Machine Learning For Commercial Real Estate

Machine learning infrastructure consists of the resources, processes and tools required to create, train and deploy machine learning models. You can also call it a component of MLOps or AI infrastructure.

Since the beginning of the industrial revolution, the entire planet has witnessed a shift in skills which has ultimately led in ever-changing professions and the need for newer skills to keep up with emerging technologies. According to the McKinsey Global Institute, they "will present multiple benefits in terms of increased productivity, GDP expansion, enhanced organizational performance and new economic success, but they will also alter the required skills of human labor."

The continued implementation of Ai and automated systems will transform and interrupt the way workers operate, particularly in industries such as real estate, financial services and banking. Research backs this up. According to a PwC report, machine learning could contribute more than $15 trillion to the universal economy between 2017 and 2030. Furthermore, 37 percent of organizations have already implemented some form of AI into their operations in 2019.

The real estate industry is well positioned to use AI and automation technologies to boost productivity, cut costs and reduce errors. We expect the number of workers performing manual tasks that only require basic cognitive abilities to decrease. On the other hand, as the interest in social and creative skills increases, so will the percentage of technological and managerial experts.

Machine learning Use Cases in Real Estate

Marketing and sales

Businesses could use AI for lead management to show personalized promotions or messages to consumers or lenders. It could also be used to analyze existing market data in order to suggest properties for brokers to buy or sell based on the broker's sales history and intent. Businesses can use Natural language processing (NLP) algorithms to fully comprehend what makes a difference to customers in a given zip code and what makes a given house stand out from the competition. Customer interaction analysis during the lending or home-buying process would be beneficial for customer service analytics.

Financial and loan modeling

Using extraction and classification technology, manual and monotonous processes such as financial modeling and data entry can be automated, allowing property investment analysts to save time, improve productivity and reduce errors. This would also free up man hours for higher-value activities like improving customer experience and doing creative tasks. Furthermore, because of its pattern recognition capabilities, AI can be useful in risk forecasting and manual property appraisal by taking into account factors such as transportation and infrastructure upgrades, changes in the environment, local community and other amenities that affect property value.

Data management

As a company grows, it must deal with large data containing lease abstracts, legal papers, offering memorandums, tax reports, ownership history records, property appraisal reports and zoning regulations. As data volumes grow, artificial intelligence could be used to classify and store documents, as well as proactively alert when a new appraisal is due, or a property's popularity skyrockets. A machine learning activated search could help people quickly analyze the massive amount of search data that is available by preprocessing and marking valuable meta data for all real estate documents in a company's repository.

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