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Rental Comp Analyzer Workbook — Complete Instruction Manual
Instruction Manual · How to use this template
The Rental Comp Analyzer Workbook is a premium Google Sheets template that helps landlords, property managers, and asset managers benchmark their rental units against comparable market listings, identify under-rented units ranked by urgency, and model a 3-year rent-up strategy with net-present-value analysis. You enter your comparable rental listings and your own unit details; the workbook automatically scores each comp for relevance, calculates rent gaps and annual revenue loss, assigns priority tiers, and projects cumulative gains from closing those gaps over three years. The workflow moves from gathering comps, to analyzing your units against those comps, to reviewing portfolio-level insights on the Dashboard — giving you data-backed ammunition for rent increases and lease renewal negotiations.
⚡ Quick start
1Step 1: Open the 'Read Me' sheet first and review the color-coding legend, sheet descriptions, and any setup notes so you understand how the workbook is organized.
2Step 2: Go to the 'Comp Listings' sheet and enter at least 5–10 comparable rental listings from Zillow, Apartments.com, MLS, or your local market — fill in every input column (Address through Listed) for the most accurate scoring.
3Step 3: Switch to the 'Unit Analysis' sheet and enter each of your own rental units — fill in Unit, Address, Beds, Sqft, Current rent, and Lease End for every unit you manage.
4Step 4: Review the auto-calculated columns on both sheets: Comp Listings will score and rank each comp, while Unit Analysis will compute rent gaps, priority tiers, annual losses, and a 3-year forecast.
5Step 5: Open the 'Dashboard' sheet to see portfolio-wide KPIs — total annual loss, gap NPV, capture percentage, and neighborhood-level breakdowns — all updated automatically from the other two sheets.
6Step 6: Use the built-in AI side-panel (click the ✨ icon) to ask questions about any cell, generate reports, or run commands like 'explain the Gap NPV for Unit 3' to deepen your analysis.
The Comp Listings sheet is where you enter every comparable rental listing you have gathered from the market. Each row represents one competing or comparable rental unit. The workbook then auto-scores each comp for freshness, relevance, and fit against your portfolio, giving you a reliable, weighted view of what the market is actually asking — not just a raw average.
✍️ Step by step
11. Start in column A (Address) and enter the full street address of each comparable listing — be as specific as possible (e.g., '742 Evergreen Terrace, Unit 2B') so you can reference it later.
22. Fill in Neighborhood (column B) with the sub-market or neighborhood name (e.g., 'Midtown', 'West End'); this is used for area premium calculations and Dashboard rollups.
33. Enter Beds (number of bedrooms, e.g., 2), Baths (number of bathrooms, e.g., 1.5), and Sqft (total square footage, e.g., 950) — these drive per-unit and per-square-foot metrics.
44. Select or type the Condition of the unit (e.g., 'Excellent', 'Good', 'Fair', 'Poor') — this adjusts the relevance score so a renovated comp is weighted differently from a dated one.
55. Enter the listed Rent as a whole dollar amount (e.g., 1850) — do not include dollar signs or commas if the column is formatted as a number.
66. In Source, note where you found the listing (e.g., 'Zillow', 'Apartments.com', 'MLS', 'Craigslist') — this helps you audit data quality later.
77. In Listed, enter the date the listing was posted or first appeared (e.g., 2026-05-15) — this drives the Days Listed and freshness scoring so the workbook can down-weight stale comps.
88. Once a row is complete, all ƒ columns to the right auto-populate: $/SF, Days Listed, Bed Rank, Area Prem, Rent/Bed, Adj Rent, Rank %, Z-Score, Score, Volatility, and Rent Dist — do not type in these columns.
99. Review the KPI cards at the top and the six charts to see how your comp set stacks up; add more comps if the data looks thin or if the Volatility metric is high.
📋 Column-by-column
| Address | INPUT — Type the full street address of the comparable listing, including unit number if applicable. Example: '310 Oak Ave, Apt 4A'. Be consistent in formatting so you can sort and filter easily. |
| Neighborhood | INPUT — Type the neighborhood, sub-market, or area name where the comp is located. Example: 'Downtown', 'Riverside', 'North End'. This value is used to calculate Area Premium and feeds into the Dashboard's neighborhood-level rollups. |
| Beds | INPUT — Enter the number of bedrooms as a whole number. Example: 2. Studios should be entered as 0. This drives Bed Rank, Rent/Bed, and is used to match comps to your units on the Unit Analysis sheet. |
| Baths | INPUT — Enter the number of bathrooms, using .5 for half-baths. Example: 1.5 means one full bathroom and one half-bath. This is used in KPI summaries and helps refine comp comparisons. |
| Sqft | INPUT — Enter the total livable square footage of the comp unit as a whole number. Example: 1100. This is critical for the $/SF (dollars per square foot) calculation and per-square-foot benchmarking across your portfolio. |
| Condition | INPUT — Enter the overall condition of the unit. Use a consistent scale such as 'Excellent', 'Good', 'Fair', or 'Poor'. This factor adjusts the comp's relevance score — a comp in 'Excellent' condition may command a premium over one in 'Fair' condition, and the scoring reflects that difference. |
| Rent | INPUT — Enter the monthly asking rent in dollars as a plain number (no $ sign needed). Example: 1950. This is the core data point for all rent benchmarking, gap analysis, and adjusted rent calculations. |
| Source | INPUT — Type the name of the listing source or platform. Example: 'Zillow', 'Realtor.com', 'Local MLS', 'Property Manager'. This helps you track data provenance and assess reliability — MLS listings are generally more accurate than Craigslist posts. |
| Listed | INPUT — Enter the date the listing was posted or first appeared on market, in date format (e.g., 2026-06-01). This is essential for the Days Listed calculation and freshness weighting — older listings are automatically down-weighted in the relevance score because stale comps are less indicative of current market conditions. |
| ƒ$/SF | AUTO-CALCULATED — Dollars per Square Foot. Formula in words: Rent ÷ Sqft. This tells you how much rent the comp commands per square foot of living space. A higher $/SF indicates a premium property or location. Typical ranges vary by market but might be $1.50–$3.00/SF in many metros. Compare this to your own units' $/SF on the Unit Analysis sheet to see if you are under- or over-charging relative to the market. |
| ƒDays Listed | AUTO-CALCULATED — Days Listed (also known as Days on Market or DOM). Formula in words: Today's date minus the Listed date. This measures how long the listing has been active. A low number (under 14 days) suggests a hot listing that was priced right; a high number (over 45 days) may indicate overpricing or low demand. The workbook uses this to discount stale listings in the overall score. |
| ƒBed Rank | AUTO-CALCULATED — Bedroom Rank. This ranks the comp's rent relative to other comps with the same bedroom count. Formula in words: the comp's percentile position among all comps sharing its bedroom count, expressed as a percentage. A Bed Rank of 85% means this comp is priced higher than 85% of other comps with the same number of bedrooms — useful for spotting outliers. |
| ƒArea Prem | AUTO-CALCULATED — Area Premium. Formula in words: the percentage difference between this comp's neighborhood average rent and the overall average rent across all neighborhoods. A positive Area Prem (e.g., +12%) means this neighborhood commands rents 12% above the portfolio-wide average. A negative value means the area is below average. This helps you understand location-driven rent differences. |
| ƒRent/Bed | AUTO-CALCULATED — Rent per Bedroom. Formula in words: Rent ÷ Beds (for units with 1+ bedrooms). This normalizes rent by bedroom count so you can compare a 1-bed at $1,500 to a 3-bed at $3,600 on a per-room basis. A higher Rent/Bed suggests the comp offers less space per dollar. Studios (0 beds) may show the full rent or be excluded from this metric. |
| ƒAdj Rent | AUTO-CALCULATED — Adjusted Rent. This is the comp's rent after adjustments for condition, freshness, and area premium factors. Formula in words: Rent × adjustment multipliers derived from Condition, Days Listed, and Area Prem. Adjusted Rent is the workbook's best estimate of what this comp would rent for if it were in baseline condition and freshly listed. Use this as a more apples-to-apples comparison than raw Rent. |
| ƒRank % | AUTO-CALCULATED — Overall Rank Percentile. Formula in words: the comp's percentile rank based on its Adjusted Rent relative to all comps in the sheet. A Rank % of 90% means this comp's adjusted rent is higher than 90% of all other comps. Values near 50% are mid-market; values above 80% are premium; values below 20% are budget. Use this to quickly identify where a comp sits in the market spectrum. |
| ƒZ-Score | AUTO-CALCULATED — Z-Score (statistical standard score). Formula in words: (Comp's Rent − Mean Rent of all comps) ÷ Standard Deviation of all comp rents. A Z-Score of 0 means the comp is exactly average. A Z-Score of +1.5 means it is 1.5 standard deviations above the mean (unusually high). A Z-Score below −2 or above +2 flags a statistical outlier that you may want to investigate or exclude. Typical range is −2 to +2. |
| ƒScore | AUTO-CALCULATED — Relevance Score (composite). This is the workbook's overall quality-and-fit score for each comp, combining freshness (Days Listed), condition, area premium, and statistical fit. Formula in words: a weighted composite of freshness weight, condition adjustment, area alignment, and Z-Score proximity. Higher is better — a Score near 100 means the comp is fresh, relevant, well-matched, and statistically consistent. Scores below 40 suggest the comp may be stale, an outlier, or in a very different sub-market. This score is used on the Unit Analysis sheet to weight comps when calculating market rent. |
| ƒVolatility | AUTO-CALCULATED — Rent Volatility. Formula in words: the coefficient of variation or standard deviation of rents within the comp's bedroom-count group or neighborhood, expressed as a percentage. High Volatility (above 15–20%) means rents in that segment vary widely, which reduces confidence in any single rent estimate. Low Volatility (under 10%) means the market is tightly priced and your estimates are more reliable. |
| ƒRent Dist | AUTO-CALCULATED — Rent Distance. Formula in words: the absolute or percentage difference between this comp's Rent and the median (or mean) rent of all comps. A small Rent Dist means this comp is close to the central tendency; a large Rent Dist means it is far from the group average. Use this alongside Z-Score to spot outliers — comps with a large Rent Dist and a high absolute Z-Score may be skewing your market rent estimates. |
📊 Reading the numbers
• KPI Cards — Beds, Baths, Sqft, Rent, $/SF, and Area Prem summarize the averages and totals of your comp set. Check that Beds and Sqft averages roughly match your own portfolio's unit mix; if they are very different, your comps may not be representative.
• Rent Trend chart — Shows how listed rents have changed over time based on the Listed dates. An upward slope means the market is rising and you may have room to push rents; a flat or declining trend signals caution.
• Rent vs Adjusted chart — Compares raw listed rents to the Adjusted Rent values. Large gaps between the two lines indicate that condition, freshness, or area factors are meaningfully shifting the true market picture.
• Rent per Sqft chart — Plots $/SF across comps. Look for clustering (most comps around $2.00/SF) versus wide spread (some at $1.20, others at $3.50), which would indicate high volatility and less confidence in a single market rent figure.
• Days on Market chart — Visualizes how long each comp has been listed. A cluster of short DOM (under 14 days) suggests a tight market; many comps over 30–45 days suggest softer demand or overpriced listings.
• Area Rent Premium chart — Shows which neighborhoods command premiums or discounts relative to the overall average. Use this to justify higher rents in premium areas or to understand why certain units underperform.
• Relevance Score chart — Displays the composite Score for each comp. Comps with high scores (70+) are your most reliable data points; low-scoring comps may be stale or poorly matched and can be deprioritized.
⚠️ Avoid these mistakes
• Entering rent with dollar signs or commas (e.g., '$1,950' instead of 1950) — this can break formulas if the column expects a plain number.
• Leaving the Listed date blank — without it, Days Listed cannot be calculated and the freshness component of the Score defaults to zero, making that comp appear stale.
• Using inconsistent neighborhood names (e.g., 'Downtown' in one row and 'downtown' or 'Down Town' in another) — this splits the neighborhood into separate groups and skews Area Premium and Dashboard rollups.
• Entering too few comps (fewer than 5) — the statistical metrics (Z-Score, Volatility, Rank %) become unreliable with a small sample size.
💡 Tips• Sort by ƒScore descending to see your most relevant comps at the top — these should anchor your rent estimates.
• Use Google Sheets' built-in filter (Data → Create a filter) on the Beds column to isolate comps that match a specific unit type you are analyzing.
• Periodically purge comps that are more than 90 days old (check ƒDays Listed) and replace them with fresh listings to keep your analysis current.
• If Volatility is high for a bedroom type, add more comps in that category to improve statistical confidence.
The Unit Analysis sheet is the heart of the workbook — it is where you enter your own rental units and see exactly how much revenue you are leaving on the table. The sheet automatically computes each unit's market rent from the scored comps, calculates the rent gap, assigns a priority tier, models a 3-year rent-up trajectory, and tracks lease renewal actions. Use this sheet to decide which units to increase first, by how much, and when.
✍️ Step by step
11. In the first section (unit detail columns), enter each of your units starting with Unit (a label like '1A' or 'Unit 101'), Address, Beds, and Sqft — these must match the format you used in Comp Listings so the cross-sheet lookups work correctly.
22. Enter Current rent — the actual monthly rent each tenant is currently paying. Example: 1650.
33. Enter Lease End — the date the current lease expires (e.g., 2026-09-30). This drives Days Left and urgency-based priority scoring.
44. All ƒ columns auto-populate: Mkt Rent, Rent Gap, Gap %, Priority, Days Left, Ann Loss, Comps, Capture, Mkt $/SF, Loss Share, Unit $/SF, Step-Up/Mo, At-Risk $, Spread, Gap NPV, Loss Rank, Confidence, Payback Mo, Yield %, and Forecast. Do not type in these columns.
55. Scroll right to the portfolio forecast section (ƒPeriod, Port. Rent, Mkt Rent, ƒGap, Cum Gain, ƒCapture, ƒROI %, ƒPV Gain, ƒCAGR, ƒMkt CAGR) — this models the 3-year rent-up trajectory for your entire portfolio and requires no additional input.
66. In the lease action tracker section (far right), enter Unit, Eff. Date (effective date of the new rent), Old Rent, New Rent, Notice (notice period given, e.g., '60 days'), and Status (e.g., 'Sent', 'Accepted', 'Pending') — the ƒ columns (Increase, Increase %, Ann Impact) calculate automatically.
77. Review the KPI cards (Mo. Gap, Annual Loss, Wtd Gap, Fcast Loss, Beds, Sqft) to see the portfolio-level summary of under-renting.
88. Study the six charts to visualize which units have the largest gaps, highest losses, and lowest confidence — prioritize action on units where confidence is high and gap is large.
99. As you execute rent increases, update the lease action tracker section so your realized gains are tracked against the forecasted trajectory.
📋 Column-by-column
| Unit | INPUT — Enter a short identifier for each rental unit. Example: '1A', 'Unit 202', 'House-3'. Keep it consistent because this label appears in charts and the lease action tracker. |
| Address | INPUT — Enter the property address for this unit. Example: '500 Main St'. If all units are in the same building, you can abbreviate (e.g., '500 Main') but be consistent. |
| Beds | INPUT — Number of bedrooms in this unit, as a whole number. Example: 2. This is used to match against comps with the same bedroom count on the Comp Listings sheet via FILTER/AVERAGEIFS formulas. |
| Sqft | INPUT — Total livable square footage. Example: 875. Used to calculate Unit $/SF and Mkt $/SF for per-square-foot benchmarking. |
| Current | INPUT — The monthly rent this unit is currently collecting, as a plain number. Example: 1500. This is compared to the auto-calculated Mkt Rent to determine the Rent Gap. |
| ƒMkt Rent | AUTO-CALCULATED — Market Rent. The estimated fair-market monthly rent for this unit based on scored comps from the Comp Listings sheet. Formula in words: a weighted average of Adjusted Rents from comps that match this unit's bedroom count, weighted by each comp's Relevance Score. A higher comp Score means that comp has more influence on the market rent estimate. This is the rent you should be targeting. |
| ƒRent Gap | AUTO-CALCULATED — Rent Gap. Formula in words: Mkt Rent − Current rent. A positive Rent Gap means you are under-renting (the market supports a higher rent). A negative Rent Gap means you are charging above market. Example: if Mkt Rent is $1,800 and Current is $1,500, the Rent Gap is $300/month. |
| ƒGap % | AUTO-CALCULATED — Gap Percentage. Formula in words: Rent Gap ÷ Current rent × 100. This expresses the rent gap as a percentage of what you currently charge. A Gap % of 20% means you are 20% below market. Gaps above 10% typically warrant immediate attention; gaps of 5% or less may be acceptable to retain good tenants. |
| ƒPriority | AUTO-CALCULATED — Priority Tier. An urgency rating (e.g., 'High', 'Medium', 'Low') based on the combination of Gap % and Days Left until lease expiration. Units with a large gap and an upcoming lease end are flagged as High Priority. Use this to sequence your rent increase actions — address High-priority units first. |
| Lease End | INPUT — Enter the date the current lease expires, in date format (e.g., 2026-12-31). This is critical for calculating Days Left and the Priority tier. If the tenant is month-to-month, enter today's date or the next notice deadline. |
| ƒDays Left | AUTO-CALCULATED — Days Left until lease expiration. Formula in words: Lease End date − Today's date. A low number (under 60 days) means you need to act quickly to issue a rent increase notice. A negative number means the lease has already expired and the tenant may be month-to-month. |
| ƒAnn Loss | AUTO-CALCULATED — Annualized Loss. Formula in words: Rent Gap × 12. This is the total annual revenue you are forgoing by not charging market rent on this unit. Example: a $300/month gap = $3,600/year in lost revenue. This is one of the most important metrics — it converts a monthly gap into an annual dollar figure that is easy to communicate to stakeholders. |
| ƒComps | AUTO-CALCULATED — Comp Count. Formula in words: the number of comps from the Comp Listings sheet that match this unit's bedroom count (via COUNTIFS). A higher comp count increases statistical reliability. If this shows fewer than 3, consider adding more comps for that bedroom type. |
| ƒCapture | AUTO-CALCULATED — Capture Rate. Formula in words: Current rent ÷ Mkt Rent × 100. This tells you what percentage of market rent you are currently collecting. A Capture of 85% means you are getting 85 cents of every market dollar. Target 95–100% Capture for fully optimized rents. Below 80% is a red flag. |
| ƒMkt $/SF | AUTO-CALCULATED — Market Dollars per Square Foot. Formula in words: Mkt Rent ÷ Sqft. This is the per-square-foot rent implied by the market comps. Compare this to Unit $/SF to see the gap on a square-footage-normalized basis. Typical range depends on your market (e.g., $1.80–$2.50/SF in many suburban markets). |
| ƒLoss Share | AUTO-CALCULATED — Loss Share. Formula in words: this unit's Ann Loss ÷ the total Ann Loss across all units × 100. This tells you what percentage of your total portfolio revenue loss is attributable to this single unit. A Loss Share of 35% means over a third of all lost revenue comes from this one unit — make it your top priority. |
| ƒUnit $/SF | AUTO-CALCULATED — Unit Dollars per Square Foot (current). Formula in words: Current rent ÷ Sqft. This is what you are actually collecting per square foot. Compare to Mkt $/SF to see the per-square-foot gap. |
| ƒStep-Up/Mo | AUTO-CALCULATED — Step-Up per Month. Formula in words: Rent Gap ÷ a defined number of months (e.g., 12 or the months remaining until lease end), representing how much you would need to increase rent each month to close the gap gradually. This is useful for phased rent increases where you cannot jump to market rent all at once. Example: a $300 gap over 6 months = $50/month step-up. |
| ƒAt-Risk $ | AUTO-CALCULATED — At-Risk Dollars. Formula in words: Rent Gap × Days Left ÷ 30 (approximated months remaining). This is the total revenue at risk between now and lease expiration if you do not increase rent. A high At-Risk $ on a unit with few Days Left means the window to act is closing fast. |
| ƒSpread | AUTO-CALCULATED — Spread. Formula in words: the difference between the highest and lowest comp rents matched to this unit's bedroom type, or Mkt Rent minus the lowest matched comp rent. A wide Spread means the market is heterogeneous and your rent estimate has more uncertainty; a tight Spread means the market is well-defined and your estimate is solid. |
| ƒGap NPV | AUTO-CALCULATED — Gap Net Present Value. Formula in words: the present value of the cumulative Rent Gap over a defined forecast period (typically 3 years), discounted at an assumed rate. Gap NPV tells you, in today's dollars, how much total value you are leaving on the table over the projection period if you never close the gap. A high Gap NPV (e.g., $8,000+) strongly justifies the effort and potential vacancy risk of raising rent. |
| ƒLoss Rank | AUTO-CALCULATED — Loss Rank. Formula in words: the ordinal rank of this unit by Ann Loss (1 = highest loss). The unit with Loss Rank 1 is the single most under-rented unit in your portfolio and should be your first target for a rent increase. |
| ƒConfidence | AUTO-CALCULATED — Confidence Level. A quality indicator (e.g., 'High', 'Medium', 'Low' or a percentage) reflecting how reliable the Mkt Rent estimate is, based on the number of matched comps, their Relevance Scores, and the Volatility/Spread of those comps. High Confidence means you can push to market rent aggressively; Low Confidence means you should gather more comps before acting. |
| ƒPayback Mo | AUTO-CALCULATED — Payback Months. Formula in words: estimated vacancy cost (e.g., one month of market rent plus turnover expenses) ÷ monthly Rent Gap. This tells you how many months of the higher rent it would take to recoup the cost of a potential vacancy if the tenant leaves. A Payback of 3 months means the rent increase pays for itself in one quarter. Payback over 12 months may mean the increase is not worth the risk of turnover. |
| ƒYield % | AUTO-CALCULATED — Yield Percentage. Formula in words: Ann Loss (the additional annual rent you would earn by closing the gap) ÷ an assumed cost basis or unit value × 100. This frames the rent increase as a return on the effort/investment. A Yield of 8% means closing this gap is equivalent to earning an 8% annual return. Higher is better; compare to your target return threshold. |
| ƒForecast | AUTO-CALCULATED — Forecast Rent. The projected rent for this unit at a future date, typically at the end of the 3-year model. Formula in words: Current rent + projected annual increases based on Gap % and assumed market rent growth rate. This is the rent the model expects you will be collecting if you execute the planned increases. |
| ƒPeriod | AUTO-CALCULATED — Forecast Period label (e.g., 'Year 1', 'Year 2', 'Year 3' or month numbers). This is the time axis for the 3-year portfolio forecast section. Each row represents a period in the projection. |
| Port. Rent | INPUT — Portfolio Rent. The total actual rent collected across all units for this forecast period. If you are running the forecast before executing increases, this may auto-fill from Current rents; if you are tracking actuals, update it with real collected rent each period. |
| Mkt Rent | INPUT — The total estimated market rent for all units combined for this forecast period. This may auto-fill from the sum of individual Mkt Rents, or you can override it with your own market projection. |
| ƒGap | AUTO-CALCULATED — Portfolio Gap for this period. Formula in words: Mkt Rent − Port. Rent for the period. This is the total monthly rent gap across all units in this time slice. |
| Cum Gain | INPUT/AUTO — Cumulative Gain. The running total of additional rent captured as you close gaps over time. This may auto-calculate as the sum of period-over-period rent increases, or you can enter actual figures. |
| ƒCapture | AUTO-CALCULATED — Portfolio Capture Rate for this period. Formula in words: Port. Rent ÷ Mkt Rent × 100. Watch this climb from its starting value toward 95–100% as you execute increases. |
| ƒROI % | AUTO-CALCULATED — Return on Investment Percentage for the rent-up effort. Formula in words: Cum Gain ÷ estimated costs (vacancy, turnover, administrative) × 100. An ROI above 100% means your cumulative gains have exceeded the costs of pursuing the increases. |
| ƒPV Gain | AUTO-CALCULATED — Present Value of Cumulative Gain. Formula in words: the Cum Gain discounted back to today's dollars at an assumed discount rate. This is the NPV version of your cumulative gains — it accounts for the time value of money, so gains farther in the future are worth less today. |
| ƒCAGR | AUTO-CALCULATED — Compound Annual Growth Rate of your portfolio's actual rent. Formula in words: (Ending Port. Rent ÷ Starting Port. Rent)^(1/years) − 1, expressed as a percentage. A CAGR of 5% means your portfolio rent is growing at 5% per year compounded. Compare this to Mkt CAGR to see if you are keeping pace with the market. |
| ƒMkt CAGR | AUTO-CALCULATED — Market Compound Annual Growth Rate. Formula in words: (Ending Mkt Rent ÷ Starting Mkt Rent)^(1/years) − 1. This is the rate at which the overall market rent is growing. If your CAGR is lower than Mkt CAGR, you are falling further behind the market over time. |
| Unit (Lease Tracker) | INPUT — In the lease action tracker section, enter the Unit identifier (matching the Unit column in the main section) for which you are recording a rent increase action. Example: '1A'. |
| Eff. Date | INPUT — Effective Date. The date the new rent takes effect. Example: 2026-10-01. This should align with the lease renewal or the end of the notice period. |
| Old Rent | INPUT — The rent the tenant was paying before the increase. Example: 1500. |
| New Rent | INPUT — The new rent the tenant will pay after the increase. Example: 1750. |
| ƒIncrease | AUTO-CALCULATED — Rent Increase in dollars. Formula in words: New Rent − Old Rent. Example: $1,750 − $1,500 = $250 increase. |
| ƒIncrease % | AUTO-CALCULATED — Rent Increase as a percentage. Formula in words: Increase ÷ Old Rent × 100. Example: $250 ÷ $1,500 = 16.7%. Check local rent increase laws — some jurisdictions cap annual increases at 3–10%. |
| ƒAnn Impact | AUTO-CALCULATED — Annualized Impact. Formula in words: Increase × 12. This is the total additional annual revenue generated by this specific rent increase. Example: $250 × 12 = $3,000/year. |
| Notice | INPUT — Enter the notice period you provided to the tenant. Example: '60 days', '30 days', or a specific date. This helps you track compliance with local notice requirements. |
| Status | INPUT — Enter the current status of this rent increase action. Example: 'Draft', 'Sent', 'Accepted', 'Rejected', 'Pending'. Update this as the process progresses so you have a clear audit trail. |
📊 Reading the numbers
• KPI Cards — Mo. Gap shows the total monthly rent gap across all units; multiply by 12 mentally or check Annual Loss for the yearly figure. Wtd Gap is the weighted average gap (weighted by unit size or rent), which gives a more accurate portfolio-level picture than a simple average. Fcast Loss projects forward what you will lose over the forecast period if no action is taken. Beds and Sqft summarize your portfolio composition.
• Current Rent vs Gap chart — Bar chart showing each unit's current rent alongside its rent gap. Units with tall gap bars relative to their current rent bars are the most under-rented and should be prioritized.
• Annual Loss by Unit chart — Ranks units by their annualized revenue loss. The tallest bar is your biggest money-loser — focus there first.
• Gap % by Unit chart — Shows the percentage gap for each unit. Units above 15–20% are significantly below market and may justify aggressive increases.
• Confidence by Unit chart — Displays the reliability of each unit's market rent estimate. Only act aggressively on units with High or Medium confidence; for Low-confidence units, gather more comps first.
• 3-Year Rent Trajectory chart — Projects your portfolio rent versus market rent over the forecast horizon. The gap between the two lines should narrow over time as you execute increases. If the lines diverge, your planned increases are not keeping pace with market growth.
• Cumulative Gain chart — Shows the running total of additional revenue captured. An upward curve means your rent-up strategy is working; a plateau means you have stalled and need to re-engage.
⚠️ Avoid these mistakes
• Entering bedroom counts that do not match the format in Comp Listings (e.g., writing 'Two' instead of 2) — this breaks the COUNTIFS/AVERAGEIFS lookups that pull comps.
• Forgetting to enter Lease End dates — without these, Days Left shows an error and Priority cannot be assigned, so you lose the urgency-based triage that makes this sheet powerful.
• Overwriting ƒ columns with manual numbers — this destroys the formulas and breaks downstream calculations on the Dashboard. If you accidentally overwrite a formula, press Ctrl+Z immediately to undo.
• Not updating the lease action tracker after executing a rent increase — the 3-year forecast and Cumulative Gain metrics will not reflect your actual progress.
💡 Tips• Sort by ƒPriority or ƒLoss Rank to create a punch list of rent increases in the order you should execute them.
• If ƒPayback Mo is very high (over 12) for a unit, consider a smaller increase that keeps the tenant rather than pushing to full market rent and risking vacancy.
• Use the lease action tracker to build a paper trail — if a tenant disputes an increase, you have the effective date, notice period, and status documented.
• Re-run the analysis quarterly by updating Comp Listings with fresh comps; the Unit Analysis metrics will refresh automatically.
The Dashboard sheet is your executive summary — it pulls data from both Comp Listings and Unit Analysis to give you a portfolio-wide view of market positioning, revenue loss, and comp quality broken down by neighborhood and bedroom type. Use this sheet for reporting to partners, lenders, or your own decision-making without needing to dig into individual rows on other sheets.
✍️ Step by step
11. This sheet is almost entirely auto-calculated — you do not need to enter any data here. All values are pulled from Comp Listings and Unit Analysis via COUNTIFS, AVERAGEIFS, and SUMIFS formulas.
22. Review the neighborhood summary table: each row shows a Neighborhood with its comp count, average rent, average $/SF, average relevance score, and average days on market.
33. Check the KPI cards at the top for the portfolio-level headline numbers: Capture %, Annual Loss, Gap NPV, Avg Mkt (average market rent), Avg Curr (average current rent), and Total Loss.
44. Study the six charts to understand your portfolio's position relative to the market across different dimensions (rent levels, losses, bedroom types, neighborhoods, comp quality, and days on market).
55. Use this sheet as your presentation layer — it is designed to communicate the story of your portfolio's rent positioning at a glance.
66. If any values look unexpected (e.g., a neighborhood showing zero comps), go back to Comp Listings and verify that neighborhood names are consistent.
77. Refresh this sheet after any changes to Comp Listings or Unit Analysis — values update automatically, but charts may need a moment to redraw.
📋 Column-by-column
| Neighborhood | AUTO-POPULATED — Lists each unique neighborhood from the Comp Listings sheet. Each row summarizes all comps and units in that neighborhood. |
| ƒComps | AUTO-CALCULATED — The count of comparable listings from the Comp Listings sheet in this neighborhood. Formula in words: COUNTIFS on the Neighborhood column of Comp Listings. A neighborhood with fewer than 3 comps may not have a reliable average rent. |
| ƒAvg Rent | AUTO-CALCULATED — Average Rent for this neighborhood. Formula in words: AVERAGEIFS of Rent from Comp Listings where Neighborhood matches. This is the simple average asking rent in that area. Compare across neighborhoods to see which areas command premiums. |
| ƒAvg $/SF | AUTO-CALCULATED — Average Dollars per Square Foot for this neighborhood. Formula in words: AVERAGEIFS of $/SF from Comp Listings where Neighborhood matches. This normalizes rent by unit size so you can fairly compare a neighborhood of large units to one with small units. |
| ƒScore | AUTO-CALCULATED — Average Relevance Score for comps in this neighborhood. Formula in words: AVERAGEIFS of Score from Comp Listings where Neighborhood matches. A high average Score (70+) means the comps in this area are fresh and well-matched; a low average Score suggests stale or poorly fitting data. |
| ƒAvg DOM | AUTO-CALCULATED — Average Days on Market for this neighborhood. Formula in words: AVERAGEIFS of Days Listed from Comp Listings where Neighborhood matches. Low Avg DOM (under 20 days) indicates strong demand; high Avg DOM (over 40 days) suggests a softer market where aggressive rent increases may cause longer vacancies. |
📊 Reading the numbers
• KPI Cards — Capture % is the single most important number: it tells you what share of total market rent your portfolio is collecting. Below 90% means significant revenue is being left on the table. Annual Loss converts that gap to a dollar figure. Gap NPV shows the present value of that lost revenue over the forecast period. Avg Mkt vs Avg Curr shows the per-unit average gap between market and actual rents. Total Loss is the sum of all units' annualized losses.
• Market vs Current Rent chart — Side-by-side comparison of what the market says you should be earning versus what you actually collect. The gap between the bars is your opportunity.
• Annual Loss by Beds chart — Breaks down total annual revenue loss by bedroom type. If 2-bedrooms account for most of the loss, that is where you should focus your comp gathering and rent increase efforts.
• Gap NPV by Beds chart — Shows the present value of the rent gap by bedroom type over the forecast horizon. This tells you where the most long-term value is being lost.
• Avg Rent by Neighborhood chart — Compares average asking rents across neighborhoods. Use this to set location-specific rent targets rather than applying a single portfolio-wide number.
• Comp Quality by Area chart — Visualizes the average Relevance Score by neighborhood. Areas with low comp quality scores need more or better comps before you can confidently set rent targets.
• Days on Market by Area chart — Shows average DOM by neighborhood. Neighborhoods with high DOM may not support aggressive increases because units take longer to lease there.
⚠️ Avoid these mistakes
• Trying to manually edit cells on this sheet — nearly everything is formula-driven, and overwriting a cell will break the rollup.
• Ignoring low comp counts in a neighborhood — if ƒComps shows 1 or 2, the averages for that area are unreliable. Go back to Comp Listings and add more data.
• Comparing Avg Rent across neighborhoods without considering Avg $/SF — a neighborhood with larger units will naturally show higher average rents even if the per-square-foot rate is lower.
💡 Tips• Screenshot or export the Dashboard charts for investor presentations or internal memos — they tell the story without requiring the viewer to understand the underlying data.
• If Capture % is already above 95%, your portfolio is well-positioned; shift focus to retention and expense management rather than rent increases.
• Use the Comp Quality by Area chart to decide where to invest research time — low-quality areas need better comps, not just more rent increases.
• Filter or hide neighborhoods where you have no units to keep the Dashboard focused on actionable areas.
📖Glossary — what every value means
| $/SF | Dollars per Square Foot. Calculated as monthly rent divided by the unit's livable square footage. It normalizes rent across different-sized units so you can compare them fairly. In many U.S. suburban markets, $1.50–$2.50/SF is typical; urban cores can exceed $4.00/SF. |
| DOM | Days on Market. The number of days a listing has been active since its posted date. Calculated as today's date minus the listed date. Under 14 days suggests strong demand; over 45 days may indicate overpricing. Also referred to as 'Days Listed' in the Comp Listings sheet. |
| Adj Rent | Adjusted Rent. The comparable listing's rent after being adjusted for condition, freshness (days on market), and area premium factors. It provides a more apples-to-apples comparison than raw asking rent by normalizing for differences in unit quality and listing age. |
| Z-Score | A statistical measure of how far a comp's rent is from the average rent of all comps, expressed in standard deviations. A Z-Score of 0 is exactly average; ±1 is within normal range; beyond ±2 is a statistical outlier that should be investigated or potentially excluded. |
| Area Prem | Area Premium. The percentage by which a neighborhood's average rent exceeds (or falls below) the overall average across all neighborhoods. A +10% Area Premium means that neighborhood commands 10% higher rents than the market-wide average. |
| Rent Gap | The difference between the estimated market rent and the current actual rent for a unit. Calculated as Market Rent minus Current Rent. A positive gap means you are under-charging; a negative gap means you are above market. |
| Gap % | Gap Percentage. The Rent Gap expressed as a percentage of Current Rent. Calculated as Rent Gap ÷ Current Rent × 100. Gaps above 10% typically warrant action; above 20% is urgent. |
| Ann Loss | Annualized Loss. The total yearly revenue forfeited by charging below market rent. Calculated as Rent Gap × 12. This is the simplest way to express the cost of inaction in dollar terms. |
| NPV | Net Present Value. The value today of a stream of future cash flows, discounted at a specified rate to account for the time value of money. Gap NPV in this template represents the present value of all rent gap revenue you would lose over the 3-year forecast if you never raised rents. |
| Gap NPV | Gap Net Present Value. The NPV of the cumulative rent gap over the forecast period. A high Gap NPV means there is substantial long-term value at stake, justifying the effort and potential short-term vacancy risk of raising rents. |
| Capture % | Capture Rate or Capture Percentage. The percentage of market rent you are actually collecting. Calculated as Current Rent ÷ Market Rent × 100. A Capture of 95% or higher is considered well-optimized; below 85% signals significant under-renting. |
| Loss Share | The percentage of total portfolio annual loss attributable to a single unit. Calculated as the unit's Annualized Loss ÷ total Annualized Loss across all units × 100. It helps you prioritize which units to address first. |
| Step-Up/Mo | Step-Up per Month. The monthly incremental rent increase needed to close the gap gradually over a defined period. Calculated as Rent Gap ÷ number of months in the step-up schedule. Useful for phased increases that minimize tenant shock. |
| At-Risk $ | At-Risk Dollars. The total revenue at risk between now and lease expiration if rent is not increased. Calculated approximately as Rent Gap × months remaining until lease end. A high At-Risk $ with few days remaining is an urgent call to action. |
| Payback Mo | Payback Months. The number of months it would take for the additional rent from an increase to recoup the estimated cost of tenant turnover (vacancy loss plus make-ready expenses). Calculated as estimated turnover cost ÷ monthly Rent Gap. Under 6 months is favorable; over 12 months suggests caution. |
| Yield % | Yield Percentage. The annualized return generated by closing the rent gap, expressed as a percentage of an assumed cost basis. Calculated as Annualized Loss (the gain from closing the gap) ÷ cost basis × 100. Higher Yield % means the rent increase delivers a better return. |
| CAGR | Compound Annual Growth Rate. The smoothed annual rate of growth over a multi-year period. Calculated as (Ending Value ÷ Beginning Value)^(1/number of years) − 1. In this template, CAGR measures how fast your portfolio rent or market rent is growing on an annualized, compounded basis. |
| Mkt CAGR | Market Compound Annual Growth Rate. The CAGR of overall market rents over the forecast period. Compare your portfolio's CAGR to Mkt CAGR — if yours is lower, you are falling further behind the market each year. |
| ROI % | Return on Investment Percentage. The cumulative gain from rent increases divided by the estimated costs incurred (vacancy, turnover, administrative effort) × 100. An ROI above 100% means your gains have exceeded your costs. |
| PV Gain | Present Value of Gain. The cumulative rent gains discounted to today's dollars. This is the NPV of your realized (or projected) rent increases, accounting for the time value of money. |
| Wtd Gap | Weighted Gap. The average rent gap across units, weighted by unit size (square footage) or rent level, rather than a simple average. This gives a more accurate portfolio-level picture because large or high-rent units with gaps matter more in dollar terms. |
| Fcast Loss | Forecast Loss. The projected total revenue loss over the 3-year forecast period if current rents remain unchanged and market rents grow at their projected rate. This forward-looking metric shows how the problem compounds over time. |
| Volatility | Rent Volatility. A measure of how widely rents vary among comps in the same group (e.g., same bedroom count or neighborhood). Expressed as a percentage — high volatility (above 15–20%) means wide rent variation and less confidence in any single estimate; low volatility (under 10%) means a tight, well-defined market. |
| Rent Dist | Rent Distance. The absolute or percentage difference between a comp's rent and the median or mean rent of all comps. Large Rent Distance values flag outliers that may be skewing your market rent estimates. |
| Confidence | A quality rating (High, Medium, or Low) indicating how reliable a unit's market rent estimate is, based on the number of matched comps, their relevance scores, and the volatility of matched comp rents. High Confidence means act decisively; Low Confidence means gather more data first. |
| Spread | The range between the highest and lowest comp rents matched to a given unit's bedroom type. A wide spread means the market is heterogeneous; a narrow spread means pricing is consistent and your estimate is more reliable. |
| Bed Rank | Bedroom Rank. A comp's percentile position among all comps with the same bedroom count, based on rent. A Bed Rank of 80% means it is priced higher than 80% of same-bedroom comps. |
| Rank % | Overall Rank Percentile. A comp's percentile rank by Adjusted Rent across all comps, regardless of bedroom count. Shows where the comp sits in the overall market spectrum. |
| Loss Rank | The ordinal ranking of units by Annualized Loss, with 1 being the unit losing the most revenue. Use this to quickly identify your most under-rented unit. |
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
1To open the built-in AI assistant, click the ✨ sparkle icon in the side panel of Google Sheets. The AI assistant is a conversational chat where you can ask questions in plain language — for example, type 'explain cell F5' to get a breakdown of what that cell's formula does, what inputs drive it, and what the current value means for your portfolio.
2You can issue commands that read your sheet and take action without breaking any formulas. For example, type 'sum column C' to get a quick total, 'fill the next row' to auto-populate a new comp entry based on patterns in your existing data, or 'color row 1 gold' to apply formatting. These are typed commands in the chat — not standalone buttons.
3The assistant includes one-click presets tailored specifically to this Rental Comp Analyzer template. Click any preset to get instant, template-specific analysis without typing a prompt. There is also a Tools tab with an 'Analyze All My Data' feature that generates a comprehensive report on a new sheet, plus an Auto-Fit option that resizes columns for optimal readability.
4You can attach a screenshot or image (such as a photo of a competing rental listing flyer) and the AI will read and extract the relevant information. The assistant can also scan your entire workbook to identify issues, and it supports translating every label into another language, building an infographic from your data, and adjusting the response tone (Friendly, Professional, or Concise) plus Smart Styling for polished formatting.
5Pro features unlock native charts, forecasts, and a full multi-page report generated directly from your workbook data. You start with a free trial of AI requests, and then a subscription gives you a bigger monthly allowance of requests to keep using the assistant for ongoing analysis and reporting.