Category: real estate education

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.  

Property Taxes by Year

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.  

Regression Points

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.  

Comparable Sales List

The results appear in a report and graph, in the section below.  

Market Analysis by Linear Regression Report

  Scatter Graph

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. It’s name notwithstanding, it can take us a big step forward.

Copyright 2021,  Frank Gallinelli and RealData® Inc. All Rights Reserved

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.

Yield on Cost — a metric for real estate investors and developers

I had a question recently about a metric called Yield on Cost, aka Return on Cost and also sometimes called Development Yield. So what is it and when and how might it be useful?

Yield on Cost is very similar to cap rate, which you’re already familiar with, especially if you’ve followed my posts, read my books or taken my online course. It’s a metric commonly used by investors and commercial appraisers, and it’s the ratio of a property’s Net Operating Income to its market value. It looks at an income property at a point in time.

What’s the difference between cap rate and Yield on Cost?  

Cap rate measures income in relation to the value of a property. Yield on cost measures income in relation to the total cost of the property.

Another way to think of Yield on Cost is as a forward looking cap rate.

Let’s try to make some sense of this by hanging some numbers on these words.

You decide you’re going to buy a property today with its 50,000 NOI at the market cap of 5% for $1 million. But you see a value-add opportunity here to make improvements and to create value. You’re going to spend money to make money.

Specifically, you’re going to upgrade these apartments and raise the rents. Remember value-add is an opportunistic approach to investing. You’re looking for a better return, and almost by definition, higher return implies greater risk. So you want to try to get a quick read on whether that higher return – in your judgment – is going to be worth the greater risk.

You’re thinking of spending $75,000 on improvements so you can bump up rents by 15%. Let’s see how that looks:

Now you have a new total cost for the property of $1,075,000 – the purchase price plus the improvements — and a NOI that’s 15% higher than before, or $57,500. Let’s use the Yield on Cost formula, which is basically a lot like the cap rate formula:

YOC = stabilized NOI / total cost

YOC = 57,500 / 1,075,000  = 5.35%

I believe you’ll see right away how this is just slightly different from the standard cap rate formula. With YOC you’re using the NOI as it is stabilized after you make your improvements; and you’re using total amount the property cost you rather than what you think it might be worth. Again cost, not value. So now…

Yield on Cost  = 57,500 / 1,075,000 or 5.35%

Your yield on cost is higher than the 5% market cap rate, and that’s what you want. You want a so-called spread between the Return on Cost and the market cap rate for your value add scenario. That spread is 0.35%.

The question that only you can answer is, is that spread worth the risk?

One way that might help you decide is to ask: What do you think the property is going to be worth after these improvements? For that you cycle back to the cap rate formula, because that deals with value as function of income. You’ll use the market cap of 5% with your new stabilized NOI

Value = NOI / cap rate

Value = 57,500 / .05 

Value = 1,150,000

Its value now, after these improvements, is $1,150,000, which is $75,000 more than your total cost:

Value (1,150,000) minus cost (1,000,000 + 75,000) = 75,000

The math here is probably simpler than the decision itself. That decision rests on your subjective evaluation of the risk involved. How confident are you that you can raise the rents by 15% after spending $75,000 on improvements? In other words —  You’ve calculated the potential reward, objectively. Now you must weigh that against the risks, — risks which you measure pretty much subjectively.

So to wrap things up… Yield on Cost is similar to cap rate except it uses stabilized net operating income after improvements and measures that against total property cost. It does that rather than weighing current NOI against current property value — which is what you’re doing with regular cap rate. Yield on Cost is extremely easy to calculate and it can be useful with value-add investments to get a sense for how improvements to a property will impact your return. It should also give you a sense as to whether the additional return is worth the risk.

Yield on Cost is often used by developers for a quick read on a potential project. Look for more about this metric in a new lesson I will be adding to my course, Introduction to Real Estate Investment Analysis.

In the meantime, if you’d like to watch my discussion of this topic in a video post, you can get that here:  https://vimeo.com/635351764

Copyright 2021,  Frank Gallinelli and RealData® Inc. All Rights Reserved

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 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.

 

Copyright 2021,  Frank Gallinelli and RealData® Inc. All Rights Reserved
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.

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

 

Copyright 2021,  Frank Gallinelli and RealData® Inc. All Rights Reserved

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.

Copyright 2021,  Frank Gallinelli and RealData® Inc. All Rights Reserved

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.

net operating income

 

Part 2 is now available here.

 

Copyright 2021,  Frank Gallinelli and RealData® Inc. All Rights Reserved

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

Copyright 2020,  Frank Gallinelli and RealData® Inc. All Rights Reserved

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 Top 10 Real Estate Finance Books Every Investor Should Read.”

investment book

I was honored to find that one of my books was featured at the top of a recent article on Motley Fool: “The Top 10 Real Estate Finance Books Every Investor Should Read.” The book, “What Every Real Estate Investor Needs to Know About Cash Flow,” was originally published in 2004, is now in its third edition, and is alive and still doing well —  a surprise certainly to me, and probably to the publisher as well.

I often get asked what accounts for the book’s long-term appeal, and I think there may be two reasons: First, I avoided “topical” or trendy content, preferring to stick with core concepts and math-based metrics don’t change with time. And second because I really dislike the get-rich quick hype that seems to characterize so many real estate books, and so I shunned that, too.

I don’t think they’ll ever make a movie out of it, but I’m satisfied if it has helped some readers make informed and unemotional investment decisions.

You can find the article here.

Are you involved in real estate education?

We’re reaching out to our followers who teach real estate investment, development, or finance to let you know that our Real Estate Investment Analysis course is available for the virtual classroom – now with volume academic pricing.

For more than a decade I’ve devoted much of my professional life to investor education, as a writer, Columbia adjunct professor, and through my company RealData. As you may know, a few years ago I created an online video course, Introduction to Real Estate Investment Analysis. It has grown to include a broad range of topics that are key to understanding how income-producing properties work, and how investors, developers, lenders, and others evaluate their financial dynamics.

With so many schools and colleges now needing to provide good content for a virtual learning environment, we’ve re-deployed the course as a resource that instructors can add to their existing curricula. We now offer volume academic pricing at a significant discount, depending on class size.

For an overview, including access to sample lessons, go to the course home page.  To see a complete course outline, click here.

If you’re involved in real estate or financial education, then I hope that this can help you provide meaningful content to your remote learners. To get a quote for volume licenses for student use or to discuss this further, please email me at education@realdata.com.

— Frank Gallinelli

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Copyright 2020,  Frank Gallinelli and RealData® Inc. All Rights Reserved

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.