Sample Report Analyze a Property Free
Methodology & Accuracy

Why investors can trust
Arvo's numbers.

Every figure in an Arvo report comes from a deterministic underwriting engine — the same formulas lenders and seasoned investors use — fed by live market data and conservative assumptions. Here's exactly how it works, and where the limits are.

How a number gets made

1

Live market data, not stale averages

Comparable sales, valuations, rent benchmarks, tax records, and walk scores are pulled live from MLS-backed sources (Zillow, Redfin) plus HUD Fair Market Rent data at the moment you run the analysis.

2

Outliers filtered before they skew your ARV

Comps are screened by distance, recency, size, and property type, then run through IQR (interquartile range) outlier filtering — so one luxury flip or distressed sale down the street doesn't distort your after-repair value. Every comp used is listed in the report; nothing hides behind the number.

ARV = median $/sqft of filtered comps × subject sqft
3

Deterministic underwriting math — AI never touches the formulas

Cap rate, cash-on-cash, DSCR, IRR, GRM, break-even occupancy, and max-offer ceilings are computed by a pure math engine using standard real-estate methodology. Run the same inputs twice, get the same answer twice. AI assists with data extraction and repair scoping — it never invents a financial metric.

Cap Rate = NOI ÷ Price  ·  DSCR = NOI ÷ Annual Debt Service
4

Conservative defaults you can override

8% vacancy, 10% management, maintenance and CapEx reserves baked in from day one. Arvo's defaults are built to keep you out of bad deals, not to make marginal ones look good — and every assumption is visible and editable.

5

Every deal is stress-tested before you see a verdict

Each analysis includes sensitivity tables (rent, vacancy, interest rate, purchase price) and downside stress scenarios — high vacancy, rate hikes, rent drops, and combined stress — so you know how much cushion a deal really has before you offer.

Worked example: how Arvo stops a runaway ARV

Illustrative example. Representative numbers that walk through the exact outlier-filter and AVM-anchor logic Arvo runs on every property. This is not a customer's deal — we publish named case studies only with documented, permissioned results.

The trap: the nearby sales are smaller, pricier-per-foot homes

The subject is a 1,500 sqft single-family home. The only recent sales within range are 880–1,100 sqft units — and smaller homes almost always trade at a higher price per square foot. Take the raw median and the after-repair value balloons.

Recent comparable saleSizeSale $/sqft
Comp A880 sqft$301
Comp B920 sqft$284
Comp C960 sqft$273
Comp D1,040 sqft$268
Comp E1,100 sqft$255
Raw median $/sqft $273 × 1,500 sqft = $409,500 comp-derived ARV

The guardrail: anchor to a size-normalized AVM

RentCast's automated valuation is computed for the exact address at its actual square footage, so it isn't fooled by smaller comps. Arvo caps the comp-derived ARV at 1.2× the AVM-anchored renovated value — anything higher is treated as size- or class-mismatched and pulled back down.

Raw comp ARV
$409,500
median $/sqft × subject sqft
Size-normalized AVM
$300,000
RentCast, exact address
AI repair scope
$30,000
from photos + local rates
ARV Arvo actually uses
$321,000
AVM + 70% of repair
Ceiling = 1.2 × (AVM $300,000 + 0.7 × repair $30,000) = $385,200  ·  raw $409,500 exceeds it → ARV pulled to $321,000

That's an $88,500 haircut off an inflated number — the difference between a deal that looks good on a spreadsheet and one that actually pencils. Every comp, the filter, and the anchor are shown in your report, so you can see exactly why the number landed where it did.

What Arvo won't do

See the methodology on a real report.

Walk through a complete analysis — comps, scoring, sensitivity tables and all — no signup required.