Tag: residential real estate

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.

New Podcast: Investing in Income-Producing Real Estate

I had the privilege recently of recording a video podcast with REICLub, where we discussed investing in income-producing real estate: deciding what kind of property you should buy, how to begin the analysis process, understanding the income stream, estimating value or worth, dealing with long-term projections, recognizing common pitfalls, investing with partners.

I invite you to view it here:


—Frank Gallinelli

The Cash-on-Cash Conundrum – a Postscript

A while back, I posted a two-part series called “The Cash-on-Cash Conundrum.” In the first installment I explained the calculation and underlying logic of CoC, and in the second I discussed some of the pitfalls of overreliance on this particular measure.

big pile of dollars

I try to keep my ear to the ground by reading and sometimes contributing to investor forums, where I continue to see a good deal of discussion on the question of what is or what should be the metric of choice for real estate investors. My unofficial and unscientific gauge of the general sentiment is that most investors agree that cash flow is king. Although I would be reluctant to crown any single measure as the absolute be-all and end-all for property analysis, I agree that cash flow is indeed a critical measure of the health of an investment property.

So what’s the big deal? What concerns me is that I see a kind of tunnel vision on this topic. I frequently hear some variation of these two statements bundled together: “Cash-on-cash return is the only reliable metric and the only one I really need,” and “IRR and Discounted Cash Flow analysis are bogus – they’re a waste of time because you just can’t predict the future.” To put it simply, these folks are saying that they trust CoC because it looks at the here and now, and they distrust IRR/DCF because it tries to look into the future.

On the surface, that argument might seem reasonable enough. Cash-on-Cash return is the property’s expected first-year cash flow before taxes, divided by the amount of cash invested to make the purchase; it’s quick and easy to calculate, and it does indeed focus on a more-or-less tangible present. A strong CoC unarguably provides a good sign that your investment is off on the right foot.

Is that the end of the story – or should it be? I think this narrow focus can cause an investor to miss some vital issues.

By adopting the “can’t predict the future” argument, aren’t you ignoring what investing is all about? You don’t have a crystal ball, but still — isn’t investing about the future, and isn’t the ability to make sensible choices in an uncertain environment a key trait of the successful investor?

I find it difficult to accept the argument that I should make a decision to buy or not to buy an investment property based on its first-year cash flow alone and without regard to projections of future performance. Ironically, there is a hidden message in this point of view: If the first year performance data is sufficient, then apparently I should believe that such data will be representative of how well the property will perform all the time. In other words, it really is OK to predict the future, so long as I believe the future will always be like the present.

I would argue that it is in fact less speculative to make the kind of projections that you typically see in a Discounted Cash Flow analysis, where you look at the anticipated cash flow over a period of time and use those projections to estimate an Internal Rate of Return over the entire holding period.

With any given property, there may be items that you can forecast with a reasonable degree of confidence. For example, on the revenue side you may have commercial leases that specify the rent for five years, ten, or even more. You may even be able to anticipate a potential loss of revenue at a point in the future when a commercial lease expires and you need to deal with rollover vacancy, tenant improvements, and leasing commissions.

You could be looking at a double- or triple-net property where you are insulated from many or most of the uncertainties about future operating expenses like taxes, insurance and maintenance.

Or, with residential property, you may have a history of occupancy percentage and rent increases that permit a credible forecast of future revenue.

Then there is the more basic question, why are you analyzing this property at all? Why are you running the numbers and making this CoC calculation? Are you trying to establish a current market value, as a commercial appraiser might? Or are you trying to make a more personal decision, i.e., will this particular property possibly meet your investment goals? And what are those goals?

Seems like I just took a nice simple metric and wove it into a more complicated story. Sorry, but in your heart of hearts you know if investing really were that simple, then everyone with a pulse would be a huge success. At the same time, it doesn’t have to be so complicated either, so long as you approach it in a reasonable and orderly way.

That orderly approach begins with deciding what you are looking to get out of this investment. Maybe you want to hold it for a few years to get strong cash flow and then sell it, hopefully for a profit. Perhaps you intend to hold it long term, less concerned with immediate cash flow (so long it as it positive), and then sell the property much later to fund your children’s college costs or your own retirement. In either case, if your plan is to buy and hold then there is one thing you can’t ignore: the future.

This approach continues with projection of the revenue, expenses, potential resale, and rate-of-return metrics, running out to your intended investment horizon. Perhaps key here is the realization that you shouldn’t really expect to nail your projections with a single try. Consider several variations upon future performance: best-case, worst-case and somewhere in-between scenarios to give yourself a sense of the range of possible outcomes.

All this brings us back to the duel between the Cash-on-Cash metric and DCF/IRR. I believe if you rely only on the former, then you are not just saying, “You can’t predict the future.” You’re saying, “If the first year looks good, then that’s all I need to know.” This is, quite literally, a short-sighted investment strategy. The takeaway here is that there should be no duel between metrics at all; that prudent investors can use Cash-on-Cash to get an initial reading of the property’s immediate performance, but they should then extend their analysis to encompass the entire lifecycle of the investment. To quote the folks at NASA (who, after all, really are rocket scientists), “It takes more than one kind of telescope to see the light.”

—-Frank Gallinelli


Your time and your investment capital are too valuable to risk on a do-it-yourself investment spreadsheet. For more than 30 years, RealData has provided the best and most reliable real estate investment software to help you make intelligent investment decisions and to create presentations you can confidently show to lenders, clients, and equity partners. Learn more at www.realdata.com.

Copyright 2014,  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.

Real Estate, Healthcare, and Your 2013 Taxes – Some Surprises Waiting for You?

Champagne, funny hats, and the ball-drop in Times Square might not be the only significant events to mark the New Year in 2013. If you are a real estate investor or a home-seller, you could have a couple of surprises lurking in your federal taxes.

The Medicare Tax

One of those surprises found its way into the Health Care and Reconciliation Act of 2010 at the last minute. If, as the the National Association of Realtors® states, it was added to the legislation at the last minute, then one has to wonder just how carefully our elected officials studied this before passing it.

     What It Is Not

There has been a lot of talk and many email blasts, claiming that this is a sales tax on real estate. It is not. It doesn’t apply to every real estate transaction, and it doesn’t get tacked on at the point of sale, the way a sales tax would. That much is clear.

     What It Is, Sort Of

The details may seem a bit daunting, but let’s try to summarize:

  • It is a 3.8% surtax on “net investment income,” which appears to include rental income, capital gains on the sale of investments (and to a limited extent on the sale of a personal residence), interest, dividends, royalties, and annuities, all net of the expenses to achieve that income.
  • It does not apply to withdrawals from IRAs and 401ks, or from veterans benefit,  life-insurance proceeds and several other types of income. (For a further discussion, see this article in Forbes.)
  • But wait, it can actually get even more complicated. According to an article in SmartMoney, there is an exception for income from sources that come from business activities. Presumably this would mean that if you derive your livelihood solely from operating rental property or from flipping houses then your rental income or capital gain from those activities is business- and not investment-related; hence it doesn’t go into the bucket of items subject to the Medicare surtax. But that same article notes an “exception to the exception” if the income is from a “passive business activity.”
  • It will never apply (should we ever say never?) if your adjusted gross income is less than $200,000 as an individual or $250,000 for a married couple filing jointly. Fire up your spreadsheet now, because there is a further test: The tax applies to the lesser of your total net investment income or the excess of your Modified Adjusted Gross Income over the $200,000 (single) or $250,000 (joint return) thresholds. (MAGI is the same as AGI for most taxpayers.) Keep in mind a couple of potential “gotchas” in regard to these thresholds. Even though your conventional (not Roth) IRA or 401k withdrawal is not considered investment income for the purpose of this law, it’s still income and could potentially push you over the threshold. Likewise, the gain from the sale of an investment property could catapult you over the line.
  • If you are selling your personal residence, you will continue to get the $250,000 exclusion for individuals, or $500,000 for a married couples filing jointly, so it is only your gain over that amount that is in play. As before you still have to pay the capital gains tax on your profit in excess of those exclusions. More about capital gains in a moment.
  • Congress did not learn its lesson from the Alternative Minimum Tax debacle, because there does not appear to be any provision to index the threshold amounts for inflation, so the tax may affect more people as time goes on.

For more information about this tax, you can refer to the articles noted above as well as a PDF summary put out by the National Association of Realtors®. You’ll find a link to that PDF here.

Capital Gains and the Fiscal Cliff

Another sobering New Year’s Day adventure is what is being called the “fiscal cliff.” Part of the wild ride into the abyss is the scheduled expiration of the Bush-era tax cuts on January 1, 2013. Here, in brief, is what it means for those of us in real estate:

  • If you sell your real estate investment property for a profit, that profit is taxed at the capital gains rate. Currently that capital gains tax rate is 15%, but if we go over the fiscal cliff on January 1, 2013, the rate will go to 20% with the potential to add the 3.8% Medicare tax to part of the gain.
  • If you sell your home for a profit and if you have a gain that exceeds the $250,000 or $500,000 exclusion (not an unrealistic possibility, especially for older homeowners who bought several decades ago – especially in what are now the more costly markets on the coasts like Fairfield County, Connecticut where I live) you may be faced with a similarly higher tax on that gain.

The Bottom Line

I believe the significance of the Medicare tax may be not so much the money it raises – probably not very much – but rather in the anti-investor mindset it reveals. The same would seem to underlie the proposals to raise the capital gains tax. Both taxes suggest to me a policy that puts investing and risk-taking in the crosshairs, that seeks to discourage rather than encourage the activities that are essential to making an economy grow.

This writer shares the opinion of many that higher tax rates on capital gains are a bad idea generally, and a terrible idea during a struggling economy. Existing businesses need capital to grow and startups need capital to launch. If our tax structure is changed to impose a disincentive to invest, then we shouldn’t be surprised to see our economy shrink even further. This WSJ article says it well.

Those who invest and who see investing as vital to our society need to keep careful watch on every new tax proposal and to keep ourselves in the conversation about those proposals. And as this Wall Street Journal article put it: “If you’re planning to sell rental real estate or other investment property, run, don’t walk, to a trusted tax expert.”

–Frank Gallinelli

Copyright 2012, 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 blog posts and 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.

You may not reproduce, distribute, or transmit any of the materials at this site without the express written permission of RealData® Inc. or other copyright holders. The content of web sites displayed or linked from the realdata.com is the copyrighted material of those respective sites.

Rate of Return on No Money Down (and Other Tales from the Deep Woods)

We frequently hear a question that goes something like this: “I’m considering the purchase of an income property where the seller will take back a second mortgage for the entire down payment. Why can’t your software figure out the rate of return on a zero-cash-down investment?”

You’ve surely heard the excuse, “It’s not the software’s fault” more times than you care to recall. This time, however, the blame really does not fall upon the software, the hardware, the astronauts, Bill Gates, el niño or any of the other usual suspects.

The problem lies in the question itself: “What is the rate of return on a zero-cash-down investment?” Let’s try posing this query a few other ways:

“What’s my return on investment when I make no investment?”

“What’s my rate of return on nothing down?”

“What’s my rate of return on nothing?”

You can see where we’re going with this. You cannot calculate the return because there is no such thing as a zero-cash-down investment. If you invest nothing, then you have no investment. You might just as well ask, “What is the height of an adult unicorn?” because you would get the same answer. No such animal.

This is not just clever semantic swordplay. Fundamental to the concept of investment is that you put your own capital at risk. (For the alert reader, capital could take a form other than cash. I once witnessed a deal where the buyer signed over a sports car as his down payment. For the sake of simplicity, we’ll just refer to the buyer’s investment as cash, with or without wire wheels.)

No-money-down violates the letter, the spirit and the algebra of conventional investment. Return on investment, by its simplest definition, is the amount of the return divided by the amount of the investment. Anything divided by zero is infinity. Hence, even a one-cent return on a zero-dollar investment would be an infinite rate of return.

In short, if you as the buyer put no cash into the deal, you have made no investment and hence you cannot calculate a rate of return. Even if the acquisition of an income property with no outlay of cash should not be called an investment, such deals do happen and can even succeed (although perhaps not as effortlessly as in the mountain of books and tapes showing how you too can amass great wealth with no cash).

If you cannot measure the potential success of a no-money-down deal using rate of return, is it time to put away your computer, trust your instincts and not bother with any kind of financial analysis? Quite the contrary. Even if you can’t measure the rate of return, you can still perform some essential analysis. In fact, caution may demand that a “non-investment” such as this, with little margin for error, be scrutinized with even greater than usual care.

In particular, there are two important issues that require careful examination: Cash flow and resale. If you are going to try to finance 100% of a property’s purchase price, you are obviously going to have to service more debt than you would if you had put some meaningful amount down. Can the property’s income cover all of its operating expenses as well as these debt payments? If not, then you’ll have to reach into your own pocket to make up the difference (so much for the “no cash, no problem” scenario).

Here is where you have to take a very hard look at the numbers. The burden of debt payments on a property with 100% financing leaves you very little wiggle room. Are the income figures realistic? Are you relying on immediate rent increases to cover your costs, increases that could initially result in vacancies rather than additional revenue? Are your expense projections based on verifiable sources and do you have the resources to handle unwelcome surprises?

A second key issue is the property’s potential resale value. (If you haven’t done so already, you should read our previous articles on “Understanding Net Operating Income,” and “Understanding Real Estate Resale.” and even take our e-course or read my books to help you understand the relationship between income and value.) Since your financing is likely to eat up most of your cash flow, the eventual resale of the property is where you will typically have the greatest chance of making money. Once again, realism is of paramount importance. Why might a new buyer give you more than you paid? Can you make physical improvements and management improvements that will make this property attractive and more valuable to an investor?

To recycle an old saying, if no-money-down deals were easy, everyone would do them. Even though you can’t calculate a conventional rate-of-return, be sure that you do the rest of your homework — cash flow projections and estimated resale — before you take the plunge.

The math surrounding vacancy and credit loss allowance is certainly simple enough. You start with your top line – Gross Scheduled Income – which represents a perfect-world situation where all units in your property are rented and all your tenants pay on time with good checks. From that you subtract an allowance to account for the warts of an imperfect world, in this case the potential rent that may be lost to vacancy and the revenue lost due to the failure of tenants to pay. Typically you will estimate the allowance as a percentage of the Gross Scheduled Income.

The result is called the Gross Operating Income (also known as Effective Gross Income). From that subtract the property’s operating expenses and the result is the Net Operating Income, the number you will capitalize in order to estimate the property’s value. An example should make this easy to see:


In this example you’ve assumed that about 3% of your potential income will be lost to vacancy and credit. As you examine this table, you’ll recognize that the greater the vacancy and credit loss, the lower the NOI and hence the lower the value of the property. There’s a lesson here, of course. The vacancy and credit loss projections you make, for the current year and for the future, are going to have a direct impact on your estimate of the property’s value. If you’re careless about these projections you risk skewing that estimate of value.

Vacancy Loss

Behind the numbers are some truisms that you want to keep in mind. The first, of course, is that vacancy and credit loss are generally unwelcome. Loss is loss. However, experienced investors will usually not fall on their swords at the first sign of an empty unit. Conventional wisdom among veterans is, “If you never experience a vacancy, your rents are too low.” I’ve never seen anyone break out the champagne upon learning of a vacancy, but there is some merit in this seemingly self-delusional chestnut. One certain way to find the top of the market is to push past it. When you reach a rate where you no longer can find tenants in a reasonable amount of time, you can pull back. The vacancy you experience will cost you something, but you’ll be sustained by your expectation that the loss will be offset by the higher revenue you can earn by maximizing your rent.

Another reality to keep in mind is that not all vacancy allowances are created equal. In general, commercial space takes longer to rent than does residential and larger spaces take longer to rent than smaller. If you have a large retail space whose lease is coming up for renewal, it might not be unreasonable to allot six months or more of rent as a potential vacancy loss. At the other extreme, a properly priced studio apartment should rent quickly in most markets, so a minimal allowance would suffice.

When making projections about future vacancy, start by looking backward. How quickly has new space been absorbed in the past? Then look forward and consider what might change. What is the likelihood of new, competing space being built? Are there reasons to expect demand to rise or fall – reasons such as new employers moving in or established businesses moving out?

Remember that your objective is to forecast as accurately as possible how this property will perform for you in the future. You can and should look at best-case, worst-case and most-likely scenarios for vacancy just as you would for income and expenses, and don’t try to convince yourself that only the best case is real.

Credit Loss

Avoiding credit loss is a problem you get one shot at solving, and that shot occurs before you sign the lease. Would you sell me your used car in exchange for an I.O.U. or a personal check? You would expect cash or a bank draft. Why would you turn over an even more valuable asset, your rental property, without similar caution? That caution, at minimum, takes the form of a credit check and some good faith money up front in the form of security deposit and advance rent.

There are numerous companies online with whom you can establish an account for checking an applicant’s credit history. Any reputable source of credit reports will expect you to provide proof of your identity and to present written authorization from the prospective tenant to obtain the report. The simplest way to accomplish the latter is to include that authorization as part of the signed rental application. A landlord association often can help you gain access to a reliable source of credit reporting.

Credit losses are a part of doing business and you’re not likely to succeed in eliminating them completely. Your best single defense against is to establish minimum acceptable credit standards and then resist the temptation to trust your instincts and make exceptions. Everyone has a dog-ate-my-homework explanation for poor credit history. Some of the stories are probably true. Nonetheless, the single best predictor of a collection problem is past history. If he didn’t pay his cell phone bill, he probably won’t pay you either.

Some investor’s simply ignore vacancy and credit loss when making their cash flow projections. You might want to call that the emperor’s-new-clothes approach, where you see what you want to see and pretend you don’t notice what’s missing. That’s not much of an investment strategy and it won’t work for very long – reality has a habit of happening whether you plan for it or not. The more prudent investor will do his or her best to minimize these losses, but at the same time work with projections that are realistic.

Copyright 2005, 2010 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.
You may not reproduce, distribute, or transmit any of the materials at this site without the express written permission of RealData® Inc. or other copyright holders. The content of web sites displayed or linked from the realdata.com is the copyrighted material of those respective sites.