After some time as a real estate professional, you can get pretty good at predicting pricing and supply-and-demand trends in the market. However, a number of new technologies developed over the last few years have helped advance the sector enormously. Here, we take a look at the ways big data and machine learning (ML) can help real estate pros make accurate predictions faster and reduce costs.
Streamlining valuations
Real estate appraisers, assessors, lenders and investors can all use AI-based automated valuation models (AVMs) to inform and optimize their valuation processes. These AVMs enable real estate professionals to incorporate more variables into their calculations and derive valuable new insights from the data they have. According to the European Center for Sustainable Finance, the absolute error on AVM appraisals stands at 9% while also providing instant, real-time valuations at low cost.
A number of companies already use ML in their valuation models, including Zillow, Redfin and Opendoor. When potential home buyers search these sites, they see estimated selling prices derived from the latest data using an AVM. Lenders such as Fannie Mae are using AVMs as well.
AI is starting to play a role in commercial property valuations too. The NYC Wide Data Project from the MIT Real Estate Innovation Lab, for example, collects data on a huge range of attributes that may impact real estate in New York City. One of the goals of this project is to better understand the factors that influence commercial asset valuation.
Assessing risk
Lenders, borrowers and insurance companies in the real estate industry need to conduct risk assessments. Like so many other aspects of the sector, a wide range of factors may influence risk. Big data and ML can help these groups include more data in their risk assessments and conduct them more quickly and accurately.
ML can help lenders to optimize their borrowing levels and rates. Freddie Mac offers a feature to lenders in its loan advisor suite that uses AI technology to help them assess borrower risk even if the borrower does not have a credit score, which has the potential to make loans available to those who would otherwise not have access.
Insurance companies can automate the assessment of risk and the calculation of premiums. For example, Lemonade, which provides renters’ and homeowners’ insurance, uses an AI-powered system to let customers purchase a policy after entering their data online.
Identifying investment opportunities
Investors need to be able to predict real estate trends to make good investments, but the huge number of factors that influence the market makes doing so difficult. Big data technologies can help you to start organizing information about these factors, and ML can analyze this data to make predictions about which homes are likely to go on the market soon and how prices will change.
ATTOM Data Solutions, for example, has collected data on more than 155 million residential and commercial properties. This dataset includes information about a variety of factors such as property size, property use, any foreclosures, school district boundaries, environmental hazards, crime risk and more. This data can be used for a variety of applications, including how likely a property is to be sold.
As another example, researchers from Madrid, Spain, developed algorithms for homes listed at a price that is substantially lower than the market price. Investors could use these programs to identify opportunities for investment.
Matching people with properties
Another use is in matching people with properties. Sites like Zillow and Airbnb already do so to some extent, but as the technology improves, it is likely to play an even larger role in the real estate sector.
By collecting information about potential renters or homebuyers and combining it with data about available properties, ML can help to match people with properties that are likely to match their needs and preferences. This customer data can include information collected directly from customers as well as insights derived from other data-gathering techniques.
Chatbots have the potential to play a significant role in this area. People looking to rent or buy real estate could chat with a bot via a website, app or smart speaker to get started in the rental or buying process. Automating the start of the process could augment the jobs of brokers and real estate agents, reducing costs to as little as $1.76 (€1.6) per lead and matching people to the right property faster.
Conserving energy
Property managers could also use AI technology to reduce their costs and provide a better experience for tenants. Sensors integrated into lighting, HVAC systems, elevators, security systems and more can help to optimize their operation and reduce energy costs.
Smart thermostats, for example, use ML to optimize their performance over time to reduce energy usage and make life more comfortable for tenants. For example, the system can learn a tenant’s habits using ML and then automatically lower the temperature before they leave for work in the winter and raise it right before they get home. Smart refrigerators can reduce their energy usage during times of peak demand when electricity is at its most expensive. Installing these technologies in a multi-tenant building can deliver significant savings over time. We buy houses Fort Worth
In the future, sensors may be able to predict when equipment such as HVAC systems, plumbing systems and appliances need repair. This approach to maintenance, called predictive maintenance, is already in use in the manufacturing industry. Over the coming years, it may begin to make its way into homes and the real estate sector as well. House Buyers
Big data and ML can help real estate professionals, as well as renters and home buyers, to improve their understanding of the ever-changing real estate market. These technologies have already started to change the sector, and they will continue to do so in the years to come.
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