• Vol 10, No 6 (2019)
  • Civil Engineering

Land Value Capture Modeling in Commercial and Office Areas using a Big Data Approach

Mohammed Ali Berawi, Nyoman Suwartha, Fathiya Salsabila , Gunawan , Perdana Miraj , Roy Woodhead

Corresponding email: maberawi@eng.ui.ac.id


Cite this article as:
Berawi, M.A., Suwartha, N., Fathiya Salsabila., Gunawan., Perdana Miraj., Woodhead, R., 2019. Land Value Capture Modeling in Commercial and Office Areas using a Big Data Approach. International Journal of Technology. Volume 10(6), pp. 1150-1156
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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
Fathiya Salsabila Department of Civil Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia
Gunawan Department of Civil Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia
Perdana Miraj Department of Civil Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia
Roy Woodhead Business Operation Systems, Sheffield Business School, Sheffield Hallam University, 38 - 40 Howard Street, Sheffield S1 1WB, United Kingdom
Email to Corresponding Author

Abstract
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Infrastructure development in Indonesia creates massive impacts on the economy. The Light Rail Transit (LRT) of the greater Jakarta (Jabodebek) project has an estimated cost of more than 29 trillion rupiahs due to land acquisition and route planning. The urban transit development may impact to the price of property including residential, commercial and offices along the route. This research aims to determine variables affecting the price elasticity of property and the correlation to station proximity. Data mining through web scrapping was used to assess the degree of correlation between price elasticity and station location. The result shows that approximately 13% of commercial property was spread over a distance of 1 km from the LRT station. The closer a property to transit station, the more likely a price will be twice as cheap compared to those further away. The findings also show variables that highly contribute to property prices, including schools and hospitals, linked to proximity  of some  transit stations located in city center of Jakarta and building density.

Commercial property; Data mining; Land value capture; LRT; Transit-oriented development

Introduction

Various types of infrastructure are included in the government’s long-term projects to create investments and equitable development in Indonesia, increasing demand on the state budget. Infrastructure development recently intensified by the government has been in the rail-based public transportation sector (Berawi et al., 2018a; Rahman et al., 2018). The existing modes of rail transportation in Indonesia are still quite limited, so to expand the network, Mass Rapid Transit (MRT) and Light Rail Transit (LRT) systems have been designed. This paper discusses the LRT project and its construction phases, which have required a total of 29.9 trillion rupiahs originating from the state budget and PT Adhi Karya, an infrastructure development company. Presently, phase A of the construction process is being carried out by Adhi Karya and is expected to be completed in the middle of 2019. In this first phase, the project financing is fully provided by the government. Primary and secondary data are taken to calculate the effects of development on property values around the available stations along the route. This calculation is intended to determine how much the construction of LRT infrastructure influences property price increases in the catchment area, thereby creating valuable insights for the government. Value capture is considered in the affected areas by constructing an LRT station as part of the project, and estimating resultant property values.

Land value capture is a public financing technique that captures a fraction of price increases from new public investments and taxes on property or required contributions for repairs. Several cities in the world have used Transit Oriented Development (TOD) to formulate policies and strategies for urban development (Suzuki et al., 2015; Berawi et al., 2019). To carry out value capture, several implementation methods are used, such as property-based developments and others relying on taxes or fees charged on property affected by  TOD (Zhang & Xu, 2017; Cordera et al., 2019). Property values are affected by infrastructure development, especially rail and its characteristics including physical attributes, location, and environment. The impacts from rail infrastructure are diverse and range from insignificant and negative influences to significant and positive attributes (Debrezion et al., 2007; Fahirah 2010). Railway investment is expected to support more compact urban structures and, therefore, serves planning purposes (Fejarang 1993; Mu & de Jong, 2012).

Physical attributes are among the most influential factors in increasing property values and include factors that directly affect land values. Generally, the physical attributes that affect property prices are divided into four categories: accommodation, materials used, age, and structural conditions (Goldberg, 1981; Zhang & Xu, 2017). Accommodation further affects the value of land prices due to variations in land uses. Based on various studies conducted, therefore, the number of rooms positively and significantly affects property prices, and prices rise with an increasing number of rooms (Listiyarko & Ennoch, 2014).

According to Fejarang (1993), property prices are strongly influenced by the characteristics of attractive locations. In other cases, the Central Business District (CBD), which is the center of many urban activities, is also considered to be an attractive quality capable of increasing property prices (Berawi et al., 2018b; Malaitham et al., 2018). Environmental attributes also affect the quality of life of the people utilizing property. However, the quality of life usually depends on the level of comfort of the persons inside and is measured based on land used for commercial or residential purposes with varying results (Medda, 2012).

Several studies have shown that the facilities around properties impact the growth of property values. Zhang and Xu (2017) carried out research in Wuhan, China with data collected in 2015 on two MRT corridor lines, 1,604 houses, 678 commercial units, and 844 office units. By taking such variables, this research considers commercial roadsides, streets, shopping malls, and different areas. The research will also take into account three types of property based on its proximity location. However, the catchment area for commercial property was only cover 300 m from transit station (Haider & Miller, 2000).

Conclusion

Property characteristics have varied impacts on property prices. Based on the benchmarking process taking into account similar TOD project in Wuhan, China, several variables had significant impacts on property prices including the CBD, main road, and schools. However, TOD in Bangkok, Thailand, indicated that distance from public transportation had no significant impacts on property prices and was replaced by job access, density of  commercial properties, distance from main roads, distance from the station, time to the CBD, and percentage of vacant building-space. TOD in Rome shows similar result to Wuhan where the location had more positive impacts on property prices than access to transportation, while building-age had negative impacts.

This indicates that property prices depend on the location of TOD and variables included on the hedonic price modeling should be adjusted based on the context of case study. This research has identified significant property characteristics based on case studies in Jakarta, Indonesia. High-density property and proximity a station of Dukuh Atas were positively correlated to property prices

Acknowledgement

This research was supported by research grants from the Universitas Indonesia and the Ministry of Research, Technology and Higher Education, Republic of Indonesia through Hibah Penelitian Dasar Unggulan Perguruan Tinggi (PDUPT) 2019 (No. NKB-1656/UN2.R3.1/HKP.05.00/2019).

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