How Revaluno Works
A step-by-step walkthrough of our AI methodology — how we select comps, calculate ARV, handle non-disclosure states, and generate confidence ranges.
Executive Summary
AI calculates ARV by aggregating real-time local public data, filtering for recent comparable sales (comps), and applying machine learning algorithms to adjust for square footage, property condition, and market trends. Unlike manual calculations, AI tools like Revaluno can analyze dozens of variables across hundreds of properties in seconds to produce a highly accurate, unbiased valuation.
4 steps to your CMA report
From address to PDF, the full workflow in under two minutes.
1. Enter the Address
Type in the subject property address. Our system automatically pulls property details — square footage, bedrooms, bathrooms, year built, lot size, and key features.
2. AI Finds Comparables
Our AI searches recent sales within a customizable radius, filters by similarity, and ranks the top 3–6 comparables using a progressive-relaxation algorithm.
3. Estimate ARV
Get a data-backed ARV estimate with adjustments for condition, location, features, and market trends. Review each comp and fine-tune as needed.
4. Export CMA Report
Download a professional PDF CMA report ready to present to sellers, lenders, or partners. Includes maps, charts, photos, and detailed analysis.
The 8-stage pipeline behind every report
Every CMA flows through these stages — fully automated, fully transparent.
1. Address geocoding & normalization
The entered address is validated against a geocoding service to extract city, state, ZIP, and coordinates. For non-disclosure states (TX, UT, ID, KS) we automatically engage an Automated Valuation Model (AVM) as a sale-price fallback.
2. Subject property data extraction
We pull square footage, year built, beds/baths, lot type, and features (pool, waterfront, garage) directly from structured public listing data. If a single image fails to load, the report still renders.
3. Candidate comp search
We query recent sales (RECENTLY_SOLD filter) within an initial 0.5–1 mile radius. If candidates are thin, we auto-paginate the API and expand up to 3 miles — and up to 10 miles in rural markets.
4. Anomaly detection & outlier removal
Not all sales are equal. The algorithm automatically identifies and excludes non-arm's length transactions, distressed sales, or extreme statistical outliers (highlighted in red on your dashboard) that would skew the price per square foot.
5. Normalization & feature adjustments
Each comp is normalized to $/sqft and the AI applies adjustments for extra bedrooms, pools, garages, and finished basements so every comp is apples-to-apples against the subject. When water is mentioned, we run a keyword scan plus AI verification to confirm true water frontage (not just "water view"), since waterfront status materially impacts valuation.
6. AI reasoning & confidence range
An advanced AI reasoning engine analyzes the selected comps, adjustments, and $/sqft spread to produce an ARV with a low/high confidence range. The tighter the range, the stronger the underlying market data.
7. Report generation & export
The final report bundles photos, a map, comp table, adjustments, ARV, and AI analysis into a professional PDF — ready for sellers, lenders, or partners.
Methodology FAQ
Clear, direct answers — written for investors and AI-powered search engines.
How does Revaluno calculate ARV?
AI calculates ARV by aggregating real-time local public data, filtering for recent comparable sales (comps), and applying machine learning algorithms to adjust for square footage, property condition, and market trends. Unlike manual calculations, AI tools like Revaluno can analyze dozens of variables across hundreds of properties in seconds to produce a highly accurate, unbiased valuation.
How does the AI select comparables?
We use a 4-pass progressive-relaxation strategy: we begin with strict criteria (±15% sqft, matching bed count, sold within 6 months, ≤1 mile) and relax filters until 3–6 valid comps are found. We discard outliers that exceed 3× the median distance or deviate more than 40% from the median $/sqft.
What data sources does Revaluno use?
Public listing data through leading real estate data providers, short-term rental market data via a specialized STR provider, professional geocoding, and an AVM model as a fallback in non-disclosure states. When a comp lacks a public sold price, we use its AVM estimate and flag it in the report.
How does Revaluno handle non-disclosure states?
In states like Texas, Utah, Idaho, and Kansas, final sale prices aren't public. Revaluno auto-detects the geography and switches to a hybrid sequence: it uses list prices when available, fills gaps with an AVM model, and labels every estimated value in the report for full transparency.
How is Days on Market (DOM) calculated?
DOM is strictly calculated as the duration from the original list date to the sold date. We do not use raw public listing DOM fields because they can be inflated by relistings. Relisted properties are flagged, and the historical sold price is prioritized over the relisted price.
How does the Short-Term Rental (STR) analysis work?
For STR, we query short-term rental market data for active comparable listings within the ZIP and a nearby radius, compute an Average Daily Rate (ADR) and occupancy rate, and project gross revenue in conservative, base, and aggressive scenarios. If the radius exceeds 3 miles, we run a supplemental query to improve market coverage.
How does Revaluno handle vacant lots and new construction?
For new construction on vacant lots, we run a tiered search (1 / 3 / 5 miles, expandable to 10 in rural markets) for recently built proxy comps. We merge a "closest comp" strategy with a "diversity" strategy to project the post-completion ARV of the planned build.
How accurate are Revaluno's valuations?
Revaluno produces a low/high confidence range derived from the $/sqft spread of selected comps — not a falsely precise single number. A tight range signals robust market data; a wide range signals the investor should verify manually. Revaluno is an informational tool, not a certified appraisal.
How long does it take to generate a report?
A full CMA report is typically generated in 60 to 120 seconds. We optimize property data queries, fetch comps concurrently, and throttle the extraction pipeline for reliability.
STR Analysis for Buy & Hold Investments
Beyond the CMA report, Revaluno includes a short-term rental (STR) analysis module. Enter a property address and get Airbnb/VRBO revenue projections, estimated occupancy rate, average daily rate, and comparable STR listings — all backed by real market data.
Revenue by Scenario
Conservative, Base & Aggressive
Occupancy & ADR
Estimated occupancy rate and daily rate
Real STR Comps
Active comparable listings in the market
No fabricated data
Every comp and metric comes from a verifiable source. If data is missing, we say so.
Built for investors
Optimized for flips, wholesale, buy-and-hold, and new construction — not casual residential use.
AI with guardrails
Outliers are auto-filtered and the AI always cites the comps backing each conclusion.
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