Data Analytics: Real Estate’s Newest Trend
Today, everywhere in real estate, companies are throwing around terms such as “Big Data” and “analytically-driven decisions” when it comes to developing new business strategies that guide decision-makers in making sound insights.
Advanced analytics has developed into the hottest trend for businesses who want to push themselves further ahead of the competition. With real estate being one of the most attractive investments for individuals, it is no surprise that real estate companies are starting to use analytics to drive their acquisitions, interactions with clients, and gauging of the market. But, just how can analytics be used to its highest potential?
Descriptive vs. Prescriptive Analytics
What is the difference between descriptive and prescriptive analytics? Descriptive analytics, essentially, looks at insights from the past. Analysts can draw conclusions from looking at past data and answer the question: “why did this happen?” This is how most statistics are presented, and then compiled into aggregate reports to tell stories of “what happened.” Prescriptive analytics, on the other hand, looks at data and guides an individual on what action to take going forward. It answers the question: “how can we make this happen?” Prescriptive analytics is still relatively new, and most companies are still trying to gain a foothold in utilizing it to make significant insights.
Raw data and statistics alone do not create much value; rather, it is the ability to extract patterns and forecasts from that data that is what is most beneficial to companies and individuals. This is why many analysts are turning towards non-traditional data, which is derived from and scattered across a variety of sources.
A simple example would be a student trying to determine which professor to choose for a class that has several instructors; students no longer sift through each professors’ old syllabi or spend time shadowing classes, rather, they read through online reviews and listen to the advice of friends who took that class before. From there, students make their own informed decision on which professor to enroll with and execute their choice. Whether it’s a hit-or-miss, students are utilizing non-traditional data sources to make informed decisions that will help them maximize their time in that class with a lecturer that best suits their learning style and needs.
Applied on a macro-level, the same goes for real estate companies. Agencies use sources such as Yelp reviews of nearby restaurants, and the number of cafes within a certain radius of a potential property acquisition, to help locate properties that are demanded highly for their targeted clients. Investment firms are trying to capture a company’s valuation by analyzing themes such as momentum and size in order to make the best investment possible. Non-traditional data has come a long way in proving to possess predictive powers that can be extremely helpful for any decision-maker, even in real estate.
To learn more about non-traditional data and how impactful it can be, check out the information here.
Big Data and Analytics in Real Estate
Hopefully, it is apparent that a thorough analysis is needed to make an informed decision about, really, anything. Real estate is no exception; it is imperative to try to optimize the choices made to guarantee that all parties are satisfied. Analytics plays a large role in evaluating an asset, ensuring risks and surprises are mitigated, while also making sure as much utility is extracted from the transactions between buyers and sellers. The article attached here explains all of the ways analytics can be used to their full potential in real estate. Big data is transforming the industry to allow sellers to play matchmaker between consumers and their ideal properties.
Access to real-time data allows investors to make more informed decisions about an asset. Sellers are able to effectively analyze their portfolios, allowing for greater insights about how to improve and sell their properties to maximize their utility. Investors are able to make educated decisions about location, expenses, and return by using data analytics.When it comes to bidding, data analytics helps investors bid in confidence about the property they are trying to acquire, hold, or rent.
Another important aspect of real estate investing is the pricing of assets. Predictive analytics can help sellers estimate rental income and appreciation of properties, while prescriptive analytics allows agents to choose the best course of action with the given data.
In order to see the marketplace move as efficiently as possible, it is crucial to start implementing the use of analytics for sellers to match buyers with properties that will best suit their needs. It starts with sellers utilizing analytics to determine the best assets to acquire, and buyers finding those agents that will execute their goals for them. Big Data is the bridge that connects these players in the market, enabling real estate to continue to be one of the most important investments a person will make. To learn more about data analytics in real estate investing, take a look at this article!
For more information on real estate investing in general, check out our other article!