For a long time, real estate has remained hesitant to accept any technology, including definitely Big Data. They’ve historically relied on past trends and professional expertise only to make important business decisions. With advanced applications, however, the future of big data in real estate is looking too bright to ignore.
Take Boston, for example, where prices of many residential properties jumped by over 171% between the years 1997 and 2014 only because they were located in the vicinity of a renowned coffeehouse Starbucks. If there was any realtor with a big data real estate software development company, they would’ve easily picked up on this trend to become the most successful real estate business in the area.
It’s not too late; many hidden trends are helping real estate businesses become an absolute success, and in this post, we’re uncovering how.
- Big data has huge potential for redefining the success parameters for the real estate business.
- The foremost use of big data in real estate is accurate property valuations.
- Through automated agents’ appraisal, insurance management, and contract reviews, big data analytics can facilitate operational efficiency.
- Smart property management and construction administration are more futuristic use cases of big data in real estate.
Top 8 Ways Big Data is Transforming the Future of Real Estate
Many real estate firms have long suffered limitations of their traditional work approaches. But now, thanks to big data applications in real estate, situations are improving for the industry rapidly. Here are some of the use cases that define the future of big data in real estate.
Accurate Property Evaluation
Property valuation is one of the functionalities of businesses engaged in real estate. More accurately, a business prices a property, both the time –while selling and purchasing, which can be defining factors for their success or failure.
Typically, realtors have relied on their experience to assess the value of a property. The only other means they have historically had is manual market analysis. Both of these means, while imperative, are not sufficient for an accurate valuation of the property. Neither of them evaluates factors like the neighborhood, amenities in the vicinity, market trends, or economic factors, mostly because real estate businesses don’t have either the entire data or the means to process it.
Big Data is the perfect solution, in this case, helping realtors accurately evaluate the value of commercial and residential property. They can easily hire AI development services to build custom predictive models. Developers can easily build and fine-tune a machine-learning model based on historical trends. A custom AI property valuation software can then be developed to ingest real-time data and overlay the hidden trends over them to more accurately predict the property value based on various factors, including demographics and economic indicators.
Forecasting Market Trends
The above-mentioned application of AI in real estate can also be used for various other use cases, including but not limited to market trend forecasting. A similar dataset can be used to derive the demand and supply gap in the real estate industry based on historical trends. For example, a realtor can easily use data points such as employment rates, vicinity pop culture places, educational institutes proximity, and income range of the demographic in a location to effectively predict the spike or sudden decline of property demand in a given area.
It is noteworthy that trading decisions in the real estate industry are heavily attuned to the emotions of the people, a factor that has historically remained hard to track and analyze. This is another area where applications of big data in commercial real estate shine.
The technology can easily ingest huge amounts of data to derive correlations between market situations, Yelp reviews of the area, social media posts, etc., to gain insights into populace emotions. This way, they can proactively predict market trends in the real estate sector, whether it means growth or decline in the demand for a certain kind of property or location. They are then able to make better investment decisions for the reselling or construction of a building.
Better Investment Decisions
Big data and AI are improving the decision-making processes of businesses across various verticals, and real estate is no exception. With data-driven insights, important business decisions can be made more efficiently while facing way fewer disagreements. The datasets can clearly show which properties would have interest from substantial of their prospect base in the near future, allowing realtors to make better investment decisions.
Other than market trends and consumers’ interests, one can hire a real estate software development company to analyze properties’ past data. Such models, when custom developed as per the market situations of the area, can help realtors decide the total investment property they would require from them and the estimated ROI of the same.
For example, a chain store might be looking for a location to open up a new franchise. In such a case they can evaluate the data regarding the building’s past renovations, insurance claims, and legal cases. They can also evaluate the demographic situation of the area to better understand the success rate of the franchise. This would put them in an ideal position to make the right decision.
Improved Construction Administration
Other than making better investment decisions, the dataset mentioned above can be used for yet another use case of big data in real estate- improving construction administration. Administrators can cross-reference relevant data, including local government circulars, economic indicators, the recent development of a niche property, etc. Through this, they can determine which kind of prospects would be most interested in buying a residential or commercial property in the area.
Thereafter, they can construct the property more attuned to the needs of the target customer base. For example, the recent development of a well-known university in the area can attract families with kids of college student age. Many restaurants, convenience stores, and rental apartments would also become highly sought-after in the area attracting investments from such interested parties. They can also determine the kind of amenities such prospects would be most interested in to make better construction decisions.
The realtors can then decide what kind of buildings would be most beneficial to build in the given area. They can even command higher prices for such properties, considering the high rates of demand.
Traditionally real estate companies have relied on manual processes of market analysis, prospect engagement, and cold calling. This limited them to scale their operations only on the basis of their hiring capabilities, assets setup and maintenance, and man-hours. By incorporating AI in real estate with applications of big data, they can overcome these conventional hurdles.
For example, they can use big data to automate data processing, contract reviews, insurance pricings, and risk assessment. Real estate businesses with Big Data solution development can process huge volumes of data quickly and with greater accuracy. Not only that, they can automate the graphical presentation of such data facilitating many audit and analysis operations.
Real estate companies can also efficiate the processes associated with finding building opportunities, optimizing supply chain networks, and finding the ideal suppliers for construction materials without spending any additional time on the process. Another operation they can automate is employee appraisal. The officer responsible can analyze employees’ performance on various tangible and intangible metrics, allowing them to make a better assessment of the value they’re offering to the company.
Enhanced Marketing Strategies
Real estate companies can collect data from both private and public sources to determine the best marketing strategies for their listings. These sources would include business surveys, property details, and social media insights that would help them understand the demand in the market, trends, and how the listed property can fit those aspects.
For example, if the trend shows that more people in a certain geographical area are interested in pet-friendly environments, the realtor can create campaigns for such pet lovers and promote the properties that allow them to keep pets. Similarly, if certain people have shown interest in finding a property specifically near educational institutes, they can deduce the prospects have a family and then can promote them family-friendly spaces with more rooms and spaces to accommodate them comfortably.
Smarter Building Management
Any property that leverages the capabilities of IoT, including edge devices, integrated surveillance cameras, sensors, or any other smart device, including door locks, refrigerators, espresso machines, etc., can be called a smart building. Now it is a well-known fact that IoT applications generate huge amounts of data. In fact, as per a release by Business Wire, IoT devices are expected to generate about 73.1 ZB (zettabytes) of data by 2025.
Now all this data can either be securely disposed off in order to secure the data privacy for the concerned users. But a better use case of this data would be real-time data processing through the custom application of big data in real estate.
This data can then be cleaned in real-time, transformed into a structured format, and then used for applications like predictive maintenance, tenant management, and optimized use of utilities.
Risk Mitigation and Property Insurance
Finance companies that offer insurance services over residential and commercial properties can perform a risk assessment of the given property with more accurate results. They can personalize their insurance offerings on the basis of insurance services offered by other finance companies to a similar target audience in the given geographic regions. They can more accurately derive interest rates that would result in the least churn rate and default rate and yet improve the number of acceptances for loan applications.
Other than property insurance, the risk assessment capabilities of big data applications in real estate can also help in mitigating those risks themselves. Companies can assess the risk associated with investing in specific projects and then can take proactive actions to avoid those risks.
Here predictive analytics capabilities of both big data and AI in real estate come in handy. The exact application here is called predictive maintenance. It uses smart sensors and edge devices to generate huge amounts of data in short and regular intervals. This data then gets analyzed in real-time to detect any anomaly in the project and alert the administrator to initiate the recovery process before any damage occurs. This reduces the depreciation rates for buildings, helping improve the net revenues for real estate.
Big Data analytics is transforming businesses across the real estate industry. From allowing them to make better investment decisions to create result-oriented marketing strategies, they can base many of the core real estate business operations on data-driven strategies. All one needs is to have a clear view of their requirements and the opportunities one can afford to explore. Thereafter, they can simply hire a real estate software development company with AI development capabilities.
Matellio is one such company that has trackable experience in both AI enterprise solutions and custom real estate software development. We can create sophisticated data pipelines for your real estate business, integrate various sources for data ingestion, create data and machine learning models, and even develop custom enterprise solutions with a comprehensive dashboard. The only thing you have to do to leverage all this expertise is to fill out this form with your requirements and our experts will create a free project plan for you with a quote.
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