4 Ways Smart Investors Use Data to Make High-Stakes Decisions

4 Ways Smart Investors Use Data to Make High-Stakes Decisions

14 min read
Scott Trench

Scott Trench is the CEO and President of BiggerPockets. Scott has dedicated his career to helping ordinary Americans build wealth in part through real estate investing. Since joining BiggerPockets in 2014, Scott has authored the bestselling wealth-building book Set for Life and joined Mindy Jensen as co-host of the BiggerPockets Money Podcast.

Experience
Scott is an active real estate investor in the Denver market, currently managing a private portfolio of about $1.5M and holds his real estate license as a Colorado broker.

He is a perpetual student of personal finance, real estate investing, sales, business, and personal development. With this knowledge, Scott stays active in the BiggerPockets Forums and has contributed hundreds of articles, market analyses, and files to BiggerPockets.

He hopes this will provide other investors the tools they need to repeat his results in just 3-5 years, giving them the option to go anywhere they want in the world, work any job, start any business, or finish out the journey to financial independence and retire young.

In addition to real estate, Scott enjoys skiing, rugby, craft beers, and terrible punny jokes.

Press
Scott has contributed to several personal finance blogs and podcasts, along with traditional news outlets including Time, CNBC, and NBC. Find out more about his story at JoeFairless.com, MadFientist, and ChooseFI.

Education
Scott graduated from Vanderbilt University with degrees in Economics and History, Corporate Strategy, and Finance.

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Data cannot solve all of your problems in real estate investing, and you should be very grateful for that fact. If real estate were a game where the winner was always the person with the best data, Wall Street would have long ago entered the market and effectively made wealth building through rental property investing completely unapproachable for the everyday landlord.

I like to think that data are, however, a big part of the puzzle.

Perhaps one could say data are up to half of the game. Maybe a third of the game. You tell me. If one half is made up of knowledge, network, operations, and qualitative assessments about location and long-term “prospects,” data account for the other half to help you narrow in on key assumptions, operate that much more efficiently, and avoid overpaying.

Without giving data more credit than they deserve, I want to discuss the critical ways that data can help guide your decision-making process, as well as how I’ve worked around an easy “database” for the kinds of decisions I’ve had to make along my journey.

BiggerPockets is investing heavily in beefing up our access to data and then using that data to solve common investing challenges. Over the next 12 months, our new feature, available exclusively to Pro and Premium members, “BPInsights” will roll out a steady trickle of cool features that we think will greatly assist rental property investors and rehabbers/flippers in their businesses. The first set of these features is already live, and I’ll explain why we started there, and how we’ll go about rolling out future features.

To begin the framework, there are a few central parts of the investing journey where we think data might be particularly important to the decision-making process:

  1. Deciding whether to invest locally or long-distance
  2. Determining which neighborhoods/zip codes to invest in within your chosen market
  3. Determining whether your deal is “good” in the context of your local market
  4. Evaluating operating performance and value-add potential

Deciding Whether to Invest in a Local Market or to Invest Remotely

When we boil it all down, the two factors that have the greatest influence on our returns as real estate investors boil down to cash flow and appreciation.

The debate is endless about which one is most important, and for all of BiggerPockets’ history, we’ve been hearing about investors who have built wealth with leans toward appreciation markets and the opposite approach working well with investors who lean toward cash flow markets. It seems that for every market and type of property, there is an investor with a success story and high-probability approach.

What we all want is the market that will provide sky-high appreciation over the next 10 years and abundant opportunities to produce excellent cash flow today.

While it is certainly possible that one or many of these markets exist, the reality seems to be that it’s a question of either/or, not a question of and/both. Just take a look at this excellent study by my colleague Dave Meyer. This chart, plotting out the CoCR (cash-on-cash return aka “cash flow”) vs. CAGR (compound annual growth rate in price aka “appreciation”) in 552 national markets:

Sure, there are markets in here that provided better than average appreciation and cash flow, and there are a couple of extreme outliers (like Detroit and Flint, Michigan) that provide dramatically different average cash flow profiles. But, as a rule, most markets tend to cluster in a reasonable distribution, and as a rule, appreciating markets offer less cash flow.

Related: What to Do If You’re Located in an Expensive Real Estate Market

Remember—this data set reflects the averages of the markets studied. Just because the average CoCR for a market is 2%, does not mean that there aren’t properties that can produce a 10% CoCR or higher. It just means that finding that great cash flow is likely to be easier and faster in a market with a higher average CoCR.

So, there’s a lot of data here. What’s the practical application of it? Well, here’s how I think about it:

I believe that there is a meaningful advantage to investing locally. Being able to see the properties, meet your tenants, preserving the option to self-manage or solve tricky problems in person—especially in the early years of investing—and building relationships has value. I can’t quite define this value to the dollar/hour level, except in the case of property management.

To me, this gives a clear preference to Denver (where I live) and surrounding cities that are an easy drive. The value of this local factor falls to zero as I get to a 50-100 mile distance.

Denver comes in offering about a 1.8% (below average but still very close to the “cluster” in the center) CoCR and a 4% CAGR (top 10%). This is generally in line with what I would have guessed about the local market.

Therefore, I need to do some self-reflection and ask myself if I am comfortable with the fact that I will not be producing massive amounts of cash flow from my portfolio, particularly in the early years of my approach. I need to ask myself if I am comfortable investing for modest cash flow and stronger-than-average long-term appreciation. If I am not comfortable with that, then I need to actualize that I need to leave my market and go to another one that offers a different return profile. If I accept that, then my strategy needs to account for the reality that I am accepting.

I’ve chosen Denver as my home and the market I invest in. I know I’m not getting cash flow like they get in Flint, Michigan. In fact, my properties probably cash flow less than yours if you are reading this! My properties do cash flow, of course. They just don’t produce a CoCR that is competitive with the average investor in a cash flow market.

I knew that going in, and that has been confirmed by my experience over the past five or so years. That’s OK. I believe that my long-term wealth will come with the long-term increase in rents and the long-term increase in property values in my local market. My experience has also certainly backed this hypothesis, although I admit that my timing has been very lucky/good.

BPInsights Proposed Solutions

I didn’t have Dave’s data set when I made these assumptions. I came to these relative conclusions on my own, prior to ever seeing it. But having it all mapped out and seeing the actual numbers in a way that I believe makes sense (thanks to Dave’s great research) certainly gives me more confidence. It tells me that Denver is not “bad” for cash flow—it’s really right in the middle.

Related: How Data Analysis Can Make You Rich in Real Estate Investments

Regardless, I certainly feel a lot better about Denver after seeing that! It affirms my suspicion that Denver produces higher than average appreciation. No changes there. Perhaps downloading the free data set here and taking a look at your local market, or other markets you are considering, might help you self-actualize how you feel about your approach, too.

We feel that we’ve made a great first step toward solving this problem with the data set in simple spreadsheet form and available for free. The next steps for us involve coming up with data that is live through the present (updated weekly) and helping investors identify appreciation/cash flow potential within their local market—at the neighborhood and zip code level.

Aside from a free spreadsheet, we have future plans to make downloading data straight from the database available to our advanced Excel users and ultimately building an interface to work with the data set for those of you who aren’t “fluent in spreadsheet.” And we’ll, of course, continue to spin up content to help you make sense of the data, highlight trends, and challenge assumptions.

Determining Which Neighborhoods/Zip Codes to Invest in Within Your Chosen Market

OK—so if the data set above has helped me feel a lot better about Denver and to me, validates hypotheses I’ve been operating against for years, the next step is understanding the dynamics at the neighborhood and zip code level.

Where are the pockets within Denver that offer the best appreciation potential? The best cash flow? Are there similar tradeoffs that I might have to make in terms of appreciation/cash flow within the city itself? Are there any areas that the data can highlight with good cash flow potential that I can drive/walk/bike to, to get a feel for? Are there any areas with good cash flow that might also offer that great opportunity for appreciation?

map with red location markers

As mentioned before, I think that appreciation will, for the foreseeable future, be anybody’s guess in given markets. I also think that many smart investors are capable of using their eyes and ears to pick up clues about local markets that might help them spot the areas offering that extra bit of upside. I’m not convinced that data will be able to solve that problem in the near-term.

But identifying the neighborhoods and zip codes with great price-to-rent ratios and helping investors quickly narrow in on those? That is a solvable problem. The challenges here lie in getting robust access to data and in the population density of the areas we are trying to study.

A zip code with just a handful of properties that have sold or rented in the past year is going to be tricky to analyze. A few outliers can really skew the averages and give a misleading picture of a given market. This can be a good and a bad thing—it can mean opportunity for an investor willing to learn the market intimately, or it can mean uncertainty and risk.

On the other hand, a neighborhood or zip code that is densely populated and has a lot of rental and transaction activity going on means that everyone in the market, and outside of it, can get a pretty clear understanding of what properties are worth and what they will rent for.

Just a few years ago, I got started by asking local investors about these areas, then doing research by looking at Craigslist and Zillow to determine rents, and then analyzing tons of deals with the BiggerPockets Calculators. Over time, that helped me amass a “data set”—mostly in my head—that gave me a pretty reasonable approximation of rents/values.

My networking and education, coupled with this private data set, helped me narrow down my neighborhoods to a select few and then clearly define my criteria for a property.

Potential Solution from BPInsights

This is the next natural evolution of the data set above. Where data exist, BPInsights plans to compile reporting to help users find out what the average “price/rent” ratio is in neighborhoods and zip codes and accompany that with an opinion of how useful that standard is for that local area.

Related: Data (Finally) Answers: Does Allowing Pets in Rentals Pay Off?

A zip code with many properties at similar price points and tons of activity is going to provide a more accurate and narrow picture of the quality of properties in it. A zip code with fewer properties, or properties with vastly different price points/quality levels, will have less reliable information. In either case, that may help accelerate the decision-making process for investors looking to narrow their search.

Specifically, we plan to provide tools and features that allow you to enter in markets and provide maps and outputs that can show you where the cash flow is, where there are lots of data points, where there are few data points, and highlight any trends. Hopefully, these features can help you narrow your search to a handful of neighborhoods and hone in on what you want.

Determining Whether Your Deal Is “Good” in the Context of Your Local Market

Sometimes, I wonder if investors coming from one market into another are blinded by how much “better” the deals are. An investor from San Francisco might not believe their eyes when they see the numbers on a quadplex in Memphis, Tennessee.

But just because the numbers are dramatically different from your local market, it does not mean that you’ve found a “good” deal.

Suppose duplexes in your area in great condition rent for $1,000 per side and sell for $100,000. If you pay $120,000, then you have not gotten a “good” deal. You paid more than others for a comparable property, and if you were to resell your property immediately, you would likely lose a substantial sum, or perhaps be underwater.

Not everyone can get a “great” deal. Not everyone will get a “good” deal. And buying a “bad” deal may even result in you building substantial wealth over time! The guy who buys that $120,000 duplex, cash flows, and sells it for $400,000 in 30 years may not really care that he paid $20,000 too much for it today.

But you still have to know if you are getting a good deal. This is real estate investing we are talking about. Your risk profile increases dramatically if you overpay for property, in particular in the first few years of your hold.

Here’s the process I use to ensure that I get a “good” deal—or at the very least, avoid a bad one.

First, I determine what I want in crystal clear definition: units, beds/baths, general square footage, approximate build year, market, and parameters or “scales.” For example, I’ll pay more, or receive less cash flow, for a property on the more desirable side of the neighborhood I’m looking at, and I’ll need more cash flow to be interested in property on the other side.

Then, I go back and look at all sold properties in the last 90-180 days. I’m looking to sanity check my wish list. If what I’m looking for hasn’t sold in the last 180 days, I’m living in a fantasyland. I’m trying to find the $300,000 triplex in the Class A neighborhood renting for $1,200/unit. Doesn’t exist. If it does, please correct me in the comments or ping me directly.

If I find properties that meet my criteria, I continually narrow my search until I’ve got in the ballpark of between five to 10 sales in the last 90-180 days. I have to be able to say, with no hesitation, that barring a deal-killer in the viewing/inspection process, I would have bought these deals. It is at this point that I have determined what a “good” deal is for me in my local market and with respect to recent comparable sales or “comps.”

To come to this conclusion for yourself in your local market, you need to access and then mentally construct a number of data sets. You need to access recent sales (perhaps through your agent). You need to get a handle on rents (perhaps through your agent and comps/research on the market). You need to question and fine-tune those assumptions.

Ultimately, for me, this boils down to finding the greatest possible amount of cash flow on a property, qualified by its relative location and appreciation prospects. I’m looking for what I believe is the best combination of those two things.

Potential Solution from BPInsights

To speed this process, BPInsights can ultimately pipe in listing and sales data into our Rental Listings page and tie our rental data to the listings to give you an approximation of rent (live as of now for Pro members). This may help dramatically accelerate the research process for many investors, as they can pull up a market at the zip code level and process and sort both active listings and recent sales by an estimation of price/rent ratio.

For example, if I see that the property with the very best price-to-rent ratio in a certain zip code still would have produced negative cash flow, I can immediately move on from that zip code and conduct a similar analysis in the next.

We also just added an offering to the BPInsights feature that could help in this area: a spreadsheet that has rent-to-price ratios for many major cities across the U.S. You can find it here.

Evaluating Operating Performance and Value-Add Potential

Once we’ve determined a market, neighborhood, and property, it’s time to add in a couple additional considerations to the areas where data-driven decision-making can make you money. This comes in the form of analyzing my property’s performance relative to market conditions and in analyzing rehab projects.

As a landlord of several years, I am sometimes more lenient with existing tenants than I should be and charge less rent than I could command with a newly placed tenant. There can be good business cases to be made for keeping tenants happy and slightly below market rent, specifically in reducing turnover. But the fact of the matter is that I am not always 100% clear on just where market rents are for properties like mine or how I am performing against those market averages. I need to know when I’m significantly under and over market. Even if I choose then not to act on that information.

I feel comfortable assessing my property’s rents to within a $100-$150 range, if not a little narrower of a range with some of my units. However, the process to determine this usually involves a less-than-scientific approach wherein I’m simply looking at other rental listings in the area and ballparking my rent based on that research and my general knowledge of the area, as well as which units are roughly equivalent to mine.

Unlike my determination of a “good” deal above, I don’t have access to sold (or in this case rented) data. So, I often have less data, and some of the “comps” that I am using will be advertised rents—they may not actually end up leasing for the full amount advertised.

I also have a hard time understanding the value of a rehab project. Again, while I can find after-repair value (ARV) by looking at comparable properties that have sold in the past 90-180 days, I also need to conduct that same study for rents. This is particularly important for folks looking to rehab and then rent or to do a full BRRRR project.

In addition to doing qualitative research, networking, and making high-level assumptions, data can be powerful tools in the decision-making process for operations and value-add. While you’ll still have to quote out any construction work, if you can get a clear picture of the outcome and where your unit will array post-rehab to the market, you can get an estimation of future rent and decide if the return/cost profile is worthwhile.

renovation interior with vinyl plank flooring and faux brick wall and gray paint

Potential Solution from BPInsights

BPInsights’ first major software feature, the rental analysis tool, begins to solve this problem by giving you a quick way to analyze where our database suggests your property’s rent should be.

We see a number of applications that can follow from this, including:

  • Periodic alerts to let you know if rents are rising, falling, or staying the same and whether you are keeping up.
  • Spreadsheets and database access that can allow you to find markets with particularly good spreads between low-end, median, and high-end rents, or that otherwise help you identify areas that might be good for BRRRR or value-add rehab projects
  • Rehab analysis tools that help you compute whether a rehab will generate enough rent/equity value to justify its cost

Conclusion

Data alone will never be the answer to successful real estate decision-making. But you must have a data set that you trust and believe in order to make high-quality real estate decisions.

To provide a data-driven solution to the problems I outlined above, we’ve invested heavily in purchasing two immense data sets—one with nationwide property detail and one with nationwide rental detail. Our expert data and engineering teams have spent a great deal of time and effort to create a merged data set that we think can estimate and aggregate rents and property characteristics better than anyone else online. And the possibilities are endless.

Some of the feature-set ideas include:

  • Calculators
    • Integrate our rental estimates to provide a starting point for rent assumptions
    • Integrate other expense data, including taxes, insurance, management, and ultimately (long-term) appropriate local CapEx and maintenance assumptions
    • Add analysis to help you understand how much a rehab might impact rents
  • Marketplace
    • Add a “market finder” application
    • Add in an assumption of rent for all listings in the marketplace from BPInsights
    • Filter all properties by rent/price ratio
  • Spreadsheets
    • Ability to download from the database to conduct your own research
    • Market overviews from our data/content teams, like the one referenced earlier
  • Content
    • Our data team can analyze the data and provide trends, forecasts, etc.
    • Our data team can provide written reports helping you understand, in general, where to find cash flow, what lead indicators to appreciation are, etc.

Rather than wait until the end when we’ve released a robust set of features all at once, we’ve decided to trickle out features one by one over the course of the rest of the year and into next year. The order in which we will tackle them will depend heavily on where we perceive the greatest problems you—our community—have and the relative amount of difficulty/time in solving those problems.

This is where you come in: What are your problems? What would you like to see? Are you excited?

Do you agree with my framework for using data in my investing decision-making process?

Join the discussion below in the comment section.