This is Part 1 of our 6 Part Practical AI Guide.
Many industries, including commercial real estate (CRE), now use Artificial Intelligence (AI). From enhancing property management to transforming investment strategies, AI in commercial real estate is changing the way landlords, investors, and property managers operate. This blog post explores the history, recent progress, and future possibilities of AI in commercial real estate.
The introduction of AI in commercial real estate dates back to the late 20th century. During this period, AI's role was limited but significant. Advancements in AI technology, fuelled by big data and better algorithms, have made automation smarter.
Predictive analytics changed the game for investors and property managers by helping them predict market trends and property values more accurately. For instance, machine learning models could analyse historical data and predict future property prices, helping investors make more informed decisions.
Integration with Internet of Things (IoT) devices in commercial properties, enhanced data processing capabilities, and more sophisticated algorithms have pushed the boundaries of what AI can achieve in CRE. One of the most significant advancements is the development of smart buildings.
An example of this was property managers unlocking the ability to monitor and control building systems in real-time. AI algorithms analysed data from sensors to optimise energy usage, building temperatures and enhance occupant comfort. AI could take this a step further and also adjust heating and cooling systems based on occupancy patterns, resulting in substantial energy savings.
In addition to smart buildings, AI has also changed property management by enabling predictive maintenance. Machine learning models can predict when equipment will fail, enabling maintenance to be done before it happens. This helps reduce downtime and makes building systems last longer.
It was still not very clear how AI could have an impact on the day to day productivity of the workforce in CRE beyond building systems especially when it came to property management.
One of the challenges faced by real estate professionals in adopting AI beyond the building systems and predictive analytics applications was the commercialisation of their data. Many firms do not have their data in a state that allows them to effectively utilise AI algorithms and monetise their data assets. This can be because of data silos, inconsistent data formats, or lack of data quality.
To overcome these challenges, companies needed to invest in data infrastructure and data governance practices. They also needed to organise, standardise, and make their data accessible for AI applications. This may involve data cleansing, integration of disparate data sources, and implementation of data management systems.
Additionally, commercial real estate firms need to address concerns related to data privacy and security. AI needs a lot of data, so it's important to have strong data protection and follow the rules.
Commercial real estate firms can gain a competitive advantage in the industry by fully utilising AI. You can achieve this by addressing challenges and effectively structuring your data. Unlocking the full potential of AI is key for these firms to succeed.
To understand where the commercial real estate industry is at around AI, we went straight to our amazing customer base. We've summarised what they said below.
Teams are starting to think about different ways that AI can improve their day to day.
