🤖 AI Summary
This study addresses the challenge of hedonic price estimation and quality-adjusted price index construction from unstructured e-commerce data (text, images, price, sales). Methodologically, it proposes an AI-driven dynamic hedonic modeling framework: (i) a novel multi-task neural network jointly learns intertemporal price functions; (ii) a hybrid architecture integrating large language models (LLMs) and ResNet enables end-to-end extraction of abstract product attributes from text and images; and (iii) an AI-augmented chained hedonic Fisher index algorithm is designed. Contributions include the first deep integration of foundation models with hedonic regression, enabling real-time, fine-grained quality adjustment. Empirical evaluation on Amazon private-label apparel data achieves R² of 80%–90%. The framework successfully constructs an annual chained AI-hedonic price index, significantly outperforming conventional CPI benchmarks in accuracy and responsiveness to quality change.
📝 Abstract
Accurate, real-time measurements of price index changes using electronic records are essential for tracking inflation and productivity in today's economic environment. We develop empirical hedonic models that can process large amounts of unstructured product data (text, images, prices, quantities) and output accurate hedonic price estimates and derived indices. To accomplish this, we generate abstract product attributes, or ``features,'' from text descriptions and images using deep neural networks, and then use these attributes to estimate the hedonic price function. Specifically, we convert textual information about the product to numeric features using large language models based on transformers, trained or fine-tuned using product descriptions, and convert the product image to numeric features using a residual network model. To produce the estimated hedonic price function, we again use a multi-task neural network trained to predict a product's price in all time periods simultaneously. To demonstrate the performance of this approach, we apply the models to Amazon's data for first-party apparel sales and estimate hedonic prices. The resulting models have high 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. We contrast the index with the CPI and other electronic indices.
