How Big Data and AI Impact Real Estate Transactions Today

By Steve Udelson

Posted on October 17th, 2019

The real estate industry has traditionally been a relationship-driven business where agents and brokers control most of the process, from setting up listings to setting commissions. As such, much of the information behind each transaction lies with the associated brokers and agents, which is why home buyers and sellers often rely on them to set listing prices, match buyers to available properties, handle closings and more. Yet the industry is now in the midst of a long-overdue digital upgrade that is making the process of selling or buying a home more transparent and efficient.

For example, rather than listings and sales data only being available within real estate brokerages databases or on the industry conglomerate known as the Multiple Listing Service (MLS), there are now a number of real estate listings sites available to the general public, ranging from broad players like Zillow and Redfin to digital brokerages, like Home Bay, that syndicate MLS listings and provide access to the public.

In addition to real estate information becoming more democratized, big data and artificial intelligence (AI) are also making the industry significantly more efficient. From listings through closings, real estate companies are utilizing big data and AI to save buyers and sellers time and money, both by integrating new technologies and processes into their offerings and by partnering with big data and AI companies that are getting involved in real estate.

How Big Data and AI Impact Real Estate Transactions Today

How real estate companies use big data and AI

Essentially any part of the real estate process can be improved through the use of big data and/or AI, and both established real estate companies and startup real estate tech firms are leveraging new technologies to better serve their customers.

One way real estate companies are using big data and AI is to improve pricing estimates. Sellers want to optimize their listing prices by listing at or below fair market value, because pricing a home above its market value can cause homes to take longer to sell, and pricing at or below market value can increase demand and drive up offer prices. This phenomenon is explained by Zillow CEO Spencer Raskoff in his book Zillow Talk. Calculating the fair market value and the optimum price can still be difficult though, as every home and market is different. Home Bay’s algorithm considers both our own data and publicly available home sale data to zero in on a home listing price ranges for clients. This is where big data and AI play a major role.

Instead of relying on a real estate agent’s intuition or manual calculations, analyzing large datasets can help sellers find the optimum listing price. Zillow’s Zestimate tool, for example, provides an estimate of a home’s value, and it uses big data and AI to try to come up with an accurate number. For example, the Zestimate tool instantly analyzes data such as listing price and days on the market to come up with accurate calculations of a home’s actual worth. The analysis has matured to the point where now half of all Zestimates are within 2% of a home’s eventual sales price.

A vision for more accuracy

In addition to analyzing market data to come up with pricing estimates, real estate companies can also use other areas of AI, such as computer vision, which uses a home’s curb appeal and quality to impact the pricing estimate. Zillow’s Zestimate tool can also analyze photos of a home to help determine the sales price, by noting in-demand features such as granite countertops or a remodeled kitchen that would indicate a higher selling price.

Big data and AI can also be used by real estate companies to improve personalization and better match home buyers and sellers. For instance, Trulia also uses computer vision to analyze features in homes like hardwood floors, and when combined with other data such as a user’s search criteria, the platform can recommend properties to those searching for a home.

Taking the pain out of closing

New technologies are also making their way to the less glamorous parts of real estate transactions, such as the closing process. Once a buyer and seller agree to a sale, there are several critical steps that can take a couple months to finalize, but big data and AI can be used to automate closing documents and speed up related processes.

One piece of the closing puzzle that is being improved by AI is the transfer of title. At closing, the seller needs to transfer the title to the property to the buyer, which is usually conducted by a title company. The current process can take weeks, as the title company conducts title searches, making sure the seller has the right to transfer the property. In fact, many buyers end up purchasing title insurance to protect against the risk of someone else making a claim that they own the property. However, some title companies, such as States Title, use AI to predict whether they can underwrite a property and then automatically generate documents so that title commitments can be made almost instantly, saving all parties time and money.

How big data and AI companies are getting involved in real estate

Beyond creating their own technologies and analyzing their own datasets, real estate companies can also leverage the work that big data and AI companies are already doing in the space.

For example, big data companies can sell datasets or ready-made solutions to real estate companies looking to improve in areas such as their ability to strategically set listing prices and to personalize marketing. Because the real estate industry has traditionally been rather opaque and siloed, many real estate companies typically have limited data based on their own sales or what they can find through public listings. Yet big data companies can provide these real estate companies with massive data sets that provide a much broader view, and from there individual real estate companies can customize how they use that data.

Attom Data, one of the largest data aggregators in real estate, has a national property data warehouse with nearly 30 billion rows of transactional-level data. Real estate companies can then hone in on specific data, such as marketing to homeowners with particular property attributes or welcoming new homeowners to a neighborhood to build long-term relationships.

HouseCanary uses machine learning to provide home valuation reports that provide actionable insights for sellers. Instead of real estate agents depending on their intuition, they can have the support of big data behind their pricing suggestions. This lends credibility as well as transparency to the valuation process.

Real estate companies can also leverage existing AI and analytics platforms to easily conduct deep data dives. This ranges from using platforms like Alteryx to analyze site selection in commercial real estate to how the Wake County, North Carolina government is leveraging a platform from SAS to predict property values for tax assessment purposes.

The best is yet to come

As these examples show, big data and AI are already having a significant impact on the real estate industry, but these technologies are still in their infancy, with further transformation of the industry on the horizon.

The industry as a whole is moving more towards digitalization, and with that, more data will be generated and easily collected. From there, real estate companies will be able to further personalize the process of matching home buyers and sellers, while providing more accurate and transparent pricing, as well as an overall quicker process from the initial search through closing.

Modern brokerages like Home Bay are already driving down costs by increasing efficiency, such as algorithm driven document auditing that flags users when offers and contract language are unusual, savings hundreds of hours of manual auditing. As big data and AI evolve, digital brokerages will be able to provide additional efficiency, such as recommended starting and counter offers based on multiple deal attributes and buyer and seller propensities.

These advances are a win-win for real estate companies and buyers and sellers alike, because increased efficiency within real estate companies drives down costs, which can then be passed on to real estate market participants. With these advances in efficiency in the pipeline, the future looks bright for the real estate industry.

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