On the morning of March 12, San Francisco based Union Bank (“Union”) announced they would be acquiring Santa Barbara Bank & Trust for an estimated $1.5 billion. Shareholders of Pacific Capital Bancorp – parent company of Santa Barbara Bank & Trust – welcomed the news, as their shares rallied over 60 percent upon announcement. Union will purchase the bank for $46 per outstanding share, which had been trading at $28, and anticipates operational integration by 2013.

The all-cash deal comes as Santa Barbara Bank & Trust continues its return to strong profitability, having reported annual net income of $73.5 million for 2011, as opposed to $27 million in 2010, and a loss of $405 million in 2009.  

The acquisition is expected to yield substantial operational synergies for Union Bank, which is the second largest bank holding company in California. Its parent company, UnionBanCal, is part of the Mitsubishi UFJ Financial Group, and has nearly 350 branches in California alone. The acquisition is meant to expand Union’s footprint to the central coast, where it has previously not operated.

Santa Barbara Bank & Trust is the largest independent banking company headquartered on the Central Coast of California, with 48 branches from Morgan Hill to Los Angeles. Between Morgan Hill and Soledad, a distance of about 65 miles, Santa Barbara Bank & Trust operates out of 12 retail branches. Its next branch is 72 miles south of Soledad in Paso Robles. Between Paso Robles and Solvang, a span of 94 miles, Santa Barbara Bank & Trust operates 8 retail branches. The remaining and majority of branches operated by Santa Barbara Bank & Trust are within 37 miles of each other, between Goleta and Thousand Oaks. One other branch is operated in Beverly Hills. 

The Congressional Budget Office (CBO) released its 2011 Long Term Budget Outlook in June of this year. There is an Abstract, a Summary, a Full Summary and the entire report (in pdf format) available at http://www.cbo.gov/doc.cfm?index=12212 . There is also a testimony of the Director of the CBO before the House Budget Committee on June 23, 2011. The report has many facets but the focus is on forecasts of federal revenue and outlays for fiscal years 2011 though 2021, the corresponding deficits, and the impact of the debt. We address them here as these are major issues for economic policy and for political discussion.

The results of the report are summarized below in Table 1 and in Figure 1. Revenue is predicted to recover quickly, reaching over three trillion by 2013. As a consequence the deficit shrinks to half a trillion by 2014, a little more than a third of the deficit forecast for the current year (2011 federal fiscal year begins October1, 2010 and ends September 30, 2011). Is the CBO’s future rosy scenario perhaps too good to be true?

For the most part, forecasting is using past experience to forecast the future. This can be particularly hazardous in today’s times since we have just experienced the worst recession since World War II, with a scale not comparable to previous recessions. This concept of incomparability is depicted visually from the following two figures for real GDP and publicly held federal debt from the Cooley-Rupert blog linked to on our homepage.

The methodology used in the aforementioned graphs is to compare previous recessions by normalizing them all to start at the peak of the previous cycle. The current cycle is displayed in royal blue, and is very clearly different than the other recessions it is being compared to. This can make forecasting from historical numbers a tricky and daunting task.

The approach adopted here is to establish our own benchmarks against which the CBO forecasts can be compared. If our results also forecast a ‘rosy scenario,’ perhaps the CBO’s predictions will hold more weight. The first benchmark we chose was already mentioned above, revenue as a percentage of GDP (Figure 1). The historical data from 1971 through 2010 was included as Appendix E to the CBO report. It is also available in Excel tables at http://www.cbo.gov/doc.cfm?index=12039. In our analysis, revenue as a percentage of GDP was regressed against the rate of growth of real GDP for the current year, as well as the previous two years, from 1971 through 2010. Economics jargon aside, we did a comparable measure. This result was used to forecast for the years 2011-2014. (The annual data for real GDP was obtained from the Federal Reserve Economic Data at http://research.stlouisfed.org/fred2/series/GDPCA)

Our forecast was then compared to the CBO forecast. Our findings of revenue as a percentage of GDP rise more rapidly for 2011 and 2012 but then level off at 18.4 percent in 2013 and 2014. The CBO forecast reaches 19.9 by 2014 and continues rising, reaching 20.8 by 2021. This is well beyond the average of 18 percent experienced in the years 1971-2010, depicted directly above in Figure 1. The level of 20.0 percent revenue to GDP is higher than any year experienced between 1971 and 2010 with the exception of one year, 2000, when it reached 20.8 percent. This is the first part of the CBO’s predicted ‘rosy scenario.’

The second part of the rosy scenario is the forecasts of the rate of GDP growth. We used the data from the Survey of Professional Forecasters, Third Quarter of 2011, which can be obtained at http://www.philadelphiafed.org/research-and-data/real-time-center/survey-of-professional-forecasters/. This table compares the forecasts from the Survey, available at the link above , with forecasts of the CBO reported in Table D-1. (Appendix D of the CBO report provides details of the CBO’s economic projections for 2010 to 2021.) The CBO has GDP recovering much more rapidly.

Forecasts of Year Over Rates of Growth of Real GDP

To be fair, the third quarter forecasts of real GDP growth by the professional forecasters were revised downward, but that just underscores the exceptionally ‘rosy’ predictions made by the CBO.

The perceivable ‘over-estimates’ by CBO of GDP growth and revenue as a percent of GDP combine by 2014 to produce a forecast for federal revenue of $3.442 trillion, an over-estimate of 258 billion. By 2015, this over-estimate grew to $390 billion. These discrepancies are based on the more reasonable forecasts of revenue as a percent of GDP, as discussed above, and by forecasts of nominal GDP by the Survey of Professional Forecasters and by the International Monetary Fund’s World Economic Outlook. There has been considerable inflation since Senator Everett Dirkson’s day, but to alter the quote by a factor of 1000, “A [trillion] here, a [trillion] there, and pretty soon you’re talking about real money.”

Total federal outlays (expenditures) as a percentage of GDP was also forecast by the CBO for 2011-2021. Once again, the approach adopted in this blog was to forecast this variable for comparison. Outlays as a percentage of GDP was regressed against the rate of growth of real GDP for the current year, as well as the previous year, from 1971 through 2010. This result was used to forecast for the years 2011-2015. The CBO forecast remains high, near the 2009-2010 levels, compared to the forecast in this blog which compares to the levels from 1980-1986, but exceeds all other percentages from 1971 through 2008, as illustrated in the following graph.

These forecasts of outlay as a percentage of GDP were multiplied by the forecasts of nominal GDP from the Survey of Professional Forecasters and from the IMF World Economic Outlook to obtain projections of Outlays for the years 2011-2015. These projections of outlay are compared to the CBO forecasts of outlay in the next table.

Our projections (in green) of the deficit are more optimistic for 2011 and 2012 than those by CBO, although still severe by historical standards. CBO’s relative optimism in this valley of gloom overtakes ours and by 2015, their forecast of the federal budget deficit is nearly a quarter trillion less than ours.

The Office of Management and Budget, the White House office responsible for devising and submitting the president’s annual budget proposal to Congress, also has forecasts of revenue, outlays and deficits for the years 2011-2016. It is remarkably surprising that their predictions are so close to that of the Congressional Budget Office’s. For example, in 2015 they only differ by 68 billion out of about 3,600 billion dollars of revenue, less than two percent of the amount being forecasted. As a benchmark, the CBO forecast for nominal GDP differs from that of the Survey of Professional Forecaster by 98 billion for 2011, and that is this year!

All of the forecasts by the CBO and the OMB of revenue and outlays are close (revenue more so than outlays), yet we face a great deal of uncertainty, both economically and politically. We may be to steadfast in our suspicions, but this closeness in forecasts makes it appear that the CBO is carrying water for the White House rather than serving as a check and a balance.

Uncertainty continues about the economy and especially the labor and housing market. In this blog we examine the total number of unemployed in California. At its peak in the first quarter of 2010, the number of unemployed reached 2, 340, 000. The California Department of Finance predicts a decline in these numbers in the five quarters ahead. In this blog we show that the California labor market has been slow to recover in recent recessions. The UCSB Economic Forecast Project is using its blog on the home page http://www.ucsb-efp.com/ to provide updates like this current topic on a regular basis.

The fraction of the population in California that is in the labor force, i.e. employed or unemployed, provides insight into labor market trends in our state. This information is also available for the Los Angeles-Long Beach-Glendale Metropolitan Division, but not for the city of Santa Barbara.

The civilian non-institutional population, ages sixteen and older, is composed of individuals not in the armed forces, or jail, prison, or hospitals. The US Department of Labor obtains estimates of this population from the US Census Bureau, and on the basis of surveys, classifies this population as in the labor force or not in the labor force. Those in the labor force are classified as employed or unemployed. The labor force participation rate is defined as the percent of the civilian non-institutional population in the labor force. A discussion of these definitions and the data is available at http://www.bls.gov/lau/rdscnp16.htm#.

I used the monthly data, not seasonally adjusted, for California and for Los Angeles. A figure of these two time series follows from January 1976 through June 2011. The shaded areas in this plot are the recessions, with the timing determined by the National Bureau of Economic Research. Note that the participation rate for California climbs until 1990, is still at a level of about 67% in 2000-01, and was still at a level 0f 65.5 to 66 % in 2007 before the Great Recession.. In the first six months of 2011, it is between 63 to 63.5 %. So since 1990, a smaller fraction of the population in California has been participating in the labor force.

The pattern for Los Angeles is similar with perhaps a more pronounced difference between peaks and troughs..

Civilian Non-Instutional Population and Employment in California

There have been at least three distinct periods of population growth in California since 1976. The first is the fourteen years from 1976 though 1989, when the growth rate was 2.2 percent per year. Remarkably, during this period, employment grew even more rapidly at 2.8 per cent per year. This behavior is exhibited in the following graph.

There was a turning point in 1990, the beginning of the second distinct period of population growth. First the population estimates caught up with the census. Second, during the years 1990-1995, the growth rate of population dropped to 0.8 percent. Employment did not grow at all for these six years, clearly affected by the 1990-91 recession. Note that following the 1980 and 1981-82 recession, employment did not grow for three years. So during and following the five recessions illustrated by shaded areas in the graph, including the most recent Great Recession, employment did not grow.

Beginning in 1996, population growth recovered, but only to1.3 percent per year, no where near as rapidly as before 1990. Employment began to grow again as well in 1996 until the recession 0f 2001.

The impact of recessions on employment in California as a fraction of the Civilian Non-Institutional Population is illustrated in the following graph. The ratio of employment to population reached 64 percent in the period 1987 through 1990 and again in 2000, indicating the potential for employment in those periods. However, since 2000, employment has not kept pace with population growth, and the Great Recession has caused this ratio to plunge back to the levels of 1976. Given that it took six years for employment to begin to grow again after the recession in 1990, it may be an even longer wait for employment to recover after the impact of the Great Recession.

Unemployment is not measured as meaningfully as employment since discouraged persons may stop looking for work and not be counted, or accept a part-time job and be underemployed. The following graph, using annual data, shows the number of unemployed as a fraction of the civilian non-institutional population. This differs from the often reported unemployment rate which is the fraction of the labor force that is unemployed. The measure graphed here is the product of the civilian labor force participation rate, as illustrated in the first graph above, and the unemployment rate. As this figure illustrates, 1987-1990, 1998-2001, and 2005-2007 were good labor market years in California when this ratio dropped below 4 percent. These were years when employment in California was growing, as illustrated in the second figure in this paper, above.

Note there is an eight to nine year cycle in this annual data for unemployment as a fraction of the civilian non-institutional population (the unemployment ratio) indicating influences more complex than the business cycle.

The California Department of Finance makes quarterly forecasts of the number of unemployed in California, so a forecast of quarterly data for the unemployment ratio was undertaken by the UCSB Forecast Project. This forecast was multiplied by a forecast of the civilian non-institutional population to obtain a forecast of the total number of unemployed in California for the fourth quarter of this year and for next year. This forecast is illustrated in the next graph.

The number of workers unemployed in Calfornia is falling , but slowly. At the current rate of recovery. It could be 2016 before good times for workers returns.

The following table compares the forecast of the number unemployed in California estimated by the Department of Finance to the Forecast Project estimates.

Forecasts of the Number Unemployed in California

A common approach is to see how local economic data compares to the national average, but this might not be as indicative of our local recovery as first thought. For example, currently the national unemployment rate is 9.1% while the average tri-county unemployment rate is 9.5%. Does this mean the local economy is having a worse recovery than the national average? Maybe. But the local economy has a much different composition than the national average. So should we expect that it will always have a lower unemployment rate?

We feel an important comparison to make is between current conditions and conditions of the past local economy. Last month we pointed you to the Cooley-Rupert Economic Snapshot that uses the same idea. In the next few weeks, the EFP blog will do a similar analysis with local data for San Luis Obispo, Santa Barbara, and Ventura county.

As frequently noted in the national press, the depths of this recession can be clearly seen when looking at the health of the labor market. A common metric of this health used in the economics profession is to look at employment. The graph below depicts the employment population ratio with data as released by the California Employment Development Department.  The ratio is defined as the number of employed persons divided by the total non-institutional working age population (16-65). It allows us to evaluate the size of the labor market, including those who are out of the labor force.

The graph shows where the ratio is relative to it’s value at the peak of business cycle activity for the last three recessions (or cycles). The current recession is depicted in blue. For example, 46 months after the start of the current recession, Santa Barbara County’s employment population ratio is 5.6 percent below its value in December 2007 (the start of the recession). Graphing the data this way allows us to compare how Santa Barbara County is doing relative to its past performance over a business cycle.

So whats to take from this graph? Well, Santa Barbara County’s labor market looked to be doing ok for about the first year of the national recession. In fact, it looked to be on the same track as it was in the 2001 cycle. Then around November 2008 the trend took a different turn. Over the next year, the employment population ratio decreased roughly 5.5 percentage points to around the level it remains today. The trend has a similar shape as it did over the 1990 cycle, not quite as deep. The interesting thing is this ratio is lower now than it was at this point during the last two recoveries and is still declining.

What exactly is going on with employment? Why the drastic change in its trend during the recovery? To look at this ratio closer, we can decompose the employment population ratio into two separate components, a labor market participation component and an employment component.

The employment to labor force ratio measures the percentage of people in the labor force (employed + unemployed) that currently have a job. It is a measure of participant success. The labor force participation rate captures notions like discouraged workers. It calculates the percentage of working age adults that are either employed or unemployed. Graphed below are these two components. Instead of showing the employment to labor force ratio we plot the unemployment rate (one minus the employment to labor force ratio). Decomposing the employment figure into these two components lets us examine where exactly the labor market is struggling.

So why was the employment population ratio flat for a year until is experienced a decline? The two graphs above imply that it was because people continued to participate in the labor market at an increasing rate two years into the recession despite the fact that labor market participants were unemployed at an increasing rate. Overall, the unemployment rate increased almost immediately and kept doing so for well over two years. The shape of the recovery in the unemployment rate is similar, although much deeper, in this recession as it was in the past two recessions. It has already started to show signs of decline. However, people in Santa Barbara County began to leave the labor market around November 2009.

Since then the participation rate has continued to decline at an increasing rate experiencing a drop of about 2.5 percentage points since July 2009. To put that in perspective, that’s a little under 3,000 people leaving the workforce over this time period. While people in the labor force are seeing somewhat better results in recent months, participation is still declining. Overall the size of the labor force is shrinking because of historically bad unemployment and because people are leaving the labor market all together.

The paths of all the series presented are plotted relative to their value at the peak of the respective business cycles, the dates of which are identified by the National Bureau of Economic Research. The snapshot presents aggregate data in four sections. The first summarizes the path of Gross Domestic Product and its components – Consumption Investment and Government Spending – and some relevant subcomponents. The next section summarizes the labor market and its important indicators including employment and hours. The third sections shows the activity in credit markets and the final section summarizes the features of industrial production and inflation.

Below is an example of the analysis for Real Gross Domestic Product. The current cycle is marked in blue and explicitly shows where we are now as compared to past cycles. 15 months after the peak of business activity, output lags significantly behind its trend in the 4 comparison cycles. Of the 4 comparison cycles, it has taken on average 5.25 quarters for output to return to at or above its peak level. However for current cycle, output continues to remain below its level in the 4th quarter of 2007.

Stay tuned to the EFP blog for similar local tri-county analysis.

The number of violent offenses known to the police, as well as their rate per 100,000 people, are exhibited in the next graph for the years 1985-2010. Since the population for the city varies a bit below 100,000 the number and this rate are not all that different. The data was obtained from the FBI uniform Crime Reporting Statistics, http://www.ucrdatatool.gov/, where a tool allows you to construct your own table. The data for 2010 was obtained from the police department, http://www.sbpd.com/goreport/maps/UCRWeb.htm

Note that the rate of violent offenses grew rapidly in the 1980’s, peaked about 1990, and has been declining ever since, including 2007-2009, the years of the Great Recession. This has puzzled most criminologists who expected crime to increase with the number of people out of the labor force. Property crime has also followed this pattern, peaking in the early nineties and declining dramatically thereafter, as shown in the next graph.

The violent crime rate is correlated with the property crime rate, as shown in the following graph for Santa Barbara. This is true for the country as a whole, as well as for  the City of Santa Barbara.

The next graph compares the rate of violent offenses per 100,000 for the nation and for the city of Santa Barbara. The rate of violent crime grew in the sixties, seventies and eighties and then has declined for the past two decades. The Santa Barbara rate is more variable or noisier but follows the national pattern.

Perspectives about Crime

We noted that crime has continued to decrease during the Great Recession despite the increase in unemployment rates. A different perspective on crime is to postulate that perhaps the same forces that motivate people to participate in legal work also motivate them to participate in crime. Since crime is predominantly a phenomenon committed by young males, in the next graph, we examine the trajectory of male labor force participation rates over time for males 16-24 years of age in the US. Note these participation rates for young males have been declining since 1979. Note also that the pattern over time for this participation rate is similar to the pattern for US property crime rates.

The nature of the home and the labor market was also changing profoundly during this period. The participation rate of women was rising from 32.7 percent in 1948 to 60 percent in 1999 and was 58.6 percent in 2010. So women were moving from the home to the labor market, changing the nature of both places. The labor market was no longer a male dominated environment, as reflected by women’s percentage of total employment, which grew from 38.5 percent in 1973 to 47.2 percent in 2010. This is shown in a graph along with the labor force participation rate of women. As the workplace became nearly evenly divided between the genders, this may have restrained anti-social behavior and limited some opportunities for crime.

A statistical analysis was conducted on the percentage changes from year to year in the US property crime rate. From 1960 through 1980, when crime was growing, these changes were predominately positive, eighteen in all, versus two negative. Between 1981 and 1990, when crime was leveling off, these changes were evenly divided between positive and negative. Since 1991, as crime has fallen, these changes have been predominately negative, seventeen in all versus two positive. As expected, since 1973, these year to year changes in the crime rate have been positively correlated with year to year changes in the labor force participation rate of men ages 16-24, lagged two years, and negatively correlated with women’s percentage of total employment.

A statistical analysis of the US property crime rate from 1960 through 2010 was conducted, substituting the labor force participation rate of women for women’s percentage of employment. The crime rate was positively correlated with the labor force participation rate for men ages 16-24, lagged two years,  and negatively correlated with the labor force participation rate for women, lagged three years. This result was used to forecast the US property crime rate for 2011 of 2735.5 per 100,000, a decline of 7.3 percent. We would expect property crime rates to continue to decline in Santa Barbara, perhaps to 2770 per 100,000 people.

Lastly, it is interesting to compare property crime rates per 100,000 people for the City of Santa Barbara, California, and the US, as illustrated in the next graph. Note that until 1996, the property crime rate in California was higher than this rate for the country as a whole. Once property crime rates started declining in the early nineties, they have been similar for all three of these geographical jurisdictions.

Author – Llad Phillips

The shaded areas in this plot are the recessions, with the timing determined by the National Bureau of Economic Research. Before the Great Recession, the ratio of condo prices to single family homes was significantly higher in Monterey than in Santa Barbara. The effect of the Great Recession was to drop condo prices relative to single family homes in both cities, but to drop the relative price more in Monterey. Now the relative price is similar in the two cities. Since the Great Recession, the price of a condo relative to a single family home is somewhere between 54 and 58 percent. The data for Monterey is more variable or noisy than for Santa Barbara, but the effect of the Great Recession is statistically significant for both cities.

The two previously posted blogs on house prices have examined the real estate data maintained by the UCSB Economic Forecast Project from various sources. The rest of this blog focuses on data from the California Association of Realtors for California, the cities of Santa Barbara and Monterey, and other regions in California. This data in the figure below is for the state, Santa Barbara and Monterey and is not seasonally adjusted.

The median price for single family units for Monterey parallels the experience for Santa Barbara, but fell to lower levels following the Great Recession. Median prices for single family units for Los Angeles closely parallels this series for California as a whole, but the corresponding price for the Riverside/San Bernardino area is much lower.

For the coastal areas of Sothern California, Los Angeles has the lowest median price for single family units, Orange County has the highest, and San Diego is intermediate.

Another perspective on the behavior of median price for single family units can be gained by examining year over year changes in these monthly prices. For example, the next graph shows the price of January 1991 minus January 1990, February 1991 minus February 1990 etc. In economic jargon, this is known as the seasonal difference in monthly data, abbreviated in the next graph as SD. Note that at the time of the Great Recession, these year over year changes in monthly prices do not fall as much for Los Angeles and for Orange County as they do for Santa Barbara and for Monterey. Whether this is affected by the smaller numbers of sales in these smaller cities or some other phenomenon needs to be investigated. Also note that median prices begin to fall before the Great Recession for Los Angeles, Orange County and Monterey, but not for Santa Barbara.

The Case-Shiller Index for home prices for the Los Angeles SMSA, seasonally adjusted, is compared in the next graph to the California Association of Realtors median price for single family units, not seasonally adjusted. Further analysis looking at fractional or percentage changes in these two measures of home prices did not reveal major differences, as shown in the final graph. However, the Case-Shiller Index shows a smaller percentage drop than the California Association of Realtors median price for single family units. This may be because the latter may have had a mix of relatively more high end homes before the Great Recession and a mix of more low end homes, under water, during the Great Recession.

Median home prices can be affected by the mix of homes being sold, i.e. how many high end versus how many low end, a composition effect. The Case-Shiller Index is based on repeat sales of the same homes. Whether this composition effect is more pronounced for Santa Barbara and for Monterey than for Los Angeles and for Orange County is a possibility that is not easy to check since there are fewer sales in the smaller cities and they do not have Case-Shiller indices. However, the percentage drops for Santa Barbara and Monterey are twice as large as the percentage drops for Los Angeles and Orange County and the difference in the percentage drop of the median price index for Los Angeles and the  percentage drop in the Case-Shiller index would only account for about a quarter of the difference in percentage drops that we see for the smaller cities compared to the larger areas.

 Author – Llad Phillips

The DataQuick median price data does not address the composition problem that the Case-Shiller index does by tracking the sale price of the same home, and hence adjusting for quality. However, the Case-Shiller price indices are not available for smaller cities such as Santa Barbara. An advantage of the DataQuick median price is that it is available for single family homes and for condos and hence provides detail.

Statistical analysis of the median price data for single family homes shows that the Great Recession continues to have an impact, depressing the median price by about $148,000. Each foreclosure has an impact with a lag of about five months, depressing the median price by about $5900 per foreclosure. Each sale or count has an impact with a lag of ten months, increasing the median price by about $677 per sale. This may be revealing the supply curve and the construction cost per square foot. There is also some evidence of a two year cycle in median home prices.

The median price for single family homes was forecast for the remaining months of 2011, based on the assumptions that foreclosure continued at an average level of eleven per month and that sales continued at an average level of 67 per month. The following graph shows that there is not likely to be a dramatic change in the median price of single family homes in Santa Barbara. The DataQuick data for June just became available. The median price is $865,000 compared to the forecast for June of $904,000.

DataQuick also provides data for the median price of condo resales in Santa Barbara, as well as their number or count. This data is maintained by the UCSB Economic Forecast Project. The following graph shows these two time series, as well as foreclosures. Of course the foreclosure data is total foreclosures, not separated for single family homes or for condos.

Statistical analysis of this data shows that the Great Recession continues to have a lingering effect on the median price of condos, depressing the price by about $166,000. Foreclosures and the number of condo sales did not have a significant effect on price.

The median price for the resale of condos was forecast for the remaining months of 2011, as shown in the following chart. The forecast of the median price for condo resales was $484,000 for June 2011. DataQuick recently released data for June and their median price for condo resales was $466,000. Their price for May was $471,750. So it appears that condo prices will not change dramatically very soon.

The ratio of the median price of single family homes to the median price of condos is shown in the next graph. Before the Great Recession, this ratio of prices varied around 1.61. After the Great Recession, this ratio increased significantly to 1.84. A topic for further investigation is whether this phenomenon is observed for other cities besides Santa Barbara, and what may be the cause of this increase in the relative price of single family homes.

Author – Llad Philips

A monthly housing price index for the USA is available at the Federal Housing Finance Agency, http://www.fhfa.gov/Default.aspx?Page=14. The series illustrated is seasonally adjusted. The Case-Shiller monthly home price index, seasonally adjusted, for ten metropolitan statistical areas (MSA’s) is shown for comparison. It was obtained from MacroMarkets. http://www.macromarkets.com/csi_housing/sp_caseshiller.asp. These two home price indices are illustrated in the graph below. The shaded areas in this plot are the recessions, with the timing determined by the National Bureau of Economic Research.

Note that the housing price indices peaked before the onset of the Great recession.

Case-Shiller home price indices for the Las Vegas and Los Angeles SMA’s are illustrated along with the Case-Shiller index for ten SMA’s. All three indices are seasonally adjusted and were obtained from MacroMarkets, The shaded areas in this plot are the recessions, with the timing determined by the National Bureau of Economic Research.

Note that Los Angeles home prices rose more than prices in Las Vegas but have fallen less.

Quarterly housing price indices for various states are available at the Federal Housing Finance Agency. The following graph compares this seasonally adjusted price data for three states, California, Arizona, and Nevada. Home prices in Arizona increased the most and fell the least

Monthly data for median home price for single family units is available from the California Association of Realtors for California and the city of Santa Barbara and is maintained by the UCSB Economic Forecast Project. This data is not seasonally adjusted.

Single-family unit median prices rose dramatically for Santa Barbara and fell spectacularly in the midst of the Great Recession. However. There may be a composition effect if sales during the Great Recession were disproportionately by the less affluent, with lower wealth and less expensive homes.

Foreclosure are available from DataQuick and are maintained by the UCSB Economic Forecast Project.. The next plot compares the patterns over time of the median home price for single-family units and foreclosures for the city of Santa Barbara. Note that the number of foreclosures increased before and during the Great Recession and have not yet declined to the low levels around the turn of the century. Also, foreclosures in Santa Barbara were high following the 1990-91 recession and took at least seven years to decline to low levels.

Statistical analysis indicates that median prices for single-family units in Santa Barbara indeed are adversely affected by foreclosures with about a six month lag. Each foreclosure drops the median price by about $4,030. Currently, foreclosures are averaging about twelve to thirteen per month, so this lowers the median housing price about $48,400. The Great Recession caused about a $341,000 drop in this median price. Whether this will be a lasting effect, or reflects the long period of half a dozen years or so for foreclosures to recover, as was the case after the 1990-91 recession, remains to be seen. So together, the impact of foreclosures and the Great Recession is to drop the median single-family unit home price by about $389,400. Single family unit prices peaked at $886,700 in Santa Barbara and at the end of 2010 were $420,000, a drop of  $466,720, much of it accounted for by the impact of conditions as proxied by foreclosures and the Great Recession.

The forecast of the median single family unit housing price for Santa Barbara for the twelve months of 2011 is shown in the graph below. It is likely that housing price will remain in the current range as long as foreclosures and the aftermath of the great recession persist.

Similar results were found for Los Angeles except the impact of the Great Recession on the price of single family units was a couple of months earlier and less dramatic than for Santa Barbara. The impact of foreclosures on price occurred with a seven to eight month lag, but of course there were many more foreclosures in Los Angeles. The combined effect of the Great Recession and foreclosures on single family unit prices was $46,000, much less than for Santa Barbara.

Results closer to those for Santa Barbara were found for Monterey.  The impact of the Great recession was $282,000. Foreclosures had an immediate impact and also an impact with an eight month lag, with a resulting drop in single family unit prices of $23,100 per foreclosure. Foreclosures in Monterey are currently averaging about 7. The total impact of the Great Recession and foreclosures was a drop in price of $424,000. Current prices are off their peak in Monterey by about $555,000.

Future topics for investigation include investigating the relationship between home prices and sales as well as foreclosures.

The Santa Barbara County Economic Summit event was held nearly three months ago on May 5, but uncertainty continues about the economy and especially the labor market. The UCSB Economic Forecast Project is using its blog on the home page http://www.ucsb-efp.com/ to provide updates on current topics on a regular basis.

Data for nonfarm employment in the United States, seasonally adjusted, may be found at FRED, the St. Louis Fed, http://research.stlouisfed.org/fred2/. Employment for June is 131,017,000. The shaded areas in this plot are the recessions, with the timing determined by the National Bureau of Economic Research.

The corresponding graph for nonfarm employment, seasonally adjusted, for Santa Barbara County follows. The data is obtained from the California Employment Development Department http://www.edd.ca.gov/ and is maintained as a data set by the UCSB Economic Forecast Project. The seasonally adjusted number of employed for June is 162,500.

Total nonfarm employment for the state of California, seasonally adjusted, can be obtained from the California Employment Development Department, referenced above, and a plot is shown below, comparable to the plots above for the nation and for the county. The seasonally adjusted number of employed for June is 14,068,600., 10.7% of the national number.

US, California, and Santa Barbara County Forecasts for the remainder of 2011

The Survey of Professional Forecasters can be found on the web site of The Federal Reserve of Philadelphia, http://www.philadelphiafed.org/research-and-data/real-time-center/survey-of-professional-forecasters/ . Forecasts of many economic variables at the national level are published quarterly. There are usually forty or so forecasters participating, listed with the quarterly forecast, such as Rajeev Dhawan at Georgia State University, Allen Sinai at Decision Economics Inc. and Mark Zandi at Moody’s Analytics to name a few. A table of forecasts for nonfarm employment follows.

Forecasts of Non-farm Employment 2011:

The California Department of Finance publishes quarterly forecasts of numerous economic variables including nonfarm employment, http://www.dof.ca.gov/HTML/FS_DATA/LatestEconData/FS_Forecasts.htm

The forecasts of non-farm employment, seasonally adjusted, for the county of Santa Barbara were provided by the author for the UCSB Economic Forecast Project. A plot of the monthly forecasts for July through December 2011 follows. We expect the actual employment numbers will fall between the upper(green) and lower(black) lines.

Summary

Nonfarm employment is forecasted to increase by 1.3% at the national level between the first and fourth quarters of this year. The corresponding figure for California is 1.2%. The outlook is comparable for Santa Barbara County where employment is forecasted to increase by 1.0%, about the level it had fallen to in June of 2009.