Big Data's Role In Digitizing Real Estate
Wed, April 21, 2021

Big Data's Role In Digitizing Real Estate

Big data is transforming business models in various industries ranging from finance to tourism, said Dima Williams of Mansion Global, an international luxury real estate listings platform. Now, big data is also making its way into real estate—a multi-pronged, multi-billion industry known for its “tradition and intuition” / Photo by: Arturs Budkevics via 123RF

 

Big data is transforming business models in various industries ranging from finance to tourism, said Dima Williams of Mansion Global, an international luxury real estate listings platform. Now, big data is also making its way into real estate—a multi-pronged, multi-billion industry known for its “tradition and intuition.” John D’Angelo, who leads New York-based Deloitte’s real estate sector in the US, noted that real estate has been a late adopter of tools and technologies, including data and analytics. 

However, in the last 18 months, the industry has been raising awareness of the importance of large data sets and applying data and analytics at work. Technology startups and behemoth real estate companies are starting to draw insights from data sets that power AI-enabled tools. The knowledge from these data sets begins to underline significant real estate decisions: what property to build, pricing, and more.

 

Digitization of Real Estate

“They need to use technologies—especially those used by their target customers—if they want to stay competitive, Nestor Gilbert of online review website Finances Online stated. Per the findings of North American trade association National Association of Realtors, 93% of realtors prefer email and phones for communication, 85% of residential firms encourage the use of multiple listing software, and 48% of firms acknowledged that keeping abreast of technology remains a challenge over the next two years. 

While indeed challenging, utilizing technology such as big data-fueled tools enables developers to better gauge potential projects. With big data tools, agents can efficiently serve clients and home shoppers and sellers can have a clutter- and redundancy-free experience. In real estate, big data is seen as a “relatively novel, non-traditional data of granular insights not before gleaned.” Some examples are the amount of light a home receives each day, noise pollution level in a neighborhood, and others. 

The information extracted from data sets will be used to augment the answers to some of the most important questions in residential real estate. Zach Aarons, co-founder and partner of MetaProp, said, “It’s not just about when [a property] is built; is it made out of wood; how many stories is it; how many bedrooms and bathrooms does it have?”

Pricing Homes

Setting an asking price hinges on the agent’s local knowledge and expertise. The agent considers similar homes, factoring in neighborhood amenities and negotiating over price strategies. Agents and developers alike rely on conventional data about the market like current supply and past sales and property to price a home. 

Developers also need to take into consideration the costs of construction. Asking prices that are too high or too low can struggle to draw in potential clients. For luxury homes, generating prices takes longer and selling them requires more effort. This makes it difficult for agents to determine the “right price” as high-end homes come with unique amenities that may be difficult to find in the market. Therefore, selling high-end abodes tends to be subjective, and this is something that agents struggle with.  

No wonder why 68.1% of respondents think that high-income apartments are overpriced while 28.9% think they are fairly priced, according to a survey by PWC, a multinational professional services network. Meanwhile, only 3% believe they are underpriced. For example, Zillow’s algorithm and home value estimation tool, Zestimate, struggles to gauge the value of luxury properties, said Jeff Tucker, an economist at the firm.  

In San Francisco, California where many homes are priced higher compared to comparable properties in other areas, Tucker said the algorithm’s margin of error for any major market is 3.6%. But pricing is still a struggle for agents and the algorithm alike, which stems from a lack of “training data” points, which are not enough in the luxury segment. Even if comparable data is scarce, technology can still connect the dots and find price benchmarks, according to Joseph Sirosh, chief technology officer at Compass. He added, “AI can actually make the best use of all available data to set the price.” 

Setting an asking price hinges on the agent’s local knowledge and expertise. The agent considers similar homes, factoring in neighborhood amenities and negotiating over price strategies / Photo by: Vadim Georgiev via 123RF

 

Boosting Profits

AI, as well as big data, can analyze properties and build the homes of tomorrow. AI-facilitated decisions can cater to residents by anticipating and enhancing their urban lifestyles. Jared Sullivan, vice president of research at CA Ventures, explained, “Data allows us and our competitors to try to fine-tune our strategies as to where we want to build and what types of products we want to build and what sort of amenities and features we’re going to need to put into those new buildings.” 

With regard to the performance of residential projects, Gabriel Morgan Asaftei and colleagues of management consulting company McKinsey said that 60% of predictive power originates from non-traditional variables. Meanwhile, traditional variables only amount to 42%. Hence, non-traditional variables are a game-changer in residential projects. For instance, the proximity of the residence to eateries may influence its desirability and financial margins. 

Chief economist at realtor.com George Raitu noted, “Some surveys of home buyers and owners show that consumers are willing to pay a premium for proximity to public transit even if they themselves are not using public transit.”

There are more ways for big data and other technologies to change the face of real estate. Big data can also aid in marketing and selling. The real estate industry may be slow at adopting new technologies, but it has the potential to keep up with other industries.