Data analytics has always been an essential component relating to the success of the real estate industry, especially in relation to expensive commercial properties that require hefty investments. Breaking down records to gain the upper hand when it comes to property funding has always been a vital process for commercial real estate brokers. In recent years, real estate data has grown exponentially, accumulating tons of historical records and archiving every unit of data in real-time. With the rise of connected devices and smart properties, the real estate market has transformed into a massive interconnected ecosystem, generating tremendous amounts of data. Gartner estimates there are now over 20 billion connected IoT devices with roughly 2.5 billion sensors located in smart buildings.
This revolutionary environment has created plenty of entirely new categories of information, far exceeding tax records and rent prices. New and improved methods for gathering data analytics have enabled industry professionals to extract valuable insights by breaking down information to the most granular level. Data analytics enables brokers and investors to see the hidden part of the iceberg and run extremely important analysis relating to property risks and potential benefits, which in turn leads to thorough, well-thought, and low-risk investment decisions. Big data is now gold to the commercial real estate industry. The only catch is that this game-changer has brought about an entirely new cluster of industry players: data collectors, processors, and analytics.
This shift requires an entirely new approach to data science and analytics. As data volumes continue to grow exponentially, the next challenge for real estate service providers is to make use of it. A proper analysis of the data pool allows brokers and investors to tap into hidden opportunities in every branch of the commercial real estate industry. Essentially, the most important reason why big data is such a valuable asset to commercial real estate is its capacity to provide better decision making, based on hidden insight. Around 70% of the world’s leading enterprises already spend more than 25% on data analytics and IT services, with plans to increase this rate in the future. In addition, a new survey from NewVantage examining the use of big data and AI among enterprises shows that 48% of Fortune 1000 companies use data science and analytics to get ahead of the competition and excel in the market.
This powerful decision-making tool encompasses many areas, from choosing convenient tenants to calculating and comparing vacancy rates. Additionally, aside from helping investors decide where to put their money with the best ROI, data science can address numerous industry issues and pain points. Data science drastically decreases deal times, increases building reliability, enforces compliance with regulations, helps track price fluctuations, enhances risk management, and determines property characteristics and profitability before it’s even built.
There is no denying the huge potential for data-driven technology, but what technologies are most applicable to address the industry’s key pain points? And which particular aspect of big data can bring value to the commercial real estate sector?
Table of Contents
3 ways big data can enhance the commercial real estate industry
Accurate property evaluation
Detecting optimal property value has always been a sensitive issue in commercial real estate. Until recently, It involved a long process filled with analyzing paperwork records of previous owners, object documents, etc. Today, with increasing competition and market expansion, it has now become even more complicated. Firstly, there is much more data accumulated across different branches of the industry. The more information there is, the harder it is to sort it all out, let alone detect useful insights. In this area, data science and analytics can help provide the most accurate price estimates. As we all know, putting the right price on a property once it’s listed is one of the first steps toward a successful deal.
Machine learning automated valuation models based on huge volumes of collected data allows brokers and appraisers to compare numerous listings simultaneously, covering any slice of the market worldwide. This enables us to take a deeper and more accurate look into a market’s average relating to a particular property type, region, etc. Additionally, data science includes market trends, which allows for making decisions with a long-term vision rather than simply reacting to what’s happening in the market at a given point in time. For example, such models are built to predict rent prices up to three years in advance, and, according to McKinsey, can forecast prices per square meter with a 90% accuracy rate. Data-driven solutions are able to provide accurate estimates as information is collected across hundreds and sometimes thousands of different sources, which unveils a clearer picture of the actual state of the market. This way, property deals can be closed much faster with fewer risks.
Such a boost in decision making benefits both investors and tenants as more transparency and accuracy during property navigation results in a faster data-to-decision process. Automated valuation modeling is an asset not just for huge investment funds, but for individual property owners who want to keep track of market prices as well. These emerging trends will increase the demand for open data platforms and large infrastructures to give all participants a reliable ‘big picture’, with even more data collected. Some startups, like CoreLogic, have databases packed with more than 4.5 billion records, complete with several hundred different analytical models that help real estate brokers and investors obtain insight from property information collected from commercial real estate management companies, brokers, mortgage lenders, security companies, and federal institutions. This collection of visualized and sorted data also enables real estate investors to evaluate property costs to proactively determine and manage risks.
Deep and comprehensive layers of property-related data can unveil critical characteristics that impact funding choices. Data analytics allows investors to visualize important parameters for maximizing ROI and the overall yield from their purchase, such as vacancy rates, building maintenance costs, specific information on object and region, possible risks, and predictive customer behaviors.
The categories of data extracted for decision-making are extremely diverse. For example, investors can access a region’s crime rate, proximity to hotels or restaurants, median household income, specific permits related to building objects in the particular area, and even the number of coffee shops to be built in that quarter in the upcoming years. In fact, a building’s proximity to community amenities accounted for 60% of fluctuation in rent price. According to a 2015 report by brokerage company Zillow, the cost of property within a quarter of a mile from Starbucks in Boston spiked more than 171% between 1997 and 2014. Such information about specific city infrastructure details provides reliable grounds for making commercial property purchases that will be most profitable from a short and long-term perspective.
Building & construction planning
3D building models
Powerful predictive capabilities of data science and learning algorithms enable property funders to not only efficiently purchase and lease existing objects but also to plan future objects with the most rational use of space. Creating 3D building models and digital constructions helps us to understand what the actual facility will look like, how it will function, and how to wire it accordingly, including piping, electricity cables, etc. Such projections can help avoid costly mistakes before the building process even starts. It also allows us to analyze construction reliability, the risks of building in a particular area, and how to better optimize object infrastructure. Additionally, it lets investors know exactly how much the property maintenance will cost, and also provides information about parameters as electricity and water consumption.
Geographical information systems
Analyzing data in a particular region allows us to add geographic characteristics to overall planning. Geographical information systems are based on analytics models derived from land-related data. This includes landowners, the price per acre, and specific information like coordinates, distance, flood zones, earthquake history and probability, and other factors that are critical for commercial real estate investments.
Time and costs estimate
Essentially, with such granular data regarding property planning with everything from land characteristics, 3D construction, and interior design, getting accurate time and cost estimates is much easier. This means investments can be made at very early stages with nearly complete accuracy of cost estimation. Predictive analytics also enables investors to determine building maintenance costs. For example, AI-enabled prop-tech startup Bldbox utilizes big data and machine learning algorithms to estimate the cost of construction to plan commercial real estate projects more productively.
What’s in store for the future?
Big data and predictive analytics are the frontrunners of the future of the commercial real estate industry. Data science significantly shifts the landscape and flow of the real estate business by creating a new powerful asset. Due to this, a new category of industry players is emerging, explicitly focused on data-driven solutions for the commercial real estate market. The industry is quickly moving from internal databases to large open data platforms in order to maximize the use of this information throughout the industry.
Data-driven decisions enable market players to significantly accelerate data-to-decision times, which makes property purchases and leasing deals close faster, with more benefits and fewer risks for investors, property owners, and tenants. Data analytics can be applied to many branches of commercial real estate, from 3D rendering and geographical information systems to automated property evaluation and integrated space management tools. While many industry professionals are worried their jobs will become redundant because of the adoption of widespread algorithms, it is much more likely that big data will instead become a powerful tool for brokers and agents. In fact, analytical tools reduce volumes of tedious and manual jobs, allowing industry professionals to concentrate more on strategic tasks.
Naturally, such an intense adoption of data science across the industry inevitably creates concerns. The key issues will revolve around who controls the data, and, essentially, how to make it more secure. With ongoing digitalization, information security is a hot issue in many industries right now, and real estate is no exception. The challenge for the technology solution providers for the commercial real estate sector will be building universally accessible, deep learning-enabled data solutions that are secure and reliable. To succeed in today’s commercial real estate market, whether with large scale projects or smaller players, companies have to continuously invest in data-enabled solutions. Ultimately, it all comes down not just to gather as much important data as possible, but to analyze and leverage it for more efficient business decisions.
Glorium has extensive experience providing software and technology for real estate solutions providers. We have a deep understanding of key industry trends and pain points, which enables us to leverage our comprehensive knowledge to help real estate projects get to market and/or enhance their existing solutions. Here is a case of a customer,
bison.box, who needed a full-scale ecosystem and data warehouse for property managers to
identify risks and opportunities for making better investment decisions.
A client turned to us upon recommendation with a request for transforming its successful application into a powerful SaaS solution. The main goal was to re-architecture the infrastructure for more flexibility and agility, shift it to the Cloud, and modernize UI/UX while keeping database structure changes to as minimal as possible.