Building powerful search for House Builders

How we have built powerful search for House Builders

In the competitive world of real estate, house-builders need a seamless, efficient, and intuitive search experience for potential buyers to find their perfect home. At iNet Digital, we have built multiple advanced search features that enable users to filter and find houses based on specific needs, including location, property type, number of bedrooms and bathrooms, and price range.

Our solutions often integrate multiple data sources and provide a lightning-fast search experience using modern technologies like React and Elasticsearch.

We have developed our own central node package that provides a suite of base functionality for housing search

Some Key Features of Our House Search Solution

Our search system is designed for house-builders, allowing potential buyers to discover available properties across different developments. The primary functionalities include:

Distance-Based Search – Users can search for properties near a specific area or postcode.
Property Filters – Buyers can refine results by number of bedrooms, bathrooms, property type, and price range.
Multi-Source Data Integration – We aggregate data from CMSs and specialist CRMs such as COINS and Eque2 to provide a unified search experience.
Fast & Efficient Searching – By leveraging Elasticsearch, we ensure rapid results for users.
Optimised Data Fetching – Using Incremental Static Regeneration (ISR), we provide up-to-date search results while minimising API requests and server load.

Aggregating Data from Multiple Developments

One of the biggest challenges when creating a property search feature is consolidating house data from multiple developments. This data is often stored in different systems such as CMS platforms (WordPress, Prismic) and specialist CRMs (COINS, Eque2). To address this:

We integrate API endpoints from different systems to pull property data.
We normalise and structure the data to ensure consistency across developments.
We implement caching mechanisms to reduce load times and improve performance.

This allows us to create a centralised property search database, ensuring that users always see accurate and up-to-date listings.

Why We Use React for the Frontend

The frontend of our search is built with React, a powerful JavaScript library for building user interfaces. React provides several advantages over traditional form-based searches:

Instant Filtering – Instead of requiring users to submit a form and reload the page, React enables dynamic filtering with real-time updates. Search results can react to user inputs in real-time.
Smooth User Experience – The use of state management ensures seamless interaction, making it easy for users to refine their search criteria without delays.
Component-Based Architecture – React’s modular approach allows us to reuse components and maintain a clean, scalable codebase.
Optimised Performance – With efficient rendering and virtual DOM updates, React ensures smooth transitions and lightning-fast results.

By using React, we provide users with an intuitive and engaging search experience that feels instant and modern.

Verto Homes Search

Using Elasticsearch for High-Speed Searching

A critical part of our search implementation is Elasticsearch, a highly scalable and fast search engine designed for handling large datasets efficiently. We use Elasticsearch because:

Speed – It processes queries in milliseconds, making searches instant for users.
Full-Text Search – Elasticsearch supports advanced searching capabilities, such as fuzzy matching, synonyms, and ranking of search results. This allows us to provide search based on keywords or human driven terms (for example "Deatched houses with 4 bedrooms in Cornwall), rather than just specific criteria of bedrooms etc.
Scalability – It can handle large datasets, making it ideal for aggregating house listings from multiple sources.
Geo-Search – Elasticsearch enables location-based searches, allowing users to find properties within a specific radius of a postcode or area.

By leveraging Elasticsearch, we provide an optimised search experience that delivers results almost instantaneously.

Optimised Data Fetching with ISR

One of the key considerations in building an efficient search system is data freshness and minimising API requests. We achieve this through Incremental Static Regeneration (ISR), a feature provided by Next.js that allows us to:

Generate static pages at build time for lightning-fast performance.
Update data dynamically without rebuilding the entire application.
Reduce API requests, improving scalability and reducing load on external data sources.

With ISR, our search system:

Loads instantly with pre-rendered search results.
Ensures data is up-to-date without requiring a full site rebuild.
Minimises unnecessary API calls, making it highly efficient and cost-effective.

This approach strikes the perfect balance between speed and real-time data accuracy, ensuring users always see fresh property listings with minimal latency.

Hayfield Homes Search

Bringing it together

By combining React for the frontend, Elasticsearch for high-speed searching, and ISR for efficient data fetching, we have built a powerful, scalable, and lightning-fast house search feature tailored for house-builders. Our system seamlessly integrates with multiple data sources, ensuring that potential buyers can find the perfect property effortlessly.

With a user-centric approach and cutting-edge technology, our house search solution delivers an seamless user experience, helping house-builders connect buyers with their ideal homes more effectively than ever before.