Understanding diverse characteristics of human mobility provides profound knowledge of urban dynamics and complexity.Human movements are recorded in a variety of data sources and each describes unique mobility characteristics. Revealing similarity and difference in mobility data sources facilitates grasping comprehensive human mobility patterns. This study introduces
House prices fluctuate spatiotemporally and when influential changes from a region happen, the effects spread out in space over time. Although many studies have introduced various models to explain the spatiotemporal dynamics in housing markets, it is always challenging to consider both dimensions in a model. Some recent studies have identified spatiotemporal interactions of house prices by combining spatial and temporal models via spatial vector autoregression. The approach, however, assumes spatial homogeneity of the variables due to insufficient degrees of freedom. Since the housing market is generally conceived as heterogeneous, we suggest an alternative model of the spatial vector autoregressive Lasso without the homogeneity assumption. As an empirical example, we examine the spatiotemporal interaction between house sales price and rent in Seoul, Korea. The results show that rent for apartments in Gangnam‐gu, a socioeconomic core of Seoul, has positive impacts on rent for apartments in surrounding suburbs rather than their sales price. Moreover, the suggested model outperforms the classical method in terms of explanation, prediction, and autocorrelation of residuals. This research is expected to provide a methodological guide to explore the interaction between house sales price and rent, and insights into the spatiotemporal dynamics of the housing market in Seoul.
This paper analyzes the spatio-temporal interaction patterns of the house sales and rent price using SpVAR model and evaluate accuracy of estimation and prediction of suggested model, compared with panel VAR model. For the case study, we analyzed the house sales and rent price by housing types for 65 sigungu of the Seoul metropolitan area. The result shows that the 1-period former neighboring rental price of apartment has effects on all types of rental prices, whereas the sale price of apartment has little influences. Especially, the increase of apartment rental prices for a particular area substantially impacts multi-family housing rental prices for neighboring areas. This result empirically supports the spillover effect of the house sales and rent price. Also, the SpVAR model predicted the direction of the house price variation more accurately than the non-spatial VAR model. This means that the spatial association in house prices by housing type plays an important role in determining sub-market house prices, and the house price of a particular housing type influences other house prices in neighboring areas.
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