Poor Quality of Data in Real Estate

Data is an important corporate asset. Data analysis helps us observe patterns and trends and draw insights, which in turn help in improving the performance of a company. However, poor quality of data is a major hurdle in most industries and the real estate sector is no stranger to it.

Data quality is an assessment about whether the data collected is in shape to serve its intended purpose. Some of the key characteristics of good data quality are accessibility, reliability, and correctness and consistency across all the business channels.
The real estate sector has long faced real problems when it comes to data availability and quality. The three key challenges the real estate faces are:

Data is not ‘the’ decision support tool
Real estate portfolios are majorly managed on the basis of contractual data. Considering the length of an asset’s lifecycle, the core data of the underlying documentation can be vast and highly intricate, depending on the types of assets, the complexity of portfolios and the number of parties involved. However, in the real estate sector, data is neither recognized nor appreciated as a decision support tool.
Data gathering and recording is more of a compliance requirement and kept as repository for any future dispute. It is not formally governed and data providers often utilize different formats, standards, and methods of moving blocks of data. Due to these, the data lacks consistency and accuracy, hence analysis drawn might not be worthwhile.
A structural approach to decision making combining experience and data helps streamline the process and improve performance.

Multiple Stakeholders
The complex web of owners, property managers, lawyers and brokers and existence of multiple data silos, where data is collected and recorded but not shared with others in the company, makes it difficult to aggregate and analyse data. Multiple sources of data collection lead to multiple versions of data which could lead to confusion in analysis of the data. For example, a company where data about the inflow and outflow of cash is collected by both the Portfolio Management Team and the Finance Team, while Portfolio Managers work with spread sheets and modelling tools, Finance Managers make use of accounting systems and financial tools. The underlying data collected by these different tools is often independent and rarely cross referenced, due to which the probability of making a wrong decision increases. In such a situation, the key stakeholders lose confidence in the data and it goes on to being considered an accessory rather than a worthwhile business tool.
Little or no automation
A major roadblock in real estate sector is that the industry is still heavily reliant on spread sheets for storing and exchanging data, despite innovations in technology. Fear of losing jobs due to redundancy of skill sets could be a potential reason why the sector has been late in adopting new technological advancements. What the sector doesn’t realise is that spread sheets lack control and auditability, are prone to human error and are not secure. To optimize activities and processes, real estate companies should work to move their critical data into structured and controlled databases, by automating their existing processes with the help of ERP systems and new cloud based data management systems. This would improve overall agility and operational performance of the companies and, access to complete and high quality niche data that the industry requires a whole would subsequently be made easier.

Lack of standards for Data measurement
Across cities, no standards are set and there are wide variations in the very nature of land measurement which leads to incorrect valuation of property. For example, stamp duty and registration is calculated based on the carpet area. However, its market value or rent is calculated on the super built up area or gross leasable area.
Different states have different units of measurements. For example, in Kerala, land is measured in ‘cent’, whereas in Maharasthra, it is measured in ‘gunta’. States do not follow a common benchmark for building construction – while some use Floor Space Index (FSI), others use Floor Area Ratio (FAR).
It is essential to standardize these units of measurement as inconsistencies in measurement could introduce variations as high as 24 per cent.

With ever-growing competition in real estate there is an imminent need for businesses to optimize their performance and encourage cooperation among their departments. To improve profitability, businesses need to identify sources of revenue as well and take care of their cost structures. Senior management should make clear which part of the business has responsibility for collecting and maintaining each data set. For too long the real estate sector has accepted the problems of poor quality data and muddled through. It is now time for companies to take practical steps in order to make real-time, better advised decisions.



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