The client is a technology firm that provides real estate professionals with property data analytics to improve customer service and boost performance. Founded in the UK, the organization was included in the list of 120+ Companies Building The Industry’s Future by CB Insights.

Project goal

When the client came to Arateg, it had a property data analytics platform for real estate professionals. The app used the U.K. database and independent sources to aggregate and process information associated with prices, rents, yields, economics, debt, construction, demographics, etc.
However, the system lacked the functionality to make deeper insights into the real estate market. For instance, there was no possibility to track property sales for different periods. Furthermore, the performance had to be improved. In addition, a big data analytics platform had an outdated design.
Although the company employed in-house designers and engineers, it didn’t have sufficient expertise in frontend web development. Cooperating with Arateg, the customer needed to extend the existing functionality and enhance software performance.

Project summary

Project duration

10 months


4 frontend developers


TypeScript, Vue.js, Vuex, Storybook, Chart.js, Jest


Since the company had in-house backend developers and UI/UX designers, our main goal was to build the frontend part of a big data analytics platform. First of all, our team analyzed the customer’s requirements and audited the existing solution.
Based on the system audit, we provided the client with meaningful recommendations on how to improve product quality.
For example, our experts suggested to:
  • improve the existing architecture by making files more structured, cleaning up the code, and facilitating infrastructure maintenance
  • use functional programming (software development by creating pure functions while avoiding shared state, mutable data, etc.) to reduce the number of errors and automate the testing process.
Then, our team built the frontend of a property data analytics platform. To provide data visualization on diagrams, tables, etc., we incorporated custom settings for axes and filters for graphs. For this purpose, our software engineers used Chart.js library that supports 8 chart types such as bar, line, area, polar, and scatter.
With the view of ensuring the highest product quality, our developers conducted unit testing (when a specific feature or module is tested for bugs and errors) with code test coverage by 80%. In addition, we created integration tests to check the interaction of the functionality parts.

During the project, our team solved the following challenges:

  1. With inspiring plans to release many product versions ahead, the customer wanted to automate the software development process for reduced costs and time-to-market.
    Our team suggested creating a user interface (UI) component library with the help of Storybook. Thanks to this solution, the client was able to reuse the existing application components and enhance the quality of new UI elements.
    Storybook is a tool that allows developing beautiful UI elements for Vue.js, React.js, Angular, etc. As of October 2020, Storybook has 54,300 stars on the GitHub open-source community.
  2. The system had to ensure high performance —fast calculations of values, data search, correct display of information, etc.
    It was difficult to enable instant information search as the system had to store and process a lot of data coming from the API. To provide instant data lookup by numerous filters (local area, address, company’s name, house number, etc.), our team implemented a trie, also referred to as a digital or prefix tree.
    This is a type of a search tree that has an ordered data structure and suits great for storing dynamic data sets. The data is decomposed in such a way that it is possible to find it in the exact place (a branch of the tree).
    Employing a trie, our frontend developers reduced the number of data processing cycles and boosted system performance, which allowed us to enable instant information search.

The key features of a property data analytics platform

  1. User registration and authorization
  2. Personal account
  3. Client personal account
  4. Role-based access
  5. Big data analytics
  6. Data visualization dashboard that includes multiple categories, for example, an Overview provides the key pricing data, Demographics contains details about people who live in a certain area (e.g., income level, education), Analyse allows comparing different dataset, analyzing supply and demand data, etc.
  7. Data search by multiple filters such as an address, local area, and organization name.


Cooperating with Arateg, the client extended the functionality of a big data analytics platform while increasing the overall system performance. Following our meaningful recommendations, the organization was able to automate the software development process, simplify infrastructure maintenance, and enhance product quality.
If you want to build a similar project, contact our custom web development company. Our experts will get back to you within 24 hours and help resolve all issues.

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