We construct a new measure of aggregate housing wealth for the U.S. based on (1) home-value estimates derived from machine learning algorithms applied to detailed information on property characteristics and recent transaction prices, and (2) Census housing unit counts. According to our new measure, the timing and amplitude of the recent house-price cycle differs materially but plausibly from commonly-used measures, which are based on survey data or repeat-sales price indexes. Thus, our methodology generates estimates that should be of considerable value to researchers and policymakers interested in the dynamics of aggregate housing wealth.

中文翻译：

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衡量住房总财富：机器学习的新见解^☆

我们基于（1）机器学习算法得出的房屋价值估算值来构建美国住房总资产的新衡量标准，该算法适用于有关房地产特征和近期交易价格的详细信息，以及（2）人口普查住房单位数。根据我们的新衡量标准，近期房价周期的时间和幅度与基于调查数据或重复销售价格指数的常用衡量标准存在重大但合理的差异。因此，我们的方法得出的估计值对于对住房总财富动态感兴趣的研究人员和决策者来说，应该具有相当大的价值。