Without data analytics in today’s data-driven world, any business is pretty much operating blind. Real Estate is no different. With large amounts tied up in capital, data analytics is quickly becoming the backbone of the real estate industry.
Data analytics involves compiling data statistics in meaningful ways for industry predictions, decisions, and motivations. Data collected may be from consumer and business surveys, government or public databases, census figures, or collating information online.
Here are 10 key ways how data analytics is revolutionizing the real estate industry.
Whether you are in real estate as an owner looking to buy or sell, in property management, a property investor, or an agent, price optimization is the key to your business success, and data analytics are key to price optimization.
To make effective decisions, buyers and sellers need to know the market they’re operating in. Agents need accurate pricing data to advise owners and investors on the best pricing decisions.
Pricing data analytics is not just limited to buying and selling prices, it is applicable to all aspects of real estate, from the pricing of services, pricing of taxes and rates, building prices, to rental prices and statistics.
Only in-depth property value information statistics can provide accurate knowledge for effective decisions on pricing. The only way to ensure effective pricing knowledge is to employ data analytics.
Data analytics can provide critical insights into the latest location trends, without the lengthy and time-consuming searches into location-specific changes.
Picking the right locations, seeing what is trending in locations, and advising others on location trends will put you one step of your competitors when it comes to real estate.
To truly analyze what is happening in a location, real estate professionals need to delve into complex data on what is selling in a particular location, which locations are selling faster than others, and what areas are not selling. Data analytics on locations can also contribute to a wider view of the location, including crime rates, school ratings, traffic congestion, and life quality.
With data analytics and the contribution of big data, complex location predictions and assessments can be created from a multitude of variables.
3. Client Demographics
What type of people are investing in real estate helps industry professionals make decisions.
Are people mainly buying or renting? What type of houses are people looking for? Developers can determine what project types to consider. Agents can more effectively target the right types of clients for sales. Homeowners can determine what renovations may or may not be wise. With data on consumers, everyone can make informed decisions.
4. Lending Demographics
What type of banks is lending to who? What is the trend in interest rates? Knowing what banks are providing can give a customer a step up when it comes to applying for lending. Agents can more knowledgeably assist clients with their lending capabilities. Banks can also benefit from real-estate data analytics, when deciding whether to lend to a client, data analytics can be used to create more effective fact-based lending decisions. Data analytics can also be used for analyzing sales prices on foreclosure.
5. Quicker and More Accurate Appraisals
An appraisal is important for anyone considering lending, buying, or insuring a home. Insurance appraisals, bank appraisals, and investor appraisals, whatever the reason for getting a property appraisal, it’s important that it is accurate. All appraisals are driven by the current market rates for sales. Data analytics provides huge volumes of accurate information on sales for similar properties. This will ensure valuators can provide price estimations far more quickly and accurately to interested parties. Data analytics will reduce the variation between valuations, and make them easier to obtain.
6. Commercial Trends
Real estate investment for commercial use is typically on a much larger scale than domestic. For any chance of success, a commercial venture requires in-depth motivation based on the current consumer market. The only way to do this accurately is by analyzing data on consumer trends.
What types of businesses exist in the industry? What are consumers looking for? Are there enough shoppers for a new mall? Are diners looking for a new restaurant? Is there a shortage of parking in the district for the development of that new parking building?
Answering these questions is a must for the planning and pitching of commercial projects.
7. Town Planning
Real estate data analytics can help with important town planning decisions. Population and usage statistics are readily available from government sources, however only with proper data analytics can they be used effectively to draw predictive conclusions.
Data analytics can help town planners and communities resolve real estate planning debates in many ways. Combining consumer trends, population demographics, and property data can show which policies are best suited to the community. This may be to determine zoning needs, drive decisions on types of permits to approve, or solve debates on the use of public areas.
Data analytics can be used to create effective fact-driven town planning strategies that better meet the needs of users.
8. Targeted Advertising
When we know what consumers are doing, we can adjust advertising to meet their needs. Real estate professionals are using data analytics to determine how best to target paid and organic traffic. Agents use consumer data to target their advertising to the market where it is most effective. Developers can also use advertising data to drive their sales campaigns. Banks use targeted advertising to reach lenders. Advertising professionals catering to the real estate business can make more informed decisions based on what types of advertising yields the most results. With data analytics, advertising is more likely to succeed.
9. Competition Analysis
Data analytics can provide performance reviews on different types of real-estate businesses or agents. For example, who is selling the most, what consumer rating a business receives, which developers are most trustworthy, or which public real estate businesses to invest in.
Review your own performance and competitors and see how customers view your performance through data-driven research. Data analytics for consumer reviews, sales figures, and profits can paint a picture of your own and your competitor’s performance.
Consumer information on real estate services can also help illuminate scams and ensure users find trustworthy and reputable agents.
Competition analysis through data improves business performance and user experience.
10. Additional Revenue Channels
Compiling data for resale is a huge industry in today’s real estate sector. Companies like Zillow and Trulia base their business models on data analytics.
For data-driven revenue, real estate professionals may be compiling comparison sales reports to help gain new clients, compiling demographic data such as employment types and rates, schools, and quality of life, or trends in population which affect real estate sales. This data may be resold, or used in marketing to help clients.
Compiling new methods of presenting real estate data analysis for consumers may provide opportunities for new online businesses in the real estate market.
Real Estate used to be gut feeling, educated guesses, and speculation. While there will always be a risk, with data analytics in real-estate, private and commercial investors alike can make far more informed decisions, which will minimize their risk.
Improved availability of quality data analytics in the real estate industry is fast providing it with tools to enhance stability and growth in the same way the internet revolutionized IT. New academic courses are even being created to assist real estate professionals with developing data analytic skills for future markets. Many paid and free services provide a wealth of data on real-estate today.
Using a data-driven approach in real estate is essential in today’s market. If you’re not using data analytics in your real-estate decisions, it’s probably a good time to look further into how you can start.