• International Journal of Technology (IJTech)
  • Vol 11, No 7 (2020)

Developing Mobile Application for Residential Property Business in Transit-Oriented Development Areas

Developing Mobile Application for Residential Property Business in Transit-Oriented Development Areas

Title: Developing Mobile Application for Residential Property Business in Transit-Oriented Development Areas
Mohammed Ali Berawi, Nyoman Suwartha, Maulindira Elrizqi, Gunawan Saroji, Mustika Sari

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Cite this article as:
Berawi, M.A., Suwartha, N., Elrizqi, M., Saroji, G., Sari, M., 2020. Developing Mobile Application for Residential Property Business in Transit-Oriented Development Areas. International Journal of Technology. Volume 11(7), pp. 1348-1358

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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
Email to Corresponding Author

Abstract
Developing Mobile Application for Residential Property Business in Transit-Oriented Development Areas

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

Introduction

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.

Conclusion

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.

Acknowledgement

       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.

References

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, 219227

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)