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Rental Comp Analyzer Workbook — User Manual
Instruction Manual · How to use this template
The Rental Comp Analyzer Workbook helps landlords, property managers, and asset managers compare rental listings in their market, identify which of their own units are under-rented, and build a data-backed 3-year rent-up plan with net-present-value analysis. The workflow moves left to right across the sheets: first you enter comparable rental listings, then you enter your own units so the workbook can score each comp's relevance and quantify the rent gap, and finally the Dashboard rolls everything up into portfolio-level KPIs and charts. Every column marked with ƒ is auto-computed — you never type in those cells.
⚡ Quick start
1Step 1 — Open the Read Me sheet and review the color legend: blue-header columns are where you type; gray-header columns with ƒ are formulas that update automatically.
2Step 2 — Go to Comp Listings and enter at least 5–10 comparable rental listings from Zillow, Apartments.com, or your local MLS. Fill in every blue column (Address through Listed) for each comp.
3Step 3 — Switch to Unit Analysis and enter each of your own units (Unit, Address, Beds, Sqft, Current rent, and Lease End). The sheet instantly calculates market rent, gap, priority, and a 3-year forecast.
4Step 4 — Open the Dashboard to see portfolio-wide KPIs (total annual loss, average gap %, comp quality scores) and neighborhood-level charts that are ready for investor reports or internal review.
5Step 5 — Use the built-in ✨ AI side-panel to ask questions like 'Which unit has the biggest gap?' or 'Explain the Fit Score formula' for on-demand analysis without leaving the workbook.
Comp Listings is where you enter every comparable rental listing you find in your target market. The sheet auto-scores each comp for relevance to your units using price-per-square-foot, bedroom ranking, area premiums, z-scores, and a composite Fit Score so you can immediately see which comps matter most.
✍️ Step by step
11. Start in column A (Address) and enter the full street address of each comparable listing — one listing per row.
22. Fill Neighborhood (the sub-market or zip name), Beds, Baths, Sqft, Condition (e.g. Excellent / Good / Fair / Poor), Rent (the listed monthly rent), Source (e.g. Zillow, MLS, Apartments.com), and Listed (the date the listing appeared, in MM/DD/YYYY format).
33. As soon as you complete a row, the ten ƒ columns to the right populate automatically: $/SF, Days Listed, Bed Rank, Area Prem, Rent/Bed, Adj Rent, Rank %, Z-Score, Fit Score, Volatility, and Rent Dist.
44. Enter at least 5 comps — ideally 10–20 — to give the statistical measures (Z-Score, IQR, Volatility) enough data to be meaningful.
55. Review the KPI cards at the top of the sheet (Beds, Baths, Sqft, Rent, $/SF, Area Prem) to confirm your dataset looks reasonable before moving on.
66. Check the Rent Trend chart to spot any outlier comps that may skew averages — delete or correct those rows.
77. Look at the Fit Score chart to see which comps the workbook considers most relevant; a higher Fit Score means the comp closely matches your portfolio profile.
88. This sheet feeds Unit Analysis: when you later enter your units, the workbook uses COUNTIFS, AVERAGEIFS, and FILTER formulas to pull matching comps and compute market rents automatically.
📋 Column-by-column
| Address | Type the full street address of the comparable listing (e.g. '742 Evergreen Terrace, Springfield'). Use a consistent format so the workbook can group comps visually. |
| Neighborhood | Enter the neighborhood name, sub-market, or zip code area (e.g. 'Downtown', 'Midtown 30308'). This value drives the ƒArea Prem and the Dashboard's neighborhood-level roll-ups, so be consistent across rows. |
| Beds | Enter the number of bedrooms as a whole number (e.g. 2). Studio apartments should be entered as 0. This feeds the ƒBed Rank and ƒRent/Bed calculations. |
| Baths | Enter the number of bathrooms (e.g. 1, 1.5, 2). Half-baths count as 0.5. |
| Sqft | Enter the total square footage as a whole number (e.g. 950). This is used to compute ƒ$/SF (rent per square foot), one of the most important normalization metrics. |
| Condition | Type a condition rating for the unit: Excellent, Good, Fair, or Poor. This qualitative input is factored into the ƒAdj Rent calculation to normalize rents across different finish levels. |
| Rent | Enter the monthly asking rent in dollars without a dollar sign (e.g. 1850). This is the raw rent before any adjustments. |
| Source | Type the name of the listing source (e.g. 'Zillow', 'Apartments.com', 'Broker'). Helps you audit data quality and track where your best comps come from. |
| Listed | Enter the date the listing was posted or first appeared, in MM/DD/YYYY format (e.g. 03/15/2025). This drives ƒDays Listed and the Days on Market chart. |
| ƒ$/SF | Rent per Square Foot. Calculated as Rent ÷ Sqft. A normalized measure that lets you compare units of different sizes on an apples-to-apples basis. Typical ranges vary by market; in most US metros, $1.00–$3.50/SF is common. Higher values indicate premium pricing relative to size. |
| ƒDays Listed | Days Listed (also called Days on Market). Calculated as today's date minus the Listed date. Shows how long a comp has been on the market. A low number (under 14 days) suggests strong demand at that price point; a high number (over 45 days) may indicate overpricing. |
| ƒBed Rank | Bedroom Rank. Ranks each comp's rent relative to other comps with the same bedroom count. A rank of 1 means this comp has the highest rent among its bedroom peer group. Use this to see where a listing sits in the pricing hierarchy for its unit type. |
| ƒArea Prem | Area Premium. Measures the percentage by which a comp's neighborhood average rent exceeds (or falls below) the overall dataset average. A positive Area Prem (e.g. +12%) means that neighborhood commands higher rents; a negative value means it trades at a discount. Feeds the Area Rent Premium chart. |
| ƒRent/Bed | Rent per Bedroom. Calculated as Rent ÷ Beds (studios use 1 as the divisor). Useful for comparing how efficiently rent scales with bedroom count. A 2-bed at $2,000 ($1,000/bed) may be a better value than a 1-bed at $1,500 ($1,500/bed). |
| ƒAdj Rent | Adjusted Rent. The comp's rent after normalizing for condition differences. For example, a 'Fair' condition comp's rent may be adjusted upward to estimate what it would fetch in 'Good' condition. This makes cross-comp comparisons more accurate. |
| ƒRank % | Rank Percentile. Shows where this comp falls in the overall rent distribution as a percentile (0–100%). A comp at 85% is priced higher than 85% of all comps in the dataset. Useful for quickly spotting top-tier and bottom-tier listings. |
| ƒZ-Score | Z-Score. Measures how many standard deviations a comp's rent is from the dataset mean. A Z-Score of 0 means the rent equals the average; +1.5 means it is 1.5 standard deviations above average (relatively expensive); −1.5 means well below average. Values beyond ±2 are statistical outliers worth investigating. |
| ƒFit Score | Fit Score. A composite relevance score (typically 0–100) that combines bedroom match, square footage similarity, neighborhood proximity, condition parity, and recency to rate how well this comp matches your portfolio. Higher is better. Comps above 70 are strong matches; below 40 are weak and may skew your analysis. |
| ƒVolatility | Volatility. Measures the spread or variability of rents in this comp's bedroom/neighborhood peer group. High volatility (e.g. above 15%) means rents in that segment are unpredictable, so setting a target rent carries more risk. Low volatility (below 8%) signals a stable, well-established price band. |
| ƒRent Dist | Rent Distance. The absolute dollar difference between this comp's rent and the median rent of all comps with the same bedroom count. A large positive distance means the comp is priced well above the median; a large negative distance means it is significantly cheaper. Helps identify pricing outliers quickly. |
| ƒIQR | Interquartile Range. The difference between the 75th-percentile rent and the 25th-percentile rent within this comp's bedroom peer group. A narrow IQR means most comps cluster tightly around the median (strong pricing consensus); a wide IQR means the market is fragmented with widely varying asking rents. |
📊 Reading the numbers
• KPI cards at the top show dataset-wide averages for Beds, Baths, Sqft, Rent, $/SF, and Area Prem — use them as a sanity check that your comps broadly match your portfolio profile.
• Rent Trend chart plots listed rents over time; an upward slope means the market is appreciating, strengthening your case for rent increases.
• Rent vs Adjusted chart compares raw rents to condition-adjusted rents — large gaps between the two bars for a comp mean its condition is significantly different from the norm.
• Rent per Sqft chart helps you spot outliers: any bar dramatically taller or shorter than its neighbors may be a data-entry error or a luxury/distressed anomaly.
• Days on Market chart reveals demand: comps with short bars (few days listed) confirm that the asking rent is market-accepted; long bars suggest overpricing.
• Area Rent Premium chart shows which neighborhoods command premiums — this directly informs where you should target rent increases most aggressively.
• Fit Score chart ranks comps by overall relevance to your portfolio. Prioritize analysis on high-Fit-Score comps and consider removing very low scorers to tighten your dataset.
⚠️ Avoid these mistakes
• Entering rent with a dollar sign or comma (type 1850, not $1,850) — non-numeric entries will break formulas downstream.
• Using inconsistent neighborhood names (e.g. 'Downtown' in one row and 'downtown' in another) — the formulas are case-sensitive for grouping, and mismatches will split your data into separate buckets.
• Entering too few comps — with fewer than 5 listings, statistical measures like Z-Score, Volatility, and IQR become unreliable.
• Forgetting to enter the Listed date — without it, ƒDays Listed returns an error and the Days on Market chart will have gaps.
💡 Tips• Sort by ƒFit Score descending to quickly focus on the most relevant comps and hide or delete low-scorers that add noise.
• Use the Source column to track data freshness — if most of your comps are from a single source, cross-check with a second source to avoid bias.
• Re-pull comps every 30–60 days to keep the dataset current; stale listings distort market-rent estimates on Unit Analysis.
• Use conditional formatting or the AI assistant to highlight comps with a Z-Score beyond ±2 for quick outlier review.
Unit Analysis is the heart of the workbook. You enter each of your own rental units here, and the sheet automatically pulls matching comps from Comp Listings to calculate market rent, quantify the rent gap in dollars and percentage, assign a priority tier, estimate annual revenue loss, and project a 3-year rent-up trajectory with NPV and payback analysis.
✍️ Step by step
11. In the first section (columns A onward), enter each unit's details: Unit identifier (e.g. 'Unit 3A'), Address, Beds, Sqft, Current monthly rent, and Lease End date (MM/DD/YYYY).
22. The formula columns instantly populate: ƒMkt Rent, ƒGap $, ƒGap %, ƒPriority, ƒDays Left, ƒAnn Loss, ƒComps, ƒCapture, ƒMkt $/SF, ƒLoss %, ƒUnit $/SF, ƒStep-Up, ƒAt-Risk, ƒSpread, ƒGap NPV, ƒLoss Rank, ƒConf %, ƒPayback, ƒYield %, and ƒForecast.
33. Review the ƒPriority column first — it flags each unit as High, Medium, or Low urgency so you know where to act soonest.
44. Scroll right to the 3-Year Forecast section (ƒPeriod, Actual, Market, ƒGap, Cum Gain, ƒCapture, ƒROI %, ƒPV Gain, ƒCAGR, ƒMkt CAGR). This section models year-over-year rent growth and shows cumulative gains if you raise rent to market levels.
55. In the Rent History / Tracker section at the far right, log each rent change: Unit, Eff. Date, Old Rent, New Rent, Lease term, Notice period, and Status. The ƒ columns calculate the dollar increase, percentage increase, and annualized impact automatically.
66. Use the Current Rent vs Gap chart to visually compare how much each unit is below market.
77. Check the 3-Year Rent Trajectory chart to present a compelling case to ownership or investors about projected revenue recovery.
88. Sort by ƒAnn Loss descending to prioritize the units costing you the most money each year.
99. This sheet feeds the Dashboard via COUNTIFS, AVERAGEIFS, SUMIFS, and INDEX formulas — every unit you add here automatically updates the portfolio-level view.
📋 Column-by-column
| Unit | Type a unique identifier for the unit (e.g. '1A', 'Suite 204', 'House-Main'). This label appears on charts and in the Rent History tracker, so keep it short and recognizable. |
| Address | Enter the property address for this unit (e.g. '100 Oak St'). If multiple units share an address, repeat it for each row — the workbook groups by Unit, not Address. |
| Beds | Enter the number of bedrooms as a whole number (e.g. 1). This is matched against Comp Listings beds to pull relevant comps via FILTER/AVERAGEIFS. |
| Sqft | Enter the unit's total square footage (e.g. 750). Used to calculate ƒUnit $/SF and to match against comps of similar size. |
| Current | Enter the unit's current monthly rent in dollars (e.g. 1200). This is the baseline the workbook uses to calculate the gap to market rent. |
| ƒMkt Rent | Market Rent. The estimated fair-market monthly rent for this unit, derived by averaging the adjusted rents (ƒAdj Rent) of the best-matching comps from Comp Listings, weighted by Fit Score. If this number seems off, check that your Comp Listings data has enough comps with the same bedroom count and neighborhood. |
| ƒGap $ | Gap Dollars. Calculated as ƒMkt Rent minus Current. A positive value means you are under-rented by that dollar amount per month. For example, a Gap $ of $350 means you could be collecting $350 more per month. A value of $0 or negative means you are at or above market. |
| ƒGap % | Gap Percentage. Calculated as ƒGap $ ÷ ƒMkt Rent × 100. Expresses the rent gap as a percentage of market rent. A gap of 15% or more is typically considered significant and worth acting on. Values above 25% are flagged as high priority. |
| ƒPriority | Priority / Urgency Tier. An automatic label — High, Medium, or Low — based on a combination of ƒGap %, ƒDays Left (time until lease renewal), and ƒAnn Loss. High-priority units have large gaps AND upcoming lease expirations, meaning you should prepare a rent-increase notice immediately. |
| Lease End | Enter the lease expiration date in MM/DD/YYYY format (e.g. 09/30/2025). This drives ƒDays Left and is factored into ƒPriority — units with leases ending soon and large gaps get flagged as High priority. |
| ƒDays Left | Days Left until lease expiration. Calculated as Lease End minus today's date. A low number (under 60 days) means you need to act quickly if you plan to adjust rent at renewal. Negative values mean the lease has already expired (month-to-month tenant). |
| ƒAnn Loss | Annual Loss. Calculated as ƒGap $ × 12. The total revenue you forgo each year by renting below market. For example, a $300/month gap translates to $3,600/year in lost revenue. This is one of the most impactful numbers in the workbook — sort by it to find your biggest revenue leaks. |
| ƒComps | Comp Count. The number of comparable listings from the Comp Listings sheet that match this unit's bedroom count and neighborhood. A higher count (5+) means the market-rent estimate is more reliable. If this shows 0–2, go back to Comp Listings and add more relevant comps. |
| ƒCapture | Capture Rate. The percentage of the market-rent gap you would recover if you raised rent to the ƒMkt Rent level. At 100%, you have fully closed the gap. Values below 100% appear in the 3-Year Forecast section to show gradual step-up scenarios where you raise rent incrementally over multiple years rather than all at once. |
| ƒMkt $/SF | Market Rent per Square Foot. Calculated as ƒMkt Rent ÷ Sqft. Lets you compare your units' market value on a size-normalized basis. Typical healthy ranges depend on your metro; compare this to the ƒ$/SF values on Comp Listings to ensure alignment. |
| ƒLoss % | Loss Percentage. Calculated as ƒAnn Loss ÷ (ƒMkt Rent × 12) × 100. Shows the annual revenue loss as a percentage of the potential annual market revenue. A Loss % above 10% is a red flag; above 20% signals urgent action needed. |
| ƒUnit $/SF | Unit Rent per Square Foot. Calculated as Current ÷ Sqft. This is what you are currently charging per square foot. Compare it to ƒMkt $/SF — if ƒUnit $/SF is significantly lower, you have room to increase rent. |
| ƒStep-Up | Step-Up Amount. The recommended incremental rent increase per renewal period (typically annual) to close the gap over the 3-year plan without shocking the tenant. For example, a $600 gap might produce a Step-Up of $200/year over three renewals. This value feeds the 3-Year Forecast section. |
| ƒAt-Risk | At-Risk Flag. A Yes/No indicator highlighting units where the combination of a large rent gap and an expiring lease creates a risk of tenant turnover if rent is raised aggressively. Use this flag to decide whether to apply the full Step-Up or a smaller, retention-friendly increase. |
| ƒSpread | Spread. The difference between your unit's current $/SF and the average $/SF of its comp set. A large negative spread means you are well below market on a per-square-foot basis. A spread near zero means you are priced in line with comps. |
| ƒGap NPV | Gap Net Present Value. The present value of closing the rent gap over the 3-year forecast period, discounted at a standard rate (typically 6–8%). This tells you, in today's dollars, how much future revenue you gain by raising rent to market. Higher values justify the effort and potential turnover risk of a rent increase. |
| ƒLoss Rank | Loss Rank. Ranks all units from 1 (highest annual loss) to N (lowest annual loss). Unit ranked 1 is bleeding the most revenue and should be your top priority. Use this alongside ƒPriority for a comprehensive triage. |
| ƒConf % | Confidence Percentage. A measure of how reliable the market-rent estimate is, based on the number and quality (Fit Score) of matching comps. Above 80% is high confidence; 50–80% is moderate (consider adding more comps); below 50% means the estimate is speculative and you should gather more data before acting. |
| ƒPayback | Payback Period. The number of months it takes for the cumulative rent increase to offset any expected vacancy or turnover costs associated with the increase. A short payback (under 3 months) means the increase pays for itself quickly even if the tenant leaves. A long payback (over 6 months) suggests a more cautious, stepped approach. |
| ƒYield % | Yield Percentage. Calculated as ƒAnn Loss recovered ÷ estimated turnover cost × 100. A yield above 200% means the annual gain from closing the gap is at least double the one-time cost of potential vacancy — a strong green light. Below 100% means the increase may not be worth the risk in the short term. |
| ƒForecast | Forecast Flag. Indicates whether a 3-year projection has been generated for this unit. Shows 'Yes' when enough comp data exists to model the trajectory. If 'No,' add more comps to Comp Listings for this unit's bedroom count and neighborhood. |
| ƒPeriod | Period. The time interval label in the 3-Year Forecast section (e.g. Year 0, Year 1, Year 2, Year 3). Year 0 is the current state; Years 1–3 project rent increases at the ƒStep-Up rate. |
| Actual | In the 3-Year Forecast section, enter (or confirm) the actual rent you are charging or plan to charge in each period. Year 0 should match your Current rent. For future years, enter your planned rent or leave blank to use the Step-Up projection. |
| Market | In the 3-Year Forecast section, enter the projected market rent for each future year. You can override the formula-suggested values if you have your own market growth assumptions (e.g. 3% annual appreciation). |
| ƒGap | Forecast Gap. Calculated as Market minus Actual for each period in the 3-Year Forecast. Shows whether the rent gap is closing, staying flat, or widening over time. Ideally this shrinks toward $0 by Year 3. |
| Cum Gain | Cumulative Gain. The running total of additional rent collected (above your Year 0 baseline) across all periods. Enter or verify this value; it may also auto-calculate depending on your inputs. By Year 3, this should represent the total incremental revenue earned from your rent-up plan. |
| ƒCapture | Forecast Capture Rate. For each period, shows the percentage of the market-rent gap you have closed. At Year 0 it may be 0%; by Year 3 the goal is 90–100%, meaning you have brought rents to market levels. |
| ƒROI % | Return on Investment Percentage. Calculated as Cum Gain ÷ estimated implementation costs (turnover, vacancy, admin) × 100 for each forecast period. An ROI above 100% by Year 1 is excellent; it means you have recouped your costs within the first year of increases. |
| ƒPV Gain | Present Value of Gain. The discounted (NPV) value of the cumulative gain at each forecast period. This is the number to cite in investor presentations — it shows the real economic value of your rent-up plan in today's dollars. |
| ƒCAGR | Compound Annual Growth Rate of your actual rent over the forecast period. Calculated as (Year 3 Actual ÷ Year 0 Actual)^(1/3) − 1. A CAGR of 5–8% is typical for an active rent-up strategy; above 10% may signal aggressive increases that carry tenant-retention risk. |
| ƒMkt CAGR | Market Compound Annual Growth Rate. The CAGR of market rents over the same 3-year period. Compare ƒCAGR to ƒMkt CAGR: if your CAGR exceeds Market CAGR, you are closing the gap; if it is lower, the gap is widening despite your increases. |
| Unit | In the Rent History / Tracker section, enter the unit identifier (e.g. '1A') to log a specific rent change event. Must match the Unit name used in the main unit table for cross-referencing. |
| Eff. Date | Effective Date. Enter the date the rent change takes effect in MM/DD/YYYY format (e.g. 10/01/2025). This creates a historical record of all rent adjustments. |
| Old Rent | Enter the monthly rent before the change (e.g. 1200). This is used to compute the dollar and percentage increase. |
| New Rent | Enter the new monthly rent after the change (e.g. 1400). Must be greater than Old Rent for an increase; can be equal for a flat renewal. |
| ƒIncrease | Increase Dollars. Calculated as New Rent minus Old Rent. Shows the absolute dollar amount of the rent change. For example, $200 means you raised rent by $200/month. |
| ƒIncr % | Increase Percentage. Calculated as ƒIncrease ÷ Old Rent × 100. A typical market increase is 3–5% annually; increases above 10% may require additional tenant communication or be subject to local rent-regulation limits. |
| ƒAnn Impact | Annualized Impact. Calculated as ƒIncrease × 12. The total additional annual revenue generated by this single rent change. Useful for aggregating total portfolio gains from all increases logged in this section. |
| Lease | Enter the lease term associated with this rent change (e.g. '12 months', '6 months', 'MTM' for month-to-month). Helps you track whether longer leases correlate with smaller increases and vice versa. |
| Notice | Enter the notice period given to the tenant before the increase (e.g. '60 days', '30 days'). Important for compliance tracking — many jurisdictions require minimum notice periods for rent changes. |
| Status | Type the current status of this rent change: Planned, Notified, Accepted, or Declined. Update this field as the increase moves through your workflow so you have a living record of where each increase stands. |
📊 Reading the numbers
• KPI cards show Monthly Gap (total dollars/month your portfolio is under-rented), Annual Loss (Monthly Gap × 12), Wtd Gap (weighted average gap % across all units, weighted by rent), Beds (total bedrooms in portfolio), Sqft (total square footage), and Current (average current rent). If Annual Loss is above $10,000, prioritize the High-priority units immediately.
• Current Rent vs Gap chart uses stacked bars to show each unit's current rent alongside the dollar gap — taller gap segments signal the biggest opportunities.
• Annual Loss by Unit chart ranks units by annualized revenue loss — the tallest bar is your most costly under-rented unit.
• Gap % by Unit chart normalizes losses as percentages so you can compare units of different sizes and rents fairly.
• Loss Distribution chart (histogram) shows how many units fall into each gap-percentage bucket — a right-skewed distribution means most units are moderately under-rented with a few extreme outliers.
• 3-Year Rent Trajectory chart plots your actual vs. market rent over the forecast period — converging lines mean your rent-up plan is working; diverging lines mean you are falling further behind market.
• Cumulative Gain chart shows total incremental revenue earned over the 3-year plan — present this to stakeholders to demonstrate ROI.
⚠️ Avoid these mistakes
• Entering Current rent as annual instead of monthly — all rents in this workbook are monthly figures.
• Leaving Lease End blank — without it, ƒDays Left and ƒPriority cannot calculate, and the unit will not be flagged for action even if it has a large gap.
• Overriding formula columns (gray ƒ headers) with typed values — this breaks the comp-matching logic. If a formula is showing an error, the fix is almost always on the Comp Listings sheet (add more comps), not in this cell.
• Entering different unit names in the main table and the Rent History tracker (e.g. 'Unit 1A' vs '1A') — the tracker will not link back to the correct unit.
💡 Tips• Sort by ƒLoss Rank to create a prioritized action list for your next rent-increase cycle.
• Use the ƒConf % column to decide where to invest time gathering more comps — low confidence units need better data before you can act.
• Enter Actual rents in the 3-Year Forecast section as you execute increases to track real vs. projected performance over time.
• Log every rent change in the Rent History tracker, even flat renewals — this builds a record you can reference at tax time or during investor reporting.
The Dashboard aggregates data from both Comp Listings and Unit Analysis into a single portfolio-level view. It shows neighborhood-level comp statistics, portfolio KPIs, and six charts designed for executive summaries, investor reports, or internal asset management reviews.
✍️ Step by step
11. This sheet is fully automatic — there are no input columns. All data is pulled from Comp Listings and Unit Analysis via COUNTIFS, AVERAGEIFS, SUMIFS, and INDEX formulas.
22. Review the neighborhood summary table: each row shows a neighborhood from your Comp Listings with its comp count, average rent, average $/SF, average Fit Score, and average Days on Market.
33. Check the KPI cards across the top: Avg Mkt (average market rent), Avg Curr (average current rent across your units), Total Loss (sum of all units' annual losses), Avg Gap % (portfolio-wide average gap), Avg Score (average Fit Score of all comps), and $/SF (portfolio average rent per square foot).
44. Use the Market vs Current Rent chart to see the gap visually across your portfolio — the wider the spread between the two bars, the more revenue you are leaving on the table.
55. Review Annual Loss by Beds and Loss by Bed Type charts to determine which bedroom configurations are most under-rented — this informs where to focus comp research.
66. Examine Avg Rent by Neighborhood and Comp Quality by Area to assess which sub-markets offer the strongest rent-increase opportunities and which have the most reliable comp data.
77. Days on Market by Area reveals neighborhood-level demand — areas with low DOM have strong absorption and can support more aggressive increases.
📋 Column-by-column
| Neighborhood | Auto-populated. Lists each unique neighborhood name from the Comp Listings sheet. No input needed. |
| ƒComps | Comp Count by Neighborhood. The number of comparable listings entered on Comp Listings for this neighborhood. More comps mean more reliable averages. Aim for at least 3–5 comps per neighborhood. |
| ƒAvg Rent | Average Rent by Neighborhood. The mean monthly rent of all comps in this neighborhood. Compare across rows to see which neighborhoods command the highest rents. |
| ƒAvg $/SF | Average Rent per Square Foot by Neighborhood. Normalizes rents by unit size for cross-neighborhood comparison. Higher $/SF neighborhoods are typically more urban, renovated, or amenity-rich. |
| ƒScore | Average Fit Score by Neighborhood. The mean Fit Score of comps in this neighborhood. A high average score (above 70) means the comps in this area closely match your portfolio; a low score (below 40) means the comps may not be representative of your units. |
| ƒAvg DOM | Average Days on Market by Neighborhood. The mean number of days comps in this neighborhood have been listed. Low DOM (under 20 days) indicates strong demand and fast absorption — good news for landlords planning increases. High DOM (above 45 days) suggests softer demand. |
📊 Reading the numbers
• Avg Mkt vs Avg Curr KPI cards: the difference is your portfolio-wide average monthly gap. Multiply by your unit count × 12 for a quick annual loss estimate.
• Total Loss KPI is the sum of all ƒAnn Loss values from Unit Analysis — this is the single most important number in the workbook. It answers: 'How much money am I leaving on the table each year?'
• Market vs Current Rent chart shows paired bars per unit or bedroom type — look for the widest gaps as immediate action items.
• Annual Loss by Beds chart helps you decide which unit type to focus on: if 2-beds are losing the most, prioritize finding more 2-bed comps and increasing those rents first.
• Loss by Bed Type chart (pie or donut) shows the proportion of total loss attributable to each bedroom configuration — a dominant slice means that bed type is your biggest revenue leak.
• Avg Rent by Neighborhood chart helps you benchmark your rents against sub-market averages — if your units are in a high-rent neighborhood but your Current is low, the opportunity is clear.
• Comp Quality by Area chart plots Fit Scores by neighborhood — neighborhoods with low scores need more or better comps before you can confidently set rent targets.
• Days on Market by Area chart reveals absorption speed — combine this with loss data to prioritize increases in high-demand, high-gap neighborhoods.
⚠️ Avoid these mistakes
• Trying to type into the Dashboard — all cells are formula-driven. If you need to change data, go back to Comp Listings or Unit Analysis.
• Ignoring neighborhoods with low ƒComps counts — averages based on 1–2 comps are unreliable and may lead to over- or under-estimating market rent.
💡 Tips• Screenshot or export the Dashboard charts for investor decks or board presentations — the charts are designed to tell a clear story at a glance.
• If Total Loss seems surprisingly high, double-check your Comp Listings for stale or outlier listings that may be inflating market-rent estimates.
• Use the neighborhood summary table to decide where to focus your next round of comp research — neighborhoods with few comps and high average rents deserve deeper investigation.
• Filter or color-code the Dashboard by bedroom type if you manage a large portfolio with mixed unit types — this helps isolate the revenue story for each segment.
📖Glossary — what every value means
| $/SF | Dollars per Square Foot. Calculated as monthly rent divided by the unit's square footage. This normalizes rent across different-sized units so you can compare them fairly. In most US metros, rental $/SF ranges from $1.00 to $3.50; higher-cost markets may exceed $4.00. |
| DOM (Days on Market) | Days on Market. The number of days a listing has been actively available for rent. Calculated as today's date minus the listing date. Under 14 days is fast absorption (strong demand); over 45 days suggests the unit may be overpriced. Also abbreviated as Days Listed. |
| Adj Rent | Adjusted Rent. A comp's listed rent after normalization for unit condition (Excellent/Good/Fair/Poor). This removes condition bias so you can compare rents across units of different quality levels on an equal footing. |
| Z-Score | A statistical measure showing how many standard deviations a data point (here, a comp's rent) is from the mean of the dataset. A Z-Score of 0 is perfectly average; ±1 is normal variation; beyond ±2 is a statistical outlier. Used to flag comps whose pricing is unusually high or low. |
| IQR (Interquartile Range) | Interquartile Range. The spread between the 25th and 75th percentile of rents within a peer group. A narrow IQR means strong pricing consensus in the market; a wide IQR means rents vary widely and setting a target is harder. Calculated as Q3 minus Q1. |
| Fit Score | A composite relevance score (0–100) rating how well a comparable listing matches your portfolio based on bedroom count, square footage, neighborhood, condition, and listing recency. Above 70 is a strong match; below 40 is a weak match that may distort your analysis. |
| NPV (Net Present Value) | Net Present Value. The total value of future cash flows (here, future rent gains from closing the gap) discounted back to today's dollars at a given rate (typically 6–8%). A positive NPV means the rent-up plan creates real economic value. Higher NPV = stronger financial justification for rent increases. |
| CAGR (Compound Annual Growth Rate) | Compound Annual Growth Rate. The smoothed annual rate at which rent grows over a multi-year period. Calculated as (Ending Value ÷ Beginning Value)^(1/n) − 1. A CAGR of 5–8% for actual rent is typical for an active rent-up; compare to Market CAGR to see if you are catching up or falling behind. |
| ROI (Return on Investment) | Return on Investment. Calculated as cumulative rent gain divided by implementation costs (vacancy loss, turnover costs, administrative effort) × 100. An ROI above 100% means you have recouped your costs; above 200% is excellent. |
| Capture Rate | The percentage of the available rent gap that has been closed by a rent increase. 100% capture means you raised rent all the way to market; 50% means you closed half the gap. Gradual step-ups may target 30–40% capture per year. |
| Gap % | Gap Percentage. The rent shortfall expressed as a percentage of market rent. Calculated as (Market Rent − Current Rent) ÷ Market Rent × 100. Above 15% is significant; above 25% is urgent. |
| Wtd Gap | Weighted Gap. The portfolio-wide average gap percentage, weighted by each unit's rent so that higher-rent units have proportionally more influence on the average. Gives a more accurate picture than a simple average when unit rents vary widely. |
| Ann Loss | Annual Loss. The total revenue forfeited per year by charging below market rent. Calculated as monthly gap × 12. This is the dollar cost of inaction. |
| Step-Up | The recommended incremental rent increase per renewal cycle (usually annual) designed to close the gap over 3 years without a single large increase that might trigger tenant turnover. |
| Payback Period | The number of months required for cumulative rent-increase gains to offset estimated vacancy and turnover costs caused by the increase. Under 3 months is excellent; over 6 months suggests a more cautious approach. |
| Yield % | Yield Percentage. Annual recovered loss divided by estimated turnover cost × 100. Above 200% is a strong signal to increase rent; below 100% means the short-term economics may not support an aggressive increase. |
| Volatility | A measure of rent variation within a comp peer group (same beds/neighborhood). High volatility (above 15%) means the market is less predictable; low volatility (below 8%) means strong pricing consensus, giving you more confidence in the market-rent estimate. |
| Confidence % (Conf %) | A reliability score for the market-rent estimate, based on the number and Fit Score quality of matching comps. Above 80% is high confidence; 50–80% is moderate; below 50% means you need more or better comp data before acting on the estimate. |
| Area Prem (Area Premium) | The percentage by which a neighborhood's average comp rent exceeds (positive) or falls below (negative) the dataset-wide average. Positive premiums indicate higher-value sub-markets. |
| Bed Rank | A rank ordering of comps by rent within the same bedroom count group. Rank 1 = highest rent among peers. Useful for seeing where a listing sits in its pricing tier. |
| Rent Dist (Rent Distance) | The absolute dollar difference between a comp's rent and the median rent of all comps with the same bedroom count. Large positive values = premium priced; large negative values = below-market. |
| Loss Rank | A rank ordering of your units by annual loss, from 1 (highest loss) to N (lowest loss). The unit ranked 1 is your most under-rented asset and should be addressed first. |
| PV Gain | Present Value of Gain. The NPV-discounted value of cumulative rent increases at each point in the 3-year forecast. Use this number in investor presentations to show the real economic value of the rent-up strategy in today's dollars. |
| At-Risk | A Yes/No flag indicating units where a large rent gap combined with an upcoming lease expiration creates a heightened risk of tenant turnover if rent is increased aggressively. 'Yes' units may benefit from a smaller Step-Up to retain the tenant. |
| Spread | The difference between a unit's current $/SF and the average $/SF of its comparable set. A negative spread means the unit is priced below its comp average on a per-square-foot basis. |
Every template ships with an AI side-panel. Type in plain language — it fills rows, explains any cell, and analyses your data for you.
How to use it
1Open the ✨ AI side-panel by clicking the sparkle icon in the right sidebar of Google Sheets. The AI assistant is a built-in chat where you type questions or commands in plain language — no formulas or code required.
2Ask it to EXPLAIN any cell — for example, type 'Explain C7' or 'What does the Fit Score in row 5 mean?' and it will describe the formula, its inputs, and what the result tells you. You can also ask broader questions like 'Which unit has the highest annual loss?' or 'What neighborhood has the strongest comps?' and it reads your data to answer.
3Give it COMMANDS that act on your sheet: try 'Fill the next row with a comp at 200 Main St, 2 beds, 1 bath, 900 sqft, Good condition, $1,750 rent from Zillow listed today' or 'Sum column G' or 'Color row 1 gold'. The assistant reads your sheet and makes changes without overwriting any formulas.
4Use the one-click PRESETS tailored to this template — these are quick-action buttons in the assistant panel that run common analyses specific to the Rental Comp Analyzer (the exact presets vary, so explore them in the panel). You can also SCAN the entire workbook for errors or insights, ATTACH a screenshot (e.g., a photo of a competitor's listing) and the AI will read and extract the data, and TRANSLATE every label in the workbook into another language.
5In the Tools tab, use ANALYZE ALL MY DATA to generate a comprehensive report exported to a new sheet — great for quarterly reviews. AUTO-FIT columns adjusts widths for readability. You can set the response Tone (Friendly, Professional, or Concise) and apply Smart Styling to polish the look.
6Pro features unlock native CHARTS, FORECASTS, and a full multi-page REPORT built from your data, plus the ability to build an INFOGRAPHIC. The AI assistant comes with free trial requests to start; after that, a subscription gives you a bigger monthly allowance of requests to keep the analysis going.