[u]PERSON OF THE WEEK:[/u] [/i]In reviewing the roots of the economic crisis, it would not be flippant to question whether anyone involved in the mess – borrowers, lenders and regulators – had the ability to understand basic arithmetic.[/b] Going forward, the next generation of mortgage bankers may have to possess a better understanding of mathematical foundations and computations relating to loan products. This week, MortgageOrb speaks with Yuri K. Shestopaloff, author of the new book ‘Mortgages and Annuities: An Introduction’ (published by AKVY Press), to discuss the role of computational algorithms and mathematical theories in reshaping the industry. [b]Q: [/b]What was the inspiration for your new book? [b]Shestopaloff:[/b] I first discovered problems with algorithms that are presently used in the financial industry when I was developing a financial software application. First, I used software engineering tools to improve system's performance. It helped, but I gradually realized that the mathematics are far from perfect. So, I started research, found a solution for that particular problem, published an article – then, the solution led to an attempt to tackle another problem, and so on. You should understand the nature of this industry and its principal distinction from many other areas. In the aerospace industry, in which I continue to do some research, the result can be easily verified. You recognize or do not recognize some object, evaluate its parameters with required accuracy or not – you hit the target or miss it. In the financial industry, however, such verification is very limited. For instance, there are many methods for computing the rate of return on an investment. The fund manager, in fact, can use any method, and nobody really knows which one is correct. Although there are some regulations, they are not very confining. All of these methods used to be considered to be independent. In my earlier book, ‘Science of Inexact Mathematics,’ I proved that these methods are approximations of one parent method, called the internal rate of return (IRR), and showed what assumptions need to be made in order to obtain each approximate method. This way, the hierarchy of methods for computing rates of return was created, and their interrelations were discovered and mathematically defined. I showed limitations of the method that is supposed to be the industry standard for certain financial institutions and funds – called time-weighted rate of return – and I showed that this method generally produces the largest error. In other words, I established the foundation on which objective valuation of rate of return and some other financial characteristics can be reliably built. Still, some principal ambiguities were present, but they were exposed, and some reasonable compromise solutions were found. [b]Q:[/b] But wouldn't it be fair to say that a good degree of ambiguity plays into the loan origination process? How can you answer that with mathematical equations? [b]Shestopaloff:[/b] Mortgage-related mathematics has slacks and ambiguities that can be used by either side – lender or borrower – although the lender is usually more knowledgeable. The difference in total payment amount can be several percent and, in some instances, more. I really would like people to know these details when they think about borrowing or lending money, as well as to understand all implications and mechanisms of quick debt accumulation, when they deal with compounding, which is usually the case in the lending business. Another thing I wanted to show is that mortgages and annuities can be arranged in a very flexible way to tailor the needs of the borrower and the lender to a particular situation. It was shown that a mortgage equation can be directly derived from the same IRR equation. Therefore, it can be used as the basis for any fancy mortgage, as well as for any mortgage reconfiguration and restructuring when there is a need. This approach delivers a very powerful instrument, but this is a double-edged sword. Rightly used, it could easily help the majority of people to avoid foreclosures, but it can also provoke bankruptcy, depending on the original purpose. [b]Q:[/b] Your book is different from other books about mortgages and annuities because it covers computational algorithms. Why is it important for mortgage bankers to have a strong knowledge of computational algorithms? [b]Shestopaloff:[/b] In my experience, mortgages and other investment vehicles are processed by software applications. When I consult system designers and programmers developing these applications, one of my duties is to tell them what mathematical and computational algorithms to use. I am trying to follow industry conventions, but the choice is not always unique – far from that. When I am trying to discuss the problem with clients, I often discover that they have a vague idea, if any, of what I am trying to convey, until I explain all the nuances. For instance, one can compute the interest rate for shorter periods using the compounding or non-compounding approach, continuous or discrete, and so on. If mortgage brokers and software developers understood what kind of enormous flexibility they could have if they used the IRR equation as the basis for their computations, then many foreclosures could be avoided. One more reason: The interest rate cannot be computed directly, the appropriate equation is solved numerically. Having efficient computational algorithms makes routine computations and analytical studies go a lot faster. In general, equations of this type have multiple solutions. Finding the right one requires knowledge of algorithms. [b]Q:[/b] Many people have blamed the push for loan quantity over loan quality as driving the crash of the U.S. mortgage banking industry. Is it possible that an absence of mathematical analysis contributed to the crisis that faced the U.S. mortgage banking industry during the past two years? [b]Shestopaloff:[/b] Any real phenomenon is defined by many factors. The mortgage market is only one factor in the whole picture. One should also look at the source of money and its stability. In that case, much instability and the resulting problems came from uncontrolled and unfounded monetary emissions. The next ‘money producing well’ was collateralized debt obligations and all the associated manipulations, such as repackaging, off-balance-sheet assets, involvement of international funds, etc. – all of these things worked as a powerful money printing machine. In such a situation, it is difficult to understand how much money is actually in the economy. In fact, money is nothing – it is a phantom. People forget that money is the measure of wealth, but not the wealth itself. Any misbalance between the measure and the real thing causes problems sooner or later. However, mathematical analysis could at least expose all consequences of lending a certain amount of money to population with known assets and incomes. Such analysis could also predict the price increase in the housing market, region by region, based on historical data and simulation models, when you know how much money was lent to home buyers and how many building permits were issued. Based on this data, it is possible to evaluate the dynamics of creditworthiness of a population. If such an analysis were to be done and be widely exposed to the public, it could somehow influence the mortgages' quality. In the borrowing spree, it would probably not influence it much. However, the data was there, and we could have made the right infer
