The Neural Real Estate

December 4, 2020 by Bonumic R&D Team

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Well before the industrial revolution, change and disruption have required workers and businesses to adapt to emerging technologies. Today, AI is transforming the real estate industry, allowing commercial real estate firms and local governments to leverage the power of AI to reduce costs, increase productivity, and boost revenues.

What is AI?

In a very broad sense, AI (artificial intelligence) is the technology that can develop logical conclusions the same way a human would – or sometimes even better. AI employs complex, highly developed algorithms and sophisticated analytics to predict future outcomes or behaviors.

Everyday examples of artificial intelligence

Today, it’s hard to imagine what life would be like without a computer or smartphone. While most people don’t realize it, AI is already used ‘behind the scenes’ in many parts of our everyday lives:

Why AI and Real Estate Are the Perfect Match

In the whitepaper Real Estate Predictions 2020: Conversational AI, global professional services firm Deloitte calls artificial intelligence a potential game changer for the real estate industry.

The firm notes real estate is facing significant disruption – and potential opportunities for improvement – as AI changes the way people work and live. In part, that’s because as property values continue to rise and business becomes more competitive, stakeholders such as investors and tenants are understandably becoming more and more demanding.

In response, real estate practitioners have begun leveraging AI to boost productivity, decrease costs, and minimize manual errors, all of which help to increase profits for the real estate brokerage and its clients.

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:

Yes, this already happened and it’s just a beginning. (source)

As real estate firms incorporate artificial intelligence, repetitive administrative tasks begin to decline, creating more time for work that can directly and positively grow revenues and increase the bottom line.

More than our brains can handle

In the real estate buying and selling business, at the very basic level, we think of data in just a few critical data points: location, price and volume. If we think deeper, we can find ourselves creating a data model where we involve parameters like property amenities, avg. price in the market, price history, supply and others.

However, we are nowhere near capable of taking thousands of data points in account when making a decision on our investment, which means we are not taking nearly as close to everything into account. However, our machines are capable of performing such tasks, and every day they are more capable and with every successful prediction – they are better.

The value of using AI in the real estate business isn’t only if we can make improvements, but if we can make new possibilities.

Being able to analyze thousands of data points in a fragment of a second and make a prediction — is what makes AI technology unmatchable to any solution today in the real estate market.

The challenge today isn’t what we can make out of all the data, that is on its natural road of progression, but the bigger challenge is – how do we gather the data necessary to train and improve AI technology.

The sources of real estate data are infinitive and rarely come in a format that professionals can use out of the box. Without being able to collect and aggregate data at high speed, it will be challenging to produce better results at almost any advanced AI solution, but also it will leave us with repetitive and manual processes of inputting and updating.

Real Estate data on autopilot

Before analyzing and acting upon the data – gathering that data is one of the most tedious tasks in real estate, having to enter and update the same property data over and over again. Real estate brokerages use many sources to track properties on the market and no matter where they store the data (spreadsheet, MLS, CRM), having to input and update manually is a painful process.

Instead, imagine if all your real estate offering emails and documents are daily scanned, organized, and listed without you even moving a finger — letting AI technology automatically update your property database while you do much more profitable things like previewing property, meeting with clients, and doing deals.

AI model to extract real estate data from documents and images

We have talked to real estate brokerage companies decided to create a Machine Learning model that will turn real estate flyers into searchable data:

Digital real estate flyers are one of the common ways of sharing listing information with clients, prospects, and other brokers. The challenge is that gathering them and manually entering information from a real estate flyer is labor-intensive and very expensive. Brokerage companies, depending on the size, spend 2-3/h a day extracting data from emails, flyers, contracts etc.

The good news is that because real estate flyers are actually free form digital marketing materials in a PDF or image type format, we can use AI technology to extract and upload key information from every page of the flyer such as floorplans, satellite maps, pricing, square footage, address, broker contact information and much more.

We took the challenge of creating the first AI step to extract, translate and understand real estate data from any given image or a document.

Computers don’t see images the same way we do — they pretty much understand images only as a file format, their size, their location and other. Therefore we had to teach and train the machine to be able to read and organize data the same way we do, but also to do it 100 times faster.

Unlike typical texts in articles, books and so on, which are usually completely sequential and where sentences are related with each other, a large amount of text in the flyers is scattered and unrelated with each other.

Using multiple computing process like OCR (Optical Character Recognition), we have extracted human readable text entities from 1000+ flyer images and PDFs to create a sizeable raw text material of real estate data that our models can use to learn more about property features.

Building this computing process will allow us in future to extract data from almost any file format as long as there is a visual representation of the data.

Nature of the texts in the real estate flyers – unlike typical texts in articles, books and so on, which are usually completely sequential and where sentences are related with each other, a large amount of text in the flyers is scattered and unrelated with each other.

Therefore our model needs to take in different parameters in account to extracted the targeted data points accurately.

Some of the basic entities we aimed at teaching our model to detect and extract:

On a sample of 786 valid real estate data flyers, we been able to accurately extract all the above mentioned entities for 756 flyers which is resulting in 96% success rate.

We explain this process in a more technical manner within our technical white paper hosted on Github currently.

Using different custom tools we are able to now import information into already existing spreadsheets, MLS or CRM systems that brokers and others use.

{Some image of all the real estate data sorted in an app or MLS system, or a gif image or maybe just a table with collected data}

Part 2 – Creating new possibilities

Automating real estate data extraction was a fundamental part in making a first step towards answering the following questions:

These are just some of the questions we are running a further research on and we cannot figure it all out on our own, which is why we are inviting others to join us and use our research to build and discover further – because all our research is completely open sources. (link to Github)

As we look to 2020 and envision the next decade, the traditional mantra of Location, Location, Location is becoming increasingly less relevant. The most successful CRE companies will likely be the ones that follow the mantra: location, experience, analytics. This requires companies to fundamentally rethink location, space requirements, users, and user preferences, and gradually shift to a service mindset.

Using analytics to create a better experience in process of buying, selling and operating will need to be our focus in the following decade – therefore, speed of collecting and standardizing real estate data will be essential in opening the chapter of real estate.

We believe there are large opportunities laying in a different kind of real estate – the neural real estate. Team at Bonumic is eager to innovate further and share our research in order to eliminate current limitations of real estate discovery, estimation, acquisition and speed so that we can, finally, focus on building a better built world.

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