442 © 2025 Conscientia Beam. All Rights Reserved. Unlocking homeownership: Key factors driving adult consumers' house purchase intentions in China Zhou Yang1 Yang Shiyu2 Vijayaletchumy Krishnan3+ 1,2,3INTI International University Persiaran Perdana BBN Putra Nilai, 71800 Nilai, Negeri Sembilan, Malaysia. 1Email: 2117341115@qq.com 2Email: 392675809@qq.com 3Email: vijayaletchumy.k@newinti.edu.my (+ Corresponding author) ABSTRACT Article History Received: 12 September 2024 Revised: 3 March 2025 Accepted: 14 March 2025 Published: 16 April 2025 Keywords Adult consumers House purchase intention TPB. This study aims to understand the factors influencing Chinese consumers' house purchase intentions guided by the Theory of Planned Behavior (TPB). Data was collected from 239 Chinese consumers through self-administered online questionnaires using convenience sampling. Partial least squares structural equation modeling (PLS- SEM) was employed to analyze the data due to its complexity and non-normal distribution. The analysis revealed that financial status and subjective norm significantly predict purchase intention. In contrast, attitude, location, and perceived behavioral control did not significantly impact purchase decisions. These findings highlight the critical role of financial resources and social influence in shaping Chinese consumers' house purchase intentions providing valuable insights for real estate market stakeholders. Contribution/Originality: This study contributes to the literature by investigating the relationships between consumer attitudes, subjective norms, perceived behavioral control, financial status, and location in shaping house purchase intentions in China. Unlike prior region-specific research, it provides a broader, generalizable analysis addressing a significant gap in national housing market studies. 1. INTRODUCTION The operation of Chinese real estate is increasingly important in the country. House ownership continues to be one of the primary means of property investment, especially considering the stock market's instability and lack of options (Carpenter, Lu, & Whitelaw, 2015). A major factor in the rise in home ownership has been government initiatives including interest rate reductions and the people's bank of China's availability of sufficient mortgage financing (Huang, He, & Gan, 2020). By 2020, the home ownership rate has been recorded at 90%, the highest globally. More than 20% of the population owns more than one home. But the recent decline in the market has potential threats and this has sparked a need to go deeper to understand the factors affecting consumers’ decision to buy houses (Feng, Wahab, Azmi, Yan, & Wu, 2022). Real estate is one of the most significant investments a person can make in their lifetime, especially in China where homeownership has always been a major priority (Arslan & Howells, 2021). Some of these policies include reduced people’s bank of China’s mortgage interest rate and mortgage loans constitute 25% of the Gross Domestic Product (GDP) by 2016 (Liu & Xiong, 2020). However, the real estate market has not been stable recently and even declined in some periods which makes the consumer more prudent in recent years (Feng et al., 2022). China’s real Humanities and Social Sciences Letters 2025 Vol. 13, No. 2, pp. 442-456 ISSN(e): 2312-4318 ISSN(p): 2312-5659 DOI: 10.18488/73.v13i2.4173 © 2025 Conscientia Beam. All Rights Reserved. https://orcid.org/0009-0004-3089-4305 mailto:2117341115@qq.com mailto:392675809@qq.com mailto:vijayaletchumy.k@newinti.edu.my https://www.doi.org/10.18488/73.v13i2.4173 Humanities and Social Sciences Letters, 2025, 13(2): 442-456 443 © 2024 Conscientia Beam. All Rights Reserved. estate industry is estimated to contribute approximately 30% of the Chinese GDP. This has changed the debt by developers and forced the government to introduce measures that would reduce indebtedness among real estate enterprises dubbed the “three red lines” and “two red lines” (Cai, Liu, & Cao, 2020; Chen, Zhou, Wang, & Zheng, 2023). The highly anticipated real estate market is now facing a crisis despite these efforts. In 2021, home sales reached 1.57 square meters but to 1.15 billion square meters in 2022 with further declines seen in 2023 (National Bureau of Statistics, 2023). The market's instability has left consumers more thoughtful in their purchasing decisions and the declining population growth with changing demographics has affected demand (Yu, Zhuo, Hui, & Zhang, 2024). China's population reached 1.41 billion by the end of 2023 with negative growth rates expected to continue. Additionally, the aging population and a high rate of homeownership indicate a reduced demand for housing in the future (Fu, Deng, & Liu, 2023). Overbuilding and vacant homes contribute to imbalances in the Chinese real estate market (National Bureau of Statistics, 2022). Urbanization has increased housing demand in cities leading to disparities between urban and rural areas (Zhang, Chen, Wu, Zhang, & Song, 2018). Financial constraints such as high housing prices and strict mortgage requirements make it difficult for many families to purchase homes (Zhang, Wang, Tian, & Zhang, 2020). Financial constraints on prospective buyers have not entirely been relieved by government initiatives, like interest rate reductions and subsidies intended to stabilize the market (Chen et al., 2023). Consumers' decisions to purchase homes may be influenced by interpersonal and social pressure to own a home which frequently puts them in a difficult situation (Hromada, Heralová, Čermáková, Piecha, & Kadeřábková, 2023). The next factor is perceived behavioral control to buy a home. First-time and young buyers need to be financially capable to deal with the regulations and the choice (Al-Nahdi, Ghazzawi, & Bakar, 2015). Most previous research in the area of home purchase intention have tended to be location-sensitive in the sense that findings derived from such studies are hardly transferable across geographical zones. For instance, research has been done by Feng et al. (2022) specifically on Nanyang City and the study could not generalize outcomes for the whole of China. Two prominent beliefs are perceived behavioral control and subjective norms that also include attitudes towards homeownership due to the government-based policies like the “three red lines”, the relative intentions of the consumer have become uncertain. Urbanization has also intensified competition for the privileged territories leading to increase in the financial problems (Xu, Qi, Li, & Ding, 2021). The commercial housing which has been a significant contributor to Chinese economy after the reform in 1998 in the housing market has issues arising from some of the current and emerging negativity and demographic changes (Yu et al., 2024). While prior literature looked at characteristics including location and price in home purchases (Hui, Dong, & Jia, 2018; Zeng, Zhang, Wang, & Zeng, 2019), there is a lack of extant literature that investigates the multiple connections between consumer attitudes, perceived subjective norms, perceived behavioral control, financial status, location, and adults consumers’ house purchase intentions in China. Therefore, the study will fill the gap and propose below research question: RQ1: Is there a significant relationship between the attitudes of adults’ consumers in China and their house purchase intention? RQ2: Is there a significant relationship between perceived subjective norms of adults’ consumers in China and their house purchase intention? RQ3: Is there a significant relationship between perceived behavioral control of adults’ consumers in China and their house purchase intention? RQ4: Is there a significant relationship between the financial status of adults’ consumers in China and their house purchase intention? RQ5: Is there a significant relationship between location and adults consumers' house purchase intention in China? Humanities and Social Sciences Letters, 2025, 13(2): 442-456 444 © 2024 Conscientia Beam. All Rights Reserved. This study seeks to build upon prior literature by analyzing the consumer attitude, subjective norms, perceived behavioural control, location, financial standing and the desire to own a home in several Chinese regions. Previous researches have examined these factors at a regional level but the current study requires broad approach to establishing the relations between these factors and decisions relating to home buying in China. The perspective provided in this study is generalizable which makes it beneficial for both policymakers and other real estate sector stakeholders in China. 2. LITERATURE REVIEW 2.1. Real Estate Industry in China China's real estate sector changed throughout the previous 20 years going from being under government control to being a commercial product and greatly boosting the country's economy. Initially, real estate was state- owned with insufficient housing and unchecked population growth exacerbating shortages (Wang, 2021; Wu, Heerink, & Yu, 2020). Reforms began in 1978 with housing reforms in 1994 and by 2017, urban residents accounted for 58.5% of the population compared to 17.9% in 1978. Real estate has emerged as a major investment sector attracting both local and foreign investors (Wang, 2021; Wang, Hui, & Sun, 2018). However, this resulted in speculative bubbles in prosperous cities such as Beijing and Shanghai (Chen et al., 2023). The government implemented measures such as stricter property purchase requirements and tightened mortgage lending but demand remains strong, driven by urban migration to manage the overheated market (Han, Zhang, & Zhao, 2021; Wu, Bian, Xue, & Zhang, 2021). Home ownership in China has grown reaching 91% despite housing affordability challenges (Cai et al., 2020). In major cities, urbanization has made home ownership a middle-class prerequisite pushing prices higher (Marfatia, André, & Gupta, 2022). The housing sentiment index is of pivotal importance for the analysis of the tendencies which take place in the housing market keeping in mind the conditions of its instability. Zhang et al. (2020) consider this aspect valid for understanding market trends after the 2008 financial crisis, especially for the policymakers. Another social factor that affects housing consumers is an aging population in society. There is a propensity to save rather than to consume especially among the aging population which has an adverse effect on housing prices (Zeng et al., 2019). On the contrary, young people consume and the high elderly people’s population dependency may affect the market (Yu et al., 2024). The government has to undertake significant efforts to keep the housing market healthy and affordable to solve these problems (Wang, 2021; Zeng et al., 2019). 2.2. Theory The Theory of Planned Behavior (TPB) posits that behavior intention is determined by three factors: behavioral intention include perceived attitude towards the behavior, perceived subjective norm and perceived control over the behavior (Ajzen, 2020). These elements contribute in defining an individual’s intention and the higher the level of the intention more often the behavior will take place (Pham Dinh, Vo Thanh, & Phan Thanh, 2022). In line with the study’s generic nature, TPB has been used in different fields such as real estate to model behaviours (Al-Nahdi, et al., 2015). The TPB serves as an adequate tool to predict behavior but its applicability covers only a subset of the factors that influence people’s behaviors (Islam, Saidin, Ayub, & Islam, 2022). One of the most important factors includes credit and loan interest which influence perceived behavioural control in home purchases (Pham Dinh et al., 2022). This is an additional important aspect of buying a property as buyers need the house close to facilities including schools, hospitals, and stores. Thus, the site improves property value and possible returns (Le Hoang, Ho, Luu, & Le, 2020). Subjective norms, which can also be social in origin, are also important for the models. Yoke, Mun, Peng, and Yean (2018) established that attitude, subjective norm, perceived behavioural control and financial factors were Humanities and Social Sciences Letters, 2025, 13(2): 442-456 445 © 2024 Conscientia Beam. All Rights Reserved. positively related to purchase intention. However, location was not a significant determinant according to this study. According to the study conducted by Le Hoang et al. (2020), we found out that the location is the most significant factor affecting the purchase intention of the real estate in the Ho Chi Minh City markets which supports previous research done by Rahman, Awang, and Jagun (2024) and Wrenn, Yi, and Zhang (2019). Kabir, Jamal, and Kairy (2024) further highlighted the influence of location with environmental factors on investment preferences in real estate. In conclusion, it can be realized that the attitude, geographical location and financial status are key determinants that affect consumers’ real estate purchase decisions. The extent of positive attitude towards property attributes is a key determinant of the propensity to purchase homes and purchase power, especially through financing, exercise a profound influence on home purchase. The developers and policymakers need to take into account while planning the projects and formulation of modes of financing (Syukor, Musa, & Tajudin, 2021; Yoke et al., 2018). The study has demonstrated that attitude, financial position, and house location are the primary three factors of a mediated model related to a home purchase choice. 2.3. Determinants of Consumers' House Purchase Intention in China 2.3.1. Attitude According to Redjo, Wijayaningtyas, and Iskandar (2020) and Rachmawati, Shukri, Azam, and Khatibi (2019), attitude is a cognitive process that entails an individual’s predisposition towards an object or activity as either positive or negative. It consists of the use of cognitions of the target object or activity, affect towards the object or activity and intentions related to such an object or activity (Breckler, 1984; Vuković, 2024). Importantly, the role of attitude as one of the key predictors of the purchase intentions is revealed in the framework of the home purchase. For instance, Judge, Warren-Myers, and Paladino (2019) and Wijayaningtyas and Nainggolan (2020) examined that attitude has a positive relationship with purchase intentions within the consumers in their respective regions: Malang, Kuala Lumpur and Australia who are buying the sustainability-certified homes. Islam et al. (2022) found a positive and significant relationship between attitude and apartment purchase intention in the Bangladeshi context. Similarly, Zhang et al. (2018) showed that attitudes had a positive influence on the Chinese millennials’ intention to purchase green buildings. Al-Nahdi, Habib, et al. (2015) and Syukor et al. (2021) have found a positive relationship between attitude and purchase intention in different situations. However, most of these studies were region-based like Wijayaningtyas and Nainggolan (2020) carried out in Surabaya, Indonesia and Feng et al. (2022) conducted in Nanyang, China. Consequently, this study will aim to collect more data across other areas to broaden its sample size and hence gain richness in relation to understanding the nature of attitude and home purchase intention in China. H1: There is a significant relationship between attitude and consumers’ house purchase intention in China. 2.3.2. Subjective Norm Subjective norm means the societal pressure to engage or not engage in a specific behavior. In China, a collectivist culture, family and societal expectations significantly influence decisions like purchasing real estate (Clark, Huang, & Yi, 2021). Family influence is crucial with Chinese home buyers often prioritizing their parents' preferences above their seeking approval before making major financial decisions. This extended decision-making process can frustrate younger buyers in a fast-paced society (Clark et al., 2021; Davis & Haltiwanger, 2024; Dong, Hui, & Yi, 2021). Property ownership is closely linked to social status in China creating immense pressure on young adults to buy homes at the cost of massive debt or significant savings withdrawals (Wei & Jung, 2017). More than 60% of mortgages in China have periods of 20 years or more (Wrenn et al., 2019). The pressure to act quickly in a Humanities and Social Sciences Letters, 2025, 13(2): 442-456 446 © 2024 Conscientia Beam. All Rights Reserved. competitive market can lead to hasty decisions driven by a strong herd mentality where one person’s purchase influences others (Chiang, Hui, & Chen, 2022). Policymakers can address these pressures by promoting rental housing as a viable and respectable option, increasing high-quality rental properties and launching government-backed campaigns to reshape social norms around renting (Bosnjak, Ajzen, & Schmidt, 2020). Additionally, social norms influence eco-friendly housing decisions with communities embracing sustainable living creating demand for green real estate (Zhao & Chen, 2021). Developers are increasingly incorporating sustainable practices aligning community norms with environmentally conscious choices (Jin, Zhao, & Santibanez-Gonzalez, 2020). H2: There is a significant relationship between subjective norm and consumers’ house purchase intention in China. 2.3.3. Perceived Behavioral Control Perceived Behavioral Control (PBC) is the degree to which performing the behavior is perceived as easy or difficult. High prices of real estate remain a major issue in China’s property market; large down payments (from 30% to 50%) make it difficult for potential buyers to secure a home (Li, Qin, & Wu, 2020; Li et al., 2023). Zhao and Chen (2021) affirm that the cost of property has a role in discouraging first-time buyers; this is coupled with income growth that lags behind the growth in housing prices (Chen, Gao, & Liang, 2023). Regulations serve as an additional layer of decision-making by implementing strict requirements for buying certain goods such as local residences which lowers demand (Tan, Tang, & Meng, 2022). For buyers, there is policy risk originating from inconsistent and unpredictable state interventions and housing market risk that creates and reinforces the perception of losing control over the timing of home purchases (Li, Cheng, & Cheong, 2017; Wang, 2021). Beliefs regarding financial control and future income also contribute to PBC. Consumers who are optimistic about the future are likely to feel that they have more control over the decisions they are making regarding purchasing products (Chiang et al., 2022) while consumers who feel economically insecure are likely to avoid long - term commitments such as owning houses and instead are likely to engage in short-term consumptions such as buying new clothes (Jin et al., 2020). Stress originating from big financial decisions also minimizes PBC. Public support measures such as first-time buyer schemes, financial incentives and low-cost effective interest rates can ease the economic burden and improve buyer confidence (Zou, Zhong, Chen, & Pu, 2024). Furthermore, it is also important to note that there are online sources that offer information about the buying process and laws which can enable buyers to make informed decisions when making complex purchases regarding the purchase of a home (Rahman et al., 2024). H3: There is a significant relationship between perceived behavioral control and consumers’ house purchase intention in China. 2.3.4. Financial Status Finance is defined as the amount of money that a person has or can borrow wages, loans, interest rates, and payment (Kurniawan, Dewi, Maulatsih, & Gunadi, 2020). Purchasing a property requires acquiring credit facilities, earnings are thus one of the most important indicators of the affordability of a home (Thanaraju, Khan, Juhari, Sivanathan, & Khair, 2019). Consumers buying real estate products generally make large payments and often undertake several interest payments; hence, financing is crucial (Islam et al., 2022). Nonetheless, stronger lending guidelines have been implemented to prevent speculative activity which some investors have engaged in (Kurniawan et al., 2020). The literature review reveals that financial factors play an important role in affecting purchase intentions as revealed in previous studies. For instance, Islam et al. (2022) established that there was a relationship between financial variables and the intention to purchase an apartment in Bangladesh. Along the same purpose, Yoke et al. Humanities and Social Sciences Letters, 2025, 13(2): 442-456 447 © 2024 Conscientia Beam. All Rights Reserved. (2018) observed that financial factors had a positive impact on residential property purchases in Kuala Lumpur. Thus, Pham Dinh et al. (2022) and Rachmawati et al. (2019) supported the proposition and also found that financial factors have a positive effect on purchase decisions in Ho Chi Minh City and Selangor, respectively. However, these reviews raise some contradicting conclusions. A survey conducted by several authors such as Sean and Hong (2014) and Kurniawan et al. (2020) noted a high significant relationship between perceived financial ability and purchase intention contrasting with findings made by Thanaraju et al. (2019) where such a correlation was not observed. This study seeks to focus on the correlation between financial solvency and house purchase aspirations in China with the hope of elucidating the financial factors that hold sway in the China real estate market because of these divergent outcomes. H4: There is a significant relationship between financial status and consumers’ house purchase intention in China. 2.3.5. Location There is a significant correlation between the place of property and its performance with the factors that involve the distance of the property to the shopping mall and other places such as schools, hospitals, and workplaces are being among the key factors that influence the value of the property as well as the performance of the property in terms of the buyer’s preference. A good location is central and less time is spent on transportation and basic needs are easily accessible as well, hence, saving time and expenses (Ismail & Shaari, 2019). This study also established that geographic location which focuses on the proximity of housing to relevant and other facilities affect property demand and future rates of return (Al-Nahdi, Habib, et al., 2015; Rachmawati et al., 2019). According to Thanaraju et al. (2019), the presence of a well-connected residential area to popular amenities make it profitable for developers whereas Kurniawan et al. (2020) affirm that location influences the choice to buy a home. Yoke et al.'s (2018) research noted that there was no relationship between location and purchase intention in a Malaysian city, Kuala Lumpur. Similarly, Syukor et al. (2021) concluded that their research revealed no effect of location on purchase intention in a housing fair. Nonetheless, several other works which include Rachmawati et al. (2019) and Le Hoang et al. (2020) establish that there is a positive and significant relationship between location and purchase intention in Selangor, Malaysia and Ho Chi Minh City. Home buying decisions can be attributed to location as suggested by the results obtained by Chia, Harun, Kassim, Martin, and Kepal (2016) and Sean and Hong (2014). The conflicting findings showing a strong correlation and others indicating no significant relationship highlight the need for further research, particularly in the Chinese context to better understand the role of location in home purchase intentions. This study will explore the correlation between geographic location and home purchase intention in China to resolve the inconsistent findings in previous studies. H5: There is a significant relationship between location and consumers’ house purchase intention in China. 2.4. Purchase Intention Purchase intention can be defined as the extent of a consumer’s willingness to make a particular purchase either of a product or a service which is usually used by marketers in their attempts to reach consumers adequately to influence their buying behaviour and get more sales (Islam et al., 2022). It means that people are ready to do something which makes this variable a good predictor of the actual purchase (Al-Nahdi, Habib, et al., 2015). Thus, the influence of the potential purchasing intentions influences such factors as location, prices, and finance for the property buyers for being successful in the real estate market (Islam et al., 2022). Home purchase intention is impacted by several characteristics, including attitude, financial status, geographic location, and other social factors. These determinants include perceived and account factors (Rachmawati et al., 2019). Attitude is an individual’s perception towards a product which comprises cognitive, affective and behavioural parts. Various financial aspects such as income, investment and mortgage rate heavily influence an individual’s Humanities and Social Sciences Letters, 2025, 13(2): 442-456 448 © 2024 Conscientia Beam. All Rights Reserved. capability of owning a home (Kurniawan et al., 2020). Ismail and Shaari (2019) found that location has been considered the most significant in-home buying criterion, including the proximity of facilities such as schools, retail centers, and hospitals. According to Al-Nahdi, Ghazzawi, et al. (2015), purchase intention is defined as a consumer's degree of desire to buy a specific product. Consequently, this research aims to determine the impact of attitude, financial status, perceived behavioral control, subjective norm and location on home buying intention in China. Real estate agents may find it helpful to be aware of these elements to match the demands of the market. Figure 1 illustrates the research framework. Figure 1. The framework proposed in this study. 3. METHODS 3.1. Research Design This study employs a quantitative research design to investigate the factors influencing Chinese consumers' house purchase intention. The study is guided by the Theory of Planned Behavior (TPB) which serves as a theoretical framework, informing the development of hypotheses and the selection of variables. 3.2. Participants The research adopts a quantitative approach as it provides reliable and applicable results through large sample sizes and statistical analysis (Al-Nahdi, Ghazzawi, et al., 2015). The target population consists of individuals in China intending to purchase a home. Surveys will be distributed through online platforms like WeChat and TikTok with respondents encouraged to share the questionnaire to increase the response rate (Malmqvist, Hellberg, Möllås, Rose, & Shevlin, 2019). Sampling involves selecting a portion of the population to make inferences about the whole. This study uses convenience sampling, a non-probability method where individuals are selected based on ease of access (Cornesse et al., 2020). 3.3. Data Collection Tool The study’s sample size is calculated using G Power with a minimum of 222 respondents. According to Cheng, Li, and Li (2020) the questionnaire, which is provided through the questionnaire star, concerns demographic Humanities and Social Sciences Letters, 2025, 13(2): 442-456 449 © 2024 Conscientia Beam. All Rights Reserved. information and elements that influence purchasing intention, including attitude, location, financial position, subjective norm, and perceived behavioural control. 3.4. Technique Data analysis uses Structural Equation Modeling (SEM) through Partial Least Squares (PLS) to assess relationships between variables. The PLS-SEM model used in this study is designed to assess the relationships between the constructs of the TPB and house purchase intention. PLS-SEM is ideal for the complex model and small to medium sample sizes used in this study (Ringle, Wende, & Becker, 2015; Sarstedt, Ringle, Smith, Reams, & Hair, 2014). The analysis involves assessing the measurement and structural models to test the relationships among constructs (Hair Jr, Hult, Ringle, & Sarstedt, 2016). Overall, this study differs from past research in several key ways. First, it applies the TPB framework to the context of Chinese real estate, offering insights that are specific to this market. Previous studies such as those by Feng et al. (2022) have been location-specific and not generalizable across China. By using a diverse sample from various regions, this study aims to provide more generalizable findings. Additionally, the use of PLS-SEM allows for the handling of complex relationships and non-normal data which are common in consumer behavior studies but often overlooked in past research. This methodological approach enhances the robustness and reliability of the findings contributing to the existing literature on house purchase intention in China. 4. RESULTS According to Table 1, this study collected data from 239 respondents; their demographic information has been shown. The study includes 239 participants with 43.10% females and 56.90% males. Most respondents are aged 35- 44 (35.15%) followed by 45-54 (31.80%) while the smallest group is 25-34 (11.30%). The majority are married (51.05%) with 46.03% being single. Income levels vary with the largest group earning 3501-5000 renminbi (RMB) (29.71%) and 27.20% falling into both the lowest (<3500 RMB) and highest income brackets (7001+ RMB). This demographic diversity allows for examining how gender, age, marital status, and income influence house purchase intentions. Table 1. Demographic information of respondents (n=239). Dimensions Items Frequency Percent Gender Female 103 43.10% Male 136 56.90% Age 25-34 27 11.30% 35-44 84 35.15% 45-54 76 31.80% 55-64 52 Marital status Single 110 46.03% Married 122 51.05% Others 7 2.93% Monthly income (RMB) <3500 65 27.20% 3501-5000 71 29.71% 5001-7000 38 15.90% 7001 and above 65 4.1. Measurement Model Table 2 summarizes the descriptive statistics for six variables: Attitude (A), Subjective Norm (SN), Perceived Behavioral Control (PBC), Financial Status (FS), Location (L), and Purchase Intention (PI) among 239 respondents. Each variable was rated on a 1 to 5 scale with mean values ranging from 3.28 to 3.41 indicating moderate agreement across all factors. Location has the highest mean (3.41) while perceived behavioral control has the Humanities and Social Sciences Letters, 2025, 13(2): 442-456 450 © 2024 Conscientia Beam. All Rights Reserved. lowest (3.28). Standard deviations are similar ranging from 1.16 to 1.21 suggesting a consistent level of variability. Median values are close to the mean showing a relatively symmetrical distribution of responses. This data highlights that all variables have a moderate influence on house purchase intentions. Table 2. Descriptive analysis. Items No. of samples Min. Max. Mean Std. deviation Median Attitude (A) 239 1 5 3.38 1.16 3.60 Subjective norm (SN) 239 1 5 3.29 1.21 3.20 Perceived behavioral control (PBC) 239 1 5 3.28 1.16 3.00 Financial status (FS) 239 1 5 3.33 1.20 3.20 Location (L) 239 1 5 3.41 1.17 3.40 Purchase intention (PI) 239 1 3 3.36 1.20 3.33 4.2. Measurement Model According to Table 3, the indicator loadings for all constructs are above 0.7 indicating strong representation of the underlying constructs. According to Table 4, internal consistency and convergent validity are confirmed by Cronbach's alpha and composite reliability values all exceeding 0.8 demonstrating high reliability (Hair Jr et al., 2016). Average Variance Extracted (AVE) values are above 0.5 supporting convergent validity. According to Table 5, discriminant validity is confirmed by the Heterotrait-Monotrait (HTMT) ratio with all values below 0.55, ensuring that the constructs are distinct and capturing unique aspects of the data. This shows the model’s robustness and validity in explaining house purchase intentions. Table 3. Indicator loadings. Items Attitude Financial status (FS) Location Perceived behavioral control (PBC) Purchase intention (PI) Subjective norm (SN) Attitude 1 0.853 Attitude 2 0.796 Attitude 3 0.834 Attitude 4 0.829 Attitude 5 0.837 FS 1 0.863 FS 2 0.82 FS 3 0.842 FS 4 0.858 FS 5 0.859 LF 1 0.844 LF 2 0.802 LF 3 0.813 LF 4 0.867 LF 5 0.805 PBC 1 0.8 PBC 2 0.816 PBC 3 0.809 PBC 4 0.782 PBC 5 0.825 PI 1 0.867 PI 2 0.877 PI 3 0.871 SN 1 0.845 SN 2 0.836 SN 3 0.876 SN 4 0.82 SN 5 0.816 Humanities and Social Sciences Letters, 2025, 13(2): 442-456 451 © 2024 Conscientia Beam. All Rights Reserved. Table 4. Construct reliability and validity. Constructs Cronbach's alpha Composite reliability (rho_a) Composite reliability (rho_c) Average variance extracted (AVE) Attitude 0.888 0.896 0.917 0.689 Subjective norm (SN) 0.895 0.905 0.922 0.704 Perceived behavioral control (PBC) 0.866 0.869 0.903 0.65 Financial status (FS) 0.903 0.905 0.928 0.72 Location 0.884 0.894 0.915 0.683 Purchase intention (PI) 0.843 0.851 0.905 0.76 Table 5. Heterotrait-monotrait ratio (HTMT) matrix. Constructs Attitude Financial status (FS) Location Perceived behavioral control (PBC) Purchase intention (PI) Subjective norm (SN) Attitude 0.387 Financial status (FS) 0.389 Location 0.383 0.546 Perceived behavioral control (PBC) 0.408 0.489 0.442 Purchase intention (PI) 0.334 0.523 0.436 0.417 Subjective norm (SN) 0.389 0.501 0.47 0.485 0.484 0.486 4.3. Structural Model The direction and strength of the correlations between the constructs were indicated by the path coefficients. With a coefficient of 0.241, Financial Status (FS) has the largest positive influence on Purchase Intention (PI), followed by Subjective Norm (SN) with 0.204. With a 0.069 score, Attitude (A) has the weakest route suggesting that it has little effect on purchase intention as shown in Figure 2. Figure 2. SEM model. Humanities and Social Sciences Letters, 2025, 13(2): 442-456 452 © 2024 Conscientia Beam. All Rights Reserved. According to Table 6, effect size (F2) values indicate that Financial Status (FS) and Subjective Norm (SN) have medium effects on Purchase Intention (PI) meaning that they significantly contribute to explaining its variance. The Variance Inflation Factor (VIF) values are all below 3 suggesting no multicollinearity issues in the model. The model explains 30.1% of the variance in purchase intention as indicated by the R² value of 0.301. The number of predictors is taken into account by the adjusted R² which is somewhat lower at 28.6% (Hair Jr et al., 2016). Table 6. SEM model output. Relationship Path coefficients F ² VIF R² Adjusted R² Attitude (A) -> Purchase intention (PI) 0.069 0.005 1.267 0.301 0.286 Subjective norm (SN) -> Purchase intention (PI) 0.204 0.041* 1.461 Perceived behavioral control (PBC) -> Purchase intention (PI) 0.099 0.01 1.423 Financial status (FS) -> Purchase intention (PI) 0.241 0.053* 1.559 Location (L) -> Purchase intention (PI) 0.119 0.014 1.474 Note: * indicate significant at the 0.05 level. The SEM model fit indices show a very good match indicating that the model fits the data well. Subjective norms and financial status are important predictors of purchase intention but attitude and perceived behavioral control do not significantly influence the outcome. These results emphasize how crucial societal influences and financial factors are when making purchases. 5. CONCLUSION The study examined the behavioral construct analysis of house purchase intentions in China including the attitude, subjective norms, perceived behavioral control, financial resources and place. In particular, it concluded that attitude did not play a critical role either because there were less significant matters such as financial position and cultural traditions to consider. This is in contrast to the findings drawn from other studies which were conducted in Bangladesh and Malaysia in particular, where attitude was deemed to be very influential (Islam et al., 2022; Yoke et al., 2018). Indeed, subjective norm affected the purchase intention positively more than perceived behavioural control and was in line with family and social pressure as reported by the earlier study in collectivistic countries (Clark et al., 2021; Wrenn et al., 2019). Perceived behavioural control did not reach the significance limit due to the small sample size despite influencing purchasers' confidence in managing financial concerns (Tan et al., 2022; Zhao & Chen, 2021). The financial status appeared to be significant and was also supported by previous literature in different regions that highlight the importance of defining the financial capability to make a purchasing decision (Islam et al., 2022; Pham Dinh et al., 2022). The tenancy and type other factors were not relevant because all the prime locations may have been occupied leaving only affordable areas especially in the major Chinese cities (Kurniawan et al., 2020). Economic solutions involve such measures as increasing the availability of cheap credit for purchasing a home as well as direct government support and tax credits for prospective buyers with raising financial literacy as to the opportunities. There is significant focus placed on family involvement and on increasing homeownership as a form of social status (Fu et al., 2023; Hsu & Chiang, 2022). The study aims at giving an understanding of the challenges of home purchase intentions in China and suggests ways of improving on market approaches and policies. In line with these findings, future research could extend this study and analyse the real- estate markets of different countries. Humanities and Social Sciences Letters, 2025, 13(2): 442-456 453 © 2024 Conscientia Beam. All Rights Reserved. 6. IMPLICATION AND LIMITATION The study enhances the understanding of consumer behavior in China's real estate sector by analyzing the relationships between attitude, location, financial status, subjective norms, and perceived behavioral control, contributing valuable insights for future research. It applies established consumer behavior theories like the Theory of Planned Behavior (TPB) to the evolving Chinese market, testing and validating these frameworks (Feng et al., 2022). Practically, the study identifies key factors influencing house purchase intentions such as social norms and confidence in the economy. These insights assist real estate developers in aligning strategies with consumer behavior while financial firms can use the findings to better assess risks and price loan products (Davis & Haltiwanger, 2024; Zhang et al., 2020). The study's limitation lies in its geographic focus on specific areas in China limiting the generalizability of its findings. Future research should broaden the geographic scope and adopt mixed-methods approaches, combining survey and interview data to better understand barriers and motivations. Long-term studies could also monitor shifts in purchase intentions over time in response to economic or policy changes (Marfatia et al., 2022). Funding: This study received no specific financial support. Institutional Review Board Statement: The Ethical Committee of the INTI International University, Malaysia has granted approval for this study on 24 May 2024 (Ref. No. INTI/UEC/2024/023). Transparency: The authors state that the manuscript is honest, truthful, and transparent, that no key aspects of the investigation have been omitted, and that any differences from the study as planned have been clarified. This study followed all writing ethics. Competing Interests: The authors declare that they have no competing interests. Authors’ Contributions: All authors contributed equally to the conception and design of the study. All authors have read and agreed to the published version of the manuscript. REFERENCES Ajzen, I. (2020). The theory of planned behavior: Frequently asked questions. Human Behavior and Emerging Technologies, 2(4), 314-324. https://doi.org/10.1002/hbe2.195 Al-Nahdi, T. S., Ghazzawi, O. H., & Bakar, A. A. 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