What are the best sources of data for estimating the fair market value of assets?

Market data, if available should be used as a first level analysis. This provides not only historical pricing data but also analyst ratings, market news, and sector performance.

When considering avenues for estimating the fair market value of assets, one might explore various data sources that offer robust insights into market trends and asset valuations. These could encompass industry reports, economic indicators, transaction databases, and specialized valuation tools. Leveraging such resources enables professionals to gather comprehensive data sets, facilitating a more accurate assessment of asset values.

Bloomberg or FactSet are two excellent sources of market data for a publicly traded company and its comps. Online brokerage tools like a stock screener provided by Fidelity are often sufficient though. Sentiment is trickier to gauge and can change at any time. It's important to be a consumer of information through papers like the Wall Street Journal and the New York Times and to even watch interviews on CNBC. Key events like monthly inflation reports, the unemployment report, and FOMC meetings also tend to move markets.

As a retired portfolio manager, who had access to both Bloomberg and FactSet for many years, I find it challenging to conduct DCF type work these days, cost approach never seemed to work anyway. Basically I have to work off SEC filings and Yahoo, believe it or not.

When determining the fair value of assets, numerous reputable sources of data can be used. The key is its availability as of the valuation date. First, consider the comparable transaction data. The research of recent transactions involving similar assets in the same market might provide useful information about the asset's fair market value. Second, market research reports are an important source of valuation inputs. Using industry-specific market research reports can assist in understanding market trends, demand-supply dynamics, and pricing benchmarks for comparable assets. Financial statements provide information about income, expenses, and profitability, which are all important variables in assessing fair market value.

Valuing private companies relying only on benchmarks in most cases will differ abruptly from the reality of the target company as is nearly impossible to find an identical peer. If you are valuing a private company for M&A purposes, a deep due diligence is required to validate assumptions and update the exercise with the real data of the target company.

The use of data has been of vital importance for companies for many years. The current trend in data analytics is also opening up new possibilities and opportunities, thanks to increasing technical sophistication and increasingly lower barriers to entry. Companies can establish a competitive advantage over their peers by monetizing financial and non-financial data from a wide range of sources. They can also present their contribution to society based on data, or address changes in market conditions earlier and more proactively, as well as derive value-added decisions from them using driver-based management models.

When estimating the fair market value of assets, financial data is a crucial source. High-quality financial data provides insight into the historical performance, profitability, and financial health of the asset or company in question. This includes audited financial statements, income statements, balance sheets, and cash flow statements. Reliable financial data allows for accurate forecasting and modeling, which are essential for determining the asset’s value. The integrity and accuracy of this data are paramount, as they form the foundation for various valuation methods, ensuring that the estimated fair market value is both credible and reflective of the asset's true economic potential.

All of this can be achieved significantly and more effectively than was previously possible through the use of financial ratios. In fact, the use of advanced data analytics is no longer a purely voluntary decision. External stakeholders, such as investment funds, analyst firms, consumers, employees and, of course, activists are using freely accessible external data to analyze companies in different dimensions. This carries the risk that companies that do not intentionally use data analytics could be overloaded by external analytical results. This could lead to unwanted public attention regarding your business activities and change the way customers perceive you. It could also result in better projections of your actual market potential.

Cost data is one of the best sources for estimating the fair market value of assets, particularly when valuing tangible assets like machinery, equipment, or real estate. Cost data reflects the actual expenses incurred to acquire or produce an asset, providing a solid foundation for estimating its value. This data includes original purchase prices, costs of improvements, and related expenses like installation or transportation. However, adjustments may be needed to account for depreciation, obsolescence, or market conditions, ensuring the cost data accurately represents the asset's current market value. By using reliable cost data, valuers can achieve a more precise and grounded estimation of an asset’s fair market value.

When estimating the fair market value of assets, expert data is invaluable. Expert data typically comes from industry specialists, appraisers, and consultants who possess deep knowledge of specific asset classes. These experts provide insights that go beyond generic market data, offering a nuanced understanding of factors such as asset condition, market demand, and potential future value. Their expertise ensures that valuations are grounded in the realities of the market, considering both current conditions and long-term trends. By leveraging expert data, you can enhance the accuracy and credibility of your asset valuations, ensuring they reflect true market value.

Today, companies operate in an extremely dynamic environment. The time available to respond to any change is becoming shorter. The role of the CFO has taken on new importance in helping companies navigate through change processes. On the one hand, CFOs are responsible for the generation of external reports and, in many cases, the preparation, interpretation and management of (financial) information. Therefore, the documentation tasks of the finance function are being replaced by a strong focus on added value. CFOs are gradually taking responsibility for the role of chief value officer – they are becoming a fundamental component of a data-driven business strategy and drivers of corporate value added.

Customer data can be a valuable source when estimating the fair market value of assets, especially intangible assets like brand equity, customer relationships, and intellectual property. Analyzing customer data allows valuers to assess the quality and sustainability of revenue streams, customer loyalty, and the overall market position. Metrics such as customer lifetime value, churn rates, and acquisition costs provide insights into the asset’s ability to generate future cash flows, which is crucial for determining fair market value. High-quality customer data ensures that the valuation reflects the true economic potential of the asset, aligning it more closely with market realities.

In a data-rich world, investment outperformance becomes more difficult to achieve if one relies mostly on widely available information that is easy to obtain. This is the reason we are seeing more and more alternative data providers that measure unconventional data points such as sentiment analysis of social media posts on an upcoming product launch that greatly influences a company's profitability, or footfall data that contribute to the success of a mall. The important things to note about alternative data is that, first of all, they must be legal to obtain and do not constitute insider information. Additionally, on a practical level, they should have a demonstrated impact on the asset's performance and ultimately valuation.

There are a lot of data nowadays. Information becomes more democratized but also more misleading with plenty of fake articles and unverified rumors. At the same time, valuation has got more precise with complex models and software programs but somehow got less accurate and robust. Before using data, valuation professionals should ask themselves 3 questions: (i) are the data relevant? (ii) are the data impactful? and (iii) are the data verifiable? For instance, number of users can be a valuation driver if there are paying customers or the company can generate revenue in other ways (data insights, ads, etc.). If not, it doesn't contribute anything to business value. Same with other non-conventional metrics such as share of mind, R&D, etc.

Business valuation is an art with a mix of science (methods). Valuing a publicly traded co is fairly a straight forward exercise; as one would need to rely on their filings to get Financial, & operational data, DCF can be estimated, to support value range a comparable co analysis can be done through platforms such as Bloomberg, Capital IQ. However, depending on which side you belong in valuing the privately held co becomes a tedious task in terms of information flow. If you are mandated by the target itself then Co management provides all the required data. But if you don’t have access to the management at all then it’s almost impossible to carry out a detailed DCF of the Target. Information holds the key.

For accurate asset valuation, consider these data sources: Public financial records (SEC’s EDGAR for company financials), market data providers (Bloomberg, Reuters for real-time asset pricing and analysis), industry reports (from firms like McKinsey for market trends and forecasts), government databases (like the BEA for economic indicators), real estate databases (MLS, Zillow for property data), and private transaction databases (PitchBook, Dealogic for private equity deals). These sources offer comprehensive data essential for determining fair market values.