Machine Learning in Real Estate

By Brian P.

Machine learning has become a new topic which has captured the interest of a great deal of people worldwide, but what exactly is machine learning? Machine learning is, as the name implies, a way for artificial intelligence to learn on its own. Though the process of machine learning is different now, before people use to data mine and tried to use programs to help other people understand it, machine learning on the other hand “The process of machine learning is similar to that of data mining. Both systems search through data to look for patterns. However, instead of extracting data for human comprehension — as is the case in data mining applications — machine learning uses that data to detect patterns in data and adjust program actions accordingly” (Rouse, 2017). Programmers are no longer concerned with helping others understand massive datasets, rather just helping the computer understand it so that it can do useful tasks with that data for us. According to the same article, there are 2 types of machine learning, supervised and unsupervised. The main difference is that supervised applies what is known from the past while unsupervised is all new data in which the AI has to make inferences.

One thing known for certain is that an AI making use of machine learning is significantly faster than how long it takes a human to learn. For instance, in the field of real estate for one person to master selling techniques for a huge diversity of customers “it takes 10,000 hours for one person to master” (Loughhead, 2016) an AI however would take a fraction of that time. The article later mentions how “the salesperson whose job can be most automated will be the first to go” (Loughhead, 2016). Though AI’s may lower costs for many sales firms dramatically, as you don’t have to continually pay them a salary to work, real estate may be safe from AI’s for the time being. A great deal of making a sale in real estate is based on the relationships built with the agent and the human interactions between the 2 people. Though the greatest disadvantage as of now is the lack of human interaction, other benefits are chatbots. Chatbots are able to help answer questions quickly for both clients and employee. Though they lack the ability to give a human answer, it is completely within the realm of possibilities as technology continues to evolve and become more sophisticated.

One tool currently being used is known as Einstein. The way this particular AI works is by reading through huge amounts of data and giving recommendations to the agents as far as reaching out to new potential customers. Einstein right now is helping the salesforce become much more efficient, but the possibilities for it are much greater than an advice machine. Another AI, currently in use by Zillow, is Mia. Mia is a CRM automation system. CRM stands for customer relationship management and “Mia plays a critical role in automatically generating requests for reviews, new customers and loyalty” (Halliday, 2016). Mia is able to maintain the CRM for Zillow, which has increased customer satisfaction and loyalty for the real estate company. The company has been getting more reviews and better reviews since the AI came into play and assisted the company. Drawbacks though for Mia however are the high cost of making an AI as such and the limited scope in which it can work. Though customer relationships are important, an AI of this magnitude feels like it should be able to do more.

As technology continues to develop and become more and more sophisticated, so too will the AI’s and the rate at which they learn. Though many fear that people will lose all their jobs to AI’s, not all have this fear. Elon Musk believes that AI’s are going to be helpful to humanity, believing that “people will have time to do other things, more complex things, more interesting things” (Osborne, 2016).

References
Halliday, A. (2016). Signpost Joins Zillow® Tech Connect Program. Reality Biz. Retrieved from http://realtybiznews.com/signpost-joins-zillow-tech-connect-program/98734582/
Loughhead, T. (2016). What the rise of machine learning means for real estate sales. Inman. Retrieved from http://www.inman.com/2016/12/14/rise-machine-learning-means-real-estate-sales/
Osborne, S. (2016). Elon Musk says people should receive a universal income once robots take their jobs. Independent. Retrieved from http://www.independent.co.uk/news/people/elon-musk-universal-income-robots-ai-tesla-spacex-a7402556.html
Rouse, M. (2017). Machine Learning. WhatIs. Retrieved from http://whatis.techtarget.com/definition/machine-learning

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