Finance & Credit Factory

In another case, Northwest Airlines’ machinists union established a “Farm Out Committee” to review any contracts Northwest contemplated offering to outside companies. Although the union doesn’t insist on doing everything itself, it negotiated with Northwest the option to bid on the business first. The union acknowledged management’s need to procure resources with the best possible quality at the best price. At the same time, Northwest acknowledged the union’s interest in keeping the work in-house. Marv Sandrin, leader of the machinists union, tells about a huge contract for modifying pylons on Northwest’s Boeing 747 passenger jets. Sandrin explains how the union managed to rescue this business from being farmed out to an outfit in Singapore.“Pylons . . . are extremely complicated,” he began.

“There were things we needed to do, such as changing some work rules, to bring the costs down.” They did, and they won the contract. Northwest Airlines procured the best-quality work for the best price. The partnership worked because each side understood the needs of the other, and each was willing to take a chance on partnering with the other.

Among practitioners, however, there is a strong belief that the risk-return profile of equities as well as corporate bonds gets more favorable with a lengthening of the planned investment period. In this case the optimal mix of assets to be held in the portfolio changes with the length of the investment horizon. From a statistical point of view, those practitioners claim that there is negative autocorrelation in the returns of equities and corporate bonds. In other words, in the long run, these asset classes show a pattern of mean reversion. This assumption seems to contradict the statistical properties of bond index returns displayed in Table our study. On a monthly basis all examined asset classes exhibit positive autocorrelation. Yet, annual total returns of US equities and corporate bonds collected from the Ibbotson Associates database are negatively autocorrelated.

In the course of this series of articles, we have seen that bond returns not only show significant deviations from a normal distribution, but also that they are correlated across time. This is an important observation in the context of long-term investments, because mean reversion of the performance of an asset class lowers its risk in the long term. So far the issue of time diversification has generated considerable interest and controversy in particular with regard to equity investments. Theoreticians such as Merton argue that markets are efficient and security returns independent and identically distributed.

As a consequence the risk-return profile of a given portfolio should not alter with the investment horizon and portfolio weights should be independent from the investment horizon.

The results of our analyses show that accurate return forecasts are essential when employing portfolio optimization approaches. However, there are various techniques that account for uncertainty in parameter estimation. One method that is particularly popular among theoreticians as well as practitioners is Bayesian optimization. Empirical studies have yielded mixed results for this methodology. For international stock and bond portfolios Maurer and Mertz (2000) show that the out-of-sample performance of portfolios that are obtained by using Bayesian estimators in a mean–variance framework is not necessarily superior. Sophisticated forecasting models might be one way out of this dilemma. Alternative approaches suggest that the skill in directional forecasts should be higher than in precise return forecasts. Dynkin et al. (2003) proposed a risk budgeting framework that relies on directional forecasts, but additionally requires the estimation of the investor’s skill with regard to the dimensions of his investment decisions.

Inour study we have highlighted a solid black line that represents the “true” efficient frontier. It is estimated with the “true” means and variance–covariance matrix of asset returns, thus neglecting skewness and kurtosis of the underlying return distributions. However, for the assessment of the effect of errors in parameter estimation on realized risk/return profiles this plays a minor role. First, we generated a set of returns by resampling the historical return series. Randomly single observations are chosen, others dropped, until time series of the same length as the original series are created. Of course, this methodology involves repetitions in the resampled return series. Estimating the parameters based on the resampled time series, we then calculate the estimated efficient frontiers that are represented by the light grey lines in provided evidence. This is the best estimate of where the efficient frontier lies or, in other words, what risk-return tradeoff can be achieved through proper diversification. Unfortunately, the realized returns will differ from our resampled time series. Assuming that the actual realizations equal our “true” underlying return series, we obtained the thin lines. They indicate the risk-return profiles of the optimized portfolios. The results are shown by the thin lines in our study. In fact, not only are the volatilities of the “optimal portfolios” higher than expected, the returns are lower than expected. Hence, they are entirely within the interior of the efficient frontier, thus inefficient. It is important to note that the actual risks of the minimal risk portfolios are slightly higher than expected, but the returns do not differ materially from the expected returns. Yet, portfolios that are optimized with respect to the maximization of return for a given level of risk or the maximization of the risk-adjusted performance suffer highly from estimation risk.

I walk away from most deals at this critical point. I make this statement because by now most people are emotionally involved in the property and the process and are reluctant to say no go to a deal. It takes me about thirty minutes to do the Five Step Property Valuation process once I have all the information I need. I use the numbers and my common sense to move to the next deal if the one I’m working on doesn’t jibe.

Should you decide the property still looks like a good idea, you’ll find your high level of preparation will put you in an excellent position for negotiation because you have all the numbers and all the facts. And believe me, numbers and facts take the emotoft out of the process. But let’s be real. There may be some hostt-Q the part of the seller, who thinks the property is worth far iiindm you’ve shown it to actually be; that happens often. But the that armed with the numbers, you are not waltzing 1% seller’s office making an arbitrary low-ball offer. Instead, ate coming in with a well-prepared case, based on research affile calculated with industry-standard formulas, and a realistic number. Kick and scream as the seller may, you’ll both know the offer you are presenting is a fair one. It just may take thest-a little longer to arrive at that conclusion.

Before we go any further, I want to point out that if after your analysis you don’t think the property is a good investment, in other words, if the net cash flow is too low, then you may need to walk away from the deal or offer a much lower price to make the numbers work. These numbers don’t lie and unless your math is wrong, the estimates you arrived at are factual. It is perfectly okay to walk away, although you may feel like you did a lot of work for nothing. But remember, every step in this process is a learning experience, and with every exercise, every calculation, every building walk-through, you are getting smarter. The next time you’ll be twice as fast and three times more thorough. Learning is never a waste of time.

These five steps—the Five Step Property Evaluation—are the most important concepts in this book. They give you the ability to evaluate property investments like the pros do. They give you power and confidence. They give you realistic, fact-based bench¬marks to make the best property choices. They give you the basis from which to have reasonable conversations with the seller. And they give you peace of mind that you are not getting into something that may not pan out.

If you spend the time gathering the income and expense infor¬mation clearly prescribed in this chapter, you’ll know what it will take to run this property and how much money you can expect to make from it. You’ll be well informed before you even talk to the seller again and certainly before you ever make an offer. I buy all my rental properties this way using these exact same five steps. I would never dream of doing it any other way. I like sleeping at night too much.

The very last step is to find out your actual profit by subtracting your loan payment from your projected net operating income. Using the net operating income of $22,368 and subtracting the loan payment of $20,010, the projected profit on this property is $2,358. Is that good or bad? Well here’s how you know. You calculate your cash on cash, which you recall, is a lot like return on investment.

To calculate cash on cash, divide your profit of $2,358 by the down payment of $25,600. For this deal, your cash on cash return is 9.2 percent. Not a bad return.

Assuming you and the seller agree to the price of $256,000, now is time to figure out what the loan payment might be. This is where your finance team member or broker could step in, but I did it myself in a matter of seconds. I just went online and typed into my Google search engine the words “mortgage payment,” and up popped numerous mortgage calculators. I entered two numbers, the loan amount and the interest rate. Instantaneously the mortgage payment schedule appeared! I love the twenty-first century.

Back to our real-world example. The numbers I used were based on our offer price and 10 percent or $25,600 down and the same interest rate of 7.5 percent. The mortgage calculator churned out a loan payment of $20,010. Certainly a more palatable number when you consider your projected net operating income, which is $22,368.