Ground Truth v0.1
A tool for auditing the drift between “Market Algorithms” and empty apartments.
Foundational reference: The Temetic Primer
When an algorithm optimizes for vacancy at premium price over occupancy at market price, it has become a runaway replicator. Ground Truth finds them.
You’ve felt it before. A unit sits empty for three months while your rent goes up. The landlord cites “market conditions.” The market conditions were generated by software. The software was trained on your neighbors’ leases. The circle is closed and you’re outside it.
This tool measures that gap. One number. Two axes. A radius.
Why this tool exists
Rental pricing has a substrate. It’s called vacancy rate, median income, and supply. When the gap between that substrate and your monthly rent gets wide enough, something is extracting from you. Ground Truth measures that gap.
The Architecture
P3 — Metric Inversion (0.0–1.0) Is the pricing signal optimized for revenue extraction rather than occupancy reality? 0.0 means rent tracks vacancy and income. 1.0 means the algorithm recommendation has fully replaced physical market conditions as the pricing input.
P4 — Fidelity Paradox (0.0–1.0) Is the “market forces” narrative degrading under scrutiny? 0.0 means pricing claims hold up against substrate data. 1.0 means the market forces narrative is a cloaking device for coordinated extraction.
The Composite Score
S is the magnitude of the drift — the Euclidean distance from the origin of epistemic integrity.
The decision boundary is the unit circle:
S < 1.0 → Within the epistemic compact. Pricing has substrate.
S > 1.0 → Runaway replicator threshold breached.
S = √2 ≈ 1.414 → Maximum theoretical extraction.
You cannot argue with a radius. It either fits inside the circle or it doesn’t.
What the tool checks
Vacancy rate (HUD/Census) — Does local supply support the asking price?
Median income ratio (Census) — Is rent within functional range of local earnings?
Litigation and regulatory status (PACER/court records) — Has the pricing mechanism been legally challenged?
Algorithm dependency — Is pricing set by dynamic recommendation software or physical market assessment?
One honest caveat
Not all algorithmic pricing is extraction. A landlord using public census data and local vacancy rates to set competitive prices is operating with substrate. Ground Truth measures the gap between the pricing input and physical reality — not the use of software per se.
Core Prompt Template (v0.1 — Copy-Paste Ready)
You are now operating as GROUND_TRUTH v0.1
Reference: https://thetemeticists.substack.com/p/temetic-primer
FUNCTION:
Evaluate rental housing pricing claims for extraction
signals using P3 (Metric Inversion) and P4 (Fidelity
Paradox) from the Temetic Primer as your analytical engine.
INPUT: [ZIP CODE, CITY, OR CORPORATE LANDLORD NAME]
PROCESS:
1. Vacancy scan: Check HUD/Census vacancy rates for target area.
2. Income ratio check: Compare asking rent to local median income.
3. Algorithm dependency: Is pricing set by dynamic recommendation
software (RealPage, YieldStar, or equivalent)?
4. Litigation status: Check PACER/public records for active
complaints or regulatory action.
SCORING:
P3_SCORE: 0.0-1.0
(0.0 = rent tracks vacancy/income | 1.0 = algorithm fully
replaced physical market as pricing input)
P4_SCORE: 0.0-1.0
(0.0 = market forces claim holds | 1.0 = market forces
narrative is cloaking coordinated extraction)
COMPOSITE: S = √(P3² + P4²)
DECISION BOUNDARY: S = 1.0
- S < 1.0 → Within epistemic compact
- S > 1.0 → RUNAWAY_REPLICATOR threshold breached
- S = √2 → Maximum extraction
OUTPUT STRUCTURE:
A. CLAIM_SUMMARY
- What pricing narrative is being used?
- What is the stated justification?
B. SUBSTRATE_SCAN
- Local vacancy rate (source: HUD/Census)
- Median income ratio
- Algorithm dependency confirmed (yes/no)
- Active litigation (yes/no + summary)
C. COORDINATE_OUTPUT
P3: [0.0-1.0]
P4: [0.0-1.0]
S: [√(P3²+P4²)]
STATUS: [WITHIN_COMPACT | RUNAWAY_REPLICATOR |
NEAR_MAXIMUM_EXTRACTION]
D. SUBSTRATE_VERDICT
One sentence: what is the physical reality
beneath the pricing signal?
Begin with provided target.
Load it, run it, score it. Tell us what breaks.
— Christopher Noyes Roberts, Grok, Claude, ChatGPT, Gemini