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Transparency in CRE through data analytics

The real estate market is intransparent and therefore it is hard to get essential client or tenant information. Besides, getting real-time information about properties comes at a high price.

Possible AI-powered Applications for the Real Estate industry

Data driven asset management

Data concerning your past transactions, present tenants, assets and contracts can serve as a basis for our data scientists to develop a model for data driven asset management. New insights will be found and combined with our external (big) data will provide for real-time, better informed, and faster decision making.

Advanced portfolio analytics

In a multi-asset portfolio, certain types of real estate are more subject to local factors, whereas other types are more subject to global factors. By combining your internal asset data with local- as well as market level data, critical risk factors can be quantified and form the basis for a data driven diversification.

Traditional process, high potential for innovations.

The Real Estate industry has still old processes and are one of the late adopters of AI. The business is mostly runned by social interaction between tenants and property owners. Data Science will not improve the social skills of real estate brokers, but can be of large value in supporting them with the right information about market developments and highly relevant information about the property.

Key components

Main property categories: offices, retail, private, portfolio's, industrial. Main stakeholders: tenants, owners, investors. Main strengths: social interaction and network.

Global footprint

Global Real Estate is worth +$200 trillion. Top players are US, China, Germany and the UK.


Economic change, politics (brexit), generational change/demographics, interest rates.

What value can bring Data Science & AI to your Real Estate company?

Collecting object data

Scraping several external data sources with information about commercial or private real estate, such as: Analysis of the building, Price development, Tenant performances, History of the building, Energy labelling, Sustainability performance, Analysis of the environment, Crowdedness around the building, Visitor flows, Area analysis (budget, type of persons).

Predictive analytics

Predictive models can be developed to assess real estate data from an asset- to a portfolio level, giving the user integrated and unique insights to optimize individual properties as well as its complete portfolio.

Visualizing data

To make data driven insights of your assets available whenever you need them, we developed an interactive dashboard that is easy to use, wherever you want. Our cloud based service provides for a modular customization based on your company specific needs. Built on top of a geographical base, layers of information and insights can be added for a real-time representation of what is most valuable for your business.

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