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Tenant business fundamentals - a foundation for value.

There is a clear distinction between tenant creditworthiness and tenant business fundamentals. A great deal of time and analysis is devoted to the former. But for retail tenants, this typically represents a focus upon secondary sources of lease payment support.

The primary, and most dependable, source of lease payments for these tenants is unit-level operations. Through a focus on unit-level operating characteristics, diverse business enterprises with different lease structures can be bound together by comparisons of unit-level operating profitability and lease coverage. The aim is to enable property managers to undertake a more pro-active posture with respect to tenant selection, tenant retention, and ongoing tenant review.

Foundation for value

Conceptually, rents paid by retail tenants are a function of business fundamentals. Tenants will pay what they can afford, although competitive real estate market pressures may permit them to pay less. The importance of this relationship suggests that investors and property managers should seek those tenants that may be expected to generate the greatest unit-level profits.

Leasing to tenants with stronger income potential has clear-cut benefits:

* Rents may more easily be adjusted upward. Real estate market conditions permitting, the tenant will be better able to absorb higher occupancy costs.

* Tenant unit-level rent coverage will be high. Strong lease coverage increases the likelihood of timely rent collection.

The conclusion to be reached is that a focus upon tenant business fundamentals stands to enhance property value.

The primary payment source

A basic tenet of finance is the importance of understanding the source of payment. For retail tenants, the essence of the rule is this: rent is paid from unit operations. If unit-level losses are incurred, then rent will have to be derived from secondary sources.

Among the most prominent secondary payment sources are tenant cash reserves, bank borrowing, and cash flow diverted from the operations of other profitable retail units. Depending upon the tenant and lease recourse, secondary payment sources may be significant. Nonetheless, the primary source is the most important for the following reasons:

* If the unit is profitable, the tenant will generally pay on time.

* If the unit is profitable, alternate tenants are more likely to be found.

* Secondary payment sources are less reliable over the long term.

The importance of unit-level operating performance is brought home in instances of tenant bankruptcy. Within bankruptcy, secondary payment sources are the first to dissolve. Primary sources are more likely to hold up.

With a choice of whether or not to affirm or reject a lease, financially troubled tenants will seldom hold on to losers. For landlords of profitable units, this means that rents are likely to be received without interruption. Less profitable properties are more likely to be returned without hesitation.

There is no universal statistic

Retail businesses have diverse operating characteristics. Electronics retailers are characterized by low gross margins and high sales volume. Clothing boutiques are typically characterized by less volume, but have higher gross profit margins. The relatively high gross profit margins generated by restaurants are offset by comparatively higher labor costs. Convenience stores have lower labor costs, but also lower gross profit margins than restaurants.

With such diverse operating characteristics, retailers can hardly be bound by any single universal statistic.

Yet, despite this diversity, owners and managers too often employ sales per square foot as if it were some sort of benchmark. Tenant business fundamentals have little or nothing to do with sales per square foot. If employed to make relative comparisons between similar retail tenants or prior-period unit sales, sales per square foot may have some value. Otherwise, this is one of the most meaningless statistics employed in real estate.

In search of similarities

While there may not be any universal statistic, retailers can be segmented. Within certain retailing sectors, similarities definitely exist. Video rental stores, for example, may share certain operational traits, as may convenience stores, book stores, restaurants, and so forth.

In today's chain and franchise culture, the operational similarities become even more pronounced. Chances are that Blockbuster Video stores have similar operating characteristics; so should Footlocker stores, B. Dalton stores, Radio Shack stores, and Wal-Marts. The implication is that the operating profitability for a Blockbuster Video store can be accurately estimated based upon predicted sales volumes. This estimate would be made based upon the known operating characteristics of other Blockbuster Video units.

The operational similarities of many retail tenants and sectors can be harnessed by the property manager both to increase and to assess lease quality. As previously discussed, retail tenants with the greatest degree of lease-coverage ability should present favorable risks. Lease quality can therefore be enhanced by targeting businesses with the most favorable operational characteristics.

On the other hand, existing tenant lease coverage also should be easy to estimate based upon reported tenant sales volumes. In this manner, the quality of existing leases may be measured.

How to do it

In order to establish a system which both captures and interprets tenant operating characteristics, financial information must be gathered. As previously suggested, financial data can be retained and captured for various retail segments or even for certain individual chains and franchised retail concepts. Once this sector or concept data is captured, it may be used to estimate current and future tenant lease coverage by taking the following steps:

1 Collect unit-level financial data. Where possible, leases should require the submission of periodic unit-level operating statements. It may also be possible to request unit-level operating data from existing and prospective tenants for other properties operated by those tenants. In the event of franchised retail concepts, samples of concept operating data may be available from the system franchisor. Lastly, unit-level operating data received from declined tenant applications also may be retained.

Through these and other means, valuable unit-level operating information, which serves as the foundation for segmenting retail concepts and analyzing retail unit-level operating characteristics, can be collected.

2 Select a sample. From the sector/concept financial data, select a sample of approximately 10 financial statements. The statements should be intentionally selected at reasonable regular revenue intervals. For example, video concept properties could be spaced at annual revenue intervals of $100,000, ranging from $500,000 to $1.5 million.

The actual number of financial statements employed can be less than 10 depending upon the range of unit sales to be analyzed and the subsequent reliability of the model as tested.

3 Capture unit sales and operating profits before occupancy cost. An example for the video rental sector appears in Figure 1.

4 Review what you have captured. Remember that this data will be employed to create a model. The results of this information should therefore appear intuitively logical. Most important are the highest and lowest sales and profit points. These points strongly influence the model to be created.

If incremental earnings are projected to rise significantly at higher levels of revenue, then earnings estimates at even higher sales levels will go through the roof. On the other hand, a sharp drop-off in profits at low sales levels will tend to weight the model downward at the lowest sales levels.

5 Run a linear regression analysis. Linear regression is a means of simply determining numerical relationships. From a series of revenue and profit points, the aim of regression analysis is to create automatically a model that will estimate unit-level profits at given sales levels.

While regression analysis may sound complex to some readers, it is actually the simplest part of creating a system to determine tenant operating characteristics. Virtually every current release spreadsheet software (and even some business calculators) will compute a regression analysis automatically.

For the video rental store example, a linear regression chart might appear as in Figure 2. The regression line is the automatically computed line of best fit between the eleven sales and profit points.

6 Test the analysis and update the model The linear regression output can be tested statistically against a random sample of financial statements to determine its accuracy. Also, because operating characteristics are subject to change, it is useful to test and update the model with more current data.

Finally, it should be pointed out that a model can employ more than just sales and operating profits. It may be both useful and informative to compile a model financial statement based upon a series of individual regression analyses on line items ranging from labor to cost of goods sold.

Making use of the system

As mentioned, the retail sector/concept model created will permit the estimation of lease coverage for existing and prospective tenants.

In this process, the model will also provide an understanding of relative retail tenant risk.

* Evaluating prospective tenants. A prospective video rental tenant believes that a location should realize annual revenues of $1 million. This estimate is based upon similarly located video rental properties.

Based upon the sector regression line, store operating profit should approximate $215,000. With an annual lease payment of $50,000, this equates to a favorable forecasted unit lease coverage ability of 4:1. If the tenant provides storelevel projections, the sector analysis can also be employed to assess their reasonableness.

* Evaluating existing tenants. A video rental operator provides monthly sales figures. Annualized sales for the last six month period are $600,000. Operating profit, based upon the regression line, should be $52,000. With annual lease payments of $80,000, the unit has a deficit lease coverage ability.

Because of the projected unit cash shortfall this tenant appears on a property management exception report to advise close tenant monitoring. Without a strong secondary payment source, timely lease collection may be in jeopardy.

With extensive property management time demands, the sector/concept analysis approach contributes to the ability to monitor properties and tenants by exception. Rather than periodically conducting an extensive financial statement analysis of every single tenant, the focus can shift to those tenants with predicted operating cash flow shotfalls.

As the last example illustrates, one of the most valuable aspects of this system is the enhanced ability to spot tenant problems before the lease stream is interrupted. This understanding of tenant operational characteristics is an aid in determining overall lease quality. Equally, it is helpful in determining a suggested course of action.

When a tenant monetary lease default occurs, the alternatives typically are: to locate an alternate same-use tenant that could employ the leasehold improvements with little modification; to locate an alternate tenant in an alternate business; or to modify the lease structure for the current tenant. Given the operational characteristics of the tenant and the retail alternatives not already represented within the trade area, finding a same-use tenant may appear optimal.

Based upon the sector regression line, a property manager also may determine that the site is or is not operated as efficiently as other comparable same-use tenants. If it is not, then an alternate tenant may be installed that is capable of paying higher rents.

Finally, the property manager may ultimately decide that it would be advisable to modify the tenant's existing lease, if the tenant's operation appears efficient and the operational characteristics of this type of tenant are favorable. In this case, the regression model will assist in lease modification by providing a better understanding of tenant business fundamentals and the primary payment Source. The sector/concept model can be used as a guidepost for determining what the tenant will be capable of paying.


* There are no universal statistics that can be employed to assess tenant performance and operating characteristics. The preceding sector/concept analysis bridges this gap by permiting tenant comparisons based upon operating characteristics. with a focus upon operating cash flow and lease coverage ability. The implications of this functional analysis technique are the more prudent selection of tenants and greater property management effectiveness.

Along the way, this simple tool will help managers be pro-active, rather than reactive, which stands to materially contribute to property performance. At the heart of this analysis technique is this thought: Understanding tenant business fundamentals is a tool that can contribute to property value. For a retail property manager, few things are more important.
COPYRIGHT 1992 National Association of Realtors
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 1992 Gale, Cengage Learning. All rights reserved.

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Title Annotation:Asset Management
Author:Volk, Christopher H.
Publication:Journal of Property Management
Date:May 1, 1992
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