Today, any business that is being built, is leveraging data in some or the other way to thrive and companies in the Real-estate industry are no exception to this.
Real-estate data includes a wealth of information that, if used properly can provide powerful insights around accessibility, profitability, and opportunity to support critical decision-making and that brings competitive advantage to the businesses.
Let’s look closely at the areas where Data is helping the Real-estate Industry today:
In digitalizing and automating real estate evaluation (predicting property prices):
Today the biggest deal-breaker in the real estate industry is of course the price. The current price or the expected price in the future of the property helps in deciding if it will be the right investment or not. With the help of data analytics, models can be created using ML algorithms to evaluate the valuation of any property based on the historical relevant information like age of property, location, and condition and can provide an evaluation in a few seconds for overall consideration.
In optimizing the buyer selection process:
AI and Analytics can not only help you find the value of your property, but they can also help you find who are the right people who are likely to be more interested in the property based on their preferences, budget, location, etc. Data that is captured by the buyers can be used to recommend to them the properties that fit their needs the most and similarly, the real-estate companies can get the details of the buyers who are the right fit for their offerings, so they can invest the right amount of time and energy to the actual buyers.
In analyzing and monitoring market trends:
Today, to sustain a real-estate business, it is crucial to have the right understanding of every aspect that plays a role in the valuation of the property. With the increasing factors, it is very important to keep an eye on the trends and understand what your competitors are doing. Also, there are so many new data points like Human mobility, Demographics, connectivity, etc. that our impacting the price of real estate. Data Analytics becomes very significant in evaluating and monitoring the market trends and understanding the impact of other factors on the real-estate cost.
In understanding patterns to anticipate future growth:
Unlike Retail, the Real estate does show a specific pattern in its growth and that usually compliments the local trends, demographics, economic stability, government policies, provisions/subsidies around buying real estate, interest rates, and so on. By using Data Analytics, all these together reflects the correlation with the price and help in uncovering the patterns that help in anticipate the future growth of the real estate, it becomes easier than it may sound to detect patterns using the power of data analytics.
In boosting profits and reducing overall development costs:
Real-estate companies invest their money majorly in two things, land acquisition and then development. Data Analytics gives the visibility into the valuation of the land to ensure they buy the land at the optimal cost and development cost can also be managed by tracking how much raw material is needed to build any building space by analyzing the historical data to minimize the wastage which results in an optimized cost of development. And again, with the help of Analytics, real-estate companies can predict the cost of the property and can sell accordingly to ensure boosted returns on each sale.
Real estate as an industry has immense potential to become completely data and AI-driven. Data-driven processes in real estate bring intelligence around property valuation, inventory, buyers’ behavior, growth patterns, expenditures, and finding out the right buyers, which streamlines all the daily operations for any mid to large-scale enterprise in the real estate industry. The data-driven approach in real estate brings improved efficiency in commercial abilities and brings visibility into what buyers want to provide maximum satisfaction.
Views expressed above are the author’s own.
END OF ARTICLE