6 Ways AI Is Reshaping the Commercial Real Estate Industry

When it comes to large properties like corporate office buildings, real estate maintenance costs can take up a sufficient part of the budget. According to the 2018 JIL Occupancy Benchmarking Report, 30-40% of office space remains underutilized. This is typically referred to as ‘silent costs’ as money losses are not visible outright. Besides paying extra for energy consumption or unused square feet, poorly managed commercial space often leads to employee dissatisfaction.

IBM has recently unveiled its AI-powered TRIRIGA solution to help real estate management professionals effectively utilize office space. TRIRIGA gathers data from various sources including Wi-Fi and IoT sensors, which is then analyzed by an AI algorithm and turned into valuable insights. This can potentially help enterprises to make better decisions about managing their working spaces.

Employees now can ‘talk’ to spaces with the help of natural language processing and the AI tool can autonomously identify users’ needs, which then can be used to rearrange entire office layouts or adjust every single employee’s workspace. Employee satisfaction plays important role in business success, and innovative tools like TRIRIGA can create a more appealing environment while decreasing maintenance costs. 

Another AI-focused company, Gridium, specializes in energy saving and property resource optimization. Machine learning algorithms automatically analyze weather data and detect suspicious spikes in energy use patterns to warn property managers. This enables building operators to react to issues on time and decrease operational costs. LinkedIn has managed to save about $100,000 in operational costs at the company’s headquarters annually using Gridium’s technology.

One of the most prominent features of AI is its ability to ‘predict’ the future. Real estate is an industry of high stakes and high rewards, where the ability to predict outcomes is especially valuable. Those who can use AI to anticipate rent and sale price fluctuations or identify the perfect timing for selling a property will have an unprecedented competitive advantage. 

It comes as no surprise that one of the most successful applications of AI and property management software in real estate is investment-focused. For example, Israeli startup Skyline AI uses predictive analysis to accurately assess property value. However, it’s important to note that Skyline has access to one of the largest data pools in the industry, which has been a significant contributor to the company’s success. Utilizing over 130 different sources of data and analyzing over 10,000 features of each property, Skyline’s prediction accuracy is unmatched.

Setting the asking price for an above-average property is a tough task, which is often used to test real estate agents’ expertise. Why is that? Agents are accustomed to standard methods of evaluating property. The number of bedrooms, the property’s size, renovation quality, and other common features are typically enough to set the right price for an average home. Luxury homes, on the contrary, are often custom-made artsy projects with unconventional designs, which are harder to evaluate. When it comes to the high-end real estate market, setting the right price often guarantees the success of a transaction. 

Another San-Francisco-based real estate and rental marketplace, Zillow, found another use case for artificial intelligence in business to partially estimate property value by analyzing photos. Machine learning techniques can assess even the most sophisticated interior details that actually ‘sell’ it to the customer. The emotional aspect is a big factor in price setting. For example, a newly renovated stylish bathroom or a granite countertop can be a turning point in the customer’s decision, thus such details need to be prominent enough to influence the price. In combination with other typical price factors like square footage, Zillow’s AI algorithm is able to set the right price with a median error rate of 2%.

On a final note, the real estate industry is leaning toward more sophisticated data and larger data pools in general. Expert realtors would definitely consider the number of nearby bars and coffee shops and most likely set the price based on intuition and their personal experience. AI, on the other hand, can find hidden non-linear relationships between data and property desirability. A study by McKinsey has found out that having two grocery stores within a quarter of a mile tends to increase property prices, but having more than four results in price reduction. 

However, such relationships are vastly different depending on the country, city, or even neighborhood. Thousands of odd variables like mobile phone signal patterns or the tone of Yelp reviews for nearby businesses can also make the difference in home desirability. This is why real estate is a perfect place for AI to shine. It unlocks the possibility to gather and analyze data more efficiently and find even the most unusual property price-influencing factors. 

Construction has a long history of suffering from budget overrun. For example, the famous Sydney Opera House was built with 1,357%, or 70 million USD, in overbudget. Although this is an extreme case, McKinsey estimated that large construction projects are usually completed with the planned budget exceeded by 80%. 

Doxel, a California-based startup, solves this problem using robotics, LIDAR imaging, and AI. The company uses autonomous robots that are able to capture 3D images of construction sites. Then an AI algorithm analyzes data and turns it into insights, which are then used by project managers to immediately react to ongoing issues. The results have been extremely promising, from eliminating overspending entirely to saving up to 11% of planned budgets. Watch the video below to see Doxel’s AI in action:

Considering lavish amounts of data produced in the real estate industry, AI’s rapid penetration into real estate software development comes as no surprise. However, there is a long road ahead before we see AI adoption at scale. 

Although real estate companies are notably progressing toward better data sets, the majority of accumulated data remains siloed and lacks standardization. The faster the organizations will learn how to make data interoperable and enforce standards, the faster both customers and businesses will be able to reap AI’s exceptional benefits. 

AI adoption is not a point in time but rather a continuous process. Old companies might have enormous amounts of historical data, but the cost and effort of structuring it would often exceed desirable short-term profits. Companies need to plan their long-term objectives and start carefully collecting corresponding data. Collaboration is also a big factor here, as sharing data sets is essential for the industry’s well-being. 

Those who value unconventional metrics like proximity to the nearest Starbucks and can utilize such data using AI-powered tools will stay ahead of the competition. AI enables industry professionals to see a much bigger picture and assess properties’ future value, risks and opportunities with a level of precision not attainable before.  

There is a plethora of attributes that influence desirability of a property. Our lifestyle is always changing and tastes shift, resulting in the constant need of tailoring the living conditions to meet the ever-growing demand. AI developers can help real-estate companies to identify those needs and wants and provide homes that meet them. 

Lastly, it’s important to recognize that AI’s role is to support humans rather than to substitute them. The home buying experience is often personal and emotional. Regardless of how refined these technologies can be, emotional AI belongs to the future, as it can’t detect and interpret humans’ complicated emotional cues. Well, at least for now.