How Machine Learning Is Revolutionising The Real Estate Sector

In the decade gone by, digital transformation and its associated technologies have become regular boardroom discussions. If we prepared a word-cloud of those discussions, data would likely outsize others, meaning it is arguably the most frequently used word in enterprise strategy brainstorming sessions. Given the vast pool of available data points, Machine Learning in real estate makes for an interesting use case and we’ll uncover a few of them.

Casually used as a synonym for Artificial Intelligence (AI), Machine Learning (ML) is a branch of AI that leverages data and algorithms to build software applications capable of emulating human intelligence in solving complex problems.

Observing the real estate sector – world’s largest asset class and the second least digitized sector in the world (as per Morgan Stanley Digitalization Index) – through a ML prism, a spectrum of opportunities waiting to be harnessed is revealed.

Machine Learning Applications in Real Estate

Advanced Pricing Models

Real estate pricing is a fascinatingly complex problem to solve. While it should be determined by a multitude of factors internal and external to the industry, it is traditionally defined by only a few:

• Historical data analysis
• Unit size
• Existing demand
• Intuition and experience

If we unlock the potential of AI and Machine Learning in real estate investment, we can include additional factors that might appear trivial at first but will help in accurate price determination.

• Median household income
• Vacancy
• Average occupancy
• Points of interest
• E-commerce serviceability
• Crime rate
• Commercial outlook of the area
• Public transport
• Predicted appreciation
• Employment opportunities
• Average monthly sunlight hours
• Rating of neighboring schools
• Parking spaces
• Expected noise levels (construction, traffic, etc.)
• Dining, entertainment, and recreation options

We can keep adding to the list and the calculation gets more complex with each data point. This requires a consistent, scientific, and self-learning approach to pricing that experience and intuition cannot deliver. Large, complex datasets require an iterative, self-improving calculation algorithm – exactly what real estate industry automation via AI and ML delivers.

Marketing

In today’s era apps, chatbots, and similar interfaces allow companies to collect considerable amounts of customer data. Machine learning models can process these vast amounts of unstructured data to deliver data-driven insights, which then realtors can use to focus their efforts on eligible customers. Focusing on converting a list of genuinely interested customers rather than cold calling and sending emails to a randomly collected list can increase overall engagement and conversion rates.

Furthermore, having sufficient information about the size, area, location, budget and overall housing preferences of a customer, allows real estate agents to engage in more personalized conversations with customers and provide a better customer experience from the outset.

ML Driven Experience Centers

Visual appeal is a key factor while making a real estate investment decision. It is as important as it is difficult to quantify. Site visit is the most straightforward approach to solve this but is often impractical. Machine Learning based image recognition models can let your clients experience a property’s surroundings from thousands of miles away. Deep Learning models can reveal key information by accessing millions of images in no time while convolutional neural networks can extract, analyze and present key visual aspects of a property such as:

• Neighbourhood activity levels
• Size and quality of streets
• Parking space availability
• Street lighting
• Greenery
• Population density
• Proximity to parks

Besides capturing the visuals, these models can improve themselves by determining the role each factor played in the final decision of the client. It is an important feedback loop that will feed future decisions of all stakeholders.

Recommender systems

This is a key aspect of Machine Learning that is yielding exponential rewards across industries. Recommendation engines are all around us, whether we recognize them or not. YouTube advertisements, sponsored search results, Amazon’s product suggestions, Tinder’s matching algorithm, Netflix recommended for you section are all examples of ML advancements.

Real estate companies can draw from these use cases for profiling their customers and listed properties. They can then match a customer to a set of properties based on selected preferences, such as location, size, budget, points of interest, and much more. They may also do a complete financial profiling of their customers to predict the properties a customer may want.

Given the inherent complexity of real estate sector, we are just scratching the surface here. As you embark on the digital transformation journey of your real estate business, you will uncover a treasure trove of information that would enable you to make informed strategic decisions.

How AI Superior can help

At AI Superior, we are experts in building end-to-end Machine Learning solutions to elevate real estate businesses performance to the next level. Whether you are looking to leverage machine learning for property price prediction or to predict key market trends, we can help you leverage your data to increase your return on investment.

Get in touch with us to find out how we can create a tailored solution that addresses your business needs