PURCHASE INTENTION OF RESIDENTIAL PROPERTY IN GREATER KUALA LUMPUR, MALAYSIA

Chung Chay Yoke1+ --- Yeong Wai Mun2 --- Low Mei Peng3 --- Ung Leng Yean4

1Senior lecturer, International Business Department, Universiti Tunku Abdul Rahman, Malaysia

2Lecturer, International Business Department, Universiti Tunku Abdul Rahman, Malaysia

3Assistant Professor, Department of Economic, Universiti Tunku Abdul Rahman, Malaysia

4Senior lecturer, Department of Accounting, Universiti Tunku Abdul Rahman, Malaysia

ABSTRACT

High housing prices in the urban area have posted a challenge for potential first-time home buyers to own a house. This study aims to investigate factors that influence Malaysians to purchase a residential property in Greater Kuala Lumpur (KL). This study examines the effect of attitude, subjective norm, perceived behaviour control, location, living space and financial factors on the intention to purchase residential property based on the Theory of Planned Behavior. The findings indicated that there are positive relationships between attitude, subjective norm, perceived behaviour control and financial factors toward purchase intention for residential property, while location and living space were found to have no relationship on consumers’ purchase intention of residential property in Greater KL. These findings will provide insightful information to the housing developer in their venture on new housing projects, marketing strategies and be meeting the homeownership needs as well as to the policy maker and bankers’ decision for economic development. The limitation of the current study is arising from its focus on the selected geographical area of the Klang Valley property market only.

Keywords:Behavioural residential property Greater KL Housing Urban Purchase intention.

ARTICLE HISTORY: Received:2 May 2018. Revised:13 June 2018. Accepted:24 June 2018. Published:4 July 2018.

Contribution/ Originality:This study contributes in the existing literature by providing better insights in terms of empirical study on the antecedents of purchasing residential property. It also broadens the usage of Theory of Planned Behavior by adding other relevant variables such as location, living space and financial factor when testing in the specific context of property sector.

1. INTRODUCTION

A basic human need is a shelter. Owning a house is a necessity to fulfil our basic physiological needs of protection, and it is believed to be everybody’s dream. However, in the recent decade, the rapid increase in house prices, particularly in the major urban areas such as Kuala Lumpur and Selangor, has attributed partly due to the shortfall in supply of low-cost house whereas oversupply on the medium and high-cost houses during the Five-year Malaysia Plans (Tan, 2012 ). According to National Property Information Centre, CEIC and Bank Negara Malaysia (Central Bank) estimated that an average shortage of 85,911housing units per year between 2011 and 2015 are particularly acute in the affordable housing category. In 2014, half of the Malaysian households earned a monthly income of RM4,585 and below. World Bank and United Nations recommended that this group could only afford to own a house that is priced up to RM165,060. However, only 21% of new housing launches in Malaysia were priced below RM250,000 in 2014 whereas, properties priced above RM500,000 shown an oversupply. This shows a mismatch between the potential house buyers and property market. In this regard, the Federal and State Governments, Syarikat Perumahan Negara Berhad (SPNB) and Perumahan Rakyat 1Malaysia (PR1MA) have initiated the affordable housing scheme for the first home buyer, with the aim to have less significant impact on reaching the demand especially in the urban area with lots of jobs supply. Between years 2009 and 2014, average house prices in Malaysia rose by 7.9% in CAGR terms, exceeding the growth in average household income of 7.3% over the same period which caused the affordability of houses across the key states in Malaysia progressively to decline (House Price Static in Malaysia, 2018 ). In line with these housing issues and scenario, this study is deemed timely to identify the factors that influence peoples’ intention in owning an affordable house in Greater Kuala Lumpur (Greater, KL). Hence, real estate developers, marketers, policy makers and bankers can use the findings to better understand, build and supply affordable housing that meets the peoples’ need.

Malaysia is a country that is located in the middle of the next high-growth region of South-East Asia and consistently ranked as among the best country in overall ease of doing business by World Bank. It will be a magnet for international talent and various nationalities, filling up the estimated 4.2 million job opportunities in 2020 and hope to be one of the world's top 20 most livable cities (The Star Online, 2013 ). Hence, the migration from rural areas will be significant due to the job employment opportunities (Greater Kuala Lumpur/Klang Valley, 2017 ).  Greater KL is the capital and commercial heart of Malaysia. It is well known as Klang Valley. This term was espoused by Malaysian Prime Minister Najib Razak as an urban agglomeration to spur the country's economic growth by the billions in the Economic Transformation Programme announced in 2010. It is defined as an area covered by 10 municipalities in surrounding Kuala Lumpur including Kuala Lumpur, Putrajaya, Shah Alam, Petaling Jaya, Klang, Subang Jaya, Selayang, Ampang and Sepang which consists of Cyberjaya and Salak Tinggi. Each municipal will be governed by local authorities. The current population in Greater KL is about 7.2 million (about a quarter of Malaysia’s total population). The population is expected to be 10 million by 2020. Urbanisation is expected to reach 70 per cent by 2020 and predicted that will be home to about 20 million by 2030 (Mohammed, 2014 ). In 2017, Location Rating Survey conducted by international knowledge, information and software provider ECA International, Kuala Lumpur ranks 118th as the most livable location for Asian expatriates out of 470 cities and rank 27th in the regional list. In fact, both of the ranking show higher (less livable) compared to the previous year and it is believed that the less livable were due to the threat of terrorism and also seasonal forest fires in neighbouring Indonesia which have increased in recent years (Chiew, 2017 ). 

To own a house in Greater KL or Klang Valley is a dream of everyone who tenanted a house or a room. However, by looking at the sky rocking house prices in Klang Valley, the house buyers have to sacrifice certain needs and set priority of which factors should be considered within their affordable limit. This study aims to investigate whether attitude, subjective norm, perceived behavior control, financial factor, location factor or living space factors are to be considered in house purchase intention within their affordable limit. Thus, the conclusion may serve as ideas for housing developers, bankers and government to establish policies to assist the house buyer to own an affordable house in Greater KL. This study also serves as a reference for future researchers to investigate further how to meet the needs of the house buyer to own an affordable house at urban area.

2. LITERATURE REVIEW 

2.1. Theory of Planned Behavior (TPB)

This study employed Theory of Planned Behavior (TPB) as proposed by Ajzen (1991 ) as a tool in modelling intention to purchase products or services. It was an improved model from Theory of Reasoned Action (TRA) (Ajzen, 1985 ) which is used to explain and forecast human behaviour more accurately. TPB added a variable reflecting a person’s perceived behaviour control in predicting and explaining human behaviour. Under TPB, Ajzen (1991 ) mentioned that important factors which influenced a person’s behavioural intention and his/her specific behaviour are attitude towards behaviour, subjective norm, and perceived behaviour control. This theory was well accepted and applied by many scholars in explaining and forecasting human behaviour in the studies related to property or real estate purchase intentions in various countries (Han and Kim, 2010 ; Phungwong, 2010 ).

2.2. Purchase Intention (PI)

Purchase intention is defined as a probability of customer’s readiness to purchase a product in the near future (Wu et al., 2011 ). According to Sidi and Sharipah (2011 ) purchase intention is referred to as a subjective judgment by customers whom it reflected after customer evaluates whether to buy a product or a service. Purchase intention is also defined as the situation in which a customer is willing to make a transaction with the retailer and probably come to consider buying some product or service (Dodds et al., 1991 ). Intention to purchase is the dependent variable which is predicted by an independent variable like attitude, perceived behaviour control and subjective norm (Ajzen and Fishbein, 1980 ; Ajzen, 1991 ). According to Han and Kim (2010 ) purchase intention is one of the most tedious tasks for any business as it is influenced by various unknown and uncertain factors that led to purchasing intention is difficult to measure under different circumstances. The higher the purchase intention will lead to higher willingness of a customer to purchase the products or services (Schuler and Adair, 2003 ; Chiew et al., 2014 ). Studies also found that a customer with strong intention to buy a residential property, he or she will be more likely to transfer the intention into actual buying behaviour (Zawawi et al., 2004 ). 

2.3. Attitude (A)

Attitude refers to the degree to which a person has a favourable or unfavourable evaluation concerning objects, people or events (Robbins et al., 2015 ). It consists of three components namely cognitive, affective and behavioural. Cognitive component is made up of beliefs, opinions, knowledge and information held by a person. The emotional or feeling part of whether that person like it or not will lead to behaviour outcomes is regarded as the affective component. Behavioural component refers to an intention to behave in certain way towards someone or something (Ajzen and Fishbein, 1980 ; Robbins et al., 2015 ). According to Kamal et al. (2016 ) they uncovered that is a significant impact between attitude and buying intention towards apartment in Bangladesh and this is supported by Saudi inhabitants to purchase real estate (Al-Nahdi et al., 2015 ). Phungwong (2010 ) also concluded that is a positive attitude served as antecedent of home purchase intention of single people in Thailand. Hence, the following hypothesis is derived: H1: There is a positive relationship between attitude and purchase intention of residential property in Greater KL.

2.4. Subjective Norm (SN)

Subjective norm is a normative belief of an individual’s beliefs affected by others such as family members who think that whether an individual should perform a particular behaviour (Rivis and Sheeran, 2003 ). Normally, an individual will perceived the pressures placed on them whether to perform the behaviour or not (Ajzen, 1991 ; Han and Kim, 2010 ). Many studies shown that reference group has strong positive influence over the intention to purchase (Panthura, 2011 ; Numraktrakul et al., 2012 ; Razak et al., 2013 ). Songkakoon et al. (2014 ) believe that children and spouse are the main parties that will change their intention to purchase decision relating to home purchase in their Thai culture. Al-Nahdi et al. (2015 ) also found that there was a positive effect between subjective norm on intention to purchase real estate in Jeddah and has similar case in Malaysia (MdRazak et al., 2013 ). Thus, the proposed hypothesis is: H2: There is a positive relationship between subjective norm and purchase intention of residential property in Greater KL.

2.5. Perceived Behavior Control (PBC)

Perceived behaviour control is defined as the extent to which the person has control over internal and external factors which either facilitate or prevent the behavioural performance (Han and Kim, 2010 ).  Various research areas showed that there is a positive relationship between perceived behaviour control and purchase intention (Teo and Lee, 2010 ; Omar et al., 2012 ). This positive relationship also applied in the real estate industry (Phungwong, 2010 ; Al-Nahdi et al., 2015 ). However, Al-Nahdi et al. (2015 ) found that perceived behaviour control had no effect on the customers’ intention to purchase real estate in Saudi Arabia. Thereupon, the following is suggested: H3: There is a positive relationship between perceived behaviour control and purchase intention of residential property in Greater KL.

2.6. Location Factor (LF)

A strategic location generally linked to the accessibility and proximity to shopping center or retailer, public transport, school, hospital or near to workplace as it is not only convenient to work and sending kids to school and it is so cost effective. Crane (1996 ) defined short distance to work as associated with secure job relation, low moving expenses, fewer job changes, low transportation cost and more time available to spend on daily activities especially for young people due to proximity to working place and readily available facilities. Study in Nasar and Manoj (2015 ) location which is adjacent to all important emergency service, road rail transport accessibility is rank third after price and quality factors in central Kerala, India. According to Property Guru Malaysia Country Manager, Fernandez (2017 ) location still remains as the top considerations when buying property even though other factors are changing as a reflection of the current mind-set of property buyers as compared previously in the 2016 survey. This result is also supported by Abdullah et al. (2012 ) which ranked this as the first factor in influencing the home buyer’s decision. However, Al-Nahdi et al. (2015 ) shown that the location factor has no effect on customers’ purchase intention on real estate among Saudis. Thus, the proposed hypothesis is: H4: There is a positive relationship between location factor and purchase intention of residential property in Greater KL.

2.7. Living Space (LS)

According to Zeng (2013 ) living space is one of the intrinsic housing attributes that consists of living room, dining room, number of bedroom, number of bathroom and etc. that will be considered by buyers before making the homeownership decision and it is more particular in Western countries (Hurtubia et al., 2010 ). More common structural attributes including the size of the living hall and dining hall, build-up size, number of bedroom and bathroom  have an effect on purchase intention (Tan, 2012; Saw and Tan, 2014 ). However, Al-Nahdi et al. (2015 ) found that living space had no effect on purchase intention to purchase real estate property in Jeddah and this result was supported by Chia et al. (2016 ) on a case study from Malaysia.  Hence, it was proposed that: H5: There is a positive relationship between living space and purchase intention of residential property in Greater KL.

2.8. Financial Factors (FF)

Often, financial attributes is taken into consideration when buying a property. Key areas regarding mortgage interest rates, household income, house price and ability to obtain financing. Financials factor accounts for almost 30 percent of the income decision for homeowners when purchasing a house (Reed and Mills, 2007 ). Saw and Tan (2014 ) found that financing is the main consideration that turns many property investors away in the case of Malaysia market when  stricter lending regulations imposed by bank and government to soften the speculation in the property market. Abdullah et al. (2012 ) found that financial factors ranked second after locational factor that influence the first time homeowner decision in Malaysia. Thus, it was proposed that: H6: There is a positive relationship between financial factor and purchase intention of residential property in Greater KL.

Figure-1. Research Framework

Source: Developed for research

The above research framework as show in Figure 1 was proposed to carry out the current study based on Theory of Planned Behavior and past literatures as proposed above.

3. RESEARCH METHODOLOGY

This is a cross-sectional study; data was collected in a single point of time. 300 sets of questionnaires were distributed and collected directly at high traffic railway stations and shopping malls in Klang Valley through self-administered method. The target respondents are those age between 25-64 years old and have plans to buy a residential house in Greater KL within the next 3 years. The questions were adapted from two past researches (Saw and Tan, 2014 ; Al-Nahdi et al., 2015 ). Section A of the questionnaires consists of respondent’s demographics and in section B covers questionnaires on the dependent and independent variables, i.e., purchase intention, attitude, and subjective norm, perceived control behaviour, location factor, living space and financial factor. Seven-point Likert Scale was used to allow respondents to rank their level of agreement to statement in the questionnaire. 1 indicates strongly disagree while 7 indicates strongly agree. In this study, the statistical tool SPSS version 21.0 was applied to analyse the data profile and the hypothesis testing. The demographic information was analysed through descriptive analysis. In order to ensure that the data used to test the hypotheses are both valid and reliable, exploratory factor analysis (EFA) was conducted using the principal axis factoring extraction method and Varimax rotation method. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy, Bartlett’s test of sphericity and anti-image correlation were included in the factor analysis in order to verify the assumptions undertaken by the factor analysis. Cronbach Alpha was used to analyse the reliability of the instrument. Regression analysis was used for hypothesis testing to investigate the relationship between the independent and dependent variables.

4. ANALYSIS AND RESULT

The profile of respondent shows and as depicted in table 1 below:-

Table-1. Profile of respondents

Respondent’s profile Category Frequency Percentage
Gender Male 158 52.7
  Female 142 47.3
Age 25-34 88 29.3
  35-44 105 35.0
  45-54 77 25.7
  55-64 30 10.0
Marital Status Single 114 38.0
  Married 166 55.3
  Others 20 6.7
Monthly income <3500 63 21.0
(RM) 3501-5000 104 34.7
  5001-7000 79 26.3
  7001 and above 54 18.0

 Source: Developed for research

4.1. Factor Analysis

Factor analysis was used to ensure that the numbers of items can be reduced to the number of concepts that were initially hypothesized (Hair et al., 1998 ). Minimum acceptable value for KMO is 0.50 with Bartlettt’s test of sphericity to be significant. Eigenvalue value should be 1 or greater. The cut-off point for significant factor loading should be at least 0.50 on each factor.

Table 2 summarizes the factor loadings for all independent and dependent variables, one of the items in location factor, loading is only 0.411, which is below 0.5 and cross-loading shown in between two construct was dropped. The result revealed all the components had eigenvalues over the criterion of 1 and Bartlett’s test of Sphericity shown significant level at 0.000; and in combination explained 69.614 percent of the variance. The KMO measure verified the sampling adequacy for the analysis, stand at 0.881, which falls into the range of ‘great’, which is above the acceptable level of 0.5.

4.2. Reliability Analysis and Testing for Hypothesis

Cronbach’s alpha was calculated to examine internal consistency of the scales used in this study. Cronbach’s alpha coefficient can range from 0.00 to 1.0 where the value close to 1.0 indicates a high internal consistency of the scale. Table 3 results shown all the variables are good which above 0.80 except the location factor is at acceptable reliabilities of Cronbach’s alpha is above 0.70.

Table-2. Factor Analysis

Variables Loadings Eigenvalue % Variance
Factor 1: Attitude   1.739 5.510
 Buying house is beneficial decision 0.614    
 Buying house is good idea 0.678    
 Buying house is a wise decision 0.827    
 Buying house is admired decision 0.759    
Factor 2: Subjective Norm   2.590 8.785
 My family thinks that I should buy a house 0.730    
 My family would want me to buy a house 0.791    
 My family agrees with me to buy a house 0.790    
 My family thinks that buying house is a wise decision 0.738    
Factor 3: Perceived Behaviour Control   2.393 7.890
 I have enough opportunity in making decision to buy house 0.696    
 I have enough time to make a decision to buy house 0.711    
 I have enough money to buy house 0.794    
 I have enough skill & knowledge about the house to make my  own decision 0.728    
Factor 4: Location   1.186 3.372
Location close to schools and nurseries is important for me when making purchase decision* 0.411    
 Location close to health center and hospital is important 0.714    
 Location close to public transport is important 0.802    
 Location close to workplace is important 0.508    
Factor 5: Living Space   1.471 4.659
 I would consider the size of the living /dining area when
making decision to buy a residential property		 0.698    
 I would consider number of bathrooms when making 
decision to buy a residential property		 0.857    
 I would consider the number of rooms when making decision
to buy a residential property		 0.684    
Factor 6: Financial Factor   1.343 4.066
 Housing price will affect my housing preferences 0.786    
 Monthly income will affect my housing preferences 0.845    
 Financing or mortgage of property will affect my housing Preferences 0.820    
F7: Purchase Intention   9.458 35.331
 I will continue to buy house in the future 0.764    
 I intent to buy house in the future 0.843    
 I plan to buy house 0.807    
I want to buy house 0.734    
Total Variance Explained     69.614
KMO Measure of Sampling Adequacy     0.881
Approx. Chi-Square     5750.415
Bartlett's Test of Sphericity   - df     325
   - Sig.     0.000

Note: * denotes that the item was dropped from further analysis due to loading less than 0.5. Information were extracted using the principal axis factoring and rotation using varimax method.

Hypothesis 1 to 6 is used to predict the relationship between independent variables which include attitude; subjective norm, perceived control behaviour, location; living space and financial factor are positively related to the dependent variable which is intention to purchase. The multiple regression analysis technique was used to test this relationship in this model. The results R square = 40.1%, this means that about 40.1% of the variation in the dependent variable can be explained by the independent variables jointly. F value = 32.935, and p = 000 <0.01 which is significant, implying that the model is adequate and fit for measurement. 

Table-3. Reliability and hypothesis testing for independent and dependent variables

Test   Reliability Hypothesis Testing  
Variables No. of Items Cronbach’s alpha Std. Coef. Beta Sig. value
Attitude 4 0.879 0.214 0.000
Subjective Norm 4 0.914 0.249 0.000
Perceived Behavior Control 4 0.902 0.251 0.000
Location Factor 3 0.708 -0.126 0.009
Living Space 3 0.876 0.083 0.148
Financial Factor 3 0.868 0.096 0.038
Purchase Intention 4 0.921    
Hypothesis R2= 0.401      Adjusted R2 =0.389 F=32.935 Significant=0.000

Source: Developed for research

4.3. Summary of Result

Table 3 shows H1, H2, H3 and H6 hypothesis are supported, Attitude, subjective norm, perceived behaviour control and financial factor have positive effect on purchase intention of residential property in Greater KL. However, H4 is rejected as the Coefficients Beta value shown -0.126 even though the significant p<0.05; H5 is rejected due to p>0.05. Meaning that, location and living space factor has no significant positive relationship towards purchase intention of residential property in Greater KL.

5. CONCLUSIONS AND RECOMMENDATIONS

The findings of this study indicated that attitude, subjective norm, perceived behavior control and financial factor have positive effect on the intention to purchase residential property in Greater KL. However, location factor and living space have insignificant relationship with house purchase intention. The two insignificant factors most likely were due to customers knowing that strategic location and bigger living space are costly, often beyond their affordability. This result was congruent to Al-Nahdi et al. (2015 ) and Chia et al. (2016 ) past studies. Thus, it shows that for those who are buying houses within the Greater KL region normally do not expect to have a bigger space and too strategic location due to the trade-off with the pricing factor. Financial factor is indeed an important factor for house buyers as this is consistent with the result with Abdullah et al. (2012 ).

With this in mind, a developer first priority in offering any housing project should consider how to reduce the building cost without sacrificing the quality and meeting the potential house buyers’ expectation. Developers can work with the local authority jointly to develop a land in sub-urban areas and in return to build infrastructure to the new township. This strategy enable to lower down the property cost, price and increase the demand. Building smaller size studio could be a wise decision to target those young working couples or university students. Alternatively, build higher density apartment substantially able to reduce the cost.

The usage of technology such as drones during surveys to check the conditions of hard to reach places and can be equipped with lenses that are able to read serial or model numbers that make the survey to be more detailed which can save the cost of building and reducing the risk of high rise building. The use of Virtual Reality (VR) technology in real estate sector obviously can save cost in building the show house, sales and marketing where buyers from far away can view the property virtually before buying.

Young adults who are an active population involve in migrating to urban area due to employment, they are advised to save more and use better financial planning to have a higher level of financial literacy and ready access to financial services before buying a house. Moreover, if the banks and Bank Negara are able to increase the flexibility of borrowing, reduce the interest rate and extend the tenor of borrowing, it is believe that availability of such facilities will enhance the ability of citizens to own a house instead of renting a house. This is aligning with the government policies of hoping to have every household to have their own roof over their head.

The current findings contributed substantially to developers or marketers in understanding how attitude, subjective norm and perceived behaviour control affects consumer’s purchase intention of residential property in Malaysia urban area particularly in Greater KL.  Developers or marketers should influence the attitude of consumers through advertising, word-of-mouth and emphasize on how owning a house can improve their quality of life, increase their sense of security and guard as a safety net against future inflation due to price appreciation of property. The marketing strategy can promote using subjective norm which family members or friends are encouraged to purchase more than one unit for the same property, rebates or discounts will be given. The developers should work together with their bankers and lawyers to provide complete information such as terms and conditions, price, house features, down payment, all related fees and loans to the consumers as to increase their perceived behaviour control and confidence level in understanding their ability to own a property. This can also help to prevent an increase in non-performing loans due to inability to pay mortgage loan in the future which increases the individuals, banks and country’s financial risk.

6. LIMITATION AND FUTURE RESEARCH

Several limitations have been identified in this study. Since this study was conducted in only three locations in Klang Valley and only involved 300 respondents, it may not represent the whole population of Klang Valley for generalisation purposes. Building a smaller size and high density property may cause various social issues such as poor neighbourhood, congested and dirty surroundings. Future research may be considered including factors such as neighbourhood, community environment and other social issues. Research could also explore and compare between high density and low cost housing area and vice versa. Future research may also consider expanding to sub-urban area, whereby transportation network factor could be a variable of interest.

Funding: This study received no specific financial support. 
Competing Interests: The authors declare that they have no competing interests.
Contributors/Acknowledgement: All authors contributed equally to the conception and design of the study.

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