Photo by Tejas Chaphalkar on Unsplash
The volume of user and location data today has piqued the interest of many startups diving deep into the real estate market. Most intriguingly, the technologies they’re leveraging is invigorating how investors and developers analyze a site for potential investment and interact with the property after the purchase.
The various paths Big Data has paved for real estate insiders can range in depth and target, so let’s look at the top 7 ways next-gen analytics is changing how professionals make their real estate decisions:
Home valuation and Zestimate
Zillow is one of the most recognizable real estate brands, partially in thanks to its innovative Zestimate tool. It aims to show the value of nearly any American property, and a 2019 update sought to pull more real-time information into its calculations.
Claiming an “error rate” of less than 2 percent, this automated valuation model refined its algorithm by gathering data from homes currently listed for sale, such as pricing and how long a property has been on the market. Prospective investors in a property can get a baseline idea of the value of the site, but if they want complete accuracy, human appraisers may still be needed.
Predicting the potential of land parcels
Budding startups are trying to impact another area of real estate investment: how developers view the potential ROI of a plot of land. Several businesses are leveraging AI by automating the process of preliminary property analysis, which can take weeks or months and involve a partnership of developers, architects and financial experts.
Some services perform real-time analysis of financial and market data along with local building regulations, giving developers a richer picture of the land they’re looking to purchase.
The hottest heat maps
For investors leaning more into understanding an area or property beyond the four walls of the listing, Big Data can offer compelling details about neighbourhoods in order to help lead to better decisions. Local Logic has created heat maps for those kinds of inquiries.
“Through our Local Analytics platform, you can search by your investment thesis and can use our platform to create heat maps with different layers of data that match what you’re looking for,” says Sara Maffey, Head of Industry Relations at Local Logic.
For a place in mind, users of Local Analytics can look over 17 scores outlining various characteristics of a community, from how close it is to grocers to how well it does for being transit-friendly. The heat maps let users see the tally of certain filtered scores, depending on the criteria inputted. The darker the colours, the closer that area is to the filter requests.
“Our data can help gut-check an investor’s feel for an area of interest,” Maffey adds.
The ease of one-click neighbourhood reporting
Adding to the suite of Local Logic features is a new tool that is best described as a one-click report. Maffey notes that Local Analytics users can now receive a complete profile of a neighbourhood of interest anywhere in Canada or the US, an analysis that often takes weeks and a dizzying array of data sources.
Through its proprietary technology, Local Logic can generate detailed reports within minutes. Maffey says, “We wanted to display this data and make it actionable by allowing investors to compare sites in an apples-to-apples way.”
Developers and investors often take a more subjective stance on a neighbourhood, but Local Logic aims to remove the guesswork out of these critical decisions of where to purchase or build a property.
“The conversation in real estate is shifting away from ‘If you build it, they will come’ to taking into account all the insights that will drive the value in the property,” says Maffey.
Next-level credit score checks
As a Deloitte report found, many tech startups venturing into real estate could energize an industry in dire need of modernization. “The entry of important actors from outside the industry – a process already underway – can have major
effects on the sector’s market structure. The market is gradually expanding in the direction of complex, interconnected, high-tech, and automated services.”
One such service is predictive analytics focused on credit scores and their effect on helping investors understand the risk and potential impacts to the valuation of a property. Some startup firms are applying complex data to model the underlying credit quality of each tenant in real time and rolling this information up into an overall score for a building.
Even IBM’s Watson, a super-computing program, has been tested to offer predictive analytical support to real estate investors, thanks to Watson’s sensors and data processing. As a Cornell University blog wrote: “By testing correlations and looking for statistical relationships within tenant type, tenant credit and sensor enabled utilities, startups using Watson are attempting to create value by letting investors know where inefficiencies are in real time. Some large companies have taken note and built their own systems to help add value.”
Managing properties through an app
Property management software has also leveled up in the past decade, as investors seek to better organize how they run the properties they own.
Crowdcomfort, for example, converts information into time-stamped and geolocated heat maps through a portal that oversees the management of building operations. So if a water pipe burst sparks an emergency, the property manager can take a photo of the damage and upload it into the right area of the app that alerts the landlord and other personnel, with the pipe location easily identifiable through an intuitive map. That task is then “created” for staff to update as they work on and finish the project.
As the Internet of Things technology ramps up, and sensors become less expensive as their innovation advances, this kind of software could be valuable to investors and landlords who want to stay on top of emergencies and maintenance issues.
A blend of machine learning and traditional and nontraditional data
The future of the relationship between Big Data and real estate development will be brightest for those businesses that make concerted efforts to find opportunities to take advantage of that vital data. That’s where machine learning and multiple data sources come in, according to a McKinsey report.
Let’s say you’re a developer who wants to identify underused but high-value parcels zoned for development, the report goes on to say. Data sources on previous transactions, such as the Multiple Listing Service, are available and are widely established as the traditional fountain of information on both residential and commercial real estate assets. But these databases have limited value for finding future potential, not having been designed for that purpose.
“Advanced analytics can quickly identify areas of focus, then assess the potential of a given parcel with a predictive lens.A developer can thus quickly access hyperlocal community data, paired with land use data and market forecasts, and select the most relevant neighborhoods and type of buildings for development. Further, that developer can optimize development timing, mix of property uses, and price segmentation to maximize value.”
A successful data-driven approach can yield potent insights that lead to a better real estate decision. In another example McKinsey cites, “an application combining a large database of traditional and nontraditional data was used to forecast the three-year rent per square foot for multifamily buildings in Seattle. These machine-learning models predicted rents with an accuracy rate that exceeded 90 percent.”
Another application for machine learning focuses on how developers view the potential ROI of a plot of land. Some businesses are leveraging AI by automating the process of preliminary property analysis, which can take weeks or months and involve a partnership of developers, architects and financial experts.
Now that you’re aware of how critical Big Data application can be for real estate investors and developers, you’ll never look at a heat map or hyperlocal amenity the same way again.