Re-Engineering a Property Discovery Platform for a Growing Real Estate Company

Project Snapshot

Business challenge

The property discovery market is intensely competitive, with platforms like Zillow, Realtor.com and Redfin setting high expectations for search speed, listing quality and user experience. Haven's platform had reached a critical inflection point where technical debt and architectural limitations were eroding its competitive position.

• Slow and unreliable property search: The search infrastructure could not efficiently query millions of listings with complex filters such as price range, property type, bedrooms, location radius and amenities. Response times exceeded acceptable thresholds, and users abandoned searches before finding relevant results. Research indicates that 53% of mobile visitors leave a site that takes longer than three seconds to load, making search performance a direct driver of engagement and conversion
• Poor organic search visibility: Property listing pages were rendered entirely on the client side, meaning search engine crawlers could not effectively index them. In an industry where organic search drives a significant share of qualified traffic, the platform was losing visibility for high-intent queries such as location-specific property searches. Competitors with server-rendered pages dominated organic rankings
• Fragmented frontend architecture: The platform's user interface had been built incrementally over years, resulting in a patchwork of React components, Vue.js modules for broker-facing tools and AngularJS legacy widgets for older listing features. Styling was inconsistent, shared state management was brittle, and developer productivity suffered as engineers had to context-switch between frameworks. This fragmentation also created inconsistent user experiences that undermined trust
• MLS data integration complexity: The platform ingested listing data from multiple regional MLS providers, each with different schemas, update frequencies and data quality standards. Synchronisation failures led to stale listings, missing photos and inaccurate status information. Buyers who encountered outdated listings lost confidence in the platform
• Image-heavy page performance: Property listings are inherently visual, with each listing featuring dozens of high-resolution photos, floor plans and sometimes virtual tour assets. Without proper image optimisation, listing pages loaded slowly, particularly on mobile devices. Slow-loading image galleries directly increased bounce rates and reduced time on page
• Lead conversion inefficiency: The platform generated leads for brokers and agents but lacked intelligent routing and qualification. Leads were distributed based on simple rules, and brokers had limited tools to manage, respond to and track them. Industry benchmarks suggest that responding to a real estate lead within five minutes increases the likelihood of conversion by up to 21 times, yet the platform's workflows did not support rapid response
• Legacy backend complexity: The backend comprised Node.js services, Python microservices for data processing and PHP modules inherited from earlier versions of the platform. These services communicated through a mix of REST endpoints with inconsistent contracts, making it difficult to add features or onboard new engineers

Solution

Advantrix Labs partnered with Haven to systematically modernise the property discovery platform, addressing search performance, SEO, data reliability, frontend cohesion and broker workflows while preserving business continuity throughout the migration.

• Micro-frontend architecture with incremental migration: We implemented a micro-frontend architecture with a modern component framework as the core, allowing existing legacy modules to run alongside new components during the transition period. A shared shell application manages routing, authentication and inter-module communication. This approach enabled the team to modernise the platform incrementally, replacing legacy modules one at a time without a risky full rewrite
• High-performance search with geo-spatial capabilities: We re-engineered the property search infrastructure to handle faceted queries across millions of listings with sub-second response times. The search layer supports filters for price, property type, bedrooms, bathrooms, square footage, lot size, year built, amenities and location radius. Geo-spatial indexing enables map-based property discovery, allowing users to draw search boundaries and see results update in real time. Autocomplete and saved-search functionality further streamline the discovery experience. These optimisations reduced search latency by 65%, keeping users engaged and moving them toward lead conversion
• Server-side rendering for SEO: We introduced server-side rendering for property listing pages, neighbourhood guides and search result pages. Each listing generates a fully rendered HTML page with structured data markup, optimised meta tags and canonical URLs. This ensures that search engines can crawl and index the full content of every listing, including property descriptions, photos, pricing and location data. The SSR strategy targeted long-tail keywords that home buyers use, such as specific neighbourhood and property-type combinations, unlocking organic traffic that client-side rendering had missed entirely
• MLS data synchronisation pipeline: We built a robust data ingestion pipeline that connects to multiple regional MLS providers, normalises incoming data into a unified schema and applies validation rules to catch inconsistencies. The pipeline supports near-real-time updates, ensuring that listing status changes, price adjustments and new photos appear on the platform within minutes of being published in the MLS. Automated monitoring flags synchronisation failures and data quality anomalies for resolution before they reach users
• Image processing and optimisation: A dedicated image processing service handles property photos at scale, generating multiple resolution variants, applying lazy loading, serving next-generation formats such as WebP and AVIF, and providing CDN-backed delivery. Listing galleries load progressively, showing optimised thumbnails first and full-resolution images on demand. This approach dramatically improved page load times for image-heavy listings without sacrificing visual quality
• GraphQL orchestration layer: We introduced a unified data orchestration layer that sits between the frontend applications and the underlying data stores. The gateway provides a unified query interface for frontend developers, eliminating the need to call multiple endpoints and reducing over-fetching. Schema stitching connects data from all underlying databases into a coherent graph
• Broker management and lead workflows: We rebuilt the broker-facing tools with streamlined lead management, including intelligent lead routing based on agent availability, geographic specialisation and response history. Broker dashboards provide performance metrics, lead status tracking and listing management. Agents receive real-time notifications for new leads and can respond directly from the platform, reducing response time and improving conversion rates

Solution gallery

Product and workflow visuals from the delivered solution.

OpenOpen

Business outcomes

By modernising the property discovery platform across search, SEO, data infrastructure and broker workflows, Advantrix Labs helped Haven regain competitive ground, convert more visitors into qualified leads and provide brokers with the tools they need to close deals faster.

• 45% increase in lead conversion: The combination of faster search, relevant results, optimised listing pages and intelligent lead routing significantly improved the path from property discovery to lead submission. Buyers found properties faster and with less friction, while brokers received better-qualified leads with the tools to respond quickly. In an industry where speed of response is a decisive factor, this uplift translated directly into revenue growth for the client and their broker network
• 50% growth in organic traffic: Server-side rendering and structured data markup unlocked organic search visibility that had been inaccessible with client-side rendering. Property listing pages began ranking for high-intent, location-specific queries, driving a sustained increase in qualified organic traffic. Given that organic search remains one of the highest-converting traffic sources in real estate, this growth provided a compounding return on the SSR investment
• 65% reduction in search latency: The re-engineered search infrastructure with geo-spatial indexing and optimised query paths reduced average search response times by 65%. Users experienced near-instant results when filtering and browsing listings, reducing abandonment and increasing the number of listings viewed per session. Faster search directly correlated with higher engagement and more lead submissions
• Improved data reliability: The MLS synchronisation pipeline eliminated stale listings and data inconsistencies that had eroded user trust. Listings now reflect accurate pricing, availability and photos within minutes of MLS updates, giving buyers confidence that what they see on the platform is current
• Scalable, maintainable architecture: The micro-frontend approach and GraphQL orchestration layer gave the engineering team a clear path to retire legacy modules while continuing to ship features. New developers onboard faster, and the separation of concerns between frontend modules, backend microservices and the data layer reduces the blast radius of changes

Technologies