The demand for interconnected smart cities, public and commercial spaces, and homes is driving a revolution in the real estate and construction industry. And this hyperconnectivity is exponentially increasing the amount of data available to the industry.

Real estate may appear to be highly immovable and tangible (concrete, even) and less susceptible to technological disruption than other industries. However, the reality is that this sector is at a crucial inflection point, and decisions companies make today on how to harness all of this multiplying and often inaccessible and confusing data will determine companies’ future in a digital, hyperconnected world.

And one of the areas ripe for the most fundamental change is the way in which real estate investments are valued and monetized.


Buildings, infrastructure, and entire cities are becoming more and more dominated by (and made more or less valuable because of) their digital content: a wealth of unstructured and structured data that can be mined by AI applications. Like consumer products today, cities will compete with each other for business and leisure attractiveness based on their “smart” features. The interconnectedness of their infrastructure, driven by AI applications, will optimize everything from traffic conditions to carbon emissions to social integration.

An even greater revolution can be foreseen for the individual home, office, or shop, with changes to people’s mobility and demographics shifting the way properties are used and shared, whether they are owned or rented, with increasing demands for the highest standards (covering energy consumption, environmental impact, and safety) and, of course, hyperconnectivity. Embedded technologies will be able to optimize utilities consumption, safety, surveillance, and even health and lifestyle by capturing our personal data within our domestic walls and offering integrated solutions and value-added services.

If bricks can connect, they can start thinking. And they will surely speak, as well, one day soon.

"Buildings, infrastructure, and entire cities are becoming more and more dominated by (and made more or less valuable because of) their digital content."


The valuation and, hence, design, development, management, and marketing of real estate properties are also heading for wholesale disruption. Huge amounts of data on the state of conservation, on the productivity of infrastructure and other public- and multi-purpose constructions, on the interplay of demand and supply in the sale and rental markets, and, finally, on the attractiveness of specific locations will progressively make these markets more transparent, liquid, and efficient.

Big data can be captured and analyzed in real time, suggesting trends in real estate and construction performances, based on the intelligence produced by AI applications. These allow developers and investors to perform optimized planning for their global asset allocation and local project selection. It also allows them to more effectively design new development plans and urban regeneration programs, as they can now capture social perspectives around a given location, making it even cooler for business and leisure, for example. They can also analyze the footfall and flows of current and prospective users to improve the usability of public spaces and private ones, with high ecological and safety standards. Attractiveness and usability could then be used for a dynamic pricing of rents (or loans): for example, offering to link the fees of merchants in a new development to the footfall of people generated.


The immediate challenge (and opportunity) around the abundance of data now available to real estate and construction companies is how to identify and harness it strategically to evaluate and boost returns for existing as well as potential, new real estate developments and investments.

A wide range of factors tied to the location of a real estate asset contribute significantly to explain and anticipate trends in its valuation. However, these are generally overlooked by players in the sector, who tend to rely on traditional and structured data. Traditional factors include information on the external surroundings – such as tourism, cultural attractions, night- versus day-life activity – and macro-economic information – such as average income, labor dynamics, age of population, and employment rate.

New operational tools can now integrate unstructured data sources and AI algorithms into the analysis, enabling companies to fine tune their understanding of the “quality” of an area or specific property.

Data sources can be clustered in three main macro groups:

  1. The state of conservation, spotting improving or deteriorating trends in the real estate assets located in the same area.
  2. The demand and supply of similar assets, in terms of size, number of rooms and characteristics, for sale and rent, both long-term and short-term.
  3. The location data, providing a view on the type of city or precise neighborhood where the asset is located, the destination of the buildings (residential, office or retail), purchasing power, types of services, and interest points.

Applying AI tools to analyze this data provides significantly more precise and measurable ways to consider new investments and increase the returns on existing ones.

Ultimately, however, because of the fast pace at which this industry is evolving, great strategy and tools amount to nothing if companies do not execute effectively and quickly. Those businesses that are not aggressively exploiting the opportunities these technologies offer today may find themselves left behind tomorrow.