ARV Calculator & Comp Validation
Get Accurate ARVs Without Guessing
AI-powered After Repair Value calculation using live MLS comps, automated adjustments, and confidence-weighted valuations.
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The single most consequential number in any fix-and-flip or wholesale deal is the After Repair Value. Get the ARV right, and your offer, your rehab budget, and your profit margin all fall into alignment. Get it wrong — even by 10% — and a deal that looked like a winner on paper becomes a loss. Despite this, most investors rely on imprecise methods for estimating ARV: picking comparable sales by proximity alone, using raw sale prices without adjustments, or relying on Zillow's Zestimate, which is a consumer tool that does not apply the appraisal-grade methodology that lenders and serious investors require.
The ARV Calculator & Comp Validation Engine applies the same fundamental methodology used by licensed appraisers — pulling comparable sales, making condition and feature adjustments to each comp, and deriving an adjusted value per square foot — but at the speed of software and with the consistency of an AI that never rushes, never cuts corners, and always applies conservative bias when the data is thin.
The engine pulls 6 to 12 months of sold comparables within a 0.5-mile radius of the subject property, filtered by property type, bedroom/bathroom count, and square footage range. Each comparable is then adjusted individually for the differences between the comp and the subject: square footage, condition grade, bedroom/bathroom count, lot size, garage presence, pool, age, and other relevant features. Adjustments are made using a market-calibrated per-unit cost methodology — not generic national averages.
The final ARV is calculated as the median of the adjusted comp values — not the mean — to protect against distortion from outlier sales. A confidence score of High, Medium, or Low is assigned based on the number and quality of available comps, and a 5% downward market uncertainty adjustment is applied to all ARV estimates by default. If fewer than 3 valid comps are found after filtering and adjustment, the system explicitly flags the result as "Insufficient Comp Data" and recommends a manual appraisal.
Input Parameters
Parameter | Type | Required | Description |
|---|---|---|---|
| String | Yes | Full property address of the subject property |
| String | Yes |
|
| Integer | Yes | Number of bedrooms in subject property (post-repair) |
| Float | Yes | Number of bathrooms (0.5 increments; e.g., 2.5) |
| Integer | Yes | Post-repair gross living area in square feet (above grade) |
| Integer | No | Lot size in square feet (required for SFR) |
| Integer | No | Number of enclosed garage spaces; default |
| Boolean | No | Does the subject have a pool (post-repair)? |
| Integer | Yes | Year built of subject property |
| String | Yes |
|
| Float | No | Search radius for comps; default |
| Integer | No | Months of sales history to include; default |
| String | No |
|
| Float | No | Conservative downward adjustment applied to final ARV; default |
| String | Yes |
|
Processing Methodology
Step 1 — Comp Pull. The AI queries the MLS sold data feed for the target market, filtered to:
Same property type as subject
Within configured search radius (default 0.5 mile, auto-expands to 1.0 mile if fewer than 6 comps found at 0.5)
Sold within configured lookback period (default 6 months, auto-expands to 12 months if fewer than 6 comps at 6 months)
Gross living area within ±25% of subject GLA
Same bedroom count ±1
The raw comp pool is filtered further to exclude:
Distress sales (foreclosure auction, REO bank sale, short sale) — these are not arms-length transactions and would artificially depress ARV
Sales with no verified sale price (listed as non-public or confidential)
Sales where the buyer and seller share the same surname (potential related-party transaction)
Step 2 — Adjustment Calculation. For each comp, the AI calculates adjustments for the following value differences between the comp and the subject property:
Adjustment Factor | Method |
|---|---|
Gross Living Area | Market-calibrated per-sqft adjustment based on active sales in the market; applied linearly |
Condition Grade | Paired-sales analysis: C3 vs C4 spread in market; typical range $8–$25 per sqft depending on market |
Bedroom Count | Market-calibrated per-bedroom adjustment |
Bathroom Count | Market-calibrated per-bathroom adjustment |
Lot Size | Per-sqft land value, applied only for differences >15% of subject lot size |
Garage | Per-space value; market-calibrated; typically $5,000–$18,000 per space |
Pool | Market-calibrated pool contribution; varies significantly by climate market |
Age/Year Built | Applied for comps >10 years age difference from subject |
Location Adjustment | Applied manually if comp is in materially different neighborhood or school district zone |
Step 3 — Adjusted Value Calculation. For each comp:
```
Adjusted Sale Price = Actual Sale Price + Sum of All Adjustments
Adjusted $/sqft = Adjusted Sale Price ÷ Comp GLA
```
Step 4 — Outlier Exclusion. Any comp whose adjusted value falls more than 2 standard deviations from the mean adjusted value of the comp set is flagged as an outlier and excluded from the final ARV calculation (retained in the report for transparency, marked with ⚠ OUTLIER).
Step 5 — ARV Derivation.
```
ARV (pre-adjustment) = Median of adjusted sale prices of valid comps
ARV (final) = ARV (pre-adjustment) × (1 − Market Uncertainty Adjustment)
= ARV (pre-adjustment) × 0.95 (with default 5% adjustment)
```
Step 6 — Confidence Score Assignment.
Condition | Confidence Score |
|---|---|
6+ valid comps, tight adjustment range, sales within 90 days | HIGH |
3–5 valid comps, moderate adjustment range, or sales 91–180 days | MEDIUM |
Fewer than 3 valid comps, or high adjustment variance | LOW — Manual appraisal recommended |
Output Format
ARV Summary:
```
Subject Property: 1847 W Elm St, Tempe AZ 85281
Property Type: SFR | 3bd/2ba | 1,420 sqft | C4 (Average → C3 Post-Repair)
Analysis Date: [Date]
ARV Estimate (Final, Conservative): $312,500
ARV Pre-Adjustment Median: $328,947
Market Uncertainty Adjustment Applied: -5% (-$16,447)
Confidence Score: MEDIUM (4 valid comps, 6-month lookback)
Range: $298,000 – $334,000 (adjusted comp range)
Recommended Offer at 70% ARV: $218,750
```
Comparable Sales Analysis Table:
Comp | Address | Sale Date | Sale Price | GLA sqft | $/sqft | GLA Adj | Condition Adj | Bath Adj | Garage Adj | Total Adj | Adj. Value | Adj $/sqft | Status |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 1920 W Elm St | 45 days ago | $328,000 | 1,380 | $237.68 | +$8,400 | $0 | $0 | $0 | +$8,400 | $336,400 | $236.90 | Valid |
2 | 1755 W Oak Ave | 67 days ago | $318,500 | 1,510 | $210.93 | -$11,400 | +$14,200 | $0 | -$8,500 | -$5,700 | $312,800 | $220.28 | Valid |
3 | 2103 S College Ave | 89 days ago | $341,000 | 1,395 | $244.44 | +$7,000 | $0 | +$4,200 | $0 | +$11,200 | $352,200 | $248.03 | ⚠ OUTLIER |
4 | 1612 W 4th St | 112 days ago | $309,000 | 1,440 | $214.58 | -$1,200 | +$14,200 | $0 | $0 | +$13,000 | $322,000 | $223.61 | Valid |
5 | 1980 S Terrace Rd | 158 days ago | $305,000 | 1,350 | $225.93 | +$10,800 | $0 | $0 | $0 | +$10,800 | $315,800 | $222.39 | Valid |
Median Adjusted Value (excl. outlier): $328,947 | ARV Final (−5%): $312,500
Conservative Bias Methodology
Median, Not Mean. The ARV is calculated from the median of adjusted comp values. The mean is susceptible to distortion from high outliers (often cosmetically renovated properties or new construction that inflate the average). The median is more representative of the center of the market and less vulnerable to outlier influence.
5% Market Uncertainty Adjustment. Even when the comp analysis is strong, the AI applies a default 5% downward adjustment to the final ARV. This accounts for: (a) the time between comp sales and the projected sale date, (b) market volatility in the preceding 90 days, (c) uncertainty in the condition assessment when photos are not available, and (d) the general principle that it is better to be pleasantly surprised than to over-promise.
Outlier Exclusion. Any comp more than 2 standard deviations from the mean is flagged and excluded. This prevents a single anomalous sale from inflating the ARV estimate.
Distress Sale Exclusion. REO, foreclosure auction, and short sales are excluded from the comp pool. These transactions occur under compulsion and do not represent true market value — including them would artificially deflate the ARV and make deals appear worse than they are, which while seemingly "conservative" actually distorts the analysis in a different way.
Insufficient Comps Flag. When fewer than 3 valid comps remain after all filters, the tool does not produce an ARV estimate. Instead, it returns an explicit "Insufficient Comp Data" flag with a recommendation to obtain a professional appraisal, a drive-by BPO, or a manual comp analysis from a local expert. A number produced from weak data is worse than no number at all.
CRM Integration
Attaches full ARV report as a note to the Property record in CRM
Populates custom fields:
ARVEstimate,ARVConfidence,CompCount,ARVDateTriggers recalculation reminder if ARV is more than 90 days old
Feeds ARV value directly into Deal Margin & ROI Scenario Modeler if pipeline integration is active
Parameter | Type | Required | Description |
|---|---|---|---|
| String | Yes | Full property address of the subject property |
| String | Yes |
|
| Integer | Yes | Number of bedrooms in subject property (post-repair) |
| Float | Yes | Number of bathrooms (0.5 increments; e.g., 2.5) |
| Integer | Yes | Post-repair gross living area in square feet (above grade) |
| Integer | No | Lot size in square feet (required for SFR) |
| Integer | No | Number of enclosed garage spaces; default |
| Boolean | No | Does the subject have a pool (post-repair)? |
| Integer | Yes | Year built of subject property |
| String | Yes |
|
| Float | No | Search radius for comps; default |
| Integer | No | Months of sales history to include; default |
| String | No |
|
| Float | No | Conservative downward adjustment applied to final ARV; default |
| String | Yes |
|
Parameter | Type | Required | Description |
|---|---|---|---|
| String | Yes | Full property address of the subject property |
| String | Yes |
|
| Integer | Yes | Number of bedrooms in subject property (post-repair) |
| Float | Yes | Number of bathrooms (0.5 increments; e.g., 2.5) |
| Integer | Yes | Post-repair gross living area in square feet (above grade) |
| Integer | No | Lot size in square feet (required for SFR) |
| Integer | No | Number of enclosed garage spaces; default |
| Boolean | No | Does the subject have a pool (post-repair)? |
| Integer | Yes | Year built of subject property |
| String | Yes |
|
| Float | No | Search radius for comps; default |
| Integer | No | Months of sales history to include; default |
| String | No |
|
| Float | No | Conservative downward adjustment applied to final ARV; default |
| String | Yes |
|
Processing Methodology
Step 1 — Comp Pull. The AI queries the MLS sold data feed for the target market, filtered to:
Same property type as subject
Within configured search radius (default 0.5 mile, auto-expands to 1.0 mile if fewer than 6 comps found at 0.5)
Sold within configured lookback period (default 6 months, auto-expands to 12 months if fewer than 6 comps at 6 months)
Gross living area within ±25% of subject GLA
Same bedroom count ±1
The raw comp pool is filtered further to exclude:
Distress sales (foreclosure auction, REO bank sale, short sale) — these are not arms-length transactions and would artificially depress ARV
Sales with no verified sale price (listed as non-public or confidential)
Sales where the buyer and seller share the same surname (potential related-party transaction)
Step 2 — Adjustment Calculation. For each comp, the AI calculates adjustments for the following value differences between the comp and the subject property:
Adjustment Factor | Method |
|---|---|
Gross Living Area | Market-calibrated per-sqft adjustment based on active sales in the market; applied linearly |
Condition Grade | Paired-sales analysis: C3 vs C4 spread in market; typical range $8–$25 per sqft depending on market |
Bedroom Count | Market-calibrated per-bedroom adjustment |
Bathroom Count | Market-calibrated per-bathroom adjustment |
Lot Size | Per-sqft land value, applied only for differences >15% of subject lot size |
Garage | Per-space value; market-calibrated; typically $5,000–$18,000 per space |
Pool | Market-calibrated pool contribution; varies significantly by climate market |
Age/Year Built | Applied for comps >10 years age difference from subject |
Location Adjustment | Applied manually if comp is in materially different neighborhood or school district zone |
Step 3 — Adjusted Value Calculation. For each comp:
```
Adjusted Sale Price = Actual Sale Price + Sum of All Adjustments
Adjusted $/sqft = Adjusted Sale Price ÷ Comp GLA
```
Step 4 — Outlier Exclusion. Any comp whose adjusted value falls more than 2 standard deviations from the mean adjusted value of the comp set is flagged as an outlier and excluded from the final ARV calculation (retained in the report for transparency, marked with ⚠ OUTLIER).
Step 5 — ARV Derivation.
```
ARV (pre-adjustment) = Median of adjusted sale prices of valid comps
ARV (final) = ARV (pre-adjustment) × (1 − Market Uncertainty Adjustment)
= ARV (pre-adjustment) × 0.95 (with default 5% adjustment)
```
Step 6 — Confidence Score Assignment.
Condition | Confidence Score |
|---|---|
6+ valid comps, tight adjustment range, sales within 90 days | HIGH |
3–5 valid comps, moderate adjustment range, or sales 91–180 days | MEDIUM |
Fewer than 3 valid comps, or high adjustment variance | LOW — Manual appraisal recommended |
Output Format
ARV Summary:
```
Subject Property: 1847 W Elm St, Tempe AZ 85281
Property Type: SFR | 3bd/2ba | 1,420 sqft | C4 (Average → C3 Post-Repair)
Analysis Date: [Date]
ARV Estimate (Final, Conservative): $312,500
ARV Pre-Adjustment Median: $328,947
Market Uncertainty Adjustment Applied: -5% (-$16,447)
Confidence Score: MEDIUM (4 valid comps, 6-month lookback)
Range: $298,000 – $334,000 (adjusted comp range)
Recommended Offer at 70% ARV: $218,750
```
Comparable Sales Analysis Table:
Comp | Address | Sale Date | Sale Price | GLA sqft | $/sqft | GLA Adj | Condition Adj | Bath Adj | Garage Adj | Total Adj | Adj. Value | Adj $/sqft | Status |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 1920 W Elm St | 45 days ago | $328,000 | 1,380 | $237.68 | +$8,400 | $0 | $0 | $0 | +$8,400 | $336,400 | $236.90 | Valid |
2 | 1755 W Oak Ave | 67 days ago | $318,500 | 1,510 | $210.93 | -$11,400 | +$14,200 | $0 | -$8,500 | -$5,700 | $312,800 | $220.28 | Valid |
3 | 2103 S College Ave | 89 days ago | $341,000 | 1,395 | $244.44 | +$7,000 | $0 | +$4,200 | $0 | +$11,200 | $352,200 | $248.03 | ⚠ OUTLIER |
4 | 1612 W 4th St | 112 days ago | $309,000 | 1,440 | $214.58 | -$1,200 | +$14,200 | $0 | $0 | +$13,000 | $322,000 | $223.61 | Valid |
5 | 1980 S Terrace Rd | 158 days ago | $305,000 | 1,350 | $225.93 | +$10,800 | $0 | $0 | $0 | +$10,800 | $315,800 | $222.39 | Valid |
Median Adjusted Value (excl. outlier): $328,947 | ARV Final (−5%): $312,500
Conservative Bias Methodology
Median, Not Mean. The ARV is calculated from the median of adjusted comp values. The mean is susceptible to distortion from high outliers (often cosmetically renovated properties or new construction that inflate the average). The median is more representative of the center of the market and less vulnerable to outlier influence.
5% Market Uncertainty Adjustment. Even when the comp analysis is strong, the AI applies a default 5% downward adjustment to the final ARV. This accounts for: (a) the time between comp sales and the projected sale date, (b) market volatility in the preceding 90 days, (c) uncertainty in the condition assessment when photos are not available, and (d) the general principle that it is better to be pleasantly surprised than to over-promise.
Outlier Exclusion. Any comp more than 2 standard deviations from the mean is flagged and excluded. This prevents a single anomalous sale from inflating the ARV estimate.
Distress Sale Exclusion. REO, foreclosure auction, and short sales are excluded from the comp pool. These transactions occur under compulsion and do not represent true market value — including them would artificially deflate the ARV and make deals appear worse than they are, which while seemingly "conservative" actually distorts the analysis in a different way.
Insufficient Comps Flag. When fewer than 3 valid comps remain after all filters, the tool does not produce an ARV estimate. Instead, it returns an explicit "Insufficient Comp Data" flag with a recommendation to obtain a professional appraisal, a drive-by BPO, or a manual comp analysis from a local expert. A number produced from weak data is worse than no number at all.
CRM Integration
Attaches full ARV report as a note to the Property record in CRM
Populates custom fields:
ARVEstimate,ARVConfidence,CompCount,ARVDateTriggers recalculation reminder if ARV is more than 90 days old
Feeds ARV value directly into Deal Margin & ROI Scenario Modeler if pipeline integration is active
Attaches full ARV report as a note to the Property record in CRM
Populates custom fields:
ARVEstimate,ARVConfidence,CompCount,ARVDateTriggers recalculation reminder if ARV is more than 90 days old
Feeds ARV value directly into Deal Margin & ROI Scenario Modeler if pipeline integration is active
Defend your number. The full adjustment table gives you a defensible, appraisal-methodology ARV that you can present to lenders, JV partners, or sellers who push back on your offer.
Catch bad deals before you make an offer. A LOW confidence score tells you the market is too thin to make a reliable estimate — saving you from over-paying based on weak data.
Speed. An analysis that would take a skilled investor or analyst 2–3 hours to complete manually is returned in under 2 minutes.
Consistency. The same methodology, the same conservative adjustments, every time — regardless of who on your team runs the analysis.
Who This Is For
Fix-and-flip investors making offers that depend on accurate ARV
Wholesalers who need to defend their MAO calculation to buyers
Lenders and hard money lenders reviewing investment loan applications
Acquisition analysts running multiple deal analyses simultaneously