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The 3 Future Trends Of Business Analytics In Real Estate

For a long time, real estate lagged in terms of technology. The application of data analytics, machine learning algorithms, and similar technology in the building world is an industry trend that wasn’t widespread until slightly over a decade ago. However, as technology and automation continue to dominate various sectors of the economy, real estate is slowly catching up. 

The real estate industry is continually under pressure to tap into big data potential and apply machine learning and tangible strategies into their operations. A 2018 KPMG Global PropTech Survey revealed that over 49% of the event’s participants believed that big data, artificial intelligence, and data analytics are the future of the real estate industry.

Consequently, some of the top players and veteran professionals in the real estate industry are already at the forefront in exploiting the potential of their decade-long transaction records, asset management, valuation, listing, and similar relevant data. 

To this end, the data provision sector is rapidly advancing (even saturating) with new startups, most of which have already established themselves as forces to reckon with, including Reonomy and HouseCanary, which have since joined the ranks of successful industry players like CoStar and Real Capital Analytics. This has, in turn, made it possible for any company with an interest in real estate to acquire as much relevant data as they can efficiently.

However, these advancements have presented a new problem for real estate professionals who are unsure how to utilize the data that is increasingly being made available, according to a recent NAIOP piece. Another 2019 KPMG Global PropTech Survey showed that about 80% of real estate firms still don’t have data-based decision-making in place, citing a significant gap in the required skill set. Only 5% of these firms have data analytics experts who can oversee the transformation efforts.

Which brings us to the question, how can the majority of real estate firms apply data analytics or data science to their diverse workflows? What are the benefits, and which future data analytics trends should we expect in the world of building?  Lastly, where can professionals acquire these skills to ace big data potential in real estate?  We look at the three future trends of business analytics in real estate and attempt to answer this question.

Trend 1: Automated PDF Extractions

Automated PDF extractions models assist in understanding the present trends in the property market by enhancing extensive data to analyze current market prices and their fairness. Statistics-based approach to valuation is gaining global implementation, with notable pioneers like US-based Zillow Zestimate, Singapore-owned UrbanZoom and Finland’s SkenarioLabs being at the forefront.

The automated data strategy aims to enhance data to establish accurate property market value estimates, providing a fair and accessible ground for willing buyers and sellers. Indexation-like approaches are also adopted alongside other highly advanced data analytics techniques, which are further employed in online machine learning and ensembles. However, the final results are different. Instead of an index, the output is a rough estimate of a property’s value. 

The main benefit is better and low-cost precision on the fairness of the property market value. Not only are these valuations beneficial to pricing properties, but also to mortgages and in helping stakeholders assess funding portfolios that support these properties. 

Notable companies already enjoying the benefits of automated PDF extractions technologies include Opendoor and Properly, which place automatic bids on assets, giving owners ready liquidity in properties. Automated valuation models are essential in understanding the present real estate market so you can assess a fair transaction price for an ongoing deal.

Trend 2: Machine Learning Algorithms

Another instance where business analytics, particularly machine learning, applies to real estate is property market price. Generally, home prices are determined by several factors that include property developers, investors, realtors, government policies, and real estate brokers. 

Machine learning comes into play by providing accurate predictive commercial real estate analytics through a ten-year data set. Additionally,  socio-economic factors like Consumer Price Index, House Price Index, Gross Domestic Product, Producer Price Index, and Effective Federal Funds Rate are gathered and deployed in the prediction technique.

To create an efficient model, different powerful machine learning algorithms are employed and integrated with the proper encoding to create an appropriate model of the transaction to ascertain if the final sale price will be higher or lower than the listed price. These machine learning algorithms include:

  • Logistic Regression
  • Random Forest
  • Voting Classifier
  • XGBoost. 

Of the four AI tools, XGBoost provides the most effective and powerful results compared to others. As one of the leading business analytics in real estate, this model can assist mortgage professionals, real estate investors, and banks in making informed decisions.

Trend 3: Accuracy And Speed

Business analytics in real estate provides more accurate and predictive analytics that are crucial to faster and efficient transactions. Comprehensive data and data sources that determine precise property values and estimates results in  more consistent property values among real estate players, including appraisers, brokers, and lending institutions. 

Furthermore, property appraisers can develop better property visualizations, such as advanced visual maps, 3-D illustrations, among others which give investors and lenders the required clarity to make informed lending or investing decisions.

Finally, easy access to credit information, reports, and similar data can accelerate the process of being approved for a mortgage. Hence, it’s time-efficient.

The Trend Continues

Although having lagged in embracing industrial automation, the real estate sector is fast catching up with healthcare, transport, and security in the use of big data in decision-making. While data science remains largely unexploited, significant efforts are being made by startups and growth-focused institutions to unlock these potentials.

Furthermore, data science in real estate is now being taught in various institutions and learning platforms such as the General Assembly, which offers an on-campus course on business analytics in real estate, Coursera, which offers pre-recorded self-study videos on data science in real estate, formal universities which provide 1-2 year study programs and PropertyQuants which offers a comprehensive training specifically on how to apply data science and machine learning to real estate.

Given how these trends are fast transforming how the industry operates, skills in big data and analytics are in high demand as more and more professionals are needed to spearhead this remarkable transformation. And that should serve as a cue to real estate professionals seeking to be at the top of this transition or at least be a part of it. 

We believe these are just the beginning of a significant transition in the building world as far as data science goes, and we’ll be updating future developments as they come. If you’d like to be a part of this journey, you can join our email list to stay up to date on these changing trends.