Consumer Confidence Down

With consumer confidence declining, the outlook for retail sales, excluding food and energy, will be affected.  This will be a drag on the demand for retail space.

Newsday reported that “Along with increased gas and food prices, New Yorkers were uneasy about government budget cutbacks, residents told pollsters. “Unfortunately, as we warned a month ago, Mideast turmoil as well as U.S. budget battles punctured the confidence balloon New Yorkers floated in January,” Lonnstrom said.”

In addition, Newsday also reported “Nearly two-thirds of New Yorkers said their finances are being strained by the cost of groceries. Nearly half said both gasoline and food prices are having either “a somewhat or very serious impact on their finances.”

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Posted in Real Estate Market Analysis, Retail Stores | Tagged , ,

Follow Up Long Island Retail

Newsday states “The Valley Stream Waldbaum’s store is among the 32 supermarkets scheduled to close as part of the Great Atlantic & Pacific Tea Co. Inc.’s restructuring plan, according to company court filings.”

The N.J.-based company, which owns stores including A&P, Waldbaum’s and Pathmark, last week filed a motion to shut down three other stores on Long Island and others in six states.”

Posted in Retail Stores | Tagged , , ,

The Long Island Retail Market & New Construction

by Jim MacCrate, MAI, CRE, ASA

The vacancy rate for retail properties on Long Island (Nassau & Suffolk Counties only) has been increasing steadily since 2004-2005 when the vacancy rate was below 4%.  One can drive through the older downtown areas and witness buildings that were constructed during 2006 and 2007 that are still vacant.  Many older retail properties have experienced tenants closing stores, declaring bankruptcy, or defaulting on their rental payments.  In many areas, this situation will not change because the basic demand generators for retail properties are not favorable for the foreseeable future.  Except for prime properties in good locations, effective rents will remain flat or increase only slightly as the overall economic climate improves.  Most retail projects are not cost feasible unless the properties are pre-leased.  If one looks at the demographics of Long Island, it is quite easy to understand the basic underlying factors affecting many retail properties.

Demand For Retail Space – Population

In Market Analysis for Valuation Appraisals, the authors, Terry V. Grissom, Stephen Fanning and Thomas Pearson, illustrate two simple relationships that can provide an understanding of a retail market.  First, the ratio of retail space in the Long Island market area to the total population can be analyzed.  As indicated in the following chart, Long Island’s population is relatively stable, especially in Nassau County.

Population Nassau and Suffolk Counties

Where is the future population growth going to come from to drive the demand for retail space without a major change in zoning?  The following chart indicates the percent change in population year over year.

Clearly, the chart indicates the trend in the population in Nassau County was negative in certain years during the last decade, while the overall population rate of growth in Suffolk County has been declining.  The amount of retail space per person has been increasing gradually.

Demand For Retail Space – Households

The second ratio that is important to consider is the number of square feet of retail space per household.  According to the US Census, in 2000, it was estimated that there were approximately 981,563 households in Nassau and Suffolk Counties.   As the following chart indicates, the rate of growth in the number of households in the bi-county area has been declining and has been relatively flat in the last couple of years.

While the number of households is leveling off, the amount of retail space per household continued to increase based on estimates in 2010.

Demand For Retail Space – Aging Population

The population on Long Island in the bi-county area is aging which will change the trends in retail sales.  The following chart indicates the change in the age of the bi-county population based on data supplied by Woods & Poole and analyzed by MacCrate Associates LLC.

The rate of the growth in the population over 65 has started to accelerate.  Retail owners and developers must take into consideration the aging population in determining the use of vacant land or the repositioning of vacant retail space.  The population under 24 is relatively unchanged, but some school districts are considering closing elementary schools which indicates that this age segment will continue to decline in the near term.  This data suggests that the population between 35 and 64 reached a peak in 2008 or so.

Retail Sales on Long Island

MacCrate Associates LLC has compiled data from Woods & Poole to calculate the historical retail sales in Nassau and Suffolk Counties and to estimate retail sales going forward.  The following chart summarizes the findings.

The trend was clearly positive from 1995 through 2006-2007 but declined drastically in 2008 and 2009.  Recovery will be very quick initially, but will, then, probably level off with a gradual increase per year tied to inflation.

MDM Appraisals, LLC and MacCrate Associates LLC analyzed the retail sales tax receipts for the bi-county area and estimated the percentage change in retail sales from 2000 through 2009 as indicated in the following chart.  It is important to note that the sales tax includes certain goods and services and may exclude certain goods and services.  It is only used as a proxy for retail sales.  The STDBonline actually estimates a lower retail potential but it does not include sales to businesses.  It is extremely difficult to obtain the actual figures for retail sales that would provide a basis for estimating the income generated from retail space. 

The Nassau County’s Comptroller’s Comments On The Proposed Nassau County 2011 Budget And Multiyear Financial Plan, dated October 6, 2010 indicated “The anticipated 2011 sales tax revenue will only recover to about the pre-recession 2008 levels.” 

The situation in Suffolk County is similarIn the 2011 Capital Budget and Proposed Capital Program for 2011 to 2013, the Suffolk County Executive stated “Suffolk County has encountered high levels of real property tax delinquencies, significant levels of unemployment, and substantial decreases in sales tax revenues. In the early part of 2010, we are seeing some improvement but we have a long way to go.”

In Newsday, it was recently reported that in 2010, Nassau County collected $1 billion, or 6.7% higher than in 2009, according to Comptroller George Maragos, while Suffolk County indicated an increase of 6.9%.  Increasing energy prices have had some impact on sales tax revenues but that does not indicate a demand for retail space.

MacCrate Associates LLC has developed the following estimates in the change in retail sales based on data from Woods & Poole.

While this indicates that retail sales jumped in 2010, it is expected that the rate of change will be relatively flat going forward at less than 2% in the bi-county area.

Conclusion

While certain communities will continue to experience low vacancy rates in the bi-county area, recent trends do not support strong demand for retail space on Long Island in general.  Any new retail space that is constructed, generally, does not attract new business and only hurts existing retail businesses.  In addition, the impact of internet sales on the demand for retail space is difficult to quantify, but many individuals prefer point and click versus going from store to store.  If the population and the number of households remain relatively stable, the only increase in retail sales will be associated with inflation, specific items such as energy, or an increase in income per household.  That cannot be anticipated in the near term.

Special Thanks to Maureen McGoldrick, MDM Appraisals, LLC and Max Ramsland, MAI, CRE, Ramsland-Vigen, Inc for their contributions.

Posted in Highest & Best Use, Real Estate Market Analysis, Retail Stores | Tagged , , | 2 Comments

Cost of Capital: Applications and Examples, 4th Edition

Cost of Capital: Applications and Examples, 4th Edition

I recommend this book for anyone involved in the valuation of business interests.  The following comments have been made by others. 

Praise for Cost of Capital, Fourth Edition

“This book is the most incisive and exhaustive treatment of this critical subject to date.”
—From the Foreword by Stephen P. Lamb, Esq., Partner, Paul, Weiss, Rifkind, Wharton & Garrison LLP, and former vice chancellor, Delaware Court of Chancery.

Cost of Capital, Fourth Edition treats both the theory and the practical applications from the view of corporate management and investors. It contains in-depth guidance to assist corporate executives and their staffs in estimating cost of capital like no other book does. This book will serve corporate practitioners as a comprehensive reference book on this challenging topic in these most challenging economic times.”

Robert L. Parkinson Jr., Chairman and Chief Executive Office, Baxter International Inc., and former dean, School of Business Administration and Graduate School of Business, Loyola University of Chicago

“Shannon Pratt and Roger Grabowski have consolidated information on both the theoretical framework and the practical applications needed by corporate executives and their staffs in estimating cost of capital in these ever-changing economic times. It provides guidance to assist corporate practitioners from the corporate management point of view. For example, the discussions on measuring debt capacity is especially timely in this changing credit market environment. The book serves corporate practitioners as a solid reference.”

Franco Baseotto, Executive Vice President, Chief Financial Officer, and Treasurer, Foster Wheeler AG

“When computing the cost of capital for a firm, it can be fairly said that for every rule, there are a hundred exceptions. Shannon Pratt and Roger Grabowski should be credited with not only defining the basic rules that govern the computation of the cost of capital, but also a road map to navigate through the hundreds of exceptions. This belongs in every practitioner’s collection of must-have valuation books.”

Aswath Damodaran, Professor, Stern School of Business, New York University

“Pratt and Grabowski have done it again. Just when you thought they couldn’t possibly do a better job, they did. Cost of Capital, Fourth Edition is a terrific resource. It is without a doubt the most comprehensive book on this subject today. What really distinguishes this book from other such texts is the fact that it is easy to read—no small feat given the exhaustive and detailed research and complicated subject matter. This book makes you think hard about all the alternative views out there and helps move the valuation profession forward.”

James R. Hitchner, CPA/ABV/CFF, ASA, Managing Director, Financial Valuation Advisors; CEO, Valuation Products and Services; Editor in Chief, Financial Valuation and Litigation Expert; and President, Financial Consulting Group

“The Fourth Edition of Cost of Capital continues to be a ‘one-stop shop’ for background and current thinking on the development and uses of rates of return on capital. While it will have an appeal for a wide variety of constituents, it should serve as required reading and as a reference volume for students of finance and practitioners of business valuation. Readers will continue to find the volume to be a solid foundation for continued debate and research on the topic for many years to come.”

Check out the table of contents.

Posted in Appraisal Process, Business Valuation Issues, Discount Rates, Real Estate Valuation Methodology | Tagged ,

Risk Management Association – The Current Status of Long Island’s Commercial Real Estate Market and What Risks Lie Ahead

Risk Management Association

Posted in Appraisal Reviews, Uniform Standards of Professional Appraisal Practice

Wall Street Has Not Learned

As a follow up to this blog post, Wall Street Has Not Learned The Lessons That Created FIRREA And The Debacle of the Seventies In Real Estate Lending and Investing!, three interesting articles seem to clearly indicate the trends will continue.

Caveat Emptor, Continued…………………
Signs of Risky Lending Emerge
Bond Sale? Don’t Quote Us, Request Credit Firms.

It is interesting that we teach our children to take responsibility for their actions, but corporate Wall Street and their advisors do not!  They pass the debt off to unsuspecting investors who can not possibly do the proper due diligence and make a buck in the process, even if the loans go bad, and they know they will, along with the lenders who make them!  They set a very bad example, along with their CEOs who lead those firms in investment banking and lending.

Posted in Appraisal Process, Ethics, Real Estate Litigation Support | Tagged , , , , , | 3 Comments

MALLS, ANCHORS & CAPITALIZATION RATES: AN EXCELLENT QUESTION

By

Maxwell O. Ramsland, Jr., MAI, CRE
Ramsland & Vigen, Inc., 302 W. Superior Street, Duluth, MN 55802
 
Shannon M. Luepke
Ramsland & Vigen, Inc., 302 W. Superior Street, Duluth, MN 55802
 
Introduction
Malls and their modus operandi are fascinating in their complexity. The first enclosed mall, Southdale in Edina, Minnesota was built in 1956, and 36 years later within five miles of Southdale the Mall of America was built in Bloomington. These two malls demonstrate a complete cycle in mall design and efficiency, and exhibit considerable contrasts in design, layout, materials, etc.
 
The typical regional or super regional shopping center, and more recently the life style center, has changed over the past 50 years. The catalyst for this transformation has been for obvious reasons: reduced costs, better customer and tenant efficiency, and most importantly, to maximize their revenue producing capability. For the smaller inline tenants as rents increased their demised square footage decreased; gone were the back rooms and storage areas, store fronts shrank as did store depths. The typical inline space went from approximately 2,500 square feet to ±1,800 square feet. Changes have also taken place for the anchor department store. In the 1960s and 1970s, the typical anchor space was near or exceeded 200,000 square feet, often approaching 300,000 square feet. By the late 1990s, anchor store sizes decreased to 100,000 to 170,000 square feet on average, with the mean being approximately 150,000 square feet.
 
What has not Changed at the Mall is the Profit Motive 
Most, if not all retail activity is driven by optimizing retail sales. As measured by the PriceWaterhouseCooper/Korpacz Real Estate Investor Survey, mall inline sales in excess of $600 per square foot of GLA represent a Class A+ mall, the very best, while average sales approximate $300+/- per square foot of GLA. With the successful mall’s first objective being to maximize retail sales, tenant space size and layout are designed to increase selling areas and minimize non-selling areas. As store sizes evolved, leasing patterns and rental rates also evolved, with rent being directly tied to sales productivity. For example, if the overall sales level of a particular mall is generating sales of $400 per square foot, and the average base rent is 7% of sales, the base rent then approximates $28.00 per square foot. Some, of course, would be higher, e.g. smaller stores near the mall’s center court, food court, kiosks, etc., and some would be lower, e.g. large inline tenants, book stores, etc. This brings up the existence of the secondary mall occupant: the anchor department store.
 
While the average mall might contain 300,000 square feet, and have 150 inline tenants, anchor department stores, on the other hand, are single tenant operations usually containing 100,000 to 170,000 square feet each. Anchor department store sales average about $180.00 per square foot, and rents are traditionally between 2% to 3% of sales, resulting in rents of between $3.00 and $6.00 per square foot when they are owned by the mall and leased to a particular retailer (Macy’s, Dillard’s, Sears, etc.).1 Many anchor department stores, however, are independently owned, and they are often bought and sold independent of the mall.
 
Critical Information Sought by Real Estate Analysts  
When a mall sells, the critical information sought by real estate analysts is the net operating income (NOI) and the sales price. For example, a mall that sells for $100,000,000, and has a net operating income of $7,500,000, will have an overall capitalization rate (OAR) of 7.5% (NOI of $7,500,000/$100,000,000 sales price = .075). Industry professionals constantly monitor the marketplace for mall sales, for the resulting OAR of a sale is a key component in valuing a mall and a leading indicator of existing valuation trends. From the above example the derivative of the transaction can be transformed to units representing the income and price per square foot in units of GLA. The corresponding NOI is then: $25.00 per square foot of GLA and the sale price is $333.33 per square foot. The OAR remains the same: 7.5% ($25.00/$333.33 = .075). Reducing the sale’s components to units of the GLA allows the analyst to make comparisons based on the industry’s most relevant common denominator.
 
Department Store’s Capitalization Rate versus that of a Mall’s Capitalization Rate  
Recently, a discussion of mall sales and anchor department stores took place at an informational meeting between representatives of Dillard’s department store and the Williamson Central Appraisal District (WCAD) in Georgetown, Texas. When the question of a department store’s capitalization rate versus that of a mall’s capitalization rate came up, Alvin Lankford, RPA, the WCAD’s Chief Appraiser, observed that those companies publishing national real estate investment survey reports feature surveys that are essentially perceived to represent only the inline portion of the mall. He went on to ask, “What impact on the mall’s OAR does the presence of one, two or more anchor department stores have when they are owned by the mall and included in the sale?”
 
An Excellent Question 
Mr. Lankford is correct that PriceWaterhouseCooper/Korpacz and RERC investor surveys do track capitalization rate information for national shopping centers, but both surveys are silent on anchor department store capitalization rates or any residual impact they may have on a mall’s overall capitalization rate. A published proxy for the department stores might be in the category of the national power center, which typically has a high ratio of single story big box properties, many of which approach the size of anchor department stores. But, power centers do not enjoy a mall’s possibly dominant location, nor do they have the department stores’ physical characteristics of larger sizes, and/or second or third stories, a condition anathema to most big box retailers. So, does the mall’s superior location offset the department stores unique physical attributes? Anchor department stores when they sell, and if leased, typically have capitalization rates of 100-200 basis points higher then those of similarly aged national shopping centers. Higher department store capitalization rates are frequently attributed to the slower growth expectations of retail sales, obsolescence due to size, multi-stories, existing trade dress, and physical recapitalization issues.
 
However, the inescapable conclusion is that there exists a symbiotic relationship between a mall and its anchor department stores. One needs the other to maximize sales and profits. However, the difference of a 300,000 square foot, 150 inline tenant shopping center versus a 600,000 square foot shopping center with two or three anchor department stores presents an entirely different set of investment and/or risk considerations for the investor, appraiser or assessor.
 
Getting back to Mr. Lankford’s question: if our hypothetical 300,000 square foot inline mall had a 150,000 square foot department store included in the sale, what impact on the mall’s NOI and OAR would the anchor department store theoretically have on the mall diagnostics?
 
Potential Theory
Our theory would suggest that more square footage, a lower NOI per square foot, and a lower price per GLA, ceteris paribus, would result in a higher OAR. Expanding our earlier example and adding an anchor department store, the hypothesis bears this out, as shown below.
Mall:                      300,000 sq. ft. x $25.00  =   $7,500,000
Anchor:                150,000 sq. ft. x $ 4.00   =         600,000
Total/Average:    450,000 sq. ft. x $18.00  =    $8,100,000
The mall now has a 450,000 square foot GLA, and an average NOI of $18.00 per square foot or, in other words, 50% more square feet of GLA, and a 28% reduction in rent per GLA. The theoretical adjustment can be calculated as follows:
 
Adjusted OAR   =       .075  x  450,000   x  $18.00
                                                    300,000      $25.00
                           =          .075   x  ( 1.5  x   .72 )
                           =          .075   x   1.080
                           =          .0810
                                    Say: 8.1%
 
The adjusted NOI would increase the resulting overall capitalization rate (OAR) approximately 60 basis points (.0810 – .0750), or an approximate 8% increase in the OAR. Employing the same methodology, the corresponding OAR rates for two department stores is 8.67%, and three stores is 9.30%; again approximately 60 basis points per anchor department store. Thus, the theory could conclude that as the total income increases, the NOI per square foot decreases, the sales price per square foot decreases, and the corresponding OAR increases.
 
This is the simple theory of the department store influence on mall OAR. The follow-up question is: Is the theory supported by market evidence? The authors’ contention is that there is ample market based evidence to conclude that the existence of one, two, three or more anchor department stores in a mall will have an incremental affect on a sale’s OAR.
 
The Model 
The process to test the department store capitalization rate evidence will include three variations on an econometric model. The empirical evidence will include 53 observations of shopping center sales that have taken place between 2000 and 2006. The independent variables include the date of sale, sold GLA (includes inline space and the anchor department stores, if any), the effective age of the mall (i.e., the year built with adjustments for upgrades), the number of anchors, and location. The dependent variable is the overall capitalization rate (OAR).   

Presented below are two graphs of the relationship of the model’s NOI per GLA and sales price per GLA experienced as an OAR. The first graph identifies the relationship of price per square foot of NOI and sales price. The second graph demonstrates the dichotomy of the model color coding the components of the mall, e.g., no anchor, one anchor, etc., and the OAR. It may be obvious to the observer that the lower NOI values represent malls with anchors, and the higher values represent essentially inline space. The model has a tendency to self-compensate for size, rental rates and other disparities. The graphs are presented below.    
 

First Graph Analysis of Data   

   Second Graph Analysis of Data   

   

The mall sales observations are summarized below.  It is interesting to note that the unadjusted data tends to confirm the theory by itself. This is not surprising insofar as the average age is approximately 20 years. 

          Difference in
    No. Ave. NOI/Sq. Ft. Ave Cap Rate Basis Points
No Anchor   24 $18.71 8.68%  
1 Anchor   10 $12.83 9.07% 0.39
2 Anchors   7 $8.18 9.22% 0.53
3 or more Anchors   12 $7.09 10.84% 2.15
Total/Means   53 $13.58 9.31% 1.02

 
 

The universe of data was tested using three options. The independent variables in all models included date of sale, the GLA sold, effective age, anchor department stores, and a subjective variable for location. The dependent variable is the OAR. The models examined three variations on the identification of the anchor department store. All variables were determined to be significant, but to varying degrees.
 
Model 1: The model employed an independent step variable identifying one, two or three or more anchor department stores. This variable produced a T-score of 2.6, and was significant to 1%. Overall, the model had an adjusted R2 of 56.2, a standard error of .013427, and a 1% significance (a 99% confidence level). While this model is significant, it presupposes that all additional department stores have an equal weighing in the formula. This may or may not be true. The theory section suggests that it may be a correct hypothesis.
 
Model 2: This model employed an independent variable for one department store, another for a second store, and a third for three or more stores. In the aggregate, the T-factor was equal to that in Model 1, but individually only the three or more anchor department store variable was independently significant. The R2 equaled 54.0, standard error was .01375, and the significance was at the 1% level. The coefficients for the department store start out lower and increase as the number of department stores increase, thus suggesting that additional department stores influence is not uniform.
 
Model 3: This model bifurcated the department stores into two independent variables, namely 1 and 2 department stores, and 3 or more department stores. The anchor store T-factor was again equal to that of the earlier models, with 3+ anchors being significant. The R2 equaled 57.1, standard error was .013286, and the significance was at the 1% level. Statistically, Model 3 is the preferred model.
The anchor department store issue is significant insofar as our hypothesis suggested that the advent of one anchor had the greatest percentage effect on the NOI, reducing the original NOI from $25.00 per square foot of GLA to $18.00 per square foot of GLA; a change of 28%. The change is not corroborated by the department store coefficient alone. Other variables must contribute to this condition providing the department store OAR increment theory is to be proven true.
 
The other independent variables were also tested. Individually: date of sale, sold GLA, effective age, and location all tested significant at <5%. Overall, all the models were significant at the 1% level.   The dependent variable is the OAR.  The means and assumptions of the independent variables are as follows: 
 
Date:                          February 2004 (tested at February 2004)
Sold GLA; Mean:    492,270 sq. ft.
Tested at:            300,000 sq. ft. no anchor
                          450,000 sq. ft. one anchor
                          600,000 sq. ft. two anchors
                          750,000 sq. ft. three anchors               
Effective Age, Mean: 20 years (tested at 10 and 20 years)
Location:  From a range of 1 being a weak location to 4 being a stronger location. In all likelihood, considering the ages of the universe, data equates to a Korpacz range of A-A+ (tested at 4).  
The model’s output, assuming the above variables and an effective age of 10 years, and three variables of department stores, resulted in the following OARs. 
 
 
 

 

Category   

  Model 1  Model 2   Model 3   Average

No anchor   

  6.94%   7.12%   7.05%   7.04%

1 anchor   

  7.71%   7.49%   7.48%   7.56%

2 anchors   

  8.48%   7.83%   7.91%   8.07%
3+ anchors   9.25%   9.65%   9.50%   9.47%

 The corresponding OAR for a 20 year old property is as follows: 

Category    Model 1    Model 2    Model 3    Average
No anchor    7.42%    7.44%    7.47%   7.44%
1 anchor    8.19%    7.81%    7.90%   7.97%
2 anchors    8.95%    8.15%    8.33%   8.48%
3+ anchors    9.72%    9.97%    9.92%   9.87%

Then, comparing the theory with the mean outputs from the 10 and 20 year age analyses, the following graph demonstrates these relationships.   

 

 
                              Theory      10 years       20 years      
No anchor            7.50%           7.04%                 7.44%
1 anchor              8.10%   60    7.56%      52     7.97%      53
2 anchors            8.67%   57   8.07%       51     8.48%      51
3+ anchors         9.30%    63   9.47%    140     9.87%     139
       Means           8.39%             8.04%                 8.44%

Conclusions  
From the empirical evidence presented herein, it is suggested that the theoretical impact of having an anchor department store in a mall sales is largely corroborated by market based evidence. Our theory discussion suggested a difference of approximately 60 basis points for malls with up to three department stores. The market based evidence suggested 50 basis points for adding one or two anchors each, and the increase for the third plus is 100 basis points. The overall conclusion suggests that anchor department stores add approximately 50 to 240 basis points to a mall’s sales OAR as opposed to sales without an anchor department store included in the sale.   
 

It is noted that traditional wisdom concludes that the existence of an anchor department store will bring stability to a mall’s financial statement, reduce the presumption of risk, and result in lower overall capitalization rates. This perception is evidenced in institutional studies measuring the difference between anchored malls and non-anchored malls, e.g., specialty malls, strip malls or power centers. All of the malls included in this study are anchored malls. The distinction is whether or not the anchor(s) are included in the sale.   

It is further noted that the studied analysis is time, age, size, and location sensitive. The coefficient for time is negative, demonstrating that OARs are decreasing at approximately .5% per annum over the study period. This decrease roughly coincides with the decline in rates measured by Korpacz for institutional grade properties over the same period. While all of the properties in the model could not be considered of institutional grade, the data conclusively suggests that the advent of an anchor department store, in all probability, should result in an increase in the transaction’s reported overall capitalization rate.   

The authors wish to acknowledge James B. Robinson, CMI, National Director of Property Taxes at Dillard’s Department Store for initiating this discussion, and Alvin Lankford, RPA, and the appraisal staff of Williamson Central Appraisal District, Georgetown, Texas.   

1Department store rents tend to be based on a base rent plus a decreasing rate of overage rent beyond a given breakpoint. Thus, as sales increase, rents increase, but rents increase at a decreasing rate. In other words, the higher the sales, the higher the rent, the lower the rent as a percentage of sales. The inverse is also true.

 

 

Posted in Appraisal Process, Capitalization Rates, Discount Rates, Real Estate Valuation Methodology | Tagged , , , , , | 1 Comment