Accurate house price forecasts are essential for efficient policy-making,investment, and risk management of mortgage loan. Nevertheless, there arefew empirical studies on the Korean house price prediction. This seems to bebecause of the large number of variables. In this study, we provide a newBayesian variable selection method for the Seoul apartment price indexforecasting, considering the uncertainty of the variables. To do this, weextend the standard Bayesian variable selection by using a more flexible andinterpretable spike-and-slab prior. This method consists of two stages:variable selection and predictive density simulation. According to ourout-of-sample forecasting experiment, the proposed model outperforms thestandard variable selection method and first-order auto-regressive (AR(p)model in the medium- and long-term horizons. Meanwhile, the AR(p) isfound to be the best for the s