Methodology
Research methods, scoring frameworks, and measurement approaches used across HomeSelf Research publications.
Scoring Frameworks
Representation Efficiency Score (RES)
Scoring Framework for Property Representation Quality
The Representation Efficiency Score (RES) quantifies how efficiently a property record conveys selection-relevant information. RES balances completeness with concision, rewarding properties that provide comprehensive representation without redundancy.
Inference Burden Score (IBS)
Measuring Computational Cost of Property Understanding
The Inference Burden Score (IBS) quantifies the computational complexity AI systems encounter when processing property records. Higher IBS indicates more challenging representations that may degrade selection performance.
Representation Completeness Score (RCS)
Measuring Attribute Coverage in Property Records
The Representation Completeness Score (RCS) measures what proportion of selection-relevant attributes are present in a property record. RCS identifies missing attributes that may prevent AI selection.
Selection Readiness Score (SRS)
Measuring Property AI Selection Preparedness
The Selection Readiness Score (SRS) is a composite score combining representation quality, trust signals, and discoverability factors. SRS predicts how likely a property is to be selected by AI systems.