Big Data has and will continue to change the way industries make critical decisions and discover new success opportunities. This is especially true for real estate businesses, as it can help them calculate property value and target specific customer groups who are more likely to buy their houses.
As the real estate industry continues to evolve, the power of Big Data analytics will be crucial in helping businesses acquire or develop valuable properties for people who need it the most.
What is real estate data analytics?
Real estate data analytics involves processing data points like demographics and infrastructure projects to assess the land intended for purchase and uncover neighborhoods that have the potential to net huge profits.
Harnessing big data analytics is crucial for real estate businesses. It allows businesses to make informed investment decisions based on facts rather than hunches. Moreover, data analytics can help agents determine the optimal density and mix of amenities to maximise property value and neighbourhood appeal.
Types of data used in real estate data analytics
In the real estate industry, processing key data points with AI and machine learning can help agents measure the true value of a property and determine if it is worth investing in. Some of these data points include:
- Property-specific data: These include details on an individual property, including location, size, age, number of rooms, nearby amenities, and transaction history. This data can be found in public records, real estate listings, and appraisal reports.
- Market data: This data point informs real estate agents of the current trends in the real estate market. Market data includes average property prices, sales volume, vacancy rates, and days on the market. They are typically found in industry reports, government statistics, and real estate data platforms.
- Economic data: GDP growth, interest rates, inflation, and employment rates can help agents understand the current state of the real estate market.
- Demographic data: This type of data informs real estate agents about the makeup of a population in a certain area, such as age, income, education, and household size.
- Geospatial data: Location-based data like maps and satellite imagery can give real estate agents a thorough rundown of the entire neighbourhood or area.
Also read: The 4 Types of Data Analytics and How to Apply Them
How is data analytics used in real estate?
The power of data analytics is invaluable in simplifying operations and uncovering investment opportunities. Specifically, data analytics can help real estate agents sift through massive sets of location, property size, and sales data to get a faster and fairer valuation of property. This, in turn, can lead to speedier transactions.
Besides that, analysing property-specific data and economic factors can help users identify potential risks in the real estate market. These can range from increased vacancy rates to a decline in property prices. With this information, real estate agents can take steps to resolve issues and secure their investments so they can prevent losses on their end.
Finally, data analytics help users predict trends in the real estate market that can boost their chances of success. For example, by analysing data on demographic shifts and infrastructure projects, agents can identify developing neighbourhoods that have the potential to net huge profits.
Real estate analytics tools
Real estate data analytics often involve handling massive sets of structured and unstructured data, including images, videos, transaction records, and market trends. Managing and storing these data points is therefore crucial to ensure that the right data can be retrieved in one place.
To make this effort easier, real estate agents should integrate unified fast file and object (UFFO) storage systems that are capable of handling multiple data types.
Real estate agents can also benefit from predictive analytics platforms like Offrs and Homebot. These platforms are designed to pinpoint properties that will likely be sold and estimate their value. This is crucial in helping agents find the best deals quickly.
Finally, real estate agents should integrate a comparative market analysis (CMA) platform like CoreLogic, which visualises properties and neighbourhoods in real-time. With this platform, agents can assess property types, mortgage details, and owner information within a given area.
Read: Top 10 Important Big Data Analytics Tools
How does data analytics help real estate in transforming traditional investment methods?
Data analytics provide numerous benefits that can transform how real estate businesses choose their investments. Some of the benefits of data analytics include:
Real estate market research
Analysing demographic data, past transactions, and market demand can help real estate agents better understand home buyers’ demands and tailor properties accordingly.
For instance, real estate negotiators may discover that there are frequent transactions for landed terrace houses in a certain neighborhood. By focusing their efforts on that area, negotiators will have an easier time securing buyers and sellers.
Study property market trends
Analysing historical sales data, economic indicators, and market conditions can help agents anticipate future trends in the real estate market. For example, agents can use this information to detect up-and-coming neighborhoods that have the potential to provide the greatest returns.
Conduct housing market analysis
Information on the area or neighborhood, lot size, amenities, and photos can help real estate agents get a sense of the property’s current state and potential value. Agents can also compare the property with similar properties to determine which one has the most appeal and, therefore, is more likely to attract higher returns.
Housing market forecast
Historical sales data can provide real estate agents with insights into current and future demand for different regions and property segments. This is especially crucial in helping agents determine which areas they should focus their investments or development efforts on.
Decision making
Besides helping agents make more accurate investment decisions, data analytics can empower agents to optimize their real estate portfolios. By analyzing the performance of all of their properties, agents can identify which properties they can hold and which they should sell.
Risk assessment
Poring through data on economic indicators and tenant turnover rates can help real estate agents better understand issues associated with a certain property. Moreover, real agents can also identify patterns that indicate potential problems down the line, such as a rise in vacancy rates or a decline in rental prices. This way, agents can stay on top of issues before they skyrocket into unfixable situations.
Property price prediction
Data on market demands, tenant demands, and competing prices can guide real estate agents in setting fair prices for their properties. This will help them attract more tenants and position themselves ahead of the competition.
Generate real estate investment trends
Another benefit of data analytics is that it can be used to predict which neighbourhoods or land can provide the most profits. By analysing information like demographic shifts, migration trends, and local business growth, agents will be able to decide which properties they should acquire.
Develop real estate investment strategies
Analysing property performance, market conditions, and visitation trends can help real estate agents pinpoint areas that either have the potential for success or are considered too risky. These findings can lead to smarter investment strategies, which, in turn, boost revenue and reduce issues down the road.
Challenges of implementing data analytics in real estate
While data analytics can be an invaluable asset for real estate businesses, it is not without its drawbacks. Some of the common challenges that businesses face with data analytics are:
- Privacy issues and compliance with data protection laws.
- Maintaining data quality and accuracy.
- Reluctancy from employees to learn new systems and technologies.
- Ensuring data is accessible to people who need it the most.
- Inability to integrate data analytics platforms with legacy systems.
Failure to address these challenges can lead to increased risk of cyberattacks, hampered performance, and massive losses from inaccurate data. For this reason, real estate businesses need to invest in advanced property analytics tools that are easy to use and contain security, accessibility, and integration features.
Conclusion
In today’s highly competitive landscape, embracing data analytics is essential to capitalise on more opportunities before other businesses catch on. Before they can do that, however, businesses will need to consider which tools best suit their needs.
By acquiring the right tools and platforms, organisations can make smart investment decisions that can boost their revenue with minimal risks.
