Hedonic Prices and Quality Adjusted Price Indices Powered by AI
This provides a more accurate method for economists and policymakers to measure inflation and price changes by accounting for product quality, though it is incremental in applying AI to an existing economic modeling framework.
The paper tackled the problem of estimating hedonic prices and quality-adjusted price indices by processing unstructured product data using deep neural networks to generate abstract attributes, achieving out-of-sample predictive accuracy with R² ranging from 80% to 90% on Amazon apparel sales data.
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. The resulting models have a very high out-of-sample predictive accuracy, with $R^2$ ranging from $80\%$ to $90\%$. Finally, we construct the AI-based hedonic Fisher price index, chained at the year-over-year frequency, and contrast it with the CPI and other electronic indices.