The rapid recovery in home prices in a number of U.S. metros has already led some observers to suggest that we are in another home price bubble. We find this unsupporting for a number of reasons, the foremost of which is that most people using this term may not fully understand what defines a bubble. We started writing about home price bubbles about 10 years ago when there was a bubble building in a number of important housing markets around the country.
One common theme used to describe bubbles in other markets is the idea that prices have risen very quickly to levels which could not be justified by underlying fundamentals. There are a number of well documented historical examples of bubbles that include the stock market in 1929, gold prices in 1979-80, and Japanese real estate prices in 1989-90. It is typically acknowledged that in the late stages of a bubble, prices keep rising primarily because they are expected to keep rising. At the time of our previous research, we suggested several definitions of home price bubbles based on the concept that home prices had risen far above levels which could be justified by any economic measure. These included the ratios of home prices to household incomes, rents, construction costs, and employment.
While these can be useful metrics in identifying when prices may be overvalued, bubbles typically move far beyond so-called “overvalued levels” and can last far longer than most people expect. The study of bubbles in other markets such as the ones mentioned above led us to develop a straightforward and easily understood “bubble indicator” which works in most markets and does not depend on a deep understanding of the underlying fundamental factors.
Our finding was that a simple measure of how much the market has increased over a five-year period has proven to be an excellent indicator of most bubbles. In the case of the U.S. stock market over the past 100 years, we found that whenever the 5-year rate of change of the S&P 500 Index has exceeded 200 percent, a significant market top has subsequently occurred. Figure 1 above shows the Dow Jones Industrial Index along with this indicator and, as seen, it identified the bubble peaks of 1929, 1987, and 1999-2000. These were followed by the overall stock market “crashes” of 1929, 1987, and the crash of technology stocks in 2000, respectively.
Figure 1: DJIA Stock Index and Collateral Analytics Bubble Indicator
While such an indicator might seem overly simplistic in identifying such a complicated phenomenon as a price bubble, its construction accounts for some of the important factors. With regard to the time frame of the indicator, stock prices can deviate from intrinsic values for short periods of times, but eventually economic realities based on earnings growth must prevail. Looking at price changes over a 5-year time frame is sufficiently long to filter out the shorter-term trends that deviate significantly from sustainable earnings growth rates.
To develop a similar rule for the real estate market, a good place to start was the Japanese Bubble of the late 1980’s. Figure 2 below shows the Japanese land price index for its six major cities and the 5-year rate of change. As seen in this case, a threshold value of 150 percent did an excellent job of identifying the 1990 bubble top in this market.
Figure 2: Japan Six Largest Cities Land Price Index and Collateral Analytics Real Estate Bubble Indicator
We found that this 150 percent rule also did an excellent job of identifying the bubble peaks in many U.S. metros in the 2005-2006 period. Figure 3 below shows the median single family price for the Los Angeles CBSA along with our “bubble indicator.” Note that the 150 percent price change over the previous 5-year period was reached in mid-2006 at a median price very close to the ultimate peak in the market.
Figure 3: Los Angeles Median Single Family Price and Collateral Analytics Real Estate Bubble Indicator
With regard to the current situation, one only needs to look at the current value of the “bubble indicator.” In the case of Los Angeles, it is far below “bubble” territory, which is what we are seeing in virtually all the important metros that have experienced significant increases in home prices over the past year. Our feeling has been that, while these price increases have been sharp, they should be viewed as corrections to the overshooting of prices on the downside in the 2009-2011 period, and not the beginning of new home price bubbles.
CBSA Winners and Losers
Each month Home Value Forecast ranks the single family home markets in the top 200 CBSAs to highlight the best and worst metros with regard to a number of leading real estate market based indicators.
The ranking system is purely objective and is based on directional trends. Each indicator is given a score based on whether the trend is positive, negative, or neutral for that series. For example, a declining trend in active listings would be positive as will be an increasing trend in average price. A composite score for each CBSA is calculated by summing the directional scores of each of its indicators. From the universe of the top 200 CBSAs, we highlight each month the CBSAs which have the highest and lowest composite scores.
The tables below show the individual market indicators which are being used to rank the CBSAs along with the most recent values and the percent changes. We have color-coded each of the indicators to help visualize whether it is moving in a positive (green) or negative (red) direction.
Top 10 CBSAs
The top ranked metros in the current month include markets from most major regions of the U.S. Of particular note is that three of the top markets are in Texas and include the large Houston and Dallas CBSAs as well as San Antonio. Two new entrants to the Top 10 List are Cambridge, MA and Providence, RI both in New England. This is noteworthy because the Northeast in general and New England in particular had been lagging the nationwide real estate market recovery. North Carolina has three of its CBSAs in the Top 10 (Charlotte, Raleigh, and Durham). These markets had also been lagging somewhat. Of note is that all of the Top 10 markets this month are exhibiting positive trends in every one of the indicators that we follow.
Bottom 10 CBSAs
The bottom ranked metros also represent an interesting mix of markets around the country. As seen in the table, nearly all have higher single or double-digit Months of Inventory Remaining. Other than Portland OR, the common theme among these is that all are relatively small markets based on both population size and sales activity. As we have discussed in the past, our ranking system is relative. This means that these markets are not as strong as the Top 10 markets and does not necessarily mean that they have weak conditions. In fact, as seen in the table above, nearly all the markets have experienced increasing sold prices over the past year.
In this month’s Outliers, we highlight the Cambridge-Newton-Framingham, MA CBSA, which is currently in the list of the Top 10 metros. As seen in the ranking table above, all of the important market indicators for this CBSA are showing positive trends on a year-over-year basis including declining inventory, lower Inventory Remaining, declining market times and lower distressed sales activity to name a few.
Within this CBSA there are numerous sub-markets. On a ZIP code level, one of particular interest is Concord MA, ZIP code 01742, which is one of the higher priced markets in this metro.
Figure 4: CBSA – Cambridge-Newton-Framingham MA | ZIP 01742, Concord, MA
As seen in Figure 4, single family home prices in this ZIP code held up better that the overall Cambridge metro since the market peak. More important, as seen above, our home price forecast models call for this Zip code to continue to outperform the surrounding metro and move above its previous peak levels over the next several years.
There are a number of reasons for the historical and forecasted outperformance of this ZIP code which include the fact that homebuyers in this ZIP code have historically been better capitalized and, thus, better able to weather declines in home prices. The average loan-to-value (LTV) ratio in ZIP 01742 has historically been between 60 and 70 percent (Figure 5) compared to approximately 80 percent for the overall Cambridge-Newton-Framingham, MA CBSA.
Figure 5: Regular & REO Average Loan to Value Ratio
About Home Value Forecast
Home Value Forecast was created from a strategic partnership between Pro Teck Valuation Services and Collateral Analytics. HVF provides insight into the current and future state of the U.S. housing market, and delivers 14 market snapshot graphs from the top 30 CBSAs.
Each month Home Value Forecast delivers a monthly briefing along with “Lessons from the Data,” an in-depth article based on trends unearthed in the data.
HVF is built using numerous data sources including public records, local market MLS and general economic data. The top 750 CBSAs as well as data down to the ZIP code level for approximately 18,000 ZIPs are available with a corporate subscription to the service. A demonstration is available upon request. Please visit the Contact Us page to reserve your trial.
To see how we can help your company with its valuation needs, please call 800.886.4949 or email email@example.com.