We’ve recently written an article about how important data analysis is to a real estate investor and we stand by it. That being said, we must also admit that, as with all things in life, one must be conscious of both the positive and negative side of things.

As much as we’d like to think that computers can solve all our problems and that math and statistics hold all the answers we must ultimately accept the fact that sometimes, there is something more at play.

Today we’d like to cover 6 things you should think about before making any decisions based on data and statistics. Undoubtedly, these will be powerful tools in your arsenal but approaching them and basing your investments on them without asking yourself a few questions first could lead to some very bad decisions. And we all know, when it comes to money, a few bad decisions can ultimately lead to financial ruin.

So, do yourself a favor and jot these down really quickly. Once you’re ready to sit down and collect some data for your new investment, whip your notes out and get started!

When It Comes to Data Analysis Consider the Source

We wish we could say you could trust any sort of data or statistic you find online, but in this day and age, we know that’s not always a given. When looking up anything online it’s best to be wary of who put that information forward, why, and whether or not they’re biased.

This doesn’t always mean that an individual or a company will provide the general public with data that is intentionally skewed (though we’d be careful of that too). More often than not, people make mistakes because they simply do not have the necessary knowledge. To put it simply, they don’t know what they don’t know. Though their intentions might be good, their information might not be.

A rule of thumb is to only look at reputable sources and companies. Look for other opinions online- what do other communities think about the provided data? Does the company or individual have a history of supplying real estate investors with unfactual statistics?

What’s more, when people want to share their own finds and statistics online and they mention the fact that this is their personal project (yes, we’ve seen plenty of these circulating around various message boards), believe them! Just because the graphs seem accurate doesn’t mean you shouldn’t double-check with other sources.

The bottom line is this. Make sure that you’re consuming information from people or companies that have no bias. If they make mistakes, as is sometimes normal, do they stand by them? Do they retract them? After all, these are your investments we’re talking about, your livelihood. And you don’t want those in the hands of untrustworthy individuals.

Watch out for Skewed Data Analysis

Though data is supposed to help us make informed decisions, if you’re not looking at the bigger picture, you might end up looking at a loss instead. Skewed data analysis, even in the age of technology, is abundant online and offline.

Let’s look at one famous example of a statistic that, on the surface, seems just fine. “95% of car accidents happen within 25 miles of one’s home” is what we’re talking about. Now, this might cause you to drive more carefully the closer you get to your house after a long day- understandable and, well, we can’t really blame anyone for careful driving.

It doesn’t seem like this data is going to hurt anyone. But that doesn’t mean we shouldn’t look at it more closely. As it turns out, one important bit of information is left out, which is the fact that these people never drove further than 25 miles from their home in the first place. So, of course, they couldn’t get into car accidents outside of that radius.

So you see? At first, you’re given a false sense of security, ‘if I drive more carefully near my home, I’ll be fine’, but the data analysis doesn’t encourage you to drive carefully all the time, which is what you should do!

Just make sure you’re not missing out on important pieces of the puzzle when using data analysis.

Question How Numbers Were Calculated in the Data Analysis

On top of making sure that your source is credible and isn’t leaving out important information, you also have to question how the numbers were calculated. Do you remember back in school during math class when the teacher expected us to show how we got to our answers? This is exactly like that, except of course we’re talking about real estate investments.

Let’s look at the cap rate example. If you’re talking to someone, anyone, about a cap rate (the rate of return an investor can expect from an investment in real estate ) make sure they know what they’re talking about. Nowadays too many people neglect to include all expenses of a property when determining the net.

Now, are they doing it deliberately or are they inexperienced? That remains to be seen. Just be wary when anyone advertises a 20% cap rate on a property- which is a percentage we’ve seen more and more often. Ask them about their calculations and get to the bottom of how they reached their conclusion.

Another example that makes our blood boil is that of an ROI, a return on investment. Because there are so many variables and people tend to only take numbers that benefit them into consideration, it’s best to ask for a rundown. What formulas did you use? How did you get to your final figure?

Let’s say someone presents you with an ROI that, unbeknownst to you, includes years-long speculation on appreciation. That appreciation might not even come to fruition, so it’s of no use to you, but it certainly makes the ROI look that much better, right? Of course, we typically want to give people the benefit of the doubt, but if they’re upstanding people then they shouldn’t mind going through their numbers with you one by one so you’ll know exactly what you’re investing in and why.

Question the Relevance of the Data Analysis

Sometimes people or companies present data analysis because, well, there is data to be presented. Does it have any relevance to you?

This is solid advice for any newcomer to the real estate investment scene! No matter how good or flashy data analysis looks, always make sure you’re looking at statistics that are of interest to you and don’t change your buying habits just because the writer is enthusiastic about their charts.

Let’s say your goal is to buy a bunch of properties so that you can rent them out, securing an additional income. If you come across a lot of articles, tables, and message boards that show off appreciation data analysis you might think it’s worthwhile to start looking at them in-depth. But what you’ll actually be doing is wasting time, because while some people might be interested in a property’s appreciation, your goal is finding out which areas or cities have the best monthly cash flow.

Say that in most cases the cities that make it on these lists are New York, LA, and San Francisco. Those with more experience know that, yes, you’ll find great properties if you’re looking for appreciation but in terms of cash flow, you’ll be out of luck and better off looking elsewhere.

See how it’s important to not get distracted by data analysis that might look interesting and appealing? At the early stages of real estate investing it might be difficult to distinguish between what’s relevant and what isn’t, but in time you’ll find it easier and easier. If you start questioning relevance early on, you’ll get to that stage that much faster!

Know the Difference Between Primary and Supportive Data Analysis

In terms of primary data, you’ll want to look at more than one factor. Why are you interested in buying a property? What are the key elements you’ll want to take into account? Let’s keep rolling with the rental properties example we used earlier.

You should be looking at an area in which the market is not in decline, right? In order to figure out what’s good and what isn’t, you could study jobs data, industry data, and population data. Again, don’t just go for one of these as you’ll never get a clear, bigger picture. If the market is in decline then you’re better off investigating other areas, neighborhoods, or even cities.

Once you find something that scored well across the board, it’s time to consider supportive data analysis. Supportive data typically looks at details. For example, is a certain state tenant or landlord friendly? If you don’t like the answer to that question it could weigh in on your final decision, but it shouldn’t be the deciding factor. Of course, how you separate data analysis into primary and supportive is up to you, but we find that this approach typically works best.

Once you have all these points, your bigger picture is complete and you can decide whether to invest or not. Often times even though a property looks profitable on paper and you want to rent it out, you might have zero luck if the people in the area aren’t interested.

Remember, Reality May Not Always Be Reflected by Statistics in Data Analysis

Without statistics, we’d all collectively still struggle to make heads or tails of the real estate market. Without statistics, we wouldn’t even know where to begin when looking for properties. Even if you’re looking to move someplace else, how likely is it that you’ll look up data analysis for the surrounding area. Is it safe? What’s the weather like? What do properties in this neighborhood cost anyway?

What we do need to consider though, is that some statistics may not always reflect real life. So you might find a property that is supposedly in an area with high crime rates. But then you move there and while yes, there may be disturbances, you’ll find that it’s actually pretty peaceful and expecting something bad to happen might be out of the ordinary.

So the bottom line is that you must take everything with a grain of salt. Use the tools at your disposal but be wary of them. Question everything. There’s never anything wrong with asking as many questions as you can when it comes to real estate investing- and if anyone claims there is it means they’re just trying to swindle you.

We hope this article has helped you in your quest for the best real estate properties to invest in. Data analysis might be confusing (at best) at first, but an inquisitive mind will be able to make sense of it in no time! Take the tips we’ve given you, grab your laptop, and get to work!

Tell us what you think! What are some other examples of skewed data analysis that you’ve seen? Talk to us in the comments down below. 

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