Since incomes and rents are closely related, evidence for the Great Housing Bubble that appears in the price-to-rent ratio also appears in the price-to-income ratio. National price-to-income ratios are quite stable. There has been a slight upward drift with the decline of interest rates since the early 1980s peak, but from the period from 1987 to 2001, this ratio remained in a tight range from 3.9 to 4.2. The increase from 4.1 to 4.5 witnessed from 2001 to 2003 can be explained by the lowering of interest rates; however, the increase from 4.5 to 5.2 from 2003 to 2006 can only be explained by exotic financing and irrational exuberance.
Figure 46: Projected National Price-to-Income Ratio, 1988-2015
If national price-to-income ratios decline to their historic norm of 4.0, prices nationally will fall 9% peak-to-trough, bottom in 2011 and return to peak pricing in 2014. A 10% decline and a nine year waiting period would be difficult on homeowners nationally, but the magnitude and the duration will not be nearly as severe for most as it will be for homeowners in the extreme bubble markets like Irvine, California.
Figure 47: National Projections based on Price-to-Income Ratio, 1986-2015
The volatility in price-to-income ratios caused by bubble behavior is clearly visible in the historic price-to-income ratios from Irvine, California. During the coastal bubble of the late 80s, in which Irvine participated, the price-to-income ratio increased from 3.7 to 4.6, a 25% increase. In the decline of the early 90s, price-to-income ratios dropped to a range from 4.0 to 4.1 and stabilized there from 1994 to 1999 before rocketing up to an unprecedented 8.6–a 115% increase. This new ratio was achieved by the extensive use of exotic financing, in particular negative amortization loans that rendered the new ratio inherently unstable.
Figure 48: Projected Irvine, California Price-to-Income Ratio, 1986-2030
If house prices in Irvine decline to the point where the price-to-income ratio reaches its average of 4.2–a ratio higher above this historic range of stability between 4.0 and 4.1–prices will decline 43% peak-to-trough, bottom in 2011 and return to the peak in 2029. The magnitude of this decline would be catastrophic to homeowners who purchased during the bubble. Twenty-four years is a long time to wait for peak buyers hoping to get out at breakeven.
Figure 49: Irvine, California Projections from Price-to-Income Ratio, 1986-2030
The Federal Reserve under Ben Bernanke began aggressively lowering interest rates at the end of 2007 in response to the severe economic downturn caused by the collapse of house prices and the related difficulties falling house prices had on the banks and other institutions that made loans using houses as collateral.  Bernanke, prior to taking the position as the chairman of the Federal Reserve, was an academic who studied the Great Depression and wrote extensively on the failures of monetary policy by the Federal Reserve at the time. He also wrote about the crisis of deflation Japan faced when their combined stock market and real estate bubbles deflated throughout the 1990s. [ii] Bernanke believed that quick and decisive action on the part of the Federal Reserve was necessary to prevent a destructive deflationary spiral as was witnessed in the United States during the Great Depression and in Japan during the 1990s. [iii] By lowering interest rates and creating price inflation, Bernanke hoped to devalue the currency and provide market liquidity through both domestic and foreign investment. Once the real rate of interest was below the level of inflation, borrowing would be strongly encouraged as the value of the currency was falling faster than the interest rate being charged. The increased borrowing would stimulate business growth and the general economy minimizing the deflationary impact of falling home prices. In theory, the lower interest rates would also serve to blunt the decline in housing prices as borrowers would again be able to finance large sums to support inflated prices.
Figure 50: CPI Adjusted Median Home Prices, 1986-2006
At the time of this writing, the results of the policies of the Federal Reserve have not become history so the consequences cannot be fully evaluated. The primary foreseeable consequence of Federal Reserve policy is rampant price inflation. An economy that relies for 70% of its value on the spending of consumers will be strongly impacted by price inflation. When a country knowingly devalues its currency, it causes a severe recession as the prices of imported goods and raw materials increase significantly. Perhaps a severe recession and price inflation is preferable to an economic depression like the one of the 1930s in America, but it is certainly not desirable. Since stagflation of the 70’s, the FED has shown a willingness to push the economy into recession before it allows inflation to get out of control. When the FED started lowering interest rates at the end of 2007, it appeared as if they may be moving down the path of hyperinflation; however, it seems unlikely they would take it to extreme. One of the primary functions of the FED is to provide a stable financial system. Once the Federal Reserve begins to see economic growth and liquidity in the debt markets, interest rates may rise as quickly as they fell in order to stop hyperinflation from occurring.
Figure 51: National Inflation Rate, 1961-2007
There will be some benefits to a devalued currency. A less valuable currency is a boon to exporters. The United States has run a chronic trade deficit for many years, and much of the recent deficit has come from inexpensive goods imported from China. The trade imbalance may correct itself with currency devaluation. Of course, this rebalancing of trade will come at the cost of more expensive imported foreign goods and a commensurate decline in spending power from US consumers. Also, prior to currency devaluation, wages in the United States were so high that jobs were being outsourced to foreign countries where people can be paid much less. Wages could not rise significantly from where they were without devaluing the dollar to prevent wage arbitrage from moving jobs overseas. [iv] The devalued currency provided some room for wage increases, and these wage increases could theoretically provide additional support for housing prices.
Figure 52: Inflation-Adjusted Projections for Los Angeles, 1987-2012
Currency devaluation and inflation eats away at the buying power of money. Although this may support house prices at marginally higher nominal price levels, real price levels, the price level adjusted for inflation, will remain unchanged. Imagine if the Federal Reserve allowed inflation to cut the spending power of the dollar in half by 2011, and imagine if this level of inflation allowed house prices to remain stable at 2006 price levels for those 5 years. Many homeowners would feel relieved their homes did not decline in value, but this relief would be an illusion as the buying power of their money tied up in the value of their houses was cut in half. Irrespective of the nominal decline in prices, the inflation adjusted prices will decline significantly going forward. In the Los Angeles market as measured by the S&P/Case-Shiller index, a decline in prices to levels of historic rates of appreciation as previously described will result in a 66% decline in inflation adjusted terms. [v] On an inflation adjusted basis, buyers during the bubble will never get back to breakeven unless there is another real estate bubble similar to the Great Housing Bubble.
An Educated Guess
Each of the four methods of house price valuation described previously makes an independent prediction of how far prices will fall, and when they will recover. Some of these will prove more reliable and accurate than others, but an average of the results of these four methods makes it possible to make an educated guess as to the percentage decline in house prices and when prices will get back to peak levels. Unfortunately, there is no reliable method for projecting the rate of this decline, but if the experience of the coastal bubble of the early 90s is a good guide, then prices should fall for 6 to 7 years before reaching a bottom. This puts the bottom of prices sometime in or around 2011 based on the peak in various markets occurring in 2005 or 2006. Prices will flatten at the bottom because it will take time to absorb the inventory of foreclosures resulting from the drop. Market psychology and the rate of foreclosures will largely determine the rate of price decline, and these forces are difficult if not impossible to model. It is possible to construct a graph that illustrates the path of house prices over time based on the methods of price valuation and assumptions about the timing of the decline.
Table 10: Summary of Predictions for National Home Prices
|Method||Total Decline||Appreciation Rate||Recovery Year|
|S&P/Case-Shiller Inflation Support||33%||3.3%||2025|
|Median House Price and Historic Appreciation||10%||4.5%||2011|
The range of predictions for the decline of national home prices is from 9% to 27% with an average of 20%. The predicted time of peak-to-peak recovery ranges from 2011 to 2021 with an average of 2018. Some will argue price drops of this magnitude are not likely, and these would be unprecedented declines; however, the increases were unprecedented as well. The Great Housing Bubble was a unique and unprecedented event.
Figure 53: National Median House Price Prediction, 2004-2019
The predictions for national prices are based on a 3.7% rate of fundamental appreciation for some combination of wage growth, rental increases and other factors. The origin point for the graph is based on the last period in which fundamentals were aligned in the 1986-1999 period (not shown on the figure). The amount of the decline, 20%, is based on the average prediction of the four methods. The rate of decline was interpolated from the date of the peak to the date of the predicted bottom based on the experience of the coastal bubble of the 1990s. National prices peaked at an approximate value of $246,000 in 2006; they should bottom out at around $196,000 in 2011, and if fundamental appreciation rates hold, they will reach the previous peak in 2018.
Table 11: Summary of Predictions for Irvine, California Home Prices
|Method||Total Decline||Appreciation Rate||Recovery Year|
|S&P/Case-Shiller Inflation Support||55%||3.3%||2039|
|Median House Price and Historic Appreciation||45%||4.4%||2023|
|* The appreciation rate of 3.9% moved up the recovery year to 2025|
The range of predictions for the decline of home prices in Irvine, California, is from 22% to 53% with an average of 41%. The predicted time of peak-to-peak recovery ranges from 2019 to 2033 with an average of 2028. Of course, since Irvine is in the heart of a bubble-prone market, recovery may happen more quickly, but then again, that would mean prices have entered another unsustainable price bubble.
Figure 54: Irvine, California, Median House Price Prediction, 2004-2025
Predictions for Irvine, California, are based on a 3.9% rate of fundamental appreciation as wage growth and rental rate growth have consistently outpaced national averages. The origin point is the intersection of the last two stable bottoming periods in 1984-1987 and 1995-1999. The 41% decline is the average of the four analysis methods, and the rate of decline is projected in the same manner as the national statistics. In Irvine, California, prices peaked around $723,000 in 2006, and they should bottom out in 2011 along with the rest of the country. If the fundamental appreciation rate of 3.9% is accurate, the previous peak will be reached in 2025–a 19 year span from peak to peak.
 Adam Posen wrote Why Central Banks Should Not Burst Bubbles. (Posen A. , 2006) His conclusions are as follows, “Central banks should not be in the business of trying to prick asset price bubbles. Bubbles generally arise out of some combination of irrational exuberance, technological jumps, and financial deregulation (with more of the second in equity price bubbles and more of the third in real estate booms). Accordingly, the connection between monetary conditions and the rise of bubbles is rather tenuous, and anything short of inducing a recession by tightening credit conditions prohibitively is unlikely to stem their rise. Even if a central bank were willing to take that one-in-three or less shot at cutting off a bubble, the cost-benefit analysis hardly justifies such preemptive action. The macroeconomic harm from a bubble bursting is generally a function of the financial system’s structure and stability – in modern economies with satisfactory bank supervision, the transmission of a negative shock from an asset price bust is relatively limited, as was seen in the United States in 2002. However, where financial fragility does exist, as in Japan in the 1990s, the costs of inducing a recession go up significantly, so the relative disadvantages of monetary preemption over letting the bubble run its course mount. In the end, there is no monetary substitute for financial stability, and no market substitute for monetary ease during severe credit crunch. These two realities imply that the central bank should not take asset prices directly into account in monetary policymaking but should be anything but laissez-faire in responding to sharp movements in inflation and output, even if asset price swings are their source.” His argument is sound, but he does point out that if bubbles get too large, the fallout can be even more disastrous than attempts to restrain them. This argues against his central point, and the Japanese example does show what can happen if bubbles are allowed to get too large. Ben Bernanke also wrote a paper titled Should Central Banks Respond to Movements in Asset Prices? (Bernanke & Gertler, Should Central Banks Respond to Movements in Asset Prices?, 2000). He professes a belief that the activities of the Central Bank should not target asset prices, although his behavior as FED chairman has been interpreted as an attempt to bolster market prices. In a later paper (Bernanke & Boivin, Monetary Policy in a Data-Rich Environment, 2002) Bernanke also explored the uses and aggregation of data by the Federal Reserve. He seemed to be preparing himself for the job of FED Chairman. The focus of policy debate and academic research in the wake of the Great Housing Bubble is likely to be the issue of asset bubbles in general. Central Banks around the world learned how to control inflation in the 1980s and 1990s, but their policies have tended to create excess liquidity which has resulted in financial bubbles.
[ii] (Bernanke B. S., Japanese Monetary Policy: A Case of Self-Induced Paralysis, 1999)
[iii] Adam Posen in his paper It Takes more than a Bubble to Become Japan (Posen A. S., 2003), outlines the causes of the prolonged recession after the bursting of the stock and real estate bubbles in Japan. He agrees with Ben Bernanke’s conclusion that aggressive monetary easing is the solution to the problem, “Central bankers should learn from Japan’s bubble the benefits of a more thoughtful approach to assessing potential growth and of easing rapidly in the face of asset price declines and not be concerned with targeting asset prices or pricking bubbles per se.” In a related paper Passive Savers and Fiscal Policy Effectiveness in Japan (Kuttner & Posen, 2001) by Kenneth N. Kuttner; Adam S. Posen, the authors reaffirm their conclusions on the mistakes of the Japanese Central Bank in handling their asset bubbles. The authors note that despite excessive borrowing of the central government, Japanese citizens continue to by government debt at very low cost and in effect subsidize their own borrowing.
[iv] In the paper Offshoring, Economic Insecurity, and the Demand for Social Insurance (Anderson & Gascon, 2008), Richard G. Anderson and Charles S. Gascon describe the problems associated with offshoring, in particular the increased demand of social services caused by the fear of offshoring.
[v] The inflation adjusted chart for the S&P/Case-Shiller index was drawn by the following method. The index value was then divided by the consumer price index date from the U.S. Department of Labor Bureau of Labor Statistics. The resulting number was converted to a baseline value of 1 so the data could be represented as a percentage change.