Real estate companies must make good use of data to benefit from the intelligence that data brings to the table. But it is imperative this data is of very high quality because business decisions backed by poor data quality result in failure. This article discusses the need for high-quality data in real estate and the various ways to improve this data.
$15 million. That’s what poor data quality is costing organizations. Yes, read that figure again. If you are a real estate player, you need access to high-quality data as well. It is this data that is going to help you provide more value to your customers and differentiate yourself in an overcrowded real estate market. Poor data results in poor decision-making. This results in missed opportunities, which in the long run results in reduced business profitability.
The cost of poor data quality in real estate
Bad investment decisions
Data accuracy is of critical importance when you want to make capital-intensive decisions. Say you invest in a property expecting it to deliver 2-fold value when you sell it. But what if your expectations are backed by data that hasn’t gone through an extensive data cleansing process. In this case, you make a wrong decision and invest in a property that won’t deliver on your ROI goals. You might get saddled with a property that gives diminishing returns as no one wants to buy it.
Redundant data
Scenarios evolve and so does data. Poor quality data is not current and is not up to date according to the changes in your target market. E.g., if there are new zonal regulations that have been notified, and your zonal data isn’t updated, you might offer wrong buying advice to your customers, and they might go onto rue their purchase decisions. This has the potential to destroy your reputation.
Wasting time on non-essentials
Leveraging bad data results in you focusing on the wrong market indicators. You are also unable to focus on the right trends, which again results in your company spending time analyzing and evaluating initiatives and offerings that will deliver no value to your business. On the other hand, your competitors will have a runaway advantage as they are maximizing the potential of good data quality to make the right decisions.
6 steps to improve the quality of your real estate data
It’s all well and good to say you need to improve data quality, but how do you go about it? Here are six steps:
1. Define a data quality process
You need to know why you need quality data, where you are going to source it from and how you are going to put it to use? This will help define a data quality process for you. What are your business priorities? E.g. if your focus is commercial real estate then you must ensure that you are able to access the best commercial data including property size, location, distressed properties, zonal laws, commercial loan data, property value, number of units, property trends and much more. You need to put in place a process that can measure value and curates this data to improve data quality.
But defining this process also includes evaluating the condition of your current data ecosystem and the quality of all the data you have collected so far. You will need to thoroughly audit your existing process to identify strengths and weaknesses and plug the weaknesses.
2. Clean up the data before use
You will collect a lot of ‘dirty data’; this is the kind of data that is of no use and if it is used, it results in confusion and time wastage. So, before you use data, you need to clean it up. Dirty data, in this case, means irrelevant data, incomplete data, inaccurate data, duplicated data, or misrepresented data. It is important that you do not feed such data into your system or it will lead to chaos.
3. Data verification and data validation
When you audit your existing data, you might come across some critical data that is missing. E.g. when you are going through customer information, you might not have entered their occupation, property inference, or even their phone numbers. Such missing data must be added so that you know you are missing out on important information.
4. Data enrichment
It’s important to understand that data is in a constant state of flux. It is changing all the time. E.g. a customer’s phone number or address can change. It is imperative that you keep checking if the data you have is current, and update it if it isn’t. Imagine a scenario wherein you have an amazing property and you know you have also maintained a list of target buyers. But, once you begin calling them, you realize that you cannot contact most of them, because their phone numbers have changed. This can be frustrating and in a worst-case scenario, you might see your competitor making a deal on that property.
5. Consistent nomenclature
All real estate companies produce copious amounts of data, and they also collect huge amounts of data from various sources. Unfortunately, they might not maintain consistent naming conventions either because they did not bother to or they were short of time. This sloppiness can cost them dearly. You shouldn’t make this mistake in your real estate company. You need to identify the right nomenclature that is self-explanatory, and easy to maintain and follow. By following naming conventions, you will be able to identify and merge records faster. This will also reduce duplication.
6. Regular data quality assessment
Managing data quality is not a do-it-and-forget-it process. You will need to monitor it constantly and assess it periodically. As we have said earlier, data evolves continuously and therefore, a data set that was meaningful for you today, might not be useful tomorrow. If you don’t assess its quality, you will be leveraging irrelevant data. Therefore, you need to carefully evaluate the outcomes that result from data usage. Both positive and negative outcomes should be assessed. This will give you a clearer picture of data quality. E.g. commercial rentals for a City are liable to change based on local economic scenarios. If you do not keep track of the changing trends, you might identify a rental price point that is no longer feasible.
Benefits of high quality real estate data
Why is it that you must focus your attention on property data quality management and make sure that your commercial real estate data is on point? The answer lies in the benefits this data brings to the table:
Grow your client base
As a real estate company, your focus should be on targeted lead data so that you have a better chance of converting these leads. Such data can be sourced from property data records, council records, courts and more. Data can also be found in population and town planning records. The whole idea is to get a better understanding of the target market so that the pitch is in line with their expectations. Quality property data will also help your sales personnel focus on a small target group of potential buyers, who are best placed to covert. Data can also help you connect with the right property sellers, who are looking for a specific type of customer.
The right data means you have meaningful facts and figures at your fingertips that result in your salespeople being able to close deals faster. Data also helps you forge new relationships that can add more value to your business, meaning your company is the first choice of both buyers and sellers.
Data helps you build and sustain a growing customer base.
Relevant choices
Real estate players spend a large portion of their time identifying the right choices for their customers. But with high-quality data at their disposal, zeroing in on the list of relevant client options becomes easier and faster. By maintaining data quality best practices, you can identify those properties that will deliver the maximum value for money to your clients and more importantly, your options are backed by statistical analysis. No longer do your clients have to wonder whether your judgement is sound, they know it is because you are offering data-empowered options.
Increased productivity
Profitability is intrinsically linked to improved productivity. But this productivity cannot be just for the sake of being more productive. It needs to have a purpose and it must not compromise on accuracy. This can only happen if you are able to take solid and proactive steps towards digital transformation with the use of technology innovators such as AI, Machine Learning, and Predictive Analytics. These technologies are underpinned by the use of data. Data-backed digital transformation helps improve productivity by automating critical, yet tedious tasks including documentation, taxes calculation, fees, and much more.
What’s more, the use of data in the right manner can also offer important business intelligence into exploring the right markets, properties, and even year-on-year property ROI. These activities, if done manually, will take a lot of time and effort and there is no guarantee that the results will be accurate. You can’t take this risk. This is where a combination of data and technology comes in to help save the day.
Take help – work with a leading real estate data processing company
You must have understood by now that maintaining data quality is not easy, and you might get the whole process wrong if you do not work with an expert. But you shouldn’t work with just any expert. You need experts on your side, who offer data cleaning and enrichment solutions tailormade for your real estate company. They are equipped to deliver data cleansing solutions including reconciliation of duplication entries, maintenance of out-of-business records, data validation, address standardization and much more.
Conclusion
There is absolutely no doubt that data is your most valued asset. But what is the kind of data you are using? Is it of very high quality or you aren’t aware of whether it is really very good or bad? If it’s the latter, you need to make sure you deploy industry-leading real estate data cleansing and enrichment solutions to ensure the data you use is accurate, relevant, and insightful. This will ensure your real estate business experiences an array of benefits including improved customer targeting, an increase in successful deals, and better business profitability.
