Using online data to construct hedonic price indices for smartphones

In this work, hedonic indices and hedonic regressions are estimated for smartphone prices. The indices are built using a special collection of big data from online retailers that comprises a large number of representative observations and a wide range of product attributes (1.9 million distinct smartphone price observations since July 2022). We demonstrate that, for smartphones, the dynamics of hedonic indices built with the techniques suggested in the literature are substantially greater than Rosstat price index. Furthermore, we indirectly validate that the skimming pricing strategy of smartphones, in which a high price is initially set for a variety and then lowered. In the case of using simple methods of accounting for quality in the consumer price index, that is common among statistical agencies, the use of pricing strategies by manufacturers and retailers, along with frequent changes in product ranges, ultimately result in the depletion of the observation sample and a bias in the consumer price index. This supports the use of hedonic regression-based techniques to enhance the ways in which price dynamics are adjusted for changes in quality. Hedonic regressions should use all quality attributes that are pertinent to price fluctuations as regressors rather than fixed effects of varieties, according to another finding. A bias in the consumer price index happens when simplified regression specifications are used. For price adjustments, Rosstat will be encouraged to collect information on all qualitative traits of the varieties. Increased confidence in the published data will result from the addition of hedonic methods to Rosstat’s toolkit, which will help make the published CPI more representative.

hedonic indices, hedonic regressions, online prices