Data science in the real estate industry: a revolution is underway.
Digitalization has reached the real estate sector, with data science playing an increasingly important role. Yet we are only at the start of a three-phase transformation process. Of central importance is the ability to interpret data and draw the right conclusions.
Data organization constitutes the basis of the digital transformation in the real estate sector – phase one, in other words. While our industry is considered low-frequency in data terms, the volumes of data generated are set to grow rapidly thanks to the Internet of Things (IoT) as well as smart building sensors. These data will provide answers to simple questions about selections and enable benchmarking, for example.
Phase two is where artificial intelligence comes in. This phase is based on big data and data analytics methods that enable large volumes of data to be structured and interpreted, and therefore allow data-driven decisions to be made. These lead to process optimization and improvements in operating efficiency.
Special challenges in the real estate industry
Data science has long been firmly established in the financial world, where up to 80% of transactions are performed using data-based computer models1. In the real estate industry, on the other hand, every transaction is unique. This poses major challenges for data science.
Nevertheless, there are solutions available:
Applying these methods makes it possible to determine the current price while also helping to estimate future income.
Extensions of these methods include:
- Online machine learning: new data is factored in regularly, generating steady improvements in the quality of the model.
- Ensemble learning: a multitude of algorithms are tested and a combination of the best of them is applied.
- Automated machine learning: the selection of datasets and parameters is automatically controlled.
These automated valuation methods have operational advantages because properties do not have to be physically visited. Furthermore, they enable the current, fair transaction price on the real estate market to be obtained. This is important for determining risk in the mortgage business (dynamic LTV ratios), for example, or for the regular valuation of real estate funds.
The third phase of this digital transformation is characterized by the merging of data models and physical buildings, which covers the entire life cycle of the property. Initial attempts are taking place at the property level with the introduction of building information modeling (BIM). Developed as a digital enhancement to the planning and construction process, BIM is increasingly used in management, refurbishment, and demolition as well. It enables the life cycles of individual building components – heating systems and elevators, for example – to be monitored on a constant basis. This facilitates preventive action to avoid more extensive deterioration, thus keeping costs down.
The automation of entire processes is on the horizon, encompassing the complete life cycle of properties: from automated identification of properties for project development to preventive renovations and maintenance, all the way to automated rental recommendations when rental contracts expire.
The trend towards more data-driven decision-making in the real estate industry is accelerating. Although the sector is still in the early stages of this paradigm shift, the potential is huge – for real estate developers and investors alike.
