Obtaining fast, actionable insights from big data is evolving the way industries operate across the board. From lead generation to function, performance, and reporting, data disciplines are unlocking new levels of potential, particularly when it comes to real estate. However, given the huge potential for data science and its related disciplines to drive success for investors, developers, and agents alike, data is not being leveraged fully and the significant opportunities for data scientists within the real estate space remain to be explored.
KPMG’s 2019 Global PropTech survey found that 80 per cent of firms in the field do not have data driving “most or all” of their decisions. This is despite the KPMG Global PropTech survey from 2018 finding that 49 per cent of participants believe that big data, AI, and data analytics are the technologies that will have the biggest impact on the industry long-term.
Let’s take a look into:
- How data is being used by those in real estate that have invested in data science & AI
- What is stopping the rest of the real estate industry from catching up
- How data skills can help you enhance your real estate career
How are businesses in the real estate/property market using data science, data analytics and AI to gain a competitive advantage?
Even though not every professional or business in real estate is currently equipped to organise or capture the insight from the abundance of data they are collecting, there are those that are unlocking and using data science, predictive analytics and machine learning effectively, to organise and qualify data, understand market trends and optimise business strategies, identify and manage risks, predict consumer behaviour and increase engagement, automate processes to enhance employee performance, and provide more tailored solutions to clients.
Here are just some of the many ways real estate professionals and businesses are using data to optimise processes:
Identifying and leveraging hyperlocal patterns to more accurately inform, access, and connect consumers to real estate that fits their lifestyle
This is based on non-traditional quantitative and qualitative variables, such as:
- mobile phone signal patterns
- tone of online reviews
- type of after school activities
- traffic bottlenecks/commute rush hours
- number of permits issued to build pools
- noise pollution levels by time of day
- neighbourhood quality/crime rate
- building energy consumption compared to others in the same postcode
- number of coffee shops in the area (also, the type of coffee shop – vegan-friendly/franchise-based/independent?).
Data analytics is providing the opportunity to identify hyperlocal patterns to formulate a more accurate picture and price indices, enabling the real estate industry to better cater to unique customer needs and as a result build loyalty and increase customer satisfaction.
Identifying hyperlocal patterns also provide the opportunity to predict the next big growth areas ahead of competitors and can help to generate a higher volume and consistent quality of leads.
Making informed property-based decisions using geographic information systems
This enables professionals to visualise, understand, and analyse location intelligence to better inform decisions relating to land ownership, zoning, property value, finalising construction plans based on climate conditions/regional restrictions, assisting in the selection of home insurance, mapping out commuting pathways etc., basically – facilitating an accurate pros and cons market and spatial based analysis of a certain location, to mitigate risks and uncover relevant insights about evaluating, purchasing, developing, upgrading and selling real estate.
Forecasting market trends and investment risks using traditional and non-traditional data types and advanced analytics
Predictive data analytics in real estate can aid in better understanding certain consumer groups and developing buyer profiles to assist brokers, determining the best use of land whether that be for public, private, or commercial use, assess online and offline user behaviour such as consumer commuting and spending patterns, monitor leisure activity preferences, buyer or renter incomes, even pre-empt potential damages/insurance risks, and set competitive property pricing based on quantitative and qualitative insight surrounding location assets etc.
For example, a Mckinsey report detailed the use of machine learning to forecast rent over a three-year period per square foot for multifamily buildings in Seattle – this yielded results exceeding ninety percent accuracy and was powered by an application that combined traditional and non-traditional data – a forecast that would not be possible without the timely collection, organisation and analysis of big data.
Streamlining the research/property assessment process and buying/renting experience for customers
The abundance of behavioural, preferential and location based consumer data is providing the real estate industry with new opportunities to stay ahead of their competitors by meeting consumer and client needs in creative ways – especially when they are actively searching for their ideal property fit online.
This includes the use of tailored apps and forums, property matching software, virtual home tours and using social media to provide real-time updates and market emerging real estate opportunities – all to help potential consumers build their interest, by identifying and narrowing down their preferences for their next property based investment prior to speaking with an agent.
It is important to note, streamlining the consumer experience does not mean replacing agents – using data science, analytics and AI to more accurately meet consumer needs, is how real estate professionals can provide a more effective and efficient service – boosting consumer loyalty, trust and ultimately, satisfaction.
So, what’s getting in the way of data being used effectively across the real estate market?
The reality is that the data real estate professionals need, exists, but the industry is still learning how to efficiently and effectively leverage it. Traditional real estate databases house information, but are not designed to automatically analyse or produce future projections so they are of limited value if you are looking to make data driven decisions in real time. On its own, raw real estate and property data doesn’t hold much value either, it is the insights that can be gained from it that carry weight, and if this data is not analysed in a timely manner, insights become stale and subtle trends can be lost – resulting in revenue loss and missed opportunities for profitable investments.
That being said, real estate has still come leaps and bounds with regards to its use of data in recent years. New startups such as Frist, HouseCanary, and Reonomy are cropping up all the time, and there is a growing trend of established real estate firms working to revolutionise, automate, and speed up their processes using data science tools and techniques.
The effective use of data by some businesses and not by others, is why there is a growing skills gap and demand for real estate professionals to become proficient in data science and analytics, and there is also opportunity for data scientists to specialise in the real estate space. One of the biggest challenges for real estate professionals today is the inability to quickly and effectively wrangle and analyse data for valuable and actionable insights.
Traditionally, the real estate and property market has relied solely on historic data, personal intuition and experience. While this has been effective in the past, sifting through today’s mass amounts of data can be laborious and time consuming, not to mention it is impossible for any one person to effectively analyse all the data available to them, and natural human shortcomings mean some insights will be missed and hidden patterns will be overlooked.
The lack of trained data professionals working in the real estate space is what is preventing the effective use of data across the property industry. So if you are someone that possesses domain knowledge or experience in consulting within the property market, or you are willing to learn – gaining skills in data, analytics and AI, will not only enable you to unearth the gems hidden in big datasets to enhance your performance as an agent, but you will also be able to future proof your career by producing insights that will make you a uniquely significant asset to employers in the real estate space.
How data science, analytics and AI skills will enable you to become more effective in your real estate career
Beyond becoming an entrepreneur in PropTech or taking up an entirely data based role, data literacy and skills can help you better perform specific functions within traditional roles in the real estate space, and also help you to accelerate your career into a multifaceted role:
Advanced analytics and machine learning help asset managers make informed and more accurate decisions. For example, if an asset manager is looking to optimise and grow their portfolio of multi-family buildings, they can use machine learning to draw from both macro and hyperlocal projections to see which cities and suburbs have the highest demand for multi-family housing. This can then be used to identify and invest in buildings in areas that are undervalued but increasing in popularity and appeal to the target audience.
Access to big data, machine learning / predictive data analytics and data visualisation tools, allows for enhanced property management by enabling managers to assess emerging trends and risks, diagnose and monitor maintenance needs, provide more accurate repair times and regulate maintenance schedules, predict customer behaviour and curate more relevant business strategies.
Real estate agent
Agents that can effectively collect, organise, extract and apply data insights and automation technologies to enhance the experience of their clients, will be able to spend significantly less time on administrative tasks, and more time focusing on developing personal relationships to increase customer retention, by more precisely understanding and meeting client needs.
Real estate analyst
Data analysts in real estate have a major advantage because they no longer have to spend copious amounts of time making calls to manually collect data or manually sort through unstructured data sets. Data science, automated processes and machine learning tools and techniques accelerate the process of collecting, organising and processing data to extract insights – giving analysts more time and opportunity for testing ideas, creative problem solving by uncovering hidden insights, and utilising more accurate data to generate profit and facilitate future planning.
Real estate broker
Big data and machine learning makes brokerage much easier and efficient, for example, mortgage brokers are able to calculate the optimal amount to lend borrowers, especially first time borrowers with limited or no credit history, or identify and forecast the risks of lending to existing borrowers using historic data and descriptive analytics.
Real estate appraiser
An appraisal is an important process in the real estate business because it provides valuable insights to home buyers and sellers. Traditionally, appraisers would have to manually investigate and study the property market in order to determine the value of property and other assets. Thanks to big data, factors such as demographics, legislation changes, in-demand and in-decline locations, market pricing etc. can all be scrutinised to provide more accurate appraisals and reporting.
With consumers observing market trends and proactively searching for their next strategic investment opportunity via online tools, the real estate and property industry is rapidly recognising the importance of leveraging and underpinning its processes, services, and decision making with historic and real-time big data insight. We are also seeing an increasing demand in the real estate sector for professionals with data skill sets and knowledge of the property market.
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