Exploring spatiotemporal dynamics in a housing market using the spatial vector autoregressive Lasso: A case study of Seoul,Korea

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.