【报告摘要】 This study examines whether price spreads, the differences between list and sale prices of houses, predict the cross-section of real estate returns. Theoretical analysis suggests that price spreads reflect adverse selection costs, thus potentially serving as predictors of real estate returns. We develop a general approach for cross-sectional prediction, along with a novel evaluation method based on the Mallows permutation model. Our empirical findings indicate that price spreads positively predict cross-sectional real estate returns. Incorporating price spreads significantly improves forecasting accuracy and significance, especially in long-term predictions. Additionally, we propose a five-factor model that includes a price spread factor, showing better performance than existing models in pricing real estate assets. Our research contributes to understanding the influence of microstructure variables on housing prices and adds to the literature on the cross-sectional predictability of returns.

【报告人简介】乔柯南，中国科学院数学与系统科学研究院副研究员。博士毕业于中国科学院数学与系统科学研究院/格罗宁根大学。主要研究方向包括资产定价、市场微观结构。研究成果发表于Journal of Financial Markets、Quantitative Finance等国际期刊。