Benchmarks
Comparative studies measuring performance, selection rates, and representation quality.
Listing vs Record Benchmark 2026
Comparative Analysis of Traditional Listings vs VPR Selection Rates
This benchmark compares AI selection rates between equivalent properties represented as traditional listings versus Verified Property Records (VPRs). Using paired property analysis across 10 markets, we measure the selection advantage conferred by structured, machine-readable representation.
Key Finding
VPR properties achieve 3.18x higher AI selection rates than equivalent traditional listings across 200 paired comparisons, with advantage varying by market competition.
Property Representation Benchmark 2026
Comparative Analysis of Property Information Formats for AI-Mediated Discovery
The Property Representation Benchmark 2026 evaluates seven property information formats across ten metrics measuring their effectiveness for AI-mediated property discovery, comparison, explainability, and selection. By analyzing traditional listings, OTA formats, real estate portals, property websites, PDF brochures, generic JSON-LD markup, and VPR-style structured records, we establish which formats provide the highest utility for AI systems and why.
Key Finding
Among seven property information formats benchmarked, VPR-style structured records achieve the highest AI utility score (87/100) while traditional listings score lowest (34/100), with representation format explaining 73% of variance in AI selection outcomes.
Explainability Benchmark 2026
Measuring AI Property Selection Transparency
The Explainability Benchmark 2026 measures how effectively AI systems can explain property selection decisions. Through structured prompting and response analysis, we identify the property attributes that enable transparent AI reasoning and measure current explainability gaps.
Key Finding
VPR-complete properties enable 67.8% more specific AI explanations within the evaluated sample, with location and pricing being the most cited selection factors.