Although the dot.com bubble was a serious crisis for software and technology companies, it was not the only bubble to burst during the decade.  Starting at the end of 2007 and running through 2010 the country and much of the world encountered what has come to be known as “the great recession” which is a paraphrase on “the great depression” which started in 1929.

The recession rippled through the entire economy and affected thousands of companies and millions of individuals.  However the burst of the housing bubble and the severe reductions in real-estate costs had the greatest human impact on ordinary consumers.

Real estate bubbles have occurred so many times over history that they have even been statistically analyzed, but the big bubble burst circa 2008 was particularly severe.  It was caused in part by speculative building of homes for “flipping” or purchases by investors rather than by homeowners who wanted to live in the homes.  In some communities in Florida and Nevada there were actually more houses on the market than there were people to live in them.

Overall the great recession was caused by a very complex set of interlocking events and mistakes.  In approximate chronological sequence they run as follows:

1. Opening up too many subprime mortgages to home buyers with low incomes circa 1995.  This was due to the urging of the U.S. government to increase home ownership among low-income citizens.

2. Basing subprime mortgages on variable interest rates, also circa 1995.  Thus when interest rates went up, thousands of subprime mortgages became unaffordable.

3. Reducing oversight of financial institutions circa 1995 due to the mistaken belief that financial markets would be self-regulating.  This lack of oversight resulted in host of new and complicated financial transactions with increasing risks.

4. Dividing and repackaging mortgages into complex financial bundles and selling the pieces, circa 2000.  These bundles were classified as low risk, which was a serious mistake due to lack of oversight and inadequate audits.

5. Repackaging and reselling mortgage segments in bundles makes renegotiation of mortgages very complicated because there is no longer a one-to-one relationship between home owners and banks or mortgage companies.

6. Overbuilding homes and condominiums due to escalating real estate costs, circa 2000.  Many homes were built for “flipping” rather than occupancy, so the U.S. soon reached a surplus of about 500,000 more homes than there were people to live in them.  This surplus was not troublesome when prices were going up, but when the real-estate bubble burst the surplus caused prices to drop more quickly than might otherwise have happened.

7. Allowing Lehman Brothers to fail in September of 2008, which triggered an abrupt and startling global financial crisis.

8. Providing TARP funds to banks and financial institutions without oversight in 2008.  Although the TARP was intended to restore financial flexibility to consumers and homeowners, the lack of oversight resulted in decreases in lending by TARP recipients, but no decreases in bonuses and compensation for officers.

9. Providing stimulus money to States without adequate oversight circa 2009.  As a result,  a significant amount of stimulus money was used to pay the pensions of retired workers and the salaries of current workers, rather than being used to create new jobs and remove unemployed citizens from welfare roles.

10. Failing to provide really effective stimulus aid for thousands of homeowners who were facing foreclosures, but who did not qualify for any of the new programs.

The result of these mistakes soon led to numerous business and personal bankruptcies, thousands of foreclosures, and thousands of layoffs.  It also led to huge losses in the stock market.  There is more that can be said about the great recession, but its impact on software companies was a reduction in sales volumes and an increase in layoffs of personnel.  In order to save money, there was also an increase in offshore outsourcing to countries with low labor costs such as India, China, the Philippines, and the Ukraine.

The interlocked factors of the Great Recession are what physicists call a “linked oscillating system.”  That is, so many things are interrelated that changes in any one of them ripple through all of the others.  Here are some examples:

• Every layoff of a worker who is also a homeowner raises the possibility of one more foreclosure.

• Every foreclosure puts one more home on the market and increases the surplus of vacant homes that already totaled more than 10% of all U.S. houses at the peak circa 2010.

• Every foreclosure lowers the property values of surrounding homes, and drives prices down.  The more foreclosures in a town or neighborhood, the greater the loss of value for entire communities.

• Many foreclosures of rental properties have the unintended consequence of putting renters out in the street, even though they had been paying their rents on time.

• Every foreclosure costs banks more money than they gain by seizing the property.  As a result foreclosures also raise the risk of bank failures.  Renegotiation of loans would be more profitable for banks than foreclosures, but the mortgages are scattered among various institutions so that simple renegotiations are not possible.  Banks seem not to grasped the essential math that renegotiation would have been more cost effective than foreclosures.

• The combination of job losses and foreclosures cut consumer spending by more than 25% compared to 2007, which is caused serious damage to retail stores, automobile dealers, restaurants, and other businesses.

• The cutback in retail sales also caused cutbacks in manufacturing, in international sales, and in shipping and transportation.  These cutbacks reduced the profits of shipping companies, railroads, airlines, and trucking companies.

• The combination of job losses, foreclosures, and business shrinkage lowered the stock market by unprecedented amounts although a partial recovery took place during the spring of 2009.  Full recovery was not until early in 2013.

• The reductions in retail sales, manufacturing, and transportation coupled with job losses have seriously reduced tax revenues at town, state, and national levels.  Almost every state and a majority of towns circa 2009 had serious budget deficits.  Some of these continued into 2013, due in part to excessive largess in pensions and health care for retired government workers.

• Due to high unemployment rates and numerous foreclosures, state and municipal tax revenues continued to decline from about 2008 through 2011, but saw some increases in 2012.

• Attempts to increase tax revenues via “tax the rich” methods backfire and cause reductions in tax revenues.  The very wealthy are highly mobile, own properties in several states, and have attorneys and tax accountants far more sophisticated than state officials.  There have been no successful revenue increases in any state that has attempted tax-the-rich programs.  In spite of the failure of this method, many states and the Federal government continue to try and push through “tax the rich” programs without understanding that revenues will decline rather than increase.

• Attempts to tax internet sales by individual states (such as Rhode Island) backfire and reduce tax revenues.  This is because major internet vendors such as Amazon and Overstock cut ties to Rhode Island companies as will most of the other major players.  The result is damage to Rhode Island companies without any corresponding increases in tax dollars.

What is technically interesting about the dot.com bubble, the Great Recession, and the housing bubble is that these issues could have been predicted and modeled using a combination of historical data and predictive analytics.  It does not require really sophisticated math to predict that if more houses are built than there are people to live in them, prices must come down.
It is also easy to predict that when home prices fall below average mortgages, there will be many foreclosures.  Municipal and state governments and the Federal government need better skills in operations research and in economic modeling than they have today.

A combination of predictive analytics and intelligent agents that bring back relevant data from web sources allow the construction of powerful planning tools that can chart risks for municipal governments, state governments, the Federal government, and corporations in many key industries.