A Data Valuation Model to Estimate the Investment Value of Platform Companies: Based on Discounted Cash Flow

Hyongmook Cheong, Boyoung Kim () and Ivan Ureta Vaquero
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Hyongmook Cheong: Seoul Business School, aSSIST University, Seoul 03767, Republic of Korea
Boyoung Kim: Seoul Business School, aSSIST University, Seoul 03767, Republic of Korea
Ivan Ureta Vaquero: Department of Business Economics, Health and Social Care, The University of Applied Sciences and Arts of Southern Switzerland, 6928 Manno, Switzerland

JRFM, 2023, vol. 16, issue 6, 1-17

Abstract: As both investment attraction and mergers and acquisitions targeting information technology and platform companies are becoming more important in the digital-centric economic environment, interest in valuing corporate data assets is increasing. Accordingly, among the income approaches used in business valuation, this study presents a data valuation model based on discounted cash flow. This model is expected to be useful for corporate investment decision-making. The assumptions used in this study for the estimation of data income include intangible asset value, exclude net asset value, and data attribution is centered on technology, human resources, and market factors. In particular, data attribution accounts comprise ordinary data research and development, data labor costs, and data advertising expenses. Data costs were divided into those incurred during collection, storage, curation, analysis, and utilization. Financial statements and related data from a real estate information platform operator over three years were collected and used to simulate the data valuation model. The simulation reveals that the operator possesses KRW 472.6 billion in data assets. Ultimately, the data valuation model developed in this study can contribute to strengthening platform operators’ investment attraction, guaranteeing financial sustainability, and transparency and data assetization.

Keywords: data valuation; intangible assets; data attribution; business valuation; investment attraction (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2023
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