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The Case of the Mysterious Sinking IRR

Users of our Real Estate Investment Analysis program sometimes call us with questions that are not about the software but about the underlying analysis. If we had a “greatest hits” list for those questions the all-time winner would be this: “My cash flow goes up each year; the value of the property goes up each year; but when I look at the Internal Rate of Return, it goes down almost every year. What’s up with that?” To see how this can happen, let’s take a look at two very simple examples.

Example #1: We purchase a property for \$100,000 all cash. It has a Net Operating Income of \$10,000, so the capitalization rate is 10%. We are going to assume that 10% is the right cap rate for this market (primarily because it make the math in our example easy to follow). Because we bought the property for cash there is no debt service and so we can also assume that the cash flow is the same as the Net Operating Income. For those who require an instant (and very abbreviated) refresher course on these concepts, use the following:

• Gross Income less Operating Expenses equals Net Operating Income
• Net Operating Income less Debt Service equals Cash Flow
• Net Operating Income divided by Capitalization Rate equals the property’s Present Value

The property is in good shape and is running well when we buy it. Our initial cash flow occurs on Day One when we spend \$100,000 in cash to make the purchase. We project that we can raise the rent 4% during the first year to \$10,400. The property is well-located, so we believe we can get a bit more aggressive over time. We’ll project that we can increase the revenue 5% in the second year, 6% in the third, 7% in the fourth and 8% in the fifth. Here is what our projections look like:

Notice that, if we sell the property at the end of one year for its full value (i.e., with no selling costs, to keep matters simple), our Internal Rate of Return (IRR) is a pleasing 14.4%. If we sell at the end of year two, our IRR for that holding period is even better, 14.92%. If we hang on to the property for five years, we see that we can expect a 16.38% IRR. The rents go up each year, the value goes up and so does the IRR. All is right with the world.

Example #2: At the same time we buy another property, also for \$100,000 cash. It too has a \$10,000 NOI, but this property needs immediate management improvements to control expenses and to get rents in line with the market. We feel sure that we can get the NOI (and hence the cash flow) to \$12,000 in the first year. That should get it on a stable footing, from which we expect a more modest 3% increase in rent each year thereafter. The rents go up each year, the value goes up each year, but what about the IRR?

At the end of the first year, we’re thrilled by a robust IRR of 32%. We worked hard; we deserve it. But if we hold the property for a second year the Internal Rate of Return drops to 22.76% — still not shabby but significantly lower than at the end of the prior year. Indeed, the longer we hold the property, the lower the IRR becomes. What, to coin a phrase, is wrong with this picture? Nothing is wrong, actually. The numbers are correct. Remember that Internal Rate of Return is a time-sensitive measurement. The biggest jump in cash flow and in the property’s value came early. The earlier it arrives, the less severely it gets discounted — it’s the “time value of money” concept. The increases that occur in years two through five are smaller to begin with and they get discounted over a greater number of years, shrinking their worth to us today even more.

Simply put, if we hold the property two years instead of one, then that second year dilutes the overall rate of return because it didn’t contribute as much (especially after an extra year of discounting) as the first year did. If we hold the property for three years, the return gets diluted still further.

At this point, someone in the back of the room is surely asking the insightful question, “So what?” Here’s what: The first property is telling us that it will perform better as an investment if we hold onto it for a while. Its rent increases are accelerating each year. Even though the increases have to be discounted — it’s that time value of money again — they’re growing at a pace that makes them worth waiting for. Hence the IRR gets higher with each year we hold on. The second property, however, has a bit more of a roman candle quality to its performance. The big flash comes early; after that, it just sputters along.

Does this mean you should immediately sell such a property? If you’re happy with the long-term IRR and could not find a replacement property with a greater yield, it might make sense to hold. Or you might be more comfortable following the words of immortal Janis Joplin: Get it while you can. To put that in more businesslike terms, you might decide to sell the property when the IRR peaks; then take the proceeds and reinvest them. Whichever way you go, the important thing is that you’ll be making an informed decision.

Better than being like this guy.

If you found this example helpful, I have a lot more educational material for real estate investors and developers. For example, check out these video lessons…

Real Estate Investment Case Studies where I take you step-by-step through the evaluation of five different property types: apartment, mixed-use, triple-net leased, retail strip center, and single-family property

Value-Add Real Estate Investments where I show how you might do something tangible or intangible to a property, but in either case, something that increases how much a person would pay to acquire that asset from you when you’re done.

Or if you’re ready for a complete training series in real estate investment, development, finance, partnerships, and more, consider Mastering Real Estate Investing.

—— Frank Gallinelli

The information presented in this article represents the opinions of the author and does not necessarily reflect the opinions of RealData® Inc. The material contained in articles that appear on realdata.com is not intended to provide legal, tax or other professional advice or to substitute for proper professional advice and/or due diligence. We urge you to consult an attorney, CPA or other appropriate professional before taking any action in regard to matters discussed in any article or posting. The posting of any article and of any link back to the author and/or the author’s company does not constitute an endorsement or recommendation of the author’s products or services.

Educating Real Estate Investors — Third Episode in My New Podcast Series

Welcome back to my new podcast series. In my first interview I answered some questions about how I got started as an investor, and I hope my experience provided some ideas for you if you’re just looking to get started yourself.

And in the second, I talked about my first commercial investment, which is where I really found my way in leveraging technology, and which led to the birth of RealData software.

In this third interview I discuss how my experiences with the software company evolved into a passion for investor education.

Below is a snippet of the video version of this podcast. You can watch the entire video on youtube, or visit our complete youtube video library (lots of good stuff there for investors). You can also listen to the audio version of my podcasts on Spotify, Apple, or on most anyplace you usually get your podcasts.

The information presented in this article represents the opinions of the author and does not necessarily reflect the opinions of RealData® Inc. The material contained in articles that appear on realdata.com is not intended to provide legal, tax or other professional advice or to substitute for proper professional advice and/or due diligence. We urge you to consult an attorney, CPA or other appropriate professional before taking any action in regard to matters discussed in any article or posting. The posting of any article and of any link back to the author and/or the author’s company does not constitute an endorsement or recommendation of the author’s products or services.

Evaluating an Income Property and the Birth of RealData — Second Episode in My New Podcast Series

Welcome back to my new podcast series. In my first interview I answered some questions about how I got started as an investor, and I hope my experience provided some ideas for you if you’re just looking to get started yourself.

Now I want to take you on the next few steps in my journey and talk about how I came to learn about analyzing income-property investments.

In this interview I tell you about my first commercial investment, which is where I really found my way in leveraging technology, and which led to the birth of RealData software.

Below is a snippet of the video version of this podcast. You can watch the entire video on youtube, or visit our complete youtube video library (lots of good stuff there for investors). You can also listen to the audio version of my podcasts on Spotify, Apple, or on most anyplace you usually get your podcasts.

UPDATE: Episode 3 is available now!

The information presented in this article represents the opinions of the author and does not necessarily reflect the opinions of RealData® Inc. The material contained in articles that appear on realdata.com is not intended to provide legal, tax or other professional advice or to substitute for proper professional advice and/or due diligence. We urge you to consult an attorney, CPA or other appropriate professional before taking any action in regard to matters discussed in any article or posting. The posting of any article and of any link back to the author and/or the author’s company does not constitute an endorsement or recommendation of the author’s products or services.

The Single-Family Home as a Rental Property Investment — Using Regression to Estimate Value

Regression – no, it’s not what your family and friends accuse you of when you want to trade in the mini-van for a two-seater stick-shift convertible (well, maybe it is, but that’s a topic for a different article). If you’re familiar with our RealData software, my online video courses, and my other blog posts here, then you know that I’m usually talking about income-producing property like multi-family, retail, office, or the like — seldom about single-family homes. And when we estimate the value of most income properties, we typically do so by looking at their income stream.

Recently, many investors (both big and small) have been buying up single-family homes to hold as rental properties, and that presents something of a conundrum: We still want to analyze cash flows and returns as any investor should, but when we think about the price we pay to acquire a home or the price we’ll get when we sell, our usual income-capitalization may not be the best approach. Simply put, that’s because most single-family residences are bought and sold based on the price of comparable sales, not on their ability to produce rental income. Often, our comparable sales approach is informal and unscientific. The neighbor got \$250k, so I guess this house is worth the same.

Or not.

Linear regression is a statistical technique we can use to approach this with more rigor. To put it into non-technical terms, it lets us look at a situation where we can take some facts that we know (dare we call them real data?) and use them to identify a trend. If a trend really does exist, that trend, in turn, allows us to predict the value of something otherwise unknown. Let’s look at some examples. Five years ago my property taxes were \$1,000. Four years ago they were \$1,100. Three years ago, \$1,200. Two years ago, \$1,300 and last year \$1,400. Given this trend, what can we reasonably predict we’ll pay this year? Right. \$1,500. How did we guess? We probably had a flashback to our junior high school algebra class (talk about regression!). In the graph paper of our mind, we plotted a perfectly straight line. The line was formed by a series of data points and it clearly suggested a trend.

Each data point on this graph represents two pieces of information, or “variables:” an independent variable (time) plotted along the horizontal x-axis and a dependent variable (the tax amount) plotted along the vertical or y-axis. The first data point, therefore, is a dot that appears where “5 yrs ago” and “\$1,000” intersect. The second point lands where “4 yrs ago” and “\$1,100” intersect and so on. The tax amount is the dependent variable because it changes as a function of time. In other words the tax bill depends on the year, not the other way around. When we play connect-the-dots as in the graphic above (hence the name linear regression), we see that those dots form a perfectly straight line. If we extend that line beyond our known data points a bit, we can see that in the current year, assuming that the trend holds up, we could reasonably expect the taxes to be \$1,500. Of course, in real life our ducks don’t always line up so nicely in a row. When they look like the graphic below, we’ll probably need computer software to fit the best possible line to the series of points. Then we can use the resulting straight line to make our predictions. There are numerous ways that we can use linear regression in real property analysis. We invite you to download a RealData® model to give the concept a spin. “Real estate value by linear regression” is a Microsoft Excel® workbook designed to help us estimate a property’s worth using the market data, or comparable sales, approach to valuation. This approach assumes that recent sales of properties that are nearby and are comparable to the subject provide the best indicators as to the value of the subject. While we might sometimes use this model with other types of real estate, let’s assume for the sake of example that we want to estimate the value of a single-family residence. Although previously sold homes may be comparable they are unlikely to be identical, either to each other or to the subject being appraised. One may have more land; another may offer more interior space; a third may boast a better layout and so on. As a rule such differences are generally reflected in the selling prices of the homes. Properties that are otherwise similar sell for more or less as a function of their distinguishing features. If we can identify some measure (index) of the appeal or amenities of the properties in a given neighborhood, then we may also be able to discern a pattern between that measure and the value of the properties — our trend line again. We can then use the pattern to predict the values of other properties in the same locale.

Our model will permit us to determine by regression analysis whether or not a linear relationship exists between selling price and some independent variable that we define. One possible technique is to use the property tax assessment as an index of value. Although assessments seldom reflect true market price, they often provide a good indication of relative value, so they’re worth a try. If the assessments and prices from a number of recent home sales in a neighborhood define a linear relationship, our model can measure the strength of that relationship and use it to estimate the worth of a home not yet sold. After we open this model we can enter the address, an index and an adjusted selling price for as many as fifteen comparable sold properties. (Regarding the term “adjusted:” We may want to correct for price inflation whenever a sale is more than a few months old.) At the bottom (after #15), we’ll enter the address and the index amount of the subject property. The program will fill in the field for the number of comparables used and compute the subject property’s estimated selling price. The results appear in a report and graph, in the section below. Notice that the program will specify a correlation coefficient. This is a new bit of terminology we didn’t see in our simplified explanation above. This number is a statistical measurement of the reliability of the relationship between the index and the adjusted selling price. To put it another way, it’s a numerical way of expressing how straight our dots line up. A correlation of 1.00 is a perfect relationship, while zero indicates that we have completely random data. In most cases, we would like to see a correlation coefficient of at least 0.80 to believe that there is a strong enough relationship between the index and selling price to use that relationship as the basis of a prediction.

As an interesting sidebar, we can see how accurately this regression analysis would have predicted the values of the homes whose actual selling prices we know. That is because the program computes and displays the selling prices that the analysis would have predicted for each of the comparables. We also see the dollar and percentage differences between the projected and actual prices. This section provides a very graphic demonstration of the accuracy — or inaccuracy — of our model’s prediction. We need to keep in mind that, as with most projections, the quality of our output is entirely dependent on the quality of our input. We certainly have to make appropriate choices for our comparables. Otherwise we can’t reasonably expect to achieve meaningful results. In addition, the kind of index we select must relate consistently to value. If we find tax assessments to be unreliable, we may want to try gross living area or experiment with a scoring system (X points for each bedroom, Y points for each bath, etc.). We may also want to consider trying for even greater accuracy in our predictions by advancing to what’s called “multiple linear regression,” a similar technique where we consider two or more independent variables as possible predictors of an outcome (i.e., a dependent variable).

A regression analysis like the one provided in this model can be very useful because of its ability to provide statistical support to what might otherwise be a subjective estimate of value. Property sellers and buyers can use it to support price negotiations; and agents can use it to enhance the effectiveness of their listing presentations. And of course, investors can estimate the initial cost and ultimate reversion value of a single-family home bought and held as a rental property. With a bit of imagination, linear regression can be used in many ways to poke and prod our analyses and projections. Its name notwithstanding, it can take us a big step forward.

POSTSCRIPT: I’ve added a detailed video case study about single-family investments to my course, Mastering Real Estate Investing, and to my mini-course Real Estate Investment Case Studies.

The information presented in this article represents the opinions of the author and does not necessarily reflect the opinions of RealData® Inc. The material contained in articles that appear on realdata.com is not intended to provide legal, tax or other professional advice or to substitute for proper professional advice and/or due diligence. We urge you to consult an attorney, CPA or other appropriate professional before taking any action in regard to matters discussed in any article or posting. The posting of any article and of any link back to the author and/or the author’s company does not constitute an endorsement or recommendation of the author’s products or services.

What Every Real Estate Investor Needs to Know About Cash Flow — thanks for the recent reviews

Many thanks to one of my favorite podcasters, Keith Weinhold, for his youtube review of my book, “What Every Real Estate Investor Needs to Know About Cash Flow.”

Also a “thank you” to Flagship Bank for including my book in their list of  “…the best commercial real estate investing books you can buy…”

—Frank Gallinelli

President, RealData, Inc.

What Happened to Your Property Management?

If you’ve taken my video course, read any of my books, listened to some of the podcasts I’ve been on, then you’re very aware that I often rant about how important it is for you to account for just the real operating operating expenses when you evaluate the worth of a property — no more and no fewer.

There is one mistake I see really often, and I want to call it out here in this video blog.

Love Your Hat! What is Your Lender Really Looking at When You Apply for a Commercial Mortgage?

If you’re not an all cash buyer, then when you purchase a piece of income-producing real estate you’ll probably need to secure mortgage financing to complete the deal. It’s essential for you to understand what your lender is looking at when underwriting that loan.

And — If you guessed that he or she is not admiring your millinery —  ok then, stick with me here. I’m going to discuss briefly a couple of key yardsticks.

Of course, this short video blog post is just the tip of the iceberg when it comes to evaluating, financing, and acquiring a successful real estate investment.

For in-depth insight into on all the key metrics and methods, check out https://realestateeducation.net/

And you’ll find the software that will do all the heavy lifting for your analysis and presentation at https://realdata.com

The information presented in this article represents the opinions of the author and does not necessarily reflect the opinions of RealData® Inc. The material contained in articles that appear on realdata.com is not intended to provide legal, tax or other professional advice or to substitute for proper professional advice and/or due diligence. We urge you to consult an attorney, CPA or other appropriate professional before taking any action in regard to matters discussed in any article or posting. The posting of any article and of any link back to the author and/or the author’s company does not constitute an endorsement or recommendation of the author’s products or services.

Video post: Understanding Net Operating Income, Part 2

In Part 1 this post, we looked at the revenue side of our NOI calculation. Now let’s look at the expense side, and how the end result – the NOI itself, is typically used when evaluating a potential real estate investment. Click the image below.

If you missed Part 1, you can watch it here.

The information presented in this article represents the opinions of the author and does not necessarily reflect the opinions of RealData® Inc. The material contained in articles that appear on realdata.com is not intended to provide legal, tax or other professional advice or to substitute for proper professional advice and/or due diligence. We urge you to consult an attorney, CPA or other appropriate professional before taking any action in regard to matters discussed in any article or posting. The posting of any article and of any link back to the author and/or the author’s company does not constitute an endorsement or recommendation of the author’s products or services.

Video post: Understanding Net Operating Income, Part 1

One topic that seems to generate a lot of interest and questions among investors I speak with is the subject of net operating income. Those who are new to real estate investing and even those with some experience are often unclear as to exactly what it is, what it means, and how to use it.

To shed some light on this topic, I’m going to try something new here – new for me at least – a video blog post. I’ll try to answer those questions by giving you a basic roadmap of how Net Operating Income is calculated, and how it’s used in real investment situations. So —  here we go with Part 1 of 2. Click the image below.

Part 2 is now available here.

The information presented in this article represents the opinions of the author and does not necessarily reflect the opinions of RealData® Inc. The material contained in articles that appear on realdata.com is not intended to provide legal, tax or other professional advice or to substitute for proper professional advice and/or due diligence. We urge you to consult an attorney, CPA or other appropriate professional before taking any action in regard to matters discussed in any article or posting. The posting of any article and of any link back to the author and/or the author’s company does not constitute an endorsement or recommendation of the author’s products or services.

New content in my online video course

Those of you who are already enrolled in my course, Introduction to Real Estate Investment Analysis, are probably aware that I’ve been regularly adding new content to the course over time.

My most recent addition is a lesson on “Phantom Income.” The lesson discusses how and when it might be possible for your taxable income to outpace your cash flow. Probably something you’d prefer to avoid if you could.

New content like this is always available at no charge to those who are enrolled in the course, but for a limited time this new lesson will be my treat to anyone who would like to view it.

So, even if you’re not already enrolled, just go to the course home page, and scroll down about two-thirds, past my smiling face, until you see the curriculum. You can find the lesson in the middle of the section called Real Estate Pro Formas. Click the Preview button to watch.

In case you missed it, I also added a three-part series this summer called, “Blend and Extend.”

This is a technique that landlords and tenants have used during difficult times in the past — a technique where a bit of give and take could potentially benefit both parties. A timely topic, I believe, given the upheaval in commercial real estate during the pandemic.

I’m making the first video in the series available as a free preview. Again, go to the curriculum, but this time expand it and scroll to the very bottom to find “Blend and Extend.” That’s where you can preview Part 1.

In the two remaining lesson in this series, I go into more specifics about the ways you might actually run the numbers on a possible lease restructuring to find a scenario acceptable to both sides. I include examples was well as an Excel model that should help you with the calculations.

Since the original release of the course, I’ve added a great deal to my core content, including a series of case study examples, as well as modules on partnerships, development projects, and value-add investments.

But I’m always enthusiastic about broadening the scope of the learning you can derive and the benefits you can reap from the course. Do you have an idea for an additional topic you’d like to see? If so, please pass along your suggestion in the comments section! Thank you.

— Frank G