Chinese Housing Market Sentiment Index: A Generative AI Approach and An Application to Monetary Policy Transmission

Kaiji Chen and Yunhui Zhao

No 2024/264, IMF Working Papers from International Monetary Fund

Abstract: We construct a daily Chinese Housing Market Sentiment Index by applying GPT-4o to Chinese news articles. Our method outperforms traditional models in several validation tests, including a test based on a suite of machine learning models. Applying this index to household-level data, we find that after monetary easing, an important group of homebuyers (who have a college degree and are aged between 30 and 50) in cities with more optimistic housing sentiment have lower responses in non-housing consumption, whereas for homebuyers in other age-education groups, such a pattern does not exist. This suggests that current monetary easing might be more effective in boosting non-housing consumption than in the past for China due to weaker crowding-out effects from pessimistic housing sentiment. The paper also highlights the need for complementary structural reforms to enhance monetary policy transmission in China, a lesson relevant for other similar countries. Methodologically, it offers a tool for monitoring housing sentiment and lays out some principles for applying generative AI models, adaptable to other studies globally.

Keywords: Chinese Housing Market Sentiment; Generative AI; Monetary Policy Transmission; Consumption; Crowding-Out; sentiment index; housing sentiment; C. predicting housing price; housing market sentiment; Housing prices; Housing; Mortgages (search for similar items in EconPapers)
Pages: 68
Date: 2024-12-23
New Economics Papers: this item is included in nep-ain, nep-big, nep-cba, nep-cmp, nep-cna, nep-mon and nep-ure
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