β系数及其稳定性对投资风险判别和资产配比选择具有重要意义。为分析房地产板块的β系数变化特征及其稳定性, 利用单指数方程测算中国A股2013—2022年房地产板块的月度和年度β系数, 并开展基于Chow检验的相邻公历月和公历年的β系数稳定性判别。研究结果表明：房地产板块的月度β系数在不同年份的变化趋势各异, 年度β系数整体先升后降; 与年度β系数相比, 月度β系数稳定性更强; 房地产板块与建筑板块β系数的整体变动轨迹具有相似性及相关性, 房地产板块β系数总体稳定性则低于建筑板块但高于金融板块。该文建议, 在使用房地产板块的中长期β系数进行投资决策时, 应根据宏观经济等因素适时修正; 进行与房地产相关的组合投资时, 需重点关注建筑业和金融业与房地产业的关联波动。

Objective: The β coefficient is a critical indicator for stock sector investment, and its stability is essential for making informed future investment decisions based on historical data. The real estate sector, known for its high investment risks and stock fluctuations, plays a crucial role in many investors' portfolios. Although there is a growing body of literature on the β coefficient of the real estate sector, research on its systematic calculation and stability remains limited. This paper analyzes the changes and stability of the β coefficient in the real estate sector, providing valuable insights for investors. Methods: Through method screening, this paper uses the single index equation to calculate the monthly and annual β coefficients of the Chinese A-share real estate sector from 2013 to 2022. After confirming data stationarity, daily data are processed through least squares regression analysis to obtain accurate and reliable monthly and annual β coefficients. The stability of the β coefficient is assessed using the Chow test for adjacent calendar months and years, and statistical analysis is conducted on the results. Ultimately, the study includes a comparative analysis between the real estate, financial, and construction sectors to provide a comprehensive understanding of the β coefficient characteristics. Results: The research results reveal the followings: (1) The monthly and annual mean β coefficients of the real estate sector are close to but less than 1. Monthly β coefficients show significant variability, while the annual β coefficient initially increases and then decreases. (2) The monthly β coefficient demonstrates stronger stability compared to the annual β coefficient. (3) The trajectories of the β coefficient in both the real estate and construction sectors are highly similar, with the stability of the β coefficient in the real estate sector being lower than that of the construction sector but higher than that of the financial sector. Conclusions: There are clear differences in the stability characteristics of the monthly and annual β coefficients in the real estate sector, and these differences vary across different sectors. This paper suggests that the followings: (1) Short-term investors should monitor changes in monthly β coefficients to predict market volatility. (2) For long-term investment decisions based on the real estate sector's β coefficients, timely adjustments should be made according to macroeconomic factors and other variables. (3) When investing across different stock sectors, investors should focus on the volatility relationship among the construction, financial, and the real estate sectors, and adopt appropriate risk hedging strategies to reasonably diversify investment risks.