The Economic Genome

The Economic Genome risk model is the first step towards the goal of creating an algorithm, which can be used to achieve a sustained stabilized economy as experienced in the mid-1940s throughout the 1950s. 

The Economic Genome model’s input data in Figure 2 is comprised of the following historical interconnecting relatable indices: Industrial Production of Durable Consumer Goods (IPDCG), The Number of New Home Sales, Unemployment Rates, Median New Home Appreciation and Housing Cost to Income ratio (HCTI). We have tracked the HCTI ratio affect on the movements of these specific interrelated indices, which collectively have shown us to positively or negatively effect the U.S. economy over the past 50 years.

The HCTI index consists of the greater Male or Female Single Median Income divided by New Home Sale Cost. We have further observed the HCTI to be the leading index that affects home appreciation, New Median Home Sales, Durable Consumer Goods sales, and the rise r and decline d of Unemployment Rates U (Jones, 2013). Figure 2 and Figure 1-4 has shown, a rising HCTI Hr lowers IPDCG home sales and appreciation, which was observed to lead to a r in unemployment rates Ur.

When HCTI levels remain elevated for an extended period of time, a recession was likely to follow (Allen and Barth, 2012). The opposite was shown to occur when HCTI ratios began to decline Hd. We noted a rising HCTI Hr is attributable to the decline of IPDCG. This eventually led to a rise of unemployment rates:
 Ur = Hr.
Conversely, a decline in HCTI Hd led to a rise in New Median Housing sales and IPDCG that leads to a decline d in the Unemployment rate Ud:
  Hd =Ud.
We have therefore concluded the rise and decline rd of H led to rd of U:
  Hrd = Urd. 

We further observed through the use of our Economic Genome model, when HCTI rises, it intersects with the rising unemployment. As viewed in, 1973, 1979, 1989, and 2005. The opposite was true when HCTI decreased as observed in the years 1968,1972,1982, 2003 and 2011 (Allen, 2013).

The following chain of events were also noted; A spike or prolonged elevation of the HCTI approaching a 6:1 ratio led to a negative effect on the New Home Sales index, as was the case in the following years: 1968-1971, 1978, 1985-1987, 1989-1990, and 2006-2008 (Wolfson, 2013). The same indices were observed acting in the opposite manner when HCTI ratios were in decline in the following years (Allen and Barth, 2012): 1974-1977, 1981-1986, 1990-1992, and 1995-1999 (Alexander and Moloney, 2011).

The Economic Genome model can be used as an accurate tool by economists to unmask abnormalities in the real-estate market, which would eventually lead us into a recession. For instance, in the late seventies, the real-estate market crashed despite the HCTI visible along the bottom of Figure 2 being approximately 5:3 (Blinder, 2013). This was likely to have been a result of efforts of the Federal Reserve to stem inflation by raising interest rates from 11% to 16%. 

From 2002 until 2008 the HCTI was well above 6:1. This leads us to ask the following question: Why did it take six years for the real-estate market to crash? The most probable answer is likely to be the substantial number of borrowers that opted to choose interest only subprime loans during this period of time; and property values continued to rise, enabling borrowers to sell or refi their homes. (Stone and Zissu, 2012). Both catalysts to these recessions were telegraphed long in advance of their impending negative results on the economy by our Economic Genome model.

According to U.S Census Bureau, subprime loans at their peak rose to just over 20% of all home loans originated, of which 35% defaulted. Subprime mortgage defaults accounted for approximately 7% of the entire housing market at its peak. The cumulative defaults of conventional mortgages surpassed those of subprime at 12%. The Credit Characteristics of these same loan pools between the 2001-2005 were virtually the same, however defaults in 2001 were approximately 1%. Note, the crisis began when the housing default rates were approaching 6%. Leading to the conclusion, a severe housing bubble would have occurred with or without the influence of subprime loans. Additional supporting data can be found here: https://20-yearsimp.blogspot.com

Viewing the data along the bottom of Figure 2, “The Numerical HCTI Ratio” we noted, real-estate booms preceded lower HCTI ratios in the following years; 1974,1984 and 1997.  As HCTI levels soared to a peak in 1979,1989 and in 2005, real-estate market crashes soon followed. This observation led us to ask, was Prof. Milton Friedman's free market economic system the main cause of the real-estate crash of 2008 as some economists have suggested, or was there the possibility of an innate flaw embedded within our financial system? Past and present data we viewed in Figure 2 suggest the latter. 

The repetition of the sequential events observed over the past decades led us to conclude, if we could resolve the HCTI Paradox explained in the following section, the Economic Genome could be used as a valuable tool to help avoid future economic recessions. (Blinder, 2013). The goal being, to create an algorithm, which will achieve a sustained stabilized economy as experienced in the mid-1940s throughout the 1950s. We would start by optimizing all the aforementioned interconnected leading indicators we identified within the Economic Genome led by the HCTI index (Green, 2013).

Figure II The Economic Genome 

Regression Analysis:

 Graphs 1-4 contains a 49 variable subset average of 33,113.000 Sampled homes sold from each of the past real-estate bubbles spanning over the past 49 years. We break down all the variables consisting of millions of homes sold into seven individual graphs to show the contrast of the correlations of each individual period. The results of the data confirms a strong correlation between the fluctuations of HCTI ratios and the number of new homes sold during the past three major recessions. Graphs 5-7 demenstrates how high HCTI ratios negatively affects the number of new homes sold, which in turn, lowers the amount of IPDCG leading to rising unemployment rate. The statistical improbability of the aforementioned sequential economic events repeating themselves over the past 49 years without causation is improbable. Further still, Graph 9 depicts a direct correlation between HCTI, IPDCG and unemployment rates, which confirms Graphs 1-8. All the data used to perform linear regressions were based on historical U.S Census reports viewed in Table 1. The following formula was used for calculating best fit: