The great acceleration is here: machine learning (ML) and advanced analytical technologies will transform the commercial real estate (CRE) investment industry in many ways, from task automation to providing granular insights into market trends and asset valuations.

I believe the impact of these technologies will impact the entire investment ecosystem from acquisition, asset management / business plan execution, disposition, and portfolio management. Time consuming tasks such as return metric calculation and property performance analytics will be more automated. Professional roles and responsibilities will be adjusted in order to ensure optimal allocation of finite resources and time. Below are some general areas where machine learning and advanced analytical technologies will transform CRE investment:

1. Market Targeting and Property Analysis

ML technologies can quickly analyze significant amounts of data such as market fundamentals, economic indicators, demographics, income, and employment trends and provide a level of predictive insight into the future performance of certain markets. This could help speed up the process for CRE professionals in targeting the markets that have the best potential for good risk-adjusted returns. At the individual asset level, the insights gain from analyzing market dynamics can provide predictive insights into the performance of individual assets with respect to rent levels, occupancy, and valuation metrics.

2. Better Risk Assessment and Granular Due Diligence

Natural Lanaging Processing (NLP) can be used to analyze lease, loan, and various legal agreements that impact properties. All this analysis can uncover the “skeletons in the closet” when it comes to imbedded provisions in legal agreements. Additionally, the sentiment analysis can be employed to gauge the “animal spirits” in the investment market to provide better market timing in terms of buy/sell/hold decisions. Overall, NLP and sentiment analysis can provide a glandular due diligence process that does not typically occur in a asset acquisition/disposition process currently.

3. Optimal Portfolio Management

Advanced analytical technologies can be integrated across an organization’s systems providing a robust portfolio management platform to provide real-time insights into performance and market trends. This will help inform portfolio management to make better decisions about asset strategy to optimize risk-adjusted returns for investors.

4. Streamlined Property Management / Predictive Maintenance

Property managers can use AI to streamline operations and reduce costs. AI-powered systems can monitor building systems and pro-actively identify issues before they become significant problems, reducing maintenance costs and downtime. AI can also analyze data from building systems, such as HVAC and electrical systems to streamline maintenance scheduling.

5. Energy Efficiency

This is one of the biggest areas of focus for investors and the many proptech firms. ML/AI enabled technologies can significantly assist building engineers and energy consultants to optimize energy usage depending on the time of day, occupancy level, and energy market place (if you are in a deregulated market). Additionally, these technologies can provide insights on which systems should be turned and works well given certain conditions (i.e. sunny day – turn on chiller 1 instead of chiller 2 because chiller 2 works well with a colder temps).

ML and advanced analytics technologies have the potential to add significant value in terms of efficiency and analysis to the CRE industry. However, with all technologies, it is up to CRE owners/investors to embrace these technologies and use them in order to keep up with the times and be competitive. Time will tell how the industry evolves, but with the rise of so many proptech firms and the next generation of professionals coming to leadership positions – I expect CRE’s technological change to accelerate.