GenAI: Changing CRE for the Better

Generative artificial intelligence (GenAI) may have only recently garnered the public’s attention, but it’s been quietly revolutionizing businesses for decades. Many sectors have adopted AI and leveraged its analytical abilities for customer segmentation and forecasting. While many sectors, including finance, manufacturing and retail, were earlier adopters, real estate — a historically slow adopter of new technologies — has lagged.

GenAI offers the real estate industry an advanced AI solution tailored to its vast datasets and specialized needs. When “fed” with proprietary and third-party data about communities, properties, tenants and the market, the technology becomes customized to perform real estate-specific tasks like automated marketing, opportunity identification, building and interior design and much more.

Key Differences Between GenAI and Analytical AI

GenAI and analytical AI are not, for the purposes of this article, the same thing. GenAI is more flexible and can provide a broader range of tasks like text, image and design generation. Analytical AI is more narrow and goal-oriented, focused on predictive tasks like forecasting and segmentation.

GenAI is creative, going beyond existing data to generate new content or ideas, whereas analytical AI relies wholly on finding patterns in given data. GenAI synthesizes and combines data in novel ways. Analytical AI analyzes data to extract insights. GenAI is still emerging but is gaining traction. Analytical AI is more established and already embedded in many industries.

The main caveat (or caution) about GenAI: unlike analytical AI, which is limited to preset goals based on available data, GenAI can sometimes generate inaccurate or biased output if it doesn’t have enough training data (or the data isn’t clean). 

That said, it’s believed that GenAI has the potential to generate between $110 billion to $180 billion — or more — in value for the real estate industry

Despite the hype, real estate organizations continue struggling to implement and scale GenAI applications, and thus, the promised value creation hasn’t yet materialized. The challenge isn’t surprising — successfully generating a competitive advantage from GenAI requires more than deploying a primary foundational model. Many steps must fall into place to capitalize on its potential.

Challenges of Implementing GenAI

Merely deploying a base GenAI model won’t automatically produce benefits. Real estate companies need the right data infrastructures, workflows and oversight to train industry-specific models. Personnel with real estate expertise and AI skills are best equipped to properly frame problems, interpret outputs and continuously improve GenAI systems. Also necessary? Robust change management for ethically and productively integrating GenAI into operations.

Transforming this technology into real estate solutions with tangible ROI requires more than hype. Organizations need a comprehensive approach addressing data, talent, processes and governance. With proper implementation and realistic expectations, GenAI’s immense opportunities can be captured and used — but not through deployment alone. Delivering its true promise in real estate remains a journey requiring cross-functional vision and execution.

There’s no need to feel discouraged, though. This incredible technology represents a chance for the real estate industry to learn from the past and transform itself into a sector at the cutting edge of innovation. Here are some incredible use cases for GenAI.

GenAI Use Cases

GenAI shines in multiple use cases, each demonstrating the depth and breadth of its transformative potential.  


It can synthesize insights from unstructured data, summarize conversations and extract signals from vast datasets, demonstrating its strength at concision. Imagine an investor with multiple properties and tenants, each with their own lease. Leases, by their very nature, are full of legal terminology. It’s challenging for property owners to sift through and find the information they need quickly and accurately. 

A commercial real estate professional could leverage a GenAI tool to help clients analyze leases to extract key details and insights. For example, this tech-powered tool could read multiple leases and summarize anticipated monthly rents or relevant market forces like local ESG regulations potentially impacting agreements. The AI could scan full lease texts quickly to identify specific parameters, such as leases with a rent below a defined price per square foot, compiling extracted information into tables and summaries.

GenAI handles the grunt work of extraction and consolidation, allowing professionals to focus on higher-level analysis and decision-making. It digests unstructured lease data at scale, accelerating review and surfacing strategic insights. Humans retain control over the final decisions informed by AI’s synthesized lease data summaries.

Facilitating Faster, More Accurate Investment Decisions

Today’s real estate decisions often rely on manually analyzing disparate data sources. For example, an investor interested in healthcare clinics might start with a macro analysis of attractive markets based on demographics, number of existing clinics, and other factors. Using broker or digital tool data, a more granular analysis of specific locations and buildings would follow. Available historical analyses would assess how past investments had faired. 

Using a GenAI tool trained on internal, first-party and third-party data, investors could ask questions like, “What are the top 15 properties most suited for medical use?” An AI-powered tool can synthesize unstructured data, including the performance of existing properties and leases, with external data like census and previous building sale records. Overlaying this multifaceted analysis on a list of specific properties for sale helps investors and brokers identify and prioritize specific buildings worth closer exploration.

Rather than painstaking, time-intensive queries, GenAI enables holistic analysis via tedious data consolidation and pattern identification, allowing those in real estate to focus on high-value assessment of the most promising opportunities. 

These use cases are just two of many possibilities for GenAI. Brokers can also use the technology to:

  • Generate customized marketing materials like brochures, listing presentations and property websites based on details of the property and market conditions. 
  • Engage and qualify leads 24/7 via conversational chatbots.
  • Analyze tenant preferences and behaviors to recommend optimal property usage and redesigns.
  • Identify potential buyers for properties based on past investments, portfolio needs and target markets.

Bankers can use GenAI for automated review and abstraction of critical details from lengthy commercial loan applications and contracts as well as for:

  • Forecasting future property values, rental income and cash flows to model risk and guide underwriting.
  • Generate personalized loan offers and proformas tailored to borrower’s needs.
  • Monitor portfolios using NLP to flag loans requiring additional scrutiny.

The applications for GenAI are vast, allowing many roles in CRE to augment their expertise with data-driven insights and automation.

Are you a commercial real estate investor or looking for a specific property to meet your company’s needs? We invite you to talk to the professionals at CREA United: an organization of CRE professionals from 92 firms representing all disciplines within the CRE industry, from brokers to subcontractors, financial services to security systems, interior designers to architects, movers to IT, and more.

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