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

By N. David Milder

Introduction

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

https://www.ndavidmilder.com/2016/09/retail-leakageleakage-analyses-should-be-treated-with-great-caution-by-analysts-and-end-users-analytical-issues.

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

esri-vs-sales-tax-retail-sales-estimates-xlsx

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.

ecommerce-retail-2013-and-projections-xls

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

By

N. David Milder

Introduction

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).

PART 3 – SOME THOUGHTS ABOUT STUDIES OF THE ECONOMIC IMPACTS OF DOWNTOWN ENTERTAINMENT VENUES


Parts 1 And 2 Can Be Found At:

https://www.ndavidmilder.com/2015/03/some-thoughts-about-studies-of-the-economic-impacts-of-downtown-entertainment-venues-part-1

https://www.ndavidmilder.com/2015/04/part-2-some-thoughts-about-studies-of-the-economic-impacts-of-downtown-entertainment-venues

The numbering of tables, figures and endnotes continues from Part 2.


Introduction

Whereas studies of the economic impacts of formal entertainment venues typically use input-output models to follow the effects of a venue’s expenditures and the relevant spending of its audiences, impact analyses of informal entertainment venues are far less enamored of this research strategy (18). Perhaps this is because they — being entities such as a park, public space, restaurant, bar, etc. — usually have far smaller annual expenditures than the formal venues. For example, the annual expenditures of Bryant Park in NYC and Millennium Park in Chicago are in the $11 million to $13 million range while the expenditures of the Metropolitan Museum of Art and the Art Institute of Chicago are $252 million and $211 million respectively (19). As was demonstrated in an earlier Downtown Curmudgeon article, the same pattern seems to hold true among entertainment venues in smaller communities (20).

Instead, Internet and library searches found that most impact analyses done on informal venues usually pay a lot more attention to how they affect the way nearby real estate properties are valued as measured by their rents, occupancy rates, sale prices, dollars invested to create or improve buildings and/or assessed values. Some of the studies on formal venues, in addition to their I-O analyses, look at impacts on nearby real estate (21).

Entertainment venues can be stimulants for nearby residential, office and hotel development – all interesting potentials for downtowners. Downtowners, of course, also will be interested in the people side of such developments, e.g., the numbers of residents, office workers/creatives, and hotel guests that entertainment venues can help attract and their potential downtown expenditure patterns. On these topics, the impact analyses have been thinner in number, the use of available data and the employment of statistical analyses.

Many impact studies fail to take into consideration the characteristics of the downtown environment in which an entertainment venue is inserted that can influence the range and magnitudes of its impacts.

The use of sophisticated research techniques, such as multiple regression models and, potentially, factor analysis, can provide invaluable insights and cope with the multi-causal situations that should frame most impact analyses. However, the use of far simpler data sources, such as a short survey of the judgements of local experts, can also be of immense value. When developers say that a park or PAC was an important reason why they built projects on nearby properties or a landlord reports that the redevelopment of a park led to higher occupancy and rents, these are not trivial pieces of evidence to be ignored. An analysis that combines both approaches promises even more reliable findings.

The types of questions that downtowners might ask about their entertainment venues’ impacts, especially the degree to which they are quantified, should inform what kind of research is needed. For example, it is one thing to want to know if a venue has had a meaningful positive impact on the nearby area; it is quite another to want to have precise data about the value in dollars of those impacts and how they compared to other causal factors.

Even a partially knowledgeable observer of the changes that occurred  in the areas close to Bryant Park, Millennium Park, the High Line, Division Street Plaza, Lincoln Center, Campus Martius Park, or Discovery Green probably feels ,with considerable certainty, that these venues have had significant positive impacts on their neighborhoods. Such “knowledge” maybe imprecise, but in many ways it still can be very useful for a significant number of policy and program development purposes.

Any decent student of the social sciences knows that in our fields, there is very little that can be explained by a single silver bullet causal factor. Meaningful explanations of social science phenomena are far more likely to utilize several causal factors. Too many of the advocacy motivated impact studies are too simplistic because of their total focus on their client organization or project. When a downtown PAC, museum or public space is highly lauded because it can increase the value of nearby properties, it is easy for leaders in other downtowns, who are eagerly looking for best practices to follow, to forget that most of the value is probably accounted for by other factors that they also must learn how to use and mobilize.

Because an entertainment venue helps attract nearby real estate projects that have $Xs of investment does not mean that its economic impact equals $Xs. The impacting factors on site selection and acquisition are quite distinct from those that determine how much money needs to be invested in a building on that site. Location also factors in again in the determination of a building’s rents and appraised value. Consequently, entertainment venues can have impressive impacts on the rents and values of adjacent buildings.

Downtown leaders should be very cautious about the lessons they learn from these impact studies. The “transferability” of a project concept from one downtown to another is usually far more difficult than the success stories painted by these studies might suggest. Impact studies, especially those that are advocacy oriented, are unlikely to detail what those difficulties might be.

Variation in Approaches

All of the studies of the impacts on real estate value use a comparison of some kind in order to prove their case, but they differ in how this is done. In many instances, the causal connection is established simply by a comparison of some measures of real estate value (e.g., rents, assessed values, occupancy rates, etc.) “before and after” the creation of the venue or its substantial improvement. This approach can be both simple and powerful as demonstrated in a previous article in this series by Beth Anne Macdonald, who used before and after occupancy rates and investment levels to effectively demonstrate the positive impacts of Division Street Plaza on proximate properties in downtown Somerville, NJ (22).

Hedonic impact studies use geographic distance from the entertainment venue as an indicator variable – if properties increase in value with their proximity to the venue, then the venue’s positive impact is established (23). However, the venue itself is not in any way directly measured. Its attractiveness in the eyes of landlords, homeowners and renters and their willingness to pay more to be closer to it are hypothesized to be the factors that explain why real estate values would increase with proximity to the venue, but they are not directly measured. The venue acts as a positive, given factor in such analyses, not a true variable. The related variation is in the variable measuring the properties’ distances from the venue.

Another approach, traditional in the real estate industry, is to find a “comparable” that is intended to function as a kind of experimental control. That involves finding an area similar to the one surrounding the venue being studied except that it lacks a similar entertainment venue, and then comparing how the two areas score on the measures of real estate value selected by the analysts. In the impact area the venue exists; in the comparable area there is nothing like it.

Each of these approaches has its analytical advantages and limitations. A before and after analysis has some possible causal attribution problems. For example, how can the entertainment venue demonstrate that its purported impacts are not the result of other causal factors that also produced similar outcomes elsewhere in the downtown? Furthermore,  the way that the entertainment venue exerts its positive influence may be increasingly shifting from producing new and increased real estate values to maintaining them at a desired level. Establishing a 95% or 100% occupancy rate leaves little or no potential upside, though helping to keep occupancy at that level can be quite an achievement. Also, what if the continued rise in adjacent real estate values is the result of people acquiring a greater desire for using such venues, not because of any improvements done to it? Does the causal impetus then still reside with the entertainment venue or with the external force(s) that favorably altered people’s evaluation of it?

Finding a truly comparable area can be far more difficult than it might sound. Which variables will be used to establish the comparability? What scores on the measures of those variables will be the comparability thresholds? Automatically, a questionable comparable area severely weakens the credibility of the analysis in which it is employed. Conversely, a credible comparable area can commensurately strengthen an impact study.

The hedonic studies are often difficult to do and are vested in a conjecture laden theoretical framework. On the other hand, they can be very powerful analytically.

None of the real estate value impact analyses reviewed for this article used measures of how the venue performs, such as attendance, revenues or number of events/performances as an impacting variable. None has used statistical data reduction techniques, such as factor analysis, that can take a number of indicator variables to create a way to score/scale a venue’s success and/or magnetism at different points in time.

Usually, it simply is the overall positive character of a venue that is treated as the impacting factor. It functions much like a switch variable – it’s either on or off. Positive impacts can only be produced by viable and successful venues, so they are the only ones usually looked at.

Not All Impacts Are Positive

It is important to remember that the economic impacts of entertainment venues on real estate can be either positive or negative – depending on the individual venue’s performance — and that the impacts will be stronger the closer the properties are to the impacting venue. These negative impacts can also be very visible. It all happens in the same neighborhood arena, whether the impacts are positive or negative, with the same causal paths probably being involved.

If Bryant Park, today, can be called a model informal entertainment venue, it should not be forgotten that in the 1970s and early 1980s it was the poster child for a badly failed, strategically located public space. Riddled with criminal activities, it induced pedestrians to walk on the other side of the street or to completely avoid the vicinity of the park. It also made leasing nearby commercial spaces extremely difficult and suppressed rents and property values. Its public reputation bordered on being infamous and the yellow press feasted upon it.

Liberty theater-Eliz NJ

Figure 2. A long vacant downtown movie theater

Formal entertainment venues also can fail as businesses and fall into states of physical disrepair, even overt decay. For example, failed and rotting downtown theaters and movie houses, such as the one shown in Figure 2, have not been uncommon sights over past decades. Their most devastating negative impacts were on proximate properties. Their renovation or repurposing often has been expensive and taken many years to accomplish.

In Silicon Valley, entrepreneurial failures are treated as important learning experiences and badges of honor. In the economic development field, failures are completely disdained and overwhelmingly ignored. Because impact studies have become so entwined with advocacy, economic development impact analyses seldom look at failures. One might wonder what we are not learning as a consequence.

Strong Venues Can Mobilize the Neighborhood as an Impacting Agent — for Good or Ill

The impacts of entertainment venues can have both direct and indirect paths to nearby properties. Together they can constitute what might be called the neighborhood’s impact. Prevailing rents, for example, are a good measure of this neighborhood effect.

BP Causal model real esatte impacts

For example, there are about 26 buildings surrounding Bryant Park. For the purposes of brevity and manageability, only four will be cited in the following discussion – otherwise Figure 3 would be a mess and rather undecipherable.

The resurgence of Bryant Park helped the Grace Building increase its rents and occupancy rates. Later on, the park influenced the conversion of an office building into the Bryant Park Hotel. After that the Durst Organization capitalized on the park’s image and popularity to attract Bank of America as its anchor tenant for One Bryant Park, a large new office building. The importance of the park to the new building being developed by Hines is reflected by its address: 7 Bryant Park. The park has had – and continues to have – direct and positive influences on the desirability of these building for investors, developers, landlords and tenants.

As can be seen in Figure 3, Bryant Park probably was only one among many local agents impacting directly and positively on the Hines development project. The other buildings that the park is helping also have had their own direct impacts. It is not only Bryant Park, but, in a very real sense, the whole immediate neighborhood, e.g., the park and the 25 other buildings, that are having a positive impact on the Hines project. Part of their impact will be due to their own individual attributes, the remainder will be due to causal influences they are passing along from the park and other nearby buildings.

As Figure 3 also demonstrates, Bryant Park does not have just one causal path, its direct one   A–>E, for influencing the Hines project. It also can work its influence along seven additional indirect causal paths through just the three impacting buildings displayed in Figure 3. For example: A–>C–>E. Suffice it to say, that taking the other 20+ buildings into consideration would produce many more causal paths through which the park can exert its influence, In this sense, the park is truly the neighborhood’s foundation stone. Question: as the neighborhood flourishes, do the impacts of Bryant Park shift from mainly flowing through direct paths to following mostly indirect paths?

This line of reasoning suggests that an entertainment venue’s potential level of indirect real estate impacts will often be influenced by the strengths of the adjacent buildings. Positive indirect impacts are far less likely when these buildings are unattractive or relatively small. On the other hand, once a sufficient bolus of acceptably attractive adjacent buildings has been achieved, indirect positive impacts can snowball.

The same kind of network of causal pathways very probably existed during Bryant Park’s troubled past and it helps explain why the park’s negative impacts were so pervasively felt and difficult to fix. That point is a good reminder that failing to keep important downtown entertainment venues popular and in good operating condition can have waves of unwanted repercussions.

The Proximate (Hedonic) Principle of Real Estate Value

Residential Impacts. There is a long standing consensus among real estate experts that the closer residential properties are to parks, the more likely they are to be impacted – positively or negatively – by it. These venues can create or destroy values on nearby properties. This impact carried through “nearby-ness” is called by some proximate value and hedonic value by economists. Proximate or hedonic value is determined mostly by a property’s distance from the venue combined with the quality and magnetism of the venue itself (24).

That parks can generate higher residential property values has been known and leveraged in England since the early 19th Century, as pointed out by John L. Crompton, who has written extensively about the “proximate principle.” Crompton cites this statement made in 1856 to show how the then New York City Comptroller believed that Central Park would be financially supported through the increased real estate values it would create:

“…(T)he increase in taxes by reason of the enhancement of values attributable to the park would afford more than sufficient means for the interest incurred for its purchase and improvement without any increase in the general rate of taxation” (25).

Crompton also details how Frederick Law Olmsted assembled data on Central Park’s impacts on abutting property values and how that data, wrapped into a package with the proximate principle, became conventional wisdom nationally among urban planners. In turn, it was soon used to stimulate park development in many other major cities across the nation (26).

Americans like living close to parks. For example, a 2001 survey by the National Association of Realtors revealed that 57 percent of voters would choose a home close to parks and open space over one that was not (27).

Not surprising, then, that over 30 studies have found parks have a positive impact on nearby property values (28).

1. Impact Magnitudes and the Reach of Impact Areas. In “The Payoff from Parks,” Howard Kozloff cites, from various sources, a number of recent projects that demonstrate how strongly parks can create significant values for the residential properties that are adjacent to them:

  • In NYC, apartment prices, by 2011, in one building bordering the new and extremely popular High Line Park , had doubled since the park opened
  • For apartments facing NYC’s Central Park, the premium for the typical apartment sale was, “ ‘more than double that for apartments in surrounding neighborhoods’ ”
  • In Dallas, a luxury residential building could be successfully developed next to a submerged expressway, because the Klyde Warren Park was built as a deck over the Woodall Rodgers Freeway
  • “New Town St. Charles in suburban St. Louis: “Land premiums for lots fronting canals are roughly 50 percent”
  • “Upper Albany, Columbus, Ohio: Homes fronting the village green garner 25 percent premiums”
  • “ Clayton, Missouri: Office properties in the business district fronting Shaw Park ‘achieve the highest lease rates in the St. Louis region, and operate at high occupancies.’” (29).

In addition, the 2011 impact study of downtown Chicago’s Millennium Park found that:

  • “Rents in apartment buildings adjacent to the park increased 22.4% since the park opened in 2004” (30)
  • “Since the park opened, over 4,800 apartment and condo units have been completed, resulting in a population increase of 71%. The development of new units was comprised of 64% of units being new construction and 36% being adaptive reuse of class B and C office space” (31)
  • “Millennium Park clearly adds value to residential real estate with views of the property. Though there are many variables which factor into the sales price of condo units, it is clear that an excess of $125 per square foot premium is paid for units with a park view. This further illustrates people’s desire to live close to parks and other activities.” Millennium Park has added real estate value to the surrounding area.” (32).

Also, in Atlanta, GA, prices at an adjacent condominium increased  from $115/PSF to $250/PSF after Centennial Olympic Park was built (33).

To provide an indication of the magnitude of the impact that the proximate principle can provide and the extent of its impact areas, Crompton cites a study of 14 neighborhood parks in suburban areas of the Dallas-Fort Worth metropolitan area. These parks were under 7.4 acres in size and unquestionably average in their features and maintenance. The study found that:

“Homes adjacent to parks received an approximate price premium of 22% relative to properties a half-mile away. Approximately, 75% of the value associated with parks occurred within 600 feet of a park and 85% within 800 feet. This distance approximates a two to three minute walk and delineated the parks’ principal areas of influence. The price effects of the parks were insignificant at a distance of approximately 1,300 feet (a quarter mile), the conventional estimate of a 5 minute walk” (34).

Peter Harnick and Ben Welle, based on their review of 30 studies, found that:

  • “While proximate value (“nearby-ness”) can be measured up to 2,000 feet from a large park, most of the value is within the first 500 feet”
  • “The preponderance of studies has revealed that excellent parks tend to add 15 percent to the value of a proximate dwelling.” (It is perhaps a good idea to keep in mind that other factors, primarily related to a dwelling’s characteristics, that account for 85% of its value.)
  • “(P)roblematic parks can subtract 5 percent of home value” (35).

Given the unexceptional character of the 14 parks cited by Crompton and the importance Harnik and Welle ascribe to park quality as a determinant of proximate value, one may wonder if parks with far better features and greater magnetism, such as Bryant Park, Millennium Park, Balboa Park, Boston Common or Central Park might have somewhat larger impact areas –though their impacts still would ebb as their distances to properties increased. The data cited above would seem to indicate that Central Park, The High Line and Millennium Park probably account for significantly more than 15% of the value of their proximate properties. But, do their greater impacts also have a wider geographic reach? If so, then what does their curve of diminishing impact over distance look like?

That entertainment venues might have impacts beyond a half mile is partially supported by research conducted by C3D’s Stephen Sheppard on a different type of entertainment venue, the Kenosha Public Museum, in Kenosha WI. A close look at a map provided in that study of the museum’s impacts on residential real estate values strongly suggests that the museum’s primary impact area, where it increased property values 21% to 36%, extended out at least about one mile (36).

An earlier study by a Sheppard led C3D team of MASS MoCA’s impacts on residential property values in North Adams, MA found that the  properties nearest to MASS MoCA  increased in value by about 24%, or $11,728 in 2004 constant dollars. A positive impact was found out to about one mile from the museum (37).

From Figure 1 in that report, it appears that at about one kilometer (3,280 ft), the increase in value had probably fallen to about $3,518 or 30% of the increased value of the properties nearest to the museum. The MASS MoCA report also found that “Property value increases of the sort identified here, while significant in aggregate, are modest in percentage terms. They average less than 5% of total property values…” (38). These findings suggest that while museums may have larger impact areas than parks, with increasing distance the magnitude of impact quickly becomes much shallower.

A lot of a park’s/public space’s real estate impact probably works through a number of factors on which proximity would be very critical to the strength of their influence:

  • The venue’s ability to be seen from the impacted property
  • The frequency the venue is used by people living or working on that property
  • The time it takes to walk from the property to the venue.

A new luxury 82 story residential tower is being built next door to NYC’s MoMA and the museum’s exhibition space will extend into the tower’s lower floors. It is interesting that the developer’s marketing strategy for the most expensive units — located above the 48th floor — uses the unobstructed views of Central Park (about 1,250 ft to the north) available at those heights as its pivotal asset (39). The developer is expecting these far from adjacent views of Central Park to generate a lot of sales value. This example suggests that the configuration of a park’s view shed may be the most critical factor determining how a park’s impact declines over distance as well as the park impact area’s geographic reach.

Museums and PACs are used by their patrons less frequently and in different ways than parks. For example, they may be less likely to be “consumed” by being viewed from across the street out of an office or apartment window. Their uses usually are  far more passive than park uses. Do these differences structure their areas of impact in different ways than parks’ impact areas are structured? Do different types of entertainment venues differ in the magnitudes of their most proximate impacts and the geographic reach of their impact areas? Do they also have different rates for their impacts diminishing over distance? The answers to these questions are critically important to the proper selection of study area boundaries for the analysis of any entertainment venue’s impacts – especially in downtowns and large neighborhood commercial districts.

2. The Multi-Causality Problem. One might also hypothesize that on the downtown or neighborhood level, there is a distinct probability that other strong impacting factors are present and that the impact of the entertainment venue cannot be properly analyzed in isolation. Also, as the distance from the entertainment venue increases, and the influence of nearby-ness ebbs, it is likely that the importance of other impacting factors also will increase. Analytical issues related to multiple impacters consequently are likely to become increasingly important as the size of the designated impact area increases. Yet, too often in the analyses of the impacts of entertainment venues on real estate values, the presence of multiple causal factors is overlooked or ignored.

One reason may be that impact studies are usually done to support the advocacy efforts of a particular entertainment venue and its management organization may not want too much discussion of other positive causal factors.

Another probable reason is that paying attention to multiple causes with methodological rigor requires sophisticated analytical skills, easy access to a lot of data and adequate funding. Sheppard’s study of the Kenosha Public Museum is methodologically sophisticated. One of its virtues is that his multiple regression statistical technique is able to “ isolate the contribution (if any) of the presence of cultural organizations to house values from the dozens of other factors that influence them” (40). He laments the paucity of such studies on arts and cultural institutions and offers as one reason the fact that “the techniques required to carry out the analysis will almost certainly be beyond the capabilities of all but the very largest organizations or public arts agencies” (41).

Even in impact studies that try to avoid overtly overstating the impact of the subject entertainment venue, the failure to focus adequate attention on other causal factors can lead to implicit overstatements. When only one cause is discussed it is easy to leave the impression that it is either the most important one or the only one. A good example of this is the study done in 2004 on Lincoln Center, and downtowners can learn a lot from it (42). The study notes – and few, if any, other knowledgeable observers would deny — that:

“Fewer and fewer people in New York City remember what the Upper West Side of Manhattan was like prior to the building of Lincoln Center. The old tenement buildings are long forgotten by the vast majority of current residents and businesses, as well as most visitors. Instead, the visitor to the Lincoln Square neighborhood today finds a vibrant and diverse residential and commercial community that continues to evolve” (43).

It then goes on to concur with a prior study’s conclusion that Lincoln Center played a “key role” in the development of this vibrant community.

The analysis provides data to show that:

  • The number of housing units in this neighborhood increased by 19.4% between 1960 and 2000, higher than Manhattan’s 9.7% or NYC’s 16.1% (44)
  • The number of households in this neighborhood increased by 16.8% between 1960 and 2000, higher than Manhattan’s 6.2% or NYC’s 13.9% (45)
  • “(T)he assessed value of taxable property in Lincoln Square has grown 2,608% since 1962, while the average for all of Manhattan was 447% over that same time period” (46).
  • “Throughout the 1980s and 1990s, the neighborhood attracted large investments in retail and commercial ventures. Most observers would agree that Lincoln Center has acted as both an anchor and a catalyst for these developments” (47).

The study focused on the Lincoln Square neighborhood that is “bounded by 58th Street on the South, 72nd Street on the North, Central Park on the East, and the Hudson River on the West” (48). While these may be this neighborhood’s boundaries, there is no evidence presented to prove that they coincide with Lincoln Center’s impact area.

As the crow flies, Lincoln Center is about 2,000 feet away from the neighborhood’s most northeastern corner at 72nd Street and Central Park West and about 2,440 feet of its most northwestern point near 72nd Street and the Hudson River (49). Central Park is about 840 feet due east and the Hudson River is about 2,200 feet due west from Lincoln Center. This is not a small study area.

Two of the study area’s boundaries very probably were, in their own rights, very strong impacting factors on local real estate values, population growth and household growth . Since its inception in 1980, the Central Park Conservancy has led an impressive overhaul of the park that has greatly increased its attractiveness and usage. As the data cited by Kozloff indicates, Central Park can have very strong positive impacts on nearby apartment prices. The appeal of the upscale residential buildings along Central Park West never significantly weakened and they include some of the most prestigious in the city, e.g. the Dakota. More recently, the city’s first “billionaire’s building” was developed at 15 Central Park West, where asking prices have reached $12,200+ PSF (50).

A long history of anecdotal reports and a perusal of real estate ads indicate that residences in Manhattan with river views also get substantial premiums in rents and sale prices. That suggests that the Hudson River also probably has strong impacts on the desirability and value of nearby apartments and their buildings. By 2004, Trump’s Riverside South had built 2,828 new residential units in five buildings along the Hudson River in a former Penn Central rail yard (51). Since then, new buildings have probably brought that total to 5,000+ units (see Figure 4).

Riverside South v2 cropped

Figure 4. Riverside South along the Hudson

The neighborhood also has some other features that influence both residential and commercial locational decisions and that probably even influenced the selection of the site for Lincoln Center: its proximity to the Midtown Manhattan CBD and its mass transit assets. The neighborhood basically abuts the Midtown CBD, and its Broadway commercial corridor has grown to the point where it now can be seen as an extension of that CBD. The neighborhood is served by four subway stations, seven subway lines and five bus lines.

Figure 5 multi causes LCPA

Looking at the causal factors associated with Lincoln Square’s residential development, as partially represented in Figure 5, it appears to have been  fairly complicated, with many factors in the equation and numerous direct and indirect paths through which they could be manifested. The presence of many strong impacting forces means that there is is a very strong neighborhood effect that now underlies its magnetism. Over time, the new residential units and their affluent residents helped bring in

  • A steady growth of  national retail chains that includes Apple, Brooks Brothers, Lululemon, Best Buy, and Zara
  • A 13 screen cinema (one an IMAX) that probably attracts at least 500,000 guests a year
  • An increasing number of prestigious restaurants, e.g., Jean George, Per Se, Picholine, Bar Bouloud, etc.

These, then added to the neighborhood’s magnetism and the complexity of analyzing Lincoln Center’s economic impacts. It should be noted, however, that this did not happen quickly, probably taking 20+ years for Lincoln Square’s magnetism to become sufficiently strong.

Certainly, this multi-causal situation provides a basis for calling in question the use of Manhattan and NYC as the comparable areas by which Lincoln Center’s impacts on Lincoln Square’s growth in housing units, households, assessed real estate values were to be established. How many other neighborhoods in Manhattan are as close to a Central Park or a Hudson River as well as a world class CBD and have seven subways lines serving them? Perhaps a few, but far from all. How many in NYC? Probably a far,far lower percentage.  Can Manhattan and NYC  then really provide the useful comparisons to the Lincoln Square neighborhood that are needed to show how Lincoln Center has impacted it?

Lincoln Center’s local impact area also still remains uncharted. Assuming that it was congruent with the Lincoln Square neighborhood did not mean it was an accurate representation of reality.

Analyzing the residential impacts of PACs in smaller downtowns will often present a similar need to deal with a multi-causal situation. For example, in downtown South Orange, NJ, a substantial and growing number of housing units have been developed near the commuter rail station – which the South Orange PAC also abuts. How much of this residential development can be attributed to the PAC? How much to the rail station? How much simply to a downtown location?

From a downtowner’s perspective, it also would have been useful to learn how the impacts of  local residents compared to that of Lincoln Center’s audiences  on local retail growth.

3. The Influence of the Income Levels of Park Impact Area Residents. Most people are not affluent enough to live across from Central Park, the High Line or Millennium Park. How successful are investments in parks that are located in less affluent areas, such as those in or near many downtowns?  In 2002, New Yorkers for Parks and Ernst & Young conducted an in-depth study of six NYC parks that gathered some data relevant to answering that question. The study was reported in the document “Analysis of Secondary Economic Impacts: New York City Parks Capital Expenditures. Final Report” (52). Among the parks studied was Bryant Park, the only one with the real estate inventory in its impact area being predominantly office buildings. The other parks studied had mostly residential impact areas.

Arts impact Table 9

Table 9 was constructed by this author from data presented in that report.  It speaks to the question of how the economic impacts of parks are influenced by the incomes of the people who live within their impact areas. The table reports on data from seven study areas. Prospect Park in Brooklyn and St. Albans Park in Queens each had two impact areas looked at by the E&Y researchers and the data on them were included in the table. Also included in the table are data on Close Lakes Park on Staten island, Crotona Park and P.O. Serrano Park, both in The Bronx. Data on Bryant Park are not included in the table because its surrounding properties are not dominated by residential uses. Each of the study areas had an “impact area” close to the park and a “control area” that was supposed to be as comparable to the impact area as possible, save for its proximity to the park.

Based on their analysis, E&Y rated each of the parks on the degree to which the financial investments in them had paid off by increasing the values of the real estate in their impact areas and the taxes that were levied on them. The impacts of the park investments were judged to be very successful, moderately successful and not yet successful. The study covered the years 1990 to 2001.

Though the sample size is admittedly small, all seven study areas were looked at with the same methodology and the pattern in the data is clear: the success of park investments increases with the incomes of their impact area’s residents.

That it’s tougher for parks to have positive impacts on real estate in poorer neighborhoods and downtowns should surprise no one, but kept in mind to assure realistic policy and programmatic expectations. That said, because something is tough to accomplish does not mean that it should not be attempted.

4. The “People’ Part of Residential Impacts. Residential development not only means physical development, but also possible associated changes in the characteristics of the people and households that occupy them. These people changes can have significant economic ramifications. Retailers, for example,  have long followed roofs and many of them have come to appreciate having lots of nearby residents with high disposable incomes. Conversely, residential development can also raise issues of gentrification through the displacement of low income households.

For example, the 2004 study of Lincoln Center’s economic impacts noted that:

“…the population that resides in Lincoln Square is, in many ways, atypical: It is slightly older, more educated, and has a higher average income than most in Manhattan. Unlike most neighborhoods in Manhattan, Lincoln Square has more residents between the ages of 25 and 34 than it does residents under the age of 24. These residents tend to be highly educated, with 75% holding at least a bachelor‘s degree, compared with just 49% across Manhattan and 27% across the five boroughs. Lincoln Square residents are also more likely to have high incomes, with almost 40% of households reporting an annual income of $100,000 or more, compared with just 24% of Manhattan and 14% of New York City households. Given these demographics, the surge in retail investment during the past decade comes as little surprise” (53).

The neighborhood impacts claimed for Lincoln Center seem quite clear. On the other hand, the NYC park improvements studied by E&Y seem to have had far less definitive impacts on the characteristics of the people who live in their impact areas.

 

Table 10 E&Y people impacts

The data in Table 10 were assembled and computed from statistics presented in the E&Y report. The table covers seven study areas, with each having an assessment of its park’s investment impacts. Each study area has its own impact area and control area. Statistics are presented for each impact and control area on per capita income in 1990, the percentage growth in per capita income (PCI) between 1900 and 2001 and the growth in the number of households (HHs) between 1900 and 2001. Looking at the column “Delta PCI 1990-2001” we find that:

  • In six of of seven study areas, the PCI grew in the impact areas. The growth ranged from a very modest 9% to an impressive 61%
  • The two impact areas for St. Albans Park had both the highest and lowest PCI growth rates, 61% and -10%. The two impact areas for Prospect Park also had dissimilar PCI growth rates, 12% and 40%
  • The growth in PCI among the impact areas does not appear to have an apparent association with the success of their park’s investment
  • In five of the seven study areas, the growth in PCI was greater in the control area than in the impact area. This probably reflects the fact that a number of other, unidentified, factors impact a neighborhood’s PCI and they, in aggregate, are often far more powerful than a park’s creation or substantial improvement.

As for household growth, which is closely tied to residential unit growth:

  • The parks that had “very successful” and “not yet successful” investment impacts showed relatively modest growth rates in their impact areas of 11%, 1%, 5% and 7%
  • The parks with moderately successful investment impacts had more robust household growth of 83%, 20% and 31%. In the St. Albans NE impact area, the combination of -10% PCI growth and 83% household growth supports the hypothesis that a lot of less affluent households were being attracted to that area. That was probably the result of affordable housing units being built
  •  The differences between impact and control areas on the household growth variable (see Delta IA-CA) are smaller than on the PCI variable, except for the St. Albans NE Study Area, and there is no apparent pattern across the study areas.

The E&Y study shows that putting an entertainment venue into a neighborhood is no guarantee that it will attract a lot more affluent people to live in that area. A lot also depends on the neighborhood’s other development assets. Lincoln Center inserted into another Manhattan neighborhood — such as around the old location of the Metropolitan Opera in the Garment District —  might have produced quite a different set of local impacts.

Impacts on Offices, Hotels and Retail

Offices and Jobs. Office development is important in many downtowns and basically the main story in some. Small and medium-sized towns are very unlikely to attract tenants needing large amounts of office space, though office activity can still be an important function for their downtowns. Office development not only impacts on real estate values, but also on how the downtown looks and operates. Many “office districts” can be deader than doornails after 5:00 p.m. and on weekends. For many retail and service operations, the characteristics of a district’s daytime population are more important than those of its residential trade area. Offices bring in jobs and the people who fill them can be important components of a district’s daytime population. They often account for high percentages of lunchtime shoppers and of those who use nearby public spaces and cultural venues.

1. Campus Martius Park in Downtown Detroit and Discovery Green in Downtown Houston. These two successful parks were projects of the Project for Public Spaces (PPS). In their documentation on the PPS website, PPS provides information about how these projects have impacted on their surrounding areas. The documentation does so in a way that is easy to read, easy to understand and useful and relevant for downtown leaders. One reason is that the quantified impacts did not require complicated research with large data gathering efforts or sophisticated statistical techniques to determine. Nor is it long winded.

With both parks, a primary impact discussed is the new investment dollars attracted by the parks, including the investments in new office buildings. Doing this does not require the collection of a large number of pieces of data. PPS also uses local experts to establish the causal connection between the the parks and their impacts. For example, regarding Campus Martius Park, the PPS website states that:

  • “The $20-million public space, comprised of less than two acres, has spurred well in excess of a billion dollars in new investment downtown and spurred the re-location from the suburbs of thousands of employees of major corporations like Compuware and Quicken Loans…” (54)
  • “Most significantly, the Compuware computer firm moved its headquarters and 4000 employees from the suburbs to a new building near the square” (55).

It then establishes the causal connection by using this quote:

  • “ ‘Compuware would not have come downtown without the park,’ notes Bob Gregory of Detroit 300. ‘They didn’t want just a building. They wanted a lively district, where their workers would have things to do’ ” (56).

PPS describes Discovery Green’s impacts in the following manner:

“Catalyst for more than $500 million in downtown development projects (completed or underway) that specifically note the park’s creation as an impetus for the dramatic investments, including the creation of the Embassy Suites Hotel, One Park Place Luxury Apartment Building and the Hess Tower (Hess Corp HQ)” (57 – italics added).

With Discovery Green, it is apparently the developers themselves who made the casual connection between the creation of the park and their investments. it’s hard to argue with them about why they invested.

This approach to impact analysis has the advantages of being relatively easy to do, easy to understand and having considerable credibility. Yet, it should be used with caution because of some potential conceptual muddles. For instance, with regard to Campus Martius Park, based on the information PPS provides, it seems legitimate to conclude that

  • The park had a major influence, perhaps even the predominant influence, on the site selection decisions of Compuware and Quicken Loans that led them to construct and occupy new buildings adjacent to it. The park undoubtedly has helped make development happen
  • It is doubtful that the park was the dominant factor in the determination of  how much money was invested in these projects. The number of employees these firms wanted to accommodate, how much space they wanted to provide for employee desks and amenities, the costs of land and the costs of the labor and materials needed to create that amount of office space were the far more likely dominant determinants of the magnitudes of those investments. The proximity and quality of the park may have impacted on investment magnitudes to the degree that the developer’s financial planning anticipated the park would provide a premium in rents and/or the building’s sale
  • Location does have an impact on rents and rents are an important determinant of a buildings value. Consequently, the park now most probably does add to the value of the adjacent buildings and the rents of their leased spaces. There is no research cited by PPS on how much of the adjacent building’s rents can be attributed to their location adjacent to the park,  but it is unlikely to be more than say  15% to 25%. Again, the park is neither the sole or the dominant causal factor
  • While these percentages are relatively modest, the park’s impact becomes more impressive when they are applied to the aggregate valuation of these buildings. For example, if the buildings together are now appraised at about $1 billion, then the park accounts for $150 million to $250 million of that valuation. That’s not exactly chump change, especially for a $20 million public space investment.

2. Millennium Park in Chicago.  The 2011 study of this very popular and successful park showed that, in contrast to its significant positive impacts on residential property values, its impacts on nearby office buildings have been negligible: “An analysis of rental rates and vacancy trends of the East Loop compared to the rest of the downtown market did not yield any significant trends. This was confirmed in conversations with active office leasing brokers in the East Loop” (58).

The brokers felt the main constraint on the park’s impact was that “the distance from the main transportation hubs on the west side of The Loop will continue to hinder demand” in the East Loop (59). This is further proof that the characteristics of the downtown environment in which an entertainment venue is inserted will influence the range and magnitudes of its impacts.

The brokers noted that views of the park did give some East Loop office spaces a strong marketing tool that helped differentiate them from their downtown competitors (60).

The credibility of these findings is enhanced because the researchers combined their secondary analysis of leasing data with consultations with commercial brokers who knew the East Loop office market.

These findings also demonstrate that a big, successful entertainment venue may not always be a spark for office development. Furthermore, they  again show that the attributes of the urban environment in which such venues are inserted will influence the characteristics and magnitudes of their impacts.

3. Bryant Park, New York City. Two interesting studies have been done of Bryant Park’s impacts on adjacent office buildings. The 2002 E&Y study, discussed above, is interesting because of its findings, its methodology and the careful way that its findings were stated. A more recent 2014 study, by Landauer Valuation & Advisory, is notable for its methodology, its credible numerical estimates of how much the park impacts on the rents of the office buildings that surround it as well as their valuations, and the prudent wording of its findings and approach (61). Both E&Y and Landauer are staffed with real estate professionals.

The E&Y study looked at four of the 20+ buildings located across a street from Bryant Park and compared their asking rents in 1990 and 2002 to those in some other nearby Manhattan CBD office submarkets. One of the buildings was rated as Class A, while the other three were Class Bs.

This sampling was presumably because the inclusion of all the adjacent buildings would have been prohibitively expensive. It is interesting that although the E&Y researchers had established the same types of impact and control areas for Bryant Park as they had for the other five parks they studied, they ignored them when examining the impacts of the park on nearby office rents. In effect, they used the other Midtown CBD office submarkets as control areas for rent related variables. That is understandable, given that is where the strongest competition for Bryant Park’s adjacent office buildings is likely located. It also reinforces the argument offered above, that the geographic definitions of such control or comparable areas may need to vary depending on the variables being analyzed.

Importantly, the E&Y study recognized that:

  • “Increases in real estate values during the 1990s (were) due to several factors including:
    • – Bryant Park renovations
    • – Improvements to Grand Central Station (sic)
    • – Revitalization of Times Square
    • – Economic conditions
    • – NY Public Library renovations” (62).

The E&Y study found that:

“Between 1990-2002, asking rents for commercial office space near Bryant Park increased from 115% to 225% as compared to increases ranging from 41% to 73% in the surrounding submarkets.” (63 )

The difference in the asking rent increases is undeniable, but the study unassertively concluded from this that “park renovation (was a) critical factor to success of the area” (64). Notably, it did not try to quantify how much of this growth in asking rents was due to the park’s renovation. Nor, based on the temporal coincidence of the park’s renovation and the strong increase in asking rents, did it attribute all the increases to the park’s renovations.

Rents combined with a cap rate can be used to generate a useful estimate of a building’s value. The E&Y researchers did not attempt to generate such estimates for the four office buildings adjacent to Bryant Park.

impacts Table 11 E&Y BP

Looking at Part A of Table 11, we can see that, in 1990, the asking rents at the Grace Building, the only one in the Class A category, did not lag behind the Grand Central submarket and lagged behind the Rockefeller Center submarket by 22%. As might be expected, the 1990 asking rents lags of the Class B buildings were far more substantial: -73% compared to the Grand Central submarket and -23% compared to the Penn Plaza Garment District submarket. By 2002, all of the buildings had higher asking rents than their comparison submarkets. This was certainly an impressive turnaround.

The three Class B buildings showed greater improvements than the Grace Building. The table’s “Delta” rows show the differences in the relative positions of the asking rents of these buildings in 1990 to their relative positions in 2002. Part B of Table 11 is based on Part A and presents the average market lag in 1990 and the average delta for the Grace Building across its two comparison submarkets as well as the same types of data for the three Class B buildings in their respective two comparison submarkets. The Grace Building had less of an average lag, -10.5%, but it also had a smaller average delta, 35.5%. In contrast, the three Class B buildings had an average asking rents lag of -48% and a delta of 65.5%. The buildings with the largest lags made the largest gains in asking prices relative to those in their competitive submarkets. Is this a result simply of the fact that they had more statistical territory to recapture or also an indication of some more interesting underlying tendencies?

None of these buildings were decayed or in bad condition. The building in the best condition was in the best competitive position. It was Bryant Park that was the force of decay and disorder. Its renovation obviously helped all of them, as the owners of all the building have attested, though it seems to have helped the less strong the most on a percentage basis. Is this a one-of-a-kind situation or will a similar result occur whenever  a public space or entertainment venue that has been dragging down its surrounding neighborhood’s attractiveness and real estate values is very successfully renovated? Also unknown, is how much the renovation of the park stimulated improvements in the Class B buildings, giving them better office spaces as well as an improved location to sell. This would demonstrate how Bryant Park’s direct and indirect causal paths function.

Following this line of reasoning, it is harder to see how a similar result flows from the creation  or successful renovation of a public space situated amidst a group of buildings that are either decayed or in marginal condition  — unless the project is so large or there are other powerful casual factors that can join with the public space to spur the substantial renovation of the existing buildings or attract the construction of new buildings.

The data in Table 11 are also consistent with the hypothesis that as the properties surrounding a public space get stronger and more desirable, the relative weight of the public space’s direct impacts on them may diminish.

The 2014 report by Landauer Valuation & Advisory stands out for its methodology. Its analysis of the park’s impact on rent rates focuses on 548 leases, of which 126 are in buildings located in the BID, with the remainder in nearby buildings that are outside of the BID. Like Sheppard’s hedonic studies, it uses a multiple regression model statistical tool to analyze the relative strengths of several potential causal variables:

  • Location: whether the space is or is not in the BID, i.e., adjacent to the park
  • Whether the space is in a Class A or Class B building
  • The square footage of the leased space
  • The type of lease, e.g., new or renew
  • Floor height – where it is in the building

In addition, it surveyed 29 appraisers, commercial brokers and building representatives.

The Landauer report also provides a model that more economic impact studies should follow when it comes to a report’s language. This description of the project is a good example:

“We have completed this exercise for comparison purposes, in an effort to quantify the approximate percent difference in value that is attributable to the rent premium derived from a location on Bryant Park” (65).

How many economic impact studies say they are an “exercise for comparison purposes” or engaging in an “effort to quantify the approximate percent difference?”

Considering the park’s specific impact on rents, Landauer reported that:

“Our statistical analysis yielded a Bryant Park-based office rental rate premium of approximately 10% to 15%. The survey of market participants offered an opinion of the premium of 14.55% on average, with a median of 15%, a mode of 10%, and a standard deviation of 6.66%.

We have concluded to a premium attributable to being located on Bryant Park of one-eighth, or 12.5%” ( 66).

Some other interesting findings of this exercise:

  • The strongest explanatory variable was the floor in the building on which the leased space was located
  • The location variable – whether or not the space was in a building adjacent to the park – was the second strongest, about 22% weaker than the location in the building variable
  • Building class and the amount of leased space both had about half the explanatory power of the location variable (67).

Landauer then used its findings about the park’s rental rate premium to assess the park’s property value premium, inserting them into a fee simple building value analysis of six office buildings that were located on the park. Extrapolations were then made to the other buildings in the Bryant Park Business Improvement District. Landauer found that:

“Further, it is our opinion that, when translating the rent premium into a property value premium, properties located on Bryant Park would be worth between 20% and 25% less, on average, if they were not located on Bryant Park” ( 68).”

Here, again, the park did not account for most of the building’s worth. However, given that the six buildings studied are probably worth $ 6 billion+, between $1.2 billion and $1.5 billion of their worth can be attributed to being adjacent to Bryant Park (69). Extrapolating that out to all 26 buildings in the BID would vastly increase the dollar value of the park’s impact on adjacent buildings.

Regarding the park’s impact on the city’s real estate tax revenues, Landauer found that

“This value premium results in increased real estate tax revenue for the City of New York, estimated to be a minimum of $33,000,000 annually. Only office rental rates and their effects on value were estimated in this report, not retail rental rates. Therefore, it is conceivable that the increased tax revenue is greater than this amount” (70).

Many of the other real estate impact studies reviewed for this article would have benefited from using a methodology similar to Landauer’s. Many more economic impact studies would benefit by stating their findings in the same prudent language used by Landauer.

Impacts on Nearby Retail.  Covering how much the audiences of an entertainment venue spend within the entertainment venue’s region, perhaps even looking closer at how much they spend in retail shops, hotels and eateries seems to be the limit of the analytical attention economic impact studies might give to impacts on retail and hospitality activities. Of course, having defined “local” or “community” at the county or multi-county level. means they do not pay much attention to the neighborhood immediately surrounding an entrainment venue — except if they are also looking at impacts on real estate values. Even these “impact on real estate value” studies seldom go into much analytical depth on retail space values, e.g., the 2004 Lincoln Center study. Sometimes this is apparently due to the analysts or their clients not feeling the subject merits the required attention or the associated costs of time and money. In other instances, such as Landauer’s Bryant Park study, the needed data were just not there to collect and analyze.

This might be considered as a remarkable pattern, given that in so many communities across the nation, downtown leaders and EDOs played important roles in bringing these entertainment venues into their districts.

Observational and anecdotal information indicate many instances where a robust retail sector exists within about a five minute walk of an entertainment venue – e.g., Union Square in San Francisco, Union Square in NYC, Lincoln Center in NYC, Boston Common, etc. Not known are the degree to which the entertainment venues have sparked and helped maintain the retail development and the channels through which this influence is exerted. For example, do the audiences of a downtown PAC add more to the coffers of nearby retailers and restaurants than the new residents attracted to the area, directly or indirectly by the PAC?

However, there also are many instances where the nearby retail is either non-existent, weak or not impressive.  NJPAC in Newark, for example, has not sparked a nearby retail revival. The Green in Morristown, NJ has attracted some major destination retailers like Century 21, but not been as attractive a location for small independent merchants. For all its success and magnetism, the retail around Bryant Park has not been as impressive as its impact on adjacent office buildings has been, though its improvement in recent years is quite noticeable. The GAFO retail has not been that robust. Food operations, on the other hand,  have been strong, with the Le Pain Quotidien and the Starbucks locations across from the park reportedly being among the highest grossing in their chains (71). More upscale shops, such as Whole Foods and Tourneau, a watch retailer, reportedly have signed leases for spaces near the park (72). Most impressive has been the development in recent years of a kind of vibrant and charming boutique/eatery cluster of attractive small shops along 40th Street from 5th to 6th Avenues. A decade ago, this block was quite dull and glum.

Do these examples demonstrate the lack of a casual connection between entertainment development and retail growth or are there, in these downtowns, unidentified factors present that inhibit the manifestation of that causal connection? Does it make a difference if the district around the venue is dominated by office activities or if it also has a significant number of residential buildings? Do the lack of a shopper bounce gained by having retail on both sides of a street or one-way auto traffic make any difference? Are some types of retail and hospitality activities helped by some kinds of entertainment venues and not others? The strength of eating and drinking establishments in or near theater districts or close to PACs, parks and public spaces suggests that they are more likely to benefit from the presence of entertainment venues. Americans for the Arts surveys have shown that the audiences of cultural and arts venues spend much more in eateries and hotels than on retail purchases. Not known is how much of those expenditures are spent in establishment close to the entertainment venues or the indirect impacts of those establishment through the residents and workers they have helped bring into their districts.

These questions cannot be answered by a study of just one entertainment venue in one downtown. That means that the needed type of study could be complicated and expensive. Americans for the Arts has assembled a very large database on cultural and arts venues and it can probably can used in such a study, while reducing its data gathering costs.

Impacts on Nearby Hotels. Many important and famous hotels are located adjacent to or within a few minutes walk of Central Park in NYC, Union Square in San Francisco, Times Square in NYC, and the Boston Common in Boston.

Observational and anecdotal information also indicate many instances where hotels have been developed following the development or rejuvenation of an entertainment venue. For example:

  • The building of the first phases of Mitchell Park in downtown Greenport, NY, was followed by the development of a 30-room hotel adjacent to it (73)
  • An office building was converted into the Bryant Park Hotel in 2001 and a 282 room hotel is now being built just west of Bryant Park on W42nd Street (74). Field observations over the years suggest that there are a lot more hotels within a five-minute walk of the park than there were a decade or two ago. How much of that growth can be attributed to the park and how much to the 1 million SF Macy’s about five blocks south and a revived and extremely crowded Times Square is an interesting question
  • As noted above, the creation of Discovery Green in Houston sparked the development of an Embassy Suites hotel nearby (75)

For the Greenport and Houston examples, city official and developer reports provide evidence for claiming a causal link between the establishment of the park and the development of the hotel.

On the other hand, there are numerous downtown parks and public spaces without a hotel on or near them. Why do some attract hotels and others do not is still an unanswered question. The demand for hotel rooms,  condition of the surrounding neighborhood and transportation access are some other possible factors. The lack of in-depth research on this question remains a problem. One thing is certain: inserting an entertainment venue in a downtown probably will not, by itself, spark the development of a hotel.

The 2011 impact study of Millennium Park stands out among the studies reviewed for this article in the depth of its analysis of the park’s impact on hotels. It reported a secondary analysis of data on the number of hotels and hotel rooms in the area adjacent to the park before and after its completion. From that analysis, and consultations with local experts, the study concluded that:

“The demand for hotels generated by Millennium Park overcame the financial challenges of the recent recession and added 18% more units since the park’s inception. With 4.5 million annual visitors going to the park each year, it’s clear that these people need places to stay. The park spurred hotel development boom, and that can be attributed to the number of people the park attracts” (76).

This analysis certainly indicates that there was significant growth in the numbers of hotels and hotel rooms after the park was completed. However, the explanation of the causal connection presented above again exemplifies the conceptual muddles that economic impact studies can fall into. To begin with, not all of the park’s visitors are from out of town and in need of a hotel room. There also is no survey evidence presented to show that people stayed in these hotels because they were going to visit the park, as the above quote seems to imply? The causal linkage may actually be the reverse of that: proximity to the park, and possible hotel room views of it, gave these hotels a strong marketing advantage for attracting guests who were visiting Chicago for a wide variety of reasons other than visiting the park. Many of these out of town hotel quests then may have gone to the park, not because of a pre-trip plan to go there, but as a consequence of taking a room in a hotel from which it could be easily seen and visited. How many of these hotel guests did what and why is still unknown – as is the causal connection.

The report also claims that: “The park spurred hotel growth that otherwise would have never existed, as no hotels were built between 1999 and 2003” (77) Again, was this because developers thought that park visitors would rent rooms or they thought that new hotel rooms in that part of The Loop with views of a large and attractive park would be very, very marketable. Also, did the recession bring land prices down to more affordable levels? The developers certainly knew what views of the park were doing for residential development.

Whether the association between the park’s development and nearby hotel development is spurious or casual remains unclear — as does the strength of any casual connection. Can it really be argued that the park accounts for the entire 18% increase in units?

Many downtowners have an increasing interest in tourism. That is often accompanied by a desire to learn more about the capabilities of downtown entertainment venues to spark hotel development. Additional research in this area would have an audience, especially in small and medium-sized communities.

Impacts on How the District Works

Most economic impact studies strive to state their findings in terms of dollars. However, if we learned anything from the Troubled Years for America’s downtowns, it was that a downtown only has its competitive advantages to the degree that it works as it is supposed to. The downtown socio-economic subsystem itself is very important.  When consumers feel afraid to walk on its sidewalks or in its public spaces, when shops offer an unattractive array of products and services, when commercial spaces are old, unattractive and graffiti strewn, when public transit is unreliable or dangerous, when parking is hard to find and fear inducing, when business owners are leaving in droves, when investments in downtown properties and businesses fall to a trickle,  the downtown obviously is not working properly.  Conversely, when the creation or improvement of an entertainment venue helps make the downtown function better, it can help attract more downtown visitors, stimulate the sales revenues of nearby businesses and spark other nearby investments in properties and businesses. While a monetary value very often cannot be assigned to these “how the district works” impacts, they still can be analytically important and many can be quantitatively assessed.

For example, DANTH’s field observations of Bryant Park since the early 1980s strongly suggest that the park’s revitalization,  with its improved appearance and new opportunities for visitors to engage in a wide array of interesting activities, attracted a large number law-abiding visitors. Their presence, in turn,  probably has reduced substantially the fear of crime and the level of avoidance behaviors in that part of the Midtown CBD. That has probably increased pedestrian activity near the park. All of that was good for local businesses and property owners.

Also, reviews of the last round of physical improvements to Lincoln Center focused on how they made the center “less austere and more welcoming and far more integrated into its West Side neighborhood” (78). That certainly sounds as if the renovations had very important positive impacts on the surrounding neighborhood, regardless of whether or not they can be stated in terms of dollars and cents. However, such comments also imply that Lincoln Center previously was having some serious negative impacts on its how its neighborhood functioned, a fact that prior impact studies failed to note, most probably because of their advocacy orientation.

The impact study on Chicago’s Millennium Park employed a “quadruple net value” approach that looks at a long list of social/cultural, economic, environmental and sensory variables. Almost all of them might speak to the issue of how the park has made the downtown area near the park work better. Some of the impacts this report looked at were:

  • Social/Cultural: decrease in reported crimes; number of public transit connections; number of parking spaces at or near thew park; number of annual visitors; number of annual events; number of residential units close to park; number of organizations that use the park; pedestrian comfort; square feet of shaded area in the summer
  • Economic: park’s impacts on residential, office and hotel development
  • Environmental: amount of green space; air quality; storm water runoff; energy conservation
  • Sensory: iconic visual elements; sounds and smells (79).

Other downtowns might find some of these variables more or less relevant, and other analysts might take issue with how particular variables were looked at, but this report certainly shows the wide range of impacts that can be considered. New or expanded entertainment venues that are housed in pedestrian unfriendly fortresses, or that create significant air or noise pollution or that severely stress nearby public transit facilities are bound to have adverse economic impacts.

C3D’s study of MASS MoCA’s impacts on South Adams, the town where it is located, looked at far fewer variables than did the Millennium Park study. However, its use of an hedonic analysis of real estate values, its analysis of the gentrification issue and its effective use of readily available data to assess the museum’s impacts on business and job growth make it a study that downtown leaders in other communities might want to look at.

ENDNOTES

Numbering continued from Part 2.

18. Though some have been done, see for example: John L. Crompton, Measuring the Economic Impact of Park and Recreation Services, National Recreation and Park Association, Research Series 2010, pp.68, http://www.nrpa.org/uploadedFiles/nrpa.org/Publications_and_Research/Research/Papers/Crompton-Research-Paper.pdf

19. See Figure 14 at: https://www.ndavidmilder.com/2014/11/bryant-park-part-3-a-comparison-to-other-entertainment-venues-on-annual-expenditures-and-annual-expenditures-per-visitor

20. Ibid,

21. See, for example: Economic Development Research Group, The Economic Role and Impact of Lincoln Center, 2004, pp.41,  http://www.edrgroup.com/library/economic-impact-analysis/the-economic-role-and-impact-of-lincoln-center.html Hereafter referred to as LCPA study

22. See: “Three Informal Entertainment Venues in Smaller Communities: Bryant Park Series, Article 4” at https://www.ndavidmilder.com/2014/12/draft-121414-three-informal-entertainment-venues-in-smaller-communities-bryant-park-4

23. Peter Harnik and Ben Welle, Measuring the Economic Value of a City Park System, The Trust for Public Land, 2009, pp. 19 p.1. Hereafter referred to as H&W.

24. Ibid. p.1

25. John L. Crompton, “THE PROXIMATE PRINCIPLE: The Impact of Parks, Open Space and Water Features on Residential Property Values and the Property Tax Base,” National Recreation and Park Association, 2004, pp.201, p.51  http://www.carolinamountain.org/sites/default/files/files/Nature%20and%20Commerce/2%20Compton_ProximatePrinciple.pdf

26. ibid pp. 53-53

27. Megan Lewis, How Cities Use Parks for Economic Development, The City Parks Forum, American Planning Association, 2002, pp 4, p3

28. H&W p.1

29. Howard Kozloff, “The Payoff from Parks,” Urban Land, August 29, 2012. http://urbanland.uli.org/economy-markets-trends/the-payoff-from-parks/

30. Dennis Jerke, Ryan Mikulenka, et al. MILLENNIUM PARK: QUADRUAPLE NET VALUE REPORT. Texas A&M University And Depaul University. Summer 2011. Pp.77, p.29

31. Ibid. p.31

32. Ibid. p.33

33. Megan Lewis, How Cities Use Parks for Economic Development, The City Parks Forum, American Planning Association, 2002, pp 4, p.2

34. John L. Crompton, “THE PROXIMATE PRINCIPLE: The Impact of Parks, Open Space and Water Features on Residential Property Values and the Property Tax Base,” National Recreation and Park Association, 2004, pp.201, p.85 http://www.carolinamountain.org/sites/default/files/files/Nature%20and%20Commerce/2%20Compton_ProximatePrinciple.pdf

35. H&W p.1

36. Sheppard Ken Stephen Sheppard, “Measuring the impact of culture using hedonic analysis.” Center for Creative Community Development, October 2010, pp.28, p.21

37. Stephen C. Sheppard Kay Oehler Blair Benjamin Ari Kessler. “Culture and Revitalization: The Economic Effects of MASS MoCA on its Community.” C3D Report NA3, 2006, pp.16

38. ibid p 17

39. Oshrat Carmiel, “Inside New York’s Newest Architectural Masterpiece for the Mega-Rich,” BloombergBusiness, May 15, 2015. http://www.bloomberg.com/news/articles/2015-05-15/moma-tower-s-70-million-duplex-newest-addition-to-nyc-skyline

40. Stephen Sheppard, “Measuring the impact of culture using hedonic analysis.” Center for Creative Community Development, October 2010, pp.28, p.9

41. ibid p.9

42. LCPA studyp24

43. Ibid. p24

44. Ibid. p27

45. LCPA study p.28

46 Ibid. p.32

47.lbid. p.34

48. LCPA study p.24

49. Distances measured on Google Maps

50. See:http://ny.curbed.com/archives/2015/02/25/15_central_park_west_pad_wants_29m_after_owner_paid_59m.php

51.Computed from data provided at LCPA study, pp.30-31

52.- Hereafter referred to as the E&Y study.  This document is a pdf file of a slideshow presentation and can be found at: http://www.ny4p.org/research/other-reports/or-smartinvestment02.pdf

53. LCPA study p.25

54. See: http://www.pps.org/projects/pps-involvement-in-the-place-led-regeneration-of-detroit/

55. See: http://www.pps.org/projects/campusmartius/

56. Ibid.

57. See: http://www.pps.org/wp-content/uploads/2011/08/discovery-green_benchmark_aug-2011.pdf

58. Dennis Jerke, Ryan Mikulenka, et al. MILLENNIUM PARK: QUADRUAPLE NET VALUE REPORT. Texas A&M University And Depaul University. Summer 2011. Pp.77, p. 30

59. ibid.

60. ibid.

61.Landauer Valuation & Advisory, “Valuation Study Of: Bryant Park Business Improvement District.” New York, NY, December 2014, pp.61. Hereafter referred to as Landauer. A big thanks to Dan Biederman for sharing this report with us.

62. E&Y study p. 38

63. Ernst & Young. “How Smart Parks Investment Pays Its Way,” Executive Summary of the NY4P / E & Y study: “Analysis of Secondary Economic Impacts Resulting from Park Expenditures,” 2002 ,pp. 16, p. 2

64. E&Y study p. 38

65. Landauer, p.28

66. Ibid, p.28

67. Computed from data on Landauer , p.24

68. Ibid., p.2

69. Computed from data on Landauer , p.52

70. Landauer, p. 56

71. Reported by a Bryant Park Corporation staff member

72. Ibid

73. See: https://www.ndavidmilder.com/2014/12/draft-121414-three-informal-entertainment-venues-in-smaller-communities-bryant-park-4

74. Landauer, p.10

75. See endnote 47

76. Dennis Jerke, Ryan Mikulenka, et al. MILLENNIUM PARK QUADRUAPLE NET VALUE REPORT. Texas A&M University And Depaul University. Summer 2011. Pp.77, p.34

77. ibid.

78. Editorial, “Lincoln Center, New and Improved,” New York Times, May 13, 2009. http://www.nytimes.com/2009/05/14/opinion/14thu4.html

79. Dennis Jerke, Ryan Mikulenka, et al. MILLENNIUM PARK: QUADRUAPLE NET VALUE REPORT. Texas A&M University And Depaul University. Summer 2011. Pp.77

80.