Why Better GenAI-Driven Real Estate Decisions Stem from Better Data Sets – SPONSOR CONTENT FROM JLL


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By Bobby Magnano, Giles Wrench, Mike Sandridge, and Ram Srinivasan

Even in its infancy, generative AI (GenAI) already brings a competitive edge to banks and financial institutions that can put its insights to work. The next frontier for GenAI is the commercial real estate (CRE) sector.

What’s been holding the CRE sector back from fully adopting GenAI?

The CRE sector has traditionally struggled with data quality and an easy interface to access and visualize data. GenAI could help fill this gap, giving institutions within the sector powerful and meaningful insights that could help them make the right decisions on their CRE holdings.

In a risk-aware sector like finance, organizations seeking AI-powered insights from their proprietary data may find that off-the-shelf large language models (LLMs) are insufficient for the task. But models trained specifically on integrated and extensive CRE data sets could provide more valuable insights. With access to CRE market information, a customized model may help financial institutions better understand industry trends, identify new opportunities, and make more-informed decisions about managing their property portfolios.

Reliable Data Required for GenAI Insights

CRE investments involve significant capital—and they carry significant risks. Financial services organizations need accurate and up-to-date information on market trends, property valuations, tenant profiles, and economic indicators.

A key capability of advanced AI is that it can rapidly process large volumes of data and automate repetitive manual work. For the data in CRE portfolios, AI can take on tasks such as data cleaning, matching, and aggregation at scale so human analysts and experts can focus on higher-value strategic work like identifying insights.

With time-consuming manual tasks now automated, CRE employees can analyze portfolios at a larger scale and surface strategies and opportunities that time and resource constraints have made it difficult to uncover. Using AI can help organizations boost productivity, enhance the employee experience, and manage risks.

But there’s a catch: AI is only as good as the data it’s trained on. Developing trustworthy AI models for complex real estate applications can be challenging for organizations with limited proprietary data. Working with narrow data sets can lead to decisions that result in financial losses or missed opportunities.

Models trained on far larger and more comprehensive CRE data sets that incorporate multiple external data sources may better analyze market conditions and dynamics. An AI system exposed to billions of data points can capture trends across the entire CRE sector to give decision makers a wide-range and real-time understanding of industry and location-specific factors. And while all models have limitations, a customized solution trained on extensive external and internal sources can improve continuously with growing representative data sets.

Optimized Investment Decisions

CRE-driven GenAI can extract insights from unstructured data sources: property descriptions, market reports, and news articles. And organizations that can train LLMs using vast amounts of historical real estate data beyond their own can more accurately predict outcomes, such as future property valuations, rental rates, and cap rates.

Financial services organizations can optimize their portfolios by combining human expertise with GenAI insights to identify underperforming assets or those with higher risk profiles, applying its insights to improve returns or reduce risks when buying, selling, refinancing, or renovating properties. Real estate investors and lenders can use AI to gain insights on real-estate utilization and portfolio optimization data, improve building efficiencies, generate 3D leasing visualizations, calculate sustainability risks, and manage investment leads.

Having access to a fuller set of AI-powered market research and real-time CRE information can supercharge the abilities of lenders and portfolio managers to streamline the underwriting process and improve efficiency.

JLL’s Hank, an AI-powered platform, dynamically optimizes commercial buildings’ energy-intensive heating, ventilation, and air conditioning (HVAC) systems, drawing data from HVAC sensors to adjust its settings that maximize energy efficiency in real time and predict maintenance issues.

Other AI platforms JLL uses include:

• Horizon: identifies and predicts upcoming CRE buying and selling opportunities

• 3D Viz and OpenSpace: create immersive photo representations and video tours of workspaces and job sites

• VergeSense: uses a ChatGPT interface to help optimize CRE portfolios

• EliseAI: uses conversational AI to help manage residents and prospects

Accelerate Decision Making

Beyond investments, introducing automation tools can also transform the employee experience for CRE decision makers, with their capabilities to rapidly extract key details from property valuations, borrower financials, occupancy rates, and other legal documents—streamlining the process of establishing borrowers’ creditworthiness and informing better decisions and more accurate projections based on more historical data.

GenAI technology can augment human intelligence. As this technology matures in the finance sector, it can help improve productivity and free employees to focus on their more strategic and creative endeavors.

“While we anticipate AI will change the nature of jobs by replacing routine tasks and improving operational efficiency, it will most likely transform roles and require a shift in the skill set of the workforce rather than making roles obsolete,” said Giles Wrench, Vice Chairman, Financial Services and Insurance, Americas Markets, JLL. “This transformation will also allow workers to focus on higher-value activities like relationship building and new business opportunities.”

Just as the CRE sector’s role of providing the right work experience has magnified since the pandemic, so have its data sets on workspaces: who uses the workspaces, how people use them, and what these users need.

The CRE sector needs AI technology to quickly consolidate and unlock insights from these data sets so it can supercharge the employee experience while also producing powerful guidance for the real decision makers: humans.

CRE Investment Landscape Transformed

Integrating advanced AI may give financial firms new ways to ideate and explore CRE insights. By developing customized models trained on vast data sets, their tools may uncover unexpected connections and market dynamics. Beyond prediction, AI’s greater value may be its ability to analyze disparate internal and external data sources from novel angles in order to surface hypotheses for human experts to validate and build on so they can make optimal decisions.

More comprehensive, more trustworthy LLMs can drive more reliable analytics, supporting better predictions, smarter CRE investments, and lower costs—driving enterprise growth while improving the employee experience. And beyond enhancing operational efficiencies, automation could transform the CRE marketplace, generating over $110 billion in annual value for the real estate industry.

“Connecting buyers, sellers, and lenders at the right time, with the right data in hand—within seconds—is going to determine success in this new generative AI era,” says Bobby Magnano, president, Financial Services, JLL.


Learn how JLL’s AI-powered platforms could bring your organization innovative insights.



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