As the COVID-19 pandemic came unexpectedly, many real estate experts claimed that the property values would fall like the 2007 crash. However, this study raises the question of what attributes of an apartment are most likely to influence a price revision during the pandemic. The findings in prior studies have lacked consensus, especially regarding the time-on-the-market variable, which exhibits an omnidirectional effect. However, with the rise of Big Data, this study used a web-scraping algorithm and collected a total of 18,992 property listings in the city of Vilnius during the first wave of the COVID-19 pandemic. Afterwards, 15 different machine learning models were applied to forecast apartment revisions, and the SHAP values for interpretability were used. The findings in this study coincide with the previous literature results, affirming that real estate is quite resilient to pandemics, as the price drops were not as dramatic as first believed. Out of the 15 different models tested, extreme gradient boosting was the most accurate, although the difference was negligible. The retrieved SHAP values conclude that the time-on-the-market variable was by far the most dominant and consistent variable for price revision forecasting. Additionally, the time-on-the-market variable exhibited an inverse U-shaped behaviour.

由于COVID-19大流行的突然到来，许多房地产专家声称房地产价值将像2007年崩盘一样下跌。然而，这项研究提出了一个问题：公寓的哪些属性最有可能影响疫情期间的价格调整。先前的研究结果缺乏共识，特别是在上市时间变量方面，它表现出全方位的影响。然而，随着大数据的兴起，这项研究使用了网络抓取算法，收集了第一波 COVID-19 大流行期间维尔纽斯市总共 18,992 个房产列表。随后，应用 15 种不同的机器学习模型来预测公寓修订情况，并使用可解释性的 SHAP 值。这项研究的结果与之前的文献结果一致，证实房地产对流行病具有相当的弹性，因为价格下跌并不像最初认为的那么剧烈。在测试的 15 个不同模型中，极端梯度提升是最准确的，尽管差异可以忽略不计。检索到的 SHAP 值得出的结论是，上市时间变量是迄今为止价格修正预测中最主要且最一致的变量。此外，上市时间变量表现出倒 U 形行为。