5 Ways AI is Changing the Real Estate Sector
The real estate sector, like many others, is slowly but surely adapting to the “data-focused” world and making some progress in defining use cases for artificial intelligence utilization. Quietly, tech-savvy CEOs and development managers are educating themselves on the application of data analytics to gain a competitive edge.
Indeed, with AI infiltrating every industry, it makes perfect sense to explore what technology like machine learning can do for your business. Already, a number of use cases have emerged, showing promise in multiple areas that can improve a company’s position in the market.
In this report, we’re going to review these use cases in detail as well as bust some myths about AI being nothing but a buzzword in the real estate industry.
The Current State of AI Adoption in the Real Estate Sector
To many people, AI sounds very futuristic, almost like a buzzword that gets thrown around way too often. While this perception is often justified in some industries, the real estate sector is different. CEOs, industry leaders, and decision-makers are already talking about the tangible benefits of the application of AI in the sector as well as the opportunities in the making.
Earlier this year, Or Hiltch, a co-founder of the U.S.-based real estate investment technology company called Skyline AI, spoke at a conference where he announced a major breakthrough.
In 2018, there was the first AI-driven real estate transaction, acquiring two multi-family buildings in Philadelphia for $26 million. This property was picked by the so-called “soon to market detection” algorithm that defined whether it was going to go to market.
This was a result of analyzing tens of thousands of data points to define such interesting data such as:
- potential economic value for a property
- Property characteristics and KPIs
- the probability of natural disasters in the area
- the state of the local real estate market
- the supply of the units that are going to be released
- many others.
Yes, this already happened and it’s just a beginning.
Property KPIs definitely stands as the most interesting and useful factor for real estate companies.
“Once we have this data, we have to figure out what it means,” explained, offering examples. “How is the building currently operating? Is it working correctly? How is it being controlled?” Commercial Observer quoted Alec Manfre, CEO of Bractlet, an infrastructure investment solution, as saying.
According to Manfre, the data supplied by AI can also allow property owners to do “analysis on how they need to invest in their buildings, what they can invest in and what their returns are ultimately going to be.”
The recent CRE Innovation Report shows that attitudes of decision-makers are positive and that the present use cases of AI in the commercial real estate sector are already quite impressive.
For example, after surveying 400 senior executives in the commercial real estate branch, the researchers found the following applications of AI and machine learning.
As you can see, “Scenario and Sensitivity analysis” is the most common area of application, which supports the words of CEOs you read above. The purpose of this analysis is to analyze a property’s financial and operational performance to make the most reasonable real estate investment decision, which often involves dealing with a lot of data.
For example, Deepki — a company accelerated by Axeleo — engages the environmental and digital transition of their real estate clients, from performance to sustainable impact. By collecting and analyzing existing data, they help them to :
- gain insight into their real estate portfolio
- reduce their environmental footprint
- leverage their assets value
“The market is evolving fast! Property managers are aware of the great opportunities generated by data analysis and technologies. Using complex analysis models, we allowed a large corporation to save €4M in only six-month. It gives a great signal to the market that leveraging data and creating value from it, is the new goldmine in the real estate sector” said Vincent BRYANT, co-founder & CEO at Deepki.
It’s safe to assume that the percentage of decision-makers applying AI will increase, as 28 percent of them think that technology will create major disruption. Moreover, 36 percent also agree that AI has the potential for significant cost savings.
These results suggest a high awareness of what AI algorithms can do for companies in real estate as well as a strong interest to use the technology to advance data analytics.
As an early-stage investor, we are seeking startups developing tech solutions with the ability to reshape an entire segment of the market. Artificial Intelligence and advanced data analytics are one of our focuses. It gives startups the power to heavily impact the marketplace in areas such as construction supervision, energy efficiency, BIM, access to property, etc.
Regarding massive investments in the real estate sector since years and years (14% of France GDP’s), all % of operational efficiency allowed by digitalization gives to those actors amazing leverage to increase their ROI. Real Estate sector is finally ready!” added Eric Burdier, Founding Partner at Axeleo Capital.
Indeed, the opportunities brought by AI-powered software in the real estate sector are worth pursuing.
How AI is Changing the Real Estate Sector
1. Improving Lead Generation and Marketing
AI-enabled consumer apps, machine-learning interfaces, and chatbots are already benefiting a lot of eCommerce businesses by collecting customer data and helping with improving lead generation and content marketing.
Melanie Sovann, a content marketing specialist at Studicus states that “a chatbot can become an amazing virtual assistant for your clients and a great way to deliver personalized content directly to leads.”
Let’s suppose that you decided to run a social media ad targeting Millennials for a new house listing in an attractive area. You could do it the traditional way and get, say, a Facebook ad to drive traffic to the listing’s page so you could get their views and possibly email addresses.
Or, you could run a Facebook ad, but when a lead clicks on it, they would end up in a Facebook Messenger chat window with a chatbot ready to answer their questions. At that point, you automatically become subscribed to that chatbot’s newsletter, meaning that it will send you content on a regular basis.
An AI-enabled 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 chatbot can ask a lead:
- What’s your price range?
- What kind of property would you like to buy/rent/sell?
- What’s your location?
On top of that, a real estate 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.
2. Predict Property Market 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. In fact, this is exactly what Skyline AI’s Or Hiltch was talking about previously in this report.
3. Advanced Property Analysis
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. Localize is an example of such software, designed to generate the following information:
- The number of sunlight hours that a property gets in a year/month
- The rating and reviews of local schools
- The statistics about the local transportation system
- Updates on the area’s dining scene, entertainment, recreation with notifications about new and upcoming openings
- Parking space availability and prices
- Sound disturbance.
This 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.
4. Property Recommendation Based on Customer Preferences
In eCommerce, real-time product recommendation engines are becoming a reality. For example, Amazon recently launched Amazon Personalize, 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.
5. Property Management
Real estate businesses can track rental and property listings, requests for maintenance, tenant applications, and other information with AI software. These results of the tracking can help to identify the most common maintenance issues, tenant characteristics and preferences, price trends in certain areas, and seasonal availability.
The Future of AI in The Real Estate Sector is Bright
As you can see, real estate businesses can benefit from using AI in many important ways. Clearly, a lot of decision-makers in the industry are aware of disrupting potential of AI-enabled algorithms, so they’re exploring the use cases as you’re reading this.
Of course, at this point, AI is just scratching the surface of the real estate sector, but it’s reasonable to assume that highly effective algorithms are already being developed.
If you are a startup in the industry looking for funding, please fill the form here!
Estelle Liotard is an experienced content marketing specialist who writes long-form marketing content and technical documentation. While working at Trust My Paper, she has developed an interest in writing software guides to help users navigate complex apps. In addition to being a writing expert, she also teaches technical writing to aspiring writers at Grab My Essay and Best Essay Education.
