Forty-one percent of commercial real estate companies are investing in automation and artificial intelligence to enhance budgeting, forecasting, and property management operations.
In the residential real estate segment, artificial intelligence and its subsets, such as machine learning (ML), help agents implement competitive pricing models and tap into advanced customer analytics for marketing purposes.
But is it an exaggeration to say machine learning will reshape the real estate market as we know it?
In this article, we’ll explore the role of machine learning in real estate together with a team of innovation analysts from Symfa, a machine learning development company focussing on business process optimization and automation.
3 Ways Machine Learning Is Used in Real Estate
Machine Learning In Real-Estate Marketing
With the abundance of data in the real estate sector, agents must interpret this data effectively. And here’s where machine learning solutions come in handy.
Feeding off structured and, most importantly, unstructured data, which exists in the form of property photos, videos, and descriptions, as well as customer reviews and social media posts, real-estate specialists can supercharge their marketing campaigns and bolster sales.
There are several ways ML revamps real-estate marketing:
- Personalization. ML models can analyze data on consumer behavior, preferences, and interests. Using these insights, real estate professionals could target the right audience with the right message, increasing the likelihood of conversion.
- Predictive analytics. With the help of intelligent algorithms, real estate agents can tap into sales, customer behavior, and trend analysis — and make better decisions about pricing, timing, and marketing strategies.
- Customer service. AI-powered chatbots and language models like ChatGPT can supercharge customer support, answering questions and providing information 24/7. By automating mundane work with ML, real estate professionals could dedicate more time to higher-value services and boost customer experience.
Compass, Inc., a licensed real estate broker from the United States, is one of the real estate companies that jumped on the ML bandwagon before it became mainstream.
Two years ago, the tech-savvy company launched the AI-assisted Video Studio platform that helps agents create sleek, professional-looking videos of property listings in mere minutes and distribute the clips among their colleagues and potential buyers. They also infused their CRM system with a recommendation engine that leverages machine learning to help agents engage with clients who have the highest likelihood of selling property in the next 12 months. And following ChatGPT’s launch, Compass became one of the first real-estate brands to start using the intelligent chatbot for customer service.
Machine Learning in Pricing Prediction and Analysis
Property valuation and pricing operations in real estate could benefit from machine learning in several ways:
- Property pricing. ML models can be trained on past real estate sales data to predict the price of a property based on multiple parameters. These include location, square footage, number of bedrooms and bathrooms, the property’s age, proximity to amenities, and more. Similarly, machine learning smartens up automated valuation models (AVMs), helping agents to assess the market value of a property based on its characteristics, recent data on comparable sales, and market trends. And with ML-powered dynamic pricing models, real estate professionals can advertise the same property at different prices to different customer groups and automatically adjust its value based on demand, seasonality, and other factors.
- Market research. Thanks to their ability to sift through vast datasets faster than humans, machine learning models help identify emerging market trends that might not be evident to the human eye. For example, ML can spot patterns in terms of regions gaining popularity, property types becoming trendy, or even the impact of new infrastructural developments on property prices. Also, machine learning analyzes historical data, current market conditions, and wider economic indicators to project future demand for properties of certain types or in certain locations. And you can get even more specific with ML, applying computer vision algorithms to property images and quickly identifying houses with particular features, such as high ceilings, hardwood floors, or granite countertops!
- Risk assessment. ML can analyze potential risks associated with certain investments. For instance, properties in areas prone to natural disasters might be priced differently, or the potential for an area’s economy to grow or shrink can impact future property values.
One example of an ML-driven real estate data analytics solution is Cherre’s integrated data ecosystem, which is available to their clients on a subscription basis and interfaces with miscellaneous software systems via APIs.
Modeled after a high-frequency trading platform, the cloud solution sources property data from disparate resources, including third-party services and applications comprising a real estate company’s IT infrastructure. Next, AI algorithms cleanse and unify the information and create compilations of all the data available on a particular property. The platform’s core features include automated risk assessment, ROI modeling, and real-time forecasting.
Machine Learning in Real-Estate Cost Management
Custom-trained machine learning models have the potential to increase cost efficiency in commercial and residential real estate. The effect is achieved through:
- Smarter property maintenance. By analyzing real-time and historical data from building management systems, cameras, HVAC solutions, and smart home devices, ML models can predict when the property and the corresponding equipment will require maintenance.
- Energy consumption optimization. When applied to the data from power systems and HVAC equipment, machine learning can suggest ways to operate buildings more efficiently, reducing energy consumption. Additional cost savings can be achieved through the analysis of room occupancy sensor data.
- Construction cost optimization. Machine learning improves budget planning in construction projects by analyzing historical construction cost data and augmenting the insights with current market trends. Additionally, ML models can help evaluate a supplier’s reliability and advise on when to purchase building materials and in what quantities.
One of the best-known examples of ML-driven cost optimization in real estate is the Edge office building in Amsterdam. Packed with IoT sensors, Edge collects heaps of data on space utilization, energy consumption, and building maintenance, while AI algorithms adjust the systems’ operations for maximum efficiency. As a result, the intelligent building uses 70% less electricity than commercial property of the same size.
On a Final Note
As you can see, machine learning in real estate can take many forms — from advanced analytics solutions to next-gen marketing tools.
However, the reliability of ML solutions depends on the quality and quantity of training data. And it is crucial to continue to update ML models post-launch, too.
Additionally, despite the tremendous automation potential, machine learning solutions produce the best results when working alongside human experts. That’s why real estate companies should treat ML like an augmentation and not a replacement for their workforce. At least for now.
