Utilizing Data Analytics for Profitable Real Estate Investments

No doubt about it: Big data and analytics tools are revolutionizing real estate investing. But just how is this taking place? Beyond saving you time trawling sales records, what lies at the core of their power in real estate investment?

Let’s dive deeper into how real estate data analytics tools collect, process and utilize information in order to assist investors with making more informed decisions.

Traditional Data Sources for Investors: County Records and Multiple Listing Services Where should investors turn for reliable property data? A great starting point is publically available property records from county authorities.

Key information included in these records includes deed details and any liens on a property as well as mortgage documents and plat maps (which contain data regarding land boundaries).

Real estate investors rely heavily on property records to identify investment opportunities. Homeowners with property that has an outstanding tax claim or is in pre-foreclosure may be motivated to sell quickly to a cash buyer.

In general, to obtain these records requires visiting your county tax assessor’s office; some states offer public property data online as well. If you live in a nondisclosure state however, only licensed real estate professionals will be able to access data through multiple listing service (MLS) databases; therefore acquiring or working with an agent might be required in order to gain access to said databases.

If you decide to work with an agent, they’ll typically send over a sheet with sales figures and rental history data; you then must make sense of this data on your own, which can be time-consuming and sometimes confusing.

No matter the source of property data – be it online, at a tax assessor’s office or through the Multiple Listing Service (MLS), house by house will likely become your focus of investigation.

Big Data in Action for Investors
Real estate data analytics tools can be immensely helpful to investors, from saving valuable time and finding potential investment properties faster to finding potentially suitable homes more quickly. CoreLogic and Zilculator search through billions of records; CoreLogic alone boasts having access to over 5 billion housing records!

These platforms aggregate and analyze the data compiled during searches to provide investors with potential leads. Some of these platforms go further in their analyses; Zilculator for instance calculates projected ROI, cash flow and even profits after tax on properties it finds suitable during its search process.

Property data analysis will only get you so far; ultimately, decisions on real estate should always take into account local demographics and migration patterns to make smart choices for renters or to assess whether a local market is hot. Simply by looking at property sales details you won’t know whether renters want something specific in an area or whether one market might be oversaturated with potential renters.

Real estate data analytics tools utilize geographic, demographic, and user behavior data to help give a holistic picture of local markets. Similar to Google Analytics but more targeted as they use real estate-specific data; such as prospective buyer or renters’ ages or what types of properties people are most interested in browsing on property websites and ads.

Imagine you’re about to invest in a neighborhood of a popular metro area and wish to perform market trend analysis for that neighborhood. How would you do so in order to determine whether people will likely move in or out in five years, whether home and rental prices will continue their steady rise, or be subject to sudden drops?

Traditional homebuying required you to access demographic reports and home price data manually before trying to understand it all yourself. Unfortunately, that approach often led to mistakes being made since to reach statistically significant conclusions quickly enough would require you to analyze far larger datasets than an investor could manage prior to making their home purchase decision.

Data analysis tools offer immense value by processing large volumes of information from multiple sources, such as reports from ATTOM, Quantarium and Terradatum. However, they also use nontraditional data sources – from Yelp reviews of neighborhoods and restaurants to mobile phone signal patterns – in order to more accurately pinpoint market trends. By tracking how people feel about specific neighborhoods–and whether or not they plan on living there–data analysis tools allow companies and governments to make smarter decisions and more accurately forecast market movements.

Predictive Analytics for Property Valuation
Nontraditional or nonlinear data sources also play a vital role in predictive analytics tools that assist investors with appraising property values. Again, sheer volume combined with an understanding that human behavior analysis plays an integral part in accurately forecasting real estate investment performance is often enough.

Traditional property valuation methods involve performing a comparative market analysis by looking at how other properties in your area have appreciated over time, taking into account factors like schools and amenities in the vicinity.

Again, mistakes can easily arise when comparing one property with only five or 20 others in its vicinity. A real estate analytics tool provides more accurate valuation based on hundreds of similar properties nearby.

Yet that is only half of the equation; another component must take into account nonlinear relationships between people’s preferences and home values. Remember when Zillow discovered that Seattle apartments appreciated more over time if they were near Whole Foods? That was an indicator of an important nonlinear effect in an urban population that valued access to high-quality organic food products.

Data analytics tools incorporate highly specific data into their appraisals, while traditional appraisal methods often overlook such invaluable insights.

Case Studies of Data-Driven Real Estate Investments
Data is already making big impactsful changes for real estate investors, like Dallas-based Metro Realty Group in Texas. Metro Realty was having difficulty accurately measuring real estate performance as well as finding lucrative investment opportunities.

TechSolutions’ Power App provided them with real-time data that enabled them to make better decisions regarding which properties to invest in and, thus, increase profitability by 18% while increasing investment accuracy by 30%. As they had access to real-time information they were now better informed when choosing which properties to buy or rent out.

Real estate investors frequently face difficulty connecting with the right client base – whether renters or buyers. San Francisco-based RE/MAX found itself struggling to attract buyers. By using data analytics tools instead, however, RE/MAX was able to recraft its marketing campaigns and target its efforts at those most likely to purchase by tracking demographics, online behavior and interests – leading to a 20% rise in leads!

Eric Fernwood recently discussed on the BiggerPockets blog how real estate data analytics helps him tailor his investment decisions according to an ideal tenant type he’s targeting.

Final Thoughts mes Big data in real estate involves using large volumes of information to gain precise, actionable results specific to your local market and goals as an investor. DealMachine is your gateway to taking full advantage of big data’s potential; using it efficiently enables you to find, analyze and secure deals of the highest value quickly and precisely.