Digital twin for the built environment

Commercial property investment analysis is time-consuming and judgment based. In traditional analysis, all the data is sourced, cleansed and interpreted manually. Comparative analysis is incomplete, as data is often sparse, leading to subjective, biased decisions. Non-traditional data, such as social referencing, mobility and key local factors, is rarely taken into account. Valuable historic deal pipeline and existing portfolio insights get ignored or lost in the email chains. Only a fraction of potential data is used, limiting risk analysis depth and quality.

Built AI is creating an AI-powered platform that will enable real estate investors to make the best data-driven investment decisions. Our cloud-based SAAS platform provides investors with a better way to make property decisions, through a combination of street-level data insights, fast & powerful financial modelling and machine learning that leverages historical datasets for dynamic learning.

In this project, BuiltAI will conduct research to build a novel data platform and analytics tools. The technology will offer an advanced ML-based data analytics tool that will allow property buyers, owners and tenants to get far more information, e.g. hyper-local, to make optimal use of their commercial property.

We will provide users with the most up to date analytics on locations, buildings, tenants in the UK, with a focus on five of the major urban centres, and include other value drivers such as footfall, demographics, supply, demand, transportation, etc. The BuiltAI platform will enable users to consider any property, identify the critical data insights in that location and for that property type. In particular, BuiltAI will provide granular data insights at a 'street level' that gives users a deeper understanding of the market than is currently available.

This research will accelerate UK economic development in built environments and improve the investment in and use of real estate space, to meet the needs of a post-covid UK.