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Local economic area analysis is a study of the demand and supply factors that can affect the value of a subject property located within the boundaries of a local economic area. A local economic analysis performed for a shopping center appraisal should obtain Information on the economic variables that either directly or indirectly affect the value and marketability of a subject property. This article discusses the major economic and demographic variables important in analyzing a local economic area, the nature of local economic analysis, and the sources of data for economic and demographic variables. [PUBLICATION ABSTRACT]

Local economic area analysis is a study of the demand and supply factors that can affect the value of a subject property located within the boundaries of the local economic area. Depending on the requirements of the study and the appraiser's judgment, the local economic area (LEA) could be a large metropolitan area, a mid-sized or small metropolitan area, a small town, a county, a portion of a county, or a portion of two adjacent counties.

The appraiser's judgment is the basis for identifying the appropriate geographic area containing economic variables that affect the subject property. The local economic area for a neighborhood shopping center is smaller than that of a regional mall. The appropriate LEA for a neighborhood shopping center could be entirely contained within a county, while the LEA for a superregional mall could include multiple counties or even an entire metropolitan area.

Moreover, the local economic area analysis of a small neighborhood shopping center at the eastern fringe of a large metropolitan area would probably not be affected by economic changes at the western edge of that metropolitan area because the consumers to the west are not in the subject shopping center's geographic circle of influence. Plant openings or closings and population changes on the west side of the metropolitan area will have little or no impact on an east-side neighborhood shopping center. However, if an in-town shopping area or downtown mall were being analyzed, economic factors affecting the western fringe of the metropolitan area could have a more significant impact. Knowledge of the local community and appraisal judgment define the scope of the local economic area and the analysis appropriate to the assignment.

Local economie area analysis performed for a shopping center appraisal should obtain information on the economic variables that either directly or indirectly affect the value and marketability of the subject property. The first step is the demarcation or delineation of the local economic area for this shopping center, the subject property. In the broadest sense, the entire metropolitan area in which the subject property exists can affect it. However, in reality, only factors in a more proximate geographic area have a substantial effect on the subject property.

To perform a local economic area analysis, the appraiser needs to identify the spatial extent of the local economic area and then investigate the major economic variables in the local economic area. Employment, population, households, and income levels are the prime demand factors. Land, buildings, and space availability are the prime supply variables. Variables that result from the interaction of demand and supply, such as prices, sales, rents, and occupancy, are also important to investigate. The purpose of investigating economic variables is to determine their effect on the supply of and demand for all types of real estate products and space.

Data on market rents, vacancies, and potential customers at the western fringe of a metropolitan area may be only marginally useful in analyzing a neighborhood shopping center at the eastern fringe of the LEA. Most of the data for the western fringe of the metropolitan area will require large adjustments. Market rents, vacancies, and customer profiles may be more usable and important when analyzing a downtown mall geographically closer to the eastern fringe of the metropolitan area.

This article addresses the following three major aspects of local economic analysis for shopping center appraisals.

* Identification and discussion of the major economic and demographic variables important in analyzing a local economic area for a shopping center appraisal.

* Discussion of the nature of local economic analysis and its relation to the market in which the subject shopping center is located.

* Identification and evaluation of the sources of data for economic and demographic variables.

Major Economic and Demographic Variables

Employment

The first economic variable to be analyzed is employment. The number of available jobs, the types of jobs available, and future job prospects provide the underlying reasons why people reside in a given geographic area. Most people live where they do because they have jobs that they expect to keep in relatively close proximity to their homes. As a result, employment is a very important variable in local economic area analysis.

Employment data is reported in one of two ways. Census-based publications report employment by residence site. These sources identify whether people living in a residential area are employed or unemployed. The second way of reporting employment data is by job site, meaning that employment is reported as the number of people who are working and whose place of employment is located at a certain site within the geographic area.

As a result, data for County A can show employment by residence site equal to 10,000 and employment by job site equal to 30,000 without contradiction. The first figure means that 10,000 of the people who live in that county are employed; the second figure means that there are 30,000 jobs currently available in the companies located in the county. The 20,000 difference represents individuals who work in County A but reside elsewhere.

Both of these employment concepts are important to local economic area analysis and shopping center appraisal. Employment by residence site is directly related to population, which is an important factor in the analysis of the retail market and trade areas. Employment by job site is used to determine a second set of potential customers for a shopping center. When office buildings and industrial parks are located in the retail trade area of the shopping center, they house potential customers for some of the retail establishments in that shopping center. Individuals who work in the area are often referred to as the "daytime population" of that area.

Employment data by residence site and job site is typically reported using industrial classification categories. The Standard Industrial Classification (SIC) System was used prior to the 2000 Census, and the North American Industrial Classification System (NAICS) has been used since 2000. An example of the NAICS categories appears in Table 1.

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Population, Households, and Families

Population refers to the total number of people living within a politically established geographic area such as a metropolitan statistical area (MSA), county, zip code, or census tract It can also refer to people living in an economically defined geographic area such as the LEA, the retail market area (RMA), or the retail trade area (RTA). Population is one of the major variables that affect retail purchases in shopping centers. Population determines sales for grocery, drug, clothing, and shoe stores, and so on.

Households are a subset of population. A household is defined as one individual or a group of two or more individuals who live together, sharing a dwelling unit. These individuals may be related by blood or marriage, or they may be unrelated. If they are unrelated, the individuals are categorized as "people of the opposite sex sharing living quarters" or "people of the same sex sharing living quarters." The U.S. Census Bureau's definition of a household is "a person or group of people who jointly occupy a dwelling unit and constitute a single economic unit for the purposes of meeting housing expenses. Households may be families, two or more persons living together, or individuals."

Households and population are related by a household compositional variable known as household size, which is defined as the total number of persons, related and unrelated, residing in a dwelling or housing unit If population size in a geographic area is 21,000 and there are 7,500 households, the number of persons per household, or household size, is 2.8 (21,000/7,500).

Families are a subset of households. The U.S. Census Bureau defines a family using the following set of standards:

1. A group of people related by blood or marriage or two individuals related by marriage residing in a dwelling unit

2. Two individuals related by marriage with one or more children who are related by blood or legal adoption

3. Two people related by marriage with a dependent parent

4. A single adult living with a child who is also single

Families are usually identified by means of the first description.

Employment by Residence Site and Population

Employment by residence site is related to population through the labor force participation ratio, which is defined as the percentage of people living in a geographic area who are part of the civilian labor force. In turn, the civilian labor force is the total number of individuals who are currently employed plus those who are able and willing to work and currently seeking employment

The relationship among population, employment, unemployment, and the labor force participation ratio is illustrated in the following example. Consider a geographic area in which the civilian labor force-measured using the employment-by-residence concept-is 11,000 residents. The number of unemployed residents is 1,000. The unemployment rate is 8.3% (1,000/12,000). If the current population of the area is 21,000, the labor force participation ratio is 57.1% (12,000/21,000).

Now assume that the appraiser analyzing this geographic area was unable to obtain a current estimate of population, but was able to get an estimate of current employment by residence and the unemployment rate. An estimate of the current population can be derived by analyzing the following relationships:

CLF=E+ U

LFPR = CLF/ P

Where CLF= civilian labor force

E = employment

U= unemployment

LFPR = labor force participation rate

P = population

In the example, E = 11,000, U= 8.33% of CLF, and LFPR = 57.1%. The population can be calculated as follows:

CLF=E+ U

CLF= 11,000 + 0.0833 CLF

CLF= 11,000/0.9167= 12,000

P= 12,000/ LFPR

P= 12,000/0.571 =21,015

Employment numbers are more readily available with a higher degree of accuracy than unemployment numbers. As a result, many real estate analysts use an employment-to-population ratio instead of the labor force-participation ratio.

The Employment, Population, and Household Triangle

Employment by residence, population, and households is strongly interrelated. If employment increases, population and households also increase. If the population increases, an increase in employment opportunities is probably the underlying cause. (This may not be true in retirement areas where population can increase without a rise in employment However, if the population of retirees in an area increases sufficiently, an increase in the employment level in the community will follow after a time.) As a population changes, the number of households will also change. This relationship will not hold if the population increase is caused by births or the return of minor children to families. It also does not apply when adult children return home during periods of economic recession, a phenomenon known as "doubling up." To visualize the relationship, consider employment by residence site, population, and households as the three corners of a triangle. They are all part of a whole, and when one changes, the other two also change.

The relationship between employment by residence and population was presented in the previous section of this article. This relationship rests on the labor force participation rate.

The population (P) and the household (H) values are related by the household size variable (P/H). If a population forecast is generated for the local economy, the number of households can be determined by dividing the population forecast by the current household size. For example, if the current household size is 2.46 persons per household and is expected to remain constant in the near future, then a population of 3,010 will contain approximately 1,224 households. If the trend in household size is declining and the appraiser expects it to drop from 2.46 to 2.35 over the next five years, then the number of households forecast would be 1,281.

The employment, population, and household triangle as displayed in Table 2 is important because if a forecast for one variable is obtained, a forecast for the other two can be determined. An employment forecast can be used to generate a population forecast by means of the existing employment-to-population ratio. Also, a population forecast can be used to determine an employment forecast by means of the existing employment-to-population ratio. A population forecast can be used to determine a household forecast by means of the existing household size variable.

Income

Income is the next major economic variable to be analyzed. The income data gathered for shopping center analysis should be both per capita income and household income. For the neighborhood shopping center selling convenience goods, especially food, the per capita income goes with the population figures. For regional malls selling shopping goods like clothing and appliances, both per capita and household income are important. Clothes are bought by people, or the population, while appliances and furniture are bought by households. Household income exists as a distribution among income categories and with accompanying descriptive statistics on the mean and median household income. A household income distribution is simply a breakdown of households into various income categories. A simple example is shown in Table 3.

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By inspecting the income distribution, the analyst can tell whether the local economy is relatively prosperous, with a high percentage of households in the upper-income categories, or relatively poor, with a high percentage of households in the lowerincome categories.

Mean or Median Household Income. An unsettled question in the minds of many analysts is the use of mean or median household income. This question is often settled by the availability of income data. The significance of the issue rests on the income distribution and the statistical relationship between the mean and the median; they are equal only in a perfectly normal distribution, the bell-shaped curve. If the income distribution is skewed in either direction, the mean moves in that skewed direction away from the median. This makes the purchasing power (discussed in the next section) based on the income measures different. Observation of the mean and median household income figures reveals that the two numbers are different and that the income distribution is skewed to the higher end of the distribution because the mean is higher than the median.

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Purchasing Power

Purchasing power is a composite variable. It is an important variable in analyzing retail market areas and trade areas. Purchasing power is calculated as the number of consumers multiplied by a measure of their income. It is important to match the measure of consumers and the measure of income properly. The census data in Table 4 provides the population and per capita income for both Census Tract 503.08 and Gwinnett County. Using the data presented in Table 4, the purchasing power for Census Tract 503.08 can be calculated in the following three ways:

* Multiplying the population by the per capita income

3,536 × $49,781 = $176,025,616

* Multiplying the number of households by the median household income

1,087 × $128,137 = $139,284,919

* Multiplying the number of households by the mean household income

1,087 × $162,557.50 = $176,700,002.50

Notice that the two estimates of purchasing power based on household income are very different, while the estimate based on per capita income times population and the estimate based on mean household income times the number of households is approximately the same. The use of median household income is not appropriate when the mean household income is available. If the mean household income is not available but the income distribution categories and the households in those income categories are provided, an estimate of the mean household income can be calculated.

The mean household income based on purchasing power is greater than the value based on the median household income because of the skewness of the distribution.

When the income data is accurately specified, the purchasing power based on per capita income and that based on mean household income will be the same value.

Population and Household Composition: Income, Age, and Size

In addition to information about the population and number of households in the local economy, the analyst also seeks information on the composition of the population and households. The principal compositional variables studied are the income distribution, which has already been discussed, the age structure of the population and households, and household size. Regarding the income distribution, high-income individuals and households demand a different quality of goods and services and often different retail stores. A simple point to remember is that a market area in which every person has a per capita income of $40,000 is not the same as a market in which half the people have a per capita income of $15,000 and the other half have a per capita income of $65,000.

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Table 5 provides an example of the distribution of population in various age categories. As a descriptive variable, the age composition of the local economy is important in both retail trade area analysis and housing market analysis. If the local economy and market area have a relatively low mean and median age, this signifies that a high percentage of the population is young. If, on the other hand, the mean and median age is relatively high, an older population is indicated. A retail market area and retail trade area that contains a high percentage of young adults will want or demand a different mix of tenants in a shopping center than that demanded by an older population. The age composition of the population can affect the desired tenant mix.

The size of the household can affect the volume of retail sales. If a market or marketability study is being performed on the basis of the number of households, two market areas with 1,000 households may not be the same and will have a different impact on retail sales. The first area could have 1,000 households with 2.25 people per household, while the second market area could have 1,000 households with 3.25 people per household. The difference is 1,000 people. The size of the household can affect the size of the housing unit that the household acquires, if it has the income to do so.

Retail Sales and Vacancy

Retail vacancies are important for indicating the economic vitality of the retail sector in the LEA, the RMA, and the RTA. Vacancy levels provide an important indicator. High vacancy levels in retail space signal an excess supply of retail space given the demand for retail goods and services. High vacancy levels can also signal the existence of the three components of accrued depreciation in the stock of retail space as consumers bypass the depreciated property to shop at a newer facility.

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Retail sales data provides information about trends in total sales in the LEA, the RMA, and the RTA. Sales volume can be traced over time and used to make inferences about the future.

Spatial Distribution and Growth Patterns

An understanding of local employment, population, households, and income at a particular point in time is supplemented with information on the spatial distribution of these economic and demographic variables and the changes that have occurred over time. The distribution of population at a point in time is easily obtained from census publications and secondary data vendors such as Claritas and the Site To Do Business. For example, Census Tract A may contain 5,000 people while Census Tract B, immediately adjacent to it, contains 7,200. This is a rudimentary analysis of the spatial distribution of a population. It shows that the distribution between census tracts is not uniform. An analysis of a street map and a visual inspection can reveal that the population within a census tract or a zip code is also not uniform.

Spatial growth analysis is a time-series study of population movements. The appraiser should discover where population is growing and where it is not. Over the past five to 10 years, for example, more population movement may have occurred in the eastern and northern sections of a county or market area than in the western and southern sections. This observation is a rudimentary spatial growth analysis. More sophisticated analyses may be performed using tables and maps that show how population and other economic and demographic variables have changed over time.

Conclusion

Once all significant variables affecting the local economy have been investigated in this way, the appraiser can identify whether the subject property and its RMA and RTAs are in a portion of the local economic area that is experiencing rapid growth, slow growth, or no growth.

Data Sources for Local Economic Analysis

The appraiser's first responsibility in performing local economic analysis for a shopping center appraisal is to find data on the local economy in the shopping center's geographic area. As mentioned earlier, the local economic area in this context is the spatial area that can affect the subject property. For a neighborhood shopping center, this might be a portion of a county, a combination of zip codes or census tracts, the geographic area within a two-mile radius of the subject property, or an irregularly shaped geographic area (customized polygon) around the subject property. For a regional shopping center, the spatial area could be a county, a combination of counties, a section of a metropolitan area, or the geographic area within a certain radius-such as 10 miles-of the subject property. Keep in mind that counties are not the same size throughout the nation.

In terms of demand, the major economic variables to be analyzed for this local economy are employment, population, and income. In terms of supply, a key consideration is the amount of retail sales generated in the existing retail establishments.

Employment by Residence and Job Site Data

Employment-by-residence data for major metropolitan areas is generally easy to find, but it is more difficult to find this type of information for smaller geographic areas within urban areas. The following list describes different sources of employment information.

* The U.S. Census Bureau and the Bureau of Labor Statistics of the U.S. Department of Labor provide employment-by-residence-site data for census tracts, zip codes, counties, and metropolitan areas (or a combination of adjacent counties).

* Each state's Department of Labor has a bureau or division that collects employment-by-job-site data on a quarterly or annual basis. This data is usually free or available for a modest fee to cover the cost of processing the request or reproducing the master file.

* The Bureau of Economic Analysis (BEA) provides a data series called "Regional Economic Accounts," which provides data on employment by place of work (job site), county residence, and NAICS category (as shown in Table l).This information can be found at www.bea.doc.gov free of charge.

* The www.Economy.com Web site provides employment data by NAICS category for all U.S. states, metropolitan areas, and counties. This information requires a fee.

* Woods and Poole Economics provides employment, population, household, income distribution, and retail sales data by county for a fee at www.woodsandpoole.com.

* Claritas does not provide employment data, but it does provide population data that can be transformed into employment data using the labor force participation rate.

The relationship between the employment-byresidence figure and the employment-by-job-site figure becomes closer as the spatial area under consideration becomes larger. The numbers are usually quite different at the county level. They are still different for a metropolitan area because workers cross these boundaries freely to go to work. The numbers become close at the national level but workers still cross borders for jobs. At the time of this writing, the two values differ by three, the number of workers on the international space station.

Population and Household Data

The U.S. Census Bureau is the principal source of population and household data. Population estimates for all counties and metropolitan areas are compiled every 10 years and limited data is available for intervening years. A regional planning agency, a local chamber of commerce, or the research department of a local electric power company can provide this data. Private vendors of census data, such as the Site To Do Business and Claritas, are convenient sources of population estimates for the aggregate local economy and smaller geographic areas such as counties and census tracts. Population and household information on one-, three-, and five-mile rings around a subject property or irregularly shaped areas around a subject property can also be obtained. Private vendors of census data provide information from the last census year, estimates for the current year, and a projection or forecast for the future, which is usually five years ahead.

Income

The U.S. Census Bureau is also the principal source of population and household data. Population estimates for all counties and metropolitan areas are compiled every 10 years and limited data is available for intervening years. Income estimates are also available from private vendors of census data. These companies tailor their research to pinpoint a particular geographic area. In addition, annual income estimates for larger metropolitan areas are available from the U.S. Census Bureau and local regional planning agencies.

Other sources of income data are the Site To Do Business (STDB), Claritas, Woods and Poole Economics, and www.Economy.com.

Forecasts of Employment, Population, and Income

Employment, population, and income forecasts are more difficult to obtain than historical data for small spatial areas. Their availability depends on the existence of an economic forecasting center or bureau of economics and business research at a state or local university that has undertaken the task of forecasting demographic and economic variables. The cooperation of the professional staff at the regional planning agency is also needed to obtain forecasts.

Private vendors of data such as Claritas, Woods and Poole Economics, and Economy.com are often the best sources of employment, population, and income forecasts. Some of these vendors provide data by census tracts and specialty areas, while others provide data by counties. However, the methodology used in making the forecasts is not fully explained by these private vendors. This lack of information can be a problem for appraisers if they have to explain how the forecasts obtained from these vendors and used in the appraisal were actually generated.

Retail Sales

The Census of Retail Trade provides information on the type and number of retail establishments on a county-by-county basis. The data includes the number of establishments and the level of sales for various categories of establishments, such as department stores, food stores, furniture stores, electronics and appliance stores, auto dealers, apparel and accessory stores, eating and drinking establishments, and drugstores. This data allows the appraiser to judge the economic vitality of the subject property's county. The appraiser can determine whether sales per retail category, such as grocery stores, have changed and by what magnitude.

The U.S. Census Bureau also provides data on the impact of e-commerce on retail trade. For example, in 2001, total retail trade was $3,141,400 million while e-commerce was $34,382 million, or 1.1% of total sales. Between 2000 and 2001, total retail sales grew by 2.7%, while e-commerce sales grew by 22.1%. Considering specific product lines, total sales for "electronic shopping and mail-order houses" was $109,238 million. The e-commerce portion of these sales was $25,680 million, or 21.1%. Book and magazine total sales were $3,864 million. These sales were 3.54% of total sales for "electronic shopping and mail-order houses" ($3,864/$109,238). The e-commerce portion of book and magazine total sales was $1,748, or 45.2% ($l,748/$3,864). This same information is available for 12 specific retail categories, such as electronics and appliances, music and videos, computer hardware and software, and office equipment and supplies.

A publication by the International Council of Shopping Centers (ICSC) is also useful to the appraiser. The Monthly Mall Merchandise Index provides information about national trends in certain retail categories. The publication provides data on sales per square foot and the annual change in sales per square foot. It also provides this data on a regional basis but does not provide it on the local area level.

The Site To Do Business and Claritas are private vendors that provide data on consumer expenditure patterns.

Conclusion

This article introduced and discussed local economic area analysis. As part of the discussions, major economic and demographic factors were identified and their data sources were provided. Two later chapters of Shopping Center Appraisal and Analysis focus on other types of studies: market analysis and marketability analysis.