How Smaller Rural Downtowns Are Faring Under the New Normal’s New Retailing


This article is a follow up to “The Changes in the Retail Industry That Are Impacting Our Downtowns” which can be found at:

It is the first of a three article series that will explore how the changes in the nation’s retailing are manifesting themselves in different types of downtowns. The changes this series of articles will look at are:

  • The emergence of the deliberate consumer
  • Reduced demand for retail spaces
  • The growing strength of e-commerce
  • The continued growth of a broadly defined “value” category of retailers
  • The decline of traditional department stores and traditional specialty retailers
  • The uneven opportunities for small merchants

There are all sorts of downtowns. I have selected a few that I know well because I either am a frequent visitor or have done research projects about them. Sometimes, both conditions are applicable.

Many of the conclusions and observations I make below should be treated as hypotheses, since I cannot claim that they are based on a rigorous, wide reaching, systematic research effort. There were obvious limits to my resources that made such a research effort impractical. Instead, I hope that the discussion below convinces readers that I have done enough number crunching, field visits, personal interviews and analytical thinking to warrant my observations and conclusions being deemed worthy of serious attention and consideration. That hope is tied to one of my main objectives for this article: to get downtowners to start thinking about how the new normal is impacting on their downtowns.

Many of the old rules of the retail game are still in effect. For example, the trade area population size and household incomes of a potential store location are still deciding factors for many retail chains. Not many GAFO retail chains (but a special few) will enter addressable market areas where the populations are relatively smallish, but many food related and neighborhood type operations certainly will. Also, the dynamics of constructive economic destruction mean that when some chains falter in a market, others will enter to try to capture that lost market share.

Downtowns in Rural Towns and Cities

Smaller Towns, With Under 10,000 Populations, May Be Better Positioned Than Many Think. As can be seen in the table below, which is based on research done by a Bill Ryan led team at UWEX’s Center for Community and Economic Development, the size of a community has an important impact on the types of retail that will take root in their downtowns. Although the study covered 300 downtowns in WI, the patterns it found probably hold true for many other states.


One of the most striking findings of this study is that the percentages of downtown sales accounted for by GAFO retailers – general merchandise, apparel, furniture and home furnishings, other miscellaneous retail stores – in all the communities with populations of 25,000 or under, ranging from 4.4% to 15.6%, are relatively low. In contrast, in downtowns in communities with populations of 25,000 to 50,000, the GAFO shops captured 44.9% of the sales.

The table’s food category contains shops that sell food for the home products and eateries and watering holes. The auto related subcategory does not contain car sales, but gasoline and car repair equipment and services. Note that:

  • In all of the communities, food related shops captured most of the downtown’s sales. These are and will be for the foreseeable future the core retail operations in the vast majority of the downtowns in these communities.
  • Together, the food related and car related establishments accounted for over 70% of all downtown sales in towns with populations under 25,000.

What this strongly suggests is that many smaller rural communities – and their downtowns or Main Streets – are not very susceptible to being impacted by the reduced demand for retail space exhibited by national and regional GAFO retail chains. Indeed, most of these communities have addressable trade areas that are smaller and less affluent than GAFO retail chains traditionally look for. The Village of Sherwood, WI, for example, has a population of about 2,800 and an addressable trade area population around 6,000 people; the Borough of Washington, NJ has a population of about 6,600 and an addressable trade area population around 46,000 people; the tourist strong Meredith, NH, has a population of about 6,200 and around 14,000 people living within a 15 minute drive shed.

The smaller downtowns certainly experienced retail vacancies as a result of the Great Recession. These were most probably caused by unemployment, wage stagnation and the emergence of deliberate consumer behaviors that translated into significantly reduced consumer expenditures. The few independent GAFO operations in the really small towns were probably hit hard, harder than the local food related and auto related establishments, because the latter were more likely to involve household needs rather than wants. On the other hand, these small towns, if sufficient household wealth is present, can attract chains in those sectors. For example, after the recession, Sherwood, WI (median household income around $93,000) attracted a 20,000 SF supermarket, the second store of an aspiring chain and Meredith attracted a Hannaford Supermarket prior to the recession.

For the small rural towns, it is doubtful that e-commerce has raked away sales revenues from their downtown merchants – their merchants are primarily in retail sectors that are least impacted by e-commerce. In contrast, e-commerce, may often have become a great benefit for local residents. For them, shopping for GAFO merchandise has always been a problem and they probably would shop for GAFO type merchandise in larger distant towns that had stronger retail, with their trip frequency dependent on the strength of their needs, the magnetism of the distant retail base and the difficulty of the trip.


Data is hard to find on the degree to which e-commerce has captured a share of this “outshopping” in the smaller towns. However, the table above provides some indication about the relative magnitudes of Internet and traditional outshopping. Based on survey data gathered by CBI in 2014, it shows how the outshopping in Laramie, WY (population of about 32,000) breaks down between respondents who take the Internet route versus those that still physically travel to out of town retailers. While the retail base in Laramie is certainly larger than those in most less populated rural communities, Laramie shares with them the trait of having comparatively weak GAFO retailing. The table shows that Internet outshopping is stronger than the traditional out of town outshopping on those items where Internet shopping is strong nationally – books and electronics, but otherwise generally lags the traditional mode of outshopping. Even so, the fact that 20%+ of Laramie’s shoppers primarily shop for clothing and shoes on the Internet is significant, since such sales are growing rapidly nationally. In these GAFO poor rural communities, the Internet is very probably not taking away sales from existing local merchants, but from the out of town merchants the outshoppers had previously patronized. This still leaves the potential for competent local merchants to appear who can claw back market share. It would be a different story, if the e-retailers were taking sales dollars from existing local merchants who were selling comparable merchandise. As I will note below, I believe that capable independent GAFO retailers can enter some smaller downtowns and win needed market share.

In these smaller communities, greater effective use of the Internet will be critical for local GAFO merchants to be more successful than they have been. It can enable them to electronically reach large numbers of potential new customers who are located in places thousands of miles beyond the boundaries of their geographically defined local trade areas. It also can facilitate quality of life retail recruitment, especially for those who are in the kinds of retail operations that are not dependent on local geographical assets or geographically defined markets.

Data on Internet use by small town rural merchants is, unsurprisingly, hard to come by. DANTH’s observations have identified three non-store e-retailers in some small rural communities, with populations around 1,500, 3,000 and 5,000 in IN and WI. They sell ceramics and clothing that could be worn by historical enactors or in theatrical productions. Their addressable markets are nationwide. One employs over 20 people, another about five. On the down side, while these operations were located on their towns’ Main Streets, they were basically closed to the public and two of them had facades that needed obvious improvements. The number of such operations in our rural small towns is hard to judge because of a lack of systematically collected data.

Most merchant online marketing in these towns probably is part of a strategy that combines it with a brick and mortar store. We have noted in several rural communities that the local EDO has advocated that local merchants use Facebook to establish their online presence because it is cheap to use and easy to create and update. How many rural small town merchants currently have e-stores is now hard to assess, but my admittedly limited observations suggest that number is relatively low. On the other hand, their use of websites and the social media for marketing, rather than sales transactions, seems to be growing. I know that everywhere I go I am seeing more websites and social media addresses listed on store business cards, internal signs, sales receipts and even on menus. This is especially the case when new shops are opened by Millennials.


Covered Bridge in Downtown Woodstock, VT

Woodstock, VT, is a town known for its parks and downtown’s attractiveness – appealing architecture, a beautiful covered bridge and an alluring town green. It only has about 3,000 residents, but there are lots of tourists visiting practically year round, a significant number of nearby second home owners and it’s the seat of a county having about 58,000 year round residents. Woodstock’s median household income is in the $47,000 range. The tourists and nearby second home owners probably have significantly higher incomes. That probably explains why its downtown has a relatively large number of retail operations.


I used photos I had taken on a recent trip to Woodstock and Google searches to identify 37 businesses in its downtown that I classified as retail. I then searched for their websites. My findings are displayed in the above table: 78% of the retailers have their own websites; a few lacked websites, but had Facebook pages, while only about 16% had neither. It was interesting to note that among those lacking an Internet presence were the supermarket and pharmacy, retail sectors where e-retailing has not been strong. In my opinion, Woodstock’s retailers are more skilled than their colleagues in most other small towns, so they are probably on the high side when it comes to having websites.

CBI’s Laramie study, though limited in geographic scope and the number of respondents (99 downtown business operators of whom 42% were retailers) does provide some additional evidence. Calculating from its reported raw data counts, I allocated all of the no website responses to the retailers and on that basis estimated that at least 57% of that downtown’s retailers use the Internet and/or a website to market their stores. I also estimated, in a similar manner, that at least 48% use social media for marketing purposes. How effective they find these e-marketing tools to be is an important, though now unanswerable question for me. Furthermore, downtown Laramie’s numbers are probably higher than other rural communities, since it has an active Main Street program as well as a SBDC and a university minutes away.

Also, DANTH’s research in Sherwood, Frederic and some other small towns in WI indicated that small town eateries and auto related operations have comparatively low rents and labor costs and need to capture a relatively small market share to be financially viable. This suggests that recession caused failures might have been easier to replace in these towns, with the return of consumer spending, than would have been the case in larger communities. Importantly, a recent analysis based Census Bureau data found that in 2015 the median household incomes in rural areas had increased by 3.4%. That was less than the 6% increase in Metropolitan Areas, but an important increase nevertheless.

Rural towns in the 5,000 to 10,000 population range also have long attracted national and regional chains providing fast food operations and convenience stores as well as a few national GAFO chains that have a saturation locational strategy, e.g., Sherwin Williams. For example, the Borough of Washington in NJ has a population around 6,500 people and Sherwin Williams, Quick Chek, Domino’s and Dunkin Donuts were located downtown before the Great Recession. Of interest, low-priced value retailer Family Dollar opened after the recession. In Gehring, NE, (popualtion 8,300)t here are a number of fast food operations that are the downtown’s strongest retail magnets as well as two dollar stores, with Family Dollar arriving post recession. The potential customer drawing power of these kinds of retailers should not be underestimated:

  • The average McDonald’s serves about 1,900 customers per day
  • A small town convenience store operator reported averaging 1,100+ transactions per day.

My field observations suggest that dollar stores, under the new normal are opening more and more stores in smaller rural communities. In no small part, this is because they only need a trade area population of about 8,000 to make their nut. Convenience stores are another category that appears to be increasing their presence in smaller communities. Of note: both the dollar and convenience stores are increasingly offering significant amounts of food related products.

gt-barrington-apparelWomen’s Apparel Shop in Downtown Great Barrington, MA

Over the 40+ years I’ve been in the downtown revitalization field, I have visited a number of small rural and suburban towns that had downtown apparel shops. Toward the end of the 1990’s I began to notice that they were disappearing and this trend seemed to strengthen greatly through the Great Recession. To my surprise, on a recent road trip through western Massachusetts and Vermont, I noted a number of downtown apparel shops in Woodstock, VT, Rutland, VT (population about 16,600) and Great Barrington, MA (population about 6,900). There appeared to be two factors shared by these three downtowns:

  • There were no traditional apparel chain shops located within about a 40-minute drive
  • All were in towns that had both strong tourist traffic and a significant number of financially comfortable second home owners living nearby. My bet is that female tourists, especially those with some spending power, would rather visit an independent and hopefully unique apparel shop than visit the stores of chains they can easily find close to their primary homes.

Suburban downtown Morristown, NJ, (population 18,500) in the post recession years, has seen a strengthening of its women’s apparel niche. These retailers are overwhelmingly independents and they are Internet savvy. The downtown’s trade area is filled with affluent households (median income about $124,000) and numerous strong malls and shopping centers that attracted practically every highly desirable retail chain Morristown might want to court, but consequently cannot. The strength of the downtown rests on its robust captive consumer markets – office workers, hotel guests, students and many financially comfortable downtown residents – who make good use of the downtown’s restaurants, bars, community theater, cinema, library, churches and town green. In other words, it has strong central social district functions.

The similarities between downtown Morristown and the rural towns mentioned above appear to me to be:

  • There were no traditional women’s apparel chains in the town
  • The downtown is filled with a lot of financially comfortable visitors and users.

Some Takeaways

This analysis suggests to me that the weakening of traditional specialty GAFO chain shops under the new normal has given capable independent small town merchants revived opportunities for growth and success. The important thing for them is that this decline occurs where their town’s outshoppers were shopping. We should not assume that all of the local market share lost by department stores and traditional retail chains will be gobbled up by e-merchants and strong value oriented retailers, though gobble up they will. A significant amount may be left on the table for independent downtown merchants to compete for and capture. Those that succeed may be the founders of our new omni-channel retail chains.

In my opinion, the Great Recession, much like the Great Depression, altered consumer behaviors, triggering the appearance of deliberate consumers across the middle class and in all geographic areas where the recession took hold. It is this aspect of our new retailing environment that has had the greatest negative impact on our smaller rural communities.

Up Next

How retailing under the new normal for our downtowns is playing out in:

  • Rural regional commercial centers
  • Suburban downtowns with lifestyle mall type retailing.

The third article in this series will cover urban downtowns and large neighborhood retail districts.


The Changes in the Retail Industry That Are Impacting Our Downtowns


N. David Milder


Since 2009, the Downtown Curmudgeon has been writing about the “new normal” that has emerged for our nation’s downtowns. One of the most important features of this new normal is the great changes occurring nationally in retailing that are having significant impacts on downtown retail growth potentials. While such growth is still possible, for many downtowns, the changing nature of our retail industry has made it much tougher to achieve.

A follow-up article will investigate how these retail changes are impacting different types of downtowns in different ways. The attention here will be on those retail industry changes.

The Reduced Demand for Retail Space

For downtowns, one of the most important changes is that retail chains, especially those in the GAFO category, are looking for far fewer new store locations than a decade ago and the new stores are significantly smaller than those constructed in the years before the Great Recession. In other words, the demand for new retail space by national and regional chains has diminished substantially. Mall construction has fallen to a trickle, while 15% to 20% of the existing malls are in danger of closing. Within the commercial real estate industry there is finally a recognition by many that, nationally, we just have too much retail space – the US has about five times more shopping space per person than any other nation. Significantly, the retail chains now are only looking at proven strong locations that offer minimal risk. In effect, this means that most downtowns – not the wealthiest and most successful — have been demoted by the chains as potential locations for their stores.

The Deliberate Consumer

One prime driver behind this reduction in retail space demand is the emergence of the “deliberate consumer.” Their more prudent spending has impacted retail sales and consequently the demand fro retail space. For over a decade prior, the earnings of middle-income households had been stagnant and the Great Recession brought about a very significant change in their consumer behaviors. These middle-income consumers bought less and became more cautious about what they purchased, giving much higher priority to needs than wants. They also paid down their credit cards and became more circumspect about using them. The strong recession even encouraged members of more affluent households to spend more prudently. In more recent years, employment prospects, incomes and the overall economy have improved and some increased consumer spending has followed. However, cautious purchasing behaviors are still seen, mostly in middle-income households, though also to a lesser extent in households in the $100,000- $250,000 income bracket.

In a large number of metro areas, the middle class is shrinking significantly in numbers, while the numbers of those with higher and lower incomes are growing. This has obvious impacts on retailers – middle market retailers are languishing and disappearing as consumer incomes polarize.


Those in the top quintile of household incomes account for a disproportionate share of our nation’s consumer expenditures. Although they account for 20% of the households, in 2015 they made 40% of the consumer expenditures for food away from home, 41% of the furniture and home furnishings expenditures, 44% of the apparel expenditures and 42% of the entertainment expenditures (see the above table). The lowest three income quintiles, composing 60% of all households, only accounted for 35% – 37% of the expenditures in those retail product categories.

Another factor threatening to suppress retail demand is the emergence of Millennials as our largest age cohort. Millennials are more interested in experiences than things and they spend much less on retail than the Baby Boomers did at a similar age.


The other major influence on the reduced demand for retail space has been the growth of e-commerce. According to the Census Bureau, in the second quarter of 2016, e-commerce only accounted for a small portion of the nation’s total retail sales, 7.5%. (It was 3.9% in the 2nd quarter of 2010.) However, as shown in the table below, when we look at sales by retail product category, e-commerce’s market share is often significantly higher. By 2013, 79.5% of the sales for music and videos, 44% of the sales of books and magazines, 32.9% of computer hardware and software sales and 28.8% of toys, hobbies and games were transacted on the Internet. The product categories least impacted by e-commerce are drug, health and beauty products, 4.7% of sales, and food and beverages, 9% of sales.

E-commerce is capturing surprising amounts of sales even in categories that have large, hard to ship products such as electronics and appliances and furniture. The research by Hortac?su and Syverson indicate that, if trends continue, these product categories will have 50% of their sales transacted online in 2017 and 2022 respectively. E-commerce also has made surprising inroads in the clothing, accessories and footwear category, where seeing, touching and trying on the products are supposedly so important, 14.9% of sales. Moreover, it is projected to capture 50% of this category’s sales by 2024, if current trends hold!

Significantly, while e-commerce has only captured about 17% of the sales for office equipment and supplies, Staples has been closing many stores and downsizing many others. Similarly, while e-commerce has only captured about 15% of the sales of clothing, accessories and footwear, many apparel firms have been hard hit. Chico’s, for example, closed 120 stores in 2015 and has been busy trying to strengthen its online presence. American Eagle is in a similar position.

That’s a lot of sales potential being taken away from downtown retailers – unless they, too, also can compete on the Internet.

Additionally, research has shown that the Internet is involved in at least 45% of all retail purchases. Many people, for example, now research the products they want and the shops that sell them before they go out shopping.

Some Green Shoots

However, the green shoots of a counter trend have surfaced – e-commerce retailers such as Amazon, Harry’s, Warby Parker, Bonobos, Blue Nile, and Birchbox have opened brick and mortar stores. Warby Parker has 32 locations and Bonobos has 20. Amazon just opened its first, whether there will be more remains to be seen. (It has taken a large space on 34th Street in Manhattan, but it’s not yet clear what it will put in there.) Also, it is de rigueur among today’s retail experts that a multi-channel approach, including both online and brick and mortar stores, is the key to success, so more brick and mortar stores opened by online retailers can be expected. The key question is how strongly will these green shoots grow?

Affordable Rents

Two positive characteristics of the new normal for our downtowns are that a lot of their revitalization efforts are successful and there is now the expectation that most of them can be vibrant and economically healthy places. However, this success has usually led to significantly higher commercial rents. Small retailers, who already have long been plagued by difficulties in raising capital and operating funds as well as burdensome municipal regulations, now often are facing unaffordable rents. In other instances, rents have been declining or holding steady. The critical factors seem to be whether retail chains are entering or leaving the downtown and whether new retail spaces are being constructed.

How Are the Retail Chains Doing?

Among the retail chains, department stores (e.g., Macy’s, Sears-Kmart, Kohl’s, JCPenny, Nordstrom, Bob-Ton) are flailing and have closed many stores. They are not only fighting the ever growing e-retailers, but also the off-price retail such as TJ Maxx. Macy’s is even trying to create in-store off-price operations. All are trying to buttress their own e-commerce operations.

Specialty stores (e.g., American eagle, Chico’s, The Gap, Talbots, Coach, Abercrombie & Fitch, Eileen Fisher, Williams Sonoma, Crate and Barrel) have also been closing locations and/or struggling to develop strategies suited to their new retail environment. They, too, are challenged by the e-retailers. The more traditional specialty chains in the women’s apparel sector have really been struggling as they compete with:

  • Foreign low-price, fast fashion operations such as Zara and H&M.
  • Off price, value –oriented and low prices chains such as TJX, Burlington Coat, Filene’s Basement, Ross, Stein Mart, DSW and the low priced dollar stores/ These have been strong post-recession performers.

Value oriented outlet malls are likewise strong performers. Indeed, some department store chains have developed their own off price/outlet chains, e.g., Nordstrom Rack and Saks Off 5th.

Supermarket anchored shopping centers are showing significant strength.


 New 25,000 SF Target in an Affluent Residential Neighborhood  About a Half Mile From Chicago’s Michigan Avenue’s Magnificent Mile

Large big box chains are plodding along, trying hard to adapt to the Internet competition and doing better where their locations’ grocery offering are robust or if they are in the home improvement sector. Walmart and Target are focusing now on using smaller formats to enter dense urban markets (see above photo), but they have rejected their use in sparsely populated locations. For example, in 2016, Walmart announced closing 154 stores, 125 of which were in the smaller Walmart Express and Neighborhood Market formats; they were disproportionately located in low-income, low-density areas.

With consumers making far fewer “trading up” purchases, mass luxury retailing has weakened, though true luxury brands are still doing better than most other retailers.

How Are the Small Independents Doing?

Systematic research on how these retailers are doing under the new normal for our downtowns is hard to find, so I must rely on my field observations and interviews:

  • Here in Kew Gardens, NY (a village in the big city) lots of the small retailers closed during and soon after the Great Recession. However, the vacancies have slowly been leased and more often than not by far stronger operators. Lots more food and beverage establishments and dollar store type operations.
  • In many parts of Manhattan, rising rents and the great recession have forced small GAFO merchants to completely disappear. Similar patterns were observed in many other large cities we visited.
  • On the other hand, in many smaller downtowns, often those with few or no GAFO retail chains, a surprising number of independent apparel shops are to be found (e.g., Morristown, NJ, Woodstock, VT, Great Barrington, MA). Some of them survived the Great Recession (a few quite surprisingly), while others opened more recently. Before and during the Great Recession, downtown independent apparel merchants appeared to be a dying breed. Why the apparent turn around? One hypothesis: not only the absence of apparel chains in their downtowns, but also their absence and/or weakness in their larger trade areas? To some degree, the growing weakness of national chains’ brick and mortar stores appears to be giving small independent retailers the opportunity to capture more customer expenditures.
  • I am seeing more and more millennial small merchants who from the get-go are adept at using websites and the social media. The problem of Internet inept independent merchants seems to be naturally “aging out.”


Retail Leakage Analyses Should Be Treated With Great Caution by Analysts and End Users: 2 Some Serious Data Issues

By N. David Milder


This is the second of a two-part article on the need to be very cautious when doing or using a leakage analysis. Part 1 focused on the analytical issues associated with this economic development research tool. It can be found at

This article will focus on the problematical data too often used in leakage analyses.

GIGO is an acronym well-worth knowing. It stands for Garbage In, Garbage Out. Too many leakage studies are very problematic because the data they use are very questionable. This is often not the result of data suppliers being slovenly in their data gathering or analysis – although sometimes, it is – but because getting information about small and medium sized firms’ sales, workforces and finances can be very difficult. To counter this problem, the leakage market data firms sometimes will try to use business census and related data to estimate the sales of the firms in the major NAICS codes that have been identified in a study area. Their resulting estimates are not based on primary research and have a number of degrees of separation from the original firm level data that is usually unknown to the data purchasers. Different research firms will have different methodologies for making these estimates. Which, if any of them, produce accurate estimates about NAICS retail categories’ sales is a key question. Unfortunately, I have not seen any studies that validate the accuracy of these estimates. This suggests caution and prudence in their use.

A Good Starting Point for the Discussion


The table above is a good starting point for this discussion. It shows the results of the leakage analyses generated by Nielsen and Esri for a city in the West that has a population of about 30,000. These two respected private firms provide a lot of the demographic and business data used in the economic development field and the table is representative of the leakage analysis data they produce. They certainly are not the only ones that generate the data used in a leakage analysis, but because of their affordable prices and easy online access, they are used by many EDOs (mostly by their consultants) looking for such information.

The table’s first column shows the NAICS retail categories being analyzed. Leakage analyses subtract retail sales in the study area from consumer expenditures. If the expenditures exceed sales, there is a leakage. Conversely, if sales are larger than consumer expenditures, there is a surplus. Columns three-five provide the data on consumer expenditures, store sales and leakages/surpluses for each category. The data in columns three and four are estimates – these firms have not directly surveyed the residents of City XYZ about their expenditures or the retail stores about their sales. That’s one reason why their leakage analysis reports can cost $50 and not $25,000. The data in column five are simply the results of subtracting the data in column four from those in column three.

Given that the numbers in column five are supposed to facilitate an understanding of economic growth potentials and possibly an assessment of the market viability of substantial private investments, looking at the table probably leaves the reader disturbingly confused. While there is agreement in direction (i.e., leakage or surplus) and quantitative closeness in some categories, e.g., gasoline stations, furniture and home furnishings, on many others the size of the leakage/surplus differs substantially, e.g., food and beverage stores, or conflicts on direction, e.g., health and personal care stores and eating & drinking places.

Looking at the estimated data in columns three and four, there is an even more divergent pattern. The estimates of both consumer spending and stores sales are in most cases substantially different. These two market data-producing firms are obviously using very different methodologies and/or formulas to generate these estimates.

How, then, do we know which, if any, of these leakage estimates can be trusted?

An Example of the Explanations They Provide

If you call these firms or look online for explanations of how they generate their estimates, they may use quite a few words, but seem nonetheless not very transparent. Here, for example, is how ESRI in “2014 Methodology Statement: Esri Data—Retail MarketPlace,” a 2014 white paper,” described how they estimated retail sales:

“Estimates of retail sales begin with the benchmark, the 2002 and 2007 CRT (Census of Retail Trade) from the US Census Bureau. Trends from the economic censuses are used to update the base along with Esri’s extensive portfolio of demographic and business databases. These include commercial and government sources such as the Dun & Bradstreet business database and economic statistics from the Bureau of Labor Statistics. Supply estimates also incorporate data from the Census Bureau’s Non-employer Statistics (NES) division. Smaller establishments without payrolls, such as self-employed individuals and unincorporated businesses, account for a small portion of overall sales. However, these businesses represent more than half of all retailers in the United States. Their inclusion completes the report of industry sales.

Esri’s model captures economic change by first differentiating employer and non-employer sales growth. Multivariate statistical techniques are used to model data that is subject to disclosure issues in CRT and NES. Sales estimates have been recalibrated against the Monthly Retail Trade (MRT) survey and independent sources to address the disparities that can exist between independent input data sources. This methodological improvement yields a more precise estimate of the retail sales attributable only to domestic households.

Esri licenses Dun & Bradstreet’s business database, which also provides sales for the retail market. Although Esri utilizes this database in the derivation of small-area estimates, the methods differ. Esri estimates retail sales only to households for implementation within the Retail MarketPlace data. Additionally, Esri relies heavily on data from both the CRT and MRT to improve estimation. Furthermore, the Dun & Bradstreet business data file is reviewed and cleaned to improve data content. All estimates of market supply are in nominal terms and are derived from receipts (net of sales taxes, refunds, and returns) of businesses that are primarily engaged in the retailing of merchandise. Excise taxes paid by the retailer or the remuneration of services are also included, for example, installation and delivery charges that are incidental to the transaction.”

This explanation shows that Esri s methodology is a rather complicated, multi-step process, which involves quite a few data sources and data manipulations. Part of those complications is the result if its recognition that the estimates based on one data source or another are in need of improvement.

Dun & Bradstreet (D&B) and MRT data are needed because:

  • The business census data are aggregated to such geographic units as states and counties that are not congruent with the non-legally defined boundaries of many study areas. Addressed based D&B data can be aggregated to the needed geographic boundaries.
  • The business census is done only every seven years and the actual data are only released two to three years after it is taken. Depending when they are used by outside analysts, the business census data can be three to six years old. The census data cannot be depended on to detail what businesses are there and what they are like. The D&B data are supposedly more recent and able to establish who is there now. ESRI’s methodology recognizes the importance of knowing how many firms by size there currently are in each retail NAICS code in a leakage analysis study area. The NES data are used to address the presence of small firms.

On a key question, whether and how Esri uses D&B’s data on firm sales, Esri’s white paper is unclear.

My take aways from Esri’s explanation of how estimates of retail sales are made are that it

  • Reveals the sources of some of the data they use, e.g., the Census Bureau, BLS, Dun & Bradstreet. However, they do not detail the data obtained for each retail firm (e.g., name, NAICS code, sales, number of employees) or, very importantly, how these data may have been statistically manipulated to produce their estimates. This is understandable to the degree that their methodology provides a competitive advantage over their rivals and therefore needs to be kept confidential. Nevertheless, it makes assessing their estimates more difficult.
  • Additionally, and perhaps even more importantly, Esri provides no indication that they have validated their estimates, i.e., shown that their estimates are accurately measuring what they are supposed to be measuring, or that they even made an attempt to do so. How, then, can we know that their methodology is sound? How can we have confidence in them?
  • Is a complicated methodology with many steps. I was taught that every time basic data are manipulated or added to there is a potential for a new error factor to be brought in. Esri has added steps to its methodology to overcome some known errors. Whether they are successful or simply add new errors is unknown.

I have not found any better explanation from Nielsen or its corporate predecessor Claritas about how they estimate retail sales.

A Closer Look at Basic Supply Side Information: Retailers –Their Number, NAICS Codes, Sizes and Sales

Accurate basic firm level data are absolutely essential to an accurate and useful to a traditional leakage analysis. Having directed numerous surveys of business firms, especially retailers and those prone to renting office spaces, and conducting countless personal interviews with business owners and managers in the USA and France, I am well aware of the difficulties involved in getting reliable data from business firms. This is especially true for the sales and finances of small and medium-sized operators.

As the following quote from Mike Stumpf indicates, I’m not the only one that questions the verisimilitude of the firm-level data that is often the foundation of a leakage analysis. From a recent email, here is Mike’s description of how ESRI looked at one area’s business mix:

“ESRI looked at the area’s business mix, then talked about the very large number of establishments in the financial sector. ESRI’s database lists every ATM as a business establishment! A grocery store with a deli, pharmacy, and liquor store can be listed as four different businesses, each with its own estimated sales, but then they do not pick up the restaurant in the Mexican grocery. Their sales estimates for one Mexican bakery were under $100,000, but they actually do more than that in a single month.”

Both Nielsen, ESRI and other leakage analysts obtain basic data about the stores in a study area from other market research firms, such as InfoUSA, Dun & Bradstreet or, its subsidiary, Hoovers, etc. that focus on firm-level data gathering. Nielsen and ESRI combine that info with data from the economic census and other sources to produce their estimates. In a sense they are heaping estimates upon estimates, with each potentially having unknown error factors that may combine through addition or multiplication.

Over the past three decades my company, for our client’s downtowns, has purchased establishment level data from some of the same business market researchers that have supplied firm level data to ESRI and Nielsen. On occasion, we obtained such data from local economic development agencies. Our experience with those data made a deep impression because:

  • There were usually error factors of 20% to 40% in the listings of firms. The errors, as might be expected, were most frequent among the really small firms that have a high probability of failure and turnover, but some of a downtown’s larger retailers were too often also omitted or listed after they had closed. We have seen annual retail churn rates in our clients’ downtowns (they include newcomers as well as closures) that can range between 5% and 30%. Unless these gatherers of firm-level data research businesses annually, which is very expensive, there is a high probability that their collected facts will be out of date. The year-to-year errors can really add up.
  • The Retail NAICS codes often may be wrong. Our discussion with one of the businesses that gathers firm-level data revealed that the downtown firms themselves were surveyed and provided their NAICS code(s). They were aware that errors were frequent. They attributed this to a difficulty in getting knowledgeable executives to respond to their survey. My review of the NAICS codes listed for retailers in the downtowns we bought data for frequently found numerous questionable codes.
  • I have supplied the NAICS code for my firm in the business census and to insurance companies and found out what many of the not large downtown business owners have told me over the years: selecting the right NAICS code isn’t easy, its takes too much time, and consequently, they just put down whatever is easiest to get the task done. Some reported giving the job of responding to the business census and enquires from firms like InfoUSA to their secretary or assistant or salesperson. I suspect that there are even substantial NAICS code errors in the US Business Census returns — especially from respondents who are the types of independent retailers found in our small and medium-sized downtowns and urban ethnic neighborhood commercial centers. DANTH, Inc. has conducted many surveys of businesses over the years, including the largest corporations headquartered in Manhattan, those in the healthcare industry, manufacturers in Nassau County, NY, etc. We quickly found there was a tendency for high-level executives to avoid responding to our survey. They felt it was a waste of their time.
  • Getting accurate firm-level retail sales data is really, really difficult. In addition to the surveys mentioned above, DANTH has also done numerous surveys of downtown merchants. Unsurprisingly, we found that there is strong resistance among most independent downtown merchants to providing such information. Interestingly, they sometimes reported that, off hand, they don’t know that information. The managers of the national chain stores also are loath to reveal their sales statistics. Many downtown managers – probably most – can attest to the difficulty of obtaining such information.
  • One of the business market researchers reported to me a few years ago that they have similar challenges getting information about a firm’s sales, workforce and finances. For the national chains, they use the chain’s average store’s sales. For the independent merchants they ask for the number of employees – which they said small retailers are more willing to provide – and multiply that by the average sales per employee for that NAICS code in the state. D&B is in the credit rating business, so small and medium-sized retailers who are concerned about their credit rating have an incentive to respond to D&B inquiries.

Getting basic information about small and medium sized business is a tough job, fraught with potential errors. Given that this type of information is such a basic building block in the often-complicated process of estimating the retail sales of a study area, it seems wise to treat such estimates with caution and prudence.

DANTH, Inc. has used leakage analyses generated by Claritas Esri and UWEX. In each instance, I created small tests of the data. Sometimes running a leakage analysis on a small of part of the study area enabled a comparison of the number of firms with my visual counts. If a NAICS category was identified as being very strong, I tried to verify that by visual inspection and interviews with local business operators. In a project in Sherwood, WI, a fast growing community, I thought that both the demographic data and the leakage analysis of one provider were badly out of date. Instead, I used the UWEX Gap Calculator and data from the Census Bureau’s American Community Survey. On another project, one of my little tests was to see if the leakage analysis had captured the sales of a food operation inside of a convention center. Finally, there is the “making sense test.” Sometimes I’ve looked at one of these leakage analysis reports and felt it just does not make sense. It just may be that the numbers do not jive in my mind with what I saw touring the study area and what I heard from local business people and economic development officials. Reports that fail the making sense test cause me to take a much closer look at the situation.

Sales Tax Data. In a number of states, retail sales tax information sorted by NAICS codes are gathered and published for the state, counties and sometimes the cities. These data are potential analytical gold for those researchers who find those geographic units congruent with their study area. Even when they are not, the validity of such data may provide a strong foundation for making needed extrapolations for the non-legally defined study areas.

Most of my project experience has been in states that do not provide such information. On the two occasions when I did projects in such states, I encountered some problems that impeded my use of the sales tax data. In one state, the sales tax is applied to only a portion of the merchandise in various NAICS categories and we could not determine what those percentages were. In the other state, the data for the counties were only provided at the two-digit NAICS code level, e.g., that of the entire Retail Trade category.

However, I have been told by some economic development consultants in California whom I respect that the state’s sales tax by NAICS code data enables them to produce more reliable leakage analyses.

Avoiding the Need for Retail Sales Estimates

In the 1990s and early 2000s, instead of the usual leakage analysis, DANTH, Inc. used an approach based on the consumers’ satisfaction with the retail services in their market area. We thereby solved the unreliable retail sales estimates issue by avoiding the subject. Our analysis focused solely on the consumer. Back then, response rates to our telephone surveys were far more acceptable than today’s 30% or so and we asked respondents if they were satisfied with the stores within a 15 minute drive in each of the higher level retail SIC or NAICS codes. Those that were dissatisfied we deemed as probable out-shoppers and open to being attracted by new retailers within the trade area. We also knew from the survey about these dissatisfied shoppers’ incomes, education levels, age cohorts, etc.

Using consumer expenditures potentials generated by ScanUS, Esri or Claritas we also provided estimates of the consumer expenditures for some of the products associated with each retail SIC/NAICS code. The expenditures of the “underserved” shoppers were, in a sense, up for grabs and prone to being spent out of the trade area. Back then we were not able to match completely BLS product categories to NAICS codes as can now be done. Our retail growth strategies were based on capturing these up for grabs shoppers and a sample of their retail expenditures.

This approach is sort of like a leakage analysis, though not the same. However, it enables an analyst to answer the same important retail growth questions, with far fewer questions about the validity and reliability of the data used.

DANTH has not used this approach recently because the cost of a really good telephone survey has become unaffordable and their findings less reliable, while the use of lower cost non-panel online surveys have serious sample issues.

Of course, others have used local surveys to ask respondents about whether they mostly buy various products in town, out-of-town or online. That, too, tells you about the leakage of shoppers and avoids the need for data about retail sales. Here, again, being able to do an affordable survey with a representative sample is a critical question.

My guess is that within the next 10 years online survey techniques will solve the sampling issue and affordable surveys will be possible. Then researchers will be able to answer the questions that leakage analyses are intended to address without needing to know about local retail sales or complicated methodologies for estimating them.

The Demand Side – Consumer Expenditures by NAICS Category

BLS Based Approaches. As I have written elsewhere, the best way of obtaining reliable, acceptably accurate data on how consumers spend their money is to survey them. However, such surveys require substantially more methodological skills and resources than most other kinds of surveys. For many consumer items, the relevant information must be obtained close to the points in time when the expenditures are made, lest memories of them quickly fade. For example, the Bureau of Labor Statistics (BLS) probably sets the gold standard for this type of research and it has half of its 14,000 survey respondents keep diaries of their expenditures and interviews the other half 5 times over 15 months. In contrast, I’ve seen many inept downtown and Main Street surveys that ask about shopper expenditures that are oblivious of the challenges involved, have no where near the rigor of the BLS surveys, have relatively few respondents and their results aren’t worth the paper they are printed on.

While the BLS expenditure data are solid and a good starting point, using them to inform estimates of consumer expenditures in a leakage analysis study area presents a number of barriers that must be overcome:

  1. The BLS uses categories to order its data that are product oriented, about the things people buy not the kinds of stores consumers buy in. The demand side retail sales data are ordered by the NAICS codes that are about the characteristics of aggregations of retail stores. To be useful in a leakage analysis, the BLS data have to be “crosswalked” into the NAICS categories. Such data crosswalks are enabled by a list of the products that are typically sold in the different retail NAICS codes that was produced by the Census Bureau. Using the list can take a lot of effort. Computerizing the list makes things easier, but also takes resources. Both Nielsen and Esri do these data coding crosswalks. This data crosswalking is a possible opportunity for errors to enter the analysis, but I think the probability of that happening are relatively low.
  2. The geographies used by the BLS are far, far larger than the size of any retail leakage study area. The 14,000 respondent BLS sample is still too small to provide enough respondents to be directly used even at the state level of analysis; the sample would average only about 280 respondents per state. Consequently, some methodology is needed to make estimates of consumer spending within retail leakage study areas that is based firmly on the BLS data. Both Nielsen and Esri have developed methodologies to make such estimates based on the BLS data. My research experience says these research tasks have significant opportunities for errors to enter into the analysis.
  3. Different types of people have different spending levels and patterns and different retail leakage study areas will vary in the kinds of people that live in them, e, g., by income, education, race, lifestyle group. The BLS data based estimation process needs to be able to adjust expenditure estimates by taking these population differences into consideration. Both Nielsen and Esri use their demographic and lifestyle segmentation databases, Prism and Tapestry, to make these adjustments. My research experience again says these research tasks have significant opportunities for errors to enter into the analysis.

The data presented in the table earlier in this article about City XYZ shows big differences in Esri’s and Nielsen’s estimates of consumer expenditures. This suggests that while they both are trying to address the problems just enumerated above, their methodologies vary significantly. Again we are posed with the problem of whose estimates, if any, should we trust?

The BLS data covers household expenditures. Used in a leakage analysis they inform only about the expenditures of study area residents. They do not appear to inform about the expenditures of day and overnight tourists and non-residents who work in the area. Yet, accurate estimates of retail sales should include the sales dollars from those types of customers. For many downtowns, the non-resident customer base can be extremely important. There consequently seems to be an imbalance between the customers included in the analysis on the demand and supply sides. Bill Ryan has recently upgraded UWEX’s Gap Calculator to incorporate data on the expenditures of tourists and people working downtown. Others should follow suit.


Also, of increasing importance, the BLS data does not tell us whether consumer expenditures are being made in brick and mortar shops or online. The total of online retail sales now only accounts for about 6.5% of all retail sales nationally. However, as can be seen in the above table,

  • By 2017 it is anticipated, if trends continue, that e-commerce will account for 50%+ of the sales for music, videos, books, magazines, computer hardware and software, toys and games, electronics and appliances,
  • By 2017 e-commerce is projected to account for 25%+ of the national sales of furniture, sporting goods, office equipment and supplies, clothing, accessories and footwear.

 The vast majority of online purchases will be going to retailers based geographically outside of the retail leakage study area, perhaps across the country or even on another continent, yet they are very close because they can be reached from a living room chair almost instantaneously via a smartphone, tablet or computer. The online purchases are, of course, leaked purchases. The important question then becomes do local merchants have a real chance to recapture these dollars? The strong trend now is for online sales to grow at a rapid pace. Younger Americans, especially the Millennials feel very much at ease with making online purchases and they will be growing in number. Older folks are getting more accustomed to buying online, but not like the younger age cohorts, and many of them will soon fade away. Consequently, a strong argument can be made that, for the foreseeable future, it will be much harder for local brick and mortar merchants to win back dollars spent on the internet than for dollars spent in other brick and mortar shops located beyond their trade area’s boundaries.

If this is the case, then knowing about how many consumer dollars in a study area are going online is, in effect, identifying “dead dollars” that now are well beyond the reach of local retailers who really have little hope of recapturing them – unless they, too, are able to become strong competitors on the internet. Unfortunately, this is the type of information that many downtown leaders need to know, but don’t want to hear.

None BLS Approaches. Leaders of EDOs in many small and medium sized communities may want a leakage analyses done for their downtown, but the don’t have a lot of money to pay for a consultant. Some consultants and economic development organizations have constructed leakage calculators that can be used by EDO leaders or their staffs either for free or for little cost. These builders of these calculators do not have the funds that take the BLS data and crosswalk then into NAICS codes, or to generate expenditure estimates at the study area level, or to adjust such estimates through the use of a lifestyle database. Instead, they take a simpler and less expensive approach:

  • They take the retail sales by NAICS code at the state level and adjust them for inflation
  • Then they divide those sales numbers by e the population of the state to get per capita sales per NAICS code at the state level.
  • Then they treat these per capita sales estimates as measures of per capita spending. They identify the population of the study area in question and then multiply the NAICS per capita sales estimates by that population number to estimate total consumer expenditures by NAICS codes for the study area.
  • They then identify the median household income in the trade area and adjust the consumer expenditure estimates accordingly.

In a lot of ways this is a simpler and easier way of estimating consumer expenditures in a study area than how Nielsen and Esrsi do it. I’ve used the UWEX Gap Calculator and can attest that its easy to use. It’s intended users are small and medium-sized downtowns, where local leaders can input their own data about the retailers in their trade areas.

The use of sales data at the state level to estimate consumer expenditures at a study area level may raise a few eyebrows. It did mine. Since the state retail sales data includes tourist sales, those dollars are also finding their way into estimates of resident retail expenditures and inflates them inversely to the actual flow of tourists through the study area. The theoretical explanation for it escapes my understanding. But, can Nielsen and Esri prove that their estimates are any better?

Given that there is no evidence that the gap calculators do any worse than the leakage reports the big private market data firms put out, who’s to say they are not worth considering. The price is right. Moreover, they are methodologically rather transparent: it’s easy to understand how all the estimates are being made.

Some Final Comments.

To properly do our research we need to live in the real world and in that real world we most relay on doing secondary or tertiary levels of analysis on primary data collected by someone else. Professionally, we should have been trained to understand that we must be very cautious about how we do our secondary analyses and transparent about our methodology when we present our findings to others. We need to be even more cautious about how we accept the secondary analyses done by others – especially when their methodology is not transparent and their findings have a significant number of apparent errors and conflict with the findings of other analysts.

That is why I argue that the estimates of retail sales and consumer expenditures generated by most leakage analyses should be treated with caution and used with prudence.

Retail Leakage/Gap Analyses Should Be Treated With Great Caution by Analysts and End Users: 1. Analytical Issues


N. David Milder


A retail leakage (a.k.a. gap) analysis essentially compares consumer expenditures sorted by NAICS codes in a trade area (demand) with the corresponding retail sales of trade area stores (supply). Sometimes it may be confined to just a few code categories or even just one.. A leakage exists when demand exceeds supply. The demand that is not met locally is seen as leaking out to shops beyond the trade area’s borders. These leakages are usually interpreted as identifying local untapped retail demand that, if recaptured, can not only support growth, but do so without taking market share away from existing retailers. Understandably, such leakage analysis findings have become the foundation stones of many downtown retail revitalization plans and strategies. Unfortunately, too many of these plans and strategies are on very shaky ground because the leakage analyses on which they are based utilized bad or incomplete data and/or the analytical framework through which they were given meaning and significance suffered from fatal conceptual errors. This article will focus on those conceptual errors.

The Appeal

This analytical tool appears to have a heap of usefulness since it seems able to answer two important questions: how much can a downtown’s retail grow and in which sectors. It also appears to have a lot of basic prima facie validity, after all, it does numerically compare retail supply to retail demand. It looks like sound, basic economics. Moreover, it makes retail growth appear safe; the leakage/untapped demand seems to identify retail sectors where growth can occur without hurting existing small merchants. It also can state the growth potential in tangible numerical terms such as retail sales dollars and sometimes, with additional calculations, the number of new stores and the amount of new retail space that would have market support.

Additionally, leakage analyses can be done quickly, easily and cheaply – e.g., an ESRI report costs $50, the calculator on the UWEX website is free to use (1). You can generate a basic leakage/leakage report in under an hour at either source.

An economic analysis tool that can apparently do so much and also be very affordable is very appealing, indeed. What’s not to like? Well, here are some strong cautions.

Problems in the Analytical Framework

These analytical problems are often found in leakage analyses and can sink a retail revitalization plan or strategy.

Retailers That Are Unfazed About Fighting for Market Share. An assumption often attached to a leakage analysis is that retailers will only go into market areas where they do not have to fight for market share. This is demonstrated when the lack of leakages in NAICS categories is interpreted as indicating low growth potential. However, it is a well known fact that many retailers are neither risk averse –- especially those that are the most successful and powerful – nor afraid of fighting for market share. For example, does Walmart really care that small merchants are already capturing most of the retail household expenditures in a market area it wants to enter? Some retailers may not care at all about local competitors, they just want to know how many of their type of customers live in a potential new trade area and how the other retailers in the area (hopefully including some of their frequent co-tenants) are doing. Other retailers may care about having to compete with some specific rivals – information that a leakage analysis itself can not provide. Conversely – and perversely —sometimes it is the presence of the rivals that attracts retailers because they figure that they share a common customer base and that together they will draw more shoppers from a wider geographic area. Years ago, the famous example of this was the proximity of Macy’s and Gimbel’s in New York City. Today, Home Depots and Lowe’s are often found within a few minutes drive of each other. Other examples are successful retail niches and clusters. For these retailers, a leakage analysis might help them define the geographic boundaries of their trade areas, but it is unlikely to have much a determining influence on their locational decisions.

It can be reasonably argued that the retailers many downtown leaders very often want to recruit, the so-called trophy retailers, are not afraid of competing for market share with other chains, if the potential rewards of sales revenues and profits can justify the risks. For these retailers, total consumer incomes and expenditure potentials are a much better indicator of retailer perceived potential rewards than any reports of sales leakages.

Small independent operators may be more concerned about not having to fight for market share, so a good leakage analysis may provide a useful indication about their ability to enter and thrive in a market area. However, given how many of them open their new shops without any detailed knowledge about the retail market area they have entered, there are bound to be some limitations to the value of a leakage analysis that could inform their locational decisions.

The Improbable “Immaculate Retailing” Assumption and the Need to Win Market Share: There is no research showing that retailers entering a market area will only recapture residential expenditures dollars that are now going to retailers located beyond a trade area’s borders, while leaving the local expenditures captured by any similar existing local retailers untouched. This assumption is a bit of fantastical thinking that has not been properly called out. For example, can anyone imagine a new retailer asking shoppers if they otherwise would be patronizing another downtown shop and then refusing to make the sales transaction if the customer said yes?

Expanding on that argument, consider the following scenario:

In Community X, with a downtown filled by small merchants, a leakage analysis shows that there is a $5 million leakage of residential expenditures for apparel. What the leakage analysis does not show is that most of the leakage is being captured by strong and attractive apparel specialty shops and department stores in the powerful 1.5 million SF Shopping Mall Beta located about 2 miles beyond the borders of Community X’s trade area.

The mall’s apparel merchants, with their very large and frequently updated selections will have a great deal of retail gravity and consequent customer drawing power. That means a very substantial portion of their trade area likely overlaps with that of any Community X apparel merchants, even though the mall’s shops are located beyond Community X’s trade area’s borders. Viewed from the perspective of Community X’s trade area, it might be argued that greater proximity could give a new apparel merchant some competitive advantage, but viewed from the perspective of the mall’s far greater strength and far larger trade area, proximity does not offer Community X’s merchants that much potential advantage. Facing that level of external competition, is it likely that a new apparel merchant in Community X could resist knowingly trying to take market share from other nearby apparel merchants?

The bottom lines here are that:

  • Recapturing leaked shopping dollars often means that small downtown merchants must compete with very powerful retailers who are located beyond THEIR trade area’s borders while their supposed competitive advantages are related to their proximity to the targeted shoppers
  • Unstated and unresearched, of course, is the fact that if these small merchants do not provide a sufficient selection of merchandise and at the right price/quality value ratios, they won’t be able to even win back the expenditures of shoppers who live next door or in apartments over their shops
  • There is no free lunch no matter what many leakage analysts may assume. Any merchant – be they large or small – must compete and win market share. There is no way around that fact! The merchants they are competing with may be located in and/or beyond their trade area
  • Of course, competition may be less fierce in some market areas than others. However, a strong argument can be made that the level of competitiveness is determined more by the capabilities of the retailers in and near the market area, than by how many sales dollars are being leaked out of the market area
  • Retail revitalization plans and strategies need to specify a lot more about the needed characteristics of new retailers that could compete successfully. Just identifying and quntifying the presence of a leakage is not enough.

The Existence of a Retail Leakage Does Not Mean All or Even Most of It Can Be Recaptured. A review of numerous reports containing downtown leakage analyses revealed that far too many of them assumed that most of the identified leakages can be recaptured. This is a basic and fatal mistake for any plan or strategy that builds upon leakage analysis findings.

Besides indicating that retail demand exceeds supply within a trade area for a specified type of goods or service, finding a leakage is usually interpreted by analysts as an indication of local unmet demand that is:

  • Legitimate to “recapture” because it is somehow owned by the trade area in which its consumers reside and recapturing these retail sales dollars will not hurt local merchants
  • Easier to reclaim, because these dollars are now being captured by more distant, less conveniently located merchants whose shops are outside the trade area than the sales revenues of local merchants.

However, the advantages of proximity do not mean that the leakage dollars are just lying there to be easily scooped up by some observant businessperson. Also, because these dollars are being captured by shops beyond the downtown’s trade area does not mean that those more distant merchants lack strong competitive advantages of their own. For example, they may be very competent operations offering larger merchandise selections, better merchandise quality, better overall values, better customer service, a more attractive shopping environment, etc. These assets may more than outweigh the proximity advantages of downtown merchants when competing for local residential retail expenditures.

Furthermore, the competency and ability of new small downtown merchants to compete are certainly not assured, as indicated by their high failure rates nationally. Whether the new merchants like it or not or whether the leakage analysts like it or not, new downtown merchants will have to take away the sales revenues from someone else, no matter the size of any leakage. How difficult it will be depends on their abilities, the strength of their proximity and convenience advantages and the strengths and weaknesses of their competitors both inside and outside of their trade area.

Consequently, it is an enormous error to assume that any identified retail sales leakage can be mostly recaptured.

The Critically Important Assessment of How Much of a Retail Leakage Can Be Recaptured Requires Well Reasoned Judgments, Not the Magical Use of a Complex Equation or Simple Arithmetic. Assessing how much of a leakage it is reasonable to expect downtown merchants can recapture is the most important part of any leakage/leakage analysis. It can only be properly done if the leakage findings are viewed from a larger analytical perspective that looks at such things as:

  • A SWOT analysis of the downtown as a retail location, with special attention paid to the magnitude and nature of the strengths of the downtown’s retail competition that may be located beyond the boundaries of the downtown’s trade area.
  • An analysis of the types of retailers who can utilize the downtown’s locational assets to successfully compete with rival retail centers.
  • The capabilities of local merchants to compete.

There is no formula to apply or simple arithmetic manipulations of the leakage data that can inform this assessment. Rather it is a judgment call. Analysts, alone or as a team, may add some quantification to this process by using their Bayesian probability estimates to assign capture rates to the leakage estimates. This assignment hopefully is based on their analysis of the strengths and weakness of the competition, the downtown as a retail location and the abilities of the retailers most likely to be recruited.

Retail Surpluses Are Often a Much Better Indicator of Viable Potential Growth Opportunities Than Leakages/Leakages. A leakage analysis can not only identify leakages, but also the surpluses that exist when a downtown’s retailers are strong and importing sales dollars from customers living beyond the boundaries of the studied trade area. In far too many studies, however, the discovered surpluses are treated as realized growth opportunities that signal no ability to generate any additional growth. They are then largely ignored. The analysts, quite obliviously, only see leakages as indicators of potential growth.

A niche analysis has its own way of viewing leakage/leakage data. It first looks for strong exiting niches that can be grown and leveraged. A NAICS category that has a surplus is probably such a raptor niche or part of one. Its surplus shows it is strong and successful. It’s real trade area is probably much larger than the one that was used to reveal its “surplus sales.” In many instances, such a raptor niche is bringing in strong customer traffic flows that, through effective cross marketing, can facilitate substantial growth among other downtown niches.

A niche analyst looks at leakages in a more ambivalent manner. On one hand, they represent potentials for future growth, but on the other, they also demonstrate weaknesses. Total leakages best demonstrate this point. The fact that there is no retailer in a particular retail sector certainly suggests an opportunity framed by obvious unmet local demand and the lack of obvious local competition. A niche analysis would call the situation a potential niche. However, a good analyst would also ask why no retailer has successfully entered this market space? Very often, there are strong factors that have impeded firms from doing so, such as:

  • The lack of appropriate spaces, a problem that also may be very expensive to remedy
  • Rents that are too high for that particular kind of retail activity
  • The lack of a local workforce having required special skills
  • While the local demand my be unmet, it is still too small to support a successful shop in that retail category
  • The competition sitting outside of the trade area is very, very strong and has a long reach.

With the understanding that it is far easier to organize, grow and leverage an existing strength than to turn a growth potential into a reality, a niche analyst will prefer finding surpluses (strong existing niches) to leakages (potential niches).