By Dave T. Garland
Editor’s Note: This is the first article in a series on Big Data analysis as relevant to the real estate industry. ‘Big Data’ analytics is the practice of using machine learning to process and draw conclusions from high-volume, high-variety, unstructured information. But what does this mean for brokers and agents?
Data analysis, in general, provides powerful tools and advantages for real estate agencies, and Big Data analytics is a shining improvement upon this concept. Big Data analytics offers a way to combine the “big picture” with individual details within a market to generate a deeper understanding of the variables that make it function.
At its most basic level, data analysis makes automatic price estimation and assessment of real estate possible: think automated valuation models (AVMs) and comparable market analysis (CMAs). Information that is usually incorporated in this type of “basic” data analysis includes: square footage of the building; lot square footage; number of bedrooms and bathrooms; central air; amenities; number of fireplaces; garage size and basement size; as well as location and information about the surroundings.
This data is collected from as many sources as possible before being processed. By comparing these criteria, specially designed software and algorithms can automatically determine the approximate price of the property in question. But at this volume and uniformity of available information, we are still firmly in the realm of “traditional” data analysis. These algorithms are too rudimentary, and the amount of data is too small.
What makes Big Data different is partially the sheer magnitude of gathered information. To qualify as “Big Data,” the information collected must be greatly varied and unstructured, incorporating as many different sources as possible, such as: available amenities; average neighborhood income; amount of interested buyers; duration of vacancy; number of parks in the surrounding area; distance from the center; businesses in the area; typical buying behavior in that specific location; and far more. This does not even begin to cover the available sources and types of information that can be used. Any relevant—as well as some seemingly irrelevant—data can be incorporated into this process.
To put the eclectic scope of Big Data into perspective, there are actually services on the market that use Big Data analytics to allow sorting of real estate listings by incredibly diverse factors, such as: average age of inhabitants; “neighborhood spirit;” stylistic preferences; and artistic inclination. So, if you wish to find a “hipster” or “artsy” neighborhood, for example, this is easily done through the accumulation of multiple data points, the normalization of the data from multiple sources and the application of mathematical formulas across the datasets to establish outputs such as trends and projections.
By looking at such “strange” examples, it becomes easy to recognize that the potential of Big Data lies in truly creative uses of the available information. Here, we can briefly look at a specific example. Relocality is a specialized online service that actually matches prospective buyers with a neighborhood best suited to their lifestyle based on information provided on their Facebook profile. This service takes advantage of both the data available in the real estate market, as well as the massive social datasets on Facebook itself.
From here onwards, possible applications of Big Data analytics in a real estate brokerage essentially break the boundaries of our usual thinking processes. For example: predictive analysis and marketing. This means that software heuristics (or rules of thumb) can discover properties with a high probability of being sold in the near future, and target this specific audience with great results.
The same principle can be applied to selling properties, wherein Big Data analytics are used to generate directed marketing strategies with significantly higher rates of success. Such predictive functionality can also be applied to estimating the ROI on real estate investments, as well as determining which are most likely to be sold quickly. And yet, these are still only a few of the possibilities available.
While this may seem crazy at first, such analytics are verifiably beneficial with ever-increasing usage and applications. However, we must also consider what will happen to traditional businesses and the associated additional costs if this practice becomes the status quo.
A direct implementation of Big Data analytics into an enterprise requires extensive organization, a specialized IT department, and large amounts of funding. This means that most small- and medium-sized businesses cannot feasibly implement any such solutions themselves. Fortunately, there are third-party services available that specialize in providing Big Data services. For companies with lower volumes of data and fewer expendable resources, this is actually a superior and desirable solution in every aspect.
And yet, despite all the recent hype, these features and uses are merely a shell of their future potential. As Big Data analysis turns more pervasive and comprehensive, and information becomes more accessible in both depth and scope, businesses may quickly find themselves struggling to remain competitive. Therefore, it may be advisable to get on board early even with the most rudimentary implementations.
While this may not seem significant at the moment, relying entirely on traditional methods is unlikely to hold up in the future. Given enough time and research, Big Data analytic methods will be able to outpace human decision-making entirely. It is better to be part of, rather than struck by, the flood of innovation.
In future articles of this series, specific techniques, theories and applications of Big Data analytics will be addressed. Readers will become familiarized with the opportunities available and given a solid foundation for proceeding with their own research and solutions.
Dave T. Garland is a principal with Rainmakers Group. Formore information, please email email@example.com or call 650-353-7757.
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