Abstract
This study examined the price spillover effect of housing submarkets in cities in the Seoul metropolitan area in South Korea by using the Granger causality test and vector autoregressive model (VAR). We found that housing prices showed a higher spillover effect within regions with similar housing market characteristics. Additionally, the spatial spillover of housing prices revealed a difference between sales price and jeonse price. The spillover of jeonse price was characterized by mutual influence among neighboring cities, while that of sales price was characterized by the influence being transferred in one direction hierarchically. Furthermore, the effects of housing price indicated a slight difference between sales price and jeonse price. Although jeonse price was mainly affected by a neighboring area (geographic boundary), sales price was more influenced by the city with the highest housing prices. Lastly, the housing price spillover tended to be expansive around the city with the highest price. These results suggest that housing price policies targeting specific regions or areas in Korea must be differentiated according to the type of occupancy (jeonse or sales), and it is essential to consider the externalities when promoting policies in the housing market wherein externalities may be significant.
Highlights
We empirically analyzed the price spillover effect of the housing sales market and the jeonse market using the Granger causality analysis and the vector autoregressive model (VAR) to target individual cities in the Gyeongeui region of Gyeonggi-do and the Northwest region of Seoul, wherein the price spillover of the Korean housing market can be examined in detail because these regions have unique submarket characteristics and form subordinate relationships
The spatial spillover of housing prices varied between jeonse market and sales market
The jeonse price spillover was characterized by mutual influence between neighboring cities, while the sales price was characterized by the influence being transferred in one direction hierarchically
Summary
There are several methods to classify housing markets, such as attributional methods and data-driven methods [1,2,3]. The former are typically based on physical characteristics (such as spatial and political boundaries), type of house (e.g., apartment and detached house), and socioeconomic characteristics (such as housing price, consumer housing preferences, income, and race). The latter are mainly based on data algorithms and hierarchical clustering [4,5]
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