Bayesian Forecasting of China’s Housing Prices: Dynamic Factor Identification and Predictive Evaluation

• 발행기관: 한국계량경제학회
• 저자명: Xia Gao
• 간행물 정보: 『JOURNAL OF ECONOMIC THEORY AND ECONOMETRICS』Vol.36 No.3, 61~105쪽, 전체 45쪽
• 주제분류: 경제경영 > 경제학
• 파일형태: PDF
• 발행일자: 2025.09.30

국문 초록

This paper applies a Bayesian Variable Selection (BVS) framework to forecast the year-on-year growth rate of China’s newly built housing price index (HPI YoY). Using a broad set of macroeconomic and financial predictors, we implement a hierarchical BVS model with rolling-window estimation and direct multi-horizon forecasting. Out-of-sample performance is evaluated against autoregressive (AR) models, the random walk (RW), and a machine learning benchmark, the Random Forest (RF). The results show that BVS consistently outperforms AR and RW across most horizons in both point and density forecasts, and dominates RF within two years (h=1-18), while RF is only slightly better at very long horizons (h=24,30,36). Horizon-specific predictors provide further insights: lagged HPI and market sentiment (RECI) drive short-run dynamics; RECI, inflation (CPI), and housing credit conditions (HPF, IHLL) matter at medium to long horizons; and demographic fundamentals (PNGR) dominate at long and ultra-long horizons. The forecast results point to a subdued housing market over the next two years, consistent with a structural break around April 2022 and the lasting impact of demographic shifts. Overall, the study demonstrates that BVS not only improves forecasting accuracy but also enhances interpretability, making it a valuable tool for academic research and housing market policy design.

목차

1. INTRODUCTION

2. BAYESIAN VARIABLE SELECTION MODEL

3. DISTRIBUTION PREDICTION USING BVS MODEL

4. DATA AND PREDICTOR VARIABLES

5. EMPIRICAL ANALYSIS

6. BAYESIAN VARIABLE SELECTION COMPARISON WITH RANDOM FOREST

7. ROBUSTNESS ANALYSIS: STRUCTURAL BREAK IN NATIONAL HOUSING PRICES

8. CONCLUSION AND POLICY IMPLICATIONS

REFERENCES

키워드

Bayesian variable selection housing price forecast macroeconomic predictors out-of-sample evaluation China