• International Journal of Technology (IJTech)
  • Vol 12, No 6 (2021)

An Analysis of Implicit Value of Property Characteristics in Residential Property Prices using a Hedonic Value Approach

An Analysis of Implicit Value of Property Characteristics in Residential Property Prices using a Hedonic Value Approach

Title: An Analysis of Implicit Value of Property Characteristics in Residential Property Prices using a Hedonic Value Approach
Zulkarnain, Tania Arvianti

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Cite this article as:
Zulkarnain, Arvianti, T., 2021. An Analysis of Implicit Value of Property Characteristics in Residential Property Prices using a Hedonic Value Approach. International Journal of Technology. Volume 12(6), pp. 1168-1176

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Zulkarnain Department of Industrial Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia
Tania Arvianti Department of Industrial Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia
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Abstract
An Analysis of Implicit Value of Property Characteristics in Residential Property Prices using a Hedonic Value Approach

Residential property prices increase every year without an equivalent increase in people’s purchasing power. To suppress these price fluctuations, the government has implemented the land banking concept, for which quantitative knowledge about factors affecting property prices could optimize its implementation. Therefore, this study aims to identify the implicit value and influence of public facilities and landscapes located around residential properties. The implicit value can be identified by taking a hedonic value approach, which is used to estimate the value of residential properties by considering the distance to public facilities and landscapes in Jakarta. The regression methods used are multiple linear regression (MLR), quantile regression (QR), and support vector regression (SVR). This study found that MLR offered the best accuracy (R2 = 0.798), followed by QR (R2 = 0.687), and SVR (R2 = 0.563). The study also found that the proximity of residential properties to Mass Rapid Transit (MRT) stations, green open spaces, reservoir lakes, health service facilities, vocational schools, marketplaces, high schools, and mosques has a significant influence on residential property prices, with MRT stations, health service facilities, marketplaces, and high schools conveying the largest implicit value.

Hedonic price method; Implicit value; Ordinary least square; Quantile regression; Support vector regression

Introduction

In addition to food (including water) and clothing, shelter is one of the three basic needs of human beings. However, every year in Indonesia, there is an increase in residential property prices that is not followed by an increase in people’s purchasing power. In 2020, approximately ten million households did not own private housing, despite members of generation X (born in 1965–1980) having been of a productive age for a relatively long time. Therefore, the issue of residential property ownership in Indonesia is deemed critical.

To suppress fluctuations in residential property prices, the Indonesian government has implemented the concept of land banking, by which the government buys and invests in land for future infrastructure development. To optimize the land banking concept, it is necessary to identify factors that significantly affect changes in land and/or residential property prices. However, in Indonesia, few studies have been conducted to identify these factors using a quantitative approach. One way to quantitatively identify the implicit values of these factors is to take the hedonic price approach to consider residential property prices.

The hedonic price model has been used in various studies to analyze the implicit marginal value of a particular characteristic. The basis of the hedonic price approach is the consumer demand theory formulated by Lancaster in 1966. Based on the theory, Lancaster showed how consumers see products as a group of characteristics. The characteristics of the product determine whether the consumer prefers to buy a particular product or not. In other words, consumer preferences can be represented as the fulfillment of customer satisfaction based on the characteristics of the purchased products (utility). In Lancaster’s theory, consumers receive the characteristics they are aiming for by purchasing products or services with the desired characteristics. When a market price is observed, the price represents several characteristics grouped together, making it impossible to know the specific value of each characteristic (Thomsen, 2021). An approach was later developed by Rosen (1974) to determine the implicit value contained in each characteristic of a product, which is known as the hedonic price model. Rosen’s theory involves two steps. First, the hedonic equation is estimated. Then, the price or implicit value of a characteristic is yielded by the derivative of the hedonic equation generated. The hedonic value of an object is found by representing different levels of characteristics, each with their own numeric values, to further constitute the price of the product. The hedonic value approach can be implemented to obtain the implicit marginal value of a characteristic (Herath and Maier, 2010).

Against this backdrop, the current research is carried out to quantitatively identify previously unidentified public amenities and landscapes that are closely related to changes in residential property prices. In Indonesia, especially in Jakarta, such quantitative research has not yet been conducted. This research also compares three regression methods, namely multiple linear regression (MLR), quantile regression (QR), and support vector regression (SVR), to obtain the best model to represent the condition in Jakarta. The variables included in this research are derived from several previous studies adjusted to the existing situation in Jakarta. Therefore, this research aims to generate an exploratory model to analyze the relationship between public amenities or landscapes and residential properties, as well as the influence of these features on changes in residential property prices in the Jakarta area.

Conclusion

Analyzing the implicit value of property characteristics using the hedonic price method can be implemented by employing several regression methods, such as MLR, QR, and SVR. This study found that the MLR model gave the best accuracy (adjusted R2 = 0.798) when compared to the other two, followed by QR (adjusted R2 = 0.687) and SVR (adjusted R2 = 0.563), consecutively. Despite the accuracy differences, the three methods yielded similar results regarding the property characteristics that significantly affected residential property prices. An increase in residential property prices was found to be influenced by close proximity between the property and MRT stations, healthcare facilities, marketplaces, and high schools. By contrast, a decrease in price was caused by close proximity between the property and green open spaces, reservoir lakes, vocational schools, and mosques.

The results yielded by the MLR model indicate that the public facilities with the highest implicit value are MRT stations, healthcare facilities, marketplaces, and high schools, respectively. These implicit values imply that residents of DKI Jakarta value these facilities the most. Hence, land banking can be implemented to suppress the residential property price fluctuations in lots located near these four public facilities in particular. However, further research must be conducted to develop and provide validation for the hypotheses of this research.

Acknowledgement

    The authors would like to express appreciation and gratitude to the Directorate of Research and Development Universitas Indonesia for funding this study through PUTI Q1 Research Grants Universitas Indonesia No: NKB- 1433/UN2.RST/HKP.05.00/2020.

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