The innovation city project was designed to alleviate the concentration of population in the capital region and enhance regional competitiveness. It aims to activate the local economy and promote balanced development through the relocation of public i...
The innovation city project was designed to alleviate the concentration of population in the capital region and enhance regional competitiveness. It aims to activate the local economy and promote balanced development through the relocation of public institutions. In other words, the construction of innovation cities is intended to improve the living environment in rural areas and develop them into innovation-driven economic hubs. Since such large-scale development projects have a significant impact on the real estate prices in the region, it is essential to evaluate these effects objectively.
This study focuses on the spatial analysis of changes in real estate prices around Jeonbuk Innovation City using GIS-based spatial analysis methods. By using spatial pattern analysis and hotspot analysis with GIS, I examined the rate of change of real estate prices. We evaluated the temporal changes of real estate prices in Jeonbuk Innovative City, with special emphasis on the spatial distribution patterns of real estate price changes.
Over a 20-year period, from 2005 to 2024, we analyzed property price fluctuations in 19 intervals to determine the quantitative relationship between the development of the innovative city and changes in property prices. The analysis revealed a strong increase in real estate prices during the project confirmation phase, but the subsequent phases showed a trend that did not reflect the expected values. Overall, while there were positive effects initially, the rate of change slowed or even declined later, indicating the impact of innovative city development on surrounding property price changes.
Next, we used kernel density analysis to examine the distribution and clustering of real estate prices. This analysis showed that we could assess the data regionally rather than relying solely on administrative statistics. In particular, the rate of increase in property prices showed clustering in certain areas, while the rate of decrease showed regional characteristics influenced by government policies on property prices, resulting in concentrated patterns in certain years.
Moreover, the spatial autocorrelation analysis of property price changes confirmed that both the rate of increase and the rate of decrease were clustered. Statistically significant characteristics were found, with a stronger tendency for clustering in the increase rates compared to the decrease rates.
The hotspot analysis of real estate price changes allowed us to derive the types of hotspots and cold spots over time. Mean values showed patterns that closely followed the median, while maximum values provided a more comprehensive understanding of property price changes in the surrounding areas of the innovative city.
Finally, this study established a long-term and localized methodology for monitoring real estate price changes that is applicable to large-scale development projects such as the Innovative City. Therefore, it can serve as a valuable resource for decision-making in real estate price policies and the formulation of regional development strategies. This understanding of the complexity of real estate price changes is expected to make a practical contribution to regional economics and policy-making.