The Big Data Advantage in Real Estate Analysis

Big data has affected how organizations do business in every industry across the world, and real estate is no exception.

Understanding the term ‘big data’ gives context to how it can be applied in real estate analysis. Gartner’s explanation is considered the go-to definition: “Big data is high-volume, high-velocity, and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision-making, and process automation.”¹

Essentially, the term refers to the processing of large amounts of data, be it historical or real-time. Algorithms are applied to unveil trends in user behavior, increase speed to market, target customers, and ensure they’re satisfied. The data sets can be structured or unstructured, with information sourced from a vast and growing range of inputs. Processing, analyzing, and identifying meaningful insights from such volumes of data is too great a task to expect from a single analyst or more traditional data processing software, which is why specifically designed software is utilized to do the analytical heavy lifting for organizations. The analysis can be done in-house or by third-party specialists, who can also help companies manage the sheer scale and noise of data.²

The five Vs of big data

The three Vs of big data, as conceptualized by Gartner, have now become five:

1. Volume: Big data entails processing high volumes of data, from social media data and clicks on a web page to data collected via mobile apps or sensors. Volumes range from terabytes to petabytes.³
2. Velocity: To yield the most relevant and recent insights, and thus inform the best decisions, data needs to be gathered quickly – as close to real time as possible. Some see the speed at which data comes in as more important than the volume.⁴
3. Variety: This refers to the wide array of available data types. Traditional data types are more structured, whereas big data typically comes in unstructured sets. Analysts often need to pre-process unstructured and semi-structured data types, including text, audio, email, and video, before they can extract value from them.⁵
4. Veracity: Increasingly important in this age of information overload is the quality and trustworthiness of data. Managing large volumes of data can be more cumbersome than useful if the data isn’t complete and accurate.⁶
5. Value: Finally, organizations need to monetize their data by transforming it into a business advantage. Gaining useful information about customers to serve their needs better is one key way to create value.⁷

The power of data

Big data can prove effective in getting your message in front of your target audience and measuring its efficacy in a world already cluttered with advertisements and content.⁸ It helps reveal consumer trends, patterns, correlations, and customer preferences.⁹

The key to using big data efficiently isn’t in how much a business has, but how it utilizes it. Organizations can take data from any source, and in any format, and analyze it for solutions to:

• Make better decisions: Big data analytics can give business decision-makers the insights they need to help their businesses stay competitive and grow. However, doing so requires more than just collecting and analyzing information; leaders need to work hard to drive culture change to grow truly data-driven organizations.¹⁰
• Cut costs: The smart use of big data can help businesses cut costs in several ways. These include reducing marketing spend through targeted advertising, optimizing supply chains, identifying potentially fraudulent customer activity, and resolving production problems faster.¹¹
• Increase productivity: Analyzing data points relating to performance metrics, employee output vs. hours worked, the effectiveness of business tools, and client-specific profitability can help organizations better focus their efforts and resources.¹²
• Manage online reputation: A survey of more than 2,000 executives found that, on average, 63 percent of company market value could be attributed to reputation. In this context, monitoring all information about a business is crucial, including social posts, surveys, and customer reviews. Analysis of this information should constantly be used to inform and update business listings, marketing strategy, and advertising.¹³
• Improve customer service: By analyzing customer interactions across data points, including searches, purchases, feedback, and reviews, and identifying behavior patterns in these, organizations can improve their service in a host of ways. They can use this information to build customer profiles and personalize their services accordingly, improve their products and operational activities, anticipate market needs, and provide more responsive support.¹⁴
• Increase revenue: Businesses can use the insights about customer preferences and order histories to drive targeted sales and develop more appealing products. Data analysis can aid in price optimization and better-tailored business strategies, which can lead to improved revenue.¹⁵

The advantages of big data in real estate

Before big data, many of the decisions made in real estate were mainly based on experience and limited analysis of trends. Now, property professionals use real-time, detailed information from multiple sources to make more informed decisions.¹⁶

Real estate service providers who focus on delivering bespoke, customer-centric property solutions can increase customer satisfaction. In addition, data insights can be used to help meet the needs of buyers and sellers. In this way, service providers can position themselves as the real estate partners of choice for prospective customers.

Searching for a property today – to buy or rent – is an online exercise conducted through apps, websites, and online forums. According to a 2021 study by the U.S. National Association of Realtors, 97 percent of home buyers used the internet to search for homes, while 51 percent found the home they purchased online. The most common actions taken through such internet searches were:¹⁷

• Virtual walk-throughs
• Viewing the exterior of a home or neighborhood
• Finding an appropriate estate agent
• Pre-qualifying for a mortgage
• Requesting more information

Realtors, investors, home buyers, and financial institutions now have access to data that’s a click away. This empowers them to make more savvy decisions, with data analysis facilitating more accurate predictions about risk and market trends.¹⁸ Benefits include:

• Risk mitigation. Predictive analytics helps assess commercial viability and thus reduce risk when it comes to real estate investments, with realtors and buyers now having access to accurate metrics drawn from reliable real estate data.¹⁹
• Simple (and fast) evaluations. Realtors use property evaluations to set the price of their properties, and home buyers and investors use these evaluations to put forward offers. Financial institutions rely on them to calculate loans and minimize losses. Through real estate data analysis and statistical modeling, appraisals can be made based on years of market data. This is increasingly happening automatically, alongside services like Opendoor, which automatically bids on houses.²⁰
• Understanding customers’ needs better. Predictive analytics provided by big data helps real estate agents better understand what their customers want and helps them respond with personalized offers based on the data.²¹
• Improved marketing strategies. Realtors can identify potential customers across a range of data points, including age, gender, preferences, interests, and region, as well as whether or not they have children or pets. This more granular approach enables more targeted and efficient marketing.²²
• Market trend forecasting. Tracking additional data points such as employment trends and income levels can grant insights into potential future events such as foreclosures, spikes in prices, or the needs of specific groups.²³
• New insurance services. Insurance companies that cater to home insurance analyze data from various sources to develop and personalize insurance offerings for customers and geographic regions.²⁴

The future of big data in real estate analysis

Ever since Google and YouTube added ‘how-to’ videos for almost every subject, people have discovered how to execute any number of tasks previously reserved for professionals. This includes handling real estate purchases. In the U.S., organizations such as CoreLogic, Smartzip, and InfoSparks offer interactive data visualization, providing important data to use for accurate decision-making.

Data analytics technology is becoming increasingly user-friendly, making the tools more accessible to people from all walks of life. The latest technology is not only optimizing the real estate industry but also improving the way we live in our homes. Examples include:

• Smarter building management: Internet of Things (IoT) sensors can help building managers monitor things like elevators, heating and air-conditioning systems, and ventilation, sending alerts if there are malfunctions. When analyzed over time, this data can enable predictive maintenance, improved tenant and resident experiences, and reduced costs.²⁵
• Transparent data democratization: The growing volume of data created through property technology platforms and digital tools is driving transparency in real estate markets. This will ultimately facilitate greater investment.²⁶

What does big data mean for the real estate industry of tomorrow? Advances in technology will continue to reshape the industry and drive more personalized customer experiences. These changes are likely to alter responsibilities, required skills, and risks in the real estate sector, and will impact margins. However, the long-term outlook for the real estate industry in terms of big data and analysis is a positive one. It’s up to industry professionals to learn how to use the analytical tools necessary to make data-driven decisions, preparing themselves for the future of real estate today.

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