Applying the Hedonic Pricing Method and Cost Benefit Analysis to Sand- Feeding Projects - an Empirical Pilot Survey in Gribskov Municipality (30ECTS)

University of Copenhagen MSc in Environmental and Natural Resource Economics

Master Thesis Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality (30ECTS)

Main Supervisor Søren Bøye Olsen (50%)

Co-Supervisor Toke Emil Panduro (50%)

Student Name Konstantinos Douvis

Student number wgk869

Submission Date 15th December 2014

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

Picture Cover.1 - Taken by the author on June 2013 presenting the coast-protection installations in Gribskov Municipality Copies printed: 4

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

Table of Contents

 Abstract…………………………………………………..5  Preface…………………………………………………....6  List of tables……………………………………………..7  List of figures…………………………………………….8  Chapter 1 -Introduction…………………………………9  Chapter 2- The theory o 2.1 Cost-Benefit Analysis………………...... 14 o 2.2 Hedonic Price Method………………...... 19 o 2.3 Previous examples of C.B.A…………………23 o 2.4 Previous literature of H.P.M…………………25  Chapter 3 - The data o 3.1 Data for the project…………………………..29  Chapter 4 – Analysis and results o 4.1 Estimating the Costs………………………....33 o 4.2 Estimating the Benefits using the HPM……..35 o 4.3 The Cost-Benefit Analysis…………………...38 o 4.4 Sensitivity Analysis  4.4.1 Discount rate (r)……………………………41  4.4.2 Time horizon……………………………….44  4.4.3 Dead-weight loss…………………………...46  Chapter 5 - Policy proposal o 5.1 Presenting the results………………………...49 o 5.2 Policy proposal……………………………....51  Chapter 6 - Conclusion…………………………………53  Bibliography …………………………………..………..57  Appendix Ι …………………………………….……...61  Appendix II ……………………………………………65

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

Abstract

This report investigates the impacts of a sand-replanting project which occurred in Gribskov Commune in the city of Copenhagen, Denmark. Due to erosion from sea tides and winds most of the sand has been removed from the beaches of Gribskov and it is stuck in the area of the new harbor. The Municipality thought of a project in order to restore the beaches to their previous situation. They are going to do sand-feeding to the beaches with a ship and that will eventually add value to the prices of the houses in the area and attract more tourists. In order to give a monetary value to these improvements a Hedonic Pricing Method has been applied to this study. By using data from houses in the area and numerous other property-related characteristics, it has been found that the sand-replanting project has a positive impact on property values and therefore the project should be implemented. However, when we take into consideration the dead-weight loss we conclude that the project should not be financed with indirect taxes.

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

Preface

This report is a 30 E.C.T.S. Master Thesis in Environmental and Natural Resource Economics. It is written for the Faculty of Science, University of Copenhagen.

I would like to express my special thanks to my supervisors, Søren Bøye Olsen and Toke Emil Panduro, who fully supported me throughout the whole process of this thesis with useful feedback, felicitous remarks and were always keen on listening to my problems, concerns and questioning.

I would also like to thank my family and friends for their precious help and support during this whole process. I know I was not the kindest person to deal with the last 6 months and I would like to thank them for their understanding.

This report is the final step towards my Master degree. This thesis reflects everything I have been taught during the last two and a half years not only in Copenhagen, but also in Melbourne, Australia. I feel obliged to thank all the people that our paths crossed into this long, but indulgent journey, because one way or another they all affected my life.

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

List of tables

Table 2.1 – Classification of goods according to their excludability and rivalry….27

Table 5.1- N.P.V. with different discount rates……………………………………51

Table 5.2- N.P.V. with different discount rates (dead-weight loss included)……...52

Table 6.1 – N.P.V. of the project with and without dead-weight loss……….…….54

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

List of figures

Figure 2.1 – Equilibrium in a hedonic price market……………………………..…21

Figure 3.1 – Gribskov Commune…………………………………………………...31

Figure 3.2 – The west side of Gribskov Commune…………………………………31

Figure 3.3 – The east side of Gribskov Commune………………………………….32

Figure 4.4.3.1- Dead-weight loss with indirect taxes……………………………….46

Figure 4.4.3.2- The change in the welfare of society by imposing an income tax….47

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

Chapter 1 - Introduction

The Municipality of Gribskov (Gribskov Commune) is a Municipality in Capital Region (Region Hovedstaden) that covers an area of 278 km (28.000 ha)(Gribskov Commune, 2004). It is in the north area of Copenhagen, Denmark. The northern part of the Municipality used to be full of sandy beaches for several kilometers. The inhabitants of the Municipality (and not only those ones) used to spend most of their weekends and summer holidays there enjoying the sea and the sand. Also, they have bought or rented summer houses next to the beaches in order to be able to stay there as much as they want to. Those houses contribute to half of property taxes in the municipality with almost 200 million DKK per year (Wandall, n.d., a). As it is easily understandable characteristics such as distance from beach, amongst many others, are affecting the price of the house in the real estate market.

Picture 1.1 - Gribskov Municipality as it is presented on its webpage. You can see all the forests (areas in green color), the lakes (areas in blue color), railways (the black lines), of course all the summer houses that there are in the Municipality (the black dots) and the different names of the areas of the Municipality.

Unfortunately, over the last years due to strong winds and sea tides the sand has been removed from the beaches and has been transferred on the eastern side of Gribskov where the new harbor is (Gilleleje). As a result, most of the beaches nowadays have no sand at all and they are full of huge or small rocks. There is no need to mention that rocky beaches are not attractive to swimmers or fishermen and of course, they are not safe for kids to play around. This leads to houses being

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality abandoned and those ones available for renting to being unwanted. Moreover, this area’s income is based on tourism. Restaurants, cafes, little shops, are all based on money brought in the area by the owners of the houses or the people coming to Gribskov for holidays. It is easily understandable how disastrous the sand deficiency from its beaches is for the Municipality of Gribskov. Keep in mind that the municipality’s annual tourism revenue is 1,056 million DKK (Wandall, n.d., b)! Here are some pictures taken in the area of Gribskov Monucipality showing the situation in June 2013:

Pictures 1.2 & 1.3 - Taken by the author on June 2013 showing sandy beaches in the Gribskov Municipality

Pictures 1.4 & 1.5 - Taken by the author on June 2013 showing rocky beaches in the Gribskov Municipality For more pictures showing the situation nowadays see Appendix II.

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

Therefore, the Municipality decided to do something in order to face the problem. In the past the Municipality made some protection installations, but sand is a prerequisite for their efficiency. Here is a picture of those installations, so that you can have it in your mind:

Picture 1.6 - Picture taken by the author on June 2013 presenting the coast-protection installations. For more pictures see Appendix II So now, as it is described by Jacob Wandall (Wandall, n.d., c) in his report “Oplæg til specialeemne: Cost Benefit Analys/Værdisætningsanalyse - Sandfodring i Gribskov kommune” (Translated in English: Launching a thesis topic: Cost Benefit Analysis / Valuation Analysis - Sand Feeding in Gribskov municipality) [Appendix I], it is their desire to do sand-replanting once a year in order to reset the coastlines to their previous standards. Their plan is to rent a ship which will sail across the beaches and it will throw away enough sand to restore the coastline and thereafter, once a year the ship will replant as much sand as it is necessary for the coast to be maintained. Wandall has estimated that this project will cost 100 million DKK for full recovery of the beaches over a 3-year period time and afterwards 5 million DKK per year for maintenance (Wandall, personal communication). There is but one thing left, which is to decide if they will proceed with the project or not. In order to be able to reach an agreement on the Municipality’s council concerning the implementation of the project, an unbiased university researcher has to do an evaluation of the project. His role will be to conduct a Cost-Benefit Analysis of the project without political influence and purposefulness. In order to do the evaluation I decided, under the supervision of Søren Bøye Olsen (Lector in the Department of Food and Resource Economics, Section of Environment and Natural Resources) and Toke Emil Panduro (Postdoc in the Department of Food and Resource Economics, Section of Environment and Natural

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

Resources), to do a Cost-Benefit Analysis, using the Hedonic Price Method. This thesis is about to use the Geographical Information System (from now on G.I.S.) to estimate the distance of the houses in the area from the different factors that could probably affect their price such as forests, lakes, the harbor and of course the coast. The goal is to compare the prices of the houses according to their distance from the beach. The best way to do it would be to compare the prices of the houses close to sandy beaches with those close to rocky ones. For otherwise identical houses, any difference in house price would reflect the value that the people put on a sandy beach. But, due to lack of time (this is a 6-months period Master Thesis) the thesis will be a simple comparison of the house’s distance from the beach. That way I will have enough results, so that I will be able to draw some conclusions and make my policy proposal. After I have calculated the benefits, I will use the costs provided by Mr. Wandall (Wandall J., personal communication) and we will calculate the Net Present Value of the project. Of course a sensitivity analysis is necessary before reaching my conclusion and doing my proposal. To sum up, the northern part of the Gribskov Municipality used to be full of sandy beaches for several kilometers. A considerable number of summer houses have been built in the area (8,716 summer houses, Panduro T. E., personal communication), which took place because of the sandy beaches. But, due to sea tides and strong winds the sand has been removed from the beaches and it has been transferred in the east part of the Municipality where the new harbor is (these is no way to escape from there so it is stuck). As a result, most of the beaches have been transformed from sandy beaches ideal for families and swimmers, to spend their vacations, into rocky beaches (with smaller or bigger types of rocks) that are unattractive for families and people who love the sea and sunbathing. This means that, the revenues of Gribskov Municipality have been decreased and this inevitably affects the standards of living as well. Less money for the Municipality means less money available to spend in social benefits. Moreover, a lot of cafeterias, restaurants and little shops have been established in the region of Gribskov. If there are no more visitors in the area their contribution to the common fund will be dramatically decreased, so the economy of this local community will suffer tremendously. As it is easily understandable, the deficiency of sand from the beaches of Gribskov Commune affects the economy of the Municipality to a great extent. Therefore, the Municipality decided to do a sand-replanting project in order to restore the beaches and of course keep their revenues high! This project will affect positively both the owners of the houses, the tenants of the houses and the Municipality (we explained in details previously how the incomes of the Municipality are affected by the summer houses and tourism). Therefore, the Municipality decided to deal with this problem and solve it as soon as possible. The research question needed to be answered though is: “What is the welfare economic impact of changing the coastline of Gribskov Municipality from a rocky to a sandy beach?”. The benefits are equal to the difference of the price between a house close to a sandy beach and an identical house next to a rocky one. So, what this thesis will try to do is to find an answer to the previous question, which is the real question needed to be answered. Keep in mind though, that this is not an easy task. In all probability, there will not be enough time to complete my research (6 months is such a short time for projects like this) and therefore I will do a much simpler research such as “How does the distance to the beach affect the price of a summer house?”. That is a simplifications I decided to do. By doing so, this thesis will be able to provide us with

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality some benefits that I would put in the Cost-Benefit Analysis and reach a first conclusion. Afterwards, I will have the opportunity to analyze the results and explain what the next step should be or what we would propose the Municipality do. Given that this project is still just an idea that the Gribskov Municipality has, this thesis could provide enough reasons to move forward with it or abandon the idea of the project. After all, some results are always better than no results. In Chapter 2, the theory chapter, we explain why we should do a Cost-Benefit Analysis in order to reach a conclusion. Also, we are providing enough reasons why we chose to use the Hedonic Price Method instead of others and some previous examples of the C.B.A being used. We are focusing more on the H.P.M. providing previous literature, the rationale behind it and a considerable number of examples being used. In the next Chapter, Chapter 3, we are presenting the data that I am going to use in my analysis. We are presenting all the data that this thesis is going to be based on and explain how they are collected and used. In Chapter 4, we are actually doing the Cost-Benefit Analysis. We calculate the benefits and the costs and in the end the Net Present Value of the project. Moreover, we are doing a sensitivity analysis by questioning the discount rate used and the time horizon used in this project, before we reach our conclusion and express our proposal. Furthermore, we are having an extensive discussion about the dead-weight loss. Moving forward to Chapter 5, we analyze the results of our research and present our proposal. We reach our conclusion and present reasons for that. The last Chapter, Chapter 6 is the one we are making our conclusion and a summary of our thesis. Of course at the end of this book there is a full list of bibliography on which some of our work is based. And also in Appendices I and II you can have a look at the full description of the project as it was presented to us by Jacob Wandall and at some pictures from the Gribskov Municipality taken by the author showing the situation nowadays.

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

Chapter 2 - The theory

2.1. Cost-Benefit Analysis

It is common sense that governments as well as municipalities have certain budget constraints while at the same time they have to deal with different problems. As a result, the politicians have to decide on which problems they will focus and after that how they should allocate their money amongst the different proposed solutions. This is not an easy decision to make therefore economists have to come up with different ways in order to make politician’s decisions easier. The most widely known amongst them is the Cost-Benefit Analysis. The “father” of Cost-Benefit Analysis is considered to be the French Jules Dupuit (18 May 1804 – 5 September 1866) who wrote the study “On the Measure of the Utility of Public Works” on 1844. Of course a massive discussion has been raised from academics all over the world concerning the paternity of Cost-Benefit Analysis and if Dupuit was the actual father of it, but still he is considered one of the first teachers of C.B.A.. Tons of ink have been split about this issue and even more papers have been published. In every seminar concerning the C.B.A. the discussion inevitably turns around him and arguments claiming both sides are given. One example of how controversial an issue it is given by Ekelund R.B. Jr, in his book Jules Dupuit and the Early Theory of Marginal Cost Pricing (Ekelund, 1968): “The name of Jules Dupuit, the nineteenth-century French engineer, has been frequently invoked in contemporary economic literature concerned with marginal cost pricing (Hotelling, 1938, pp. 242-44; Nelson, 164, vii-viii) and cost-benefit analysis (Prest and Turvey, 1965, p. 682). Although his contributions in the area of utility theory (Stigler, 1950), consumers’ surplus (Houghton, 1958), and price discrimination (Edgeworth, 1912) were, by any standard, remarkable for the time, his role as proclaimed mentor of the modern theory of marginal cost pricing and, more generally, of cost-benefit theory has been largely unexplored and often misunderstood. The result has been a general confusion among modern theorists concerning his achievement in this area1. Most writers have not bothered to investigate Dupuit’s original works and, following Hotelling’s original attribution, have simply accepted Dupuit as the first marginal cost theorist. Ragnar Frisch, Hotelling’s first critic, may be placed in this camp (Frisch, 1939, p. 145). Such neglect has probably been nurtured by the relative obscurity of his writings and by the fact that, until recently, only two of his economic articles have been translated into English (Dupuit, 1844, 1849b)”. In a Cost-Benefit Analysis (from now on C.B.A.) the economist is calculating the costs of the project and the benefits gained from its future use. These must be measured in the same value system in order to add it all up to get a single number, called the Net Present Value (from now on N.P.V.) of the project. N.P.V. is estimated by discounting all the future benefits and comparing them with the costs of the project. If the N.P.V. is positive then the C.B.A. is an asset during the discussion/negotiation about the implementation of the project from the government or the municipality. On the other hand, if the N.P.V. is negative then the project will

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality probably not be an efficient one, from an economic perspective. But the reader should keep in mind that a C.B.A. is just a tool which is used in order to create a stable basis for negotiations over the implementation of a project. It is not a given fact that if one project’s N.P.V. is positive, we should implement it at all costs. Different projects proposing solutions to a problem can have a positive N.P.V. and therefore the C.B.A. will indicate that we should implement them all. But as I mentioned in the beginning, governments have budget constraints and therefore they have the last word on which project should be implemented. All in all, C.B.A. should not replace the authority and responsibility of policy makers. There are multiple reasons for that some of which are: equity concerns, international regulations, ethical considerations, non-economic criteria etc. To make it even clearer, a C.B.A. has certain steps that the researcher needs to follow. These are: Step 1: Defining the project. To begin with, the researcher needs to present the reallocation of the resources being proposed and identify clearly the beneficiaries and the losers of the project (if any) Step 2: Identifying project impacts. In this step the researcher needs to consider all the possible effects/impacts that the project in discussion will bring in the environment and the society. Step 3: Identifying economically relevant impacts. Benefits and costs of the project should be taken into consideration. The reader must keep in mind though that environmental impacts should be included only if they affect utility of individual directly, change the output level, quality or price of some commodity. Moreover, transfer payments should not be included, given that they do not constitute a using-up of real resources. Step 4: Physically quantifying impacts. This means that the researcher should take into consideration all the physical amounts of costs and benefits that will occur over time. Step 5: Monetary valuation of impacts. All the physical quantities occurring from the project must be valued in common units (money). These prices indicate the relative scarcity of the resources. However, the researcher must transform all the market prices into the same price level, consider all the future flows of these values as well as estimate these values where no price exists. That is what we call economic valuation. Step 6: Discounting of costs and benefits. All benefits and costs must be converted into present value term. Keep in mind that the sooner the benefits are received, the more highly valued they are while costs seem less onerous the later they occur. Step 7: Applying net present value. In this step the researcher asks whether the sum of discounted benefits exceeds the sum of discounted costs. If the Net Present Value of the project is higher than 0 then the project represents an efficient shift in resource allocation and it should be implemented. Step 8: Sensitivity analysis. A general rule of thumb is that none of the predictions made in a C.B.A. can be made in perfect foresight. Therefore, the researcher can conduct a sensitivity meta-analysis of all the key parameters that can change such as: discount rate, time horizon, physical quantities of inputs and outputs etc. which influence the final result.

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

Of course, as I said earlier, the fact that a project has a positive Net Present Value on its C.B.A. does not mean that it will be implemented. Apart from a positive result in a C.B.A., a project also needs to have a strong lobby that will put pressure on the government/municipality in order to be implemented. There are multiple projects that could have a positive impact on the Municipality and/or the environment, but that does not mean that they will be implemented. For example, the construction of a hospital in an island far away from mainland sounds like a great idea. However, a C.B.A. is needed in order to even start discussing about it and of course a lobby to put pressure on the national government to do it. Let me give an example. Let us assume that a municipality wants to build a new school and they are considering 3 different projects. Project 1 is constructing a new school, Project 2 is renovating the old school and project 3 is renting a building for some years. After doing a C.B.A. for all the projects, we found that Project 1 has a N.P.V. of 100m.DKK, Project 2 has a N.P.V. of 70m.DKK and Project 3 has a N.P.V. of -25m.DKK. Now, if the municipality based its decision purely in C.B.A. they would prefer Project 1 and construct a new school, given the fact that Project 1 has bigger N.P.V. than Project 2, and Project 3 is out of question because of its negative N.P.V.. But, and here is the question, “Is it the best choice to prefer Project 1 or is it not”? It is possible that Project 1 asks for 200m.DKK in order to be implemented (costs) while Project 2 asks for only 35m.DKK. As a result, the Municipality may prefer to implement Project 2. By doing so, they save a lot of money (165m.DKK), which they can use in different projects. We should always keep in mind that each government or Municipality has budget constraints and more than at least two problems to deal with simultaneously. It is easily understandable that C.B.A. is just a tool and not a given fact for economists to prove which projects are efficient and should be implemented. The main purpose of a C.B.A. is to provide a solid foundation over which the Municipality will base its discussion and decisions. It is not only Municipalities or Governments that are using C.B.A. in order to make their decisions. Even the European Union is using it. There is a certain provision on the Treaty of European Union in 1992. More specifically, Article 130r-3 is stating that:

“In preparing its policy on the environment, the Community shall take account of: - available scientific and technical data; - environmental conditions in the various regions of the Community; - the potential benefits and costs of action or lack of action; - the economic and social development of the Community as a whole and the balanced development of its regions” (E.U., 1992)

Therefore, it is obvious that C.B.A. is a powerful tool used worldwide before making a decision on the implementation of a project even from the E.U., the U.S.A. or smaller countries. No matter how small or big a piece it is in the worldwide chessboard, they are all using C.B.A. and there are various reasons for that. First of all, the most important reason for doing a C.B.A. is because it eliminates the possibility to choose the least poor option. By contacting a C.B.A. in 3 different projects/options there is always a chance that the C.B.A. concludes that none of them will be beneficial. On the contrary, other techniques used in the past such as Cost-Efficiency Analysis (C.E.A.) and Multi-Criteria Analysis (M.C.A.), can only suggest which one from a number of suggestions would be the best option. As a

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality result, the government can save a huge amount of money from its budgets. Let me give you an example. In the region of Korinthos, Greece the Municipality has a very serious problem to face. Due to lack of rainfalls, there is water deficiency in the region. In order to face the problem the Municipality is considering two different projects. Project 1 is to put a water tax so as to reduce unnecessary consumption or waste of water. Project 2 is to expand the existing water tanks in the suburbs of town in order to collect more water during rainfalls. In that way they will be able to cover the increasing people’s needs in periods of deficiency. The Municipality could easily adopt either of these two projects, but a C.B.A. proved that neither of them was efficient. After some discussion, the Municipality decided to “transfer” water from a village’s river nearby. As a result, the Municipality was “forced” to think of an alternative way given the fact that neither project was efficient according to the C.B.A.! Another reason for doing a C.B.A. is that “C.B.A. seeks explicit preferences rather than implicit ones” (Pearce et al., 2006a, pp. 35-36). A C.B.A. is based on real choices that are revealed by people. Both Revealed Preference Methods and Stated Preference Methods are using real data in order to reach their conclusions. Hedonic Price Method and Travel Cost Method (Revealed Preference Methods) are using real markets and they are describing actual behavior. On the other hand, with a Stated Preference Method, the researcher is creating a questionnaire and he is interviewing people asking them to “state” their preferences. Most of the time, the researcher is sending away questionnaires (or question people himself at their houses) in order to collect all the information needed to do his analysis. By doing so he can be 100% sure that everyone is considered for this project and that he cannot be accused of being biased. Of course there are multiple questions in order to identify strategic bidders and people who are lying. In this case, and if the sample is a representative sample of the population, the results are reliable and you can base your conclusion on real facts and not simple guesses. Moreover, a C.B.A. is providing all the necessary information not only for the beneficiaries of the project, but for the losers as well. It is commonplace for policy- makers to make a decision taking into account only the ones who are gaining the benefits of the project. But they are ignoring the losers who might be even a bigger percentage of the population. This could be a result of the different nature of losers and beneficiaries. For example, imagine that in a village there is a problem with bees. They are attacking kids and flower shops all over the area. The Municipality is considering using pesticides in order to kill the bees and protect its citizens. And it might be a good solution for the kids and the shop owners. However, at the same time the village has a massive production of apples. Apples are the main source of income for the majority of the villagers. Therefore, if the Municipality decides to use pesticides, it will destroy the production of apples. Bees are an important factor for the blossoming of the apple trees. If the Municipality conducts a C.B.A., it will be certain that they will consider not only the flower shop owners, but the apple producers as well. And there is a strong possibility that the C.B.A. results of these two projects (the one taking consideration of only the citizens and the other taking consideration of the apple producers as well) will be totally different. Last, but not least, in a C.B.A. the preferences of all individuals and not simply a small percentage of the people affected by the problem are the ones that the researcher relies on to reach his conclusion. Educational background, age, gender and a number of other facts are not affecting our sample. Everyone affected can and will

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality express their preference before we reach our conclusion. Some people are considering this to be weakness of the C.B.A., but I believe that we should definitely include all people’s choices. One way or another they are all part of our community and their decisions are affecting us all. They are able to vote and therefore they are able to affect our results with their preferences.

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

2.2 The Hedonic Price Method

As I have already mentioned above, a C.B.A. can be conducted using two methods, either Revealed Preference Method or Stated Preference Method. Stated Preference Methods are the Contigent Valuation Method (C.V.M.) and the Choice Modelling (C.M.). In a C.V.M. you must design your own questionnaire and make your own survey. This requires a huge amount of work and many hours spent on it. These two factors make it impossible to use it in a 6-month Master Thesis. C.M. also requires a lot of time and it is not appropriate for a 6-month Master Thesis either. So, Revealed Preference Methods are the most appropriate for our C.B.A. We can use either Travel Cost Method (T.C.M.) or the Hedonic Price Method (H.P.M.). The H.P.M. “estimates the value of a non-market good by observing behavior in the market for a related good” (Pearce et al., 2006b, p.93). The H.P.M. is using a value of a marketed good which is easily observed through the market and then with the use of statistical techniques it isolates the price of the characteristic that we are looking for. We must always keep in mind that many market goods are nothing more than a function of characteristics. For example, let us consider the price of a house. A house consists of a number of bedrooms, number of bathrooms, but also, air quality, distance from highway, distance from the beach or even distance from public transportation. All these characteristics are included in the price that we are paying (or at least we are willing to pay) for this house, but cannot be considered independently. For example, if someone wants a house really close to the highway there is no market value for that. Nevertheless, by using the H.P.M. we can compare the price of otherwise identical houses, but with a difference in the distance from highway. The difference in their prices will reflect the willingness to pay for the specific good we are looking for (here is the distance from highway). As a result, I decided to use the H.P.M. in order to do my C.B.A.. However, there are also some difficulties with the H.P.M.. There are some problems that the researcher should confront. First of all, some people claim that it is likely that the individuals lack adequate information. Therefore, their decisions could be biased and so will be our survey. That could be true, but on the other hand it is also a given fact. Not anyone in our society has the same educational background or the same I.Q. level to understand everything that is going on around him and that is affecting his life. For example, does anyone in the society have a clear idea what effect an increase in the discount rate of the banks has on his everyday life? Of course he does not. But still, he needs to be aware of it. Let me give you one more example. Recently in Switzerland they made a referendum if they want a salary increase. I am convinced that most of the people did not think that a higher salary will cause more expensive products and as a result a decrease in exports, which will also lead to a shrink of the G.D.P.. This doesn’t mean that most people are not making any choices affecting our markets or that they are inappropriate for our survey. Every single person has the right to vote therefore his choices are affecting us all. It is the same thing with our sample. Our sample is, and should, consisting of people from all different ages, genders, educational background and social statuses. Moreover, another problem of the H.P.M. is that of multicollinearity. For example, a house next to the seaside is really close to the beach but it also has better air quality. As a result, the difference in house price could be because of both these

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality two things. Researchers cannot distinguish which of these two characteristics the consumer values more. Therefore, it is common that researchers are “failing” to identify one of the characteristics in their analysis and take the risk of creating biased results. To be honest it is almost impractical to distinguish between two characteristics that are not directly marketed and therefore a common acceptance is needed in these kinds of problems. Last, but not least, the extent of the market is crucial. The researcher must be extremely careful when he is defining the property market and he should include all the houses that any individual will choose from, but not a single one more. If you include in your sample more or fewer houses that the ones examined by the individuals then your sample will be biased and so will your results. That is a tricky thing to do and sometimes this is a friction point among researchers and between the researcher and his employer. The H.P.M. has a two stages approach for the estimation of the benefits. The first stage, the simpler one, is the estimation of the hedonic price equation. First, you gather all the information needed about prices and attributes of the “good” (in our case houses) and then you run a regression analysis by choosing the explanatory variables (size and number or bedrooms etc.) and the function form. By doing so, the researcher is ending up having calculated marginal prices for the characteristics of the houses. Then he is ready for the second stage of the H.P.M.. In the second stage, the researcher combines the marginal prices of the characteristics together with socioeconomics attributes of the consumers (income or else) in order to estimate the parameters of the behavioral equations of the consumers (utility functions). Let us have a closer look at the welfare economic background of the H.P.M.. To start with, we must say that a differentiated commodity is described by its characteristics Z (where Z= z1+z2+…+zn). The commodity’s market price P is determined by the price of these characteristics P=P(Z). So, consumer’s utility is defined by two goods: the differentiated good Z and the composite good X representing all other goods (e.g. income left after purchasing good Z). In order to get the consumer’s utility function we calculate the socioeconomic characteristics αj of j j the consumer j and we have U = (x,z1,z2,…,zn;α ) and we know that he has a budget constraint of yj=x+P(Z), given that he only purchases the commodity Z (y is income). Now, we know that the consumer maximizes his utility by choosing a version of a differentiated good Z and the amount of x subject to the budget constraint. The utility maximizing consumer will choose Z and X according to the first order conditions so that the following equation (1) is satisfied for every zi:

(1) M.R.S.z,x = = = Pzi

Equation (1) states that the marginal rate of substitution between Zi and X must be equal to the marginal price of Zi. The marginal price of the characteristic Zi is equal to the partial derivative of the hedonic price function P=P(Z) with respect to the characteristic Zi. This equation simply says that the consumer is willing to pay Pzi for a marginal change of z. This could be used to estimate the willingness to pay for this specific characteristic. The previous welfare economic analysis established that the marginal price of a given characteristic of a differentiated commodity is a relevant welfare economic measure of changes in utility as a result of changes in the characteristic. Keep in mind though that the marginal price of a characteristic zi is not

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality directly observable, but it may be possible to estimate this implicit price from the market data on trade in a differentiated commodity with zi as one of its characteristics. Now, we need to estimate the consumer’s bid function θ which shows the consumer’s optimal bid for a given variety of Z as a function of the characteristics. We will use the utility function provided above slightly formulated considering that J J the amount left to spend for X is the income minus the bid θ for Z: U (y0-θ,Z; α )≡U0. We can describe the utility function of consumers with indifference curves for given values of y. Each indifference curve will reveal a constant combination of utility for the expenditure of housing and z for a given level of income. Unfortunately for us, people are heterogeneous. This means that people with different incomes have different indifference curves, even if they have same preferences. If we suppose that consumers consider the hedonic price function as exogenous then they maximize their utility subject to their budget constraint and to the hedonic price function. Moreover, the hedonic price function comes from the equilibrium of demand and supply of housing. We must not disregard that sellers as well have isoprofit curves (curves that ensure the same level of profit). On the supply side of the market, producers are profit maximizers. A producer’s profit Π can be described as: Π = H·P(Z) – C (H, Z, δκ), where H is the number of units Z produced, C (·) is a cost function for the producer with characteristics δκ and P(Z) is an exogenous equilibrium price that the producer faces. Supply behavior of the producers can be described by an κ κ κ offer function defined by: Π0= H·Φ - C(H, Z, δ ), where Φ represents the price the producer will accept for a particular variety of Z. The marginal price Φzi, a producer is willing to accept for a characteristic zi is equal to the marginal cost of producing that characteristic. So now, we have finished describing the first stage analysis of the H.P.M. and that is what we are going to do in this thesis. In order to do a second stage analysis you need to have much more data available and considerable time to devote. This thesis will only make a first stage analysis in order to reach a conclusion, so the second stage will not be presented here. Now, let me show you what we have discussed before in figure 2.1 before we move forward:

Figure 2.1 – Equilibrium in a hedonic price market. Modified version of Clark and Cosgrove’s (1991) wage offer/acceptance functions

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

In Figure 2.1 H.P.F. is the Hedonic Price Function. Πa is the profits of seller a (isoprofit curve), Πb is the profits of seller b. Ui is the utility function of buyer i (indifference curve) and Uk is the one of buyer k. If we add all the points in which the isoprofit curves of the sellers meet the indifference curves of the buyers we get the hedonic price function. At any point along the hedonic price function, buyers’ marginal willingness to pay (and sellers’ willingness to accept) for a change in Z is given by the derivative of the hedonic price function with respect to Z.

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

2.3 Previous examples of C.B.A.

C.B.A. is a worldwidely used method. Countries, governments and international organizations all over the world are using it before they reach any conclusions regarding projects they are considering. For example OECD has published a book with guidelines for its countries (OECD Publications, 1997). But OECD is not the only International Organization which has published guidelines. E.U. is also one of them, which is quite logical, given the fact that as I mentioned earlier, in the Maastricht Treaty there is clear provision of using a C.B.A. in all E.U. decision making procedures (European Commission, 2009). As it is easily understandable, C.B.A. is a basic element of decision making procedures all over the world. The U.S.A., the United Kingdom and Canada are some other examples of countries issuing guidelines for conducting a C.B.A. and how to interpret the results. A C.B.A. has been conducted for multiple different reasons. Some of the best known examples are the installation of wind-turbines in Messanagros, Rhodes in Greece and the construction of a new bridge connecting Rio and Antirio in Achaia, Greece. Both those C.B.A. were conducted using a C.V.M. and questionnaires. Although that is a time-consuming procedure, it provides the most accurate results possible, eliminating the possibility of a mistake. These were such significant projects that a C.B.A. was needed before reaching the final decision. Moreover, there are various different reasons why a C.B.A. was conducted. The most representative example is the C.B.A. of the Iraq war by Gregory Scoblete (Real Clear Politics, 22 March 2008). In his Analysis he is concluding that the Iraq war created more costs (losses) for the U.S. Government than benefits (gains) and as a result Iraq war was a “mistake”. Another example of a C.B.A. is the Case Study Example of the Hedonic Pricing Method—Values of Environmental Amenities in Southold, Long Island (Opaluch et al., 1999). In that case a H.P.M. was used. The citizens of the town of Southold were looking for new space in order to expand their city, due to increased population density. Their city was surrounded by open-spaces, farmlands, wetlands, major roads and zoning. So, the municipality used the H.P.M. in order to calculate the most cost-effective pieces of land that they should use for the expansion. The prices of the houses are increased by 12.8% if they were next to open space, or by 16.7% within certain areas of zoning. Moreover, for every acre of wetland next to the house, its value increased by 3%. On the contrary, properties next to major roads or farmland had a 16.2 and 13.3% per-acre lower value respectively. As a result, the managers could calculate which areas would be the most beneficial to be preserved from the expansion. Let me give you another really simple example. Imagine yourself when you are out of shampoo. Then you have to go to the supermarket and buy a new shampoo for you or your family. Once you go there you have to find the proper section where all the shampoos are set. The next step is to choose the shampoo you want. But, this is not an “easy” choice. You have to choose from different brands, different colors (yes, color DOES matter and there are multiple surveys to prove so- not needed to be presented here), different smells, different quantities and of course different prices. As a result, the consumer has to take into consideration all the things that determine the

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

“proper shampoo” for himself and then make a choice. Of course there are more things that can affect the choice of a specific shampoo, such as past experience or the use of any allergic substance. All these factors are influencing our choice of the shampoo. And it is the individual that keeps in mind all these factors and he is making the decision of which shampoo he will purchase. As it is easily understandable, a C.B.A. is happening inside his head. He is calculating the pros and cons (benefits and losses) before he makes a decision that will maximize his utility. So, we have proved that even the slightest decision in our everyday life premise a C.B.A.!

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

2.4 Previous literature of H.P.M.

As we mentioned previously, there are many goods that are simply the sum of multiple characteristics. Here, we are taking into consideration houses. When the buyer is purchasing a house, he is paying for a certain amount of rooms (bedrooms, bathrooms etc.), but also for a certain level of air quality, noise level, distance from highways, distance from metro station, distance from the beach and a numerous other characteristics. And the main problem that we have to face is that most of the time the buyers don’t even know that they are doing so. Most people, when asked, reply that they are looking for a place with a certain number of bedrooms and of a certain size, but they fail to mention all the rest characteristics. Yet, they base their decision on these as well. By using the H.P.M. we are having the opportunity to estimate the value of these characteristics by comparing the prices of the houses. For example in Gribskov Commune there will be a lot of houses with the same number of bedrooms or bathrooms or of the same size. But a quick look at the house market will show that the prices are not the same. Therefore, it is easily understandable that the differences on the house prices will be due to differences on the other characteristics such as air quality etc. And that is what the H.P.M. can help us to do. For otherwise identical houses (size, number of bedrooms-bathrooms) the difference in the price will be due to difference in air quality, distance from highway and distance from the beach amongst many others. Afterwards we can isolate the characteristic that we want and estimate the price that the consumers are putting on it. That is my plan. If we want to estimate a hedonic price function, we need to take into consideration all the relevant characteristics of the houses. Therefore the hedonic price function should consist of the sum of the house characteristics (number of bedrooms, size of garden etc.), the neighborhood characteristics (distance from school, crime rate etc.) and the environmental characteristics (water quality, noise level etc.). We assume that the buyers of the houses have subconsciously put a price in all these characteristics and they have chosen the proper mixture that matches their preferences. Of course, the H.P.M. has some disadvantages that Palmquist (1991, 2003) presented in detail. If we want to focus on some of them we could not overlook the problem of omitted variable bias. This is the case when the “emitters” are causing more than one impact. For example, if the government decides to use pesticides to protect its citizens from bees, this will not only destroy the apple farmers (for the reasons we explain in the previous Chapter), but it will also pollute the local river. As a result, if the researcher only includes one of these two externalities his results will be biased and therefore unreliable. Another problem is the one of multi-collinearity. As I explained previously, it is possible that two different characteristics could not be distinguished. For example, a house next to the city center is close to school, but also close to the bus station. Researchers have difficulties in distinguishing which of these two characteristics the buyers considering more and therefore the results could be questionable. Moreover, market segmentation is another major problem that the researcher has to deal with. For example if we want to deal with traffic noise in London, should

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality we consider the whole city as one market or split the city into 4 different groups (North, East, South, West)? Is it going to help our research or is it going to make things more complicated? In any occasion, it is always better to use a segment of the market than a bigger market from the appropriate one. Furthermore, the problem of expected versus actual characteristics level is not a negligible one. In many cases the prices of the houses are increasing (or decreasing) due to future changes in the area. For example, the prices of houses next to a park will fall dramatically if it is announced that the park will be replaced by a waste dump. Last, but not least is the problem of risk attitudes. Kask and Maani (1992) have proved that a H.P.M. to the value of risky environmental events can produce biased estimates. Fortunately, this is not the case in this study as I am addressing in a certain area. These are potential problems that I will have to face during my research, but despite all these the H.P.M. is a reliable method which if used properly, can provide us with all the necessary information. The H.P.M. is a topic that has raised a lot of controversy and many well- known researchers have spent most of their lives using it and trying to face its problems. Hanley et al. (1997, 2007) dedicates one chapter in his book Environmental Economics in theory and practice to the H.P.M.. Ridker and Henning (1967) were the first ones who used the H.P.M. to environmental valuation and tried to explain the stages of a H.P.M.. Moreover, there are multiple researchers that used the H.P.M. to prove their point. Examples are Bjorner et al. (2003, c.f.: Nelson J. (2007)) who proved a relation between house prices in Copenhagen and noise levels generated from traffic in the same city and Garrod and Willis (1992) who found a significant relationship between house prices in England and woodland cover. Another example is Brookshire et al. (1981) who proves that an improvement in air quality in 14 neighbourhoods in the South Coast Air Basin in California can create benefits per home per year. Furthermore, researchers do not only limit themselves in an analysis of the H.P.M., but also they are conducting a meta-analysis of the results in order to test their theories. For example, Smith and Huang (1993) made a meta analysis on 37 H.P.M. studies and they concluded that “…there is a systematic relationship between the modeling decisions, the descriptions used to characterize air pollution, the condition of local housing markets, and the conclusions reached about the relationship between air quality and house prices”. Of course we could not consider this passage through the previous literature of the H.P.M. integrated without mentioning the Palmquist (1991, 2003). He is the one who spent tones of ink trying to explain, understand and test the H.P.M. using multiple examples and testing different approaches on the method. His work cannot be compared with no one mentioned above and his remarks are always distinctive and felicitous. However, before I start my analysis it is necessary that I make reference to the Total Economic Value (from now on T.E.V.) of the project. Any project has some benefits that will arise from its implementation and these are what we are looking for here. These benefits constitute its Economic Value. The T.E.V. consists of two kinds of values: the use value and the non-use value. These two are divided into smaller subcategories. The use value consists of the actual (or planned) use of a good and option value. On the other hand, the non-use value could be divided into three subcategories: existence value, bequest value and altruistic value. Let me give a definition to these values as simply as I can:

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

 Use value: the value of the actual use of the good, planned use and possible use of the good  Option value: the value that a project has for someone just because he wants to have the option of using it, whether if he will ever make use of that option or not.  Existence value: the value that someone is willing to pay to keep a good in existence, when it has no actual use for himself or anyone else.  Bequest value: the value that someone is willing to pay in order to maintain a good so as the next generations will have the option to make of it.  Altruistic value: the value that someone is willing to pay in order to maintain a good so as others (from the current generation) can make use of it. This is the most controversial type of non-use value. Our project has both use and non-use values and of course all of them are taken into account. But, the values that we are going to focus on are the use values (both actual use and option one). Given the fact that our time horizon will be 50 years then the bequest value is out of question since there will be only one future generation in the whole life of this project-assuming that a generation has 40 years of life, before a new one comes up. Also, the existence value should be not counted, because the people who are going to pay for this project are the owners of Gribskov’s summer houses. Therefore, the existence value does not apply, because they bought these houses in order to be close to the sea. That is actual use and not existence value. Obviously, altruistic value is such a controversial value that I would not even consider it for this project. For example, an elderly couple can always pay the money just because they would love to know that there is a sandy beach for the kids of the area to play around and for young couples to walk on the beachside at a sunset and not because they would like to swim. To sum up, I will take into consideration only the actual use value and the option value for this project, simply because the people who are going to pay for this are the inhabitants of Gribskov the vast majority of whom are going to make use of the sandy beaches. Furthermore, the reader should keep in mind that the beaches of Gribskov Commune (that I am investigating here) are common good. All the goods are fall into 4 categories according to their excludability and rivalry. Excludability means that you can exclude someone from using the good by putting a price on it. Rivalry means that the use of this good from one individual reduces the ability of another individual to use it. A quick look at table 2.1 will help the reader to get a clear picture:

Table 2.1 – Classification of goods according to their excludability and rivalry

Excludability YES NO YES Private Good Common Good Rivalry Ex. Congested, toll Ex. Congested, road non-toll road NO Club Good Public Good Ex. Non-congested, Ex. Non-congested, toll road non-toll road

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

As we said Gribskov beaches are common good which means that there is no excludability from them. As a result the municipality will face the problem of free- riding, meaning that a lot of people will come to the beaches and enjoy the sea and the sun without even contribute to the costs of this project. Such people will be residents of Copenhagen or other cities that will take advantage of the fact that the beaches are full of sand now and therefore they are going to use it by spending their leisure time there. So there is a danger that if a lot of people outside Gribskov choose to spend their weekends or vacations there, then there will not be enough space for the inhabitants of the Municipality who, in the end, are the ones who will pay for the project. Being the one who has the property rights and the one who needs to find a way of financing the problem, the Municipality of Gribskov should keep in mind all the above. It is not an easy issue to deal with, but how the Municipality of Gribskov will finance the project is beyond the scope of this thesis. Before we move forward to our analysis we must take into consideration some externalities that may affect our project. The most important one is the climate change, an externality that cannot be foreseen and therefore can influence our results. We are going to use a time horizon of 50 years and within this timeframe the temperature could possibly rise between 1.8 and 4oC (IPCC, 2007). Moreover, the sea level will rise and as a result our coasts would be influenced. Another possibility is that an economic crisis will strike Denmark as well (E.U. is still in crisis nowadays and most of the countries face serious socio-economic problems, even Germany). This will result in a dramatic drop in the house pricing market and therefore our results would be affected. All in all, there are multiple externalities that we need to take into account while doing this analysis, but on the other hand, we cannot base our analysis on the externalities and not the real facts that we face nowadays. We just need to mention and be prepared for all the different possibilities. As a result, I think that H.P.M. is the best method to follow for this C.B.A. despite its difficulties and specifics. There is already a property market in that area therefore it won’t be too difficult to collect the data of the houses. Then I can use the G.I.S. and R and find out the answer to my question which is “What is the welfare economic impact of changing the coastline of Gribskov Municipality from a rocky to a sandy beach?”. Once I have an answer then it will be easy to estimate the benefits of this project. To sum up, H.P.M. may receive negative criticism, but I believe that it is the best method to follow in order to reach my goal and make an as much as possible unbiased C.B.A. for all the reasons stated in this Chapter.

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

Chapter 3 – The data

3.1 Data for the project

For our project I am going to use data from the Gribskov Commune. My supervisor Toke Emil Panduro sent me all the relevant data that I will use. Under his supervision I will use the program Q.G.I.S. and all the data needed to find all the necessary information in order to complete my survey. The data that I am referring to consists of information about forests, lakes, the harbor, summer houses and the coast side. As Mr. Panduro informed me (Panduro T.E., personnal communication) it was quite simple to gather all the relevant data. That is because in Denmark, data concerning structural house characteristic and sales prices all over the country are frequently gathered. Structural house characteristics have been collected since 1976 and been registered in the “Bygnings-og Boligregisteret” (BBR). As far as sales prices are concerned the data have been collected since 1966 and registered in “Ejendomsstamregisteret” (ESR). Moreover you can make use of “Krydsreferenceregisteret” (KRR) which contains geographic coordinates for every house in Denmark (Hansen, 2000). KRR has the ability to supply you with a common key in order to be able to combine the BBR and ESR data. You can construct location-based variables by using ArcGIS 9.2. There are data provided by The Danish Geodata Agency (2011) in the kort10, by Danmarks Miljøundersøgelser (2000) in the “Area Information System” (AIS) and by Naturgas Midt-Nord (2000) in the Danish Address and Road Database (DAV) that you can make use of. The variables can be calculated using Euclidian distance or road network distance. The forest proximity variable was measured as Euclidian distance in steps of 100 meters to the nearest forest. The forest variable was delimitered at the distance of 600 meters from the forest edge. The forest proximity variable has a scale between 6 and 0 (with 6 being the forest edge and 0 being a distance of 600 metres). There are several other measures of forest proximity or availability that have been formed or analyzed in H.P.M., but will not be presented here. As far as the data on sales prices are concerned, I used data for single family houses from 1992 to 2004. I constructed dummy variables for each sales year (taking 2004 as the reference) in order to subtract time variation. After removing some incomplete or erroneous observations (missing or implausible technical entries), the remaining 5659 observations formed the basis of my analysis. To start with the actual research, I download the program Q.G.I.S.. It is free on the internet and you can easily find a link to download it. In Q.G.I.S. you can insert different data such as rivers, streets, forests, harbors, beaches etc. and then after proper calculations you get a table with all the relevant information included. For example you can add a municipality with all its rivers, forests and the rest and then add all the houses. Afterwards, with the proper calculations you can get tables with the distance of each house from forests, coasts etc. As far as our project is concerned, in this program I added multiple vectors. One vector was added for Gribskov Commune and one for the harbor, the forests, the

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality lakes and the beaches. By doing so, I included all the environmental factors that affect the price of a house in the area. In the end and right before I start doing my research I added a vector with the summer houses in the area. It included all the summer houses and all the different characteristics that they have. Characteristics such as renovation, number of rooms, garden and so many more, were all included. After I added all the vectors, I examined each one of them separately. I started with the harbor and I created a line by connecting all the points around It. I did that by following the orders [Tools – export knots – “give name” – OK]. After I had all the knots around the harbor, I needed to find a table with the distance of each house from the harbor. In that way I could make use of these characteristics when I compare the prices of the houses. So, I went to Q.G.I.S. and followed the orders [Vector – Analysis Tools – Distance Table]. In there I had to add the starting point of the calculation and the final one. So, you should simply put the following choices [Point layer: VH_poly (this will be the vector of the summer houses in the area) – key – harbor_not (as we are talking about the distance from the harbor here) – unik (which is a new column that we added in the table with the characteristics of the harbor_not, to make sure that each spot of the harbor will have a single unique number and it will not be calculated twice)] and of course we had to pick the option [use only the closest point]. In that way I calculated a table with all the distances of each house from the harbor and I could use this table to my calculations later on in R. As you can imagine I did exactly the same thing for the lakes and the forests. So I created enough distance tables that I can use when creating my equation in R in order to take into consideration all the relevant information. So now, when I create my equation in R, except for the number of bedrooms, bathrooms, garden, size of the house and other characteristics, I will have the distance from the harbor, the closest lake and the closest forest to take into consideration. By doing so, we will have a more complete image about the houses in the area. There is only one thing missing from Q.G.I.S. and this is the distance from the beach. In my research I will simply use the same way I described before in order to get the “coast_nots” and have a table where the distance of each house from the beach will be presented. I will get my results simply from the distance from the beach whether it is a sandy or a rocky one. If I wanted to have a more detailed equation and get more precise results, I would have followed the steps described below. Calculating the distance from each beach is not as simple as the previous calculations. The problem that we have to face here is that the beach is not the same all over Gribskov. And that makes sense given the fact that if the beach was rocky or sandy all over Gribskov there would be no need for this project. So, before calculating the distance of each house from the beach I would need to identify the different beaches. I visited Gribskov myself (back on June 2013) together with Jacob Wandall and took multiple photos in order to be sure that I comprehended the full size of the problem. So, by using my personal experience and by confirming it from Google Earth, I separated the beaches of Gribskov Commune into two categories. The first category is the beaches with sand which a lot of tourists are visiting, because swimming is feasible. The second category is beaches with smaller or bigger rocks where swimming is not recommended. You can have an idea about the situation nowadays by taking a look at the pictures in Appendix II. As I said, I separated the “coast_nots” that I had created before by giving them two different colors, the red one for rocky beaches (the last two pictures) and the yellow one for sandy ones (the first two pictures). In order to get an idea about the

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality results I took from my research take at look at figures 3.1 (Gribskov Commune), 3.2 (West side of Gribskov Commune) and 3.3 (East side of the Gribskov Commune). Keep in mind that the yellow dots showing the beaches of the area that are covered with sand and the red dots are showing the beaches with bigger or smaller rocks. General view of Gribskov Commune:

Figure 3.1 – Gribskov Commune. It is created by the author using the Q.G.I.S. program, showing the different beaches of Gribskov Commune.

In more detail the Gribskov Commune in Q.G.I.S. looked like this:

West side of the harbor.

Figure 3.2 – The west side of Gribskov Commune. It is created by the author using the Q.G.I.S. program, showing the different beaches of Gribskov Commune.

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

East side of the harbor:

Figure 3.3 – The east side of Gribskov Commune. It is created by the author using the Q.G.I.S. program, showing the different beaches of Gribskov Commune. After I have done all that, I opened the attribute table of every single section of the beach and I added a new column by following the orders [Click pencil – Add column – “new name” – Fields and values- update- “new name” – record – check rownum – OK - click Save – unclick pencil]. By doing so, I made sure that every single spot of the beach will be calculated only once. Afterwards, I made the distance calculations again for every single section of the beach the same way I did it before. So now, I have all the distance tables I would need to put in R in order to get my results. This means that now, I have a distance table for every single beach separately (rocky and sandy ones). But, due to lack of time I will not do these calculations. Finally, I am done with Q.G.I.S. and now I need to start working with R and do my statistical analysis and O.L.S. regression.

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

Chapter 4- Analysis and Results

4.1 Estimating the Costs

Moving forward we are getting into the actual Cost-Benefit Analysis. It is common sense that in order to do a C.B.A. you need two things: the costs and the benefits. Most of the time, when an organization (in our case the Gribskov Commune) wants to implement a project and they are asking for a C.B.A., they already have an idea about the future costs of the project. They may have forgotten to add some of the costs (whether on purpose or not it is not our business to figure out) or ignore some of them (standard conversion factor, dead-weight loss), but in general each organization has already a fixed number of the costs when they are asking for a C.B.A.. This is facilitating the researcher’s life. In general costs of a project we call all the costs of moving from the present situation into a future new one. When a researcher is estimating the costs of the project he must keep in mind 4 things in general: The loss of present income, the reduction in costs, new income and new costs related from the new facilities. As it is easily understandable this is not as an easy issue as it seems. Jacob Wandall, is a representative of the citizens of Gribskov. He was the guy who asked for this C.B.A. and it is thanks to him that I can do it. Jacob lives in Gribskov and he has first-hand knowledge of the problems caused by the erosion of the beaches and the costs of the project. So, due to his “expertise” and lack of time, I will use his estimations of the costs in order to do my C.B.A.. After all, he asked for the analysis, he gave us the costs and therefore we must play by his rules. In his report called “Oplæg til specialeemne: Cost Benefit Analyse / Værdisætningsanalyse - Sandfodring i Gribskov kommune” (Translated in English: Launching a thesis topic: Cost Benefit Analysis / Valuation Analysis - Sand Feeding in Gribskov municipality) (Wandall, n.d., d) he has proposed the following solution: A ship will transfer sand to the beaches equal to the quantity of sand missed all these 15 years in order to reinstate the coastline and afterwards every year it will simply replenish the lost sand. This means that it will need 20m3 of sand for each of the 30km coast (which is equal to 2 ship cargos) for the reinstatement of the coast line to the previous situation, which will create 25 meters of more beach (In Danish: Ideen var at tilføre lige så meget sand som er forsvundet på 15 år, og derefter hvert år tilføre lige så meget som der forsvinder så kysten vedligeholdes. Det ville betyde at der blev tilført ca 20 m3 sand (2 store lastvognslæs) for hver af de 30.000 meter kyst. Det ville i snit genskabe 25 meter sandstrand langs hele kysten). Furthermore, Mr. Wandall takes this proposal one step further and he has estimated the costs. According to his calculations this will cost 45 m. kr. for the restoration (it will need 600.000 m3 of sand) and afterwards 3 m. kr. /year for the maintenance of the coast line (50.000 m3 of sand). He even proposes a way to collect all the money needed, but this is beyond the scope of this report and we will not get into details (In Danish: Udgiften til projektet blev beregnet til 45 mio. kr. i retablering (600.000 m3 sand) og derefter ca. 3 mio, kr. ( 50.000 m3) om året. Udgiften skulle dækkes som en forbrugsafgift opkrævet over skattebilletten (efter reglerne i lov om

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality kystbeskyttelse) af de ca. 14.000 kystnære grundejere i kommunen – langt de fleste sommerhuse). During a communication I had with Jacob Wandall via e-mail (Wandall, personal communication), he informed me that the costs estimations have been changed upwards since our last meeting in June 2013. He mentioned that the situation on the coast line has dramatically worsened and that according to the new estimations 100 million kr. will be needed for the restoration of the beaches and then 5 million kr. /year for the maintenance of the project. So, I will keep on doing my analysis with the last numbers that Jacob gave to me. To sum up, the costs of the project will be 100+5xT (where T is the number of years that the project will run for) million kr. The costs of the project were more or less standard and Jacob was really helpful by doing the calculations needed. What is left for us is to estimate the benefits, which is far more difficult. Nothing is a given fact and benefits were the main reason for this thesis. But before we move forward we must take into consideration two more things, which are the standard conversion factor and the dead-weight loss. If these two things are not included in our calculations our results will be biased and this will lead to a wrong policy proposal. So, let us see in detail these two very important factors influencing the results of a C.B.A.. We will have a discussion about the dead-weight loss into subchapter 4.4 - Sensitivity Analysis [4.4.3 – Dead-weight loss]. To start with the standard conversion factor, we must make an introduction into the production of goods. The purpose of production is to satisfy consumer’s needs and preferences. But, the price a consumer has to pay for a product reflects the consumer’s willingness to pay for this specific good plus indirect taxes and subsidies. On the other hand, factor prices paid by the consumers only reflect the value of inputs from a producer’s point of view without indirect taxes. As a result, in C.B.A. factor prices must be converted into consumer price level and reflect the marginal value product of factors of production. If we want to calculate approximately the indirect taxes that a consumer has to pay when he buys a product we can compare the Gross National Product (G.N.P.) and the Gross Factory Income (G.F.I.) of the country. The Standard Conversion Factor (from now on S.C.F.) for goods only traded domestically is calculated as the ratio between the G.D.P. (in consumer prices) and G.F.I. (in factor prices). The Danish Ministry of Finance has set the s.c.f. in 1.35 (DMU Report 2000) and that’s what I am going to use as well. If we multiple the s.c.f. with every year’s costs separately or with the total costs of the project, this will not change our results.

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

4.2 Estimating the Benefits using the H.P.M.

Now, that we have the costs calculated it is time to move on to the most demanding part of our analysis. In this chapter we are going to estimate the benefits from this project. We have everything we need in order to create our equation and try to estimate how much the price of a house is affected by having a beach nearby. The most difficult step for making a Cost-Benefit Analysis in our field is to estimate the benefits of the project. And that is simply because you have to estimate the price of a product that is a non-market good. But, that is exactly what are we going to try right now. Still the best way to do my research would be the one I will describe below, but this will require more time and I do not have this luxury right now. Therefore, I decided to do a more simple analysis. Instead of doing a regression with all these different beaches and characteristics, I decided to do a simple regression in R. I will write an equation with some of the different characteristics of the houses (not all of them, but those that I think are the most important ones) and of course I will add the variables: distance from the harbor, distance from the forests, distance from the lakes and of course distance from the coast. This one, the last one, is the most important variable for our research from now on. Once I got a number from this variable I will simply multiplied it with the distance of each house in the area from the beach and I will get my benefits. The first thing we have to do before we start using R is to create a folder in our hard disc (c) where we will save all the data that we are going to use. That will make it easier for R to locate where the different data are and insert them to the system. The most common name used is “DataForR” and that is what I used as well. Now, we need to transfer all the data that we got from Q.G.I.S. in this folder in order to have everything gathered together. I chose to copy all the data from their previous folder into the new one and I managed to create a folder with the data that affect the price of the houses in the area such as distance from lakes, forests, harbor, distance from the beach and of course all the characteristics of the houses. Now that I have all the data gathered, I can start my calculations. First of all, we have to set the tank from which we will trawl our data and then add all the relevant data one by one (habour_dist, forest_dist, coast_dist and VH). Of course we also need to install some packages that will help us get our desirable results. As it is easily understandable we have numerous tables to put into R. As a result, we definitely need to merge them and to remove the duplicates if any exist. Now, everything is ready for doing our regression. I will do a simple O.L.S. regression in order to get my results. After doing my regression I will simply ask R to summarize my data. When I ran this regression model I got some really interesting results. It seems that the variable “coast_dist” has a positive value of 702.28DKK/m. This means that if a house is closer to the beach by one meter, its price is increasing by 702.28 DKK. So now, in order to get my benefits I simply need to multiply the distance of each house from the coastline with 702.28. That way I will get the increasing of all the houses value in the area by having a beach next to them. At this point we must present exactly how we made the calculation of the benefits for this project. The benefit calculation presented above provided us with the

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality value of 702.28DKK/m. This means that every single house in the area will have its value increased by this number for every single meter it gets closer to the beach, either a rocky or a sandy one. So, now we have to make a choice. We should decide which houses we will include in our calculations. If we take a look at the house data we will see that there are houses with distances from the beach ranging from 32 to 9.000 meters. As it is easily understandable it would not be logical to include all these houses in our benefit calculations. The rationale behind this is that for houses with a distance longer than 500 meters from the beach the owners will have to take the car or a bicycle in order to get to the beach. So the factor “distance from beach” will not be as a strong factor during the decision of which house they should buy. As a result this thesis will take into consideration for the calculation of the benefits only the increase in price for the houses which are closer than 500 meters to the beach. Now, if we do the calculations for every single house (distance from the beach (m.)x702.28DKK/m) we get a certain number for the benefits. To give an example a house which is 50 meters away from a beach will see its value be increased by this project by the distance from beach (50) x The increase in price (702.28) = 35,114DKK. If we do the same for all the houses which are closer than 500 meters and we add them all together, we will get our benefits from the sand feeding project. To sum up, for our C.B.A. the benefits used will be B= 322,172,316.4DKK. The reader has to keep in mind that the variable of “coast_dist” has a high standard deviation, but that is something that I had anticipated. The high standard deviation is caused by the fact that in my variable both sandy and rocky beaches are included, which means that this price could get really high if we include only sandy beaches or really low if we include only the rocky ones. This was something that I was expecting and of course it is one of the factors that we need to keep in mind when presenting the results and our policy proposal. Moreover, the positive value of the variable is something anticipated. And that is completely normal given the fact that having a beach nearby your summer house is certainly increasing its value whether it is a rocky one or not. After all, you bought that house in order to be close to the beach. In conclusion, the variable “coast_dist” has a positive value with high standard deviation and these two things clearly prove that our calculations are on the right way. As I stated earlier, if I had more time I could do a deeper research and I could have more precise results for my analysis. Not that I cannot draw my conclusions from what I got before, but if I was doing a deeper analysis I could have better numbers and give a more secure policy advice. Right now, I can only give a hint on what I think the Municipality should do. But still, simple results are better than no results! If I had more time I could do an analysis with more precise results by doing simply the following: First of all, we would have to set the tank from which we will trawl our data again and add all the relevant data one by one. As it is easily understandable we would have numerous tables to put into R. As a result, we definitely would need to merge them and to remove the duplicates if any exist. Once we have done that we would need to rename the distance calculations and to remove any obsolete variables. The next step will be to create a spatial point data and at last we will need to write a shape file which will check the distance calculations in QGIS. Of course more calculations would be needed in order to get my final results, but now you have an idea how many calculations would be needed and imagine how much workload they demand. Because, here I would have separated the sandy and the

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality rocky beaches and I would have estimated a variable of how having a sandy beach near-by your property affects the price of the property. The difference from the simple regression I did before is that now I have a number for the sandy beach and not for generally speaking any beach nearby the house. Therefore I would have had more precise results and my N.P.V. would be more secure. Furthermore, this thesis would lead to a better and safer proposal to the Municipality without any doubts and with no hesitation. But, as I mentioned earlier, I can still make a proposal and defend my results.

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

4.3. The Cost-Benefit Analysis

In this chapter this thesis is finally going to provide us with some results. We have gathered all the necessary information in the previous chapters and now the time has come to get some results and be ready to present our conclusions. Of course a sensitivity analysis will be more than necessary and small modifications will be in need, but in general this chapter will reveal our answer to the question “What is the welfare economic impact of changing the coastline of Gribskov Municipality from a rocky to a sandy beach?”. There is a basic decision rule in order to decide if you will accept a project/policy or not and that is showing in Equation (2):

-t -t (2) {∑WTAi,t (1+s) - ∑WTPi,t(1+s) }>0 where ∑ means the sum, WTP is the Willingness-To-Pay, WTA is the Willingness-To- Accept, i is the ith individual, t is time and s is the discount rate. This rule states that if the benefits from the project (the money that the people will get from the implementation of the project-WTA) outweigh the costs (the money that people have to pay in order to get the project done-WTP) then the project should be accepted and implemented. We can simplify the rule into Equation (3):

(3) N.P.V. = >0

where B stands for Benefits, C stands for costs, s stands for the discount rate and t stands for time. This equation simply tells us that if the Benefits minus Costs of a project discounted into the present money value exceed the 0 price, then the project is cost-worthy and it should be implemented. No need to say that if the N.P.V. of a project is higher than 0 then this project is profitable, if the N.P.V. is lower than 0 then the project is detrimental and that if the N.P.V. of a project is equal to 0 we are neutral as far as the implementation of the project is concerned. As far as our project is concerned, in the previous chapters we calculated the costs and the benefits of it. So, now we have everything we need in order to do our C.B.A. and reach a conclusion. What is left is to pick the proper number for the discount rate and the time horizon of the project. It may seem simple, but especially the discount rate is the most controversial part of a C.B.A.! And of course the time horizon is not as simple as it seems. In this project I made the choice of these two factors. And I had to chose between different discount rates. First of all, there is a proposal for a discount rate of 3% just like Dubgaard et al., did in their report Cost-benefit analysis of the Skjern river restoration in Denmark (Dubgaard et.al, 2003a). Moreover this is the discount rate recommended by the Danish Environmental Research Institute and the Ministry of the Environment (Møller et al., 2000, p. 140) and of course it has been proposed by the E.U. (European Union, 2014) We could also use a discount rate of 6-7% as it was suggested by the Ministry of Finance in 1999 (Ministry of Finance, 1999, p. 72). Last, but not least there is the new proposal of the Danish Ministry of Finance about a

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality declining discount rate for this kind of projects of 4% for the first 35 years, 3% for the next 35 and then 2% for the rest of its life (Danish Ministry of Finance, 2013). This thesis will make use of the last proposal of declining discount rate by the Ministry of Finance for this project, but we will return to the discussion about the discount rate later on. Furthermore, I will need to decide on the time horizon of the project. Most studies are using an infinite time horizon, but I think this will not be the situation here. I choose to select a time horizon of 50 years and I will explain the reasons for my choice in the next chapter. All in all, I believe that 50 years is the proper horizon for a project like this and I will present enough proofs for this later on. So, everything is set and we are finally ready to get our results (with a given time horizon and discount rate). All we need to do is to add the numbers that we have in the equation that I presented before. So, let us see what the sign of this project is:

The rule is shown in Equation (3):

N.P.V. = > 0

So, we have:

- > 0

With the proper calculations this will be:

– ( + )x1.35

 ( + )– 257,310,064.7>0

Now, we can add the benefits and get:

 298,019,168.8 – 257,310,064.7> 0

 N.P.V. = 40,709,104.1 >0

Let me explain what I did on the calculations before. First of all, I added all the numbers that I got in my previous calculations and from Jacob Wandall. The benefits are a given number which is fixed. But, the costs are a completely different story. As Jacob stated clearly in his report (Wandall, n.d., e) the costs will not be paid all at once. Instead, 100,000,000 kr. will be paid the first 3 years for the restoration of the sand and thereafter, 5 million kr. will be paid annually for the maintenance. So, that is what I did: First of all, I separated the fraction. I distinguished the discounted value for benefits from the discounted value for costs. That way I could continue with my

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality calculations far more easily. Afterwards, I started by calculating the costs. The costs will be 100,000,000 kr. for restoration and then 5,000,000 kr. for maintenance per year. Afterwards, I separated the amount of 100 million into the 3 years of restoration period equally. This will give me 33,333,333 kr. per year. I assume that the Municipality could do it like 35,000,000+35,000,000+30,000,000, and that would only change my results by 132,348 kr., which is negligible (we are dealing with millions here!). So, I discounted the 35,000,000 kr. by 1.04 (the discount rate of the first year of the project) and I did the same for the next two years [35,000,000/(1.04)2 and 30,000,000/(1.04)3]. And then I added the maintenance costs per year discounted as well. I added each year separately, because both the restoration and the maintenance costs will be paid annually and therefore they should be discounted separately for every year and not simply summed over the 50 years, cause that would mean that all the costs will be paid in full in year 50. That would lead to miscalculations and therefore biased result. When I made the calculations I got a number for the costs of this project and that was 257,310,064.7 DKK. The next step was to enter the benefits so as to be able to do the subtraction and get a Value for the project. Once I got the benefits, I divided them into 3 years and I simply discounted it to Net Present Value by dividing the number with the discount rate (1.04) raised in the power of 1 for the first year, 2 for the second one and 3 for the third year. I did this because every year I will get some money back from this project, given that some of the beaches would be restored after year 1 and the same after year 2. In year 3 all the beaches will be perfectly restored. To sum up, as we can see for a declining discount rate of 4% for the first 35 years and 3% for the next 15, and a time horizon of 50 years, the C.B.A. seems to have a positive value and therefore we must conclude that the project should be implemented.

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

4.4 Sensitivity Analysis

4.4.1 Discount rate (r)

Every time the discussion goes around what the discount rate should be in a C.B.A. opinions diverge and a long discussion is starting considering which discount rate would be the proper one for the given project and more importantly if there is a need for a discount rate at all. Thousands of pages have been written and hundreds of reports have been published regarding this issue, but still a clear answer is not imminent. In this subchapter, I will try to present all the different proposals and justify my choice for the discount rate I used before. To start with, we must say that discounting means that a given benefit in the future is less valued than the same benefit now. The discount rate is nothing more than a trade-off relation where society refrains from present consumption to achieve a greater future consumption. Also, we must keep in mind that in real life it is often difficult to find precisely when benefits occur and therefore researchers typically assume that all the benefits occur in the end of every year. Moving forward, I will provide you with the example of Dubgaard et al. and their report Cost-benefit analysis of the Skjern river restoration in Denmark (Dubgaard et.al, 2003b, p.18). In their analysis they are constantly using a discount rate of 3% for the costs and the benefits. But when it comes to the actual C.B.A. they are using 3 different discount rates (3, 5 and 7%). They argue that the “Danish Environmental Research Institute and agencies under the Ministry of the Environment recommend a social discount rate of 3% in social cost-benefit analysis” (Dubgaard et.al, 2003c, p.18,), but at the same time they state clearly that the “the Ministry of Finance (1999) recommends a social discount rate within the range of 6–7%”( Dubgaard et.al, 2003d, p.18). As we can see prominently in Dubgaard’s analysis the discussion about the proper discount rate is a continuous one and quite controversial at the same time. It is typical that even within the same country (in our case Denmark) there are two different opinions and both are from government sources. The Ministry of Finance supported a discount rate of 6-7%, which means that it sees future generations as less important that today’s one. Or to make it more politically correct, the Ministry believeed that the future generations will be richer than the today’s one, so they prefer to spend more money on today’s problems and let future generations deal with their own problems. On the other hand, the Ministry of the Environment and the Danish Environmental Research Institute suggest a discount rate of 3% which means that there should be a difference on how we value future generation’s wellbeing , but not as big as to create moral problems. The reader has to keep in mind that a discount rate close to zero means that our generation needs to save more money than we are currently using. It is stated that “…The lower the discount rate, the more future consumption matters, and hence more savings and investment should take place in the current generation’s time period.” (Pearce et al., 2006c, p.185). This means that a discount rate of 1% leads to higher investments today and a higher value for costs.

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

Also, the E.U. seems to agree with the suggestion of the Danish Ministry of the Environment about a discount rate of 3%. In the official webpage of the Ministry of Finance we can find that “The use of sensitivity analyses is recommended, applying a rate of 3 percent, which in the guidelines is explained by reference to the Ramsey condition, with p=1.5, µ=1 and g=1.5.” (Danish Ministry of Finance, 2014). At the same website you can also see the proposal from the Ministry of Environment that we discussed in the previous paragraph. That will make it really easy to do the comparisons needed. Furthermore, there is a third proposal concerning the discount rate of this kind of projects. And that is a zero discount rate and there are multiple reasons for this suggestion. To start with, there are those who support the theory of “sustainable development”. If you check the report of the Brundtland Commission of the United Nations (Brundtland, 1987): “Sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs. We must note two important things. First, “future generations”(plural) implies “for a very long time,” where long means long compared to a human lifetime.” Second, the arithmetic of steady growth shows that steady growth of populations or of rates of resource consumption for modest periods of time leads to sizes of these quantities that become so large as to be impossible. The combination of these two observations leads us to the First Law of Sustainability”. In other words, sustainable development states that our planet has enough resources to feed all generations (both current and future ones) and that because of technology innovation, future generations will have more and better means to support themselves and fulfill their needs. As a result, they believe that there is no need for neither underestimate nor overrate the value of money today compared to let’s say 20 years from now. So, a zero discount rate will be a fair discount rate for every project and every generation. Additionally, there are people who support the zero discount rate for one more reason. And that is the existence of moral objections about intergenerational fairness and equity. There are those who support that we should care equally about people today and in 100 years from now and that we should not base our decision about our expenses on possibilities and predictions concerning future wellbeing and evolution. As an example there are those who support that it is possible that the next two generations might have more money to deal with or even have more natural resources to use (the research for extraction of natural gas in some areas in north Sweden or south Mediterranean sea have just started), but at the same time it is highly likely that the next two generations will need to face global warming as well. And recent studies have showed that global warming will need huge investments of money in order to be faced effectively. Just to give you an example, the only proposal for dealing with global warming is to reduce C02 emissions and turn into a greener economy (wind turbines, solar panels etc.). You can easily understand that these solutions in a heavily industrialized environment will ask for huge investments of money. No need to mention that the climate change politics are divided into two categories: adaptation and mitigation ones. The adaptation ones are the policies that every single country can implement individually in order to face the results of climate change (less rainfalls, higher temperature etc.). The mitigation ones are the most difficult ones to be faced. And that is simply because they call for international cooperation. No single country can reverse the results of global warming. Every single country is only a small player in the world’s chessboard of energy. As a result, every single country will need to

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality face her problems relying on her own possibilities while at the same time participate in a global discussion about facing global warming. And this is even more complicated than it sounds. This means that they will have to face their own problems whether have more resources or not. As a result, they argue that a zero discount rate is the most appropriate one to be used from the perspective of fairness and equity. Last, but not least, there is a new proposal by the Danish Ministry of Finance which proposes a declining discount rate of 4% for the first 35 years of projects like that, 3% for the next 35 years and last a discount rate of 2% for the rest of the project’s life. This was a good decision because the declining discount rate secures that, on the one hand, we take into consideration the future when we are talking about projects that require a long time horizon and on the other hand this discount rate will reduce the possibility of making an, economically speaking, bad decision. Bad decision means the decision that will cause loss of money for the Municipality/Government. However, a time declining discount rate could be the solution to the “tyranny of discounting”. People seem not to discount with a steady discount rate, but with a time declining one. Therefore, that is what we must do as well when we are talking about long time horizons. As it is easily understandable the discussion about the proper discount rate is a long and ineffective one. If we take into consideration that within the same country there are numerous suggestions and that the two most important Ministries for the kind of projects that we are dealing with (Ministry of Environment and Ministry of Finance) propose two different numbers for the discount rate, we can get an idea about the difficulties and the different opinions within different (or even the same) countries. I am not really optimistic that a clear answer to the question “What the discount rate on a social project should be?” will ever be given. But I can reassure you that a clear answer is not on the horizon. To sum up, there are four main proposals for the discount rate. 3%, 5%, 7% or a decling one should be used in order to get your results. You can pick any number you want, express your arguments for doing so and still there will always be an argument against your choice. By keeping that in mind, I decided to use a declining discount rate of 4% for the first 35 years and 3% for the rest 15. Of course this thesis will present the results with a discount rate of 2, 5 and 7% and make the proper comparisons, but for all the reasons that I stated clearly previously in this chapter I decided to use a declining discount rate for this project.

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

4.4.2 Time horizon

After we have made our decision about the discount rate then it is time to deal with another questionable issue, the one of the time horizon of the project. Finding the proper time horizon of a project is not a simple issue, especially if we keep in mind that in some cases the time horizon can influence the results of the project by changing the outcome. (Remember that in the equation of a C.B.A. [Equation (3] time (t) is used as an exponent in the denominator, which means that the bigger the time horizon, the bigger the denominator and the smaller the total number of the equation). To start with, let us suppose that we want to use a time horizon between 100 and 500 years. In many cases this time horizon was used in C.B.A. for projects with a long lifetime (e.g. forest replanting). The problem with such a long time horizon is uncertainty. You may make some predictions for the future and base your choices on these, but how sure can you be about what nature or the environment will look like in 500 years from now? For example, in 100 years from now, the climate change committees state that the natural environment will face a dramatic change and an increase of 2oC is more than a certainty. So, nothing will be the same and our C.B.A. will be based in a wrong assumption therefore our suggestions will be wrong. Furthermore, there is another proposal for a time horizon of 20-50 years. This might seem quite logical, given the fact that we are talking about environment and therefore all the changes needed request time. As a result, some may say, a time horizon of 20-50 years will be ideal. But, there is always an argument against it. And that is the following. Let us assume that the administrative council of a Municipality conducts a C.B.A. for a project with a time horizon of 20 years (let us make use of the lowest point. The results apply to the highest one as well). If the project is implemented this means that the Municipality has to keep the same policy for 20 years. If, for any reason, they change their policy, then the project will not be the most efficient one and therefore their decision to implement it, was wrong and they lost some money that they could use to face other kind of problems. Also, if for any reason there is a change in the Council of the Municipality (elections or else), then the new Council should continue implementing the project otherwise the investment made will be in vain. The chances are that this will not be the case therefore a vast majority will be needed in the voting for the implementation of the project in order to secure that we are making the right choice. On the other hand, most of the time we are using a small time horizon when we are conducting a C.B.A.! For example 3, 5 or 10 years are usually the time horizon for some projects, because it is more easily manageable. When you have a small time horizon you can adjust your policy or you can minimize the risk of the uncertainty. When you are about to implement a project for 3 years from now you can have a clearer picture of the socioeconomic conditions under which the project will be implemented and therefore you can react to the unforeseen situations better than if you had a time horizon of 20 years. Moreover, by using a small time horizon you have to deal with smaller number in the C.B.A.’s equation (remember that the denominator has t as an exponent!) and that will facilitate your calculations.

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

Last, but not least, there is one more possibility. You can simply use a 2 year time horizon. In that case the implementation of the project requests a fixed cost (for the reinstatement of the environment) and then a certain amount of money (costs) per year for the conservation of the project, and given that the benefits from this project are constant over time per year, then the best solution is to use a 2 year time horizon. The costs as well as the benefits are standard from the second year forward, so there is no need to perplex our lives with higher numbers. The same results will apply whether we use a 3-year period time or 50-year period time. Therefore, we can get the result the easiest way possible. The problem with this approach is that there is not enough time to have a respectable amount of benefits, and as a result your conclusion may be biased. Certainly, most of the researchers that are conducting a C.B.A., decide to create a table with multiple results. For example they create a table with different results for 3, 4 or more time horizons and they present it to their employer. This way, they are giving the freedom of choice to their employer to decide about the time horizon of the project. So, they are not responsible for the possible results or any possible negative surprise during the implementation. And that is something that has been done repeatedly. To sum up, in a C.B.A. a time horizon from 2 to 500 years can be used, depending on the project and various other reasons. It is up to the researcher to decide (in collaboration with the Council that decided the project) the proper time horizon and argue about it. Of course it is not such a controversial issue as the discount rate, but more than one answer can apply here as well. Providing a table with multiple time horizons will give the researcher some extra time, but eventually he will be asked to state one final number for the project.

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

4.4.3 Dead-weight loss

As far as the dead-weight loss is concerned a C.B.A. must consider the sources of financing. This is because if a government finances a project through (non-lump sum) taxes, this will add to the distortions of the market and it will imply that raising revenue represents a resource cost (termed as dead-weight loss). But let us take a step backwards. Putting an indirect tax will cause distortion in the market through 3 ways: 1. Income effect. Consumption becomes more expensive (given that a tax drives the price higher). This means that if a consumer wants to keep consuming the same products, he must reduce the consumption of all of them due to budget constraint. 2. Substitution effect. The good that the government has put tax on becomes more expensive compared to its substitutes. As a result, its consumption will decrease and the consumption of its substitutes will increase (given that it has a price-elasticity different than zero). 3. Ongoing research. This could affect labor supply negatively If we want to have a clear idea how an indirect tax is affecting the market we can have a look at figure 4.4.3.1:

Figure 4.4.3.1- Dead-weight loss with indirect taxes. (DMU Report 496) On the horizontal axis we can see the quantity of a product x and on the vertical axis the price p. DD is the demand curve of a consumer for this specific product. In the equilibrium, the consumer will buy a quantity x0 in the price of p. Now, if the government decides to put a tax t the equilibrium will change and we will move to the point where we have a quantity of x1 and a price of p+t. In this situation we have 3 areas in our figure, area A, area B and area C. These three areas represent: Area C is the consumer’s surplus (decreased due to the increase in the price of the product). Area A is the tax revenue that the government will take advantage of. And finally, area B is the dead-weight loss due to the indirect tax.

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

Of course in cases of goods with very inelastic price demand such as cigarettes or in cases were lump-sum taxes exist (e.g. television licenses) then dead-weight loss is of less importance. But this is not the case here. Before we move forward we must explain the term of Marginal Cost of Public Funds (from now on M.C.P.F.). The M.C.P.F. estimates the welfare economic costs of a project financed by taxes (in most cases indirect). It is given by the equation (4):

(4) M.C.P.F. = = 1+ ,

where A=tax revenue and B= dead-weight loss. The second part of the equation (B/A) is what we call the tax distortion factor. It is the sum of economic loss (B) divided by the change in revenue (A). So, as we have showed previously an income tax has two distortion effects: An income and a substitution one. Income effect is caused because now the real income per working hour is decreased, so the individual will need to supply more labor hours in order to achieve the same economic level. Substitution effect means that wage rate per hour is worth less compared to one leisure hour. As a result the incentive to work is decreased. Figure 4.4.3.2 shows exactly what will happen to the economy with an income (indirect) tax:

Figure 4.4.3.2- The change in the welfare of society by imposing an income tax. (DMU Report 496) In the horizontal axis we can see the hour of work an individual is willing to supply in the market. In the vertical axis is the gross wage he is asking for to supply this hour of work. DD is the demand curve for labor. SS is the supply curve of labor. In the equilibrium we are in L0 and we have a wage of W0. When we put an income tax (t) we move the supply curve upwards (SS+t) and we have a new equilibrium with L1 labor hours (less than in the initial situation) and a wage of w1 (higher than the initial situation). Now as we saw in the previous Figure [Figure 4.4.3.2] the tax revenue is the area B and the dead-weight loss is the area D. Area A is an economic surplus. So, by imposing an income tax we push the labor supply curve higher while at the same time the demand labor curve remains steady. This leads into a lower labor L1 which also means lower production level and a higher wage W1 which also means higher gross income level. As a result, area D represents the dead-weight loss caused

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality because the marginal value productivity of labor (W1) differs from the individual’s marginal benefit of labor (W1+t). The Danish Ministry of Finance (DMU Report 496) is estimating and recommends the dead-weight loss from taxation equal to 20% of tax revenue. This means that the M.C.P.F. = 1.2. Of course these estimations are changing from one to another. For example Norway recommends a dead-weight loss of 0.2, Sweden proposes 0.3, U.S.A. suggests a 0.25 and U.K., Finland, Germany, France and the Netherlands are using a dead-weight loss of 0. As it is easily understandable from these numbers dead-weight loss raises a huge discussion and is at least controversial. Now, it is quite interesting to see how our results are changing by inserting dead-weight loss into our discussion. For this project our thesis provided us with a result of N.P.V. = 40,709,104.1 DKK. This was without taking into consideration the financing of the project. That is because when this thesis started I had no knowledge of how this project is going to be funded. Now, I am assuming that this project is going to be funded through public funds and most likely indirect taxes (maybe income?). Will this assumption affect my results? We can find an answer to this question by redoing the calculation of the N.P.V. of the project and this time we will add the M.C.P.F. that Danish Ministry of Finance is proposing. Now if we take equation (3) and we add everything discussed above we have:

N.P.V. = >0

=> N.P.V. =

=> N.P.V. = 298,019,168.8 – 308,772,077.64

=> N.P.V. = - 10,752,908.84 DKK

As we can observe if we finance this project with public funds (indirect tax) this will lead to a negative N.P.V.. The M.C.P.F. is increasing the costs of the project by 51,462,012.94 DKK (dead-weight loss). As a result we have no other choice but to conclude that this project is a bad idea for the Municipality of Gribskov. However, there is a long discussion amongst economists, politicians and investments consultants concerning the existence of dead-weight loss and if we need to take it into consideration during the C.B.A.. Some people suggest that we should not include the dead-weight loss in our calculations simply because they do not believe in its existence. They claim that there is no such thing as a loss when you put an indirect tax. We are not going to get into an endless conversation about the dead- weight loss. We will simply use the results we found with and without it and we will make our proposal based in those two.

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

Chapter 5- Policy proposal

5.1 Presenting the results

At last, we have everything we need in order to present our results. In the previous chapters we have discussed our options and now it is time to present what we have found during our research. In this sub-chapter I will present all the results that I have found and all the possible outcomes. I will not make a policy proposal until the next sub-chapter, in which I will present all the possible outcomes and everything we will need to keep in mind. To start with, we must take one more look at the C.B.A.’s equation (3):

N.P.V. = >0

As it is easily understandable all variables (benefits, costs and the denominator) are affected by the time horizon that will be chosen. The benefits will occur gradually over the first 3 years. Of course we need to express them in Net Present Value, therefore a discount will be needed. The same applies for the costs. During the first three years a lot of money will be needed for the reinstatement of the coast (100 mio.kr.) and thereafter a steady cost will apply. Also, it is obvious that the denominator is highly affected by time. Additionally, the decision for the discount rate (r) that will be used is affecting the results of our C.B.A.. A high discount rate will lead to a higher denominator, which in turn will lead to a lower result in our N.P.V.. So, what we can realize from this is that our choice of the discount rate used will influence the result of our C.B.A., exactly like the choice of a time horizon will. This is a huge problem for every single researcher. There are two ways of solving that kind of problems and I am going to present them right away. The first one is to choose a discount rate yourself and a time horizon as well, argue as much as you can to defend your choices and then present the result that you think is the right one based on your research. After all, you are the one asked to present a result, so your opinion is the one valued. On the other hand, if the researcher wants to minimize his involvement in the results, he can present a table with all the possible results. By doing so, he secures that the decision is totally on his employer’s shoulders and he has the minimum responsibility possible. That is easily done by presenting a table where on the vertical axis you insert all the possible discount rates and on the horizontal one all the possible time horizons. Then you simply do the calculations and you present that table to your employer. Then it is up to him to decide about the discount rate and the time horizon and see the result himself. If you decide to present only one number you gain advantage because most of the time, your employer has no idea about what you are doing in a C.B.A. and therefore you can save yourself a lot of time from questioning about your results. For example if you present only one number then there is no room for questions such as “So, what do you think we should do?” or “Which one is the outcome?” etc. The answer is pretty straightforward and it leaves no room for doubts. But, on the other

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality hand this means that you accept the full responsibility of the result. You have made the choice of the discount rate and the time horizon, so the decisions made by you are influencing the final number. If this number turns out to be wrong and a false investment has taken place then you are the only one to blame for. You can always argue that you followed the orders of your employer and the numbers that he proposed, but still, the decision was based on your results. On the other hand, you can avoid providing only one result and taking the full responsibility of the outcome by presenting a table of results to your employer. You can have a table with 6, 10 or even 20 values depending on how many variables you have that are affecting the result. For example, in our case you can present a table with 16 different prices. You can have 4 columns with different discount rates (2, declining, 5 and 7%) and 4 rows with different time horizons (10, 20, 30 and 50 years). When you fill in the prices, you can take that table to your employer and ask him to make the choice by saying “Pick the discount rate and the time horizon that you think will apply and get your price”. That way you have no responsibility if a detrimental investment occurs. No matter which way will you choose to present your results you will always be under moral attacks. If you decide to present only one value to your employer, the opponents of the project will argue that you made all the choices yourself and they had nothing to say about it. They can also argue that you choose the discount rate and the time horizon that served your desired result and that the C.B.A. is at least questionable (we mentioned before that the discount rate is the most questionable matter on a C.B.A., so be prepared). If, on the contrary, you present a table with multiple results and ask them to pick the discount rate and time horizon themselves (because you do not want to make any of the choices and affect the result), you can be accused of reluctance to help and inability to conduct a C.B.A.! They can always argue that by presenting them a table with 16 numbers you confused them more than you helped and that was not the desirable result. All in all, in a C.B.A. the researcher receives all the complaints and the accusations if the project fails to meet its targets and deliver the desirable profits/benefits. But, if the C.B.A. is met with success and the project turns out to be functional and profitable, then the acclaimer can go to the Municipality and the city council. They were the ones who thought of conducting the C.B.A. and of course they were the ones who proposed a solution to the problem. To sum up, no matter what the result will be, the researcher is the bad guy.

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

5.2 Policy proposal

Finally, the time has come to give my policy proposal to the Gribskov Commune. The research has been completed and we reached some results. So, now what is left is to present those to the Municipality and give them an answer to the question “What is the welfare economic impact of changing the coastline of Gribskov Municipality from a rocky to a sandy beach?”. Before I start, I would like to make a quick flashback to the situation nowadays. Due to erosion from the sea and strong winds and tides, huge amounts of sand have been removed from the coasts of Gribskov and they have been transferred to the east side of the Municipality, next to the new harbor. As a result, most of the sandy beaches have been transformed into inhospitable environments for swimmers and families with kids who want to spend their weekend on the beach. Some beaches with huge rocks or some others with smaller ones are a common sight in the Municipality nowadays. As anyone could see on the pictures I provided you with in Appendix II and previous chapters, there are beaches full of sand, most of which however, are in the west side of the commune, where a protected area exists and in the east side where the harbor is. The sand is stuck in there, because there is no way to escape from the harbor. Moreover, there are some areas with neither enough sand, nor big enough rocks as well. These areas also need to be changed slightly or just need a little bit more sand so that the coast is enlarged. Last, there are rocky beaches or even areas with no coast at all. As we can see in Appendix II, there are multiple rocky beaches where swimming is not recommended. Moreover, it is dangerous for families with small kids to go swimming there, because kids are in danger of falling and hitting on the rocks. Therefore, it is easily understandable why a sand-replanting project is needed in the area. We are trying to change an area like this into an area like the ones presented in previous chapters. For pictures showing the situation nowadays see Appendix II. All in all, in Gribskov there are three types of beaches: rocky ones, sandy ones and some beaches with sand and small rocks. All of them need to be replanted by sand because the sea takes away most of it every year. Therefore, it was crucial we have a C.B.A. to prove if it was cost-worthy for the Municipality. So, the time has come to present my results and make my proposal to the Gribskov Commune. As we saw previously, there are two ways of doing so, both of which I will do. What I mean is that I will present a table with 4 different prices and afterwards I will give the price that I think will occur. I think this will be the best way to do it, because the Municipality will have the whole image of the project and of the different results. To start with, let’s have a look at the table 5.1:

Table 5.1- N.P.V. with different discount rates.

S=2% S=4% and 3% S=5% S=7% T=50years -11,417,676.1 40,709,104.1 66,285,436.2 89,473,142.3

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

Table 5.1 presents the N.P.V. of the project under discussion when we use 4 different discount rates. 2% which is the lowest from the rest just in order to make a comparison, 5% and 7% as the Danish Ministry of Finance proposed in the past (the Ministry proposed a discount rate of 5-7%, so I decided to use both in my table) and the declining one of 4 and 3% as the Danish Ministry of Finance has suggested lately, just to make the comparisons afterwards and see how the discount rate affects our results. As we can see it shows an increase in the N.P.V. as the discount rate of the project is increasing which we expected. In this table we took into consideration the different discount rates, the time horizon and the standard conversion factor. As we can see there are four different prices depending on the combination of discount rates. If we base our proposal only on this table we should conclude that the project should be implemented given that it’s N.P.V. is clearly positive. However, we need to investigate a few more assumptions before we make our decision. Moving forward, we will take the next step and we will recalculate the previous table by adding one very important thing to it. For the reasons that we stated clearly in the previous chapters, we are going to do the same calculations, but this time we will include the dead-weight loss in our table and see if it will affect our results. Table 5.2 summarizes our findings:

Table 5.2- N.P.V. with different discount rates (dead-weight loss included).

S=2% S=4% and 3% S=5% S=7% T=50years -75,641,701.6 -10,752,908.8 21,052,181.8 51,002,305.5

Table 5.2 presents the N.P.V. of the project under discussion when we use 4 different discount rates. 2% which is the lowest from the rest just in order to make a comparison, 5% and 7% as the Danish Ministry of Finance proposed in the past (the Ministry proposed a discount rate of 5-7%, so I decided to use both in my table) and the declining one of 4 and 3% as the Danish Ministry of Finance has suggested lately, just to make the comparisons afterwards and see how the discount rate affects our results. As we can see it shows an increase in the N.P.V. as the discount rate of the project is increasing which we expected. In this table we took into consideration the different discount rates, the time horizon, the standard conversion factor and the dead- weight loss. As we can see now that we have included the dead-weight loss in our calculations the results are highly affected. The inclusion of the dead-weight loss pushes our results downwards. This leaves us with no choice, but to reject the project given that the N.P.V. of it is negative for the declining discount rate that we are looking for. All in all, what will this thesis conclusion be? Our research leads us to the following conclusion: The sand replanting project should be implemented by the Gribskov Municipality, but it should not be financed by indirect taxes like income tax, given that an indirect tax will cause a distortion of 51,462,012.9 DKK and it will lead into a negative N.P.V.. The Municipality must find another way to finance the project. How the project should be financed is something that we are not qualified enough to deal with and it is beyond the scope of this thesis.

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

Chapter 6- Conclusion

Gribskov Commune is a Municipality in Capital Region (Region Hovedstaden), in the north area of Copenhagen, Denmark. The northern part of the Municipality used to be full of sandy beaches for several kilometers. The inhabitants of the Municipality (and not only those ones) used to spend most of their weekends and summer holidays there enjoying the sea and the sand. Unfortunately, over the last years due to strong winds and sea tides the sand has been removed from the beaches and it has been transferred to the eastern side of Gribskov where the new harbor is. As a result, most of the beaches nowadays have no sand at all and they are full of huge or smaller rocks. It is easily understandable how disastrous the sand deficiency from its beaches is for the Municipality of Gribskov. Keep in mind that the municipality’s annual tourism revenue is 1,056 million DKK (Wandall, n.d., f)! Therefore, the Municipality decided to do something in order to face the problem. In the past the Municipality made some protection installations, but sand is a prerequisite for their efficiency. So now, it is their desire to do sand-replanting once a year in order to reset the coastlines to their previous standards. Their plan is to rent a ship which will sail across the beaches and it will throw away enough sand to restore the beaches and thereafter, once a year the ship will replant as much sand as it is necessary for the coast to be maintained. Jacob has estimated that this project will cost 100 million DKK for full recovery of the beaches over a 3-year period time and afterwards 5 million DKK per year for maintenance (Wandall, personal communication). So, there is one thing left, which is to decide whether they will proceed with the project or not. In order to be able to reach an agreement on the Municipality’s council concerning the implementation of the project, an unbiased university researcher has to make an evaluation of the project. His role will be to conduct a Cost-Benefit Analysis of the project without political influence and purposefulness. In this C.B.A., I used the Hedonic Price Method in order to get a number for the Benefits of the project. In the early stages on this thesis I had to make a choice. I could do a huge analysis and get into details of the benefits arising from the project or do as a simple analysis as it gets in order to get a number for my benefits and then explain those numbers. Given the time constraints that a Master Thesis requires (6 months), I had to go with the second choice. This thesis is a simple O.L.S. regression presenting the benefits of having a beach nearby your house. Any beach was included whether it was a rocky or a sandy one. The estimates that I got were the lowest value of my benefits. This means that if the N.P.V. of the project even with these lowest values is higher than zero, then the project should be implemented, because, no matter what the other regression would have shown (the one with all the different beaches), the N.P.V. would still have been higher. As a result, even with this conservative numbers this thesis still can provide us with a result about the sand-replanting project. Furthermore, some may accuse me of being a conservative, simply because I used the simplest possible regression. I totally disagree with this accusation. First of all, being a conservative does not mean that you are just simplifying everything because you do not think employers really care. On the contrary, it could mean that you simply want to take into consideration the basic things that matter and not let

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality yourself being detuned from various other factors. I was not trying to influence the results by making them as simple as possible. I was trying to prove that even with a basic regression you can get a hint whether the implementation of the project would be a good idea or not. Moreover, I believe that being a conservative is an advantage and not a handicap when we are talking about a C.B.A.. As I clearly mentioned earlier in this thesis, the most difficult part of a Cost-Benefit Analysis is to estimate the benefits that you are going to have from the implementation of the project. What is usually done is that we have an exaggeration on the benefits that will arise from it. I prefer to believe that this is because of the euphoria arising from the new project and the charm of the unknown rather than a knowingly “mistake”. No matter what the truth is, I believe that being a conservative when calculating the benefits is not a bad thing to do. That way you can be 100% sure that the results you get are the “worst” possible and that no matter what, you got the lowest bound of your N.P.V.. All in all, if you decide to change anything in the benefits that you have entered in the N.P.V., the results can only get higher because of your conservatism in the first place. This means that no matter what will you do from now on, your decision can only get better and in no case worse. Simplifying it, let us imagine three occasions. In the first one, you have found that the N.P.V. has a positive sign. You can conclude without hesitation that the project should be implemented. That is because it has a positive N.P.V. even with the simplest regression that you could do. As a result, your N.P.V. can only get higher! In the second scenario, you have found a negative N.P.V. and the number is really big. This means that, presumably, the project should not be implemented because it will need huge changes in the way you have calculated the benefits and that might not be easy. Moreover, with a huge negative number, it is highly likely that no matter what change you make in the calculation of the benefits, the project will be detrimental even with “fixed” numbers for the benefits. Last, but not least, the third scenario is the one that you will have a negative (or positive) N.P.V., but really close to 0. In this case, you can conclude that a more thorough analysis would be needed in order to reach a conclusion about your proposal. But, you can also argue that, given that with the simplest regression that you could do, you got a number really close to 0, this means that a more precise and deeper calculation of the benefits would probably give you a positive N.P.V. and therefore you can propose to do the further research. Moreover, you can argue the further analysis will provide you with higher benefits (and never lower ones-given that you were conservative at the beginning) so the project should be implemented. Everything else will be steady except for the benefits, therefore the N.P.V. could only increase and therefore the project could be implemented. Keep in mind that we are talking about the situation that the N.P.V. was really close to zero. As far as the sand-replanting project of the Gribskov Commune is concerned, by taking into consideration all the things mentioned in this research and the calculations made, I can conclude that it would be a cost-worthy investment. But, and here is the tricky part, it should not be financed by indirect taxes. To start with, take a look at table 6.1 presenting the results found before:

Table 6.1 – N.P.V. of the project with and without dead-weight loss.

N.P.V. N.P.V. with dead-weight loss 40,709,104.1 -10,752,908.8

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

On the left side of table 6.1 we have the N.P.V. of the project when we include the declining discount rate that the Danish Ministry of Finance proposed (4% for the first 35 years, 3% for the rest 15), we have a time horizon of 50 years and we have calculated the standard conversion factor. On the right side we have the same N.P.V., but this time we have included in our calculations the Marginal Cost of Public Funds. As we can see the N.P.V. is decreased by 51,462,012.9 (dead-weight loss). This table clearly shows something that we need to keep in mind. For a given time horizon (50 years), a declining discount rate of 4% for the first 35 years and 3% for the next 15 (according to the guidelines from the Danish Ministry of Finance) and standard conversion factor taken into account in the calculation of the costs we have found a positive N.P.V. for the sand-replanting project of Gribskov Municipality. However, when we add to our calculations the dead-weight loss (arising from the finance of the project through indirect taxes) we come up with a negative N.P.V. for the same project. And that is a really interesting result. These two things that I described before prove that we are on the right path and that our calculations are logically made. If we had at least one of these two (N.P.V. with and without dead-weight loss) moving on the opposite direction, this could be a sign that something went wrong with our calculations. But, for now, we can be sure that our table, on which we based our policy proposal, is the right one. Before this thesis is finished and we can make our proposal to the Gribskov Municipality, we need to discuss one more thing. In this thesis, we have calculated the benefits and the costs only for the Municipality of Gribskov. But, what about Municipalities like Helsingør or Halsnæs, which are next to Gribskov Municipality? Aren’t they taking advantage of the sandy beaches as well? If that is the case, shouldn’t we take into consideration the benefits from these municipalities as well? By doing so it is common sense that we are increasing the benefits of the project without affecting its costs. As a result, our N.P.V. is going to increase and therefore we should propose the implementation of the project without hesitation. Moreover, as I mentioned in a previous chapter, most bureaucrats and some economists all over the world simply choose not to believe in what we call dead- weight loss. They believe that when we put an indirect tax (like income tax) there is no such thing as a dead-weight loss. As a result, they believe that dead-weight loss should not be included in our calculations and they only see the N.P.V. of 40,709,104.1 DKK for this project. According to their point of view, and taking into consideration what we said previously about the benefits of the Municipalities next to Gribskov, the project should be implemented. I must mention here that when this thesis started, I had no information about how this project is going to be financed and therefore I could only make assumptions. It is the Municipality who need to find a way of financing the project and this is beyond the scope of this report. Another problem that I had to face during this thesis was time. This was a 6- month Master Thesis about a complex problem and if I am asked to do something like that again in the future one thing is for sure. I would make a lot of things differently. 6 months is simply not enough time for a project like this and the researcher could not afford “wasting time”. For example, I would never waste time trying to divide the coastline into sandy and rocky beaches. It was a time-wasting procedure that in the end was not necessary, given that I reached my conclusion without making use of it. Moreover, next time a project like that occurs, I think it is clear from this thesis, that the researcher needs to find a way of financing the project. And that is

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality because our conclusion that “this project should not be financed through indirect taxes” is at least unclear. Yes, the project should not be financed with indirect taxes due to the dead-weight loss, but it is sure that the Municipality will ask you “Then how you think we should finance it?” and you need to have an answer. You are the economist, so you have the knowledge to propose something. And in every probability if you fail to propose another way of financing the project, then they will put an indirect tax no matter what. And that is something you do not want to happen. Last, but not least, another thing that this thesis could have investigated before reaching a conclusion could be the houses included in our calculations. What I mean is that, in this thesis we included the increase in the price of the houses that are closer than 500 meters from the beaches only. The rationale behind it was that people living in these houses will most probably walk to the beach, so the variable “distance from beach” would affect their decision of which house they should buy. Once again, if I had more time, I could do a sensitivity analysis for this one as well. I could simply make the same calculations for house less than 600 meters from the coast or 400 meters from the coast and see how this ±100 meters affect my results. That way I would feel that my results are more secured. All in all, if we take into consideration all the things discussed above we can reach a quite strong conclusion. And this conclusion could be quite strong for all the reasons stated previously in this chapter. This thesis did not include the benefits from the municipalities close to Gribskov; it only included the prices of the houses which are closer than 500 meters from the beach, dead-weight loss is taken into consideration whether the people of Gribskov Municipality believe in it or not, sensitivity analysis was made for all the things that could affect our results (discount rate, time horizon, dead-weight loss) and we were really conservative in our calculations.

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

Bibliography

1. Bartik, T. J.: 1988, Measuring the benefits of amenity improvements in hedonic price models, Land Economics 64(2), 172–183 2. Bateman et al., Economic Valuation with Stated Preferences Techniques, A manual, Department for Transport, 2002 3. Bateman I., Lovett A. A. and Brainard J. S.: 2005, Applied environmental economics: a GIS approach to cost-benefit analysis, Cambridge University Press. 4. Boardman, N. E. (2006). Cost-benefit Analysis: Concepts and Practice (3rd ed.). Upper Saddle River, NJ: Prentice Hall. ISBN 0-13-143583-3 5. Bockstael N. and McConnell K.: 2007, Environmental and Resource Valuation with Revealed Preference- A theoretical Guide to Empirical Models, Springer, Dordrecht, The Netherlands 6. Brookshire et al., “Experiments in valuing public goods” in Smith V.K. (ed.), Advancesin Applied Economics: Volume 1 Greenwich, CT: JAI Press, 1981 7. Brundtland G. H., The World Commission on Environment and Development, 1983. The report was published as: Our Common Future by the Oxford University Press in 1987 8. Danish Ministry of Finance, Ny og lavere samfundsøkonomisk diskonteringsrente, 2013/31/5 URL: http://www.fm.dk/nyheder/pressemeddelelser/2013/05/ny-og-lavere- samfundsoekonomisk-diskonteringsrente/ 9. Day, B., Bateman, I. and Lake, I.: 2007, Beyond implicit prices: recovering theoretically consistent and transferable values for noise avoidance from a hedonic property price model, Environmental and Resource Economics 37(1), 211–232. 10. DMU, Report 2000, [3.3.9-3.3.10] pp 100-101. 11. DMU, Technical report 496, pp. 8-16 12. Dubgaard et al. (2003), Chapter 6 in: Brouwer R. & Pearce D. (Eds.): Cost- Benefit Analysis and Water Resources Management, Edward Elgar Publishing, Cheltenham, UK, p.124-150, 2005 13. Ekelund R.B. Jr., Jules Dupuit and the Early Theory of Marginal Cost Pricing, Vol. 76, No. 3, pp. 462-471, Journal of Political Economy, The University of Chicago Press, 1968 14. E.U. Publications, The Maastricht Treaty, Title XVI, Environment, Article 130 r, 3, 7 February 1992. 15. European Commission, Impact Assessment Guidelines, 15/01/2009, SEC (2009) 92 16. European Commission, Guide to Cost-Benefit Analysis of Major Projects. Evaluation Unit, DG Regional Policy, 2008. 17. Fryd O., Backhaus A., Birch H., Fratini C., Ingvertsen S.T., Jeppesen J., Panduro T.E., Roldin M.K., and Jensen M.B. (2013), Water sensitive urban design retrofits in Copenhagen - 40% to the sewer, 60% to the city, Water

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

Science and Technology. URL: http://www.iwaponline.com/wst/06709/wst067091945.htm 18. Freeman, A.: 2003, The Measurement of Environmental and Resource Values Theory and Methods, Resources For the Future, Washington, DC. 19. Garrod G. and Willis K., Valuing Good’s Characteristics: An Application of the Hedonic Price Method to Environmental Attributes, (1992), Journal of Environmental Management, 34(1):59-76 20. Gribskov Commune, Om Gribskov Commune, Kort om Gribskov Commune, 2014 URL:http://www.gribskov.dk/gribskov/web.nsf/0/F545856BC5EB7BDEC1257 24B003D9018?OpenDocument 21. Hanley et al., Environmental Economics in theory and practice, second edition, Palgrave Macmillan, 2007 22. Hanley N. and Barbier E. B., Pricing Nature: Cost-benefit Analysis and Environmental Policy, Edward Edgar Publishing Limited, 2009 23. IPCC, 2007: Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, Pachauri R.K. and Reisinger A. (eds.)]. IPCC, Geneva, Switzerland, pp.104 24. Jensen Cathrine, (2012), There goes the neighbourhood- A hedonicstudy of the effects imposed by an onshore turbine on its closestneighbours, Master Thesis, Forest and Landscape, Department of Economics, Policy and Management Planning 25. Jensen, C.U., Panduro T.E., Lundhede T.H. (accepted). The Vindication of Don Quijote: The impact of noise and visual pollution from wind turbines on local residents in Denmark, Land Economics 26. Jim C.Y., Chen Wendy Y., Consumption preferences and environmental externalities: A hedonic analysis of the housing market in Guangzhou, Department of Geography, The University of Hong Kong, Pokfulam Road, Hong Kong, 2006 27. Kask S.B. & Maani S.A. 919920, Uncertainty, Information and Hedonic Pricing. Land Economics, 68(2): 170-184. 28. Kolstad C. D., Environmental Economics, London: Macmillan, Oxford university Press, 2000 29. Koundouri P, Kountouris Y., Remoundou K., Valuing a wind farm construction: A contingent valuation study in Greece, Elsevier Press Ltd, 21 January 2009 30. LaGreca Simone (2014), Assessing Enhancements of Urban Quality of Life / The Effect on Property Values of an Urban Renewal Project in Vesterbro, Copenhagen, Master Thesis, Department of Food and Resource Economics 31. Loomis J. and Feldman M., Estimating the benefits of maintaining adequate lake levels to homeowners using the hedonic property method, Water Resources Research, Volume 39, Issue 9, September 2003 32. Møller Fl. Et al..: Samfundsøkonomisk vurdering af miljøprojekter (Economic Valuation of Environmental projects), DMU, 2000 33. Mitoula R., Sustainable Regional Development in E.U. & Reconstruction of the Greek Urban Envrionment, Stamoulis Publications, 2006 34. Navrud S. and Pruckner G. J., Environmental Valuation – To Use or Not to Use?, Kluwer Academic Publishers, 1997

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

35. Nelson J. P. (2007), Hedonic Property Value Studies of Transportation Noise: Aircraft and Road Traffic, in: Baranzini A. et al. (eds): Hedonic Methods in Housing Market Economics, (Springer, 2008) 36. Newell, R. G. (2003). "Discounting the Distant Future: How Much Do Uncertain Rates Increase Valuations?". Journal of Environmental Economics and Management 46 (1): 52–71. 37. OECD, Regulatory Impact Analysis: A Tool for Policy Coherence, 11 September 2009 38. Opaluch, James J., Grigalunas T., Diamantides J., Mazzotta M., and Johnston R., Recreational and Resource Economic Values for the Peconic Estuary System. Report prepared for the Peconic Estuary Program, Suffolk County Department of Health Services, Riverhead, NY by Economic Analysis, Inc, 1999. URL: http://www.ecosystemvaluation.org/hedonic_pricing.htm 39. Palmquist, Raymond B. (1991). Hedonic Methods in J. B. Braden and C. D. Kolstad (eds.), Measuring the Demand for Environmental Quality, Amsterdam: North Holland Publishers. 40. Palmquist (2003), The Bedford Researcher with CD-ROM: An Integrated Text, CD-ROM, and Web Site, Bedford/St. Martin's; Pap/Cdr edition. 41. Palmquist, R. B.: 2005, Chapter 16 property value models, in M. Karl Gran and J. Vincent (eds), Valuing Environmental Changes, Vol. 2 of Handbook of Environmental Economics, Elsevier, pp. 763 – 819. 42. Panduro T.E. (2008), House prices and Forest Amenities-A hedonic valuation, Master Thesis, Department of Geography and Geology 43. Panduro T.E. and Thorsen B.J. (2013), Evaluating two model reduction approaches for large scale hedonic models sensitive to omitted variables and multicollinearity, Letters in Spatial Ressource Sciences. URL: http://link.springer.com/article/10.1007%2Fs12076-013-0103-x 44. Panduro T.E. and Veie K.L. (2013), Classification and valuation of urban green spaces - A hedonic house price valuation, Landscape and Urban planning. URL: http://www.sciencedirect.com/science/article/pii/S016920461300162X 45. Pearce D., Markandya A., Barbier E. B., Blueprint 1: For a Green Economy (Blueprint Series), Earthscan, 1989 46. Pearce D., Giles A., Mourato S., Cost-Benefit Analysis and the Environment, Recent Developments, OECD Publishing, 2006 47. Perman R., Ma Y. and McGilvray J., Natural Resource and Environmental Economics, Addison Wesley, Chapters 2,3, 1996. 48. Renda A., Schrefler L., Luchetta G. and Zavatta R., Brussels Assessing the costs and benefits of regulation, Study for the European Commission, Secretariat General, Final Report, 10 December 2013 49. Richardson H. S. (2000). "The Stupidity of the Cost-Benefit Analysis". Journal of Legal Studies 29 (S2): 971–1003. doi:10.1086/468102 50. Ridker R. G. & Henning J. A. (1967), The Determinants of Residential Property Values with Special Reference to Air Pollution, The Review of Economics and Statistics 49(2):246-257. 51. Rosen, S.: 1974, Hedonic prices and implicit markets: Product differentiation in pure competition, Journal of Political Economy 82(1), 34–55. 52. Scoblete G. (2008): A cost Benefit Analysis of Iraq, Real Clear Politics, 22 March 2008. URL:

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

http://www.realclearpolitics.com/articles/2008/03/a_cost_benefit_analysis_of_i ra.html 53. Smith V. K. and Huang J. C., Hedonic Models and Air Pollution: Twenty-Five Years and Counting, Kluwer Academic Publishers, Printed in the Netherlands, Environmental and Resource Economics 3: 381-394, 1993. 54. Sterner T., Policy Instruments for Environmental and Natural Resource Management, Routledge, 2002 55. U.S. Environmental Protection Agency, Guidelines for preparing economic analyses, National Center for Environmental Economics, Office of Policy, December 2010 (Updated May 2014) 56. U.S. Environmental Protection Agency, Introduction to Benefit-Cost Analysis, Last updated on 30/06/2014 57. Vlahou A., Environment and Natural Resources, Economic Theory and Policy, Volume 1, Kritiki Publishing, 2001 58. Von Grewnitz K. and Panduro T.E. (accepted), An alternative to the standard spatial econometric approaches in hedonic house price models, Land Economics 59. Wandall J., Personnal communication. Phone: +45 2299 8099. E-mail: [email protected]. Oplæg til specialeemne: Cost Benefit Analyse / Værdisætningsanalyse - Sandfodring i Gribskov kommune (Appendix I) 60. Wooldridge , Introductory Econometrics, A modern Approach, Fourth Edition, 2006 South-Western Cengage Learning, 2009 61. World Commission on Environment and Development, "Our Common Future", 1987 62. Zhou Q., Panduro T.E., Thorsen B.J., Arnbjerg-Nielsen C.(2013), Adaption to extreme rainfall with open urban drainage system – An integrated hydrological cost benefit analysis, Environmental management. URL: http://link.springer.com/article/10.1007%2Fs00267-012-0010-8 63. Zhou Q., Panduro T.E., Thorsen B.J., Arnbjerg-Nielsen C.( 2013 ), "Verification of flood damage modelling using insurance data",Water Science and Technology. URL: http://www.iwaponline.com/wst/06802/wst068020425.htm

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

Appendix I

Oplæg til specialeemne: Cost Benefit Analyse / Værdisætningsanalyse - Sandfodring i Gribskov kommune

Gribskov kommune udgør med sine 30 kilometer over halvdelen af kyststrækningen af Nordsjællands kyst mellem Helsingør og Hundested.

Kysten er kommunens ubetingede største aktiv: Gribskov er den store sommerhuskommune – næsten halvdelen af de 30.000 bebyggede grunde i Gribskov er sommerhuse. Og af disse ligger 90% indenfor 1 km. fra kysten. Sommerhusene bidrager med halvdelen af ejendomsskatterne i kommunen – ca. 200 mio. pr. år. Turistindustrien er det største erhverv. Gribskov Kommunes årlige turismeomsætning er på 1.056 mio kr. Analyser fra Visit Danmark (se vedhæftet oplæg fra 2011 kystkonferencen http://www.masterpiece.dk/UploadetFiles/10852/25/DKK2011_program(1).pdf under session Værdien af smukke kyster) viser at sandstrande og børnevenlig kyster er det som turisterne kommer efter.

Nordsjællands kyst er en erosionskyst. Kysten bliver nedbrudt så der forsvinder ca. ½ meter om året svarende til ca. 50.000 m3. Nedbrydningen kan forsinkes en smule med hård kystsikring men problemet flyttes til naboen – svindet på de 50.000 pr. år er ikke til at undgå. Det sediment der flyttes er mest sand – jo mere finkornet jo mindre bølger/strøm skal der til. Det flyttes på grund af bevægelse i vandet – strøm og bølger. Kun ved så lave vanddybder (sjældent over 2-3 meter vand), at bølgerne kan sætte bevægelse i sandet sker der sandtransport. På grund af den overvejende vestlige vind sker bevægelsen fra vest mod øst. Sandet ender til sidst på store sandbanker ude i haver (fx Gilleleje Flak, Munkerup i kattegat og Disken i øresund). De seneste 50 år er de meste af Gribskovkysten blevet kystsikret med stensætninger langs strandfoden, bølgebrydere og høfder. Det har forhindret kysttilbagetrækningen men resultatet er, at stranden forsvinder.

Sådan ser det de fleste steder nu:

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

Sådan så det ud mange steder for 25 år siden:

Kysten kan retableres: Denne situation var basis for at en række foreninger og kommunen gik sammen om at designe et projekt til retablering af kysten.

COWI blev bedt om at lave et skitseprojekt (http://www.gribskov.dk/gribskov/web.nsf/Vindue?ReadForm&db=C1257258002FD 751&iD=F8B2B6BD389F46C7C125765D004E2A32). Ideen var at tilføre lige så meget sand som er forsvundet på 15 år, og derefter hvert år tilføre lige så meget som der forsvinder så kysten vedligeholdes. Det ville betyde at der blev tilført ca 20 m3

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality sand (2 store lastvognslæs) for hver af de 30.000 meter kyst. Det ville i snit genskabe 25 meter sandstrand langs hele kysten. Udgiften til projektet blev beregnet til 45 mio. kr. i retablering (600.000 m3 sand) og derefter ca. 3 mio, kr. ( 50.000 m3) om året. Udgiften skulle dækkes som en forbrugsafgift opkrævet over skattebilletten (efter reglerne i lov om kystbeskyttelse) af de ca. 14.000 kystnære grundejere i kommunen – langt de fleste sommerhuse.

På grund af lokalpolitisk uenighed valgte kommunens Tekniske udvalg at bringe projektet til afstemning blandt de potentielle bidragsydere. Så længe at afstemningen er vejledende er der ikke noget problem heri – men man valgte fra starten at sige at man ville følge resultatet. Denne handling var på kanten af lov om kystbeskyttelse om kommunalbestyrelsens ansvar og/eller grundlovens bestemmelser §42. stk 6.

Ud af 14.000 stemmeberettigede stemte 3.000 imod og 1.500 for forslaget. På det grundlag valgte kommunalbestyrelsen af lade forslaget falde i juni 2011.

Kommunens evaluering af processen efterfølgende konkluderede, at der var bred tilslutning til projektet og at udgifternes størrelse for den enkelte blev accepteret. Det der var modstand mod var principperne i betalingsmodellen. Mange havde stemt nej fordi de mente at det var kommunen eller staten der skulle betale – ikke den enkelte grundejer.

Som borgmesteren efterfølgende har pointeret, er problemet ikke løst og alle fagfolk (kystdirektoratet, COWI, Niras, HDI osv.) er enige om at der ikke er anden løsning end sandfodring.

Siden da har vi haft en meget hård vinter, som flere steder har skrællet flere meter af kysten. Visit Nordsjælland har lanceret en kampagne for Nordkystens Riviera. Dette har genoplivet ideen om projektet i en let justeret udgave. Men positionerne er stadigt fastlåst - diskussionen savner stadig fakta og nye argumenter.

Ide til en række samfundsøkonomiske analyser: En række gode emner for et specialeprojekt (eller evt. bachelorprojekt) kunne være en eller en kombination af følgende: En CBA analyse af effekten af kystretablering (med sandfodring) med inddragelse af flere (alle) effekter. Omkostningssiden er nogenlunde kendt; benefitsiden er det store spørgsmål. Der må formodes at være en væsentlig øget rekreativ værdi for dels lokale beboere i kommunen, dels turister i området. Opgørelse af benefit er potentielt mulig enten ved benefit transfer, eller som egentlige primære værdisætningsstudier (som beskrevet nedenfor – NB. Hvert af disse er i sig selv et relativt stort specialeprojekt!) Effekten på turistøkonomien (fx aktivitet, detailomsætning, andet erhverv, antal overnatninger, priser på sommerhusudlejning). Med fokus på hvor stor betydning sandstrandene har for turisterne i området. Evt. en stated preference undersøgelse på turisters WTP for sandstrande kontra stenstrande (vil nok kun være mulig at gennemføre i sommerhalvåret) Effekten på huspriserne og dermed beskatningsgrundlaget. Herunder: Hvordan har prisudviklingen været i områder hvor der er sandstrand ift. der hvor stranden er forsvundet.

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

En hedonisk pris-analyse (Husprismetoden) vil være oplagt. Der er masser af (sommer-)huse i området, så der vil formodentlig være et stort datamateriale (hushandler) tilgængeligt, og ved nogle kyststrækninger er der pt. stenstrand mens der er sandstrand ved andre En stated preference undersøgelse på lokale beboere (”Hvis man opkrævede pengene til kystbeskyttelse over statsskatten, hvor meget ekstra ville du så være villig til at betale om året for at få 25 meter sandstrand hvor der ingen strand er i dag?”). Ville evt. svare nogenlunde til den folkeafstemning der allerede er gennemført i området, men med payment vehicle hvor alle danskere betaler via skatten og ikke kun lokale. Dette kunne give nogle spændende adfærds- og metodemæssige overvejelser i et speciale. Resultater kunne evt. sammenholdes med resultater fra HPM analyse ovenfor, hvis begge gennemføres En analyse af afstemningen i maj måned. Fx geografisk fordeling af stemmerne (helårs/sommerhus, øst/vest for Gilleje, første række/længere tilbage) – hvad var årsagen til et ja og et nej – opfølgende telefoninterview. Fokus på adfærd, etik, den velfærdsøkonomiske tilgang, hvor vi som sådan er ligeglade med hvem der betaler, kontra den virkelige budgetøkonomiske verden, hvor det tydeligvis har en betydning for policy.

Hvis en eller flere studerende kunne være interesserede i at udføre et sådant speciale er jeg sikker på, at kommunen også ville være interesserede i at bistå med råd og hjælp fx ved tilrettelæggelsen af dataindsamlingen.

En veludført specialeopgave med afsæt i denne problemstilling ville kunne sætte en ny dagsorden og få en meget betydelig plads i debatten/beslutningsgrundlaget. Ideelt set skal resultater fra specialet helst foreligge i efteråret 2013, hvor der er kommunalvalg.

Mvh Jakob Wandall

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

Appendix II

Pictures showing the situation of the coastline back in June 2013. They are taken by the author during his visit in the Gribskov Municipality back then.

Picture 1 – Sand-protection installations Picture 2 – Sand-protection installations

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

Picture 3 – Sandy beach Picture 4 – Sandy beach

Picture 5 – Rocky beach Picture 6 – Rocky beach

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

Picture 7 – Sandy beach Picture 8 – Sandy beach

Picture 9– Sandy beach Picture 10 – Sandy beach

Picture 11 - Rocky beach Picture 12 – Rocky beach

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

Picture 13 - Beach with sand and small rocks Picture 14 – Beach with sand and small rocks

Picture 15 – Erosion from the sea almost destroyed that beach

Applying the Hedonic Pricing Method and Cost Benefit Analysis to sand- feeding projects - An empirical pilot survey in Gribskov Municipality

Picture Back Cover.1 - Taken by the author on June 2013 presenting a beach of Gribskov Municipality during sunset