Geospatial Analysis of Virtual Property Prices Distributions and Clustering - Dwi Sugianto; Andhika Rafi Hananto (International Journal Research on Metaverse, 2024)
This paper presents an analysis of property prices in the virtual world, focusing on geographical distribution and district comparisons. Utilizing a dataset of virtual properties, we applied scatter plot analysis, cluster analysis using DBSCAN, and box plot comparison to identify key patterns and opportunities within this market. The scatter plot analysis revealed that property prices are unevenly distributed, with higher prices clustering in specific regions, indicating areas of higher desirability and value. The DBSCAN clustering identified distinct high-value clusters, each containing 10 to 67 properties, and highlighted 1,067 properties as noise, suggesting a dispersed distribution of lower-value properties. Box plot comparisons across districts showed significant variations in property values. Some districts exhibited higher median prices, with the highest at 35,452.60 MANA, while others had lower medians. Variability within districts varied, with some showing a wide range of prices and others more uniform values. Outliers suggested unique investment opportunities in both premium and undervalued properties. For virtual real estate investors, the findings emphasize the importance of location and strategic investment. High-value districts and emerging areas offer potential for significant returns. Developers and urban planners can use these insights to focus on high-demand areas, enhancing project value through strategic investments in infrastructure and amenities. This study highlights the dynamic nature of the virtual real estate market and the importance of ongoing research to understand factors influencing property values. Stakeholders can make informed decisions and capitalize on opportunities in this evolving market.