For centuries, real estate investors and buyers have made decisions based on intuition and retrospective data. Real estate is the biggest investment many of us will ever make. Even for professional real estate investors and asset management companies, picking what to buy, where to buy, or what and where to build, is a huge investment.
What Exactly Is Real Estate Data Analytics?
Real estate data analytics is the method of analyzing raw data to obtain the information that will hep tho grow business. This information may help investors, marketers, builders in different ways. Such research is generally performed with the assistance of advanced real estate predictive analytics tools with the power of artificial intelligence and big data. Data analytics can help real estate professionals enhance operational efficiency, increase revenues, improve customer service efforts, optimize marketing campaigns, and respond faster to emerging market trends.
And yet the majority of people are making these decisions based on gut instinct and historic performance history.
When it comes to any other investment class, there is a standard warning: past performance doesn’t indicate positive future performance. So why is that warning being so easily and readily ignored when buying real estate?
A Better Way Forward: Real Estate Data Analytics
We should never ignore gut instinct. But we can be wrong. An area that was up-and-coming ten years ago maybe not as attractive for buyers or renters (commercial or residential) anymore. High-performance and high yield areas of cities and towns don’t stay that way forever.
Real estate is an ever-shifting commodity and investors need to be careful when selecting where to invest for potential as well as present-day performance.
As consumer habits and needs change, so does what we need from real estate. Only a few decades ago, the suburbs were a shining beacon on the hill in many cities. Now buyers and renters want some of the convenience of the downtown core, without living in the center, making smaller retail areas and supermarkets an attractive and necessary part of the urban landscape.
Real estate data analytics backs this up. According to Zillow data, homes in Boston within a quarter of a mile of a Starbucks increased in value 171% between 1997 and 2014, which is 45 percentage points more than homes outside of that radius. In Seattle, apartments within a mile of a Whole Foods and Trader Joe’s (specialty supermarkets) increased in value faster than those in other neighborhoods.
Real estate investors and buyers are now turning towards non-traditional data sources to unlock valuable insights they can’t get from traditional sources. Property prices, asset yields, sale prices, crime statistics: all of these are useful traditional sources. But now these aren’t the only sources of data that investors and portfolio managers can use to capture value or making divestment decisions.
Capturing Hyperlocal Value
There is and has always been a huge gulf between buyers and sellers, renters and landlords. Especially when those selling or leasing property own a portfolio. Owners might visit an area a few times, or send staff and contractors to do that. Owners will read reports and make decisions from behind a desk.
Decisions on what to buy, what to sell and what to invest in are only as good as the data someone can access. Whereas buyers or those looking to lease a property – whether for commercial or residential purposes – are more careful, in many respects. They either know an area already, or are going to spend more time assessing everything from the cleanliness of the streets, to parking, to the presence of coffee shops, restaurants, bars, or whatever makes an area attractive from the perspective of someone living or working there.
That is the value of hyperlocal knowledge. It makes a difference as to whether a house sells or is leased quickly, or not. It impacts whether a shop or office is bought or leased in a few weeks, or months. Ultimately, hyperlocal factors play as much an influence on the speed at which a vacant property sells or is leased as the quality of interior and square footage, and this has an impact on what price the market will accept for an asset.
For investors to capture this value, they need to assess thousands of data points quickly. Taking too long to do this means opportunities will be missed and poor investment decisions made.
Using Non-Traditional Data
There are thousands of non-traditional data points that property owners should consider, such as the number of coffee shops within a specific distance, proximity to fashionable restaurants and Yelp reviews for nearby businesses. These are powerful driving factors for real estate investment analytics. Nearly 60 percent of predictive power about the sale/lease value of a property – and how quickly a new owner/tenant moves incomes from non-traditional data points.
Deriving value from data analytics is no trivial task. Real estate businesses and investors, who want to gain valuable insights from data analytics need to establish clear processes for data collection, interpretation, and incorporation into decision making. Manually aggregating and analyzing large volumes of disparate data can be time-consuming. To face this challenge, companies are employing machine learning algorithms, which makes it easier to collect, process and interpret both traditional and non-traditional data sets.
Combining data from disparate sources helps to create a clearer hyperlocal knowledge and identify a location’s potential that is so often overlooked or not even accessed when making traditional investment decisions. While the technology is still in its nascent stages, the opportunity it brings to the real estate sector is too great to ignore.
Types of Real Estate Data Analytics
Descriptive analytics – This form of analytics explains what happened over a particular period of time (trends in real estate). For example, how much rental income has gone up in the past five years? What has been the number of vacancies in the past year? Descriptive analytics uses data from a broad variety of sources to provide valuable insights into the past.
Diagnostic real estate data analytics – Here, it examines historical data to explain why something happened. Diagnostic analysis, for example, can dig deep to reveal why rental properties have a high turnover rate.
Predictive real estate data analytics – Just as the name suggests, predictive real estate data analytics forecasts what might happen in the future.
Prescriptive real estate analytics – The primary goal of this research is to recommend what needs to be accomplished in order to take advantage of an opportunity or prevent a potential problem. Let’s say someone would like to invest in an Airbnb property for example. Should they purchase a home with 2, 3 or 4 bedrooms?