LANDMARQ produces probabilistic risk opinions based on observable public data. This page explains what our scores are, what they are not, and the basis on which they should be used in capital allocation decisions.
LANDMARQ produces a Community Risk Score (CRC) and an associated probability of community opposition, referred to as P(NIMBY), for individual land parcels. These outputs represent a probabilistic opinion derived from the analysis of observable public data across seven variable categories.
A LANDMARQ score is analogous to a credit rating or an actuarial risk estimate: it reflects a systematic, data-driven assessment of relative risk based on historical patterns and observable signals. Like a credit rating, it is an opinion, not a guarantee.
LANDMARQ scores are not:
LANDMARQ's pilot backtest was conducted on 30 completed utility-scale projects across California, Texas, and New York. The model was assessed using only data available at the start of each project, before any capital was committed.
The backtest yielded directional alignment with historical permitting outcomes on the pilot sample. This figure should be understood in the following context:
The pilot backtest result should be treated as a directional indicator of model usefulness, not a performance warranty. Developers and investors using LANDMARQ scores should treat them as one input in a broader diligence process, alongside independent legal, planning, and community engagement advice. They are not a standalone decision-making tool.
LANDMARQ's scoring methodology is grounded in peer-reviewed academic research on infrastructure siting, community opposition, and permitting outcomes. The variable categories and their relative weighting reflect empirical findings from published literature in energy policy, urban planning, and environmental justice.
All inputs to the model are drawn from observable public data. LANDMARQ does not use proprietary, confidential, or non-public information about any parcel, project, developer, or community. The score is designed to be fully auditable: every input is traceable to a publicly observable signal.
Users should also be aware of the following methodological constraints:
Scores reflect conditions at the time of generation and should be refreshed prior to final investment decisions.
LANDMARQ's current scoring methodology is grounded in peer-reviewed academic research and calibrated against a proprietary dataset of 30 completed utility-scale projects. The model has not yet been independently audited by a third-party academic institution or professional advisory body.
We are currently in the process of selecting an independent advisory board comprising practitioners from energy policy, infrastructure finance, and spatial data science. The purpose of this board is to provide ongoing methodological oversight and external validation as the model scales from pilot deployment toward enterprise-grade deployment.
Until independent advisory oversight is formally established, users of LANDMARQ scores should treat the methodology as self-authored and apply appropriate professional judgment. This does not affect the auditable nature of the underlying inputs, as every signal is drawn from observable public data, but it does mean the weighting logic and calibration methodology have not been externally reviewed at this stage.
LANDMARQ provides data-driven risk intelligence as an analytical tool. Our outputs are probabilistic opinions based on systematic analysis of public information. We are not a licensed legal practice, a registered investment advisor, or a professional planning consultancy.
LANDMARQ does not accept liability for:
Users of LANDMARQ scores and reports accept that those outputs constitute analytical opinions only. All material capital, legal, and permitting decisions should be made in consultation with qualified professionals.
Before making capital allocation, development, or investment decisions informed by LANDMARQ scores, users should seek independent legal, planning, environmental, and financial counsel. LANDMARQ is a first-screen diligence tool and is not a substitute for qualified professional judgment on any specific project or site.
The San Diego pilot is active across 29,890 permit records and 151MB of GIS layer data. The current priority is acquiring the proprietary outcome dataset that calibrates and defends the model.