Contact

Menu

Article

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.

April 21, 2021

hero_image_data.jpg

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.

Global Real Estate

Credit Suisse Asset Management is a leading provider of real estate investments. 
Our broad array of real estate solutions spans a range of geographies and
investment types.

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:

  1. Online machine learning: new data is factored in regularly, generating steady improvements in the quality of the model.
  2. Ensemble learning: a multitude of algorithms are tested and a combination of the best of them is applied.
  3. 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.

Get in touch

Contact us for information about investment opportunities and to find out how we can help you achieve your investment goals.

1 Source: https://www.cnbc.com/2018/12/05/sell-offs-could-be-down-to-machines-that-control-80percent-of-us-stocks-fund-manager-says.html. Data as of 05.12.2018.

Source: Credit Suisse, unless otherwise specified.
Unless noted otherwise, all illustrations in this document were produced by Credit Suisse Group AG and/or its affiliates with the greatest of care and to the best of its knowledge and belief.
This material constitutes marketing material of Credit Suisse Group AG and/or its affiliates (hereafter "CS"). This material does not constitute or form part of an offer or invitation to issue or sell, or of a solicitation of an offer to subscribe or buy, any securities or other financial instruments, or enter into any other financial transaction, nor does it constitute an inducement or incitement to participate in any product, offering or investment. This material does not constitute investment research or investment advice and may not be relied upon. It is not tailored to your individual circumstances, or otherwise constitutes a personal recommendation. The information may change after the date of this material without notice and CS has no obligation to update the information. This material may contain information that is licensed and/or protected under intellectual property rights of the licensors and property right holders. Nothing in this material shall be construed to impose any liability on the licensors or property right holders. Unauthorized copying of the information of the licensors or property right holders is strictly prohibited.
Copyright © 2021 CREDIT SUISSE GROUP AG and/or its affiliates. All rights reserved.