/ CASE STUDY • #010
Baizid Landmark
Real estate platform with map search and SSR-driven SEO
/ HERO
Baizid Landmark
Real estate platform with map search and SSR-driven SEO
CLIENT
Baizid Landmark — real estate
ROLE
Full-stack — listings, search, map integration, SEO and performance.
DURATION
3 months
YEAR
2025
/ OVERVIEW
Rebuilt the company listings site on Next.js with server-side rendering for every property page, an advanced filter UI and an interactive map that stays in sync with the result grid.
The challenge
The legacy site rendered everything client-side, so listings were invisible to search engines and bounce was high. The team also wanted a map-driven search that felt instant.
How I approached it
- →SSR migration: Moved every property and listing page to SSR so Google indexes the full content with proper meta and JSON-LD.
- →Map-grid sync: Built a synchronized map + grid — panning the map filters the visible listings, clicking a card centers the pin, both update the URL for shareability.
- →Performance: Compressed and lazy-loaded imagery with next/image; LCP dropped from 4.1s to 2.0s on a 4G connection.
- →Structured data: Added schema.org RealEstateListing markup so listings show rich results in search.
/ OUTCOME
What it did in the world
+35% Organic Traffic
Full SSR plus structured data drove a step-change in Google impressions and clicks within 60 days of launch.
2× Faster Load
LCP improved from 4.1s to 2.0s — image pipeline and route-level code splitting cut critical render time in half.
Map-Synced Search
Pan the map and results update. Click a card and the map pans. URL-state means everything is shareable.
Google Rich Results
Listings appear with price, beds and images directly in Google search results via schema.org markup.
/ STACK
Built with
FRONTEND
- Next.js (App Router)
- React
- TailwindCSS
- Mapbox GL
BACKEND
- Node.js
- REST API
- Image pipeline
- PostgreSQL
TESTING & SEO
- Lighthouse CI
- JSON-LD
- next-sitemap
- Cypress
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