Hedonic Prices and Quality Adjusted Price Indices Powered by AI

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We develop empirical models that efficiently process large amounts of unstructured product data (text, images, prices, quantities) to produce accurate hedonic price estimates and derived indices.

• To achieve this, we generate abstract product attributes (or ``features'') from descriptions and images using deep neural networks.
• These attributes are then used to estimate the hedonic price function.
• To demonstrate the effectiveness of this approach, we apply the models to Amazon's data for first-party apparel sales, and estimate hedonic prices.