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AI Optimizes the Back Office, but What About the Physical Office?

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Artificial intelligence in the back office automates repetitive cognitive tasks and helps teams quickly find and summarize the information they need.

However, companies are leaving potentially big savings on the table by not using AI to optimize their offices, factories and facilities, according to a report from commercial real estate giant JLL. Real estate costs are typically among the top three highest business expenses.

Companies incur ongoing operating expenses in commercial real estate, which include paying for leases, utilities, maintenance, repairs and the like. This is different from capital expenditures, or the amount of money spent if the company builds its own offices or plants.

By finding inefficiencies in how offices and facilities are run, AI systems can help companies renegotiate leases, consolidate under-used workspaces and optimize energy use, among other tasks, the report said.

Here are areas in commercial real estate that could yield big savings:

1. Optimize space as employees return to the office.

Using AI to predict employee office use can cut costs. By monitoring work schedules, badge swipes, office occupancy through sensors and other metrics, companies can lease the right amount of office or building space they need or downsize, according to the report.

One global financial institution saved more than $120 million a year by analyzing occupancy of its offices to determine actual office use and predict future demand, per the report.

The state of office occupancy is in flux as more companies want workers to return to the office. The PYMNTS Intelligence report “Back-to-Office Mandates Drive Demand for Fast Food, Weekend Shopping and Subscriptions” found that 63% of remote workers are back in the office full time.

About 80% work either in-office or in a hybrid setting, while 17% are fully remote, the report revealed. That’s a decline from the peak of the pandemic when half of employees worked remotely.

2. Predict energy use.

Other savings can be realized by tapping into the mounds of data the buildings themselves generate, such as electricity use, plumbing, air conditioning and ventilation metrics. AI ties this information with weather forecasts to optimize heating, cooling and electricity use in the office, according to the JLL report.

For example, the report said tools like JLL’s Hank use machine learning and real-time data to adjust HVAC systems, reducing energy expenses by as much as 40%.

The operations of buildings account for 30% of global final energy consumption and 26% of global energy-related emissions, according to the latest metrics from the International Energy Agency.

AI systems can also incorporate the use of renewable energy sources, per the JLL report. In one case, a global bank incorporated sustainability into its leasing decisions, enabling it to meet regulatory requirements and internal targets.

By fine-tuning office environments to suit employee needs, companies can also attract employees to the industry, the report said.

3. Make better financial decisions, such as whether to downsize offices.

AI systems can more quickly help companies evaluate whether to stay in the building or leave, plan locations and simplify cash flow models, among other use cases. In one example, a company generated over $250 million in capital from property sales and leasebacks, according to the report.

With 34% of commercial leases set to expire in the next two years, these decisions will become increasingly important, the report said.

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Artificial intelligence helps break barriers for Hispanic homeownership

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For many Hispanics the road to homeownership is filled with obstacles, including loan officers who don’t speak Spanish or aren’t familiar with buyers who may not fit the boxes of a traditional mortgage applicant.

Some mortgage experts are turning to artificial intelligence to bridge the gap. They want AI to help loan officers find the best lender for a potential homeowner’s specific situation, while explaining the process clearly and navigating residency, visa or income requirements.

This new use of a bilingual AI has the potential to better serve homebuyers in Hispanic and other underrepresented communities. And it’s launching as federal housing agencies have begun to switch to English-only services, part of President Donald Trump’s push to make it the official language of the United States. His executive order in August called the change a way to “reinforce shared national values, and create a more cohesive and efficient society.”

The number of limited-English households tripled over the past four decades, according to the Urban Institute, a nonprofit research organization based in Washington, D.C. The institute says these households struggle to navigate the mortgage process, making it difficult for them to own a home, which is a key factor in building generational wealth.

The nonprofit Hispanic Organization of Mortgage Experts launched an AI platform built on ChatGPT last week, which lets loan officers and mortgage professionals quickly search the requirements of more than 150 lenders, instead of having to contact them individually.

The system, called Wholesale Search, uses an internal database that gives customized options for each buyer. HOME also offers a training program for loan officers called Home Certified with self-paced classes on topics like income and credit analysis, compliance rules and intercultural communication.

Cubie Hernandez, the organization’s chief technology and learning officer, said the goal is to help families have confidence during the mortgage process while pushing the industry to modernize. “Education is the gateway to opportunity,” he said.

HOME founder Rogelio Goertzen said the platform is designed to handle complicated cases like borrowers without a Social Security number, having little to no credit history, or being in the U.S. on a visa.

Loan officer Danny Velazquez of GFL Capital said the platform has changed his work. Before, he had to contact 70 lenders one by one, wait for answers and sometimes learn later that they wouldn’t accept the buyer’s situation.

The AI tool lets him see requirements in one place, narrow the list and streamline the application. “I am just able to make the process faster and get them the house,” Velazquez said.

One of Velazquez’s recent clients was Heriberto Blanco-Joya, 38, who bought his first home this year in Las Vegas. Spanish is Blanco-Joya’s first language, so he and his wife expected the process to be confusing.

Velazquez told him exactly what paperwork he needed, explained whether his credit score was enough to buy a home, and answered questions quickly.

“He provided me all the information I needed to buy,” Blanco-Joya said. “The process was pleasant and simple.”

From their first meeting to closing day took about six weeks.

Mortgage experts and the platform’s creators acknowledge that artificial intelligence creates new risks. Families rely on accurate answers about loans, immigration status and credit requirements. If AI gives wrong information, the consequences could be serious.

Goertzen, the CEO of HOME, said his organization works to reduce errors by having the AI pull information directly from lenders and loan officers. The platform’s database is updated whenever new loan products appear, and users can flag any problems to the developers.

“When there are things that are incorrect, we are constantly correcting it,” Goertzen said. “AI is a great tool, but it doesn’t replace that human element of professionalism, and that is why we are constantly tweaking and making sure it is correct.”

Jay Rodriguez, a mortgage broker at Arbor Financial Group, said figuring out the nuances of different investors’ requirements can mean the difference between turning a family away and getting them approved.

Rodriguez said HOME’s AI platform is especially helpful for training new loan officers and for coaching teams on how to better serve their communities.

Better Home & Finance Holding Company, an AI-powered mortgage lender, has created an AI platform called Tinman. It helps loan officers find lenders for borrowers who have non-traditional income or documents, which is common among small business owners.

They also built a voice-based assistant called Betsy that manages more than 127,000 borrower interactions each month. A Spanish-language version is in development.

“Financial literacy can be challenging for Hispanic borrowers or borrowers in other underserved populations,” Pierce said. “Tools like Betsy can interact and engage with customers in a way that feels supportive and not judgmental.”



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Artificial intelligence helps break barriers for Hispanic homeownership – The Killeen Daily Herald

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Artificial intelligence helps break barriers for Hispanic homeownership  The Killeen Daily Herald



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Artificial intelligence helps break barriers for Hispanic homeownership – Richmond Register

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Artificial intelligence helps break barriers for Hispanic homeownership  Richmond Register



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