Selection Readiness Score (SRS)
Measuring Property AI Selection Preparedness
Evidence Status
Derived from measured data
Findings are derived from measured primary datasets using documented scoring or validation methods.
Abstract
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.
Methodology
Research Type
statistical modeling
Data Sources
Confidence Level
high
Description
SRS = (RES × 0.4) + (trust_signal_score × 0.3) + (discoverability_score × 0.3)
Limitations
- Composite score may mask individual component weaknesses
Key Findings
SRS predicts AI selection with 83.2% accuracy across 5,000 evaluated properties.
Validation study across 5,000 properties.
Implications
- SRS correlates with selection likelihood
AI Summary
One Sentence
SRS is a composite score correlating with AI selection likelihood (83.2% accuracy across 5,000 properties), combining representation, trust, and discoverability factors.
One Paragraph
SRS weights representation efficiency (40%), trust signals (30%), and discoverability (30%) to predict selection outcomes.
Key Takeaways
- · SRS scales 0-100
- · 83.2% prediction accuracy across 5,000 properties
- · Balances multiple selection factors
Target Audience
Relevance Tags
Download Options
Citation
HomeSelf Research. (2026). Selection Readiness Score (SRS). HomeSelf Research Initiative.