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Why better GenAI-driven real estate decisions stem from better data sets

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

Already in its infancy, generative AI (GenAI) is already providing a competitive advantage 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 has stopped the CRE sector from fully adopting GenAI?

The CRE sector has traditionally struggled with data quality and a simple interface to access and visualize data. GenAI can help fill this gap and provide institutions in the sector with powerful and meaningful insights that can help them make the right decisions about their CRE holdings.

In a risk-aware sector like finance, organizations seeking AI-driven insights from their proprietary data may find that standard large language models (LLMs) are inadequate to the task. However, models trained specifically on integrated and comprehensive CRE datasets can provide more valuable insights. With access to CRE market information, a tailored model can help financial institutions better understand industry trends, identify new opportunities and make more informed decisions about managing their real estate portfolios.

Reliable data is required for GenAI Insights

CRE investments involve significant capital – and they involve significant risk. 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 quickly process large amounts of data and automate repetitive manual work. For data in CRE portfolios, AI can take on tasks like data cleansing, matching and aggregation at scale so that human analysts and experts can focus on more valuable strategic work like identifying insights.

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

But there’s a catch: AI is only as good as the data it’s trained on. Developing reliable 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 lead to financial losses or missed opportunities.

Models trained on much larger and more comprehensive CRE datasets that contain multiple external data sources can better analyze market conditions and dynamics. An AI system exposed to billions of data points can capture trends across the CRE sector to give decision makers a broad and real-time understanding of industry and location-specific factors. And while all models have limitations, a tailored solution trained on extensive external and internal sources can be continuously improved with growing representative datasets.

Optimized investment decisions

CRE-powered GenAI can extract insights from unstructured data sources: property descriptions, market reports and news articles. And organizations that can train LLMs using large amounts of historical property data in addition to 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, using their insights to improve returns or reduce risk when buying, selling, refinancing or renovating properties. Real estate investors and lenders can use AI to gain insights into property utilization and portfolio optimization data, improve building efficiency, generate 3D leasing visualizations, calculate sustainability risks and manage investment leads.

Having access to a full suite of AI-powered market research and real-time CRE information can supercharge lenders’ and portfolio managers’ ability to streamline the underwriting process and improve efficiency.

JLL’s Hankan AI-powered platform, dynamically optimizes commercial buildings’ energy-intensive heating, ventilation, and air conditioning (HVAC) systems and pulls 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 buying and selling opportunities for CRE

• 3D Viz and Open space: create immersive photo representations and video tours of workplaces and workplaces

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

• EliseAI: uses conversational AI to manage residents and leads

Accelerate decision-making

Beyond investment, the introduction of automation tools can also transform the staff experience for CRE decision makers, with their ability to quickly extract key details from property valuations, borrower financials, occupancy rates and other legal documents – streamlining the process of determining borrower creditworthiness and informing better decisions and more accurate forecasts based on more historical data.

GenAI technology can increase human intelligence. As this technology matures in the financial sector, it can help improve productivity and free up employees to focus on their more strategic and creative pursuits.

“While we expect AI to change the nature of jobs by replacing routine tasks and improving operational efficiency, it will most likely change roles and require a shift in workforce skills 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 such as relationship building and new business opportunities.”

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

The CRE sector needs AI technology to rapidly consolidate and unlock insights from these data sets so that it can take over the employee experience while producing powerful guidance for the real decision makers: people.

CRE Investment Landscape Transformed

Integrating advanced AI can provide financial firms with new ways to generate ideas and explore CRE insights. By developing custom models that are trained on large data sets, their tools can reveal unexpected connections and market dynamics. Beyond prediction, AI’s greater value may be its ability to analyze various internal and external data sources from new angles to present hypotheses for human experts to validate and build upon so they can make optimal decisions.

More comprehensive, more reliable LLMs can drive more reliable analytics, support better predictions, smarter CRE investments and lower costs – driving business growth while improving the employee experience. And in addition to improving operational efficiency, automation can transform the CRE market and generate overhead $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 – will determine success in this new era of generative AI,” says Bobby MagnanoPresident, Financial Services, JLL.


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

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