Threshold market analysis of Western North Carolina.

Ullmer, James ; Ha, Inhyuck

ABSTRACT

A predictive model is developed to ascertain the suitability of local retail markets to support new business locations as a prerequisite to designing effective development strategies. This paper explores the feasibility for the establishment of new retail enterprises in the twenty-three counties of Western North Carolina. This paper uses a unique method involving ZIP codes to identify fifty-two distinct markets. The tourist demand index is developed by using visitor information. The number of actual business establishments in a market is compared to the predicted number to determine where markets are saturated and where new business entry is feasible.

INTRODUCTION

Ascertaining the capability of a rural market to support a particular type of business enterprise is a prerequisite to designing effective development strategies. Several factors can contribute to the vitality of a local retail market, but the most fundamental one is the size of the market in terms of potential customers. Consequently, business owners and regional development specialists frequently inquire about the population base necessary to provide adequate revenues for a particular type of business.

The Southern Appalachian Mountains are the unique geographic feature of Western North Carolina. In terms of demand in the retail sector of the economy, they have a profound influence on the highway design and the subsequent buying patterns of people living in the mountains. Moreover, the demand for retail goods associated with tourism is also significantly influenced by this same mountain landscape.

Standard location quotient methods are inadequate tools for defining relevant market areas in such regions. Topography is as important as distance in defining the bounds of local retail markets in such areas. This study incorporates the effects of topography in defining local retail markets. The Southern Appalachians, which includes the Great Smoky Mountains National Park--the most visited national park in the National Park System--plays another role in retail demand in the region. It creates year-round tourist demand in Western North Carolina. The model developed in this research attempts to capture both the local demand and tourist demand for the various local retail markets the 23 counties of Western North Carolina in an attempt to identify potential business opportunities in retail trade in the region.

In this research, demand threshold analysis is employed as the tool for market investigation. Population thresholds are traditionally defined as the minimum population needed to support a particular type of retail establishment at an acceptable rate of return on investment (Shaffer, 1989). There have been many estimates of market thresholds in the past (See Berry and Garrison, 1958a and 1958b; Murray and Harris, 1978; Salyards and Leitner, 1981; McConnon, 1989; Schuler and Leistritz, 1990; Deller and Harris, 1993; Coon and Leistritz, 2002). This study deviates from existing research in two distinct ways. First, this paper employs a unique method of market identification involving ZIP codes, which will be explained in the following section. Secondly, a proxy variable involving souvenir shops is incorporated in our model to estimate tourist demand. Hence, the model developed in this study employs two independent variables--population to capture retail demand and a proxy variable for tourist demand.

This analysis concludes with a threshold matrix for potential business location in the region. The number of actual business establishments is compared to the number of predicted establishments in each retail activity--as determined by our threshold analysis--in the attempt to identify potential opportunities for new business location. The study will begin with a discussion of our method.

METHOD

Postal delivery routes in the 23 contiguous counties of Western North Carolina were--and are--heavily influenced by mountain geography where ridge lines and mountain valleys (coves) gave rise to highly irregular postal regions that mimicked the topography, which are evident today in ZIP code boundaries. These same geographical features are evident in the shopping patterns of retail customers in the region. For example, a consumer may shop in his or her mountain cove, even though retail establishments twenty miles closer are just over the ridge line. With this phenomenon in mind, it appeared that the most propitious method for defining local markets was to identify them as unique combinations of postal ZIP codes.

Two sources of information were tapped to delineate these individual markets. First, a series of telephone surveys concerning consumer buying patterns was conducted with Directors of the various chambers of commerce in the Western North Carolina Region. Second, the knowledge of economic development specialists of the Institute for the Economy of the Future was obtained. These development specialists were also long-time inhabitants of the region, and shared both their personal and professional expertise about the shopping patterns of the region. This process yielded 52 distinct markets that could be identified as combinations of ZIP codes. A table of the 52 markets and their identifying ZIP codes can be found in the appendix (See Appendix 1).

The data concerning the population of each ZIP code was taken out of The Sourcebook of ZIP Code Demographics, 16th edition, published in 2002. Unlike some similar studies, this research used the North American Industry Classification System (NAICS) instead of the old Standard Industrial Classification (SIC) to determine the number of permit holders by retail activity for all ZIP codes in Western North Carolina (ZIP Code Business Patterns 2002). Data on the number of business establishments--both in terms of the raw number of business concerns and total number of employees--was gathered by the three digit NAICS code for all types of activity in the retail trade sector of the economy, as well as for the retail related industries of Accommodations and Food Services & Drinking Places. Altogether, data for fourteen different industries was gathered. A description of the different industries and the number of establishments and employment in each industry in Western North Carolina is given in Table 1.

MODEL

Previous research that has dealt with the study of rural retail trade has employed models in which population was the dependent variable and the number of establishments was the independent variable (Schuler and Leistritz, 1990; Deller and Ryan, 1996; Coon and Leistritz, 2000). The dependent variable in this study is the number of business establishments in each market--which can be argued to be more consistent with economic theory--as determined by the number of business permits issued. The data for the independent variable used to capture local demand was obtained from the 2000 population figures for each market area.

Communities that rely heavily on tourist demand--as is the usual case for municipalities in western North Carolina--have been shown to have approximately twice the number of retail establishments that don't have tourism as an essential component of demand (Ryan, 1998). Consequently, another unique aspect of this study is that a second independent variable was employed in an attempt to capture tourist demand. The number of souvenir shops weighted by the number of employees was used as a proxy for tourist demand (1). Hence, the following linear equation was specified to capture the effects of both local and tourist demand on retail establishments:

B[P.sub.ij] = [[beta].sub.0] + [[beta].sub.1] [P.sub.ij] + [[beta].sub.2] [T.sub.ij]

Where:B[P.sub.ij] = number of business establishments of NAICS group j in market i, [P.sub.ij] = level of population of group j in market i, [T.sub.ij]= number of souvenir shops weighted by the number of employees of group j in market i, and [[beta].sub.0], [[beta].sub.1], and [[beta].sub.2] are parameter vectors of each group j.

INTERPRETATION OF REGRESSION RESULTS

The ordinary least square (OLS) regression results in the various markets for each particular industry are shown in Table 2. The coefficients for the parameter estimates of both independent variables are revealed as well as their accompanying t-statistics. The critical value for [t.sub.49/.05] = 2.01 and the critical value for t49/.01 = 2.88. The adjusted R-squares are disclosed as well as the F-statistic for each industry. The critical value for [F.sub.2/49/.05] = 3.08 and the critical value for [F.sub.2/49/.01] = 5.06. Finally, elasticity estimates were obtained on both the population and tourism variables.

The interpretation of the coefficient estimates on the independent variables in the model is straight forward. The parameter estimates for population ([X.sub.1]) attempts to capture the effects of local retail demand on the number of establishments for each retail activity in each market. On the basis of the population parameter estimate for the motor vehicle and parts dealer industry (NAICS code 441), 1445 (the reciprocal of the coefficient, .692) people are necessary to generate sufficient local demand for one retail establishment. All of the parameter estimates on population are statistically significant at .05. In fact, all t-values on this variable that attempts to capture local demand, except for the accommodations industry, are significant at .01.

The approximate number of employees in souvenir shops ([X.sub.2]) is a proxy variable that attempts to capture the effects of tourist demand on the number of local establishments. The parameter estimates for each industry are positive, as expected, and for the most part are statistically significant, an indication that employment in souvenir shops is a reliable proxy variable. The t-statistics on tourism are significant at .05, except for NAICS code 441, motor vehicle and parts dealers, NAICS code 444, building material, garden equipment and supplies dealers. The insignificant t-value on the tourism variable in the motor vehicle and parts industry is not surprising in that tourists would not likely buy or service their automobiles in tourist areas--except in an emergency. A similar result in the building material, garden equipment and supplies dealers classification is somewhat expected because those purchases would logically be almost entirely from local demand.

The high adjusted R-squares for the regression equations in each industry indicate that the model has good explanatory power. Moreover, the estimated F-statistics for all of the regression equations are statistically significant at .01. These two results--high R-squares and significant F-statistics--indicate that the model employed in this study is a reliable predictor of market thresholds.

Finally, elasticities were calculated for both population and tourism. The elasticity coefficients for each independent variable are shown below in Table 3. While the elasticity coefficients on each variable were inelastic, except in one instance, the location of business establishments seems much more sensitive to local demand than tourist demand, at least as measured in this study.

THRESHOLD MATRIX

The model developed in this research appears to have significant explanatory power as indicated by the high adjusted R-squares and very significant F-statistics. Therefore, a threshold matrix for potential business location was developed in the following manner. First, the regression equations for each of the fourteen different industries in the study were used to calculate the predicted number of business establishments for each of the 52 markets in the region. By comparing the predicted number of businesses for each industry in each market with the actual number of establishments for those respective industries in those respective markets, the potential for new business locations can be identified. If the predicted value is greater than the actual value, the potential for new business may exist. The threshold matrix is shown below in Table 4. A positive number indicates location potential, whereas a negative value denotes market saturation.

Some markets in the matrix indicate a high degree of saturation. This outcome is misleading in the Asheville and Hendersonville market areas. These two urban areas--especially Asheville--are regional hubs. The retail demand in those does not just consist of just local and tourist demand, but also attracts considerable regional demand.

LIMITATIONS

Although the model developed in this research appears highly reliable, when considering a revitalization program as a community leader or opening up a new business of a certain type in a market included in this study, two limitations of this study should be mentioned. First, the calculated results of possible room for additional establishments of a certain kind do not guarantee that a new business will be profitable. There might be reasons people stay in business even they are incurring economic losses--i. e., they may have some non-economic motive for being in business. Such behaviour, not uncommon in some retail endeavours, could not be captured in this study.

One more thing to consider is that according to the NAICS codes, a business might be classified in a certain type of retail activity, while also offering goods and services in another area of retail trade. For example, a full-service hotel could also have a restaurant, but may be only coded as a hotel, but not as a restaurant. A mass merchandiser such as Wal-Mart may be coded as a "general merchandise store," hiding the fact that it competes with shoe stores, music stores, electronic stores, apparel stores, or other retail categories having their own NAICS code.

No potential entrepreneur should base his or her decision on whether to open up a new establishment strictly on the basis of this study. However, it can provide initial information on whether it may be feasible or not to open a certain type of establishment in a specific market. Possibilities for further research could include a more refined study that involves the usage of four, five or six digit NAICS codes, instead of the broader three digit code employed in this study. Then one could, for instance, check for market saturation in a narrowly defined industry such as sporting goods.

CONCLUSION

The novel demand threshold analysis employed in this study provides regional economists with a new method for defining the boundaries of local markets when topography--in this case, the Southern Appalachians--has a major effect on retail shopping patterns. By using ZIP code information, local retail markets were defined, and secondary data on both the dependent and independent variables was obtained. Moreover, the innovative econometric model developed by this research--which has business permits as the dependent variable, and includes explanatory variables for both local and retail demand--seems to be well specified. Both t-statistics on the independent variables and the test for overall fit--adjusted R-squares and F-statistics--are, for the most part, highly significant.

REFERENCES

Berry, B. & Garrison, W. (1958a). A Note on Central Place Theory and the Range of a Good, Economic Geography, 34, 304-311.

Berry, B. & Garrison, W. (1958b). Recent Developments in Central Place Theory, Proceedings of the Regional Science Association, 4, 107-121.

Coon, Randal C. & Leistritz, F. Larry. (2002). Threshold Population Levels for Rural Retail Businesses in North Dakota, 2000, Agribusiness & Applied Economics Miscellaneous Report No. 191. Fargo: North Dakota State University, Department of Agribusiness and Applied Economics.

Deller, S. C. & Harris, T. R. (1993). Estimation of Minimum Market Thresholds Using Stochastic Frontier Estimators, Regional Science Perspectives, 23(1), 3-17.

Deller, Steven C. & Ryan, William F. (19960. Retail and Service Demand Thresholds for Wisconsin. Center for Community Economic Development. Staff Paper 96.1. Madison: University of Wisconsin-Extension.

McConnon, J. C., Jr. (1989). Market Potential for Retail Business in Maine, University of Maine Cooperative Center, Bulletin no. 3006, Orono, Maine.

Murray, J. & Harris, J. (1978). A Regional Economic Analysis of the Turtle Mountain Indian Reservation: Determining the Potential for Commercial Development, prepared for the Federal Reserve Bank of Minneapolis. Minneapolis, MN.

Ryan, Bill. (1998). Retail Mix in Rural Communities. Center for Community Economic Development. Staff Paper 98.25. Madison, WI: Univ. of Wisconsin-Extension. Retrieved March 1, 2006, from http://www.uwex.edu/ces/cced/lets/lets998.html.

Salyards, D. M. & Leitner, K. R. (19810. Market Threshold Estimates: A Tool for Business Consulting in Minnesota, American Journal of Small Business, 6(2), 26-32.

Schuler, Alan V. & Leistritz, Larry F. (1990). Threshold Population Levels for Rural Retail Businesses in North Dakota. Agricultural Economics, Miscellaneous Report, No. 132. Fargo: North Dakota State University, Department of Agricultural Economics.

Shaffer, R. (1989). Community Economics: Economic Structure and Change in Smaller Communities. Ames, Iowa: Iowa State University Press.

ESRI Business Information Solutions. 2002. The Sourcebook of ZIP Code Demographics, 16th Edition.

U.S. Census Bureau. 2002. ZIP Code Business Patterns 2002.

ENDNOTES

(1) The weighting was done in the following manner. Employment data was available only in ranges. The mid-point of each range was used as the weighting component. The operative ranges were: 1-4, 5-9, 10-19, 20-49.

James Ullmer, Western Carolina University Inhyuck "Steve" Ha, Western Carolina University

Table 1. Number of Establishments by Industry

 Industry Statistics

NAICS No. of
3-Digit Industry Description Firms Employment

441 Motor vehicle & parts dealers 687 7120.5
442 Furniture & home furnishings stores 335 2129.5
443 Electronics & appliance stores 153 1061.0
444 Building material, garden equipment & 459 6998.0
 supplies dealers
445 Food & beverage stores 513 12049.5
446 Health & personal care stores 302 3808.0
447 Gasoline stations 691 4290.5
448 Clothing & clothing accessories stores 543 4117.5
451 Sporting goods, hobby, book & music 266 1734.0
 stores
452 General merchandise stores 222 10583.0
453 Miscellaneous store retailers 701 3595.5
454 Non-store retailers 188 1541.0
721 Accommodations 521 9187.0
722 Food services & drinking places 1750 31484.0
 Total 7331 99699.0

Source: ZIP Code Business Patterns 2002

Table 2. Linear Regression Results of the Threshold Models

 Intercept Population ([X.sub.1])
NAICS
3-Digit Coeff. t-stat Coeff. t-stat

441 -0.630 -0.58 0.692 *** 17.25
442 -0.621 -0.69 0.291 *** 8.67
443 -0.211 -0.58 0.128 *** 9.46
444 0.680 -0.83 0.406 *** 13.35
445 0.175 0.28 0.427 *** 18.69
446 -0.497 -1.04 0.274 *** 15.51
447 0.689 0.89 0.624 *** 21.75
448 -0.254 -1.15 0.245 *** 4.03
451 -0.505 -0.66 0.174 *** 6.12
452 0.253 0.62 0.178 *** 11.73
453 0.484 0.44 0.365 *** 9.01
454 0.611 1.50 0.119 *** 7.94
721 1.938 1.30 0.124 ** 2.25
722 0.061 0.03 1.319 *** 16.34

 Souvenir Shops
 ([X.sub.2])
NAICS
3-Digit Coeff. t-stat Adj. R-sq F-value

441 0.015 0.62 0.867 167.9
442 0.065 *** 3.25 0.686 56.8
443 0.030 *** 3.77 0.728 69.2
444 0.010 0.57 0.797 101.1
445 0.063 *** 4.60 0.901 233.2
446 0.044 *** 4.19 0.865 164.2
447 0.020 1.15 0.913 270.1
448 0.276 *** 7.62 0.663 51.3
451 0.104 *** 6.15 0.676 54.2
452 0.025 *** 2.75 0.779 91.0
453 0.275 *** 11.40 0.855 151.1
454 0.031 *** 3.51 0.662 50.9
721 0.265 *** 8.09 0.633 45.0
722 0.367 *** 7.63 0.896 221.3

*** significant at 99%, ** significant at 95%

Table 3. Elasticity Estimations on Population and Tourism

 Mean of Elasticity
NAICS Business
3-Digit Industry Description Permits Population Tourism

441 Motor vehicle & parts 13.21 1.02 0.01
 dealers
442 Furniture & home 6.44 0.88 0.05
 furnishings stores
443 Electronics & appliance 2.94 0.85 0.05
 stores
444 Building material, 8.83 0.90 0.01
 garden equipment
 & supplies dealers
445 Food & beverage stores 9.87 0.85 0.03
446 Health & personal care 5.81 0.92 0.04
 stores
447 Gasoline stations 13.29 0.92 0.01
448 Clothing & clothing 10.44 0.46 0.13
 accessories stores
451 Sporting goods, hobby, 5.12 0.66 0.10
 book & music stores
452 General merchandise 4.27 0.81 0.03
 stores
453 Miscellaneous store 13.48 0.53 0.10
 retailers
454 Non-store retailers 3.62 0.64 0.04
721 Accommodation 10.02 0.24 0.13
722 Food services & 33.65 0.77 0.05
 drinking places

Mean of Population (in thousands) = 19.53, and mean of tourism = 4.73

Table 4. Threshold Matrix--Potential Number of New Establishments

Market 441 442 443 444 445 446 447

Andrews 1 -1 0 -1 -1 0 -3
Arden 3 3 1 -4 4 4 6
Asheville-East 0 3 -4 4 4 -1 1
Asheville-North 3 -2 3 0 -10 -4 0
Asheville-South 7 -6 -4 -2 -3 -5 -1
Asheville-West -17 -4 0 5 3 -2 -1
Bakersville 0 1 1 2 3 1 3
Barnardsville 1 -1 -1 2 1 0 2
Black Mountain 4 0 1 2 -7 1 2
Blowing Rock 3 -12 2 -3 3 0 0
Boone -8 -2 -2 -5 3 -1 -4
Brevard 3 7 -1 -4 4 -2 7
Bryson City -6 -2 -1 0 -6 -1 0
Burnsville 0 -2 1 -3 -1 2 -2
Cashiers -1 -6 1 -3 -4 -1 -1
Cherokee 7 10 4 5 2 8 1
Columbus 4 2 0 2 1 2 3
Elkin -5 -2 -1 6 0 2 -2
Forest City -12 2 5 5 4 2 -2
Franklin -9 -3 -2 -7 -3 1 0
Hayesville -3 -1 -1 -4 0 -1 -1
Hendersonville 0 -4 -2 -13 1 -5 7
Highlands 1 -5 -3 -6 -5 1 0
Hot Springs 1 0 0 2 1 -1 0
Jefferson 0 1 -1 -1 1 0 0
Lake Lure 0 1 0 0 -1 1 -1
Lansing 2 -1 -1 2 0 0 2
Leicester 2 2 0 1 3 2 6
Lenoir 4 -14 -2 3 -1 2 -11
Linville -1 0 0 0 -3 -2 -2
Marion -1 3 -2 5 -1 2 -5
Mars Hill 2 1 1 4 1 -1 2
Marshall 1 2 1 -1 2 0 5
Millers Creek 4 2 1 2 3 1 3
Morganton 15 11 2 10 -4 2 8
Murphy -1 -3 -3 -3 1 -3 -4
Nebo 3 1 1 4 3 2 2
Newland 9 -3 1 -3 -3 1 4
North Wilkesboro -4 7 1 -2 3 2 -1
Robbinsville -2 1 0 0 0 -2 -4
Rosman 2 1 0 3 -1 0 1
Rutherfordton 4 4 1 3 0 1 -3
Sparta 1 3 -1 1 2 1 -2
Spindale -2 0 -2 -1 0 -1 -3
Spruce Pine -3 -1 0 -3 1 -3 -3
Swannanoa -3 -2 1 4 0 -2 3
Sylva 2 6 -1 3 0 1 -2
Tryon 0 0 1 1 -3 -4 1
Waynesville 1 2 1 -2 2 -1 -4
Weaverville 7 3 2 0 0 2 5
West-Jefferson -6 0 0 -8 -1 1 -2
Wilkesboro -9 2 0 1 0 0 -9

Market 448 451 452 453 454 721 722

Andrews 0 1 -1 -1 2 -1 -3
Arden 3 2 4 -1 -3 -3 3
Asheville-East -28 -3 -4 17 3 21 -3
Asheville-North 0 -10 3 -11 -6 0 -41
Asheville-South 2 -1 -1 -6 -3 3 -22
Asheville-West -17 0 -1 -12 -3 0 11
Bakersville 2 1 1 1 -2 3 4
Barnardsville 0 0 1 2 1 -1 1
Black Mountain 8 -4 1 -1 -1 1 -7
Blowing Rock -12 4 1 -1 0 -4 7
Boone -12 -16 -2 -2 -5 -3 -18
Brevard 4 -3 2 2 2 -8 2
Bryson City 0 -2 0 2 1 -16 -3
Burnsville 1 -2 -4 -2 0 -2 5
Cashiers -2 -1 -1 -7 -3 -4 -8
Cherokee 31 6 4 -1 2 -11 31
Columbus 3 2 1 3 2 3 3
Elkin 10 8 -2 15 2 17 8
Forest City 1 1 -3 1 0 7 19
Franklin -8 1 -3 -3 -3 -6 -7
Hayesville 1 -1 -2 0 1 0 5
Hendersonville 0 -5 4 -2 0 -5 -4
Highlands -10 -1 2 -14 2 4 -3
Hot Springs 0 -2 1 1 1 0 2
Jefferson 0 1 -3 1 -1 0 -3
Lake Lure 3 1 1 0 0 -4 -8
Lansing 0 0 1 2 1 2 2
Leicester 2 1 1 2 2 3 11
Lenoir 1 1 0 4 3 7 3
Linville 1 -1 0 0 1 -1 -4
Marion 2 5 -1 1 -1 1 -6
Mars Hill 2 0 1 3 1 1 5
Marshall 2 0 0 3 -1 2 6
Millers Creek 1 1 1 4 0 3 6
Morganton -2 5 0 8 5 7 1
Murphy -12 -2 -3 0 -2 -2 -14
Nebo 2 1 1 3 1 3 8
Newland 0 -3 0 -6 0 -4 -8
North Wilkesboro 2 0 2 2 -2 7 5
Robbinsville 2 1 -2 1 1 -4 2
Rosman 2 0 0 2 1 -2 2
Rutherfordton 0 3 2 3 1 2 -2
Sparta 0 -1 0 0 0 1 -3
Spindale -1 0 -1 -2 0 0 -8
Spruce Pine -4 -3 0 -2 -2 -1 4
Swannanoa 0 0 1 3 0 2 6
Sylva 1 5 1 2 1 10 5
Tryon 2 1 1 -9 -1 -1 -11
Waynesville 18 6 -5 -5 1 -30 11
Weaverville 0 2 3 -1 -2 -2 9
West-Jefferson -2 -1 -1 1 1 2 6
Wilkesboro -1 1 1 0 1 2 -10

Estimate = Predicted Value--Actual Value

Appendix Table 1. Market Area by ZIP Codes

Market Population ZIP Codes

Andrews 8603 28901 28905
Arden 34872 28704 28732 28730 28776
Asheville-East 16089 28805
Asheville-North 32948 28804 28801 28810 28814
Asheville-South 24660 28803 28813
Asheville-West 54884 28806 28715 28816 28728
Bakersville 7403 28705
Barnardsville 2954 28709 28757
Black Mountain 17027 28711 28770 28762
Blowing Rock 3098 28605
Boone 38400 28607 28618 28679 28684
Brevard 24437 28712 28768 28766 28718
Bryson City 8900 28713 28702 28733
Burnsville 14145 28714 28740 28755
Cashiers 2827 28717 28774 28736
Cherokee 11670 28719 28789
Columbus 12090 28722 28756
Elkin 26255 28621 27020 28642 28676
Forest City 42673 28043 28018 28019 28020
Franklin 26856 28734 28763 28775 28781
Hayesville 9151 28904 28902 28909
Hendersonville 78218 28739 28791 28792 28742
 28726 28729 28731 28735
Highlands 2975 28741
Hot Springs 2359 28743
Jefferson 6783 28640 28631 28617
Lake Lure 2090 28746 28720
Lansing 3304 28643
Leicester 9360 28748
Lenoir 75880 28645 28638 28667 28630
Linville 354 28646 28616 28664 28662
Marion 29864 28752 28749 28737
Mars Hill 9515 28754
Marshall 11122 28753
Millers Creek 8428 28651 28665
Morganton 80880 28655 28612 28637 28671
Murphy 15301 28906
Nebo 7421 28761
Newland 19905 28657 28622 28604
North Wilkesboro 35325 28659 28635 28649 28669
Robbinsville 7218 28771
Rosman 4473 28772 28708 28747 28708
Rutherfordton 19214 28139 28167
Sparta 11310 28675 28663 28644 28627
Spindale 3932 28160
Spruce Pine 9129 28777 28765
Swannanoa 9459 28778
Sylva 22348 28779 28723 28788 28783
Tryon 7240 28782 28773 28750
Waynesville 54257 28785 28786 28751 28745
Weaverville 18996 28787 28701
West-Jefferson 12587 28694 28693 28626 28615
Wilkesboro 16421 28697 28606 28624 28654

Market ZIP Codes

Andrews
Arden
Asheville-East
Asheville-North 28815 28802
Asheville-South
Asheville-West
Bakersville
Barnardsville
Black Mountain
Blowing Rock
Boone 28691 28692 28698 28608
Brevard
Bryson City
Burnsville
Cashiers
Cherokee
Columbus
Elkin 28683 28668
Forest City 28024 28040 28074 28076 28114
Franklin 28744
Hayesville
Hendersonville 28758 28760 28784 28790 28710
 28727 28724 28793
Highlands
Hot Springs
Jefferson
Lake Lure
Lansing
Leicester
Lenoir 28611 28633 28661
Linville 28653 28641
Marion
Mars Hill
Marshall
Millers Creek
Morganton 28690 28666 28628 28680
Murphy
Nebo
Newland
North Wilkesboro 28670 28685
Robbinsville
Rosman
Rutherfordton
Sparta 28623 28672
Spindale
Spruce Pine
Swannanoa
Sylva 28725
Tryon
Waynesville 28721 28716 28707 28738
Weaverville
West-Jefferson 28629
Wilkesboro