11 Jun '21

The Power of Data in Real Estate: the Key Competencies you need to know

Introduction

As we explored in a prior article, the accurate and effective use of data is increasingly important within the Real Estate sector and is a key enabler to a number of central activities for the industry. In particular, we highlighted the importance of a having comprehensive data strategy and implementation plan in place, in order to take full advantage of the industry’s changing dynamics.

In this article, we are expanding on our thinking by creating data competencies that specifically relate to the Real Estate sector. Given the large number of data points available within Real Estate, organisations who can implement these competencies will be best placed to gain a competitive advantage amongst their peers within the industry. The competencies we have created are based around the DCAM (Data Management Capability Assessment Model) framework, as the industry standard framework for Data Management created by EDM Council members. 

According to their website, DCAM defines the scope of capabilities required to establish, enable and sustain a mature Data Management discipline. It addresses the strategies, organizational structures, technology and operational best practices needed to successfully drive Data Management across your organization, and ensures your data can support digital transformation, advanced analytics such as AI and ML, and data ethics. You can read more about it here: (https://edmcouncil.org/page/aboutdcamreview).

With the DCAM model in mind, it is important to think about how the use of data matures in an organisation. The DCAM framework is designed for organisations to assess how mature their capabilities are and measure this over time. The same should be the case when it comes to data use as well. However, all too often we see organisations jumping straight into the deep-end hiring top-end scientists and statisticians to undertake roles that are not necessarily required from the start. 

Good data equals good insights

Whilst it is certainly plausible to have insights without the need for good data, we see time and again the issues this creates. Insights become tainted, data is manipulated to achieve the required outcomes and creating this valuable information becomes an expensive overhead and a chore rather than an efficient, well-oiled machine.    

For those firms who are serious about making data a commodity in their organisation and using it to provide real value, we recommend focussing on maturing both your data capabilities and also the way you use data.

For this reason, we have developed a data-use evolution map. This helps firms to consider both their current and desired data-use needs and highlights the steps required to achieve their goals. The competence evolution map is based on four pillars:

  • Basic – Reporting
  • Foundational – Analysing 
  • Intermediate – Predicting 
  • Advanced – Self learning 
Diagram

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Basic Competencies

Central to the success of the more advanced competences is a requirement to ensure that the basic building blocks are well covered and act as a strong groundwork for the foundational, intermediate, and advanced competencies.

Asset Performance Reporting, Management Reporting and Internal Cost Analysis will be very reliant on the Data Governance and Data Quality Management capabilities being achieved. Looking at each in turn, effective Data Governance is key to ensure that an organisation’s data is well managed within a defined ownership model and that any changes to this model are clearly understood along with the implications of doing so. Organisation-wide accountability over data is one of the critical components of this capability.

Data Quality Management is a key capability and covers a variety of data dimensions such as completeness, consistency, timeliness, validity, accuracy and uniqueness. All of these dimensions allow for greater confidence in the data utilised for a firm’s analysis and reporting. Furthermore, it also facilitates the decision-making abilities within an organisation based on the relative maturity of the firm’s data quality capability.

Foundational Competencies

Looking towards the foundational competencies, Data Ethics is a particularly interesting and very topical area at the moment. Given the sheer amount of data now available to organisations there are increasingly large risks with the holding of this data, understanding what it is being used for and also customer awareness around this. This is particularly relevant to Real Estate firms due to the volume of data (including personal data when interacting with private individuals as clients). In many cases this requires a re-evaluation of the traditional frameworks used. Indeed, the avalanche of news stories in the media in recent years on this (particularly within the context of ‘Big Tech’) means firms can no longer ignore this subject and need to actively plan their data ethics strategy.

For more information on Data Ethics please see DTSQUARED’s 3 part blog series this subject.

Another topical Foundational competency surrounds ESG (environmental, social and governance) analysis. Given the current focus on climate change and ESG as a whole, many real estate participants are focussed on ensuring that their real estate assets are environmentally friendly where possible and also need to be able to report on this.

This creates various challenges from a data perspective as the number of data points required to assign an ESG rating or similar are varied and depend on the particular aspect of ESG. For example, organisations may be interested primarily on energy efficiency as a measure to track. Similarly other organisations may be focussed on the sustainability of materials used for construction or the ethics associated with raw material extraction. 

Whilst there are a variety of external companies that may assign or calculate ESG data points, a number of challenges still remain. For example, there may be a lack of common standards across the different providers for reported data. Similarly, the methodologies used may not be consistent, leading to challenges in being able to sort, rate and manage the final metrics. Lastly there may be issues in timing between the raw data used in the metrics and the reporting date of the end consumer. Notwithstanding the above, real estate companies will still need to ensure that this data is correctly managed within the organisation in line with the Data Governance and Data Quality principles highlighted above and that the desired ESG metrics are captured when reporting on this data. 

Intermediate Competencies

As we move towards the intermediate competencies, we can see the increased importance of data management for external data as well as internal data for the Real Estate sector. The table below shows some examples of the different internal and external data types that may be useful for Real Estate organisations:

Graphical user interface, table

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Given the large and diverse nature of the ‘external data’ above firms may need to consider how they source this data and then incorporate into their technology and data architecture. There are many organisations that cater for this need such as specialist real estate providers such as Glenigan (www.glenigan.com) and also macro data such as from firms such as Moody’s (www.moodys.com). Nevertheless, even when the required data is captured it will still need to be governed, reviewed and managed before it may be effectively utilised as part of an integrated data strategy. 

As well as considering the data sourcing for this information, organisations should also carefully consider some of the other DCAM capabilities such as Business and Data Architecture and Data & Technology Architecture. Although the design of each organisation’s desired data and technical architecture will vary based on a variety of factors, the sample illustration below shows how a typical high level target architecture may look for a Real Estate firm:

By designing and implementing a suitable architecture utilising both internal and external data, incorporating the desired data management processes (data governance, data quality, and data architecture), firms will be able to establish strong foundations that support higher levels of data capabilities within the Real Estate sector. 

Advanced Competencies

In considering the more advanced competencies we can see increased reliance on Analytics Management in order to assess, interpret and ultimately allow the organisation to act on the data available. Within DTSQUARED we are already beginning to see increased focus and demand on analytics provision and management within many organisations. It is nevertheless important to ensure that the right foundational building blocks are in place in order to ensure that the data being analysed has the desired level of governance and quality associated with it. Failure to focus on these points will result in redundant effort being spent on analysing data which does not fulfil the desired criteria and so will lead to incorrect conclusions being drawn. 

The Real Estate organisations who can draw the strong trends, intelligence or other inferences from the many data points available, will be able to put themselves and their customers in the strongest position. For example, advanced demand and supply analysis in order to understand current and future price, occupancy and space trends will be increasingly important for property owners and investors and they attempt to balance their portfolios to make the most of these changes.

In addition, organisations may also be able to benefit from the addition of ‘SMART’ technology both inside and outside of buildings. For example, appliances such as HVAC systems can supply data concerning usage and overall general health that will allow owners to more accurately plan for maintenance and ultimate replacement in the most cost-efficient manner. In addition, real estate will also be able to provide usage information so that space utilisation may be optimised at different times of the day or week. Shopping centres may similarly give footfall and other data associated with usage. By having access to this data and using the appropriate analytical methods, firms will be able to optimise their portfolio for a variety of key areas and for a number of different metrics. 

Conclusion

By leveraging ‘The Power of Data’, Real Estate participants will be able to significantly enhance their commercial abilities, improve efficiencies and extend their client offerings and services. As the sector evolves, data will play an increasingly important role not only in Real Estate but more broadly across all industries. It is therefore essential that firms equip themselves now, in order to benefit from this changing dynamic and get ahead of their competition. To take advantage, Real Estate firms must ensure they have a robust data strategy and implementation plan that is consistent with their business ambitions in order to thrive in the future.

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