Determining the factors affecting housing values in Indonesia using hedonic price analysis

The real estate sector is an important part of the economy, both as a provider of basic human needs and as an investment asset. One of the challenges in the real estate sector is to gain an in-depth understanding of the factors affecting house prices, as these factors are determined not only by the physical characteristics of the dwelling, but also by regional and environmental factors. Therefore, a comprehensive analysis is required to identify the main variables affecting house prices. House prices are usually analyzed through Hedonic Price Analysis ( HPA), which helps to identify the effects of the characteristics of the housing. This research aims to analyze the factors that influence house prices in Indonesia. This approach allows to identify the specific contribution of each building feature, region and environment to the house price. The research adopts a quantitative approach using log-dog based HPA and transforms the house price into natural logarithm form as the dependent variable. Data are collected from surveys and official sources and analyzed on 19 independent variables. The research was conducted in Pattallassang District, Gowa City, South Sulawesi Province, Indonesia with a sample of 120 houses. The sample was selected based on data availability and variation in house prices in the area. House price data are collected in Rupiah (Indonesian currency) and then converted into Turkish Lira (TL) according to international standards to ensure consistency in the analysis methodology. The results of the analysis show that 11 of the 19 independent variables tested have a significant effect on house prices. These significant variables include building age, plot size, building size, air conditioning presence, presence of garden, presence of kitchen, number of rooms, number of bathrooms, presence of security guard, distance to city center and distance to bus station. Among these variables, the presence of a garden has the largest effect and shows the highest correlation coefficient of 0.249. This emphasizes that green areas are one of the primary factors for housing choice. On the other hand, the other 8 variables such as distance to education, health, commerce and recreation areas do not show a significant effect on house prices. In order to make the results more representative, it is recommended to expand the study area and analyze the dynamics of house prices by including additional variables such as market trends, socio-economic conditions and other data that change over time.