Journal of Fundamental and Applied Sciences ISSN 1112-9867 Research Article Special Issue Available online at http://www.jfas.info FACTOR ANALYSIS ON THE PUBLIC OPEN SPACE AFFECTING THE HOUSING PRICE IN KUALA LUMPUR, MALAYSIA M. Z. Asmawi *,1, N. M. Noor 1, A. Abdullah 1, T. Paiman 1, A. R. Abd Aziz 2 1 Kuliyyah of Architecture and Environmental Design, International Islamic University Malaysia, Kuala Lumpur, Malaysia 2 City University, Menara City U, Petaling Jaya, Selangor, Malaysia Published online: 16 April 2018 ABSTRACT There is an urgent need for public open spaces within the urban fabric as it provides significant services to the environmental quality of the areas. It is noticeable that many cities in Malaysia have the issue of the loss of public open spaces to give way to other developments particularly housing development. While, about the economic growth of the nation, there is an increasing trend in the housing price in cities. As such, the aim of the research is to study and examine the characteristics of the relationship between public open spaces and residential property value using GIS-Hedonic pricing modelling in Kuala Lumpur, Malaysia. Thus, the objectives which are to study the relationship between public open space and property residential price and to examine the elements of open space which influence the house price. A structured close-ended questionnaire survey with a total of 200 respondents was conducted to obtain the primary data in Taman Melati and Taman Wangsa Siaga, Kuala Lumpur. The results demonstrate that the establishment of the relationship between open space and house price is positive, with the factors of built-up area, an attractive design and provision of a parking area are preferable for the buyers. Strategic location and proximity to facilities and infrastructure are the main house attributes in influencing the house price. Keywords: Residential, Open Space, Factor Analysis, House Price Author Correspondence, e-mail: zainora@iium.edu.my doi: http://dx.doi.org/10.4314/jfas.v10i5s.56 Journal of Fundamental and Applied Sciences is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Libraries Resource Directory. We are listed under Research Associations category.
M. Z. Asmawi et al. J Fundam Appl Sci. 2018, 10(5S), 691-704 692 INTRODUCTION Reviews on the impact of open space on residential property values found that parks nearly always positively impacted property value 1. An evaluation of the economic values associated with open space provides many services including green areas for outdoor recreation, and enhanced scenic quality has been found to contribute positively to property values and positive impacts on residential home values 2-4. However, this is contradicting with varies studies that found out features such as mixed land use zone, road accessibility, municipal council amenities, distance to train stations and commercial centres have been proven to be the major influence in increasing the value of residential properties. Users' needs, quality of the physical features and the spatial structure of the space are three main factors that are related to the effective use of green open spaces. Access to quality open spaces is associated with improvement in well-being, user satisfaction, quality of life, and it contributes to social inclusion. The quality of the physical features, side by side with the spatial structure of the layout, have a direct impact on how open spaces are used regarding the type of activities, its duration and number of people visiting the open spaces. However, open spaces in the most deprived areas appear to be of lower quality and often experience less maintenance when compared with wealthier areas 5. Open spaces may also provide recreational benefits to users who do not own adjacent property or live within the park municipality. Open spaces such as parks and recreation areas can have a positive effect on nearby residential property values and can lead to proportionately higher property tax revenues for local governments. Hence, this research was embarked to analyse the significance of open space existence by using hedonic pricing model. Contextually, Malaysia experiences the loss of green space to give way to other developments, particularly in Klang Valley. This situation raises the question on how much importance public open space to economic matters, including the factor for house pricing, becomes one of the factors for implementation in planning and development decision 6. The aim of the research is to study and examine the characteristics of the relationship between public open spaces and residential property value using Hedonic pricing modelling in the selected residential area in Kuala Lumpur. Thus, the objectives are: i. to study the relationship between public open space and the price of property residential ii. to examine the elements of open space which influence the house price.
M. Z. Asmawi et al. J Fundam Appl Sci. 2018, 10(5S), 691-704 693 1. RESEARCH METHODOLOGY The approach adopted in conducting the structured and close-ended' questionnaire survey is a sample survey in which its objective is to arrive at subjective inferences about the population in the selected sites based on the information supplied from the sample 200 population. Therefore, the targeted population selected encompassed by those reside in Taman Melati (fig. 2) and Taman Siaga (fig. 3) within the areas of Kuala Lumpur (fig. 1). All these townships were selected based on the following criteria: i. It had been established for more than ten years; ii. It had been developed by Prestige developers; iii. The areas have a high density of population; iv. The location is in highly urbanised areas, and v. The availability of open space within the sites. There are four sections in the questionnaire form: profile of respondent; house details; factors influence the house price; and hedonic pricing model in housing price. From the survey conducted, the study could derive the understanding pattern of house ownership and the identification of the preferred elements of open space and the prioritisation of the main factors related to the provision of public open space. Fig.1. Two study areas (Taman Melati and Taman Wangsa Siaga).
M. Z. Asmawi et al. J Fundam Appl Sci. 2018, 10(5S), 691-704 694 Fig.2. Questionnaire survey has been collected within a 400m radius of open space (1.204 ha.) at Taman Melati residential area. Fig.3. A total of 1.271 ha. Open space is available for Taman Wangsa Siaga residential area. Data from this study were collected within a 400m radius of the open space.
M. Z. Asmawi et al. J Fundam Appl Sci. 2018, 10(5S), 691-704 695 2. RESULTS ANDDISCUSSION The number of respondents who participated in this study was 200 residents. The collected data was significant because it was distributed to quite a big sample size. The suggested minimum sample size was five for one variable 7. A one hundred sample size is also acceptable, however, a sample size more than two hundred is much more adequate to fulfil the factor analysis. The results are divided into several subsections which are descriptive statistics, reliability analysis, and factor analysis. Descriptive Statistics Descriptive Statistics refers to the frequency and percentages of profiles of respondents among the residents. Table 1 illustrates the demographic profiles of respondents according to variable gender, age, occupation, monthly income and education level respectively. According to their gender, 56% of the respondents are female, and 44% are male. Based on the variable age, the respondents' age range between 20 and above 60 years old. Most of the respondents are between 41 50 years and 51 60 years old, respectively with 40% and 37%. The age group is higher since the questionnaire given to the homeowner who well versed about their house's detail. As shown on the occupation profile, 31% of the respondents are the government worker, followed by 25.5% private sector and 20% of self-employed. It is followed by the monthly income level, ranging between 28% of RM8,001 - RM 12,000, 27% of RM 5,001 - RM 8,000 and 22.5% of them have less RM 5,000 income per month respectively. Notably, 32.5% and 31.5% of the respondents are bachelor degree and diploma holder.
M. Z. Asmawi et al. J Fundam Appl Sci. 2018, 10(5S), 691-704 696 Table 1. Profile of respondent Demographic Factors Frequency Percentage Gender Male 88 44.0 Female 112 56.0 Age 20-30 4 2.0 31-40 30 15.0 41-50 80 40.0 51-60 74 37.0 60 and 12 6.0 above Occupation Government 62 31.0 Servant Private 51 25.5 Worker Selfemployment 40 20.0 Retired 13 6.5 Not Working 34 17.0 Monthly < RM 5,000 45 22.5 Income RM 5,001-54 27.0 RM 8,000 RM8,001-56 28.0 RM 12,000 RM 12,001 35 17.5 - RM 16,000 > RM 10 5.0 16,001 Education Primary 1.5 Level School Secondary School 33 16.5
M. Z. Asmawi et al. J Fundam Appl Sci. 2018, 10(5S), 691-704 697 Diploma 63 31.5 Bachelor 65 32.5 Degree Master 23 11.5 Degree Ph.D 15 7.5 Reliability Analysis In this study, the focus is to look at the factors that contribute to the selection ofresidential units close to the open space among the house owners. The reliability analysis result showed that the Cronbach's Alpha was 0.888 for 35 items. In this study, the reliability analysis result showed more than 0.65. Therefore, there was an internal consistency of the scales. Hence, this instrument used in this study had a high-reliability value. Factor Analysis Factor analysis was used to construct the new factors choosing the green area near residential area done among the house owner at Taman Melati and Taman Wangsa Siaga residential area. Bartlett's test of sphericity and the Kaiser-Meyer-Olkin measure of sampling adequacy are both tests that can be used to determine the factorability of the matrix as a whole. The results value of Bartlett's test of sphericity is significant (p<0.001, p=0.000) as shown in Table 2. Also, the Kaiser-Meyer-Olkin measure is 0.819 which is greater than 0.6. It is suggested that if Bartlett's test of sphericity is significant, and if the Kaiser-Meyer-Olkin measure is greater than 0.6, then factorability is assumed 7. Thus, based on the results, it is appropriate to proceed with Factor Analysis to examine factors that affecting a decision of house owner regarding the green area near their property.
M. Z. Asmawi et al. J Fundam Appl Sci. 2018, 10(5S), 691-704 698 Table 2. KMO and Bartlett s test KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of.819 Sampling Adequacy. Bartlett's Test of Approx. Chi- 1108.576 Sphericity Square df 55 Sig..000 These initial communalities represent the relation between the variable and all other variables before rotation. If many or most communalities are low (<.30), a small sample size is more likely to distort results. Table 3 has listed 11 factors that have the initial communalities are above.30, which is good. Table 3. Communalities Communalities Initial Extraction Attributes2.645.751 Attributes3.621.741 Attributes4.592.684 Price3.613.704 Price4.623.803 Price5.283.321 OpenSpace1.577.646 OpenSpace4.502.530 OpenSpace7.585.649 OpenSpace8.522.563 OpenSpace9.501.555 Extraction Method: Principal Axis Factoring. The Total Variance Explained in table 4 shows that there are three components with initial Eigenvalues more than 1.0. The first component explains 38.307% of the total variance, followed by 22.377 % and 11.968% respectively.
M. Z. Asmawi et al. J Fundam Appl Sci. 2018, 10(5S), 691-704 699 Table 4. Total Variance Explained Total Variance Explained Rotatio n Sums of Squared Extraction Sums of Loading Initial Eigenvalues Squared Loadings % of Cumul % of Fact Tot Varian ative Varia Cumula or al ce % Total nce tive % Total 1 4.2 38.307 38.307 3.84 34.9234.924 3.480 14 2 4 2 2.4 22.377 60.685 2.16 19.7054.628 2.258 62 7 4 3 1.3 11.968 72.653.938 8.52563.153 2.840 16 4.57 5.257 77.909 8 5.55 5.069 82.978 8 6.45 4.172 87.150 9 7.36 3.295 90.445 2 8.30 2.806 93.251 9 9.29 2.645 95.896 1 10.23 2.115 98.011 3 11.21 1.989 100.00 9 0 s a
M. Z. Asmawi et al. J Fundam Appl Sci. 2018, 10(5S), 691-704 700 Extraction Method: Principal Axis Factoring. a. When factors are correlated, sums of squared loadings cannot be added to obtain a total variance. The factor pattern matrix as listed at Table 5 below contain the coefficients for the linear combination of the variables.a total of twenty-four items were eliminated because they did not contribute to a simple factor structure and failed to meet a minimum criterion of having a primary factor loading of.4 or above, and no cross-loading of.3 or above. Table 5. Pattern Matrix Pattern Matrix a Factor 1 2 3 Attributes2.818 Attributes3.856 Attributes4.845 Price3.811 Price4.906 Price5.591 OpenSpace1.749 OpenSpace4.717 OpenSpace7.767 OpenSpace8.803 OpenSpace9.761 Extraction Method: Principal Axis Factoring. Rotation Method: Promax with Kaiser Normalization. a a. Rotation converged in 4 iterations.
M. Z. Asmawi et al. J Fundam Appl Sci. 2018, 10(5S), 691-704 701 3. RESEARCH FINDINGS Based on the sample test, the variable of the model, including a dependent variable and 30 independent variables, they are transaction price, as the dependent variable and independent variables categories into Factor Influence Housing Price (strategic location, size of built up, attractive house design, provision of parking area, good view, adequate infrastructure and utilities, adequate open space, road, and transportation network); Housing Attribute (location attraction, lot type end lot, lot type intermediate lot, lot type corner lot, building condition, construction materials, built up original size, built up after renovation, house age, number of bedroom); and Open Space (easy access, frequency using open space, reason for going open space, availability active activity, availability passive activity, softscape quality, adequacy facility, maintenance open space, location strategic, size adequate, facilities suitable for the users, cleanliness well kept) are using actual figures. Importance of the following factors in influencing the house price Based on the factor extraction, it has been identified following factors has become important decision-making in buying a house which is the size of the built-up area, an attractive house design, and provision of parking area. With the increasing housing price nowadays, it inevitable to choose a compact built up the area where it can reduce the construction material and at the same time it can reduce the housing price. In general, housing buyer chooses a small lot but it must include an appropriate balance of built form, and open space can be found near the residential area. An attractive house design which falls under housing externalities. If housing externalities are pervasive, the price of houses in a neighbourhood, the level of investment and maintenance efforts will depend on the importance of these effects 8. Finally, a provision of parking area has become an important factor when choosing and buying a house. A study by The Nielsen Global Survey of Automotive Demand 9 survey in 2014. They have found that Malaysia has 93% car ownership, placing third in the world. By that, Malaysia also has the highest incidence of multiple car ownership globally with 54% of households having more than one car. Therefore, it is not a surprise, a provision of parking area among the highest factor in choosing their housing. House attributes in influencing the house price When it comes to individual properties, a combination of many different factors determines the prices. The location is still one of the most important factors when it comes to valuing the price - not just the geographical location, but also its proximity to key amenities and facilities. From the analysis, intermediate lot houses have been chosen as one of the highest factors in
M. Z. Asmawi et al. J Fundam Appl Sci. 2018, 10(5S), 691-704 702 buying a house. In general, the intermediate lot has the lowest price while corner lot house is more expensive since it has more land size compared the others. Building condition or property's boiler is one factor which people often fail to deliberate when considering house prices. A faulty furnace can lead to severe and potentially costly problems in the future, and replacing an old boiler that has come to the end of its life is an expense no-one wants to have to deal with. Since homes - and their fixtures and fittings - decay naturally over time, deferred maintenance and avoidance of general upkeep is a common explanation for why some homes sell for less than others 9-10. Importance of open space following factors in influencing the house price Open space should be conceived of as an outdoor room within a neighbourhood, somewhere to relax, and enjoy the urban experience, a venue for a range of different activities including entertainment activities, sports events, and most importantly of all a place for walking or sitting-out 11. Respondents have chosen five characteristics of open space near their residential area. Among the features are there must be easy access to the open space, availability of active activity such as badminton and basketball courts, adequacy of the facility at the open space such as benches and gazebo, maintenance of the open space area and facilities, and finally, open space's location must be strategic. The provision of high-quality open spaces can help to establish the character of a new residential neighbourhood and provide a venue for markets, fairs, or other community gatherings. 4. CONCLUSIONS The research analysed the relationship between the availability of open space and house price in the selected sites. As studied in the literature reviews, it demonstrates that the relationship established in a positive pattern. Meaning to say that, the international experiences suggest that the closer a house to an open space, the more expensive it could be sold. In this regards, the results on the sites also reflect the same pattern of relationship, i.e. positive manner, but unfortunately the strength of the relationship is relatively low. In the local context, the respondents did not relate the aspect of physical (planning requirement) such as the size and location as a priority when they responded to the question of the elements of open space in relation to the housing price. They saw the aspect of park management as the main priority to set the relationship between open space and housing price as they thought that the element of good maintenance would support the quality of open space that affect the housing prices.
M. Z. Asmawi et al. J Fundam Appl Sci. 2018, 10(5S), 691-704 703 5. ACKNOWLEDGMENTS: This work was financially supported by the Ministry of Higher Education under the Fundamental Research Grant Scheme (FRGS) no FRGS/2/2014/SS08/UIAM/02/1. 6. REFERENCES AND NOTES 1. Ayah Abbasi, ChahamAlalouch, Glen Bramley, (2015). Open Space Quality in Deprived Urban Areas: User Perspective and Use Pattern, Procedia - Social and Behavioral Sciences, Volume 216, 6 January 2016, Pages 194-205, ISSN 1877-0428,http://dx.doi.org/10.1016/j.sbspro.2015.12.028. 2. Bourassa, Steven C. &Hoesli, Martin & Sun, Jian, 2006. A simple alternative house price index method," Journal of Housing Economics, Elsevier, vol. 15(1), pages 80-97, March. 3. John L. Crompton (2001). Perceptions of How the Presence of Greenway Trails Affects the Value of Proximate Properties. Journal of Park and Recreation Administration,19(3), 33-51. 4. Sander, H.A. and R.G. Haight. 2012. Estimating the economic value of cultural ecosystem services in an urban area using hedonic pricing. Journal of Environmental Management 113:194-205. 5. Xuyu Wang, 2011. The Application of SPSS in Empirical Research of Housing Hedonic Price. 2011 International Conference on Multimedia Technology, Hangzhou, 2011, pp. 3262-3265. doi: 10.1109/ICMT.2011.6003072 6. Zainora, A. M., Norzailawati, M. N., &Tuminah, P. (2016). A Spatial Analysis on GIS- Hedonic Pricing Model on The Influence of Public Open Space and House Price in Klang Valley, Malaysia, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 829-836, doi:10.5194/isprs-archives-xli-b8-829-2016, 2016 7. J. C. Coakes and C. Ong, SPSS Version 18.0 for Windows Analysis Without Anguish. 1st Edition. Dougall Street, Milton: John Wiley & Sons Australia, Ltd, 2011. 8. Rossi-Hansberg E, Sarte P, and Owens R (2010) Housing externalities Journal of Political Economy 118(3): 829 858. 9. http://www.thestar.com.my/business/business-news/2014/04/16/car-ownership-inmsia-third-highest-in-the-world/#5sydugs8dmrppvcc.99 10. http://www.rightmove.co.uk/hidden-property-issues.html 11. C.W. Thompson (2002). Urban open space in the 21st century. Landscape and Urban Planning 60 (2002) 59 72
M. Z. Asmawi et al. J Fundam Appl Sci. 2018, 10(5S), 691-704 704 How to cite this article: Asmawi M Z, Noor N M, Abdullah A, Paiman T, Abd Aziz A R. Factor analysis on the public open space affecting the housing price in kuala lumpur, malaysia. J. Fundam. Appl. Sci., 2018, 10(5S), 691-704.