Hedonic Prices and Quality Adjusted Price Indices Powered by AI
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866910001246765056 |
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| author | Bajari, Patrick Cen, Zhihao Chernozhukov, Victor Manukonda, Manoj Vijaykumar, Suhas Wang, Jin Huerta, Ramon Li, Junbo Leng, Ling Monokroussos, George Wang, Shan |
| author_facet | Bajari, Patrick Cen, Zhihao Chernozhukov, Victor Manukonda, Manoj Vijaykumar, Suhas Wang, Jin Huerta, Ramon Li, Junbo Leng, Ling Monokroussos, George Wang, Shan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_00044 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Hedonic Prices and Quality Adjusted Price Indices Powered by AI Bajari, Patrick Cen, Zhihao Chernozhukov, Victor Manukonda, Manoj Vijaykumar, Suhas Wang, Jin Huerta, Ramon Li, Junbo Leng, Ling Monokroussos, George Wang, Shan General Economics Economics Machine Learning 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. |
| title | Hedonic Prices and Quality Adjusted Price Indices Powered by AI |
| topic | General Economics Economics Machine Learning |
| url | https://arxiv.org/abs/2305.00044 |