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These Are the Types of Data Changing the Real Estate Industry

Data is at the heart of real estate, in its appraisals, price indices, forecasts and sales histories. But, until only recently, the industry hasn’t done a lot with this data. Appraisers used in-person inspections prone to human error and misjudgment. Investors and developers used an imperfect mix of gut feeling and retrospective data to make significant decisions. And consumers, starved for hard data, chose their real estate agents based on unreliable third-party recommendations.

As Nobul CEO Regan McGee put it to Digital Connect, “The real estate industry is awash in data, yet we as a sector have not really embraced the tools available to leverage that flood of information.” McGee is one of the innovators working to change that (more on his company below) as real estate on the whole moves to embrace new analytical methods and data sets.

This article explores a few ways that data is transforming the real estate industry.

Capitalizing on Big Data in Property Analysis

Big data refers to large stores of data sets too complex for everyday software to mine and analyze. However, real estate investors and developers can mine these mountains of data using machine learning and AI. And the rich insights contained therein are vastly improving real estate decision-making processes.

Big data in real estate contains a heady mixture of traditional and non-traditional data: everything from the standard vacancy rates and rents per square foot in a neighborhood, to unconventional metrics like public bathrooms per square mile and average user ratings of bars in the neighborhood. In combination, these variables paint a strikingly accurate picture of where potential lies in real estate – in other words, where to invest and develop.

“Match Score” Algorithms to Empower Consumers

Historically, real estate consumers have been shut out of a lot of data – valuable information that could have made their experience easier and cheaper. Nobul, mentioned above, is one of the companies acting to amend that industry opacity. And it’s using data to get the job done.

Nobul uses a proprietary algorithm to sift through mountains of data related to real estate agents: location, sales histories, verified reviews, etc. Consumers can access these insights to find relevant, quality real estate agents that share their values and match their budget.

It’s one example of how data can be used to create a more equitable, transparent experience for real estate consumers.

AVMs for Lightning Quick Property Valuations

Automated Valuation Models (AVMs) are real estate appraisal tactics that leverage statistical modelling to provide fast, accurate property valuations. The best ones pull from diverse data sets and have been shown to outpace human appraisers in both accuracy and efficiency.

Moreover, AVMs are a hedge against fraud, malfeasance and general bias in the appraisal sector. Because statistical models can’t be influenced by money or subjectivity biases, some experts believe they constitute a fairer appraiser than their human counterparts.

Data has always been a part of real estate. But with innovations like big data analytics, match score algorithms and AVMs, innovation is finally catching up with real estate data.

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