How to Tell if a Beta Is Levered or Unlevered

Introduction

Most cost-of-capital errors in PE and M&A modeling start at the beta step, not at the WACC build. An analyst grabs a number off Bloomberg, plugs it into CAPM, and never checks whether what they pulled is levered (the equity beta of one specific company at one specific capital structure) or unlevered (the industry asset beta that strips out leverage). On a typical mid-leverage comp, the two answers can differ by enough to move cost of equity by 100 to 300 basis points, which materially moves the valuation in any DCF.

The fastest test: if the number is tied to a single company's observed stock returns, it is levered. If it has been adjusted by dividing out the company's debt-to-equity ratio using the Hamada formula, it is unlevered. Bloomberg's BETA function returns a levered equity beta by default (raw or Blume-adjusted, both levered) from a 2-year weekly regression against the local benchmark. Damodaran's NYU Stern tables explicitly publish both: "Average Beta" is the levered industry mean, "Unlevered Beta" is the asset beta, and "Unlevered Beta corrected for cash" adjusts further for cash on the balance sheet. Yahoo Finance "Beta (5Y Monthly)" is levered. S&P Capital IQ has explicit IQ_BETA_1YR / 2YR / 5YR fields (all levered) plus a separate Unlevered Beta field. Read the source's label and the question is answered.

For the underlying theory, the Hamada derivation, and the relationship to the M&M propositions, the conceptual guide on levered vs unlevered beta covers the formulas in depth. The rest of this post is the source-by-source identification reference, sanity-check heuristics for when the source is silent, the relever workflow analysts actually run on a comp set, and the interview version of the question.

Before opening any provider, get the principle right. The question always reduces to one thing: is the number tied to a single company's observed stock returns, or has someone already stripped out the leverage effect?

Most published single-name betas are levered by default. The standard output of every regression-based beta service, Bloomberg, S&P Capital IQ, Yahoo Finance, FactSet, Value Line, is the levered equity beta of an individual company's stock returns regressed against a market benchmark. Unlevered betas are derived numbers: someone has to take the levered beta, the company's debt-to-equity ratio, and a tax rate, and apply the Hamada formula. So if you found the number on a single-name screen, it is overwhelmingly likely to be levered.

The big exception is industry beta tables. When a research provider averages across many companies in a sector and publishes the result, the explicit unlevered column is usually present. Damodaran's NYU Stern industry data, the Kroll Cost of Capital Navigator, and most paid valuation databases publish unlevered betas alongside the levered industry averages. If you pulled the number from an industry table rather than a single-company quote page, check the column header before doing anything else.

The third clue is context. If you found the beta in a DCF memo, a banker pitch, or an equity research note, look at how the author uses it. A beta plugged directly into CAPM with no relevering step is almost certainly being treated as levered. A beta that the author describes averaging across comparables and then relevering to a target is unlevered up until the relevering step. A beta that comes from "the industry" without any single ticker attached is almost always unlevered.

The cleanest way to remove ambiguity is to memorize what each major provider returns by default. The good news is that the conventions are remarkably consistent: nearly every single-name source returns levered beta, and the industry-level providers explicitly publish both.

Bloomberg

Bloomberg's BETA function returns a levered equity beta by default. The terminal displays both a Raw Beta, the straightforward ordinary least squares regression of the security's returns against a benchmark, and an Adjusted Beta, the raw beta blended toward 1.0 using the Blume adjustment. Both numbers are levered. The Blume adjustment changes the magnitude of the levered beta, but it does nothing to the leverage assumption: the stock's debt-to-equity ratio is not stripped out either way.

Blume Adjustment

A weighted average used by Bloomberg and several other data providers that pulls a raw historical equity beta toward 1.0 using the formula Adjusted Beta = (0.67 × Raw Beta) + (0.33 × 1.0). The reasoning is that observed betas tend to revert toward the market average over time, so a smoothed estimate may be a better forward-looking input than a single regression result. The Blume adjustment is applied before any leverage adjustment, so both the raw and adjusted versions remain levered. The smoothing changes magnitude, not the leverage assumption.

The default settings on the BETA screen are a 2-year window with weekly observations against the local benchmark index (the S&P 500 for US-listed names). You can change the time window (1Y, 2Y, 5Y, 10Y), the frequency (daily, weekly, monthly), and the benchmark from the screen itself, but unless you have changed the defaults, that is what you have pulled. There is no built-in unlevered beta field on the standard BETA function: if you want an unlevered version of a Bloomberg beta, you have to compute it yourself using the company's debt and equity balances.

When the question is "which kind of beta did I pull from Bloomberg," the answer is always "a levered one, smoothed or not." Identify the time window and the smoothing from the screen, but assume levered unless someone has clearly told you they ran the Hamada transformation themselves.

Damodaran's NYU Stern Industry Tables

Aswath Damodaran publishes free industry beta tables at pages.stern.nyu.edu, updated each January with the prior year's regression results. The tables are organized by sector with US, global, emerging-markets, European, Japanese, and other regional cuts available. The 2026 vintage was last updated January 2026 and is the data set most US-based valuation analysts default to when they need an industry beta for a private-company DCF or a sanity check on a single-name comp.

Asset Beta

The systematic risk of a company's business operations alone, with the effect of financial leverage removed. Also called unlevered beta. An asset beta represents the beta the company would have if it were financed entirely with equity and carried zero debt. It is the appropriate intermediate input when applying comparable-company betas to a target with a different capital structure, because business risk is independent of how a firm chooses to finance itself.

Each table has explicit columns: Industry Name, Number of Firms, Average Beta (the levered industry average), Market D/E Ratio, Tax Rate, Unlevered Beta, Cash/Firm Value, and Unlevered Beta corrected for cash. Three things to take away. First, the "Average Beta" column is levered: it is the mean of individual firms' equity betas across the sector. Second, the Unlevered Beta column has been computed by Damodaran himself using the Hamada formula on each firm's beta and capital structure, then averaged. Third, "Unlevered Beta corrected for cash" goes a step further by stripping out the cash on the balance sheet, which should not bear business risk (a meaningful adjustment for tech, biotech, and pharma names that hold large cash balances relative to enterprise value).

The labeling is explicit, so identification is straightforward: read the column header. If you grabbed the Average Beta column thinking you had an unlevered industry beta, you grabbed the wrong one. For most private-company DCFs and LBO models, the "Unlevered Beta corrected for cash" column is the appropriate input, relevered to your target's capital structure.

Yahoo Finance and Google Finance

The beta number Yahoo Finance displays on a stock's main quote page is labeled "Beta (5Y Monthly)" and is the levered equity beta calculated from 60 monthly stock returns regressed against the S&P 500. There is no unlevered version anywhere on the Yahoo Finance interface: if you need one, you have to compute it yourself using the company's debt and equity balances from the most recent balance sheet.

Google Finance shows a similar levered equity beta on the quote page without labeling the time window explicitly. Treat it as levered for identification purposes, but expect a different number than Yahoo because of different window or frequency choices. Both Yahoo and Google are fine for sanity-checking a single name's beta against another source, but neither is appropriate as an input for a formal DCF without further work, both because the underlying data quality is below what a paid terminal provides and because there is no straightforward way to standardize the regression window across a comp set.

S&P Capital IQ

S&P Capital IQ offers three off-the-shelf levered beta fields: IQ_BETA_1YR (a 52-week OLS beta against the local benchmark), IQ_BETA_2YR (104-week), and IQ_BETA_5YR (60-month). All three are levered. The platform also exposes a separate Unlevered Beta field that the platform calculates from each company's reported debt and equity. If you are pulling from the desktop application or the Excel plug-in, look at the field name on your formula or the column header on your data pull: it will explicitly tell you which one you have.

Equity Beta

The systematic risk of a company's equity returns, capturing both the underlying business risk of its operations and the additional financial risk created by debt in its capital structure. Also called levered beta. Equity beta is what every single-name beta service (Bloomberg, S&P Capital IQ, Yahoo Finance, FactSet, Value Line) returns by default. It is the beta plugged directly into CAPM when calculating the cost of equity for the company as it is currently capitalized.

Capital IQ uses the S&P 500 as the benchmark for US stocks, S&P/TSX Composite for Canadian stocks, MSCI EAFE for developed-market non-US stocks, and MSCI Emerging Markets for everything else. If you are running a cross-border comps set, the benchmark differences alone introduce noise into the levered betas, which is one more reason to unlever, average across the asset betas, and relever rather than averaging raw levered numbers.

FactSet, Value Line, and the Kroll Cost of Capital Navigator

FactSet's beta service is structurally similar to Capital IQ: explicit field names for levered (typically 60-month or 24-month regressions) and a derived unlevered field. The default output is levered. When you pull a beta column into Excel from FactSet, the column header should disambiguate.

Value Line publishes a single beta number for each covered company, computed as a Blume-style adjusted beta against the NYSE Composite over five years of weekly observations. It is levered, smoothed, and intended primarily as a forward-looking equity-risk estimate for retail investors and traditional asset managers. It is not the input most investment-banking valuation work defaults to, but if you encounter it in a research report or a buy-side memo, identify it as levered and Blume-adjusted.

Kroll's Cost of Capital Navigator (formerly Duff & Phelps) is the leading subscription product for cost-of-capital inputs at independent valuation firms and corporate finance teams. It publishes both levered and unlevered industry betas, size-premium add-ons, industry-risk premia, and country-risk premia. The Navigator's output is explicitly labeled in the interface, so identification on Kroll data is by reading the field name. Kroll's own primer on developing and selecting CAPM betas walks through the methodology in detail and is the most accessible practitioner reference outside the academic literature. For US private-company valuation, Damodaran's tables are the free reference and Kroll's Navigator is the paid institutional reference.

Source-by-Source Reference Table

Here is the full identification reference in one place.

The pattern is clear. Single-name screens return levered. Industry tables publish both. When in doubt, read the column header or field name on whatever you pulled.

Sometimes the number arrives without a header. You see "beta: 1.15" in a draft memo, a teaser, an investor presentation, or a research note, and the author has not labeled it. Four sanity checks resolve almost every case.

Capital structure check. A company with near-zero net debt has a levered and unlevered beta that are essentially identical, because the leverage adjustment is multiplying by something very close to 1.0. For mature large-cap technology names with net cash positions, the distinction barely matters and you can treat the published number as effectively unlevered for relevering purposes. A heavily levered utility, REIT, or LBO-backed sponsor portfolio company with a published beta above 1.0 has almost certainly already been levered: the elevated equity volatility is the leverage effect showing up.

Magnitude check. Published industry unlevered betas typically sit between 0.5 and 1.5, with most US sectors clustering between 0.7 and 1.2. If the number you are looking at is well outside that range (say 2.0+, or below 0.3), the source is probably reporting levered beta on a high-leverage or high-volatility name, or it is a single regression on a stock with idiosyncratic price action in the window.

Context check. If the surrounding text uses the beta directly in CAPM with no relevering language, the author is treating it as levered. If the surrounding text describes the beta as "industry," "sector," "asset," or "business risk," it is unlevered. If the author describes "averaging across comparables and adjusting for capital structure," whatever number appears after that step is unlevered until the relevering step appears.

Provenance check. Where did the number come from? A single-name screen on Bloomberg, Yahoo, or Capital IQ is levered. An industry table from Damodaran or Kroll is published both ways with explicit labels. A research report typically discloses its source in a footnote; read it.

Once you have identified what you have, switching between the two is mechanical. The Hamada equation, named after finance professor Robert Hamada, is the standard formula used in practice and the one expected in interviews. To unlever (strip out the leverage effect from a levered beta):

To relever (apply the unlevered industry beta to a new target's capital structure):

Where $\beta_{l}$ is levered (equity) beta, $\beta_{u}$ is unlevered (asset) beta, $T$ is the marginal tax rate, $D$ is the market value of debt, and $E$ is the market value of equity. The market-value point matters: book values for equity in particular can be wildly off the market value, and using book values is the single most common mechanical error in this calculation.

Worked example. You pulled a Bloomberg adjusted beta of 1.40 for a comparable company. The company's market cap is $8.0 billion, its market debt is $5.6 billion (so D/E = 0.70), and its effective tax rate is 25%. The unlevered beta is 1.40 / (1 + 0.75 × 0.70) = 1.40 / 1.525 = 0.92. The leverage effect was responsible for moving the company's pure business-risk beta from 0.92 to the observed 1.40. If your target has a different capital structure, you relever this 0.92 at the target's D/E and tax rate, not at the comp's.

For the underlying derivation, the relationship to the M&M propositions, and the variant formulas that allow for a non-zero debt beta, the conceptual guide on levered vs unlevered beta covers the theory in depth. This post stays focused on identification and application.

The identification question matters because the answer feeds directly into the comparable-company workflow that every M&A, LBO, and equity-research analyst uses to estimate cost of equity for a target. The workflow is the same whether you are valuing a public target with a stated capital-structure change, a private company with no observable beta, or an LBO target where the post-close D/E will be radically different from the current standalone capital structure.

The steps look mechanical but they encode the entire reason this question matters. Step 3 strips out the financing noise that contaminates raw levered betas. Step 4 absorbs the idiosyncratic differences across firms in the sector. Step 5 puts the leverage back on at the target's own capital structure. The output of step 6 is a beta that genuinely reflects the systematic risk of the target's business as it will be financed, not the leverage of some other company.

Master interview fundamentals: Practice 1,000+ technical and behavioral questions with AI-powered feedback, download our iOS app for comprehensive interview prep covering exactly this kind of valuation walkthrough.

The identification step is not where most candidates lose the question. The losses come from a small set of errors that survive most interviews because the candidate thinks they have done the right thing.

The rest of the high-frequency errors:

Get the complete guide: Download our comprehensive 160-page PDF, access the IB Interview Guide covering DCF mechanics, comparable-company analysis, and the full menu of technical interview questions including the beta walkthrough above.

In a technical round, the beta question almost never arrives as "define levered and unlevered." It arrives as a small case: the interviewer hands you a number or a setup and asks you to work through it. Three representative exchanges:

"If I show you a beta of 1.2 from Bloomberg, what do I have, and what do I do with it?"

You have a levered equity beta, almost certainly raw or Blume-adjusted, from the default 2-year weekly regression against the local benchmark. If you are valuing this company as-is, you can plug it into CAPM. If you are using it as a comparable to value a target with a different capital structure, you unlever at this company's market D/E and tax rate, take the median across the comp set, then relever at the target's D/E and tax rate before using it in CAPM.

"Damodaran's industry tables publish both 'Beta' and 'Unlevered Beta.' Which one do I want for a private chemicals company, and why?"

The Unlevered Beta column, ideally the "corrected for cash" version if the comps in the industry hold meaningful cash balances. The Average Beta column is the levered industry average and embeds the leverage choices of the companies in the sector, which are not your target's leverage choices. Relever the unlevered industry beta at your target's actual or projected D/E and tax rate before plugging into CAPM.

"My comp set has eight names with reported betas. Two just did major debt issuances in the last six months. What do I do?"

You have two options. Drop the two affected comps and run the workflow on the remaining six, accepting a smaller comp set. Or extend the regression window beyond the issuance date if your data provider allows, so the regression is run on returns from a period when the comps had a stable capital structure. The wrong move is to leave the two comps in with a short window: their levered beta reflects equity returns produced under one capital structure, and the D/E you are using to unlever reflects another. The unlevered output will be biased.

The pattern across all three: state what you have, name the workflow, and say what comes out the other end. The interviewer is testing whether you understand the relationship between the beta source, the company's leverage, and the cost-of-equity output, not whether you can recite the Hamada formula.

The identification question is not deep theory, it is a discipline of reading the screen and respecting the provider's convention. The conventions are stable: single-name screens return levered, industry tables publish both with explicit labels. The relever workflow is mechanical once you know which kind of number you started with. And the interview version of the question is testing whether you understand the chain from source to formula to CAPM, not whether you have memorized a definition.

If you are mid-DCF and you have a beta on your screen right now, run the 30-second test, identify the source, and decide whether you need to unlever before doing anything else. If you are preparing for technicals, practice the three exchanges above out loud until the workflow comes out of your mouth without thinking. The cost-of-equity step is the place where sloppy valuation work gets noticed, and the beta identification is where the sloppiness starts. Get this right and the rest of the WACC build, DCF walkthrough, and comparable-company analysis become easier to defend in the room.

For the underlying theory, the Hamada derivation, and the relationship to the Modigliani-Miller propositions, see the levered vs unlevered beta conceptual guide. For applying this to a private company where no observable beta exists, and for the broader question of when to use cost of equity versus WACC as your discount rate, the related posts cover the next layer of the valuation stack.