The data used in Scientific Infra & Private Assets’ (SIPA) privateMetrics analysis originates from a combination of proprietary models, data aggregation, and a variety of third-party sources. This approach is designed to address the endemic lack of raw market data in private markets.

Here’s a detailed breakdown:

• Transaction Price Dataset:
  • SIPA collects a representative set of over 10,000 private market transactions (entries and exits) since 2013.
  • The transaction data includes primary transactions in private companies, including entry, exit, and take-private transactions but excludes fund-level transactions and fund-reported NAVs.
  • These transactions are sourced from PitchBook, Capital IQ, fund manager annual reports, and other publicly available data sources.
  • The data is processed via artificial intelligence to ensure accuracy and consistency.
  • Each transaction is analyzed and validated, ensuring key information is available, including risk factors, financials, and PECCS or TICCS classes.
• Company Characteristics Dataset:
  • SIPA builds a large database of “prediction data” representing the investable universe by geography and market segments.
  • This database includes data for the same factors used in the model of deal prices, such as size, EBITDA margin, and leverage.
  • The company characteristics data comes from audited accounts, public markets, deal characteristics, and the PECCS segments to which each firm belongs.
  • The data is aggregated from multiple sources, including Orbis, Pitchbook, and annual company accounts, and processed via artificial intelligence.
• Financial Data:
  • Annual financial data for private companies is sourced from a combination of private company filings with regulators, tax authorities, and other government agencies.
  • Financial data also comes from primary data vendors who obtain such data from private companies.
  • The financial data is subject to a rigorous quality check, standardization, and review process before being added to the database.
• Market Data:
  • The model incorporates a market factor based on country-specific public market and sector-specific valuations.
  • Financial market data updates produce new market factor loadings, reflecting changes in the valuation of publicly listed peers.
• PECCS Classification:
  • SIPA utilizes the Private Company Classification Standard (PECCS®) to classify companies.
  • PECCS covers all aspects of the value drivers of private companies, including activity, lifecycle phase, revenue model, customer model, and value chain.
  • PECCS classifications are included as factors in the asset pricing model.

SIPA’s data collection and analysis are designed to overcome limitations in traditional private equity data. Traditional approaches often rely on contributed data and appraisals, which can be stale, lagged, and asynchronous. SIPA’s quantitative approach aims to provide more robust and representative data for private market investors.

To ensure the quality and reliability of the data, SIPA employs several techniques:

• Model-Based Approach: SIPA uses a factor model to explain the average level of private deal prices over time. This model is calibrated monthly with the latest market transactions.
• Shadow Pricing: SIPA estimates tens of thousands of shadow prices that are, by construction, accurate on average within each segment. These shadow prices are used to calculate metrics at the segment or index level.
• Bias Prevention: SIPA’s indices do not suffer from survivorship, selection, or other reporting biases common in private market data.
• Quality Control: SIPA subjects its data to rigorous quality checks, standardization, and review processes.