Research on Submarket Effects of Real Estate Valuation Based on Bayesian Probability Model. A Comparison Between Cities

Abstract

Submarket effects are essential for real estate valuation since they could be used to increase both the prediction accuracy of housing prices and the interpretability of the machine learning model. In this paper, a Bayesian probability model that divides the housing market based on the housing location is proposed to forecast house prices, and discover key factors in house prices. A comparison of the key influencing factors affecting the real estate market in Hangzhou and in Chengdu is provided. The experimental results show that the key influencing factors in corresponding functional areas of different cities are similar, which sheds a light on creating a unified model for the real estate valuation.