大数据中的隐藏错误：以房产记录为例
Hidden Errors in Big Data: The Case of Property Records
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中文总结 AI 辅助
本文审计两个中介房产数据集，发现其存在1-2%售价偏差、12-15%覆盖错误等问题，错报高度一致，会影响财产税累退性估计，凸显开放行政数据的重要性。
中文摘要 AI 辅助
大数据是学术研究及公私领域部署的AI模型的基础，对中介数据集的依赖随时间大幅增长。中介房产记录是 gentrification、不平等、美国财产税研究的常用数据，也是房产估值模型的输入，是典型案例。本文审计了两个知名中介房产数据集，发现数据错误会导致关键经济不平等指标出现偏差。首先，我们记录到，2018-2021年伊利诺伊州库克县1-2%的匹配交易中，中介提供的售价比实际价格偏差超过5%；契约和房产特征报告中的缺失数据及概念差异，导致12%-15%的交易存在覆盖错误。其次，我们发现中介的错报高度一致：同一交易中，中介常出现相同的报告错误。第三，为说明这些错误的重要性，我们测量了其对财产税累退性估计的影响，发现错误会导致不同数据源的估计值出现显著偏差。这些发现适用于美国另外两个大县，凸显了开放行政数据及中介提供数据来源和谱系透明度的关键重要性。
英文摘要
Big data are the foundation for an increasing share of academic research and AI models deployed in both the public and private sectors, prompting substantial growth over time in reliance on brokered datasets. Brokered property records, which are ubiquitous in studies of gentrification, inequality, and the property tax in the U.S. and serve as inputs to property valuation models, are one notable example. In this paper, we audit two prominent brokered property datasets, finding errors in these data which bias key measures of economic inequality. First, we document that for 1-2% of matched sales in Cook County, IL, from 2018-2021, broker-provided sale prices differ from ground truth sale prices by more than 5%. Moreover, missing data and conceptual differences in the reporting of deed and property characteristics lead to coverage errors ranging from 12 to 15% of transactions. Second, we show that misreporting is highly consistent between brokers: more often than not, brokers make identical reporting errors for the same transactions. Third, to illustrate the significance of these errors, we measure their impact on estimates of property tax regressivity, finding that they drive significant wedges between estimates depending on the data source. These findings generalize to two other large counties in the U.S., and highlight the crucial importance of open administrative data and transparency from brokers regarding data provenance and lineage.
