The real estate sector has always been slow to adopt innovations. While there are many reasons for its reluctant attitude to artificial intelligence, the biggest factor lies in the essence of the technology. AI applications are only as powerful as the quantity and quality of the data sets fed into them. With the continuous digitalization of our world, the number of data sources has been exponentially growing. More importantly, large enterprises are starting to work toward better data organization. Every piece of information extracted from anything from user-generated content to property price fluctuations can be tracked, analyzed and turned into valuable insights.
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.
When it comes to large properties like corporate office buildings, real estate maintenance costs can take up a sufficient part of the budget. 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.
AI can power the solution to help real estate management professionals effectively utilize office space. AI 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 can create a more appealing environment while decreasing maintenance costs.
Another AI can specialize 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.
Searching for a new place to live is often a rather daunting process. Almost every home-searching engine filters out the most suitable homes based on attributes like price, location, number of rooms, size, etc. Although this model has proven to be effective, it still often leaves potential buyers with far more offerings than they are willing to look through. This is where AI-based algorithms come into play.
The AI system shows best-matching offerings on top of the search results and recommends other listings accordingly. It also considers the preferences of other users that have looked at similar properties, which helps identify customers’ tastes after few search inquiries, allowing for a superior level of personalization.
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 in real estate is investment-focused. AI uses predictive analysis to accurately assess property value.
Every real estate agent dream of their ideal client, while clients often dream about houses they can’t afford. AI also has a special CRM that analyzes thousands of attributes to distinguish customers with real intentions to buy a property from those who are browsing out of curiosity. Moreover, the algorithm can identify what type of property the customer is looking for. This allows agents to save time and efforts by dealing with customers that match agents’ niches.
Construction has a long history of suffering from budget overrun. AI 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.
If you face challenges in understanding the requirements of your customers, AI can be of help. You simply need to turn the information you have while engaging with your customers into data that AI can use to provide recommendations according to the preferences of your customers.
A big part of the real estate industry is mortgage lending, which is data-intensive by definition. Bank statements, credit history, proof of the income, and many other papers are required for your bank to give you a shot at lending. This process is time-consuming for both parties: clients struggle to hand in everything right and lenders need to process all the data and evaluate it. Considering enormous property prices, there is no room for mistake in the mortgage lending business.
The mortgage lending sector currently uses optical character recognition (OCR), which helps lenders to automatically read data from borrowers’ documents. OCR successfully penetrated the industry a few decades ago, but the technology has one major limitation — it can accurately pull information only from template-based documents. Unfortunately, the majority of documents are unstructured, which makes OCR rely on humans to validate the work.
Machine learning tools, on the other hand, are able to capture significantly more information with a higher accuracy and less human interference. This means that the mortgage lending sector is about to experience a revolution. Loan auditors are now able to evaluate three times more compliance reviews compared to the previous industry average.
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 in real estate is investment-focused. AI uses predictive analysis to accurately assess property value.
Since AI has the ability to analyze patterns in vast amounts of data, it can be used to make reasonable predictions of the future value of a property. For example, an AI algorithm can combine current market data from marketplace and CRM as well as consider public information such as transportation network characteristics, crime rates, schools, and buying trends.
The number of property attributes or market data points can exceed tens of thousands, which is definitely a kind of analysis no human analyst or market research is capable of conducting.
AI-enabled consumer apps, machine-learning interfaces, voicebots & chatbots are already benefiting a lot of eCommerce businesses by collecting customer data and helping with improving lead generation and content marketing. An AI-enabled voicebot & chatbot is able to answer typical questions without any page opt-ins, sales customer support messages, and desperate attempts to get a lead’s contact data.
For example, here are just some of the question ideas that a voicebot & chatbot can ask a lead:
On top of that, a real estate voicebots & chatbot can qualify leads by asking them specific questions and allowing to fill out forms. This customer data could later be used to learn more about the target audience as well as how to follow up.
AI-powered software can be a solution for improving the ability of a real estate business to provide accurate, comprehensive information about property to clients. These information would give a real estate agent an excellent opportunity to provide as much useful information to clients as possible, thus getting them more positive reviews and deals.
An AI-enabled service that suggests products, customizes funnels, and provides tailored search results based on real-time analysis. If real estate businesses utilize a self-learning AI algorithm on a listing website, an app or a CRM system, they can teach it to generate recommendations based on customer preferences such as past purchases, views of specific properties, and search filters. As a result, your business will be able to personalize the customer experience and interactions with your digital products.
Companies could potentially use AI for lead management to display personalized promotions or messages to prospective buyers or lenders. AI could also be used for analyzing existing market data to recommend properties for brokers to buy or sell, based on the broker's past sales records and intent. Natural language processing (NLP) algorithms could be used to understand what matters to customers in a given zip code and what's unique about a given house from listings. Analysis of customer interactions during the lending or house-buying process would be useful for customer service analytics. AI-powered voicebots & chatbots could also be used to answer any question from a potential prospect in a jiffy and make personalized offerings based on user preferences, without the need for a sales agent.
Manual and repetitive processes, such as financial modeling and data entry, can be automated using extraction and classification technology, which would help real estate analysts save time, increase productivity and minimize errors. This would also help free up employee time to perform more value-added activities, such as improving customer service and performing creative tasks. In addition, due to pattern recognition abilities, AI can also be useful in forecasting risk and in manual property appraisal by taking into account factors like transportation and infrastructure improvements, changes in the environment, local neighborhood and other amenities that have an impact on property value.
As a company grows, it has to deal with massive datasets of lease abstractions, property appraisal reports, offering memorandums, legal papers, tax reports, ownership history records, zoning regulations and so on. As the amount of data grows, AI could be used to classify and store documents and even proactively alert when a new appraisal is due or a property explodes in popularity, for example. Machine-learning enabled search could help people quickly analyze the massive amount of search data that exists by pre-processing and tagging useful meta-data for all property-related documents in a company's repository.
If you face challenges in understanding the requirements of your customers, AI can be of help. You simply need to turn the information you have while engaging with your customers into data that AI can use to provide recommendations according to the preferences of your customers.
Selling or renting property is a daunting task, but this is made much simpler by the AI integrated real estate management system. Use the system to monitor the purchase process in real time, ensure no compliance checks are missed and close deals more quickly.
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