Why Does My Zestimate Fluctuate? Model Overfitting for Platform Ad Revenue
SSRN Electronic Journal
2023
- 718Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Machine Learning (ML) algorithm-generated price estimates are increasingly common in home sales, used car sales, and short-term rentals. These algorithmic prices are informative to consumers but online platforms unilaterally control the underlying algorithm design. The revenue model for these platforms may not be fully aligned with the interests of consumers. Further, these price estimates tend to fluctuate significantly over time. It is therefore puzzling if the fluctuations reflect real changes in demand, or are simply artifacts of the platform’s opaque choice of algorithm design. In this paper, we develop an analytical model grounded in the housing market. We show that the platform, relative to consumers (homeowners), prefers to induce excess market entry and sales volume. The platforms can achieve this objective by pricing excess features (compared to statistically optimal choice) that result in an over-fit Machine Learning model and excessive fluctuations in the algorithmic prices. The consumers (homeowners) are worse off under this platform’s optimal (over-fit) relative to the statistically optimal (best-fit) model choice. These results have implications for regulating algorithmic prices offered by online platforms.
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