In a world where data-driven automation and technology-enabled transformation are becoming more important to businesses than ever before, it’s no wonder that real estate is starting to rapidly outpace other industries. But to build a strategy that will enable property managers to capitalise on their market position, they have to be able to identify what’s going on in the industry—and how best to respond. That’s where data comes in.
How is data relevant to the real estate industry?
Data allows real estate businesses to explore new ways of thinking about their business through analysis based on large datasets (e.g. images, documents, IoT sensor records or mobile device data), built on top of existing infrastructure. In some cases, the industry even uses big data to achieve business outcomes.
Big data is a term used to describe the collection and analysis of large amounts of information, which can include structured and unstructured data. It takes advantage of the capabilities of modern computing systems and technologies such as databases, meta-data management tools, semantic search engines, social media applications, and mobile devices.
The real estate industry relies heavily on technology as its foundation for success. However, there are gaps between what we know about our customers today compared with tomorrow’s needs—a gap that data helps fill.
Biggest challenges in leveraging data for real estate businesses
Data is a powerful tool that can help real estate and property businesses make more informed decisions, but it’s important to understand the barriers to using data in business.
- A lack of infrastructure: While many organisations are now using cloud-based tools to store and analyse their data, there’s still a need for more robust systems that support multiple users. In addition, these systems must be able to handle large amounts of information on demand—something not possible with traditional databases or file systems.
- Lack of understanding around what makes sense: The most common mistake people make when they first start analysing huge volumes of information is assuming that everything they see represents relevant information about their target audience (or another aspect). However, if they don’t know why something matters or how it relates to their goals then nothing will change as a result!
- Lack of a plan: When it comes to using data analytics, instead of jumping right in, property managers need to take some time upfront to lay out how they want their business to use this information and what they hope will be achieved by doing so. Once the goals are well-defined, then they can start planning out what tools and resources they need in place before diving in headfirst.
- Lack of resources: It’s easy to think that one doesn’t need to invest in data analytics because it’s not an immediate ROI. However, without the right tools in place and someone who understands how these systems work, real estate companies may end up spending more time than necessary trying to get anything useful out of their data—and that could be extremely costly for their business.
How is data being used in real estate?
Property technology, also known as PropTech, is a broad term that encompasses a range of technologies and practices used in real estate. It can be used to accurately capture data on properties across different phases of construction. PropTech has the potential to automate a lot of the data collection and management processes that were once done manually, but can still be done by people. Here are some examples:
- Using a property management system (PMS) for rental properties, property managers can collect information about their tenants, including their income and credit scores, as well as their rental history. Property managers can also use the system to collect information about the condition of their units so that they know what problems they should address before renting them out again.
- With a mortgage tracking system, businesses can collect information about their clients’ financial histories and credit scores, so that they can qualify for home loans they might not otherwise qualify for.
In this context, PropTech data refers to the collection of data that is gathered from PropTech tools in the real estate industry. This data includes information related to property sales, listings, leases, mortgages, tenant management, rental agreements, building maintenance activities, and other associated aspects of managing a property or portfolio. This kind of data can be incredibly valuable for real estate professionals, as it can help them to better understand the current market trends, identify opportunities for new investments, and better manage their existing investment portfolios. By leveraging this data, real estate professionals can make more informed decisions that will yield higher returns on their investments.
PropTech data is being used by real estate professionals to enable better decision-making, improve the customer experience and improve asset management. It’s also being used to optimise operations and improve the quality of life for residents. More specifically, PropTech data is used to make more informed decisions about sales prices and property value. This allows asset managers to price homes at levels that maximise their return on investment while also ensuring that homes are sold within a reasonable time frame (usually six months or less). When data is used in this way, it allows property managers to avoid overpricing or under-pricing their properties at any given moment in time.
Data can also be used to help developers anticipate the amount of interest there will be in a given development project before construction even begins. This allows them to make strategic decisions about pricing and selling the property as they see fit – without compromising on quality or losing money by selling too cheaply or quickly.
Once property managers have a better idea of what data they need and why, it’s time to start putting together a plan for collecting it. The first step is understanding the purpose of data collection strategy. If the goal is simply to gain insights into specific areas or demographics, then a lot of data might not be needed. However, if there are broader business objectives behind the business’ decision (such as increasing sales), then this becomes much more important to collect large amounts of information across different systems throughout the organisation and use them to achieve the specific objectives. Once these aspects have been addressed and plans laid out, then it’s time for action!