The Global Impact of Big Data on Real Estate
Learn how real estate companies can leverage the power of big data to enhance decision making, improve customer engagement, and streamline engineering and construction.
We are about to enter the era where reliance on data analytics tools will become a fundamental aspect of decision making. As big data continues solidifying its importance in other industries, real estate has been somewhat hesitant to catch up. Those who have adopted real estate software are already reaping significant benefits. In this article, we are exploring how big data in real estate provides value for consumers, realtors, as well as investment and construction firms.
Value for Consumers
The absolute majority of future homeowners start looking for property online, but they will most likely turn to a real estate agent later anyway. One of the biggest reasons for this trend lies in the multitude of parameters that really influence consumers’ property evaluation. Think about it, most search engines try to offer as many filtering attributes as possible, but it’s never enough for a consumer to make the final decision. ‘4 beds, 2 baths, 1,200 sqft’ — there are thousands of homes that will suit this description. Given that online real estate search is here to stay, there are several ways of how big data can make search engines far more granular.
Localize.city is among our favorite disruptors of online real estate search. The New York-based startup aims to answer often critical questions like: ‘How much sunlight does an apartment get?’, ‘Will a building vibrate because of the train station nearby?’, ‘Are there any good primary schools in the neighborhood?’, ‘Do noisy trucks use the street often?’, ‘Are there any construction projects expected nearby?’
This way, an apartment seeker gets a far better picture of what living in a particular property will feel like and receives a significantly more narrowed list of choices that fit his or her criteria. To make this possible, Localize employs more than 320 AI professionals, urban planners, and cartographers, among others. Unsurprisingly, the app also has an integrated recommendation engine, which tailors search results based on previous inquiries and interests. Interestingly enough, the app also helps realtors to detect hot leads, as a CRM-integrated algorithm shows real estate agents when customers are actively searching.
Another US-based startup, Trulia, uses innovative computer vision algorithms to look under the hood of customer preferences. When home seekers click on a particular property offering, Trulia’s algorithm deciphers the image to detect unconventional preferences like specific wall colors, kitchen tabletop materials, floor types, and others.
Trulia’s approach tackles the often neglected emotional part of this huge long-term financial commitment. Many of us have tastes that we are simply unaware of. Most customers won’t ever use ‘tabletop material’ filter to choose an apartment, but it can still influence their decisions subconsciously. This is why we are likely to see more applications of artificial intelligence in real estate in the near future.
Value for Investors
AI has taken almost every investment niche by the storm. By some estimates, ML-based models make up 80% of all trading decisions in the financial sector. The penetration of data science in finance shouldn’t be surprising as the best trading decisions are made by carefully analyzing past transactions, recognizing patterns in data, and monitoring social media sentiments — things that AI is far more efficient at than humans.
When it comes to property investment, however, prices are heavily influenced by largely subjective factors that often can’t be found online. In other words, these factors fall into non-traditional data.
Experienced investors have been aware of the power of alternative data for decades, but they still rely on intuition when making six-figure decisions. The reason is that by the time an investor collects millions of data points, identifies the most important ones, and finds clear patterns between them, the investment opportunity might be gone.
The investor may check the proximity to a highly-rated gym or figure out if Starbucks plans to open its coffee shop in the near future, depending on the type of property and its intended target audience. ML algorithms can perform exactly this type of property evaluation, but faster and more accurately.
For example, London-based startup Proportunity uses ML to accurately predict which homes will increase in value over a specific period of time. On top of the historical pricing data, the algorithm also processes millions of non-traditional data points including transportation availability, crime, etc. Although you might think that this looks like an ideal tool for CRE investors, Proportunity utilizes its advanced analytics system to help first-time property buyers to ensure that they invest in homes, the value of which will appreciate over time.
The most prominent startup in the CRE investment niche right now is probably Israel-based Skyline AI. The company uses advanced AI algorithms to accurately predict property value, enabling investors to use market anomalies to their advantage and confidently invest in high-risk, high-reward opportunities.
Since its foundation in 2017, Skyline AI has already accumulated more than $25 million in funding. Such an increasing interest from top investors was foreseeable because, in addition to Skyline’s access to one of the biggest CRE transaction databases that is getting daily updates, the company also comprehensively researches the impact of alternative data on CRE prices. In its recent white paper ‘The Map is Not the Territory: Discerning the True Opportunity Within Opportunity Zones’ researchers prove that non-traditional data, like average commute times, fluctuations of the number of Airbnb offerings, or car ownership rates is critical to the accurate identification of investment opportunity zones.
Skyline AI’s case is just the tip of the iceberg. Data science offers an investment potential that is yet to be unleashed. As companies aggregate more data points, study the most crucial relationships between them, and train ML algorithms based on the findings, AI will accelerate decision making to the point when investors without access to these tools will be too slow to timely capitalize on the opportunities. However, it’s crucial to understand that housing still remains a highly subjective area. AI tools are here to support or oppose one’s investment idea rather than generate it in the first place.
Value for Construction
Big data is on its way to change the fundamental real estate area — engineering and construction. Similar to traditional real estate investment firms, construction companies tend to assess a project based on a limited amount of data and industry experience. Moreover, large construction projects can easily last a decade, which can significantly skew the evaluation accuracy. With big data analytics, companies can leverage their internal historical data such as regional spending trends, project size, and subcontractor performance to identify factors that really influence profit margins along with the best projects to bid on.
Data can tackle some of the on-site issues the construction industry faces, too. As a multifaceted process that varies from project to project, construction can benefit from real-time data analytics to boost on-site productivity.
For example, by tracking employee movements with the help of drones or on-site smart sensors, managers can better locate certain equipment and materials to increase production efficiency and ensure higher safety. In fact, drones are currently disrupting the construction industry by providing a bird’s eye view both figuratively and literally. In collaboration with ML-powered analytics software and computer vision, UAVs can detect construction errors in real time, help identify logistical issues, and predict risks.
Bound with telematics data, GPS-enabled devices can also monitor machinery productivity, cycles, and condition. Project managers can use these data analytics tools to better understand how resources are getting allocated and how employees manage their time.
This is especially relevant for construction. The 2020 National Construction Payment Report by Levelset and Fieldwire shows that 69% of contractors say that poor job site coordination ‘sometimes’ or ‘always’ causes projects to run over budget or past deadlines, and 75% of the survey respondents say that their companies spend less than half of their time doing actual construction work.
Advanced ML applications can also make the design phase more efficient. The industry is currently testing generative design solutions to avoid the notoriously tedious and complicated process of merging different mechanical, electrical, and plumbing (MEP) systems. Most of the time, there are different teams working on each part of the MEP, which makes clashes almost unavoidable. By consolidating building information modeling (BIM) and ML-powered generative design solutions in one application, the algorithm can autonomously create the routing systems encompassing all MEP components.
This is exactly what US-based startup Building System Planning has done with their GenMEP, an add-on to the popular BIM solution called Autodesk Revit. In a nutshell, GenMEP comes up with a 3D routing for every MEP element, considering a building’s particular geometry and ensuring that different system routes don’t intersect.
Given the aforementioned evident advantages of data-based decision making for commercial real estate and the industry’s immense profit opportunities, the comparatively low number of data-centric firms in this sector can seem rather unexpected. While there is a certain degree of industry conservatism, most firms simply fail to implement these technologies at scale.
For an average company to capture value from their data, a side project carried out by a few newly hired data scientists can hardly bring tangible results. In most cases, a company’s approach to data management will need a property management software and a complete data strategy revamp. There are three essential parts of this transformation:
- It all starts with data cleaning. This is among the most crucial yet undervalued steps on this list. Far too often companies resort to self-operating tools for data cleaning, which rarely perform adequately. This is why it’s critical to set long-term goals, figure out exact business areas to be impacted by data analytics, identify clear use cases, and clean relevant data.
- Companies should establish solid data governance practices. This proved to be especially tricky for construction firms, as their datasets are often scattered across multiple departments and stored in different systems and formats. Moreover, each project often implies the introduction of new methods and approaches to data tracking, which complicates data management even more. This is why it’s crucial to create a centralized easy-to-update data management system.
- Organizations need to find experienced data professionals. While there is still talent scarcity, it’s time to realize that a successful data-science team needs to include business users as well. It’s not about building a healthy corporate culture but about aligning technical expertise with long-term business goals — something that too many large companies fail to achieve.
In simple terms, organizations need to treat this transformation as a serious technological revolution. Imagine a factory that transitions from manual labor to robotics-based, semi-autonomous production. It’s an all-permeating process that needs input from almost every part of the organization to succeed.
Those lavish amounts of data that real estate companies have been aggregating over the years can serve a far more valuable purpose than record-keeping. Being data-heavy by default, the real estate industry approaches the majority of processes through the lens of data analytics rather than intuition.
The predictive power of these emerging technologies implies an unprecedented value for everyone in the chain: from architects and builders to realtors and tenants. The noticeable reluctance among some industry players should be considered by others as their chance to emerge as leaders. This is why in such traditional industries as real estate and construction, involving consulting partners is often a keystone of success. Companies without major experience in big data analytics will most likely become overwhelmed by a constant stream of their unstructured data and the need to organize it properly.
Iflexion is here to help.
We look at the real-life examples of artificial intelligence applications in the real estate industry and suggest what to keep an eye on in the nearest future.FULL ARTICLEPublished: February 27, 2020Updated: December 03, 2020By Yaroslav Kuflinski
In this article, we explore how AI can help small businesses to increase efficiency, enhance employee experience and make better strategic decisions.FULL ARTICLEPublished: July 22, 2020Updated: December 03, 2020By Yaroslav Kuflinski
Every day, we send 294 billion emails, 500 million tweets, and 65 billion messages over WhatsApp. What can ecommerce organizations do with that rising sea of information? We have some answers.FULL ARTICLEPublished: October 02, 2019By Ekaterina Pioryshkina
WANT TO START A PROJECT?