We analyze the role of macroeconomic uncertainty in predicting synchronization in housing price movements across all the United States (US) states plus District of Columbia (DC). We first use a Bayesian dynamic factor model to decompose the house price movements into a national, four regional (Northeast, South, Midwest, and West), and state-specific factors. We then study the ability of macroeconomic uncertainty in forecasting the comovements in housing prices, by controlling for a wide-array of predictors, such as factors derived from a large macroeconomic dataset, oil shocks, and financial market-related uncertainties. To accommodate for multiple predictors and nonlinearities, we take a machine learning approach of random forests. Our results provide strong evidence of forecastability of the national house price factor based on the information content of macroeconomic uncertainties over and above the other predictors. This result also carries over, albeit by a varying degree, to the factors associated with the four census regions, and the overall house price growth of the US economy. Moreover, macroeconomic uncertainty is found to have predictive content for (stochastic) volatility of the national factor and aggregate US house price. Our results have important implications for policymakers and investors.

我们分析了宏观经济不确定性在预测美国所有州和哥伦比亚特区房价同步中的作用。我们首先使用贝叶斯动态因素模型将房价变动分解为国家，四个区域（东北，南部，中西部和西部）和州特定因素。然后，我们通过控制广泛的预测变量，例如从大型宏观经济数据集得出的因素，石油冲击以及与金融市场相关的不确定性，来研究宏观经济不确定性在预测房价走势中的能力。为了适应多种预测因素和非线性，我们采用随机森林的机器学习方法。根据宏观经济不确定性的信息内容，我们的研究结果为全国房价因素的可预测性提供了有力的证据。尽管在不同程度上，这一结果还延续了与四个人口普查地区以及美国经济总体房价增长相关的因素。此外，发现宏观经济不确定性对国家因素和美国房屋价格的（随机）波动具有预测性。我们的结果对决策者和投资者具有重要意义。人们发现，宏观经济不确定性对国家因素（随机）波动和美国总房价具有预测意义。我们的结果对决策者和投资者具有重要意义。人们发现，宏观经济不确定性对国家因素（随机）波动和美国总房价具有预测意义。我们的结果对决策者和投资者具有重要意义。