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The real estate industry has always used data. From setting prices to finding properties, data helps take the guesswork out of a business that can involve a lot of money changing hands.
What has evolved over the years is how that data is analyzed. Business intelligence in the world of real estate has made it easier for organizations to make informed decisions, and it has also helped make information clearer to buyers. Here’s a look at how data is involved in the real estate industry, and how it continues to progress.
Inside the data
There is a lot of data involved in selling a home. A price is set based on comparable recent sales or what’s on the market at the moment. There are average and median sale prices to consider, as well as days listed on the market and factors that played into homes that never ended up selling. A Comparative Market Analysis (CMA) considers elements like size, location, condition, and amenities in assessing a home’s worth.
A home that is overpriced won’t sell quickly, and if the price is too low, there are financial opportunities being left on the table. Making data-driven decisions allows all parties to feel that they have done everything they could to sell a home for a fair price that everyone is happy with.
How realtors can use data
What started as a tool to make it easier to crunch the numbers has become a way to assess many more elements of a property, or a potential property. Real estate companies can use analytics to determine the earning potential of a location for businesses based on information like traffic, population, rental incomes, and the ROI of other businesses in the area. Just as it can do in other industries, analytics can also help real estate organizations keep track of their competition.
Real estate has been an area where predictive analytics is nothing new. The information such as earning potential has allowed agents to make informed predictions about areas worth investing time in. As the technology has gotten more sophisticated, the predictive elements have become even more reliable. Real estate organizations can use real-time financial and market data to get the most accurate picture possible about land or other valuable properties.
Artificial intelligence (AI) is another technology that real estate organizations have been using more frequently to their advantage. Some real estate agents use ChatGPT to draft descriptions of listings, and Zillow has a proprietary predictive AI model that estimates the value a property could sell for.
Customers have interacted with the technology in a number of ways. They can take a virtual tour of a property from their computer at home, or superimpose furniture from a store’s website over a room in a house they’re considering buying. Customers have also likely read marketing materials generated by AI, or had correspondence of another kind with AI when dealing with an agency.
Real estate agents don’t expect the technology to take over their jobs anytime soon. They see it as a tool that can help them, but they know it has room for improvement. It is up to the agents to make sure the information is correct, or at least to make sure they are not putting out unreasonable numbers. Realtor.com, for example, has a team of engineers monitoring AI results every day, testing the results to ensure they turn out close to what is expected. For its part, Zillow cautions its predictive AI should not be considered an official appraisal.
Real estate is an industry that has always been flush with data. As technology has advanced, organizations have been increasingly able to harness that data to make informed decisions. Whether it’s in real estate or any other industry, in order to make the kinds of data-driven decisions that put an organization ahead of the competition, you need to have the right analytics solution in place. It also helps to have the right people making sure the correct data is producing the correct outcomes. In real estate, you may have heard it’s all about location, location, location. However, there’s an argument to be made that real estate – and all business these days – is about data, data, data.