Institutional investors looking to diversify away from traditional residential real estate investments have identified the gaps in modern purpose-built student
How is data analytics being used in real estate?
What is Predictive Analytics?
Although the term may sound complex, predictive analytics is not a difficult concept to understand.
In a nutshell, predictive analytics is the art ofby using past data to predict future trends. It is at the core of data science where such patterns are not easily detected by even human experts. Although predictive analytics is not synonymous with a fortuneteller, it comes pretty darn close to it. Such analytics prove very useful to certain sectors, since it creates a reliable forecast of what might happen in the future by including what-if circumstances, thus also taking into account risk issues.
Why Predictive Analytics?
Today, organizations, big or small, are turning to predictive analytics to simply increase their competitive advantage and their bottom line. It is all about creating smooth running decision making processes for users and to produce new insights that lead to improved actions. By using such analytics, a model can be created to predict values for different or new data. Although some risks may be involved, such risks stem from an overreliance on these tools. The tools are not bullet proof, since it is about analyzing big data, which is based on probabilities and correlation.
Real Estate Markets and Analytics
Real estate markets are complicated, volatile and always changing and real estate investors want to be able to evaluate whether their potential property is a good investment or not. At the end of the day, all they want is to make wise investment decisions that maximize their return. With predictive analytics, investors will be able to objectively assess when and where to invest, optimize their selections and determine real value of properties. Additional metrics such as price forecasts, market cycle risks and desirability analytics are also key to such investors.
Guiding the Real Estate Investor
Predictive analytics helps guide the investor into managing their rental property in a more effective way, which results in a higher return on investment. For example, in , the company is able to predict what key variable should be focused on and what needs to be done to increase future occupancy rates, which in return will increase future cash flow. The same analysis can be used for the design of the home – should an investor buy a home with 1, 2, or 3 bedrooms and what number of bathrooms should be included? All critical facts that go into determining the investor’s highest return when it comes to purchasing a property.
Vs. Traditional Analytics
Sites such as real estate investors in making wiser investment decisions by using predictive analytics in order for investors to make more informed decisions. In the case of , big data is being mashed to help real estate investors properly assess housing market conditions, price forecasts, risk metrics, demographics shifts and market fundamentals in any given neighborhood throughout the United States. Unlike traditional analytics where big data was collected and analyzed to provide a retrospective perspective in what went wrong, today big data provides a snapshot of the big picture. This means that real estate investors can easily use these tools to make the best possible decision when it comes to buying an investment property.and have been created to help
Such analytics can provide potential real estate investors with a number of benefits when it comes to trends, resulting in significant cost savings and operational efficiencies primarily based on accurate real time forecasts. Investors are quickly able to determine worthy investments, neighborhood forecasting and predictive maintenance which all make for a more informed investor and a higher marketing return on future investments. When it comes to buying an investment property, identifying the trends are critical. Why? Because such analytics assesses the future home values and risks for an investor which in turn provides “green and red flags” so to speak that will indicate whether the real estate investor should or should not buy the potential investment at hand.
Ability to Buy the Best Properties
Predictive analytics allows real estate investors to in a way predict what will be the best property design to buy in the future, what is popular and what isn’t. Investors want to get the most from their money so if there seems to be a growing demand for rentals by young families, an investor will not need to purchase a huge investment property to target the young family. A two-bedroom home will be more than enough for the targeted tenants. By using such analytics, an investor is defining the sought after home attributes that are in demand, which will get the investor the most return on the investment.
Predictive analytics can also identify who the target tenants will be for a real estate investor. When using such analytics, the real estate investor will be able to determine the makeup of the target tenant and how it will change in the upcoming years. Will there be a consistent flow of people looking to rent in the neighborhood? Will these future tenants be the same in 5 years as they are today or will the composition of the tenant completely change. Why is this important? This will tell the investor the following: Whether they will have people renting their homes in 2-3 years; What target groups should they be focusing their rental properties on, i.e. University students, Young Families, Single Professionals etc. This will also assist with focused marketing to get new tenants. It will also provide investors with the ability to know whether their target tenants will be able to afford the rent to maintain a positive cash flow.
In summary, the breakdown and understanding of big data can provide potential real estate investors with an incredibly detailed picture of the real estate market they are interested in which gives them a competitive advantage in their target market. It is all about getting a hold of the data, filtering it and looking at the results to fit your needs and what they should look like in the future.