If you are in the real estate business, chances are that you have heard some version of the industry is “being disrupted by” and “big data”, “AI”, or “analytics”. Multiple survey results and reports published during recent years indicate that the majority of executives in the RE industry now believe that harnessing the potential of big data and AI offers a huge competitive advantage (e.g. take a look at KPMG 2019 survey report or the Pwc report). But how far are we in the transformation process? Has the industry unlocked the full potential of data, advanced analytics, and AI?
We are not there yet. While digital transformation and innovation in proptech are in the acceleration phase, according to KPMG, “only 25% of surveyed RE companies have a well-established data strategy that enables the capture and analysis of the right datasets”. In other words, almost the entire industry believes utilising data and analytics to be a competitive edge, but only a fraction of them have a good strategy to get there. While I don’t have many examples from other industries to benchmark against, I still think that the pace is not fast. In this article, I want to address a few factors that slow down this transformation process.
Data science and especially machine learning strongly relies on data volume, quality, and how it can be accessed:
The most common data-related challenges I have faced or heard about may not be specific to the RE industry, but nevertheless, contribute to slowing down digital transformation. The first challenge is that relevant data are scattered across multiple sources in different formats and with various degrees of quality and reliability. A lot of relevant data, especially if produced by the public sector, are unstructured or not machine-readable, i.e. in the forms of PDF documents or papers. Another problem is that property data are not standardized. Many of the processes in the RE industry are manual and accordingly, data is produced by a human entering non-standardized information in some system. Lack of standardized data production leads to inconsistent data across different sources. Property features are not consistent, and subjective features like property conditions do not follow one widely accepted standard that renders comparisons next to impossible.
The hard truth about data science is that the largest chunk of time is spent extracting, cleaning, and organizing data to get it in workable shape. So the better and more structured the raw data, the more time is left for the part of the process that directly brings the value.
RE companies should have a clear vision on how data science should be leveraged for maximum impact and develop a digital transformation strategy to realize the vision as efficiently as possible. It should be clear by now that digital transformation should not be just forming a siloed department of engineers and data nerds quietly doing their job. It is about being a core of company strategy and implementing holistic changes in how the company works. It includes building strong digital culture, retraining employees to work with AI systems and analytics tools, implementing automation of the parts of the workflows, etc.
Different players in the RE industry need to continuously monitor the market pulse in order to stay ahead of the curve in the fast-changing world. It is clear that in addition to the traditional macro-level insights, micro-level data like accessibility and crowd behavior are very important for comprehensive market insights and precise valuation. Building advanced analytics tools that mine timely data, provides a package of information capturing location and market state, and provides good predictions can make a huge impact on ROI and profitability. However, building such capabilities can be a massive project; it requires time, substantial funding, a tech team with specific skills, and most probably, changes in the way people work. It is difficult to explain the high costs and long time scales of such projects and convince decision-makers that it’s worth it if they don’t have a tech background. And in fact, this seems to be the case.
“More likely than not (65% of cases), it isn’t a digital or technology specialist leading digital transformation”. – KPMG 2019 survey report
On the other hand, does digital transformation really mean every company operating in the RE industry should have advanced analytics systems built and maintained in-house? Maybe not. In many cases, it might be more efficient to purchase these capabilities as SAAS or AIAS (AI as a service) from tech companies with expertise and infrastructure in place. But still, even if outsourcing the analytics capabilities, any RE company still needs a sound digitalization strategy, good understanding opportunities of advanced analytics, and a data-driven decision mindset.
Even the most advanced AI systems nowadays rarely are fully autonomous, but rather they support humans in decision making. Data science products are no exceptions: employees might need some retraining to work with such systems and probably adapt their workflows. Of course, it is not necessary for the end-user to understand how machines learn and produce output, but it is definitely necessary to be comfortable extracting information from data visualization rather than from lengthy reports and excel tables. I have met with such old-school types who had a hard time interacting with analytics tools and getting information from the data visualizations on dashboard applications. People might be reluctant to give up established practices they are comfortable with. This is part of building digital culture in the organization to provide opportunities to retrain employees and inform the benefits of data-driven decision making.
To summarize, the challenges harnessing advanced analytics in the RE industry are a lack of good digital transformation strategy, having a good understanding of how to adapt internal processes to produce well structured and good quality data, and building strong digital culture throughout the company.
The sheer size of the RE market and its relatively slow pace of adopting tech innovations drives AI startups like CHAOS to disrupt. A fast-growing number of tech startups showcasing the predictive potential of long historical records of transactions, valuations, predictive maintenance, and property investors and managers are increasingly willing to partner up with them.
Dr. Valeri Tsatsishvili is a data scientist with solid experience in academic research in the Finnish Center of Excellence projects. He holds B.Sc in Physics, MA in Music, Mind, and Technology, a Ph.D. in Mathematical Information Technology. Interdisciplinary background gave him the opportunity to build data-driven analysis solutions on various domains spanning from music/speech recordings to brain images. In CHAOS he continues to cross different fields such as urban planning, real estate, and sustainability. In his free time, he enjoys listening to prog rock/metal music, playing his guitar (metal as you’ve guessed), and following developments on the sports/luxury car market. On the scale of Mr. gut feeling and Mr. logic, he is geared towards the 30/70 mix, relying more on data or facts-based decision making in his daily life.
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