The role of artificial intelligence in real estate.
Artificial intelligence is a very complicated topic. That might be one of the reasons why the real estate business is one of the last sectors to adopt it. Morgan Stanley states that real estate is the second least digitized sector globally. However, now just like many other businesses, AI is transforming the landscape of the real estate industry. Additionally, according to the Forbes report, AI is estimated to add a staggering 15.7 trillion US dollars to the world economy by the end of 2030. Artificial intelligence in real estate is a major contributor to the US economy. This article discusses the current status of artificial intelligence in real estate business and the most impactful ways it’s impacting the real estate industry.
There are many different ways of how artificial intelligence is already helping the real estate business. For example, real estate agents utilize the power of machine learning in order to respond to their clients more efficiently. Moreover, it also assists brokerages precisely market their listings and enables marketplaces to provide people with accurate data with the least median error. One of such most significant examples is more accurate Zestimates by Zillow with only 1.9 percent of average median error rate. Machine learning algorithms allow real estate businesses to analyze and classify the bulk amount of unstructured and uncategorized data. This way, the leading real estate marketplaces become able to offer precise and more detailed values for the property. Bots with machine learning algorithms utilize the power of artificial intelligence to determine everything from the paint color to home prices that customers are seeking. Analyzing hundreds of thousands of relevant yet unstructured data points with the help of AI allows for:
- KPIs (Key performance indicators) and property characteristics
- Probable economic value for a certain property
- The current standing of the local real estate industry
- The probability of misfortunes in the area, such as natural disasters
- The units supply which is about to be released
By far, the most useful and interesting factor for the real estate industry is property KPIs which allows real estate agents to answer the following:
- How is the property currently being operated?
- How is it being controlled?
- Is the property working the way it is supposed to?
Moreover, the data gathered by the AI bots also enable the property owners to find the best way to invest in their owned buildings. It also predicts what property owners should invest in getting the maximum returns. These features are the reason for the drastic adaptation of AI in the real estate business. According to the CRE innovation report of last year, the decision-makers and real estate agents have a positive attitude toward adopting new technologies, and the current landscape of AI acquisition in the real estate industry is quite impressive. The CEO and co-founder of Deepki, Vincent Bryant, says that AI allows a large real estate corporation to save about 4.85 million US dollars every six months. With that said, AI provides real estate agents with more effective and efficient strategies to empower their customers. Not only is it making the whole selling and buying experience seamless, but it also offers precise data by mitigating uncertainty. Here are some of the most important ways of how AI is helping real estate.
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Whether it’s about managing a small home maintenance system or maintaining large properties such as corporate office buildings, AI is playing its part. This technology can also provide information regarding the latest rental trends in a particular area. Property owners are also using it to determine if they need to raise tenants’ prices on the basis of specified data when the lease is expired. The benefits that AI brings are endless, and it can also be used to analyze building expansion and automation. It can be very helpful to find out the potential and expected returns based on data and stats that impact profits, expenses, and rents. According to the JLL Occupancy Benchmarking Report of 2018, on average, 30 to 40 percent of an office building space remains under utilized. As the money losses can’t be simply identified, such expenses are typically known as silent costs. Businesses have to pay for unused spaces, and it also costs an extra amount of money on energy consumption that leads to employee dissatisfaction. For that matter, an AI solution comes in handy, and one of the best examples of such solutions is IBM’s TRIRIGA. It basically is an AI-powered solution that provides efficient real estate management and allows professionals to effectively utilize their square feet. The solution collects information from many different sources, such as IoT (Internet of Things) sensors and Wi-Fi. Then it further analyzes the data by using AI algorithms and converts them into KPIs that provide valuable insights. This way, it helps businesses and enterprises to manage their office buildings and workspaces more efficiently by making better decisions. NLP (Natural Language Processing) is yet another AI feature that allows employees to “talk” to spaces. As a result, the AI solution autonomously identifies their needs and stores it. It helps the employer and the higher management to use that data to rearrange the working space and office layouts for the employees. Not only does it achieve employee satisfaction, but it also increases the probability of success for businesses. That’s why tools such as TRIRIGA are very helpful that allows businesses to create more attractive environments by reducing costs. Gridium is yet another example bridging the gap between AI and the real estate industry. It’s a company that offers solutions based on AI for property resource optimization and energy saving. The solution that Gridium utilizes patterns and automatically detects suspicious spikes in energy. It warns real estate agents and property managers by analyzing weather data. It allows businesses to decrease their operational costs by taking precautionary measures on time. By using the Gridium solution, LinkedIn saves about 100,000 dollars each year in operational costs. Real estate management tools powered by AI can track tenant applications, requests for maintenance, and property and rental listing. It allows people to identify seasonal availability, the latest price trends in a particular area, tenant preferences and characteristics, and maintenance issues.
It’s very daunting for people to search for a new home or office building that perfectly matches their unique needs. Information technology helps people to search for homes seamlessly. Thanks to the searchable listings online, that has made the whole process more streamlined. Almost all the home search solutions allow interested buyers as well as real estate agents to narrow down their search. The solutions achieve specific search functions by providing several types of filters such as location, number of bedrooms, area, and others. Undoubtedly this technique is very useful and saves lots of time. Still, this technique leaves almost all the people with far more options than their requirements, and that doesn’t exactly solve the problem. That’s where AI jumps in to save the day. Many modern searching solutions utilize the same search criteria in order to figure out the customer’s interests and preferences. These solutions choose properties and characteristics based on customer’s search behaviors to create short and more precise listings. Such listings only contain the best-matched results that customers are looking for, which saves both time and confusion. For example, Zillow takes the search history of a particular customer and combines it with somewhat similar search patterns to create a list. This list contains the results that most of the prospects with similar searching behaviors actively search. It allows the system to only show the results that a customer is most interested in. It works just like the way Amazon’s book recommendations algorithm does. Many organizations have come up with applications and solutions powered by AI that serve the customers with a conversational interface. These solutions can answer both easy and complex questions such as:
- How many cars can the garage of a certain house accommodate?
- Does a particular house have a pool?
- How many rooms have attached baths?
These applications add an extra and very impactful layer of detail that drastically mitigates the searching time.
Intelligence is what allows us to predict, and it’s undoubtedly one of the most critical features of Artificial Intelligence. The value of the right prediction increases even more in the real estate industry. That’s because it’s all about having high rewards as well as high stakes. An organization with such a solution that can anticipate the right timing to sell a certain property or identify the sale and rent rate fluctuations will surely have an unprecedented advantage as compared to its competitors. That’s why the most successful applications, even in the list of solutions powered by AI, are the ones that are investment focused. For example, Skyline AI accurately and precisely anticipates the property values by using predictive analysis algorithms. It’s also equally important to note that the software efficiency is directly proportional to its available data pool.
That’s because the bigger the data pool, the more precise the results. It’s also one of the biggest reasons for Skyline AI’s success as it has one of the biggest data pools in the real estate industry. It approximately uses 130 distinct data sources and examines about 10,000 different features of every property. It allows the software to provide customers with results that are second to none with unmatched accuracy. It’s very difficult for real estate agents to set the asking price, especially for a property that is above average. That’s because real estate agents are accustomed to evaluating the properties using standard and traditional methods. They normally use renovation quality, property size, and number of beds and baths to set the price as these features have been enough in the past. On the other hand, the luxury and above-average houses are very hard to evaluate using the standard procedure because they have an unconventional design with custom-made projects. Setting the right price for a high-value property is actually one of the most important factors that define the success of a transaction. Zillow can also partially estimate the value of any property using artificial intelligence just by analyzing photos. The machine learning algorithms that this platform uses allows it to even assess the most delicate, custom made, and sophisticated interiors and details. AI allows solutions to figure out nonlinear relationships between people’s desires and data. It even counts off variables such as online reviews, mobile phone signals in the area, etcetera to calculate the right value of each particular property. Because of such reasons, AI goes hand in hand with real estate and reveals the possibility to collect and examine data more effectively and efficiently. Moreover, it brings the most suitable and right pricing by analyzing unique price-influencing factors.
Most real estate agents understand that they usually receive uninterested leads and website visitors. That’s because there is a large swath of people who visit online real estate marketplaces just for the sake of passing the time, as opposed to the more serious potential buyer. Moreover, many people just want to look at the latest trends. That’s where deep learning algorithms help agents to find serious buyers by qualifying them on the basis of various factors. Many platforms also use NLP (Natural Language Processing) techniques to find ideal clients. Not only does it save plenty of real estate agents’ time, but it also helps them to close their leads more often. REX, a brokerage based on AI, is yet another example that targets a narrow audience looking for luxury real estate. It identifies the most serious buyers by analyzing their behaviors and earlier activities. Moreover, the platform also considers everything from customers’ previous purchases and the ads type they interact with. In simple words, such platforms signify that technology can do a lot better job to find the right leads as compared to humans. AI Chatbots are also assisting real estate agents in finding the right customers by answering their queries and questions. Additionally, chatbots also ask simple questions to customers in order to collect the most important and useful information such as:
- Determining the customers’ price range
- The type of property they’re interested in
- Area of the real estate
These types of questions allow chatbots to categorize leads and filter out the most interested ones. Chatbots also ask customers to fill out forms to collect more data that a business can later use for email campaigns and follow-ups.
This is yet another very important factor where AI helps both customers and real estate agents. That’s because construction doesn’t have a very extensive history of experiencing budget overrun. Sydney Opera House construction is one of the most popular examples of budget overrun, which was constructed with 70 million US dollars or 1357 percent over budget. Undoubtedly, it’s an extreme example. But the fact of the matter is that almost all the mega construction projects are completed while exceeding 80 percent or more of the planned budget, according to McKinsey stats. In order to solve this problem, AI is yet again helping the real estate industry. A California based startup, Doxel offers a solution that utilizes the power of AI, LIDAR Imaging, and Robotics. The platform captures the construction sites’ 3D images with the help of autonomous robots. These images are then fed to AI algorithms that analyze this data and convert it into valuable insights. It helps the project managers and engineers to immediately react to any current and upcoming issues on time. The results that this platform offers are incredibly promising and effective that allows project managers to save up to 11 percent of the planned budget. Such platforms usually use Artificial Neural Networks that not only predicts the possible cost overrun but also allows managers to save even from their planned budget. The most common historical data and factors that these neural networks use are:
- Project size
- Contract type
- The competence level of working teams
- Start and end dates of the project
AI systems can also help the staff to enhance their knowledge and skills quickly by remotely accessing the training materials. Not only does it reduce the time, but it also mitigates the chances of budget overrun.
Mortgage lending is one of the biggest parts of the real estate industry, and it is data-intensive by definition. A bank requires documents such as proof of income, credit history and bank statements to offer people a shot at mortgage lending. For both banks and customers, this process can be very time consuming. The customers need to obtain all the documents, and the lenders also need time to process and analyze all that data. Additionally, there must not be a single mistake while processing and analyzing this data because of the astronomical prices of property. In order to manage this problem, the mortgage lending sector has been using OCR (Optical Character Recognition) technique for decades. It can read and analyze data automatically from the documents that borrowers provide. However, like many other technologies, it comes with one big limitation. The documents that can only be used for OCR must be in a specific format to come up with accurate data insights. If the documents are not in the necessary format, then the solution won’t be able to provide useful results, and most of the documents are not in that format. That’s why human input is currently necessary to validate the results that OCR provides. On the other hand, machine learning techniques and solutions are able to provide more accurate and informative results. Human interference is also negligible, and that’s why more and more lenders are acquiring AI for mortgage lending. According to an estimate, the AI-based solutions provide results that are three times more accurate than earlier methods. An AI-based solution to analyze borrower documents is actually a combination of both OCE and machine learning tools. Not only do these solutions allow lenders to reduce their staffing costs, but they also save time for both parties. A solution that uses both ML and OCR is now usually called Capture 2.0, and it’s just a matter of time that it becomes the industry standard.
Not all of the customers know the definition of a perfect property, and many can’t choose from the range of available options. It’s usually very challenging for customers to select the right option according to their unique needs when it comes to renting, selling, or buying real estate. AI technology solves this problem by analyzing customer preferences and providing the most suited available options. The property recommendation engines have already become a reality. They work on the same principles as the product recommendation engines do, such as Amazon Personalize. In the real estate sector, these solutions provide customers with the most suited property on the basis of real-time analysis, such as customers’ previous interactions, preferences, and purchases. Not only does it help customers find the right property options, but it also helps the businesses to increase their sales.
Every real estate agent wants to provide their customers with a seamless and efficient transaction experience. No other technique streamlines and enhances the transaction process the way machine learning does because it offers:
- Auto fillable data fields
- Detailed reporting
- Solid compliance checks
- Smarter mobile apps
- Faster closing times
All of these features provide customers with a reduced time and minimized possibility of manual data entry errors. Many organizations are using such machine learning techniques within their CRMs that generate automatic reports about valuable data insights. It helps real estate agents to learn about their customers instantly and saves their time to manually look for each transaction. Additionally, at the day end, it also helps brokers to assess their performance and customer satisfaction accurately. Dotloop is one of the most successful solutions that use machine learning techniques and sophisticated algorithms. It allows the managers to learn about their staff’s performance to ensure the best outcomes for their customers. It also streamlines the transaction process for customers for an enhanced, personalized, and seamless experience.
Now that it’s easy to understand how AI techniques such as machine learning, automation, natural language processing, and others are benefitting the real estate industry, it’s time to analyze how it can help the investors. Here are some of the most important examples that provide a better understanding of whether integrating AI is a good option for investors or not.
Although it’s already briefly mentioned in the above section, it’s important enough that it bears repeating in this context. Artificial intelligence provides software solutions with the capability of predictive analytics. It offers more insights into the current and future value of any property. This information can be very useful for investors. First of all, it can help investors to determine if a particular real estate meets their bottom-line way before they consider putting an offer. Moreover, it can also inform investors about the possible returns they will receive and when will be the best time to utilize their exit strategy.
The predictive analytics feature of AI not only helps investors to save money, but it can also gain them a competitive edge. In simple words, if an AI solution can help investors to find out the right value of any property, then it can also indirectly help them to decide what they should offer. If an investor comes up with the right and educated offer, they will automatically be able to gain a competitive edge.
Artificial intelligence can be very useful if an investor is going with a buy and hold strategy and needs to manage property management every now and then. The real estate investment and property management systems powered with AI work on the same principles as the artificially intelligent CRM (Customer Relationship Management) does. As a CRM helps the real estate agents to minimize the number of errors and streamline the transaction process, it can also help the investors to automate the investing procedure. These are some of the most interesting and effective ways how AI can help investors, and it ensures that the future of AI in real estate is very bright. Let’s have a look at how it’s going to look moving into the future.
As we have already seen how artificial intelligence is helping the real estate industry and how it can help investors as well. But the fact of the matter is that currently, AI is only scratching the real estate industry’s surface. The algorithms that it’s currently using are continuously evolving, and the current landscape seems very promising for the bright future of AI in the real estate industry. However, it’s a long and bumpy road ahead before the real estate industry adapts artificial intelligence techniques on a larger scale. Real estate organizations are considerably advancing and acquiring ways to use better data sets. But the majority of collected data is still unstructured and lacks classification and standardization. The speed at which companies are realizing the importance of interoperable data and enforcing universal standards is directly proportional to the number of benefits that both customers and the real estate industry are receiving from AI. It’s also very important to understand that the benefits of the adoption of AI are not a fixed point in a given time. In fact, it’s an ongoing and continuous process. The big giants in real estate have access to astronomical amounts of data. However, in order to categorize and classify that data, the institutions are often stuck with efforts that exceed the cost and time that a company can afford. Big companies must focus on their long-term goals and profits and start structuring their data using new methods. Another important factor that determines the future of AI with real estate is the collaboration of different organizations. It’s essential for the industries that big companies join forces and combine their data to come up with better and more useful data sets. The bigger the data, the better the results that AI tools will produce. That’s how artificial intelligence enables industry experts to comprehend a bigger picture and greater good. Realizing it will also help them to assess the opportunities, risk factors, and future values of properties with accuracy that’s not attainable otherwise. There are a plethora of variables and data points that impact the desirability of certain property at a given time. For example, new trends, taste shifts, changes in our lifestyle, and other factors result in continuously changing requirements. AI techniques and tools can help the real estate sector to identify such behaviors on or even before time. The most important point of utilizing the power of AI is to help humans instead of substituting them. Everyone needs to understand it because the property purchasing experience most of the time is emotional and personal. That’s why one of the major features of AI in real estate’s future will be emotional AI.
Artificial intelligence in real estate and its various techniques are becoming smarter day after day. While it’s not possible to predict the exact future, it’s safe to say that it’s going to make things a lot easier for real estate agents, brokers, and investors. We hope this guide will help you to understand why AI is important for your real estate business. With that said, using AI for your real estate business is surely a wise idea that will help you to take your business to the next level.
Some great articles by authors on medium on this topic —
Oksana Tunikova, has written this brilliant piece on how technology and its multi-layered approach will impact real estate.
ChatbotNews, has written on how chat bots will improve the real estate industry.