There’s no denying that big data and data analytics tools are transforming the real estate landscape. But just how is this happening? What is behind the power of analytics in real estate investing—beyond simply saving you time trawling through sales records?
Let’s dive deeper into how real estate data analytics tools source, process, and use data to help investors make better decisions.
Traditional Data Sources for Investors: The Local County and the MLS
First: Where does reliable property data come from? The first port of call for any real estate investor is publicly available property data contained in county records.
The key information held in these records includes the deed details and information on any liens against the property, as well as mortgage details and plat maps (e.g., details about land boundaries).
These records are invaluable for a real estate investor because they help identify potential investment opportunities. A homeowner whose property has a lien against it (say, a tax claim) or that has an underwater mortgage or is in pre-foreclosure is likely to be motivated to sell to a cash buyer.
Typically, obtaining these records means going to a local county tax assessor’s office, though some states have the public property data available online. That is, unless you are in a nondisclosure state, in which case you’d only be able to access the data through the multiple listing service (MLS). The MLS is available to licensed real estate professionals, so you’d need to earn a license or work with an agent to get the data.
If you do choose to work with an agent, they’ll typically send you a sheet full of sales figures, rental history data, and so on. It’s then up to you to make sense of it, which is time-consuming and, frankly, can be confusing.
Regardless of whether you’re able to access property data online, at a local tax assessor’s office, or through the MLS, you’ll likely end up going through the data house by house.
Big Data in Action: Tools and Techniques for Investors
This is where real estate data analytics software can be useful. From the simple perspective of saving valuable time and identifying potential investment properties more efficiently, real estate data analytics tools will trawl through public records and MLS sales records for you, identifying potentially suitable homes. Analytics platforms like CoreLogic and Zilculator search through billions of records: CoreLogic boasts having access to records covering 99% of the U.S. population, or over 5 billion housing records.
These platforms aggregate and then analyze the data, presenting the potential investor with potential leads. Some platforms are more detailed than others in what they include in the data analysis, but Zilculator, for example, calculates projected ROI, cash flow, and even profits after tax on properties it identifies as suitable during the search.
Having said all this, property data-crunching will only get you so far. Ultimately, real estate decisions should always take into account local demographics and migration patterns. You won’t know what renters are looking for in a particular area or whether a local market is hot or not just by looking at property sales details.
Real estate data analytics tools use geographical, demographic, and user behavior data to help you really get a feel of a local market. They do this similarly to Google Analytics, but in a much more targeted way because the data are real estate-specific. For example, you can get insights into prospective buyers’ or renters’ ages or what types of properties people are looking at the most on property websites and property-related ads.
Applying Data Analytics to Identify Market Trends
Let’s imagine you’re about to invest in a specific neighborhood of a popular metro area. How do you perform market trend analysis? How do you know whether people are likely to move into or leave the area within the next five years, whether home and rental prices here will continue growing steadily, or whether they are vulnerable to decline?
Traditionally, you would have to gain access to demographics reports and home price data and try to make sense of what was going on manually. That, of course, is how mistakes are made: To make statistically significant conclusions, you would have had to analyze data sets far larger than any investor can over a short period of time before making a home purchase.
The benefit of data analysis tools is that they process vast quantities of data from multiple sources, including reports from ATTOM, Quantarium, and Terradatum. But these tools also use what we call unconventional or nontraditional data sources to identify market trends more accurately. These data sources range from local Yelp reviews of neighborhoods and restaurants to mobile phone signal patterns. In other words, they’re attempting to track how people feel about specific neighborhoods—and whether they’ll want to stay there.
Predictive Analytics for Property Valuation
Nontraditional or nonlinear data sources also power predictive analytics tools that help investors in appraising property values. Again, there’s a combination of sheer volume and an acknowledgment that analyzing human behavior goes a long way toward predicting real estate investment performance.
Traditionally, if you want to do a property valuation, you would perform a comparative market analysis by looking at how other properties in the area have appreciated over time. You would consider factors like local schools and amenities.
Again, there’s ample room for mistakes here if you’re only comparing the property you’re about to buy with only five or even 20 others. A real estate analytics tool will give you a more accurate valuation based on thousands of other similar properties nearby.
But again, that is only half of the equation. The other half takes 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 in proximity to Whole Foods? That’s an important nonlinear relationship right there: an alchemic reaction between an urban population that valued access to high-quality, organic food.
Data analytics tools factor this type of highly localized data into their appraisals. Traditional appraisal methods easily miss such valuable insights.
Case Studies: Success Stories of Data-Driven Real Estate Investments
Big data is already making big changes for real estate investors. One recent example is Dallas-based real estate investment firm Metro Realty Group. Metro Realty’s pain points were accurately measuring real estate performance and identifying lucrative new investment opportunities.
The firm then partnered with Power App, developed by TechSolutions. The result was an 18% increase in profitability and a 30% rise in investment accuracy. Because the firm now had real-time data, it could make better decisions about which properties to invest in.
Another issue real estate investors often face is an inability to connect with the right client base, whether that’s renters or buyers. San Francisco-based real estate agency RE/MAX was struggling to attract buyers. Using real estate data analytics tools, however, it was able to recraft its marketing campaigns and target the right audience by tracking demographics, online behavior, and interests. The result? A 20% increase in leads.
And on the BiggerPockets blog, Eric Fernwood has shared how he uses real estate data analytics to fine-tune his investment decisions based on a very specific category of tenant he’s trying to attract.
Final Thoughts
Big data in real estate is all about using vast quantities of information to gauge precise, granular results relevant to your local market and specific goals as an investor. And to seamlessly integrate these insights into actionable strategies, consider leveraging DealMachine. It’s your gateway to maximizing the potential of big data in real estate, enabling you to find, analyze, and secure the best deals with precision and efficiency.
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Note By BiggerPockets: These are opinions written by the author and do not necessarily represent the opinions of BiggerPockets.