How To Find Ethnic Makeup In Your City

Does your trade expanse population include more homeowners or renters? Infant Boomers, Gen-Xers, or Millenials? Which ethnic groups are represented in the population? If they are habitation owners, how likely are they to purchase home furnishings, renovate their homes, or spend leisure-time landscaping their yards?

To analyze marketplace opportunities for your downtown, you need to examine data and ask questions like the above about residents of your trade area(southward). This data must include the absolute number of residents, as well every bit their household characteristics. Current and projected demographic, lifestyle and consumer spending data about your trade area from secondary sources can provide this information.

Demographic and lifestyle information about your merchandise area can requite you a starting point for an in-depth assay of specific business organization and existent manor development opportunities. This data besides can help the broader customs understand how information technology is changing.

Demographic Information
Lifestyle Data
Spending Potential Data
Using GIS to Clarify Your Market

Demographic Data

Information technology is well understood that production preferences vary across dissimilar groups of consumers. These preferences relate directly to consumer demographic characteristics, such as household type, income, age, and ethnicity. For this reason, information technology is not just the amount of demand that truly matters to a local economy. The mix of consumers likewise has a major impact on a local economy, and therefore must be thoroughly examined in all trade area analyses. Unfortunately, far as well much information oft is included in these studies. An enormous amount of information is readily available from a variety of private and public sources, leaving the reader with tables and tables of demographic information overload.

Relevant Data Categories

Interpretation of demographic data is often missing in market analysis. What does the information say about how the market is changing and how consumers spend their fourth dimension and money? Specifically, what does the data suggest about new business or real estate opportunities downtown? The post-obit provides a starting indicate in your understanding and estimation of demographic data in relative to retail spending.*

Population and households data allow you to quantify the current market size and extrapolate future growth. Population is defined as all persons living in a geographic area. Households consist of one or more persons who live together in the same housing unit—regardless of their human relationship to each other (this includes all occupied housing units Households tin can exist categorized past size, composition, or their phase in the family unit life cycle. Typically, demand is generated by the private or the household as a group. And so, the entire family unit influences a household buy, such every bit a computer or TV. Private purchases, on the other hand, are personal to the consumer. Predictable household or population growth may signal futurity opportunities for a retailer. An analysis of household and/or overall population growth provides the "large motion-picture show" of potential retail demand in a community. However, further analysis is necessary to identify retail preferences within a community.
Household income data is a good indicator of residents' spending ability. Household income positively correlates with retail expenditures in many product categories. When evaluating a market, retailers wait at the median or average household income in a trade expanse and will seek a minimum number of households within a certain income range before establishing a business or setting prices. Another common exercise is to analyze the distribution of household incomes. Disbelieve stores may avoid extremely loftier or low-income areas. Some specialty way stores target incomes above $100,000. A few store categories, such every bit machine parts, are more commonly constitute in areas with lower household incomes. Run across the following box for more details on household income. Remember, though, that using income equally the sole measure of a marketplace'south buying preferences tin can exist deceptive. You need to consider all categories of demographic data when analyzing a market.
Highly flush households with annual incomes higher up $100,000 contain i of the fastest growing segments of the U.S. population, increasing past more than 2 percent each year since 2000. They are strong consumers, as well equally physically active and civic-minded. Gearing a retail mix toward this segment may require a focus in luxury goods and services. High-end department and technology stores, as well as cultural civilities similar museums and concert halls, are frequented by the almost flush households inside a population.Heart-income households with annual incomes betwixt $20,000 and $50,000, are much more mindful of their expenses than highly affluent families. These households tend to exist more frugal and selective in their ownership behavior, shopping at discount outlets for groceries and other appurtenances rather than high-end stores. Big box stores are particularly popular for middle and low-income households.Low-income households with annual incomes below $20,000 are in a different situation than affluent and eye-income households. Families at this income level are living in poverty and thus spend very little on appurtenances and services across the board.
Age is an important cistron to consider because personal expenditures alter as individuals grow older. We've already noted that purchases change throughout a family unit's life cycle, and that holds true for individuals, besides. I important phase of life, and a category that's growing as baby boomers age, is the 65 and older group. Realizing and catering to the needs of an crumbling population tin can be benign to any retailer. Consumer spending on drug stores and assisted care services flourish in areas with a large elderly population. Accordingly, drug stores often practice well in communities with a larger number of people over the age of 65. In general, though, older populations tend to spend less on the majority of goods and services. Studies indicate that nightlife and entertainment spending (restaurants, confined, and theaters) by people over 65 is roughly half that spent by those under 65. Older adults also spend considerably less on clothes than other age groups. On the other end of the spectrum, toy stores, twenty-four hour period intendance centers, and stores with infant care items do well in areas with many children and infants. Clothing stores and fast nutrient establishments also thrive in areas with a loftier boyish population. Some entertainment and recreational venues, such as movie theatres and golf courses, serve a broad section of the population. Others, such as water parks or arcades, target certain historic period groups.
Didactics levels likewise effigy into the socio-economic condition of an surface area. Because income increases with advancing educational attainment, many retailers focus on income level rather than education. At that place are some exceptions to this, though. Bookstores are oft cited by developers equally a business whose success is direct correlated with the number of higher educated individuals in the trade expanse. Similarly, calculator and software stores are often located in areas with high levels of educational activity. In full general, areas with high levels of educational attainment tend to prefer "the finer things." That is, they may have a preference for shopping at smaller, non-chain specialty retail stores located in their downtowns. They too tend to visit cultural establishments like museums and theaters at a frequency over three times greater than those without a college degree. On the other hand, less-educated populations generally have lower incomes and thus tend to prefer shopping at discount retail outlets and chain stores. This group likewise spends more coin on automobile maintenance and tobacco products than those with a college degree.
Occupational concentrations of white and blue-collar workers are used equally another gauge of a market place's gustation preferences. Specialty clothes stores thrive in centre to upper income areas and those with above-boilerplate white-collar employment levels. 2nd-hand article of clothing stores and used car dealerships are successful in areas with a higher concentration of blue-collar workers. Role supply stores and large music and video stores are especially sensitive to the occupational profile. These retailers target growth areas with a majority of white-collar workers.
Ethnicity is some other factor retailers consider when choosing merchandise to carry. Information prove that ethnicity affects spending habits as much as other demographic characteristics, such equally income and age. Tastes in goods and services vary between ethnic groups, and local retailers are wise to cater to the different needs of ethnic groups in their trade area. Ethnicity influences retailers' product mix, including the lines of clothing they acquit, and their advertizement.Retailers that apply segmentation based on race and indigenous groups must make certain their efforts effectively measure out the true preferences and behaviors of the customs.
Housing ownership and charge per unit of housing turnover is an important factor for numerous retailers to consider. Abode ownership directly correlates with expenditures for habitation furnishings and habitation equipment. Furniture, appliances, hardware, paint/wallpaper, floor covering, garden centers and other home improvement products all prosper in active housing markets.

*Adjusted from:
White, J.R., & Grayness, 1000.D. (Eds.). (1996). Shopping centers and other retail properties: Investment, evolution, financing and management. Hoboken, NJ: John Wiley & Sons.
Waldfogel, J. (2010). Who benefits whom in the neighborhood? Demographics and retail product geography. In Glaeser, E.L. (Ed.), Agglomeration economics (pp.181-209). Cambridge, MA: National Bureau of Economic Inquiry..

Comparing the Primary Merchandise Area with Other Areas

Demographic statistics are specially useful if they are presented in comparison with other places. To run across how your trade expanse differs from other places, it is useful to provide ii comparison sets of data: comparable communities and the state or United States every bit a whole.

Comparing your trade area with other communities and the country allows demographic baselines to be established. These baselines will help decide whether your trade area has low, median, or high values in each demographic category. For instance, subsequently examining demographics for your trade area, it may announced that there is a high proportion of white-collar workers. However, this ascertainment cannot be verified until yous know what constitutes an average number of white-neckband workers.

Comparable communities can include five or six cities of similar size in the aforementioned region or land. The cities chosen should reflect similar distances from metropolitan statistical areas (MSAs) of the region. Depending on the geographic size of your primary trade area, you will demand to select similar-sized trade areas.

In improver to comparable communities, adding country or U.S. statistics will provide a broader benchmark for comparison your customs. State and U.Due south. data will include a blend of urban and rural areas. Appropriately, information technology will not be limited to "similar places." Notwithstanding, differences between the trade area and the state or United States (such as per capita income) volition be used after in your analysis of retail or service business opportunities.

Demographic Data Sources

Detailed local census data is readily available free via the Internet through the U.S. Agency of Census at http://www.census.gov/. Census data can be retrieved at several geographic levels (county, city/village, census tract, zero code, etc.). The U.South. Census website includes a link to its user-friendly data-filled website chosen American FactFinder at http://factfinder2.census.gov. Employ American FactFinder to view, print, and download statistics well-nigh population, housing, industry, and business. Using FactFinder, y'all can as well find U.Southward. Census Bureau products; create reference and thematic maps; and search for specific information.

In addition to the Census Bureau, there are numerous, nationally recognized data firms that tin provide demographic estimates for a detail trade surface area. While much of these firms' data is based on the U.Due south. Census and other public sources, they add together value by providing annual updates. They also package the information in user-friendly comparative formats that make it like shooting fish in a barrel to compare i geographic expanse with another. Furthermore, you are able to tap into the noesis of skilled demographers who accept designed information products centered on particular industry needs. These firms provide a style to society reports by simply calling a price free number or downloading the data directly using their software. Prices charged by these firms accept become more and more than affordable as competition has increased.

Following is an example of a demographic comparing written report assembled for a sample community from a private data source. The "subject area merchandise area" column reflects demographics data on the trade surface area. The "Comparable communities" cavalcade reflects the demographic averages of v or six other places and the "State" column provides a 3rd level of comparison.

Sample Demographic Comparison Report

Lifestyle Data

Calculation consumer lifestyle information takes the market analysis a step further. This data recognizes that the way people live (lifestyle) influences what they purchase as much every bit where they live (geography) or their age, income, or occupation (demography). Lifestyle data enables you to include people'south interests, opinions, and activities and the effect these have on ownership beliefs in our assay.

Theory backside Lifestyle Segmentation

In his 2010 newspaper "Who Benefits Whom in the Neighborhood: Demographics and Retail Product Geography," Joel Waldfogel examines the relationship between a customs's lifestyle characteristics and its product preferences. He concludes that retail is stimulated by large concentrations of populations of similar characteristics and tastes. As a upshot, a community can develop product mixes targeted to specific high-potential customer segments.

Concentrations of lifestyle segments create need for specific products or services. This tendency to clusteris based on the premise that "birds of a featherflock together." Did you ever notice that the homes and cars in whatever item neighborhood are usually like in size and value? If you could wait inside the homes, you'd observe many of the aforementioned products. Neighbors likewise tend to participate in similar leisure, social, and cultural activities.

The quality of a segmentation system is directly related to the data that goes into them. High quality and useful systems permit you to predict consumer behavior. In a retail business organization targeting tourists, for case, the systems permit the business concern to identify products and services that appeal to this marketplace segment. The usefulness of a partition arrangement depends on how well the data incorporates lifestyle choices, media use, and purchase behavior into the basic demographic mix. This supplemental data comes from various sources, such as automobile registrations, magazine subscription lists, and consumer product-usage surveys.

Lifestyle Data Sources

Several private data firms offering lifestyle cluster systems. The firms employ data from the U.South. Census and other sources to separate neighborhoods throughout the United States into singled-out clusters. They utilize sophisticated statistical models to combine several primary and secondary data sources to create their ain unique cluster profiles. Most models first with data from U.S. Census block groups that contain 300-600 households. In rural areas, the data is more typically amassed by zilch lawmaking.

One example of a cluster system used in modern demographic analysis is ESRI's Tapestry Sectionalisation http://www.esri.com/data/esri_data/tapestry.html. The system divides every U.S. neighborhood into 1 of 65 unique market segments based on socio-economic and demographic characteristics.

Sample Lifestyle Segment Summary

As an case, qualitative data provided by ESRI for its "College Towns" designation includes:

Demographic: Most residents are between the ages of 18 and 34, and live in unmarried-person or shared households. The racial profile is typically similar to the nation every bit a whole, with three-fourths of college boondocks residents being white.
Socioeconomic: Because many students but work part-fourth dimension, the median household income ranks well-nigh the low end of ESRI'south measurements. Nigh of the employed residents work in the service industry, holding on-again, off-again campus jobs.
Consumer Behavior:Convenience dictates nutrient choices. With their busy lifestyles, residents in higher towns ofttimes eat out at fast-nutrient restaurants and pizza outlets during the week. Because many college students are new residents to a town, bedding, bath, and cooking products are popular purchases. Music and nightlife venues are extremely popular in college towns.

The Tapestry Division besides includes quantitative data, such as the purchase potential alphabetize that measures potential demand for specific products or services. The index compares the demand for each market segment with need for all U.Southward. consumers and is tabulated to correspond a value of 100 as the average demand. Values higher up 100 indicate residents are more likely to buy that product or participate in the respective activity. Conversely, values below 100 indicate residents are less likely to purchase the given product. For instance, an index of 120 means that the spending potential in the tapestry segment is twenty percent college than the nation every bit a whole. Sample data for the "College Boondocks" segment is equally follows:

Consumer Beliefs	Index to U.S.
Buy books	106
Shop at convenience stores	114
Go to confined/clubs	214
Nourish movies	135
Owns a van or minivan	27

From this data, a clear picture of the important demographic, socioeconomic, and consumer beliefs of residents in higher towns emerges. ESRI's Tapestry Segmentation organization provides similarly useful information in all 65 unique marketplace segments information technology identifies.

Other examples of sectionalization systems include Experian's Mosaic® Us at http://world wide web.experian.com/marketing-services/consumer-segmentation.html and Nielsen'south PRIZM at http://www.claritas.com/MyBestSegments/Default.jsp.

Cautions Regarding Lifestyle Sectionalization

Lifestyle sectionalization generalizes the types of customers in your trade area, which is helpful in making sense of a complex market. This simplification, nevertheless, may non fully capture the particular traits of your customer base of operations or may overlook the richness of groups in your area. Furthermore, since data are not continually updated, lifestyle segments are based on a snapshot in time. This works well if social and economic conditions remain abiding; still, significant changes may make the segment less representative of reality. Therefore, although lifestyle segments can greatly help you sympathize customers in your trade area, you should accept care not to place too much weight on segmentation systems. Instead, regard the information as a role of the mix of demographic data.

Spending Potential Data

Estimates of household spending give a sense of the size of a market in dollars. For case, secondary data are bachelor that allow you to estimate the size of the local nutrient or eating place market place, based on the number of households in your merchandise area. Private information are also available to provide refined estimates based on local demographics. It is of import to remember that these estimates mensurate the amount of spending past households residing in your trade expanse, not necessarily spending within your trade area that besides includes not-residents. Conversely, residents of your trade area may choose to spend outside your trade area.

Consumer Expenditure Survey

The 2-part Consumer Expenditure (CEX) Survey is the main data source for spending-potential estimates that covers a whole range of household spending from dining to travel expenditures. The Bureau of Labor Statistics conducts the survey of American households continuously throughout the year and has been doing so since 1980. The results of the survey provide a comprehensive snapshot of household spending and are used to revise the of import Consumer Price Index (CPI), which significantly influences both national markets and policy.

The CEX survey includes a Diary Survey of daily purchases and an Interview Survey of general purchases over time. The Diary Survey reflects tape-keeping by consumer units (individual and household shoppers) for ii consecutive week periods. This component of the CEX collects data on pocket-sized, daily purchases that could be overlooked by the quarterly Interview Survey. The Interview Survey collects expenditure data from consumers in five interviews conducted every 3 months. The information from both surveys is integrated to provide a comprehensive database on all consumer expenditures.

Individual Data Providers

Although the Consumer Expenditure Survey data alone provides valid and reliable estimates for your market, some individual information companies refine the CEX survey information for more than sophisticated estimates that may testify useful.

For instance, ESRI uses Tapestry Segmentation lifestyle segments (see above), together with CEX data to estimate household spending. A provisional probability model links spending by the consumers surveyed to all households with similar socioeconomic characteristics. The results are spending estimates based on the demographics of a particular merchandise area, which are reported together with the average spending per household and a spending- potential alphabetize. The index compares the spending of the trade area's households to the national average (encounter the following Sample Spending Potential Report).

Sample Spending Potential Report

Using GIS to Analyze Your Marketplace

Demographic assay is useful in understanding purchasing characteristics for different market place segments. While demographics tin be collected and analyzed without the use of geographic information systems, GIS often aids and enhances the analysis. Since almost downtown professionals may not be experts in GIS, you will probably desire to enlist consultants, planners and/or marketing information providers to offer technical mapping aid. Following are two examples of using GIS to assist in demographic analysis.

Using GIS to Visualize Trade Area Demographics

Demographic data for a trade area are often reported as single values for each demographic category. For example, the trade area income is reported as one value, even though income can vary beyond the trade surface area. GIS, however, tin brandish demographic values in finer detail past geographic unit (zip lawmaking, census block group, etc.). Mapping these variations may reveal valuable, visual information that tin can exist used to prove the attractiveness of a downtown location and aid in business recruitment and expansion.

Effective demographic mapping requires an understanding of some basic cartographic concepts. Perhaps the most of import concept is an understanding of the problems associated with demographic densities. By nature, downtown population density is ordinarily college than a similar-sized area on a community's fringe. Moreover, many business organisation owners would view the large concentration of customers as a competitive advantage over a not-downtown location. Nonetheless, a map showing the number of people in each geographic unit of measurement (e.thou. census block grouping) does non always bear witness this human relationship.

As a real world example of this problem, consider the following of the La Crosse, Wisconsin, expanse depicting the population by demography block grouping. Why does this type of map fail to accurately stand for the number of customers in downtown? The problem is that the sizes of demography block groups differ. While the U.S. Census Bureau tries to control the number of households in each cake grouping, it is not always possible to make the units the same size. Issues associated with geographical barriers (rivers, mountains, etc.), the nature of population distribution (sparse or concentrated), and household size tin can crusade wide variations in geographic sizes and population numbers in census block groups.

Equally a issue, demography block groups covering big geographic areas tend to dominate the viewer's eye on a map. When these large demography cake groups are located away from downtown, it appears that downtown has a pocket-sized population compared to the outlying urban areas. Additionally, there may be many more than block groups with smaller populations located in a smaller area. Notwithstanding, their modest size and small population values can get obscured on a map. Consequently, the larger number and grouping of these smaller block groups need to be addressed. GIS can tackle this problem past creating a map that accurately depicts population density.

Sample Map Showing Population by Cake Grouping

The post-obit map illustrates the same La Crosse, Wisconsin, area with an equivalent color scheme. However, this new map has undergone a transformation and now accurately depicts the surface area'due south population density. Here, the viewer sees that there is a large population clustered around the downtown and a relatively small population toward the urban fringes. The story told by the population density map would non be seen in a single population value representing the entire trade area. As a result, the new map aids in showing the potential of downtown every bit a business location and can be used every bit a valuable business recruitment tool.

Sample Map Showing Population Density

Using GIS to Analyze Company Demographics

The previous section discussed how GIS could create a useful visual representation of demographics. However, GIS is non express to producing maps and graphics. GIS tin as well be used equally an analytical tool in demographic analysis. As an example, consider the problematic nature of assembling demographics for not-local visitors. Profiling visitors is essential in the study of tourists, commuters and other market segments. While collecting demographics for the surrounding resident marketplace is a straightforward process, visitors can come from a wide area. Obtaining and analyzing demographics for every expanse that produced a visitor is unrealistic using traditional methods. In these instances, GIS can be used to profile demographics of the non-local marketplace.

Many businesses dependent on visitors, such every bit hotels, maintain client address lists. These addresses are useful in market place analysis because knowing where visitors live provides data about their neighborhood demographics. What'south more, starting with a company'south address, GIS can be used to quickly identify the census block grouping, or neighborhood, where a customer lives. Each census block group is accompanied past rich demographic data available through the U.S. Census Bureau or through private data providers. Specifically, each census cake group, or neighborhood, includes information on income, population, occupation, educational activity, age and housing. This information tin can exist entered into a GIS and used as a surrogate for demographic data on each private visitor.

Using this neighborhood demographic information every bit a surrogate is based on the premise that "birds of a feather flock together." As discussed in the section on lifestyle partition systems, people with similar demographics tend to live virtually one another. Therefore, the demographics of a neighborhood as a whole tin can be used to stand for the demographics of an individual visitor from that neighborhood. Using addresses, GIS tin can determine every neighborhood that produced a visitor and extract the demographics of these neighborhoods. The demographics extracted from each visitor neighborhood can be combined to produce a useful demographic contour of the visitor market.

The demographic profile is even more than useful when information technology is given some perspective. Similar to the comparable communities assay discussed before in this section, the company demographic contour can be used to determine what makes visitors demographically different from the general population. Instead of comparing local community demographics to those of other communities, the company demographics can be compared to the demographics of a larger region. For instance, if visitors primarily originate from a 3-state area, the visitor demographic profile can be compared to the demographics for the entire population of those three states. These demographic profiles of the community visitors and the larger region tin exist compared on a category by category basis.

The post-obit example explains the steps used in GIS analysis of visitor demographics.

Step 1. GIS is used to map the locations of visitor addresses. As an case, the post-obit shows a map of visitor origins for a sample community.

Sample Map Showing Place of Visitor Origin

Step ii. Once the visitor origins have been mapped, GIS is used to decide the neighborhoods containing each company and extract the associated neighborhood demographics. These neighborhood demographics are used as a surrogate for the demographics of an individual visitor. The post-obit is a map of one sample neighborhood showing visitor origins, as well as some of the demographics associated with the neighborhood.

Sample Map Showing Neighborhood and Demographic Attributes

Step three. GIS is used to combine all of the demographics extracted from every visitor neighborhood. Combining the neighborhoods creates a demographic profile of the visitors. To assistance in the assay, GIS also creates a demographic profile of the larger region. The regional demographic profile includes every neighborhood in the region instead of just those neighborhoods that produced visitors. These 2 profiles are then used to examine differences in visitor demographics. For instance, the table shown below compares several demographic categories. The start column contains the demographic category; the second column shows the visitor demographic profile; and the third column depicts the profile created for the larger region. In this example, GIS was able to demonstrate that visitors originated in neighborhoods that had higher incomes, a greater proportion of college-educated residents, more executive and professional employees, a college rate of home ownership, and more vehicles per household than the overall three-state region.

Sample Table Comparison Demographics of Visitor Neighborhoods Compared to Region

Demographic Category	Demographic Visitor Profile	Demographic Regional Profile
Males	48.7%	48.nine%
Females	51.three%	51.1%
Average Household Size	ii.half dozen	2.five
Median Age	36.5	36.5
Age Less Than 18	25.four%	26.seven%
Age 16 or More	77.3%	76.2%
Historic period 25 Or More than	66.6%	64.one%
Historic period 65 or More	12.6%	12.nine%
Median Household Income	$48,231	$37,561
Average Household Income	$lx,973	$40,302
Per Capita Income	$21,564	$xv,694
Didactics: High School	xx.0%	21.v%
Education: Some College	12.4%	xi.iii%
Education: Associate Degree	4.4%	4.4%
Teaching: Available's Degree	13.1%	7.6%
Instruction: Graduate Degree	seven.1%	4.one%
Occupation: Executive	15.4%	11.2%
Occupation: Professional	17.eight%	13.viii%
Occupation: Technician	3.6%	three.6%
Occupation: Sales	13.3%	11.2%
Occupation: Clerical	15.9%	16.1%
Occupation: Services	ix.6%	12.v%
Occupation: Production	9.4%	11.1%
Home Owner	75.one%	67.3%
Home Renter	24.9%	32.7%

About the Toolbox and this Section

The 2011 update of the Downtown and Business concern District Market Analysis toolbox is a result of a collaborative effort involving University of Minnesota Extension, Ohio State University Extension, and University of Wisconsin Extension. The update was supported with funding from the Due north Central Regional Middle for Rural Development.

The toolbox is based on and supportive of the economical restructuring principles of the National Trust Main Street Center. The Wisconsin Principal Street Programme (Wisconsin Department of Commerce) has been an instrumental partner in the evolution of this toolbox.

This section builds on work originally completed past Matt Kures, Pecker Pinkovitz and Pecker Ryan of University of Wisconsin Extension. This section includes new methods added by Ryan Pesch of Academy of Minnesota Extension and Glen Halsted, graduate student at the Academy of Wisconsin-Madison. This department was edited by Mary Vitcenda of the University of Minnesota Extension.

Source: https://economicdevelopment.extension.wisc.edu/articles/demographics-lifestyle-analysis/

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