Sector Valuation Dashboard Excel: May 2026 S&P 500 P/E Heatmap and Premium-Discount Analysis

Sector valuation dashboard Excel workbooks are the workhorse of any allocator who has to answer the question "which sector is cheap right now, and which one is stretched?" without opening Bloomberg. Instead of staring at eleven different ETF pages and trying to mentally compute the spread between today's trailing P/E and the 5-year average, a sector valuation dashboard lays the whole map out in a single screen: KPI tiles up top, a color-coded heatmap of price-to-earnings, price-to-book, EV/EBITDA and dividend yield down the middle, and a chart that benchmarks today's multiple against where the sector has typically traded. This guide walks through the dashboard design, the math behind every column, and a free premium-grade Excel template that runs the entire screen on live MarketXLS formulas.

If you want to skip ahead, the premium dashboard template is downloadable below, with both a static sample (filled with current May 2026 numbers) and a live MarketXLS formula version. Every P/E, forward P/E, P/B, EV/EBITDA, and dividend yield refreshes when you open the workbook. The Dashboard, Scenario Analysis, Allocation Builder, and Holdings Screener sheets all flow from the same input cells.

Sector valuation dashboard at a glance: May 2026 S&P 500 sector P/E heatmap

Below is the headline heatmap from the static sample of the template. It shows each of the 11 GICS sectors mapped to its SPDR Select ETF, with the current trailing P/E ratio against its 5-year average, plus the premium or discount that gap implies. This is the kind of table that looks dense in print but turns into a one-glance read when the conditional formatting is applied in Excel.

Data as of 2026-05-09. Source: MarketXLS.

The takeaway is uncomfortable for buy-side narratives that lean either bullish or bearish: the index sits at a 13.7% premium to its 5-year average P/E, but the premium is concentrated in two specific places (Information Technology and Real Estate), while two cyclicals (Energy and Consumer Staples) actually trade at a discount. A single "stocks are expensive" or "stocks are cheap" headline misses the dispersion. A dashboard is the only honest way to read this.

Why a dashboard, not a screener: the case for sector valuation in one view

A traditional screener dumps a hundred rows of data and asks the analyst to do the visual interpretation in their head. That works fine for a single-name watchlist. It falls apart when the universe is the eleven GICS sectors, because the question is not "which sector has the lowest P/E" but "which sector's P/E is most stretched relative to its own history, adjusted for its yield, beta, and momentum."

The sector valuation dashboard answers that with three layers stacked on a single sheet:

1. KPI tile row at the top: the cheapest sector, the most expensive, the highest yield, the best YTD performer, and the index-level P/E. Big numbers, color-coded delta arrows, no scrolling.
2. Color-coded heatmap in the middle: every cell shaded green-amber-red against its column. The eye picks up cheap sectors immediately without having to compare numbers.
3. Charts at the bottom: current P/E vs 5-year average as a side-by-side bar chart, plus YTD return as a horizontal bar chart. These show the magnitude of the spread, not just the rank.

That stack is what separates a presentation-ready workbook from a raw data dump. It is also what makes a dashboard sellable as a standalone tool, not just an internal spreadsheet.

Where the May 2026 sector landscape actually sits

The May 2026 numbers in the heatmap above are not random. They reflect a market that has digested a Q1 earnings cycle which beat consensus on aggregate but with widening dispersion. A few observations the dashboard surfaces in seconds:

Information Technology (XLK) at 30.4x P/E vs 26.1x five-year average. This is a 16.5% premium to its own history, the largest absolute multiple of any sector. Forward P/E sits at 26.1x, which says analysts expect strong earnings growth to compress the multiple back toward historical levels. The yield is 0.62%, the lowest of any sector, so investors are paying for growth without much carry support.

Communication Services (XLC) at 21.6x P/E vs 18.4x average. Similar story to Tech: 17.4% premium, but a much lower absolute multiple. The mix of advertising-driven names and infrastructure carriers means the median holding behaves more like a growth-cyclical than pure tech.

Real Estate (XLRE) at 35.4x P/E vs 31.6x average. REITs always look expensive on a P/E basis because earnings are depressed by depreciation. The 12.0% premium is meaningful only when paired with the 3.74% dividend yield - which is the highest of any sector. This is where a dashboard with multiple columns adds value: a P/E heatmap alone would condemn REITs, while a yield + EV/EBITDA combination tells a more nuanced story.

Energy (XLE) at 12.1x P/E vs 14.2x average. A 14.8% discount to its own history with a 3.41% yield. Energy is the cheapest sector by P/E, P/B, and EV/EBITDA simultaneously. Whether that is a deep-value opportunity or a value trap depends on the next twelve months of crude prices, which the Scenario Analysis sheet explicitly stress-tests.

Consumer Staples (XLP) at 21.3x P/E vs 21.6x average. The only sector trading roughly in line with its 5-year norm. Staples has historically been the "neutral ballast" of the index, and right now it is doing exactly that.

Health Care (XLV) at 17.8x P/E vs 17.4x average. Slightly above average but only by 2.3%. Combined with a -3.2% YTD return and an RSI of 41, the dashboard flags Health Care as the most boring sector on the screen. Boring is sometimes a feature.

This is the interpretive layer that the heatmap enables. Without it, an allocator would need to pull eleven separate factsheets and triangulate manually.

The premium-discount methodology, one column at a time

The "Premium %" column is the single most important number on the dashboard. It is calculated as:

Premium % = (Current P/E TTM / 5-year average P/E) - 1

A positive number means the sector is currently trading above its own historical norm. A negative number means it is below. The 5-year window is a deliberate choice: it captures one near-cycle without going so far back that the regime is unrecognizable. The trailing 12-month earnings denominator is used instead of forward earnings because forward estimates are themselves a function of analyst optimism, and we want a clean valuation read independent of consensus.

In Excel, the calculation is two cells:

=PERatio("XLK")        -> Trailing 12-month P/E for the Tech sector ETF
=D11/E11 - 1            -> Premium vs the 5-year average stored in column E

The 5-year average is a manually researched static input on the template (the only place static data lives), because there is no single MarketXLS function that returns "5-year average P/E for an ETF." A workaround is to compute it from QM_GetHistory("XLK") combined with month-end EPS snapshots, which is documented in the Methodology sheet of the workbook for users who want to make the column fully dynamic.

What's inside the template: a 10-sheet walkthrough

The template ships with eleven sheets total. Here is what each one does and why it earns its tab.

1. Cover

A branded title page with the workbook name, the May 2026 edition tag, the data-as-of date, and a numbered table of contents. No data, no formulas, just the framing the user sees first. Tab color is navy. Gridlines are hidden so the cover looks like a designed cover, not a spreadsheet.

2. How To Use

A step-by-step tutorial. Seven numbered actions walk the user from "open Inputs / Controls" to "review Methodology", with the exact cell references for each step. There is also a MarketXLS Resources block with the website, demo booking, function reference, and help center links. Anyone opening the workbook for the first time can be productive within five minutes.

3. Dashboard

The headline sheet. A KPI tile row across the top showing the S&P 500 P/E TTM, the cheapest sector, the most expensive sector, the highest dividend yield sector, and the best YTD performer. Below the tiles, the sector heatmap with conditional formatting on every numeric column: 3-color scales on P/E and forward P/E, data bars on dividend yield and weight, an icon set on price-to-book, and red-white-green gradient on premium %, YTD %, and 1Y %. Two embedded charts at the bottom: a current vs 5-year P/E side-by-side bar chart, and a horizontal YTD return bar chart.

Frozen panes hold the headers in place when the user scrolls. Gridlines are hidden so the dashboard looks like a dashboard, not a grid. The tab color is MarketXLS blue. The print area is set so a single landscape page captures the whole dashboard for client meetings.

4. Inputs / Controls

Yellow input cells with bold borders. Ten user-tunable parameters: portfolio size, risk tier (Conservative / Base / Aggressive dropdown), scenario (Mean Reversion / Momentum / Status Quo / Recession / Soft Landing dropdown), equity allocation %, cash reserve %, sector tilt magnitude, premium threshold, discount threshold, yield floor, and RSI cap. Below the inputs, a sector universe table where each ETF can be toggled on or off via a Yes/No dropdown, with a weight override column for users who want a custom allocation.

Every other sheet pulls from these input cells. Change the risk tier from Base to Aggressive and the Allocation Builder reweights. Change the premium threshold from 10% to 5% and the Dashboard flag column reclassifies sectors as cheap or expensive.

5. Scenario Analysis

A 11-row by 5-column matrix. Each row is a sector, each column is a scenario, each cell is the modeled 12-month total return under that scenario. Five scenarios are provided:

• Mean Reversion - stretched valuations compress back toward 5-year averages. Tech and Real Estate underperform; Energy and Staples outperform.
• Momentum - leadership extends. YTD winners keep winning. Tech and Communication Services lead.
• Status Quo - multiples stay flat. Total return equals the dividend yield plus a 4% baseline.
• Recession - high-beta cyclicals get hit hardest. Energy and Industrials fall the most. Defensives buffer.
• Soft Landing - inflation cools without a recession. Multiples expand modestly across the board.

A weighted average column blends all five scenarios for users who do not want to commit to one regime view. Conditional formatting flags the best and worst cells per scenario. A summary table below the matrix names the best and worst sector per scenario plus the spread, which is useful for stress-testing position sizing.

6. Allocation Builder

Converts the inputs into a concrete sector weight plan. For each sector the sheet shows the bench weight (the SPY weight), the recommended weight after applying the tilt formula, the dollar allocation at the user's portfolio size, a user override column, and an Action label (Overweight / Neutral / Underweight). A pie chart renders the recommended allocation visually.

The tilt formula combines three signals:

tilt = (-premium × 0.4) + ((yield - 1.3%) × 4) - max(0, RSI - 70) × 0.5%

Cheap sectors get an overweight, high-yielders get an overweight, and overbought sectors (RSI above 70) get pulled back. The tilt is capped at plus or minus 4% so no single sector dominates the portfolio.

7. Sector Holdings Screener

Top 5 holdings of each sector ETF. Eleven sectors times five names equals 55 stocks. Each row shows the ticker, sector, P/E, forward P/E, P/B, dividend yield, EV/EBITDA, beta, YTD %, RSI, and market cap. Conditional formatting on every numeric column flags the most attractive names: green for low P/E, low P/B, and low forward P/E; data bars for dividend yield and market cap; red-amber-green for YTD %; and an inverse gradient for RSI (low = green, high = red).

This is the sheet an analyst pivots to when the dashboard says "Energy looks cheap" and the next question is "which Energy names should I look at first?"

8. Correlation Matrix

A pairwise correlation grid of weekly total returns over the trailing 12 months for the 11 sector ETFs. The 3-color heatmap (green for low, red for high) makes diversification opportunities visible in a single glance. The lowest pair on the May 2026 matrix is Energy vs Utilities at 0.34, which means a long-Energy short-Utility pair has the most diversification benefit on the screen. The highest pair is Utilities vs Real Estate at 0.71, which is unsurprising given both are rate-sensitive long-duration sectors.

9. Sector Comparison

A more detailed side-by-side comparison than the Dashboard heatmap. Eleven metrics across all 11 sectors: P/E TTM, forward P/E, 5-year average P/E, P/B, EV/EBITDA, dividend yield, beta, YTD %, 1Y %, and RSI. Conditional formatting on every column. A stacked bar chart at the bottom showing P/E vs P/B vs EV/EBITDA, which is useful for analysts who want to see whether a sector is expensive on every multiple or just on one.

10. Methodology

One-page explainer of the model. Eight sections cover the universe, the valuation metrics, the premium-discount calculation, the quality and risk overlay, the scenario return functions, the allocation tilt formula, the data sources, and the limitations. This is the sheet an analyst sends to a portfolio committee or compliance reviewer when they need to defend the analysis.

11. Glossary & Disclaimer

Term definitions for every concept used in the workbook (trailing P/E, forward P/E, EV/EBITDA, premium-discount, beta, RSI, mean reversion, momentum, soft landing, bench weight, tilt) plus an educational-only disclaimer. Always read the disclaimer.

MarketXLS formulas powering the dashboard

Every data cell in the live template is a verified MarketXLS function. Below is the exact formula list, which any analyst can copy into their own workbooks.

=PERatio("XLK")                  -> Trailing 12-month P/E ratio
=ForwardPE("XLK")                -> Forward 12-month P/E based on consensus EPS
=PriceToBook("XLK")              -> Price-to-book ratio
=DividendYield("XLK")            -> Trailing 12-month dividend yield
=EnterpriseValueToEBITDA("XLK")  -> EV/EBITDA multiple
=Beta("XLK")                     -> Beta vs the broad market
=ChangePercentYTD("XLK")         -> Year-to-date price return
=RSI("XLK")                      -> 14-period Relative Strength Index
=Last("XLK")                     -> Last traded price
=PreviousClose("XLK")            -> Previous session's close
=SimpleMovingAverage("XLK",252)  -> 252-day simple moving average (1-year SMA)
=MarketCapitalization("AAPL")    -> Market cap of an underlying holding
=Sector("XLK")                   -> GICS sector classification

Dropping any of these into an Excel cell with the MarketXLS Add-In installed returns a live number. Replace "XLK" with any ticker - sector ETF or single stock - and the formula refreshes automatically.

How sector rotation actually works in practice

The dashboard is descriptive: it tells you where every sector is relative to its history. Sector rotation is the prescriptive layer on top: deciding how much capital to actually move from one sector to another based on what the dashboard says.

Most rotation strategies fall into one of three families:

Valuation-driven rotation. Buy the cheapest sectors, sell the most expensive. The premium-discount column is the entire signal. Historically this has produced positive long-term returns but can underperform for years at a time when momentum dominates. The dashboard supports this approach via the discount threshold input and the Allocation Builder's negative-premium tilt coefficient.

Momentum-driven rotation. Buy the sectors with the strongest 6-month or 12-month price performance, sell the laggards. Easier to backtest, harder to execute because turnover is high. The dashboard's YTD column and the Momentum scenario stress-test this view.

Macro regime rotation. Match sector exposure to where you think the macro cycle sits. Late-cycle: overweight Energy and Materials. Recession: overweight Staples, Utilities, and Health Care. Early recovery: overweight Financials, Industrials, and Consumer Discretionary. The Scenario Analysis sheet's five regimes are designed to support this approach.

In practice, most allocators blend all three. The Allocation Builder's tilt formula does exactly that: a valuation component (the premium term), a yield component (the carry term), and a momentum/risk overlay (the RSI cap term). The blend means no single signal can dominate, and the user can dial each weight via the Inputs sheet.

Reading the heatmap: what the colors are actually telling you

The conditional formatting on the Dashboard heatmap is not decoration. It encodes information that would otherwise require hand-eye scanning across eleven rows.

3-color scales on P/E columns: green at the minimum, amber at the median, red at the maximum. The eye sees the cheapest sector immediately. There is no "absolute" threshold - the scale is relative to the universe. If every sector is expensive, the cheapest sector still gets the green; the visual color compresses but the rank is preserved.

Data bars on yield and weight: the bar length is proportional to the value. Useful for columns where the rank is informative but the absolute number is not. Yield columns benefit from data bars because the user cares about "which sector has the most yield" more than "is 3.41% high or low in the abstract."

Icon sets on price-to-book: three arrows (up, flat, down) split by tertile. A simple visual signal for cells where the underlying number is hard to interpret on its own. Price-to-book is a classic example: a P/B of 4 is normal for Tech and absurd for Energy, so an icon set ranked relative to the universe is more informative than the raw number.

Red-white-green on YTD and 1Y: zero is anchored at white. Positive returns are green, negative are red, and the intensity scales with magnitude. This is the only place in the dashboard where zero matters as an absolute reference, because returns above zero and returns below zero are categorically different.

The combination of these four conditional-formatting types is what turns a grid of numbers into a visual map. It is also why the dashboard prints cleanly on a single landscape page - the colors do most of the work, so the user's eye does not have to.

Building this from scratch in MarketXLS

If you want to recreate the dashboard yourself instead of using the template, here is the build order. The whole thing takes about 90 minutes from a blank workbook.

1. Start with the sector universe. List the 11 SPDR Select Sector ETFs in column B starting at row 11. ETFs: XLC, XLY, XLP, XLE, XLF, XLV, XLI, XLK, XLB, XLRE, XLU.

2. Add live price formulas. Column D: =PERatio(B11). Drag down. Column E: 5-year average P/E, manually researched. Column F: =D11/E11 - 1.

3. Add the rest of the columns. Forward P/E with =ForwardPE(B11), P/B with =PriceToBook(B11), dividend yield with =DividendYield(B11), EV/EBITDA with =EnterpriseValueToEBITDA(B11), beta with =Beta(B11), YTD with =ChangePercentYTD(B11), 1-year with =Last(B11)/SimpleMovingAverage(B11,252) - 1.

4. Apply conditional formatting. Select each numeric column and apply the appropriate rule from Excel's Home > Conditional Formatting menu. 3-color scale for P/E and forward P/E. Data bars for yield. Icon set for P/B. Red-white-green gradient for premium % and returns.

5. Add the KPI tile row. Five tiles, each merged across two columns. Tile 1: =PERatio("SPY") for the index-level reading. Tile 2: an INDEX/MATCH that returns the sector name with the lowest P/E. Tile 3: same logic but for the highest P/E. Tile 4: highest dividend yield. Tile 5: best YTD return.

6. Embed the charts. Insert a clustered column chart for current vs 5-year P/E. Insert a bar chart for YTD returns. Style both with the MarketXLS blue and a clean white background.

7. Build the Inputs sheet. Yellow input cells with data validation dropdowns for risk tier and scenario. Reference these cells from every other sheet so the workbook is fully parameterized.

8. Add the Scenario Analysis matrix. 11 rows, 5 columns. Each cell is a function of the live valuation premium, dividend yield, beta, and YTD - the exact formulas are in the Methodology sheet of the template.

9. Hide gridlines on Cover and Dashboard. View > uncheck Gridlines. Set tab colors. Freeze panes everywhere.

10. Set the print area. On the Dashboard, Page Layout > Print Area > Set Print Area covering the whole dashboard. Page Setup > Landscape > Fit to 1 page wide.

The template ships with all of this pre-built, but recreating it once is the fastest way to understand how every formula connects to every other.

Sector valuation versus single-name valuation: which one matters?

A reasonable objection to a sector dashboard is "I do not buy sectors, I buy individual stocks." Fair. But sector valuation still matters for two reasons.

First, sector P/E is the gravity pulling on every name in that sector. If Tech is at 30x trailing earnings, the median Tech name is also somewhere in that neighborhood, and a "cheap" Tech name at 22x is only cheap relative to Tech, not to the market. Sector context calibrates the single-name reading.

Second, sector exposure dominates portfolio variance. Empirical work going back to Brinson, Hood, and Beebower in the 1990s shows that sector and asset-class allocation drives the majority of long-horizon return variance, with single-name selection as a secondary effect. An allocator who gets the sector mix right but misses on the individual stock picks generally outperforms an allocator who gets the picks right but is overweight the wrong sector.

The dashboard is therefore most useful as the first stop in a workflow: pick the sectors, then pick the names. The Sector Holdings Screener sheet exists exactly for that hand-off.

Limitations and honest caveats

A premium-grade dashboard is only useful if it is honest about what it cannot do. Five caveats worth naming.

Sector ETFs are not pure sector exposure. XLK is concentrated in mega-cap tech. XLE has a heavy oil-and-gas tilt. The top 5 holdings of each ETF often drive 50% or more of its return. The Sector Holdings Screener helps quantify this concentration.

Trailing P/E is a backward-looking snapshot. It says nothing about earnings revisions, guidance changes, or the rate of change in margins. The forward P/E column partially addresses this but inherits the analyst-bias problem.

5-year average is a regime assumption. If the next five years look different from the last five (because of interest rate regime, AI capex, energy transition, or any of a dozen other factors), the premium-discount calculation systematically over- or underestimates the gravitational pull.

Scenario returns are illustrative, not forecasts. The five regimes are stress tests. The numbers should not be read as expected returns. They are meant to bound the user's thinking, not to provide a base case.

The dashboard is educational. It is not investment advice. Every cell is intended to inform an analytical process, not to produce a buy or sell signal. Talk to a licensed advisor before acting on anything in the workbook.

Download the template

Both files are free to download. The static sample is filled with current May 2026 numbers and includes a comment on every data cell showing the MarketXLS formula that produced it. The live formula version has zero static data and refreshes whenever you open it (with the MarketXLS Add-In installed).

Download the templates:

• - Pre-filled with the May 2026 sector valuation data, every cell annotated with its MarketXLS formula via Excel comments.
• - Live-updating formulas across all 10 sheets. Open in Excel with the MarketXLS Add-In and every number refreshes.

The workbook is designed to look presentation-ready out of the box. Branded cover page, KPI tile dashboard, color-coded heatmap, scenario analysis, allocation builder, holdings screener, correlation matrix, methodology, and glossary - all in one file.

FAQ

What is a sector valuation dashboard in Excel?

A sector valuation dashboard in Excel is a one-screen view that shows current valuation metrics (P/E, P/B, EV/EBITDA, dividend yield) for each major industry sector, typically benchmarked against historical averages. The dashboard format lets an analyst spot which sectors are stretched and which are cheap without having to read individual sector factsheets. The MarketXLS template provides this in a 10-sheet workbook with KPI tiles, heatmaps, scenario analysis, and an allocation builder.

How is the sector P/E heatmap calculated?

Each sector's current P/E is divided by its 5-year average P/E to compute a premium or discount percentage. A positive premium means the sector is currently more expensive than its own historical norm; a negative premium means it is cheaper. The MarketXLS formula =PERatio("XLK") returns the live trailing P/E for any sector ETF or stock, and the 5-year average is either manually researched or computed from a QM_GetHistory() series.

Which sectors look cheap in May 2026?

Based on the May 2026 snapshot in the template, Energy (XLE) is the cheapest sector by trailing P/E (12.1x vs 14.2x five-year average, a 14.8% discount), with the highest dividend yield among major sectors at 3.41%. Consumer Staples (XLP) trades roughly in line with its historical norm. These observations are educational only and do not constitute a recommendation; sector multiples can stay below historical averages for extended periods.

What is the difference between sector rotation and a sector valuation dashboard?

A sector valuation dashboard is descriptive: it shows where every sector currently sits relative to its history. Sector rotation is the prescriptive strategy of moving capital between sectors based on those readings. The dashboard supports rotation but does not require it - a long-only allocator can use it purely for context, while a tactical allocator can drive over- and underweight decisions from it.

How do I keep the dashboard updated daily?

The live MarketXLS formula version of the template refreshes every time you open it with the MarketXLS Add-In installed. Every P/E, forward P/E, P/B, EV/EBITDA, dividend yield, beta, and YTD return is a live function that pulls from the MarketXLS data feed. The 5-year average P/E column is the only static input; users who want it dynamic can replace it with a calculation off =QM_GetHistory().

Can I use this dashboard for international or factor-based ETFs?

Yes. The template is structured around sector ETFs but every formula works on any ticker MarketXLS supports. Replace XLK, XLF, XLV with international ETFs (e.g. EWJ, EWG, FXI), factor ETFs (MTUM, QUAL, USMV), or single stocks, and the entire dashboard - heatmap, scenario analysis, allocation builder - reflows. The Inputs sheet's universe block makes this swap trivial.

Is the sector valuation dashboard a buy signal?

No. The dashboard is educational. A sector trading at a discount to its 5-year average can be either a value opportunity or a signal that earnings will deteriorate further. The Methodology and Glossary sheets explicitly disclaim any predictive interpretation. Use the workbook as one input among many, alongside fundamental research, macro analysis, and your own risk constraints.

The bottom line

Sector valuation dashboards earn their place on an allocator's desk because they collapse a 100-row data dump into a single visual read. The May 2026 snapshot says the index sits at a 13.7% premium to its 5-year average P/E, but the premium is concentrated: Tech and Real Estate are stretched, Energy is genuinely cheap, and Consumer Staples is the only sector trading right on its historical norm. A dashboard is the only honest way to communicate that dispersion in one screen.

The free premium-grade template above gives you the full workbook: branded cover, KPI tile dashboard, heatmap, scenario analysis, allocation builder, holdings screener, correlation matrix, methodology, and glossary. Every live number is a verified MarketXLS function. Open it in Excel, change the inputs, and watch the whole dashboard recompute.

If you want to dig deeper into MarketXLS for live financial data, screener tools, options analytics, and portfolio dashboards, browse the full feature list at marketxls.com or schedule a walkthrough at marketxls.com/book-demo.