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Advanced Analytics & Artificial Intelligence in Real Estate Advanced Analytics & Artificial Intelligence in Real Estate 2021 Prepared by Investa 2 3 Contents Part 1: Foundations 4 Part 3: Challenges and risks 17 Property is the largest Introduction 5 What are the challenges with Artificial Intelligence? 18 What is Artificial Intelligence (AI)? 6 a. Demonstrating a return asset class in the world on investment 18 What is Machine Learning (ML)? 6 b. Scarcity of technical skills 18 c. Data quality and quantity 19 and the time is now What is Deep Learning (DL)? 6 d. Integration 19 What are the different types What are the risks of Artificial for Investa to bring of Artificial Intelligence? 6 Intelligence? 20 a. Narrow (or weak) AI 6 a. Cybersecurity 20 b. Artificial General Intelligence b. Legal responsibilities 20 advanced analytics and (AGI) or strong AI 7 c. Failure 20 d. Regulatory non-compliance 20 What is the history of Artificial e. Reputation 20 artificial intelligence Intelligence? 8 Part 4: Applications and use cases 21 Why is Artificial Intelligence into the value chain popular now? 9 What are the applications of Artificial Intelligence? 22 What are the benefits of a. Real estate 22 Artificial Intelligence? 9 b. Commercial real estate 24 a. Reduced “human error” 10 b. Less risk 10 Artificial Intelligence as a c. 24/7 10 Service (AIaaS) 25 d. Repetitive tasks 10 e. Digital assistants 10 What is the future of Artificial f. Faster decision making 10 Intelligence? 25 a. The AI-enhanced organisation 26 Part 2: Force multipliers 11 b. Autonomous everything 26 c. Pervasive knowledge 26 Force multipliers of decision d. Enhancing the human intelligence 12 experience 26 Six force multipliers 12 Conclusion 27 a. Explore 12 b. Evaluate 12 c. Experiment 15 d. Engage 15 e. Execute 16 f. Expand 16 Investa Advanced Analytics & Artificial Intelligence in Real Estate 4 1 Foundations 5 Introduction thereby provide a better user As our models and data sets experience for tenants, investors become more sophisticated over Big data, Artificial Intelligence and customers – or risk being time, we will see increasing levels (AI), proptech and digital left behind. of return on the investment in this transformation get a lot of capability. attention and rightly so. At Investa, we are actively investing in data and decision Part 1 of this article defines AI, Data from Google shows that all intelligence. We believe that it Machine Learning (ML) and Deep of these terms have experienced is first about asking the right Learning (DL) and looks back at its significant growth over the past questions, then having the right history, how we got here and how five years in particular. people, data and technology to we can benefit. Using just the lens of the property find the answers. Part 2 looks at six force multipliers industry, it’s clear that many Investa believes that organisations that businesses use to drive a ROI organisations in property are will derive a real competitive from data and AI. sitting on vast, ever-growing, advantage by building decision quantities of data. Part 3 focuses on some of the intelligence capability, and challenges and risks that come But it’s not just about data. that the opportunity is “now.” with AI. In the next few years, data will Forward-thinking property Part 4 outlines some specific become increasingly available. companies will progressively real estate use cases of AI. Leaders in the real estate industry be able to see and realise value will need to evolve quickly to that the general property market unlock meaningful insights and cannot see. Part 1: Foundations Source: https://trends.google.com/trends/?geo=US Investa Advanced Analytics & Artificial Intelligence in Real Estate 6 1 Foundations 7 What is Artificial What is Machine a Narrow (or weak) AI Intelligence (AI)? Learning (ML)? Narrow or weak AI operates AI refers to the wide-ranging Understanding the difference within certain limitations and is a simulation of human intelligence between AI, machine learning, and simulation of human intelligence. by machines. deep learning can be confusing. It’s often focused on executing a single (or narrow) task really well. The goal of AI is to solve the kind of Venture capitalist Frank Chen problems or perform the types of made this distinction: Much of narrow AI is powered tasks that are usually completed by machine learning and deep “AI is a set of algorithms and by humans, with our natural learning. This AI can often intelligence to try to mimic human intelligence. outperform humans in a specific intelligence. Machine learning is niche task – especially if the There are two main types of AI: one of them, and deep learning task relies on historical knowledge The first is “Weak AI” or Artificial is one of those machine learning and data. Narrow Intelligence; and the techniques.” second is “Strong AI” or Artificial Some examples of narrow AI ML feeds a machine (computer) General Intelligence. include technology that is broadly data and uses statistical used: Google, Siri, Alexa, self- AI will typically demonstrate some techniques to help it learn how driving cars, chatbots, translators, of the following characteristics: to progressively get better at social network recommendations. planning, learning, reasoning, a specific task. ML consists of The most famous example here is problem solving, perception, both supervised learning and IBM’s Watson. motion, manipulation and to a unsupervised learning. lesser extent, social intelligence At Investa, we have been using and creativity. What is Deep Learning (DL)? AI/ML in our business for years. For example, we use cybersecurity According to Artificial Intelligence Deep Learning (DL) is a type of AI software to detect potential : A Modern Approach, by Stuart machine learning that runs data threats, and search engine AI Russell and Peter Norvig, AI can through brain-inspired neural across our internal files and be defined by four fundamental network architecture. The neural emails. approaches: networks allow the machine to go deep in its knowledge-base, In addition to these baseline use 1 Thinking humanly making connections and cases, we are focused on narrow 2 AI use cases that make an impact Thinking rationally weighting data. b Strong AI systems tend to be on our business, customers, Artificial General Intelligence 3 more complicated and are often Acting humanly investors and employees. (AGI) or strong AI What are the different types the subject of science fiction. We 4 Artificial General Intelligence is Acting rationally of AI? For example, identifying new the creation of a machine with are not yet deploying AGI into our development sites to acquire, businesses, but it is definitely on The first two approaches centre AI falls typically under two human-like intelligence that can or what tenants are the best fit our radar for the future. around thought processes and categories: Narrow (or weak) AI be applied to any task. It seeks to to occupy our buildings. This reasoning, the latter two focus and Artificial General Intelligence replicate the cognitive abilities of application of narrow AI allows us on behaviour. (AGI) or strong AI. the human brain. to augment what we do as humans and remove some of the repetitive When presented with an unfamiliar tasks in our business. task, a strong AI system should be able to apply knowledge from another domain or task to find a solution autonomously. Investa Advanced Analytics & Artificial Intelligence in Real Estate 8 1 Foundations 9 History of Artificial Intelligence 143 14 Warren McCullough and Walter Pitts Donald Hebb publishes the book, publish A Logical Calculus of Ideas The Organization of Behavior: Immanent in Nervous Activity. The A Neuropsychological Theory. The book paper proposed the first mathematic proposes the theory that neural model for building an artificial neural pathways are created from Artificial intelligence had its start in antiquity network. experiences and that connections by mathematicians and Greek philosophers. between neurons become stronger But when we think of artificial intelligence in the more frequently they're used. Why is AI popular now? modern-day terms, its history spans less “Investa understands the benefits that than a century. Here is a timeline of some of the key milestones in AI. Increased computer power has been a significant driver for AI, can be derived by leveraging both the especially infrastructure speed, intelligence in our data, as well as availability and scale. What used 156 154 152 150 to be run in specialised labs with in our people, to ultimately drive access to supercomputers, can The "Dartmouth Summer Research The Georgetown-IBM machine Arthur Samuel wrote the first Alan Turing publishes Computing now be deployed on the cloud at business value.” Project on Artificial Intelligence" at translation experiment automatically game-playing program for checkers Machinery and Intelligence, proposing Dartmouth College is organised by translates 60 carefully selected with sufficient skill to challenge a what is now known as the Turing Test, a fraction of the cost. Allen Newell (CMU), Herbert Simon Russian sentences into English. human player. a method for determining if a machine Jonathan Callaghan, CEO, Investa (CMU), John McCarthy (MIT), Marvin is intelligent. Minsky (MIT) and Arthur Samuel (IBM). The term "Artificial Intelligence" Harvard undergraduates Marvin Minsky Thanks to critical mass and the was coined by John McCarthy. This and Dean Edmonds build SNARC, the conference is considered the birth of first neural network computer. awareness of natural language artificial intelligence. Claude Shannon publishes the personal assistants, like Siri “Before Empirical CRE entered human to compute. Humans excel paper "Programming a Computer for Playing Chess." and Alexa, AI has also become the market (commercial real at creativity and ambiguity – using Isaac Asimov publishes the "Three mainstream. Large players are estate), data providers focused on the insights from AI to support Laws of Robotics." investing heavily in it and AI has limited groups of properties using decision making and negotiation.
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