soil, and use durable materials. Factors such as distance to fault lines (10.99%) and type of company (15.59%) were also found to be influential on preferences, but retrofitting status (9.04%) was relatively less important. While the most preferred housing models had features such as solid soil, steel structural system, and compliance with modern regulations, the least preferred models included risk factors such as weak soil, old regulations, and proximity to fault lines. The model's accuracy was verified by Pearson's R (0.964) and Kendall's Tau (0.860) coefficients. The study demonstrates the impact of earthquake risk on individuals' housing preferences and provides important findings for developing safety and resilience-oriented strategies.\u003c/p\u003e","manuscriptTitle":"Analysis of Factors Influencing Housing Preferences of Individuals Considering Earthquake Risk Using Conjoint Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-18 06:02:24","doi":"10.21203/rs.3.rs-6470770/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"58e09e12-b45d-458c-8a98-e5b8b106becb","owner":[],"postedDate":"April 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-22T20:23:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-18 06:02:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6470770","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6470770","identity":"rs-6470770","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}