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来源类型Working Paper
规范类型报告
DOI10.3386/w29880
来源IDWorking Paper 29880
Human-Algorithm Interactions: Evidence from Zillow.com
Runshan Fu; Ginger Zhe Jin; Meng Liu
发表日期2022-03-28
出版年2022
语种英语
摘要Using bi-weekly snapshots of Zillow in three US cities, we document how home sellers and buyers interact with Zillow's Zestimate algorithm during the sales cycle of residential properties. We find that listing and selling outcomes respond significantly to Zestimate, and Zestimate is quickly updated for the focal and comparable houses after a listing or a transaction is completed. The user-Zestimate interactions have mixed implications: on the one hand, listing price depends more on Zestimate if the city does not mandate disclosure of sales information or if the neighborhood is more heterogeneous, suggesting that Zestimate provides valuable information when alternative information is more difficult to obtain; on the other hand, the post-listing update of Zestimate tracks listing price more closely in non-disclosure and heterogeneous neighborhoods, raising the concern that the feedback loop may propagate disturbances in the sales process. However, by leveraging COVID-19 pandemic as a natural experiment, we find no evidence that Zestimate propagates the initial shock from the March-2020 declaration of national emergency, probably because Zestimate has built-in guard rails and users tend to adjust their confidence in Zestimate according to observed market outcomes.
主题Microeconomics ; Economics of Information ; Industrial Organization ; Market Structure and Firm Performance ; Industry Studies ; COVID-19
URLhttps://www.nber.org/papers/w29880
来源智库National Bureau of Economic Research (United States)
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资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/587552
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GB/T 7714
Runshan Fu,Ginger Zhe Jin,Meng Liu. Human-Algorithm Interactions: Evidence from Zillow.com. 2022.
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