NYC real estate brokerage earns 7 #1 rankings and early AI search visibility

The challenge

A residential real estate brokerage in Upper Manhattan was competing in one of the most competitive local search markets in the US. It needed visibility for neighborhood- and property-specific searches, plus more buyer and seller inquiries.

The objective

Grow qualified organic traffic, rank for high-intent seller and buyer keywords, strengthen domain authority and build visibility in AI-powered search.

Our strategy

We expanded topical authority around the brokerage's neighborhoods and property types, strengthened E-E-A-T signals, fixed technical and structured data foundations, and built authority off-site.

Implementation

• Content: published 4 blog posts and 3 new keyword-targeted pages; expanded cornerstone content and key seller-representation and market pages.
• E-E-A-T: added author attribution, author bio boxes, and agent biographies with license details.
• Technical: added FAQ and page schema, fixed structured data errors, added Open Graph tags and meta descriptions, and improved internal linking and site architecture.
• Authority: acquired 155 high-quality backlinks and published Q&A-platform answers to support brand and entity recognition.

Results (month over month)

• AI mentions by platform: Google AI Mode 15, ChatGPT 5, Gemini 3.

Key learnings

• Neighborhood-level content plus E-E-A-T signals (named, licensed experts) win high-intent local real estate searches.
• FAQ schema and answer-focused content create an early footprint in AI search before most competitors are even measuring it.

Client name withheld for confidentiality. Figures are taken from the client's ad platform and analytics reporting for the period shown.