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Last updated: May 31, 2022 • Real Estate Investing

How Data Is Changing Decision-Making in Real Estate Investment

Until recently, agents and investors primarily made real estate decisions on the basis of historical trends and industry experience. However, thanks to the revolutionary power of big data and today’s cutting-edge technologies, it is now possible to access accurate real estate data in real-time. The insights gleaned from big data enable investors, real estate firms, and developers to clearly identify opportunities and accurately assess the risk of potential investments. 

What is big data?

The term ‘big data’ refers to large volumes of data – whether structured or unstructured – and applying computer analysis and AI (artificial intelligence) to identify trends and patterns. Essentially, no industry is without the presence of big data. For example, eCommerce and mobile device tracking combined with the Internet of Things (IoT) allows investors, brands, and other business management professionals to monitor vast amounts of highly complex information. Everything from which web page visitors stay on the longest to patterns such as which demographic repeatedly buys property within a particular price band in a specific neighborhood, and so much more. Such colossal amounts of data are beyond the capabilities of the humble Excel spreadsheets that were once utilized to track and analyze information. 

How does the real estate sector use big data?

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One of the primary ways in which big data impacts the real estate industry is by enabling professionals to gain insights into small yet crucial variations between neighborhoods. Traditionally, real estate professionals and investors would have to profile neighborhoods manually, and the results would be vague, categorizing large areas together.

With big data analyzed through CRE software, key differentiators can be identified, even from one city block to the next. Zoning codes, price fluctuations, future infrastructure planning, and crime rates are just a few of the factors taken into account when big data analyzes real estate information. 

The real estate sector benefits more broadly from big data, too. For example, retailers use it to track customer traffic patterns and improve the flow of their store layouts. Building managers use it to identify trends in the requirements of property maintenance. Agents and brokers use big data to identify key client prospects, and mortgage lenders rely on it to assist in determining a borrower’s risk when assessing a funding request. There are many examples of the impactful use of big data throughout the real estate industry – deepening the understanding of customer needs and trends, and assisting in decision-making and risk assessment. 

Broader scope of analysis

Big data analysis can assist professionals in assessing the general conditions of the mortgage industry, any insurance risks, and establishing the extent of purchasing power that households with median income have based on a range of variables, including their location. 

Big data is also pivotal in improving the accuracy with which predictive algorithms assist in making informed investment and real estate development decisions. In addition to this, the use of alternative data is also bolstering the valuable insights that big data affords. 

Alternative data, such as from search engines, social media, and other non-traditional sources, assists real estate professionals in making even more valuable predictions. In addition, many firms and investors leverage this type of data to create algorithmic models and predictive analyses to indicate future living, buying, pricing, and investment trends.

Other sources of alternative data include online reviews, surveys, and local business searches. This data can assist investors in identifying micro-level trends as opposed to general trends at a neighborhood level. According to a report by McKinsey, alternative data (also known as non-traditional values) are a powerful source for predicting trends and property valuations, assisting in investment decision-making. 

Property evaluations

property value concept

As previously mentioned, leveraging big data can greatly assist in appraising a property. Investors will also often run a comparative market analysis, including factors such as proximity to shops, schools, and other amenities, crime rates, etc. When datasets are fed into predictive algorithms and AI software, insights into price trends, sales volume, current supply and demand, property characteristics, and more can lead to a highly accurate property evaluation. 

Investors arguably benefit the most from big data

Given that sound real estate investing has always involved the careful assessment of risk, it’s arguable that investors stand to benefit the most from big data. Ultimately, successfully managing property investments means accurately managing risk. Utilizing big data in combination with cloud computing and AI is transforming the way in which investors can measure potential risk. Critical information regarding a property portfolio can now be far more easily digested – and fast. 

For example, let’s say an investor wanted to add a new retail property to their portfolio. In this instance, big data can be used to identify which demographic areas are primed for the most growth. In addition, through the use of big data analysis, investors can accurately assess the financial burden of a particular investment opportunity through predictive modeling upon development costs, capital outlay, property appreciation, marketing expenses, and more. 

Big data allows investors to essentially access pivotal insights; with the click of a button, they can assess with accuracy what the projected expenses and income are likely to be for a specific investment proposition – both initially and over the longer term. 

Property development profitability

architect engineer design working

Developers have come to understand that this new era of big data opens up a whole new world of possibility when it comes to boosting a development’s profit margins. By sourcing sets of clean data and feeding them into sophisticated AI software, developers can leverage the resulting insights to pinpoint which parcels of land are the most likely to become a high-yielding investment. 

This level of big data leveraging is reliant on systems that are capable of cross-referencing a variety of relevant data sets, including:

  • Social media/alternative sources: for example, identifying an increased interest in a particular area from a specific demographic (professionals, executives, students, etc.) 
  • Local government information: for example, pending planning permissions, proposed changes or upgrades to local infrastructure, etc.

Fine-tuning a development for optimal ROI

To maximize an investment’s ROI (return on investment), developers leverage big data insights to determine how best to cater to the needs of its residents’ demographic. Big data not only assists developers in identifying where best to build, but what features and amenities their target demographic would be attracted to. By developing properties that are in keeping with what their potential tenants/residents expect, developers can help to ensure that their projects can be optimally priced. 

Digitization of management procedures

businesswoman hand working laptop

The more real estate and investment professionals embrace the digital era, the more powerfully they can leverage big data. More and more companies and investors are digitizing their management processes and using big data analytics to assess and forecast investment and development opportunities. Additionally, big-data-driven algorithms are being utilized to monitor the performance of each portfolio asset, and insights are used to best refine each investment’s strategies.

Data-driven strategies

Big data insights can also give investors an indication as to when they should consider disposing of an asset. It is essential to compare a property’s performance against others within a portfolio to ensure that the ROIs are optimized. If the net operating income or capital outlays no longer justify the investment returns, it may well be time to sell

Today, real estate investors apply data-driven predictive analytics to just about anything they need to assess: identifying future tenant trends, comparing similar rental properties or locations, interest rate patterns and predictions, totaling the cost of an investment, including start-up costs, and much more. Being able to access and easily interpret real-time data regarding every aspect of an investment property’s performance (and in relation to the current market conditions) gives investors a powerful edge that their professional predecessors could have only dreamed of. In addition, the data analytics reveal patterns that help in clearly identifying whether an asset is performing well or poorly in relation to the market conditions it sits in. Furthermore, when this valuable data is presented to them via an easily understandable dashboard, real estate professionals and investors are better informed regarding the validity of any potential investments. 

The challenges of leveraging big data in real estate investment

building house models chess game

Not surprisingly, investors and developers are typically highly enthused at the prospect of leveraging big data to inform their decisions and optimize their returns. However, there is often some discrepancy between the quality, accuracy, and availability of data and the ability to source and extract data sets efficiently. Investors and developers need access to real-time data to assist them in staying ahead of the market and identifying golden opportunities before their competitors. 

That said, feeding low-quality or inaccurate data into predictive algorithms software can have extremely negative repercussions. Investors need to be able to trust the accuracy and reliability of the analysis they use when making critical decisions. 

Successfully applying big data analytics to real estate investments can be a challenge. Training algorithms to accurately analyze data requires the input of vast amounts of high-quality, real-time data in order for a Machine Learning (ML) model to function well. Oftentimes, data needs to be prepared and essentially ‘cleaned’ before it can be used for analytics, and this can be a costly and time-consuming exercise. Additionally, once you have the necessary data sets successfully integrated with your ML models, it can be challenging to scale your data collection processes effectively.

For this reason, many professionals in the real estate industry, including developers, construction companies, investment houses, and software firms, outsource their data collection entirely. Leaders in the industry are quickly opting for complete automation practices in their data collection, and this will become the industry standard in the coming years. 

Final thoughts

Big data assists investors in acting more strategically, helping them to minimize risk and make well-informed investment decisions. It also assists them in getting in early and following projected trends through predictive analytics, allowing them to purchase property that is prime for high-yields and secure good deals ahead of the rest.


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