Social media sentiment and house prices: Evidence from 35 Chinese cities (with Martin Berka) Click here for the latest version

Abstract: We develop a new social media sentiment index by quantifying the tone of social media posts on Weibo related to housing between 2010 and 2020 across 35 largest cities in China. We find that the social media sentiment index significantly predicts house price changes for up to six quarters ahead, after controlling for the economic fundamentals. A 1% increase in an accumulated social media sentiment index results in an 0.81% increase in the house price inflation the following quarter, ceteris paribus. Our results cannot be explained by the announced policy changes, unobserved fundamentals and survive a battery of robustness checks. They instead give support to theories of social learning as well as animal spirits.

Rezoning and housing price: Evidence from Beijing, China

Abstract: We study the impact of the merger of suburban merged into urban districts, using evidence from Beijing. We use a Difference-in-Differences method and panel data to estimate the impact of the merger on housing prices in both rezoned and non-rezoned areas. Our results suggest that the merger led to a significant increase in housing prices in the rezoned areas, while the opposite happened in the non-rezoned border districts. In both cases, the impact is localized. Additionally, we see the merger hurts poor people, as the decline in housing prices is larger for low-priced properties in non-rezoned border districts, while the increase in housing prices is smaller for low-priced properties in rezoned areas. Furthermore, our results suggest that the merger had a positive spillover effect in surrounding counties, with the effect decreasing as the distance to the rezoned districts increased. Our findings are important in understanding the impact of place-based policies on housing and their potential spillovers.

Illuminating the effects of the US-China tariff war on China's commercial rental market

Abstract: We study the impact of the US-China tariff war on China's 34 prefectures' commercial building rents. By utilizing a Bartick-style tariff exposure, we find a one standard deviation increase in the U.S. tariff exposure measure is associated with a 0.41 standard deviation decrease in commercial building rent growth after one year, ceteris paribus. When allowing for dynamic coefficients, we find such negative impact become significant in the second half of 2018, when the U.S. initiated a series of tariff actions specifically targeting China, under Section 301. We also identify heterogeneity in rent response, with high U.S. dependence amplifying the negative impact and better financial situations, social stability, innovation and location mitigating it. Robustness checks confirm the stability of our findings. This research contributes to the literature on the indirect effects of trade policy changes and provides insights for firms and government policymakers in addressing the challenges posed by trade wars.