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Hierarchical Bayesian hedonic regression analysis of Japanese rice wine: is the price right?

Wakuo Saito (Center for Social Innovation in Market Design, Keio University, Tokyo, Japan)
Teruo Nakatsuma (Faculty of Economics, Keio University, Tokyo, Japan)

International Journal of Wine Business Research

ISSN: 1751-1062

Article publication date: 22 November 2022

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Abstract

Purpose

This paper aims to formulate a hedonic pricing model for Japanese rice wine, sake, via hierarchical Bayesian modeling estimated using an efficient Markov chain Monte Carlo (MCMC) method. Using the estimated model, the authors examine how producing regions, rice breeds and taste characteristics affect sake prices.

Design/methodology/approach

The datasets in the estimation consist of cross-sectional observations of 403 sake brands, which include sake prices, taste indicators, premium categories, rice breeds and regional dummy variables. Data were retrieved from Rakuten, Japan’s largest online shopping site. The authors used the Bayesian estimation of the hedonic pricing model and used an ancillarity–sufficiency interweaving strategy to improve the sampling efficiency of MCMC.

Findings

The estimation results indicate that Japanese consumers value sweeter sake more, and the price of sake reflects the cost of rice preprocessing only for the most-expensive category of sake. No distinctive differences were identified among rice breeds or producing regions in the hedonic pricing model.

Originality/value

To the best of the authors’ knowledge, this study is the first to estimate a hedonic pricing model of sake, despite the rich literature on alcoholic beverages. The findings may contribute new insights into consumer preference and proper pricing for sake breweries and distributors venturing into the e-commerce market.

Keywords

Citation

Saito, W. and Nakatsuma, T. (2022), "Hierarchical Bayesian hedonic regression analysis of Japanese rice wine: is the price right?", International Journal of Wine Business Research, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJWBR-10-2021-0056

Publisher

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Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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