LEVEN Real Estate — Building a multilingual luxury platform for global buyers

The Challenge

LEVEN Real Estate operates in one of the most competitive luxury real estate markets in the world. Their clients — international buyers from Latin America, Europe, and the Middle East — research properties in their native language before ever contacting an agent. The firm needed a digital platform that could match the sophistication of the properties they represent while serving a truly global audience.

The existing website was functional but lacked the content depth, multilingual capability, and neighborhood-level architecture needed to compete with larger brokerages for international search traffic. Property pages existed, but the surrounding content ecosystem — neighborhood guides, market insights, and lifestyle information — was thin or missing entirely.

Our Approach

We approached the LEVEN platform as a content architecture challenge first and a development challenge second. Before writing a single line of code, we mapped out the information architecture that international buyers actually need — organized by neighborhood, property type, and buyer journey stage.

Neighborhood Content Architecture. We designed and built dedicated pages for over 15 New York and Miami neighborhoods — from SoHo and Tribeca to the Upper West Side, Brooklyn Heights, and Gramercy. Each neighborhood page includes a lifestyle description, property characteristics, transportation access, and embedded listings. These aren’t thin SEO pages — they’re genuine buyer resources structured for both human readers and search engines.

Multilingual Implementation with WPML. The platform serves buyers in English, Spanish, and Portuguese. We implemented WPML with careful attention to the details that most multilingual builds miss: proper hreflang tags for each language variant, translated URL slugs (not just content), language-specific meta titles and descriptions, and a language switcher that preserves the user’s current page rather than redirecting to the homepage.

Front-End Components. We built custom UI components for property cards, neighborhood grids, notification emails, and the seller valuation form — all designed to align with LEVEN’s brand aesthetic of understated luxury. The email notification system for property valuations was built directly in Gravity Forms with custom-coded HTML that matches the site’s visual identity.

SEO Foundation. Every page was built with structured data in mind. Neighborhood pages include LocalBusiness and Place schema. The site architecture uses a clear hierarchy that both Google and AI models can parse. We implemented FAQ sections on key neighborhood pages with FAQ schema markup — a direct contributor to both featured snippet appearances and AI citation potential.

The Results

The LEVEN platform is now a multilingual buyer resource that competes with significantly larger brokerages for international search traffic. The neighborhood content pages rank for location-specific queries across multiple languages, and the structured data implementation has improved the site’s visibility in Google’s AI Overviews for luxury real estate queries. Key outcomes include:

• 15+ neighborhood pages with dedicated content, structured data, and multilingual support
• Full WPML implementation across English, Spanish, and Portuguese with correct hreflang, translated slugs, and language-specific SEO
• Custom front-end components including property cards, seller valuation emails, and neighborhood grid layouts
• Schema markup implementation (LocalBusiness, Place, FAQ) supporting AI visibility
• Ongoing development relationship with continuous improvements to the platform

What We Learned

The LEVEN project reinforced a principle we apply across all our real estate work: content architecture precedes design. The most beautiful real estate website in the world won’t generate international leads if the content isn’t structured for multilingual discovery. The firms that win international buyers are the ones that invest in genuine, structured, neighborhood-level content — not just property listings with a language toggle.

This project also informed our approach to AI visibility for real estate clients. The combination of structured data, comprehensive neighborhood content, and multilingual implementation creates exactly the kind of authoritative, structured information that AI models rely on to generate recommendations. LEVEN’s investment in content depth is an investment in both current search visibility and future AI discoverability.