What is Real Estate Analytics?
As it pertains to this industry, real estate data analytics is the practice of using computing resources to assemble large amounts of information about properties and neighborhoods. This can yield insights that no human could ever obtain due to the sheer amount of time it would take to digest all the information. Big data systems can be set up to collect information from recent sales, the MLS system, government reports and market forecasts.
This allows us to make projections regarding a variety of things of interest. For example, let’s say property investors want to know what the business future of a district in a city is. Utilizing artificial intelligence, we can develop methods for compiling available information about things like demographic trends, buying and selling habits, micro- and macro-economic developments and even individual properties and their owners. This can even be taken a step further to model the entire region over several years, providing probabilistic views of what the district will be like at specific points in the future.
How Do We Use This Information?
Let’s say a residential property flipper wants to figure out what their exit point on a location should be. Looking at real estate analysis trends like population growth and income in a region, we can project when the market is likely to max out returns. It’s even possible to model the behavior of other flippers on the market, allowing us to guess how long pressure from their presence in the market may actually allow sales to continue to go long. By assembling these projections grounded in data analytics into reports, we can then ensure that folks directly involved in buying and selling will have a better idea of what we want to buy and when we want to sell it. It can even allow us to guess which parts of a town might be ready to heat up, permitting us to buy before the big wave hits.
If you want to know when it might be time to move in on a target property, it’ll be there in the data. You can set standards for what counts as a buy and wait for the market to come to you. Let’s say you have a strike price for purchasing a location that has been on the market for 300 days. If there’s no evidence of activity regarding the property, your analytics package can flag it, inform someone and verify that followup is done.
Who is Involved in This?
Becoming a more data-centric operation means undertaking a massive cultural shift. Folks with math and programming backgrounds are essential to the task. Due to market-wide demand for such talent, there’s a good chance that, unless you run a massive operation, you won’t be doing this in-house.
At the same time, staff members have to be on-boarded with the cultural change. In fact, you may need to identify resistant team members who will have to be moved to other roles or retired. Over time, though, the development of a data-centric business model will put you ahead of the game, whether you’re working as a buyer, seller, or facilitator.