Local Market Transparency Score (LMTS)
A research-grade index that measures how clear or opaque a ZIP’s pricing signal is. When LMTS is low, simple AVMs and comps are guessing through data fog. Our underwriting stack adjusts for that fog so sellers see trustworthy pricing, timelines, and confidence bands.
Transparent vs. Opaque Markets (and Why Sellers Should Care)
Transparent markets price efficiently; opaque markets misprice. The Local Market Transparency Score (LMTS) tells us which world you’re selling into so we can:
- Set realistic expectation ranges, not fantasy single numbers.
- Decide how wide your confidence bands should be on price and timeline.
- Know when an AVM/Zestimate-style number is probably fine—and when it’s dangerous.
- Decide whether a fast, net-first cash exit or a longer retail listing is likely to serve you better.
LMTS doesn’t try to say whether prices are “high” or “low.” It measures whether the signal behind those prices is clean (high LMTS) or noisy and fragile (low LMTS).
LMTS lives inside a broader research stack:
How LMTS is Calculated (v1 Composite)
LMTS outputs a 0–100 score (100 = very clear, 0 = very opaque) using three observable components. Plain English first, math second.
Inputs (What We Watch)
- List-to-Sale Variance (LSV) — Average absolute % gap between the original list price and the eventual sale price in a ZIP. Higher variance ⇒ the market is guessing, re-trading, and overshooting.
- Price Reduction Frequency (PRF) — Share of listings that require ≥1 price cut before they go under contract. Higher PRF ⇒ weak initial price discovery.
- Data Availability Score (DAS) — A 0–100 score for how complete, fresh, and internally consistent local public records are (tax assessments, deed history, physical attributes, etc.). Higher DAS ⇒ a cleaner baseline.
Intuition: LSV tells us how wild the final prices are, PRF tells us how often sellers had to admit they were wrong, and DAS tells us whether the underlying data is even trustworthy to begin with.
Reading LMTS: Clarity Bands for Real-World Decisions
LMTS is designed to be seller-facing and human-readable. We group the score into four clarity bands:
Strong signal. AVMs/comps are usually in the right neighborhood. Confidence bands are relatively tight. In a clear market, price isn’t the risk—time is. Since the price is known, we can often pay closer to market value in exchange for immediate speed and certainty.
Some noise, some clarity. We can still lean on comps, but we widen the bands and pay more attention to condition, micro-location, and timing.
High data fog. Single-point pricing becomes risky. We emphasize scenario analysis, timeline risk, and the gap between “best case” and “most likely.”
Very low transparency. Public records are thin, outcomes are erratic, and list-to-sale spreads are wide. Here, certainty itself has value—and we lean hard on our Cost of Certainty and FOS frameworks.
As LMTS falls, we stop pretending that a single number can summarize your outcome. Instead, we show you net-first ranges and timelines, then let you choose between: (a) a fast, documented cash exit, or (b) a slower, more variable retail path.
LMTS hints at whether you’re in a ZIP where Zillow-style estimates and basic CMA sheets are usually fine—or where they’ve been consistently off.
For Local Home Buyers USA, LMTS modulates our confidence bands, our Cost of Certainty curve, and how aggressively we discount for unknowns.
Combined with LESI, FOS, and RVI, LMTS tells us which markets feel like transparent trading floors and which ones still feel like foggy basements.
LMTS Mini-Calculator (For Intuition, Not Production)
This lightweight calculator lets you plug in either example presets or your own rough assumptions for LSV, PRF, and DAS to see how LMTS behaves. The production version uses richer pipelines, but the shape is the same.
LMTS Inputs, Attribution, and How It Feeds Offers
LMTS is intentionally auditable. Each component lives as its own dataset in the Research & Data Catalog .
Core inputs
- List-to-Sale Variance (LSV) — engineered from historical listings and closed transactions; author: Justin Erickson and PropTechUSA.ai research; license: Research & Data Catalog.
- Price Reduction Frequency (PRF) — engineered from listing status change logs; author: PropTechUSA.ai Research; same catalog/license.
- Data Availability Score (DAS) — coverage/freshness/consistency rating of tax & assessment records; author: PropTechUSA.ai Research; same catalog/license.
Offer design link-in
LMTS feeds directly into:
- Our Unified Net Offer Sheet (see the framework), which lays out timeline, fees, and net proceeds side-by-side.
- The Cost of Certainty curve (pricing time and risk), which adjusts how much “certainty premium” we charge—or absorb—for each ZIP.
- Our friction and condition metrics like Friction to Offer Score (FOS) and Renovation Value Index (RVI).
Net result: your offer isn’t just “a number.” It’s a clarity-adjusted, time-aware scenario, backed by the same research grade tooling we use to run portfolios.
Common Questions About LMTS
A few of the most frequent questions we get from sellers, agents, and partners.
What problem does LMTS actually solve? ›
Most pricing tools quietly assume transparent markets. Many ZIPs simply aren’t. LMTS exposes where the signal is foggy so you don’t over-trust a single comp sheet or AVM. It’s a meta-signal that tells you how much weight to put on any valuation in the first place.
Is LMTS itself a valuation model? ›
No. LMTS is a clarity index, not a price. It rides alongside valuation models, telling us how aggressive or conservative to be with confidence bands, timing assumptions, and net-sheet scenarios.
Where do the LMTS inputs come from? ›
LSV and PRF come from historical listing and closing data; DAS comes from coverage and freshness checks on public records. The datasets are described in the Research & Data Catalog .
How often is LMTS refreshed? ›
We refresh LMTS at least monthly at the ZIP level, with higher-frequency updates in volatile micro-markets. Drift, stability, and methodology changes are tracked in internal model cards and version notes.
How should a homeowner use LMTS in practice? ›
Treat LMTS as your humility gauge. If it’s high, a tight CMA and a transparent cash offer are both reasonable to lean on. If it’s low, widen your expectations, focus on net (not just sticker), and decide how much you value speed and certainty versus chasing a best-case number.
Research and educational content only. Local Home Buyers USA and PropTechUSA.ai do not provide legal, tax, or investment advice. Always confirm state-specific rules and implications with your attorney, title company, and tax professional.
Real-World Seller Insights
Fresh how-tos and market tips from Local Home Buyers USA — powered by PropTechUSA.ai.
