AI applied to real estate - soon a reality.
With ChatGPT, Artificial Intelligence is on everyone’s lips. But concrete use cases are nowhere to be seen. In real estate, the only exciting use currently found is realtors using ChatGPT to create adverts.
Yet, it is bound to change. ChatGPT release was the Sputnik moment of the AI race. Moon landing is imminent. OpenAI is just a company among others, with Cohere, Github, HuggingFaces, DeepMind, or even Google, Baidu, Nvidia, Microsoft, Meta, Tencent, or IBM also being part of the race. The amount of private investment in AI went from $6 billion in 2013 to $176 billion in 2021 according to the AI lab of Standford. And all these companies which are developing solutions and products and raising funds, won’t leave out an industry as big as real estate.
Artificial Intelligence is on its way to become a powerful assistant, able to help human operators to discriminate data, organize it, synthesize it, and make initial recommendations. The issue is that there is no qualitative data in real estate. Documents, rental agreements, and contracts have surely been digitized but the texts have, on the other hand, not been digitized or analyzed. Plans are fixed with reference frames that are not organized at the scale of the assets. Even financial data is divided and can hardly be linked with public demographic, consumption, or transport data. Real Estate won’t be able to benefit from AI as the industry still hasn’t gone through a data revolution.
That’s why AI should first be used as a data extraction tool to catch up with more data-driven sectors (manufacturing, transportation, etc.). Only then it will be able to use this data to optimize asset management (acquisition, transfer, redevelopment) with a strong impact on returns. Private equity and VCs have just entered this new era as they are now choosing target companies based on quantitative criteria. Far from glittering announcements, the main work is now happening in the shadows. And the first to commit to it will have a decisive advantage after the phase of compression of interest rates which was a great equalizer of performance.
Welcome to the magical world of AI. Or is it all smoke?
Artificial Intelligence is a field of computer science that makes a computer system that can act like human intelligence. It uses machine learning algorithms, such as reinforcement learning and deep learning neural networks to operate.
The goal of Machine Learning is to enable computer systems to learn from data, so they can generate accurate results without the need for human intervention. It is all about extracting knowledge from data. It involves training a model on a large dataset, so it can learn how to correctly answer similar questions. The technology will have to learn a set of rules, like being able to differentiate different typefaces a contract can hold, or understand the difference between a title and a paragraph, to be able to work properly.
To put it simply, it simulates human learning patterns to learn, grow, and develop itself by continually assessing data and identifying patterns based on past outcomes. It basically makes a guess based on previously seen examples. These examples are provided by humans, turning what you would read as letters into explicit content for the machines, which in turn, will train on their own. And with each mistake, the system will correct itself to get it right the next time around. The learning part of Machine Learning is all about figuring out how to make the best possible guess. With practice, the machine gets smarter, to the point where we, humans, can no longer understand how it works. A big black box of some sort.
Within the realm of machine learning, you will find words such as deep learning, neural networks, or applications such as natural language processing or image recognition. An innovative world often known as just: Artificial Intelligence.
But despite the magic effect it has, it doesn’t hold the secret formula to have a mind of its own. AI still heavily relies on human intervention through what is called reinforcement learning – or learning thanks to human feedback.
The currently popular tool ChatGPT, while being an impressive tool to answer any questions, requires the help of Open AI’s thousands of developers to know right from wrong. A long and tedious work awaiting AI developers and others.
So, in the end, is AI the perfect, magical tool? Not yet. AI technology is still in the beginning stages, learning to keep on getting better. Does it have all the answers? No.
Is it able to correctly answer all the time? No.
We need to accept that AI is currently working within a range of errors and that it makes mistakes. Just like a human would. Because in the end, it is a duplicate of the humans developing it.
Why should you bet on Natural Language Processing?
A useful technology to handle large amounts of documents in an easy manner. It relies on large language models (LLM), which are types of machine learning models that can handle a wide range of NLP use cases. NLP refers to a branch of AI giving computers the ability to understand texts and spoken words in very much the same way human beings can. Meaning that technology is actually being able to read and understand your data. And thus, able to search and extract useful information, automatically index your documents, or analyze a large quantity of data at once. As the real estate industry has a written culture (leasing agreements, contracts, on-site inspection reports…) rather than a quantitative one, NLP is crucial.
Not only will it help you gain time, as you will no longer spend hours looking for specific information, but instead a simple query will suffice. But it will also improve the accuracy of your data, by checking the veracity of information coming from diverse sources.
NLP is a side of AI that is often not talked about enough, considering the advantages it brings to the table. Especially, in the real estate industry, where a large amount of ever-evolving documents have to be treated, inputted in a database, checked, and then updated times and times. Data extraction is a costly, time-consuming process thanks to a large volume of lengthy but crucial documents. To manually sort it and search is bound for human error and idle work, with dozen of excel sheets to go through. With technology, your extraction time will be reduced to seconds so you can use accurate insights to make informed decisions. Another perk of this technology is that it scans and reads documents, highlighting missing fields or errors, and empowering teams to work with accurate information.
NLP is a powerful tool for real estate managers, as it can help them to extract valuable information from large amounts of data, make sure of its accuracy, and keep it fresh and updated. All of that, in order to support asset managers make better decisions about their assets.
Made in AI.
Asset management doesn’t use plans efficiently because it is too expensive to create and maintain. How does AI-powered construction documents in minutes sound? Unlikely you say?
Not so much with swapp.ai or Finch. Their AIs are able to deliver accurate, detailed, and complete construction documents faster than ever, or anyone. An “AI-driven architecture” which can automatically generate digital models including distribution networks, for example, handle real-time alterations and the production of technical parts.
A technological revolution that will change the way the whole industry works. Real estate developers and architects can now rely on technology to automate the construction documents phase and spend less time managing production processes. For example, Swapp indicates that it has already contributed to the automation of more than a hundred projects with a projection time to generate models of less than 15 minutes. A real gain of time compared to the usual weeks or months when done manually. Soon we will be able to deal more efficiently with plans, even old-fashioned blueprints.
Discover how Stonal uses Artificial Intelligence to extract and classify data to ensure the liquidity of your assets.