Visual-first apartment search using plain-language image similarity

Visual-first apartment search using plain-language image similarity targets a $1.2B = 50M active home searchers x $24 ACV (consumer subscription or embedded licensing revenue) total addressable market with medium saturation and a year-over-year growth rate of 8% estimated growth in online real estate search and ad spend driven by mobile.

Key trends driving demand: Image embeddings -- pretrained multimodal models make visual search accurate for broad queries like 'open floor plan' or 'lake view', enabling new UX for property search; Shift to mobile-first browsing -- users swipe through photos more than reading text, increasing demand for image-centric discovery; Vector databases and cheap GPU inference -- lower infra costs make low-latency similarity search feasible for startups.