The worlds of physical property and virtual technology are coming together via the use of AI and machine learning...
to bring enhancements to the real estate industry. While nothing is as tangible as buildings and properties where people live, work, eat and play, AI in real estate is being applied to drive even more value from the ways people experience, interact with, and buy and sell property.
Assisting real estate operations and paperwork
Even in this digital age, most real estate processes are still paper based. With financing and loan applications, floor plans and surveys, tenant applications, and insurance policies, real estate is positively swimming in paperwork and documentation, making AI in real estate a prime opportunity.
Managing all of this to make sure that everything is up to date and that no information is lost is a significant challenge for owners and managers of multiple properties. Natural language processing (NLP) tools are being applied to help process all the text and documents to quickly locate information that might otherwise be hard to find.
Similarly, real estate listing companies such as Zillow are using intelligent process automation tools with AI-enabled capabilities to enter data into various different systems using autonomous bots, replacing tedious, repetitive and error-prone human labor and saving many man-hours.
In addition to keeping information clean, up to date and timely across systems, AI in real estate is streamlining processes and speeding up tasks that have otherwise taken days, weeks or even months.
For example, some real estate property owners are using AI-based systems to locate and communicate with vendors and property management suppliers. These systems are also tracking data from the properties and proactively recommending maintenance and management tasks based on identified patterns, as well as the best times to procure supplies that have highly variable pricing.
Changing the way real estate sales and marketing is done
AI systems are being used primarily by commercial real estate marketing and sales professionals to improve the way that properties are being sold and promoted. Machine learning systems can identify which tenants provide the best returns to landlords and which leads and prospects match those tenant patterns. AI-based systems are generating, validating and enriching leads, as well as providing insight into prospects' growth patterns, behaviors and preferences. This information can enable agents to strategically reach out when it's clear that a prospective tenant will need more space.
Likewise, by analyzing marketing performance data, AI systems are using machine learning to identify and target prospects that are more likely to respond to a certain marketing message and personalizing those real estate ads in ways that resonate with prospects' preferences.
For example, in commercial real estate, landlords looking for high-growth tech businesses can tailor their offerings to meet the needs of a millennial-focused workforce. Other real estate holders looking for law firms or accounting firms can also focus on different professional needs.
Additionally, real estate professionals are making increased use of chatbots and conversational assistants to interact with existing and prospective tenants. These systems are available 24/7 to help with a variety of tasks.
Rather than having to call a property management office or wait until business hours to reach someone, these chatbots are available all hours of the day to answer general questions about things like facility amenities, property details and square footage, or inquiries about the status of maintenance requests. These AI-powered chatbots save real estate companies by reducing customer service costs and providing around-the-clock customer support.
AI-enhanced property research and analysis
In addition, AI in real estate is helping companies buy and analyze real estate properties. Some notable real estate holding companies are using machine learning to estimate a property's market value based on the property's characteristics and previous home sales. They can also use machine learning to identify pricing patterns that can be used to predict future value.
Real estate company Zillow has pioneered its AI-based approach to provide accurate and timely information and estimates on real estate pricing using machine learning and by mining a wide range of data sources.
More than just advanced statistical modeling, machine learning-based systems can also mine social media for residents' sentiments related to real estate in a region or increasing concerns about quality of life, crime and other issues. These can help real estate companies make decisions about rent prices and whether or not to invest in a given property.
Furthermore, some more advanced AI-based systems are helping real estate investors assess risk based on set parameters or notify an investor when a deal matches their criteria. These AI-based systems can monitor websites that advertise real estate listings or available leases and can keep track of trends in pricing and availability.
Using NLP and by processing unstructured data, these systems can process information from an almost unlimited number of websites and databases on a daily basis and can quickly spot real estate trends, both positive and negative, that are worth paying close attention to.
Because tenant rental and loan defaults are a significant risk for many real estate property owners, some firms with large numbers of leaseholders use AI-based systems to keep an eye on how their tenants are doing. These systems can see any warning signs of pending financial problems, as well as review historical data to predict loan defaults, making the risk assessment process more efficient.
In the AI-enabled future, real estate companies can leverage the power of AI to truly be aware of the market and provide benefits that extend far beyond simply giving people a place to live and companies a place to set up desks.
Indeed, in 2016, real estate company Inman pitted a bot against three real estate professionals to provide house recommendations to a real person. The person preferred the answers provided by the bot over those of the three human agents. Is this a sign of things to come?