貝氏多層次模型在台灣不動產市場估價之應用─以台北市住宅建物為例An Application of Bayesian Inference in the Real Estate Market – A Case Study of Taipei Collective Housing
How to estimate housing prices precisely has always been an important issue in the real estate market. Most studies adopt parametric or non-parametric methods to deal with problems such as heteroskedasticity or non-monotonic phenomena which come from less influential attributes or from characteristics which can not easily be realized. Researchers have attempted to adopt certain methods such as non-parametric methods to recover from these failures but they still do not work well. This paper therefore tries to re-examine the issue of heteroskedasticity in the housing price model. By using data for collective housing-type buildings in Taipei, this study employs the Hierarchical Bayesian model to bridge the relationship between attributes and housing prices. By means of a random effect device, the location effect gives rise to a non-monotonic effect on regressors that affect housing prices. Besides, capturing the heteroskedasticity effects results in the Bayesian model providing a better estimation than OLS.
Key words: hedonic equation, Bayesian inference, Markov Chain Monte Carlo