Published at : 17 Dec 2020
Volume : IJtech
Vol 11, No 7 (2020)
DOI : https://doi.org/10.14716/ijtech.v11i7.4481
Mohammed Ali Berawi | Department of Civil Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia |
Nyoman Suwartha | Department of Civil Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia |
Maulindira Elrizqi | Department of Civil Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia |
Gunawan Saroji | Center for Sustainable Infrastructure Development, Faculty of Engineering, Universitas Indonesia |
Mustika Sari | Center for Sustainable Infrastructure Development, Faculty of Engineering, Universitas Indonesia |
Transit-oriented
development (TOD)
areas are being constructed in Indonesia, particularly in Jakarta, since the
issuance of the Regulation of Minister of Agrarian
Affairs and Spatial Planning in 2017. This development has led to an increasing supply
of apartment units in Jakarta, but this is contrary to the
declining residential property sales,
which was at the worst level in 2019. Therefore, this study
aims to determine the factors that influence consumer preferences in buying
residential properties in the TOD area, which serves as the basis for planning
residential apartments in the next TOD area development. This study adopts both
quantitative and qualitative methods through desk study and benchmarking, as
well as questionnaire surveys and fuzzy logic to achieve its objectives. The
results obtained here showed that the order of priority factors
for consumers intending to buy property is financial, property
type, and demographic factors.
Consumers intending to rent property prioritize property type, financial, and
demographic factors. Moreover, the higher one’s income means the lower one’s interest in living in the TOD area. Another
objective of this research is to harness many mobile
internet users in Indonesia, reaching 53% of the total population, to develop a
mobile phone-based application that serves as the platform for sale and
purchase transactions of residential properties in the TOD area. The proposed
application has six features: My Preferences, which provides recommendations for suitable apartment units according to
user preferences; Search My Apartment; My Store; Mortgage Simulation; Payment
Gateway; and Profile Settings.
Customer preferences; Fuzzy rules; Residential property; TOD
Development of the transit-oriented development
(TOD) area is rife in Indonesia, especially in Jakarta, after the issuance of
guidelines for developing TOD areas by the Ministry of Agrarian Affairs and
Spatial Planning in 2017 (Berawi, et
al., 2019c). The rise in the development of TOD areas has
resulted in an increased supply of apartment units in Jakarta, which is
relatively high, especially in 2019 that has increased by 20,234 units (Colliers,
2019). However, this is contrary to the level of sales
that decreased by 5.78% from the previous quarter (Bank
Indonesia, 2019). To encourage the level of residential property
sales in the TOD area, it is necessary to determine in advance consumer preferences
and behavior so that the developments are carried out on target.
Consumer preferences are
individual attitudes towards a series of objects, which are usually reflected
in the decision-making process, based on whether individuals favor an object or
not (Abdullah et
al., 2011). As consumers often optimize their satisfaction
from consuming goods to meeting their daily needs given their income (Krugman and
Wells, 2006),
their preferences to product selections are triggered by many factors, such as
material substance, product brand, ease of instruction for product usage, as
well as legality and recognition by state regulations (Voicu, 2013). Therefore, companies need to involve their
potential customers’ preferences in their product or service development (Chia and
Harun, 2016).
Furthermore, concerning the
development of TOD that aims to increase the ridership of public transportation
(Berawi, et
al., 2020b; Saroji et al., 2020), the government needs to determine the preferences
of potential consumers for the properties in the TOD area. Consumer preferences
in purchasing property, particularly residential properties, are influenced by
various factors, such as location (Kauko, 2007), property feature (Manganelli,
2015), environment (Zhang and Xu,
2017), finance (Xiao and Tan,
2007),
demography (Haddad et
al., 2011), and many more.
The distance between the
property and the city center, schools, business centers, and social facilities
is a significant consideration for consumers in purchasing a property; hence
location is vital (Hei and
Dastane, 2017). Besides, proximity to public transit will
increase land and property values (Berawi et
al., 2020a). On the contrary, property features assessed from
the building designs also play a significant influence. Moreover, the
environmental factors, including the area’s condition and security level around
the property, have never been ruled out in determining property purchases (Zhang and Xu,
2017). In addition, customers consider their ability to
pay (Anastasia,
2015), related to the price, payment, and repayment
methods. Lastly, demographic factors, such as marital and family status, are
essential because the more family members, the more living space will be needed.
Customer preferences are
often expressed in a vague colloquial way at purchase time. Various fuzzy
methods can be considered to develop new and more accurate ways to understand
customers’ product choices based on this kind of information (Barajas and
Agard, 2011). Many studies have been conducted using fuzzy
techniques regarding customer’s behaviors and preferences. There are two
methods to determine and revise the priority of customer demands (Chen et al.,
2004): first, to classify the customers’ demands using
natural language processing techniques to obtain their expectations and second,
to determine the revised priority of the customers’ demands using a fuzzy logic
inference. Kwong et al. (2007) developed a
methodology to determine the significance of engineering characteristics and
their influence using the fuzzy technique, while Földesi et al. (2007) used fuzzy numbers to represent customer
assessments to classify the relationship between customer satisfaction and attribute-level
performance and identify whether or not some of those attributes have a
non-linear relationship with satisfaction.
In the development of the
TOD area, good communication and information exchange must be established so
that all stakeholders can contribute and work together effectively and
appropriately. With the fact that 53% of Indonesians are internet users (Siswoko,
2019), there is an opportunity to develop an online
platform that unites all stakeholders to ensure the sustainability of property
businesses in the TOD area. Previous studies regarding the development of
mobile phone applications in the transportation sector, particularly in
transportation data collection, route planning, traffic safety, ride-sharing,
etc., have been extensively conducted in several countries (Siuhi and
Mwakalonge, 2016). The utilization of information and communication
technology (ICT) in the property business process has also been widely
researched (Najib Razali
et al., 2014).
However, the development of a mobile application for property transactions,
particularly those located in the TOD areas, have not been studied yet.
This study focuses on
developing a mobile phone-based information system using fuzzy rules that
considers the perspective of prospective consumers for residential properties
in the TOD area, providing property unit recommendations for the consumers and
facilitates them in the transaction process.
The results of this study show that there are three main factors influencing consumers in buying or renting residential property in the TOD area: financial factors (property prices and consumers’ income), property type concerning the number of people or family members occupying the property, and consumer demographics. For customers intending to buy residential properties, the order of influential factors is finance, property types, and demographics. For consumers intending to rent an apartment, the order of influential factors is property type, followed by financial and demographics factors.
Considering the high number of mobile phone users in Indonesia, a mobile phone-based application developed to serve residential property transactions for purchasing and renting, particularly in the TOD development area, is an opportunity to leverage the property business process. The mobile application proposed in this study has six main features: My Preferences, Search My Apartment, My Store, & Payment Gateway, Mortgage Simulation, and Profile Settings.
This
research was supported by a research grant from the Ministry of Research and
Technology/National Research and Innovation Agency, Republic of Indonesia,
Contract No. NKB-2661/UN2.RST/HKP.05.00/2020.
Abdullah, F., Abdurahman, A.Z.A., Hamali, J., 2011. Managing Customer Preference for
the Foodservice Industry. International
Journal of Innovation, Management and Technology, Volume 2(6), pp. 525–533
Anastasia,
N., 2015. The Rational and Irrational Factors Underlying Property Buying
Behavior. Journal of Economics and Behavioral Studies, Volume 7(2), pp.
183–191
Bank Indonesia, 2019. Residential Property Development Q3 2019.
Divisi Statistik Sektor Rill, Departemen Statistik?, Bank Indonesia. Available Online
at
https://www.bi.go.id/en/publikasi/survei/harga-properti-primer/Pages/SHPR-Tw.III-2019.aspx,
Accessed on July 28, 2020
Barajas,
M., Agard, B., 2011. Selection of Products based on Customer Preferences
Applying Fuzzy Logic. International Journal for Interactive Design and
Manufacturing, Volume 5(4), pp. 235–242
Berawi,
M.A., Aprianti, L., Saroji, G., Sari, M., Miraj, P., Kim, A.A., 2020a. Land
Value Capture Modeling in Residential Area using Big Data Approach Method. Engineering
Journal, Volume 24(4), pp.
249–259
Berawi,
M.A., Leviakangas, P., Muhammad, F., Sari, M., Gunawan., Yatmo, Y.A.,
Suryanegara, M., 2019a. Optimizing Search and Rescue Personnel Allocation in
Disaster Emergency Response using Fuzzy Logic. International Journal of
Technology, Volume 10(7), pp. 1416–1426
Berawi,
M.A., Saroji, G., Iskandar, F.A., Ibrahim, B.E., Miraj, P., Sari, M., 2020b.
Optimizing Land Use Allocation of Transit-Oriented Development (TOD) to
Generate Maximum Ridership. Sustainability, Volume 12(9), pp. 3798–3818
Berawi,
M.A., Suwartha, N., Salsabila, F., Gunawan., Miraj, P., Woodhead, R., 2019b.
Land Value Capture Modeling in Commercial and Office Areas using a Big Data
Approach. International Journal of Technology, Volume 10(6), pp. 1150–1156
Berawi,
M.A., Wicaksono, P.L., Gunawan., Miraj, P., Rahman, H.A., 2019c. Life Cycle
Cost Analysis of the Transit-Oriented Development Concept in Indonesia. International
Journal of Technology, Voulme 10(6),
pp. 1184–1193
Chen,
C.Y., Chen, L.C., Lin, L., 2004. Methods for Processing and Prioritizing
Customer Demands in Variant Product Design. IIE Transactions, Volume
36(3), pp. 203–219
Chia,
J., Harun, A., Mohd Kassim, A.W., Martin, D., Kepal, N., 2016. Understanding
Factors that Influence House Purchase Intention Among Cunsumers in Kota
Kinabalu: An Application of Buyer Behavior Model Theory. Journal of
Technology Management and Business, Volume 3(2), pp. 1–257
Colliers,
2019. Property Market Report | Q1 2019 Jakarta Apartment. Available
Online at
https://www2.colliers.com/en-id/research/colliers-quarterly-property-market-report-q1-2019-jakarta-apartment,
Accessed on July 29, 2020
Demšar,
J., Zupan, B., Leban, G., Curk, T., 2004. Orange: From Experimental Machine
Learning to Interactive Data Mining. Lecture Notes in Computer Science,
Volume 3202, pp. 537–539
Földesi,
P., Kóczy, L.T., Botzheim, J., 2007. Fuzzy Extension for Kano’s Model using
Bacterial Evolutionary Algorithm. In: ISCIII’07: 3rd International
Symposium on Computational Intelligence and Intelligent Informatics, pp.
147–151
Haddad,
M., Judeh, M., Haddad, S., 2011. Factors Affecting Buying Behavior of an
Apartment an Empirical Investigation in Amman, Jordan. Research Journal of
Applied Sciences, Engineering and Technology, Volume 3(3), pp. 234–239
Hei,
C.P., Dastane, O., 2017. Buying a Dream Home - Considerations of Residential
Property Consumers in Malaysia. Singaporean Journal of Business Economics
and Management Studies, Volume 5(9), pp. 19–35
Jabareen,
Y., 2005. Culture and Housing Preferences in a Developing City. Environment
and Behavior, Volume 37(1),
pp. 134–146
Kamal, M., Pramanik, S.A., 2015. Customers’ Intention towards Purchasing Apartment in Dhaka
City, Bangladesh: Offering an Alternative Buying Intention Model. Australian
Journal of Business Science Design & Literature, Volume 08(35), pp. 24–32
Kauko,
T., 2007. An Analysis of Housing Location Attributes in the Inner City of
Budapest, Hungary, using Expert Judgements. International Journal of
Strategic Property Management, Volume 11(4), pp. 209–225
Khaled,
M.C., Sultana, T., Biswas, S.K., Karan, R., 2012. Real Estate Industry in
Chittagong (Bangladesh): A Survey on Customer Perception and Expectation. Developing
Country Studies, Volume 2(2),
pp. 38–45
Koeri,
R., 2016. A Study of Factors Influencing Customer Choice Decision in Renting
Apartment in Bangkok. Master of Business Administration, Graduate Program,
Bangkok University, Bangkok, Thailand. Available Online at https://pdfs.semanticscholar.org/7841/a64e4024a0299b7dfd2ed4b3e5651785cb5b.pdf
Krugman,
P., Wells, R., 2006. Consumer Preferences and Consumer Choice. In: Economics, pp. 253–280
Kwong,
C.K., Chen, Y., Bai, H., Chan, D.S.K., 2007. A Methodology of Determining
Aggregated Importance of Engineering Characteristics in QFD. Computers &
Industrial Engineering. Volume 53(4), pp. 667–679
Manganelli,
B., 2015. Real Estate Investing: Market Analysis,
Valuation Techniques, and Risk Management. Springer
Mu,
R., de Jong, M., 2012. Establishing the Conditions for Effective
Transit-Oriented Development in China: The Case of Dalian. Journal of
Transport Geography. Volume 24, pp. 234–249
Najib Razali, M., Abdul Rahman, R., Mohd Adnan, Y., Mohd. Yassin, A., 2014. The Impact of Information and Communication
Technology on Retail Property in Malaysia. Property Management, Volume
32(3), pp. 193–212
Opoku,
R.A., Abdul-Muhmin, A.G., 2010. Housing Preferences and Attribute Importance
Among Low-Income Consumers in Saudi Arabia. Habitat International, Volume 34(2), pp, 219–227
Saroji, G., Berawi, M.A., Sumabrata, J., Ibrahim, B.E.,
Miraj, P., 2020. Creating Added Value for Urban
Transit in Developing Country: A Case Study of Transit-Oriented Development
Project. Engineering Journal, Volume 24(4), pp. 33–47
Siswoko.,
2019. Dinamika Data Aplikasi Informatika 2019 (Dynamics of Informatics
Application Data 2019). Hilos Tensados, Volume 1, pp. 1–476
Siuhi,
S., Mwakalonge, J., 2016. Opportunities and Challenges of Smart Mobile
Applications in Transportation. Journal of Traffic and Transportation
Engineering (English Edition), Volume 3(6), pp. 582–592
Tan,
T.H., Cheah, Y.Y., 2013. Locational, Neighborhood, Structural and
Socio-Cultural Attributes of Housing in Homeownership Decisions. Journal of
Chemical Information and Modeling, Volume 53(9), pp. 1689–1699
Tangirala,
S., 2020. Evaluating the Impact of GINI Index and Information Gain on
Classification using Decision Tree Classifier Algorithm. International
Journal of Advanced Computer Science and Applications, Volume 11(2), pp. 612–619
Voicu,
M.C., 2013. Characteristics of the Consumer Preferences Research Process. Global
Economic Observer, Volume 1(1),
pp. 126–134
Xiao,
Q., Tan, G.K.R., 2007. Signal Extraction with Kalman Filter: A Study of the
Hong Kong Property Price Bubbles. Urban Studies, Volume 4(4), pp.
865–888
Zeng,
R., 2013. Attributes Influencing Home
Buyers’ Purchase Decisions?: A Quantitative Study of the Wuhan Residential
Housing Market. Master's Thesis,
Southern cross University, Lismore,
NSW
Zhang, M., Xu, T., 2017. Uncovering the Potential for Value Capture from Rail Transit Services. Journal of Urban Planning and Development, Volume 143(3)