AI in Real Estate – Will Artificial Intelligence Replace Agents?

It should be surprising that considering the current investments in PropTech, real estate has been slow to adopt innovation. But this phenomenon is changing, and the potential of AI in real estate is gradually being recognized.

And how it’s changing? You’ll find out in sections:

• The growing role of AI across industries.
• AI in real estate.
• Challenges and limitations of AI in real estate.
• AI in symbiosis with real estate agents.

The Growing Role of AI across Industries

AI has a tremendous transformative capacity. Taking industry after industry by storm, AI helps companies automate operations and increase business efficiency.

A PwC report predicts that AI could give the global GDP a 14% boost from 2017 until 2030. Gartner further reports that as many as 37% of organizations have adopted some form of AI into their operations.

According to Gartner, the spike in adoption, a 270% increase, can cause a reduction in employment. According to a 2019 Brookings Institute study, 25% of all U.S. employment (36 million jobs) is marked as highly susceptible to bear the brunt of automation, causing a significant decrease in human labour.

Considering the global shift toward AI-based solutions across industries, some of that employment reduction seems inevitable in the coming decades. But does the same fate await real estate agents?

Before answering that question, let’s look at the current technology adoption in commercial real estate (CRE).

Software vs spreadsheet used at real estate companies. Source: Altus Group

While many tasks and operations are already handled by appropriate software, there’s still a considerable chunk where spreadsheets are still used.

And even though 48% of executives admitted to “using or already trying out AI and machine learning” in the same Altus Group study, the data suggests we’re still far from seeing real estate agents being replaced with AI.

The current adoption of AI aside, there’s also the specificity of the real estate industry that calls for the human factor’s presence. Therefore, instead of replacing human real estate agents with artificial intelligence algorithms, AI is more likely to work with agents, helping them make data-based decisions.

AI in Real Estate

Property purchase is a complex, emotion-dependent process, heavily relying on a human overseer to direct a sale tactfully. It takes a considerable amount of experience and observational skills to close a deal at the right moment—AI solutions can’t yet accurately detect emotions.

There’s also the matter of answering phone calls, showing interested buyers around a property, and negotiating.

Because of its specificity and reliance on interpersonal know-how, the real estate industry needs human real estate agents. But AI-powered software can help agents in a wide range of activities:

Automated Valuation Models

Automated valuation models (AVM) can estimate the value of a property based on numerous factors. Various AVM algorithms use a different combination of data to value a property, e.g., historical data, age of a home, images of a house, market value, trends, neighbourhood. Because the analysis involves such a wide variety of sources, AVMs often give a more accurate property valuation than that made by a real estate agent.

• Example: Altum AI

Altum AI offers innovative solutions in the field of real estate data. The extensive database can access the most accurate home data, relocation data, and sustainability data. The solution supports companies from the ex. the insurance industry, fin-tech in processes related to the automation of valuations and risk management. And in this case, we had the pleasure to be a part of this exciting project working with Altum AI as our client!

It is currently one of the most advanced solutions for business clients in the Dutch market. The creators of Altum have many years of experience in the real estate industry, so their algorithms are based on the most useful data related to the subject of valuation.

Smart Property Analysis

Some of the property characteristics can be objectively evaluated by a real estate agent. Still, AI-infused real estate software can make a faster and more accurate calculation regarding various property aspects. For example, detailed annual sunlight amount, time of best light during the day, sound disturbance, parking space availability, plowing, etc.

• Example: Localize

Localize is a real estate listing website that calculates seasonal light variations, crime rates in an area, or commute times for different means of transportation, among many other search criteria.

Aid in Property Development

AI can be an invaluable asset in the hands of developers and investors interested in building in a specific area. When fed with accurate datasets, AI software can suggest plots or building sites based on city zoning, trends in the industry, currently available, and future local infrastructure.

• Example: Deepblocks

Catering to property developers, Deepblocks’s algorithms analyze market trends, financial data, zoning regulations, and local infrastructure to suggest optimal construction sites.

Mortgage Calculation

Mortgage calculation is a tricky matter. However, by using AI, real estate agents can complete the process much faster—smart algorithms can quickly review mortgage applications screening them for completeness or errors. AI can also be employed to calculate income, analyze key documents, and check various income sources.

• Example: CoreLogic

CoreLogic is a robust, AI-powered platform for a variety of business tasks in real estate. CoreLogic’s MindBox gives real estate agents and brokers a handy toolset for income calculations, documents check, loan underwriting, and many more.

Property Recommendation Based on Customer Preferences

Smart, self-learning algorithms on listing websites or built into CRM systems can give property recommendations based on customer preferences. AI-infused solutions also work to refine marketing activities. For example, by analyzing past purchases, imagery, and messages, AI can suggest approaching a buyer during every step of the purchase journey.

In 2015, Inman conducted an experiment where a bot and a broker gave property suggestions based on customer preference. After a three-day experiment, the bot managed to consistently propose properties that better reflected the customer’s preferences vs the broker’s suggestions.

• Example: Trulia

Trulia, one of the most popular real estate listing websites in the U.S., has an AI-powered algorithm built into the platform to refine its recommendation system. Using machine learning, computer vision, and engagement models, the website learns and remembers customer preferences. Trulia’s algorithm even analyzes details included in property images.

Long-Term Customer Relationship Management (CRM)

A customer sold shouldn’t become a customer gone: CRM systems can aid agents in maintaining long-term relationships. A real estate buyer’s journey shouldn’t end on a single purchase, and smart CRM systems help agents assist their customers during moving, renting, selling, or buying more properties.

• Example: Freshsales

Freshsales has an AI-based lead scoring that analyzes real-time behavioural customer data on top of its rich feature package, helping real estate agents sell properties more effectively.

Chatbots

Intelligent chatbots can be invaluable for real estate agents. Set up on property listing websites, chatbots can collect leads, answer technical questions, and even give potential homebuyers a virtual tour.

By answering common questions such as square footage or lease terms, chatbots let real estate agents focus on more demanding tasks. Plus, chatbots are available 24/7.

• Example: Apartment Ocean

Apartment Ocean provides real estate agencies with customizable chatbots that understand natural language and can answer listed properties questions.

Automation of Manual Tasks

Reports, legal papers, zoning regulations, requests for proposals, various analyses, or agreements are just a few of the documents that real estate transactions generate. AI-powered tools can review these documents, scanning them for inaccuracies and inconsistencies (missing signatures, empty fields, invalid characters, etc.)

Moreover, some AI solutions generate appropriate documents automatically, freeing agents from performing time-consuming tasks.

• Example: iManage

iManage is a secure and friendly online environment for storing, analyzing, and managing contracts, lease terms, reports, etc. The built-in AI algorithm extracts crucial information contained in the documents to provide agents with actionable insight.

Challenges and Limitations of AI in Real Estate

And what are the obstacles to AI in the Real Estate market?

While these AI-enhanced operations are invaluable to spur growth and increase sales, the technology itself isn’t devoid of challenges, which have to be taken into account to draw out the best AI in real estate.

Low-Quality Datasets

Since data galore is the fuel of AI, relevant and current datasets are crucial for AI algorithms to make informed decisions. Without them or with a limited amount of them, not only does AI lose credibility, but it also lacks a proper foundation to formulate insight.

Reluctance toward New Technology

One of the adoption challenges of AI in real estate is the Luddite-like approach of some real estate agents, especially when it comes to property valuation. In the past, automated valuation models tended to have a higher margin of error, which might partially explain the reluctance.

Contact Preference

While technology is already ingrained in human lives, usage distribution is far from being even. Some buyers prefer dealing with an agent from the get-go until the completion of a purchase.

AI in Symbiosis with Real Estate Agents

For the time being, it’s not likely that AI will replace real estate agents. Instead, AI can give real estate agents a solid foundation for growing their businesses and improving operations. By acquiring access to data-based information, real estate agents better understand and insight into properties, markets, and buyers. AI, therefore, works alongside real estate agents to give them a disruptive advantage in the industry.

But as is the case with every technology: to operate well, AI needs an overseer and appropriate talent who knows how to curate datasets and configure the software. And in this matter, we can always help your business!

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