26 MOODY’S ANALYTICS / Regional Financial Review / May 2010 Higher living costs discourage people from migrating to a given area while si- multaneously encouraging residents to leave. Empirical evidence suggests there is a negative correlation between population growth, of which migration flows are the key determinant, and living costs (see Chart 1). Los Angeles and New York have experienced persistent net domestic migration outflows even during expanding business cycles. On the other hand, southern areas with low living costs have benefited from substantial migration inflows. Along with labor force productivity growth, population growth de- termines an area’s economic potential. Thus, an area’s cost structure is critically impor- tant to its long-term performance. This article presents the most recent update of the Moody’s Analytics metro area cost of living index, which considers the costs of energy, retail goods, housing, insur- ance and transportation. The article also ex- amines alternate measures of living costs. Methodology The Moody’s Analytics cost of living index is a nationally indexed composite av- erage of five key cost of living components. The weight of each component of the index varies depending on the metropolitan area, and in various metro areas, some compo- nents make up a larger portion of the total cost of living. For example, housing costs constitute 42% of the overall cost of liv- ing in Victoria TX as compared with 65% in San Jose CA. Energy costs constitute 3% of the overall cost of living in San Francisco as compared with 15% in Laredo TX. Annual expenditures are calculated for each of the five index components in every metro area and subsequently indexed to their respective national benchmark. These ratios are applied to the various compo- nents of U.S. living costs. The results are summed and indexed to the annual national expenditure average. The cost of living index does not incorporate a moving average, a technique used to reduce volatility. By using unadjusted and unbiased data, the cost of living index accurately depicts living costs in any given metro area at a specific point in time. National expenditure patterns are derived from the Bureau of Labor Statistics’ annual consumer expenditure survey. One of the largest inputs into living costs is retail expenditure. This category includes expenditures on a wide variety of goods such as food, apparel, entertainment and household furnishings. The cost index for this expenditure category is equal to na- tional expenditures on these items adjusted for the difference between retail wages and salaries per employee in the metro area and in the nation. If wages and salaries per em- ployee are higher and rising more quickly in a metro area than in the rest of the nation, producers must pass through price increases to compensate for elevated unit labor costs. Retail expenditures constitute between 20% and 34% of a metro area’s living costs, de- pending on the area. Notwithstanding the recent severe down- turn in house prices, housing expenditures are the single greatest component of household expenditure and are represented as such in the cost of living index. On average, the cost of housing accounts for 52% of total living costs as estimated in the Moody’s Analyt- ics cost of living index. Because of its large weight, the cost of housing is the cause of most of the annual variation in the cost of living index. Housing costs are also the most volatile component of the cost of living index, varying widely depending on region. Housing costs are estimated by consid- ering both mortgage payments and rent outlays. Monthly mortgage payments are estimated for each metro area using house price data from the National Association of Realtors. This house price metric is preferred because, unlike other price measures, it re- flects actual prices paid. A five-year average of the house price data is used to counteract price biases that might arise from the mix of homes sold in any one year. The base value is extended using price growth in the Federal Housing Finance Authority’s repeat-sales house price index, which, unlike the NAR data, is not subject to a mix bias. Annual homeowner expenditures are also calculated by assuming a 30-year mortgage with an 80% loan-to-value ratio. ANALYSIS R egional living costs are closely related to quality of life, migration patterns, and, by extension, long-term economic potential. For example, both Ames IA and Wichita Falls TX have per capita incomes that are be- low the U.S. average. After adjusting for living costs, however, both metro areas have above-average living standards, at least as measured by relative cost. By contrast, the Santa Rosa and San Diego metro areas in Califor- nia have above-average per capita income, yet relative living costs remain high. U.S. Metro Area Cost of Living Index Update BY CHRIS LAFAKIS & STEVEN G. COCHRANE
13
Embed
u.s. Metro Area Cost Of Living Index Update - Economy · the overall cost of living in San Francisco as ... U.S. Metro Area Cost of Living Index Update ... Census Bureau’s New York
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
26 MOODY’S ANALYTICS / Regional Financial Review / May 2010
Higher living costs discourage people from migrating to a given area while si-multaneously encouraging residents to leave. Empirical evidence suggests there is a negative correlation between population growth, of which migration flows are the key determinant, and living costs (see Chart 1). Los Angeles and New York have experienced persistent net domestic migration outflows even during expanding business cycles. On the other hand, southern areas with low living costs have benefited from substantial migration inflows. Along with labor force productivity growth, population growth de-termines an area’s economic potential. Thus, an area’s cost structure is critically impor-tant to its long-term performance.
This article presents the most recent update of the Moody’s Analytics metro area cost of living index, which considers the costs of energy, retail goods, housing, insur-ance and transportation. The article also ex-amines alternate measures of living costs.
Methodology The Moody’s Analytics cost of living
index is a nationally indexed composite av-erage of five key cost of living components. The weight of each component of the index varies depending on the metropolitan area, and in various metro areas, some compo-nents make up a larger portion of the total cost of living. For example, housing costs constitute 42% of the overall cost of liv-
ing in Victoria TX as compared with 65% in San Jose CA. Energy costs constitute 3% of the overall cost of living in San Francisco as compared with 15% in Laredo TX.
Annual expenditures are calculated for each of the five index components in every metro area and subsequently indexed to their respective national benchmark. These ratios are applied to the various compo-nents of U.S. living costs. The results are summed and indexed to the annual national expenditure average. The cost of living index does not incorporate a moving average, a technique used to reduce volatility. By using unadjusted and unbiased data, the cost of living index accurately depicts living costs in any given metro area at a specific point in time. National expenditure patterns are derived from the Bureau of Labor Statistics’ annual consumer expenditure survey.
One of the largest inputs into living costs is retail expenditure. This category includes expenditures on a wide variety of goods such as food, apparel, entertainment and household furnishings. The cost index for this expenditure category is equal to na-tional expenditures on these items adjusted for the difference between retail wages and salaries per employee in the metro area and in the nation. If wages and salaries per em-ployee are higher and rising more quickly in a metro area than in the rest of the nation, producers must pass through price increases to compensate for elevated unit labor costs.
Retail expenditures constitute between 20% and 34% of a metro area’s living costs, de-pending on the area.
Notwithstanding the recent severe down-turn in house prices, housing expenditures are the single greatest component of household expenditure and are represented as such in the cost of living index. On average, the cost of housing accounts for 52% of total living costs as estimated in the Moody’s Analyt-ics cost of living index. Because of its large weight, the cost of housing is the cause of most of the annual variation in the cost of living index. Housing costs are also the most volatile component of the cost of living index, varying widely depending on region.
Housing costs are estimated by consid-ering both mortgage payments and rent outlays. Monthly mortgage payments are estimated for each metro area using house price data from the National Association of Realtors. This house price metric is preferred because, unlike other price measures, it re-flects actual prices paid. A five-year average of the house price data is used to counteract price biases that might arise from the mix of homes sold in any one year. The base value is extended using price growth in the Federal Housing Finance Authority’s repeat-sales house price index, which, unlike the NAR data, is not subject to a mix bias. Annual homeowner expenditures are also calculated by assuming a 30-year mortgage with an 80% loan-to-value ratio.
ANALYSIS
Regional living costs are closely related to quality of life, migration patterns, and, by extension, long-term economic potential. For example, both Ames IA and Wichita Falls TX have per capita incomes that are be-low the U.S. average. After adjusting for living costs, however, both metro areas have above-average living
standards, at least as measured by relative cost. By contrast, the Santa Rosa and San Diego metro areas in Califor-nia have above-average per capita income, yet relative living costs remain high.
U.S. Metro Area Cost of Living Index UpdateBY CHRIS LAFAKIS & STEVEN G. COCHRANE
MOODY’S ANALYTICS / Regional Financial Review / May 2010 27
ANALYSIS �� U.S. Metro Area Cost of Living Index
Rental expenditures are estimated by ex-tending monthly rental payments reported in the decennial census with the growth in the FHFA home price indices. Over sufficiently long periods of time, there exists a strong correlation between rental rates and house prices, which underpins the rental expen-diture estimation methodology. The rental price for New York incorporates data from the Census Bureau’s New York City housing and vacancy survey as well; since the decennial census value covers only a small portion of the market, it does not meaningfully repre-sent the rental market in New York.
Moody’s Analytics estimates of metro area homeownership rates are then used to reconcile the costs of owning and renting. The composite average is compared with the national average.
The third component of the cost of living index is household utility expenditure. Util-ity expenditures cover outlays on electricity and heating fuels. Expenditures are calcu-lated by multiplying demand for a particular energy fuel by the price of that fuel. Data from the Department of Energy’s Energy Information Administration is used to calcu-late the specific demand for each fuel type in a metro area. This approach is taken because calculating utility costs based on a fixed amount of electricity and other fuels would bias the cost of living index for this compo-nent, since demand for heating and cooling varies considerably by region, as do the type of fuels used.
For natural gas and heating oil, the ap-propriate state-level prices were used at the metro area level, as the primary variation in these prices is due to state-level taxes. For electricity, however, the price per kilowatt-hour for each metro area was obtained from the EIA, which publishes prices for specific energy providers. Metro areas are mapped every year to their primary energy provider to determine the cost of electricity in each area. Price data from the primary cooperative or publicly owned utility are used for those few areas not served by a privately owned utility. Household utility expenditures account for approximately 8% of the cost of living index.
Automobile insurance expenditures are a small portion of living costs, accounting for
just 6% of the cost of living index. The ex-penditure data come from the National As-sociation of Insurance Commissioners, which estimates a policy-adjusted average expendi-ture for each state. The state average is used for all metro areas within a state, as no finer regional breakdown of the data is available.
Public transportation expenditures are generally not an appreciable portion of overall living expenses in most regions, ac-counting for only 1% of total consumer ex-penditures nationally. In those areas where it is important, public transportation is a substitute for private transportation. Thus, no separate estimate of public transporta-tion expenditures is included in the cost of living index. The relative cost of private transportation is used as a proxy for all commuting-related costs.
Transportation expenditures are the smallest and most consistent component of the cost of living index across metro ar-eas. This component uses gasoline outlays to determine the variable-cost portion of consumer transportation spending. Vehicle prices are not used because they vary little across regions. To accurately estimate gasoline consumption at the metro level, commuting distance, traffic congestion, and retail gasoline prices must be considered. Metro area transportation costs are esti-mated by multiplying local retail gasoline prices, which are obtained from the Oil Price Information Service, by an estimate of the necessary number of gallons purchased per household for work and normal travel. The number of gallons purchased is determined by dividing the estimated number of miles driven by the estimated vehicle efficiency in each census division. This census division es-timate of gallons per household is adjusted to the metro area level by incorporating actual metro area and census division com-muting times obtained from the decennial census. The use of actual commuting times allows Moody’s Analytics to more accurately reflect the varied traffic conditions faced by residents within each metro area.
ResultsConsistent with prior years’ results, areas
with the highest housing costs are also the
areas with the most expensive living costs (see Table 1). These areas include the North-east Corridor, southern Florida and coastal California (see Chart 2). For the third con-secutive year, San Jose has the nation’s high-est cost of living, with costs 50% greater than the national average. This, however, is an improvement from 64% greater in 2007 and is back in line with its relative costs of the earlier years of this past decade. All five metropolitan areas and divisions with the highest living costs are in California.
Housing costs, which were the primary catalyst for rises in the cost of living index for many metro areas through much of the past decade, have now become the primary reason for relative costs to falter in 2008, narrowing some of the historical differences (see Chart 3). The national house price correction began in early 2006 and was in full swing by 2008, particularly in Arizona, California, Florida, Nevada, and parts of the Northeast (see Chart 4). Housing costs in 2008 accounted for 51.8% of total living costs as estimated by the Moody’s Analytics cost of living index, compared with a peak of 53.0% in 2006. Housing costs have ac-counted for an average of 52.1% of total liv-ing costs since 2000.
California still has the highest-cost metropolitan areas, with San Jose and San Francisco ranking first and second. Similarly, two New York metro divisions—Bridgeport CT and Nassau-Suffolk NY—still rank among the top 10.
But some changes are appearing in the rankings. First, California metro areas are now less dominant among the top 10 high-cost areas. Whereas nine of the top 10 were in California in 2005—Bridgeport was the only exception—only five of the top 10 were California metro areas in 2008, largely reflecting rapidly falling house prices at that time. Honolulu HI, Naples FL and Newark NJ now are among the high-est ranking. Naples ultimately faced house price declines of similar magnitude to the California metro areas, but its price decline was slower to appear.
Aside from California metro areas, the top quintile among the 384 metro areas and divisions in the U.S. is dominated by
28 MOODY’S ANALYTICS / Regional Financial Review / May 2010
FROM MOODY’S ECONOMY.COM 1 FROM MOODY’S ECONOMY.COM 1
-1
0
1
2
3
4
5
85 95 105 115 125 135 145 155
Chart 1: Costs, Growth Negatively Correlated Based on top 50 metro areas
Sources: Moody’s Analytics, Census Bureau
Average annual population growth, % 1998-2008
Cost of living, 2008, U.S.=100
FROM MOODY’S ECONOMY.COM 4 FROM MOODY’S ECONOMY.COM 4
Chart 4: …Occurred in Housing Bust Areas Change in housing costs, %, 2007-2008
Source: Moody’s Analytics
Greater than 5% decline 0% to 5% decline
0% to 7% increase
Greater than 7% increase
U.S. metro average=2.6% increase
FROM MOODY’S ECONOMY.COM 2 FROM MOODY’S ECONOMY.COM 2
Chart 2: Living Costs Are Highest on the Coasts Living cost by metro area
Source: Moody’s Analytics
Low, below 90
Average, 90 to 100
High, 100 to 110
Very high, above 115 U.S. metro average=95
FROM MOODY’S ECONOMY.COM 3 FROM MOODY’S ECONOMY.COM 3
Chart 3: The Largest Declines in Living Costs… Change in relative living costs, 2007-2008
Source: Moody’s Analytics
Decrease Modest increase, 0 to 1.8 Large increase, above 1.8
Average=-0.7
areas in Connecticut, Florida, Massachu-setts, New Jersey, the New York City area, and the coastal metro areas of Washington State. Within this quintile, costs have fallen over the past 10 years in the California and Massachusetts metro areas. Massachusetts house prices were among the first to falter at middecade, followed closely by many of the southern California metro areas. In all others that make up this top 20%, relative living costs rose over this period, no more so than in Honolulu and Naples. Rising costs through 2008 in Florida are also exemplified by a shift into the top quintile of Jacksonville and Orlando since 2002. Among others, Dallas, Fort Worth, Austin and Salt Lake City can be found in the top 20%. Salt Lake City is a newcomer to this top ranking because of its rise in house prices and because it was among the last of the major metro areas to suffer a downturn in prices.
The lowest quintile of metro areas re-mains dominated by the Midwest, mostly concentrated in Illinois (excluding Chicago), Indiana, Michigan, Ohio and Wisconsin. These are joined by areas of western Penn-sylvania and upstate New York. Other small areas in the mid-South and Southeast are among these lowest-cost areas. Danville IL is holding firm to its bottom ranking among all metro areas with costs 22% below aver-age. Significantly, every metro area in the lowest quintile saw its relative costs con-tinue to fall from 2002 to 2008. Thus, their comparative cost advantage continued to improve. Many of these areas, particularly in the industrial Midwest, have suffered from the deindustrialization of their economies, and their ever-falling relative costs offer them some potential for revitalization as the U.S. economy strives to become more cost competitive within the global economy.
The distribution of the cost of living across metro areas has changed since the early years of this past decade with a slightly more even distribution around the U.S. index today (see Table 2). In 2008, 101 of the 384 metro areas had a COLI greater than 100, or above the U.S. norm. In 2005 this figure was 96; in 2002 it was 82. As during the years following the 2001 recession, two factors led to this change. First, through the expan-sion that ended in late 2007, many midsize metro areas experienced rapid growth, particularly if they were peripheral to large metro areas. Much of this growth was fu-eled by homebuilding and the rapid rise in housing values. Also, businesses followed the population movement outward from the metro area centers, often to be closer to their workforces.
But within this trend, the average and median of the COLIs across all the metro
ANALYSIS �� U.S. Metro Area Cost of Living Index
MOODY’S ANALYTICS / Regional Financial Review / May 2010 29
ANALYSIS �� U.S. Metro Area Cost of Living Index
TABLE 1
2008 Cost of Living IndexIndex: U.S. = 100
2002 2005 2008 2002-2008
Index Rank Index Rank Index Rank Change in living costNew England
PacificAnchorage AK 104.8 49 103.8 66 106.6 53 1.8Bakersfield CA 98.3 99 105.8 55 100.2 98 1.9Bellingham WA 98.4 97 100.9 84 106 58 7.6Bend OR 98.1 101 99 101 102.4 81 4.3Bremerton WA 101.2 72 100.8 87 106 58 4.8
TABLE 1 (cont.)
2008 Cost of Living IndexIndex: U.S. = 100
2002 2005 2008 2002-2008
Index Rank Index Rank Index Rank Change in living cost
ANALYSIS �� U.S. Metro Area Cost of Living Index
36 MOODY’S ANALYTICS / Regional Financial Review / May 2010
Chico CA 106.0 39 112.8 36 107.2 48 1.2Corvallis OR 95.4 132 93 158 98 120 2.6El Centro CA 89.3 274 91.9 171 89 245 -0.3Eugene OR 94.6 149 94.4 142 98.3 117 3.7Fairbanks AK 98.8 94 98.3 103 102.6 77 3.8Fresno CA 100.6 77 108.5 45 102.3 82 1.7Hanford CA 95.7 127 97.4 110 94.6 162 -1.1Honolulu HI 115.9 20 127.9 13 141.5 3 25.6Kennewick WA 95.2 139 90.9 186 91.5 203 -3.7Longview WA 92.9 172 89 234 91.7 200 -1.2Los Angeles CA 111.4 29 121.5 21 119.4 19 8.0Madera CA 103.1 58 106.2 53 101.3 88 -1.8Medford OR 97.4 108 101.4 79 100.1 101 2.7Merced CA 101.8 64 108.8 42 94.7 160 -7.1Modesto CA 101.8 65 105.8 55 95 157 -6.8Mount Vernon WA 102.6 61 101.5 78 106.8 51 4.2Napa CA 118.7 16 124.4 17 117.5 24 -1.2Oakland CA 135.6 4 137.7 6 135.9 6 0.3Olympia WA 100.9 74 97.9 106 102.5 79 1.6Oxnard CA 119.2 15 128 12 120.4 17 1.2Portland OR 102.6 62 100.7 88 107.4 47 4.9Redding CA 105.4 43 113.4 35 107.9 42 2.5Riverside CA 104.0 54 114.9 33 106.8 51 2.8Sacramento CA 108.2 33 115.7 32 104.2 66 -4.0Salem OR 95.3 136 92.7 160 97.6 126 2.3Salinas CA 125.5 8 135.1 7 115.4 28 -10.1San Diego CA 125.1 9 131.1 10 120.5 16 -4.6San Francisco CA 145.6 1 145.1 1 147.7 2 2.1San Jose CA 145.2 2 144 2 149.6 1 4.4San Luis Obispo CA 112.9 26 117.1 29 112.8 31 -0.1Santa Ana CA 131.5 6 142.5 3 137.6 4 6.1Santa Barbara CA 121.7 13 131.2 8 119.2 21 -2.5Santa Cruz CA 141.1 3 142 4 132.2 7 -8.9Santa Rosa CA 130.9 7 138.7 5 126.9 11 -4.0Seattle WA 111.7 28 111.3 39 123.2 14 11.5Spokane WA 92.7 177 92.1 170 96.7 134 4.0Stockton CA 110.4 30 116.6 30 101.4 87 -9.0Tacoma WA 101.2 73 100.7 88 106.4 56 5.3Vallejo CA 118.0 18 126.8 15 112.3 33 -5.7Visalia CA 97.2 111 102.7 73 99.2 109 2.0Wenatchee WA 92.0 199 88.9 239 95.2 152 3.2Yakima WA 90.4 240 90.5 196 93.6 176 3.3Yuba City CA 96.6 118 101.9 77 94.4 165 -2.2
ANALYSIS �� U.S. Metro Area Cost of Living Index
TABLE 1 (cont.)
2008 Cost of Living IndexIndex: U.S. = 100
2002 2005 2008 2002-2008
Index Rank Index Rank Index Rank Change in living cost
MOODY’S ANALYTICS / Regional Financial Review / May 2010 37
ANALYSIS �� U.S. Metro Area Cost of Living Index
areas remained, oddly, virtually the same (see Table 2). Between 2002 and 2008 the average COLI fell in the highest and lowest quintiles and rose in the three intermedi-ary quintiles, shifting the overall distribu-tion downward slightly at the tails and narrowing the difference slightly between the top quintile and the next two. The path toward this shift, however, was not lin-ear, as can be seen from the intermediate shifts for the 2002-2005 and 2005-2008 periods. Thus it is difficult to say whether the 2002-2008 shifts are indicative of a longer-term trend, but if so, it would in-dicate that cost differences between the highest quintile and the next are narrow-ing, minimizing somewhat the differences in comparative advantage, at least as it refers to costs.
ImplicationsThe relative cost of living across the
regions continues to change over time. Recently the change continues a trend that began in 2007 in which living costs began to recede in metro areas with formerly hot housing markets as house prices began to decline. Many of these areas are in the South and the West, where accelerating economic and population growth has steadily driven up costs over time. There is some indication now that this is reversing slightly.
Historically the Northeast has had the most stable cost structure. The region has an abundance of well-paying jobs, along with high population density and slow growth, which support the region’s high, but stable, cost of living. The cost of living has gradually fallen in the Midwest, where the ongoing restructuring among many manufacturing industries weighs on economic and popula-tion growth.
Low living costs can be a critical growth driver for some metro areas. In particular, small metro areas often develop as lower-cost bedroom communities for major metro areas, even though the smaller area may have few internal economic drivers. By contrast, occasionally individuals choose to remain in a certain area even though the
cost of living is high. An expensive metro area can still attract a positive flow of do-mestic migration if its costs are competitive within the region. The recent general move-ment downward of costs in the highest-cost metro areas indicates some improvement in their comparative advantage, which should help to support their economies as econom-ic recovery continues in the coming year as well as further into the future.
Other cost measuresAlternative cost measures exist for re-
gional economies such as the consumer price index and the ACCRA (formerly known as the American Chamber of Commerce Researchers Association) living cost index, yet each differs greatly in both methodology and focus from the Moody’s Analytics cost of living index.
The purpose of the CPI is to track price changes over time and is thus not designed to measure comparative living costs across regional economies. The composition of the goods and services consumed by households and their relative prices vary substantially across regions. In addition, the CPI measure is available only for a handful of economies at the metro area level of detail.
The goal of the ACCRA index is more similar to that of the Moody’s Analytics index in that it attempts to capture relative
price levels across regional economies at a point in time. However, there are a number of differences between the two measures.
The Moody’s Analytics cost of living in-dex is published annually and is based on a variety of data sources primarily published by federal agencies. Sources include the Census Bureau, the Department of Energy, the Energy Information Administration, FHFA, the Bureau of Economic Analysis, and the National Association of Realtors. In con-trast, the ACCRA index is released on a quar-terly basis and is based on comparative price surveys culled from a network of chambers of commerce, economic development orga-nizations, and similar entities.
The ACCRA index represents price dif-ferences across regional economies for a common basket of goods and services. This provides a consistent basis for cost com-parison—with regard to the same basket of goods and services—across cities and metro areas. The limitation of this approach, how-ever, is that it fails to account for differences in spending patterns across regions and product substitution. The Moody’s Analyt-ics index does not have this limitation, as it measures relative prices for the goods and services actually consumed regionally. Such methodological differences make it difficult to compare the cost of living measure from ACCRA with our measure.
TABLE 2
Average Metro Area Cost of Living Index by QuintileIndex: U.S.=100
1st 2nd 3rd 4th 5thDifference between
1st and 5th quintiles
2008 113.9 98.6 92.4 87.9 83.7 30.2
2005 115.0 97.3 90.8 87.5 83.7 31.3
2002 114.3 97.2 91.2 87.7 84.0 30.3
Change Between 2008 and 2005 -1.1 1.2 1.6 0.4 0.1
Change Between 2005 and 2002 0.7 0.1 -0.4 -0.2 -0.3
Change Between 2008 and 2002 -0.4 1.4 1.2 0.2 -0.3
Source: Moody’s Analytics
84 MOODY’S ANALYTICS / Regional Financial Review / May 2010
About Moody’s Economy.comMoody’s Economy.com, a division of Moody's Analytics Inc., is a leading independent provider of economic research, analysis and data. As a well-recognized source of pro-prietary information on national and regional economies, industries, financial markets, and credit risk, we support strategic planning, product and sales forecasting, risk and sensitivity management, and investment research. Our clients include multinational corporations, governments at all levels, central banks, retailers, mutual funds, financial institutions, utilities, residential and commercial real estate firms, insurance companies, and professional investors.
With one of the largest assembled financial, economic and demographic databases, Moody's Economy.com helps companies assess what trends in consumer credit and behavior, mortgage markets, population, income, and property prices will mean for their business. Our web and print periodicals and special publications cover every U.S. state and metropolitan area; countries throughout Europe, Asia and the Americas; and the world's major cities, plus the U.S. housing market and 58 other industries. We also provide up-to-the-minute reporting and analysis on the world's major economies on our real-time Dismal Scientist web site from our offices in the U.S., the United Kingdom, and Australia. Our staff of more than 50 economists, a third of whom hold PhD's, offers wide expertise in regional economics, public finance, credit risk and sensitivity analysis, pricing, and macro and financial forecasting.
Moody's Economy.com became part of the Moody’s Corporation in 2005. Moody's Economy.com is headquartered in West Chester PA, a suburb of Philadelphia, with offices in London and Sydney. More information is available at www.economy.com.