Investment decision making and other AI applications

Within real estate investment AI tools already exist, including AI assisted forecasts and pricing models. However, historically real estate has been slow at adopting new technologies. The McKinsey Global Institute (MGI) believes that generative AI could generate USD 110‒180 billion or more in value for the real estate industry. To incorporate AI and generative AI systems into real estate investing, companies can utilize existing foundation models for internal tasks such as GPT-4 or Microsoft Copilot. Additionally, companies can purchase off-the-shelf AI powered products and services.

A good example of AI being used as a ‘co-pilot’ is its potential to enhance and improve the real estate investment decision making process. With the ability to synthesize insights from unstructured data and write commentary, investment analysts can use AI to interpret and query large data sources to provide a more comprehensive understanding of properties and markets at breakneck speed. AI can also leverage predictive technology to forecast rental growth, assess market demographics and pull comparable properties. Investment committees may even see new AI committee members casting votes on transactions and helping to make investment decisions or AI computed investment strategies.

AI has many other uses in the investment process, including data standardization for portfolio analytics, while firms with large datasets could create customized benchmarks. Similar to data interpretation, AI can ‘read’ existing documents, like leases, to summarize key themes and variables and flag important terms like expected monthly rent, market forces, tables of information and present them in a standardized format for easy comparison. AI can help draft comprehensive market reports and quarterly fund reports or respond to routine data requests faster than a human.

Advanced property analysis can provide accurate and comprehensive information about properties to investors. It can utilize data from a variety of sources and analyze past transactions to help get a sense of which neighborhoods are primed for growth and which are headed for potential downturn. It can also analyze property owner behavior to help investors find distressed properties, off-market deals and opportunities for value creation. AI could also be used to value properties using a ‘mass appraisal’ focused on systematic and automatic analysis of broad databases and self-learning models.

Investors have large amounts of proprietary and third-party data on properties, communities, tenants and the market, which can be used to customize existing generative AI tools that can, in turn, perform real estate tasks. These tools could identify opportunities for investors at lightening speed, revolutionize building and interior design, create marketing materials and facilitate customer journeys.

AI can also be used as a tool for predicting demand and managing rental income streams for properties. By analyzing rental rate data, AI can help predict optimal unit pricing using real-time data, particularly for residential assets which typically have shorter leases. Future possible applications include real-time tenant credit analysis, market data trends, adaptation of floorplates and amenity spaces to tenant demand and automated valuation models.

Alternative data such as mobile data, package flows and market-based amenity demand can also be added to traditional underwriting models. The integration of AI has the potential to create more efficient operating models, a stronger customer experience, tenant retention, new revenue streams and smarter asset selection. Generative AI can also be used in other ways such as for virtual reality tours of properties to allow the visualization of desired furnishings. Furthermore, it can be used for creative content including images.

In terms of managing properties, AI has the potential to create greater efficiency for real estate, including the ability to better manage property operations, such as through energy management. AI can help drive down costs and improve property performance and efficiency. In terms of asset management, generative AI can help collect and analyze property-level data more effectively, which should lead to enhanced budgeting and forecasting.

The companies which are first to implement AI into their real estate management will benefit from greater operational efficiencies. This should give them an edge over competitors, allowing them to improve their operational margins and profits, and charge lower management fees.

No matter the application, for the foreseeable future, firms will still need people to review AI output before it is released or relied upon for decision-making, but AI can play a significant role in speeding up the process and improving the breadth and quality of the analytics.