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International Journal of Creative and Open Research in Engineering and Management

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ISSN: 3108-1754 (Online)
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Peer Review: Double Blind
Volume 02, Issue 05

Published on: May 2026

SALES FORECASTING IN AUTOMOBILE INDUSTRY USING DATA ANALYTICS

M. Mugilan

M. Showmiyan

Department of Management Science (MBA)

Hindusthan College of Engineering and Technology Coimbatore

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Plagiarism Passed Peer Reviewed Open Access

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Abstract

The Indian used automobile market has emerged as one of the fastest-growing pre-owned vehicle segments globally, yet the determinants of resale pricing remain fragmented and inadequately examined through large-scale empirical analysis. This study investigates the key factors that influence used car resale prices in India using a dataset of 140,904 vehicle listings sourced from secondary automobile platforms. Employing exploratory data analysis (EDA), descriptive statistics, Pearson correlation, segmental cross-tabulation, and temporal depreciation modelling, the research examines the impact of brand equity, vehicle age, kilometres driven, fuel type, transmission, ownership history, and geographic location on resale value. Findings reveal that the market is positively skewed, with a mean resale price of ₹7.62 lakh against a median of ₹6.83 lakh, reflecting a volume-dominant affordable segment inflated by a premium luxury tail. Maruti Suzuki commands 38% of market share, underpinning an affordability-driven demand structure. Vehicle age and kilometres driven demonstrate strong negative correlations with price (r = −0.404 and r = −0.214 respectively), while ownership history exerts a pronounced 'ownership penalty', with resale prices declining from ₹8.78 lakh for first-owner vehicles to ₹3.86 lakh for third-owner or beyond. SUVs, though representing only 24.9% of listings by volume, contribute disproportionately to market value. Accidental history and geographic factors further moderate pricing. These results offer actionable insights for buyers, sellers, dealers, and policymakers, and lay the groundwork for predictive modelling incorporating macroeconomic variables.

Keywords: Used automobile market; resale price determinants; vehicle depreciation; exploratory data analysis; brand equity; ownership penalty; Indian automotive sector

How to Cite this Paper

Mugilan, M. (2026). Sales Forecasting in Automobile Industry Using Data Analytics. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(05). https://doi.org/10.55041/ijcope.v2i5.179

Mugilan, M.. "Sales Forecasting in Automobile Industry Using Data Analytics." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i5.179.

Mugilan, M.. "Sales Forecasting in Automobile Industry Using Data Analytics." International Journal of Creative and Open Research in Engineering and Management 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i5.179.

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  • Published on: May 07 2026
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