The real estate sector had its share of the roller coaster ride when it comes to data availability and its quality, but no one can be blamed for it. These data quality challenges are a result of how the real estate industry operates.
More than 70 percent of the information real estate firms require to manage their business, is generated by a wide range of disparate but interrelated parties including brokers, lawyers, property advisors, appraisers any many more. Each of these identities in the complex real estate ecosystem holds their share of data, which makes data aggregation and analysis of that data a very big challenge.
Identify key data stakeholders
This certainly can solve the problem – but partially. Even if it gets clear as to where to find the data, there is no guarantee that each of the stakeholders collected the data in a comprehensive yet consistent manner. Global real estate players combat a completely different problem of data with regional nuances and differences between asset classes where the details required for managing a property varies. I.e., managing a real estate portfolio in the USA will require different data, and then the data or information required to manage the same kind of portfolio in the UK or in Germany. This makes the already complex process of data collection from internal and external sources, much more difficult.
Real estate firms operate in a world characterized by increasing complexities and uncertainties, making it all the more important for them to make the right business decisions. And for that, they need the ability to analyze humongous volumes of information and trust the insights they gleaned. Data always has mattered to the real estate business, but it’s time when having good data matters more than ever. Good quality information is critical to the success of real estate companies across the globe.
3 challenges with real estate data
Three real estate data challenges include lack of standard definition for data, the existence of multiple data silos and information from it used by different departments in a real estate firm, and big time dependency on spreadsheets for data storage and exchange. But these challenges can be addressed with help of well-thought business process improvement.
1. Dependency on spreadsheets
Real estate as an industry has been really reluctant at adopting technology & tools. More than half of the realtors still use and are more than dependent on spreadsheets when it comes to tracking and exchanging critical data. Lack of customized enterprise resource planning (ERP) is also responsible for this. All said and done, the niche data that real estate players require has no natural home in information systems used by real estate companies and finally, they resort to spreadsheets as a stopgap arrangement. Lack of data security, control over access, and the ease of human error are some of the well-known challenges when working with spreadsheets. Nothing wrong in saying that it is “not the right tool”, to manage important information that drives your real estate business decisions.
2. Data in multiple-silos
Real estate tops the list of industries maintaining multiple data silos. Data from multiple silos is further used by different departments of a real estate company – without any overlap between them. This is a clear indication as to no one across the organization has the real view of what exactly is happening.
A prominent example to this data mismanagement is how, asset and portfolio management teams in a real estate organization interact with finance teams – or rather we can say because they don’t interact – data silos come in existence. Asset managers conveniently use spread-sheets, whereas finance teams legitimately use property management accounting systems. Now the underlying data in spreadsheets and reporting tools are created and collected independently, but usually never gets integrated.
Lack of integration of such data points is always disastrous and is the reason why both senior management executives and front-line workers don’t have confidence in the data they use. Data is valuable only if the one using it is confident about what the data is telling them.
3. Standard data definitions
Standard data definitions, which provide the context in which data can be understood and to assess whether or not insights can be derived from it, are a prerequisite for reliable data. “Measuring floor areas” gives us a classic example of how different definitions lead to different results. Ideally, it should have a standard approach and give standard results, but interestingly a research claims that differences in methodologies used for area measurement can result in variations of up to 24%.
This is not only with area measurement. Similar discrepancies prevail in almost all types of measurements, from lease start dates to net rents, and many more. All this makes it necessary to define the basic data elements with more clarity and precision – and also implement it consistently as the stakes are really high. Wrong and unclear definitions will make the data to give out an inaccurate overview of the portfolio performance and risks, and all these lead to sub-standard decision making.
Above mentioned issues prove that real estate industry as a whole is facing a significant challenge in managing their data. From collecting data at the source due to disparate people holding the information, lack of standard definitions and non-availability of data management solutions putting data in silos, each of it needs an immediate solution.
Real estate sector is muddling its way through poor data quality. As said by a visionary “the price of light is less than the cost of darkness“, the cost of managing real estate data with help of data consultants would be much less, as compared to the cost of lost sales opportunities.