Conducting data analytics for commercial real estate firms

Key quotes:

• "According to Manfre, the data supplied by AI can also allow property owners to do “analysis on how they need to invest in their buildings, what they can invest in and what their returns are ultimately going to be.” The recent CRE Innovation Report shows that attitudes of decision-makers are positive and that the present use cases of AI in the commercial real estate sector are already quite impressive."
• "These results suggest a high awareness of what AI algorithms can do for companies in real estate as well as a strong interest to use the technology to advance data analytics."
• "AI-enabled consumer apps, machine-learning interfaces, and chatbots are already benefiting a lot of eCommerce businesses by collecting customer data and helping with improving lead generation and content marketing."
• "AI-powered software can be a solution for improving the ability of a real estate business to provide accurate, comprehensive information about property to clients."

Key quotes:

• "New analytical tools with predictive capabilities will dramatically affect the future of urban development, reshaping the real estate industry in the process."
• "Real estate tech companies such as SpaceQuant, Mashvisor, SMartZip, Enodo Score, and Zillow all rely on large datasets for simulating future investment outcomes or assessing potential market trends."
• "New algorithmic logic enables predictions based on datasets aggregated over the past 20 years by government websites or large real estate data platforms such as CoStar."
• "By looking at the statistical meaning of the data, the real estate industry is now invited to look at the probability of deals success.” If predictive capabilities open new horizons to urban development and real estate, data accuracy is the heart of the matter."
• "After interviewing Jasjeet Thind [4], VP of Data Science at Zillow, the explanation is clear: the number of datasets is such that processing them and adapting the infrastructure to their ever-growing size makes it hard to achieve long time span prediction, accuracy, and a large set of property types."

Key quotes:

• "The calculation is based on variety of public data records, market information, user data points by using Big Data analysis formula developed in-house."
• "Additionally, they encourage consumers and homeowners to expand online data by doing other investigations such as comparing market prices for around areas, working with a real estate agent, getting an appraisal from an expert and visiting the house."
• "The analysis combines several main components, including property characteristics in the area, crime rate, quality of life, pollution, race and ethnicity, population growth, family household, house value, business field, employment, schools and future home value."
• "Latest Big Data collections made it all possible for real estate website to provide that information to potential buyers."
• "Based on the data visualization, population size in Tarzana is higher by 3,000 residents than in Calabasas, as shown in Figure 6."
• "Real estate agencies, such as Zillow, estimate values based on Big Data collection tools and using their algorithm.They combine information from variety of sources and provide insightful information to buyers, sellers or brokers."

Key quotes:

• "The modern-day investors search for, and demand, a relationship with their commercial real estate (CRE) sponsor, thereby enhancing their confidence in the partnership."
• "The secret to being successful in a fast-changing environment and meeting the expectations of investors is reacting ahead of time and developing a better understanding of what present-day real estate investors are looking for."
• "Technology has completely revolutionised the manner in which real estate sponsors and investors interact, communicate and handle different aspects of business."
• "Harnessing the power of technology correctly permits firms to mitigate potential future issues and focus their attention on developing the business, whilst also developing more efficiencies and culminating in a comprehensive experience for the investor."
• "Technologies such as AI, Blockchain and Machine Learning operate more efficiently by being able to access and use data that is structured."
• "Experiencing fast and smart transactions, fulfilled in a cost-effective manner has elevated the demands of these investors within the real estate industry."

Key quotes:

• "There is huge pressure on the real estate industry to unlock the potential of big data and incorporate machine learning and evidence-based approaches in their workflows."
• "This presents a specific problem for real estate — how do we harness large data sets to understand individual sub-market performance? Automated valuation models help us understand the property market in the present, by helping to assess a fair transaction price for a deal today."
• "Alternatively, PropertyQuants offers a rapid 11-week live interactive online course specifically on “Applying Data Science and Machine Learning to Real Estate.” It includes a bootcamp module to help participants get started in the world of programming and data science, which is followed by real estate data science and GIS modules, thus covering all the major techniques listed above."
• "Conclusion The real estate industry is likely just at the beginning of a significant shift towards greater use of data and data-driven decision making."
• "There is now the opportunity to learn these techniques and methods — specifically for real estate — and investing the time to upgrade could benefit a range of participants."
• "Real estate researchers could begin to use data and machine learning to produce game-changing insights and unlock the value of large datasets."