Naming rights - Econometrics

Econometrics

Naming Rights

What is that?

Carlon, Yulong Naming Rights 27/05/2013 1/31

Contents

Introduction

Previous Literature

Data and Variables

Models

Residuals

Tests


Conclusion / References


Introduction

About the topic. This kind of deal are becoming more common in world. Ex: Allianz, Germany, Brazil and Australia. Financial companies see in this kind a good opportunity Ex: Long-term brand, Relationship with sports. (Parmalat). That's why banks have signed more than $2 billion worth of naming rights contracts since 1998, with deals ranging from five to 30 years. Ex: Barclays Center – 20 years, $400 Million (2007).

Introduction

Previous Literature

Data and Variables

Models

Residuals

Tests


Contents



Previous Literature

Valuing naming rights: Greg C. Ashley, Michael J. O’Hara. (2001) Historical background about this kind of deal and when this is started. It is a paper more focuses in explain a relationship between the market and the deals of naming rights. In their model they use 13 variables, some of them are dummy variables. With a total of 98 observations from different national (US) leagues (NHL, NFL, NBA and MLB). What in a Name? Price Variation in Sport Facility Naming Rights: Timothy D. DeSchriver and Paul E.Jensen (2003)

Explain the classification of the name rights in the financial view. As intangible asset, is very hard to quantify how this contributes for the creation of value for one company and how to organize in an accounting balance. How to management this kind of asset and which variables and considerations are important when 2 agents decide to negotiate this kind of deal. Local traditions.

Introduction

Previous Literature

Data and Variables

Models

Residuals

Tests


Contents


The data used on this work to determine the Price of Deal in a contract of Naming Rights include observations about the characteristics of facilities, environments where these facilities are located and economics. Our random sample is compose of 95 observations, which each one is a facility and the data are the type of Cross Sectional. Price of a deal is our dependent variable The model used to determine the Price of Deal are based on that used by Greg C. Ashley, Michael J. O’Hara. (2001): P = XB + δR + ε P: Price of deal X: Independent Variables (what a sponsor is willing to pay for naming rights) δ: Dummy variables about the type of the structures. ε: Error Term


Data and Variables


Data and Variables


Data and Variables

Cost of Construction All values are in millions of dollars (US$). This variable means the cost of construction of the facility. Max Value: US$1500 - New York Yankees Stadium. Min Value: US$1 - Lambeau Field, Wrigley Field, Comiskey Park, Sun Devil Stadium.

Games Shows how many matches have in own facility during the last year. As min value we have 5 and as max value we have 50. The values are more concentrated around the interval of 5 to 25.


Data and Variables

Population The variable Population shows the population in a region where the facility is located Max Value: 38041430 – California Min Value: 2855287 – Utah The values are more concentrated around the interval of 2855287 to approximately 6897012.

Income The variable Income shows the income on average of population in a region where the facility is located, per year. Min value we have US$42279 – Wisconsin Max value we have US$68875 – Indiana The values are more concentrated around the interval of US$42279 to approximately US$55000.


Data and Variables

Othfac Number of other structures that a facility has. Trend: Stadium(0) or Arena(2) The transformation in a quantitative variable improved R-Square of the model.

Capacity How many persons a facility can receive in a match Min Value: 8000 Max Value: 91704. The values are more concentrated around the interval of 20000 to approximately 70000. Security Measures


Data and Variables

Contract How many years has the last contract between a facility and company. In standard, the contracts have duration around 20 years. Max value: 43 – Kiel Center Min value: 0 – Lambeau Field and some others. Interruption and Long Term Contracts.

Teams How many teams play in a same facility. The values are more concentrated around the interval of 1 to approximately 4. Max value: 8 – Staples Center. Min value: 1 – Comercial Park and some others.


Data and Variables

Age How many years have the facility since the start of your construction. The values are more concentrated around the interval of 14 to approximately 60. Max value: 101 – Fenway Park. Min value: 14 – PNC Parl and some others.


Data and Variables

Multi

Explains if a facility is a multi user arena or not. In this variable we considered a multi arena if the facility have structure not only to organize sportive events but social too. 55% of the facilities aren’t multiuser arena. 45% of the facilities are multiuser arena.

Owner Explains if the owner of the facility is public or private. In the beginning the most part of facilities are from public owner but during the years this was changing. 30% of the facilities are public 70% of the facilities are private

Factype Explains if a facility is indoor or outdoor. If have a roof or not. 48% of the facilities have a roof, are indoor 52% of the facilities don’t have a roof, are outdoor

49 46

Factype

Outdoor

Indoor


Data and Variables

Introduction

Previous Literature

Data and Variables

Models

Residuals

Tests


Contents



Models

We used the Ordinary Least Squares (OLS) to estimate our model The general form of our model is: Model (1): Price Deal = β₀+ β₁Othfac + β₂Factype + β₃Owner + β₄Population + β₅Income + β₆Game +

β₇Construction + β₈Multi + β₉Age + β₁₀Teams + β₁₁Contract + β₁₂Capacity + μ

Model (2): Price Deal = β₀+ β₁Othfac + β₂Factype + β₄Population + β₆Game + β₈Multi + β₁₀Teams + μ

Model (3): Price Deal = β₁Othfac + β₃Population + β₄Game + β₅Multi + β₆Teams + μ (no constant)

Model(4): Price Deal = β₁Othfac + β₃Population + β₅Multi + β₆Teams + μ (no constant)


Models

Relation between variables. Ex: Team & Price of Deal (55,36%), Capacity & Factype (47,12%) No perfect colinearity.


Models

Model (1)

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Significant Level


Models

Model (2):

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Significant Level

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Models

Model (3)

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Models

Model(4):

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1%

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10%

Significant Level

Introduction

Previous Literature

Data and Variables

Models

Residuals

Tests


Contents



Residuals

Estimators and Residuals The signals of the residuals change on casual way and this can suggest some correlated errors. The OLS model regression have some hypotheses about their estimators. If one of these conditions are violated you cannot have a inference and consequently is impossible to get the estimators. If we have a presence of Heteroskedasticity, the variance of the error term isn’t constant, we will need to use the robust errors and assume the normality about the errors. If the errors aren’t normal the estimators will remain non distorted and consistent, but we cannot do the inference.

AWA

Introduction

Previous Literature

Data and Variables

Models

Residuals

Tests


Contents



Tests

Test: Breusch-Pagan. With the Breusch-Pagan test, we will estimate if the variance of the residuals are dependent of the values of the independent variables. Results: The P-Value is lower and because of that we refuse the null hypotheses of homoskedasticity: Our model present heteroskedasticity. Test: Ramsey Reset. Ovtest creates new variables based on the predictors and refits the model using those new variables to see if any of them would be significant. It’s a way to detect specification error Results: The P-Value present a higher value, consequently we fail to refuse the null hypothesis and our model result formally correct. We don’t need more variables, because the model doesn’t have omitted variable bias.

http://www.youtube.com/watch?v=lQ3D4CqHbJM

http://www.youtube.com/watch?v=0RrXcm89FAo


Tests

Model (2):

1%

5%

10%

Significant Level

Robust vs Non Robust Model(2)

Equal

More Sig

Less Sig



Introduction

Previous Literature

Data and Variables

Models

Residuals

Tests


Contents



Conclusion

A intangible asset(Naming Rights) can be viewed by different point of views and consequently you have many different kind of models to explain it. Is hard to find data to explain the phenomena. In other words our intuitive thinking carry us to think in variables more specific of the facilities. But they are hard to be captured statistically and because of that don’t make part of the model. Our model try to explain the price of deal based in some economic and social variables. We try to add some variables to describe the characteristics of the structure, because we think that this can have a strong impact in the end of the price. “Modern” structures have different values from “old” structures. Other important point about facilities is the fact of the historical value of each one. This is more hard to create if the facility is arena. More specific more easy to create a identity. Arenas tend to have a higher value than stadiums and in a long term have more ways to increase their value than a stadium, so we can see a tendency of increasing of the numbers of arenas in contrast with the number of the construction of stadiums. Price is a consequence of a synergy from variables from different natures: Historical, economic, social, technological.




References

1. Wooldridge J. (2002), "Introdutory Econometrics: A Modern Approach 2. Valuing naming rights: Greg C. Ashley, Michael J. O’Hara. (2001) 3. Price Variation in Sport Facility Naming Rights: Timothy D. DeSchriver,Paul E.Jensen (2003) 4. NBA.com 5. NFL.com 6. NHL.com 7. MLB.com 8. MLS.com 9. futebolfinance.com 10. en.wikipedia.org 11. ESPN.com: SportBusiness – Stadium Name Rights 12. Brandcritical.blogspot.com(Are Stadium Name Rights an Effective Marketing Practice?) 13. Stata.com



Obrigado

谢谢


Sports

Naming rights - Econometrics