How we grade properties
A strict rubric with bounded weights. AI is only used for the location adjustment.
Overview
We grade facilities with a deterministic, additive rubric. Scores start at 50, then earn or lose points from build year, access model, climate coverage, unit count, and location tagging. Location gets a bounded AI adjustment, while every other input is deterministic. Each driver is capped so no single input can dominate. Every score has a receipt with drivers and rubric versioning.
Location module (separate from quality)
Nearby ≠ compZip is for filtering. True comps rely on precise location and trade area context: lat/lng, geohash for nearby queries, market/submarket tags, and normalized address. “Nearby” does not automatically mean “comparable.” Submarket fit + quality + unit-type match make a comp, not just distance.
Normalized address, lat/lng, geohash, market/submarket tags for clustering.
AI provides a bounded location + market-fit delta with an explanation saved to “Why this value.”
“Nearby ≠ comp.” Access model, climate, and unit mix must align for a true comp set.
Quality Index
A single, legible Quality Index (0–100) with rubric versioning (rubricVersion: v1.1). Scores are deterministic, derived from strict inputs, and never inflated by narrative text. Location AI adds a bounded adjustment only.
Category scores (0–100 each)
Five categories roll up into the Quality Index. Weights are bounded and versioned for consistency across facilities.
Build year + climate coverage with strict deductions for missing inputs.
Access model emphasis (drive-up vs indoor) with bounded impact.
Proxy from build year + indoor access (no marketing claims).
Unit count bands only; missing unit count is penalized.
Lat/lng + submarket tagging plus bounded AI market-fit adjustment.
Unit-type specific adjustments
The rubric is strict on missing data. If critical fields are absent, we apply automatic deductions.
Comp Score (beyond distance)
Comp Score blends distance + submarket fit + quality delta + unit-type match + access model match + climate match. Distance alone is not enough.
Example Comp Explanation
Why comp
- Same submarket tag
- Climate/indoor match
- Drive-up mix within 5%
Why not
- Slightly newer build date
- Access hours differ (24/7 vs 9-9)
Estimated sell rate (Est. Sell)
ModeledEst. Sell is a deterministic model derived from scraped web rates plus facility context. It is designed to estimate likely sell-through pricing, not to replace observed web rates.
upliftRaw = sizeBase + configAdj + marketAdj + reliabilityAdj
uplift = clamp(upliftRaw, 0.06, 0.22)
estSell = baseWeb × (1 + uplift)
estSellPerSqft = estSell ÷ sqft (when sqft is known)
- Unit size bucket (smaller units typically carry higher sell-through uplift).
- Unit configuration (climate and indoor/outdoor adjustments).
- Market context (Quality Index and bounded location AI delta).
- Reliability context (freshness of scrape and confidence score).
- Deterministic: the same inputs always produce the same estimate.
- Bounded: uplift is capped between 6% and 22% to avoid unrealistic jumps.
- Signal-based: combines product, location, and data-quality inputs instead of a fixed multiplier.
- Transparent: web rate remains visible, and Est. Sell is always labeled as modeled.
Estimated occupancy
Hybrid modelEstimated occupancy blends listing-based occupancy evidence with a deterministic reliability-scaled model. It is directional and not PMS truth unless PMS is explicitly connected and shown.
modelPoint = fundamentals + pricing + promos + competition + local modifiers
modelRange = clamp(modelPoint ± halfWidth)
listingWeight = f(coverageScore, freshnessScore, volatilityScore)
finalRange = (listingWeight × listingRange) + ((1-listingWeight) × modelRange)
- Competition and overbuild: supply pressure balanced against local demand density with conditional penalties.
- Seasonality and local shocks: snowbirds, universities, military PCS, lease-ups, turnover, disasters, employer shocks, remodeling.
- Promos and pricing position: interpreted as demand pressure with anti-double-count safeguards.
- Facility fundamentals: quality, access, climate mix, unit count, and age priors.
- Deterministic: same inputs always yield the same output.
- Bounded: point/range are clamped with per-domain budgets and top-3 seasonality control.
- Uncertainty-first: stale or thin coverage lowers listing weight and widens range width.
- Transparent: blend math, active modifiers, applied caps, and quality scores are shown in explainability views.
FAQ
No. Distance is one factor. Comp Score blends submarket fit, quality delta, unit-type match, access model, and climate alignment.