Real estate ultimate guides

Why is the quality of data so important in real estate?

Data is a valuable organizational asset that should not be overlooked. Data analysis allows to identify patterns and trends in data and to develop conclusions, which in turn aid in the improvement of a company's performance. However, poor data quality is a significant barrier in most businesses, and the real estate industry is no exception. Data quality is an assessment of whether the information gathered is in good enough shape to accomplish its intended function.

When it comes to data availability and quality, the real estate industry has long struggled with significant issues. Regarding data flaws in any organization, poor quality information submitted at the beginning of the information chain via data entry interfaces is without a doubt the most significant matter of concern. This article presents several easy, practical, and inexpensive pointers that answer the question and can be used to significantly enhance the data quality of information that has been input by people.

Take a simple Approach

Keep it minimal; do you really require all of that information? A large number of online forms or application forms are simply included as standard, but very often, a large number of the data entry sections are unnecessary and should be removed. The difficulty is that the system may still need the user to go through them, making the entire process tedious.

You should check your data on a regular basis to determine which information is necessary, which information is pleasant to have, and which information is irrelevant. Create your forms with this type of ranking in mind, and make it simple for visitors to fill out only the information that is absolutely essential to be submitted into the system.

Record both good and bad

When a consumer or employee adds information into a form, do you record both the valid information and the preceding information that failed? The faults are just as crucial as the data that has been approved. By gathering both sets of information and analyzing them, you can immediately see where you need to make improvements to the data entry process. In the absence of a data storage system, you run the risk of losing out on possible new customers. Thus there is a strong business incentive to do so.

Even when dealing with genuine, accepted data, you will come across many of the instances in which data has been input improperly or has been the victim of repeated exploitation. This information is fed into downstream systems, resulting in additional effort, increased expenses, and poor judgments. You no longer have any justifications for not starting to profile your data entry data and use this insight to develop new data input methods.

Prevention is better than cure

Data cleansing downstream from the entry point is expensive, monotonous, time-consuming, and susceptible to mistake because of the nature of the data. It has the potential to lengthen service lead times and complicate your information chains without adding value. Analyze the usual data cleansing functions that you have built to deal with low-quality data in your company's database system. This is often done using software or by hand through data entry by data entry experts.

If you are unable to prevent defects at the source, make sure that you have procedures in place as near to the source as feasible to catch faults before they enter the business process. Always remember to include a feedback loop to the form designers whenever you develop an error-checking procedure or clean-up process. This allows logic to be incorporated at the source wherever possible.

Missing data entries

In a perfect scenario, end-users would always complete web forms entirely. However, in the real world, people may overlook particular data entry forms or be confused about what to write into them. Although you may design digital forms such that customers are unable to proceed until all sections are completed, doing so could result in frustrated customers because no one likes having to read through long forms attempting to figure out what he or she has forgotten or neglected.

As a result of all of this, it is typical to have customer-entered information that is partial or lacking sections. For example, a client may forget to input a zip code when entering an address or may decline to give an email address on an online form for privacy concerns. While you can't usually compel individuals to share data they don't want to provide, you can use data-quality tools to detect fields in a database that are missing or look to be erroneous.

Accurate data for decision making

There will always be companies involved in the purchase and sale of real estate. However, the quality of a company's judgments is only as good as the data that informs them. When making investing decisions, investors will gather the most up-to-date information available and make an informed conclusion. A greater understanding of the facts will result in better investment choices.

Owners and landlords rely on precise data to help them make decisions about their properties. They rely on data to keep track of the current situation of the market and the risks associated with prospective investment opportunities. The greater the degree to which these companies can rely on the available data, the greater the likelihood that they will make smarter investment decisions.


Solving the problem of data quality requires going to the source of the problem and putting in place precautionary measures that improve the user's satisfaction with the experience. Simply by implementing basic, useful, and straightforward online forms using the services of Questionscout at the point of data entry, you can significantly reduce costs and complexity throughout the organization.

With ever-increasing competition in real estate, organizations must maximize their efficiency and foster cross-departmental cooperation. Businesses must identify income streams and manage their cost structures in order to increase profitability. For a long time, the realtors have tolerated and muddled through the difficulties of poor data quality. It is now time for businesses to make practical efforts to make better-informed judgments in real-time.