Poor Data Quality Challenges Real Estate Industry Today
No one can fault the real estate business for its data availability and quality roller coaster. Real estate operations cause these data quality issues.
Brokers, lawyers, property advisors, appraisers, and others generate about 70% of the information real estate firms need to run their businesses. Each identity in the complicated real estate ecosystem has data, making data gathering and analysis difficult.
Key data stakeholders
This somewhat solves the issue. Even if the data is found, there is no guarantee that each stakeholder collected it comprehensively and consistently. Global real estate players face a separate data dilemma with regional idiosyncrasies and asset class disparities in property management details. Managing a real estate portfolio in the US requires different data than in the UK or Germany. This further complicates data collection from internal and external sources.
Real estate enterprises must make smart business decisions in an increasingly complex and uncertain world. They need to examine massive amounts of data and trust their findings. Good data is now more important than ever in the real estate industry. Real estate companies worldwide need high-quality data.
Three problems with real estate data
Lack of a common definition for data, the existence of many data silos and the usage of information from them by various departments within a real estate firm, and a heavy reliance on spreadsheets for data storage and interchange are three issues with real estate data. However, these difficulties can be overcome with the aid of thoughtful business process reform.
1. Using spreadsheets exclusively
The real estate sector has been quite slow to accept new technology and techniques. Spreadsheets are still used by more than half of realtors, who rely heavily on them for sharing and tracking important data. This might also be attributed to a lack of tailored enterprise resource planning (ERP). Ultimately, the niche data that real estate players need have no place in the information systems utilized by real estate corporations; therefore, as a last resort, they turn to spreadsheets. Some of the well-known difficulties with using spreadsheets are the lack of data security, lack of access control, and vulnerability to human mistakes. There is nothing wrong with claiming that the tool you are using to manage the vital data that informs your real estate company decisions is “not the correct one.”
2. Multiple data
The number one industry with various data silos is real estate. A real estate company’s many divisions use data from other silos in other ways, with no overlap in their operations. This is a blatant sign that nobody within the corporation truly understands what is taking place.
How asset and portfolio management teams in a real estate business communicate with financial teams—or, perhaps more accurately, how data silos form because they don’t engage—is a notable example of this data mismanagement. Spreadsheets are a useful tool for asset managers, but accounting software for property management is a genuine tool for finance teams. The underlying data used by spreadsheets and reporting tools today is now developed and gathered separately, rarely ever getting combined.
Front-line employees and senior management executives both lack faith in the data they utilize because of the severe consequences of not integrating such data points. Only when someone using the data is certain of what the data is showing them is the data valuable.
3. Common definitions for data
Reliable data requires standardized data definitions, which provide data with the context necessary for understanding and determining if insights can be drawn from it. We have a great illustration of how different definitions produce different outcomes in “Measuring floor areas.” It should ideally follow a consistent methodology and produce uniform results, but interestingly, a study says that discrepancies of up to 24% can be caused by different approaches used to measure an area.
This applies to more than just area measurement. Similar inconsistencies can be seen in practically all metrics, including lease start dates, net rents, and many more. Because the stakes are so high, it is vital to define the fundamental data elements with more clarity and precision and to apply them consistently. Incorrect and ambiguous definitions can cause the data to provide a misleading picture of the performance and risks of the portfolio, which will ultimately result in poor decision-making.
Frequently asked questions:
What are the implications of having poor data quality?
Your company could suffer significant losses if the data it uses are of poor quality. It can result in faulty analysis, poor interactions with customers, and poor judgments of a company’s business.
How does a good database benefit real estate?
Data can help real estate agents evaluate a building’s history, condition, and previous renovations and redesigns. This decreases investor and buyer risk by reducing property and potential unknowns.
What are the website marketing obstacles for real estate agents?
Most real estate agents struggle with lead cost versus the conversion rate. When you prioritize lead capture above nurturing, this can happen. Lead costs will skyrocket if you acquire or buy leads from Google Ads, Zillow, etc.
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