Land Value Capture Modeling in Residential Area Using Big Data Approach Method

Abstract

The increase in the number of train ridership in Jakarta induced the government in constructing more rail infrastructures for example Light Rail Transit as a new option. However, this project is not supported by sufficient funds. Therefor new funding sources are needed to include all process which comprises of development, operation, and maintenance. Land Value Capture modeling is one of such solutions because it refers to the idea that all portions of land which arises from the presence of public infrastructure need to be returned. This research was carried out to obtain hedonic price modeling as a basic reference in capturing land values, by identifying variables that affect the increase of residential property prices obtained using big data approach and web scraping techniques. The result acquired from scraping Lamudi.co.id and Rumah.com websites was 1,237 properties located in South Jakarta. Furthermore, by plotting ArcGIS, 105 data located in catchment areas about 1 km from LRT line Dukuh Atas-Cawang station were obtained following Transit-Oriented Development (TOD) standard. According to the calculated SPSS, the increase in residential property value was approximate to the mall and CBD area with significant values of 0.845 and 0.819, which indicate a highly potent correlation.