Municipal Land Use Policies and Urban Development

Or Levkovich and Jan Rouwendal Department of Spatial Economics, VU University, De Boelelaan 1105, 1081 HV Amsterdam. This version: March 26 2015

Acknowledgements

Lars Brugman, Ramona van Marwijk (Kadaster , the Dutch land registry)

Key words: land use policy, urban growth, road infrastructure

JEL codes: R52, R21, R33

Abstract

In this paper we investigate land use policy in the Netherlands by analyzing transactions of ready-to-be developed land provided by the Dutch Land Register (Kadaster). The Netherlands is a densely populated country where land use restrictions are abundant. The factual tightness of these restrictions and their impact on urban development is not clear. Previous literature has mainly concentrated on residential land prices, but we also observe transactions for residential land. The distinction is important because zoning has the potential of segmenting the market for land by spatially separating the various uses by zoning. After reviewing the relevant literature we develop a model of an urbanized area with dispersed employment and housing. We use this model to derive equations for the prices of residential and industrial land in the various locations of these urbanized area. In a competitive land market, arbitrage ensures that land prices for all uses present in a zone are equalized. This provides a clear benchmark case. Preliminary results confirm the segmentation of the land market and substantial differences in the prices of industrial and residential land that is ready to be developed.

1. Introduction

In recent years a large literature has paid attention to the impact of land use restrictions on the prices of housing and residential land. Some of these analyses have also considered the implications of such policies on employment and wage growth. There has, however, been less attention for land use policy that relates to firms and their land use. This is perhaps somewhat surprising, as an important function of land use policy is to mitigate the (impact of) external effects of specific types of land use on neighboring lots, and particular industries are an important source of such effects. A possible explanation is that land use restrictions to prevent external effects have a clear economic rationale, whereas this is not so clear for restrictions on residential land use. It is, nevertheless, likely that there will be implications of land use policy with respect to housing and firms. For instance, if households are in favor of land use restrictions with respect to housing to preserve the value of their property, they will presumably also, and for the same reason, be in favor of restrictions with respect to industrial land use. One would expect, therefore that similar restrictions would exist for both types of land use. More specifically, this reasoning suggests that places with high housing prices due to land use restrictions will also have high prices for industrial land. In this paper we study the impact of land use policy on the prices of newly developed residential and industrial land in the Netherlands. The Netherlands is a densely populated country where land use restrictions are important. Since the 1960s land use plans, that stipulate which types of land use are permitted at any location, implying that all others are prohibited, cover the whole country. As a general rule these land use plans tend to ‘freeze’ the existing situation. This does not mean that changes are impossible, but they usually require time consuming administrative procedures with an uncertain outcome. Urban growth is facilitated by the development of large scale plans for new residential or industrial land use. These plans are often developed in cooperation by the municipality and one or a few large developers. The land that is to be developed is withdrawn from its original use, existing land use restrictions are lifted and new ones imposed, and then the development can start. An important characteristic of this planning process is that the land market is segmented, The amounts of agricultural, residential and industrial land are determined by the land use plans and arbitrage between these segments is not possible, or at least made difficult. It is therefore possible, and at least to some extent likely, that prices for these three types of land use differ. For instance, the literature referred to above suggests that the price of residential land may be higher than that of urban land at the edge of the city, due to land use restrictions that aim to preserve open space. It is less clear what to expect

with respect to the price of industrial land, but on the basis of the above reasoning one may conjecture that something similar will hold. The purpose of the research is to investigate this issue on the basis of information about the prices of newly developed industrial and residential land provided by the Dutch land register (Kadaster). That is, we want to measure the observed land prices and to explain them. We consider the municipality as the main actor. The reason is that land use plans are determined at the municipal level and that municipalities are always involved in the large scale urban extensions that we consider. Without changes in the land use plan, development of new residential and industrial areas is impossible. The changes in the land use plan determine the extent of the extension and where industrial and residential development will take place. The extensions must be facilitated by infrastructure of various types (roads, electricity, water, sewage) and the municipality is always involved. At least until the onset of the current crises, many municipalities were also active in land policy: they bought land to be developed from the original users and sold it to the developers. The revenues were used to cover the cost of the infrastructure and often a net benefit resulted. Understanding the mechanism through which municipalities use land regulation in order to achieve growth has both policy and scientific value. Understanding the process can help evaluate whether existing municipal land regulation indeed helps to achieve growth, and whether it is socially optimal. Moreover, little is known regarding the regional and economic effects of the allocation and value setting for industrial land developments in the Netherlands. Shedding light on this issue could direct decision makers which aims to encourage growth by increasing a region’s industrial activity.

2. Land policy and urban development Early urban economics literature has focused on the relationship between land prices, land uses, land densities and transportation networks. This is particularly studied in the framework of the monocentric model (Alonso, 1964; Mills, 1967; Muth, 1969; Wheaton, 1974). Spatial growth depends much on the price structure of lands, and it occurs when the value of land in urban use is greater than its value in agricultural use (Brueckner, 2000; Capozza & Helsley, 1989). Capozza and Helsley (1989, 1990) argue that the price of urban land can be decomposed into four main additive elements - The value of accessibility, value of agricultural land, cost of conversion and a growth premium, which is the value of expected rent increase from future development. Conversion of land from agricultural to urban use will occur when its urban rent is equal to the agricultural rent plus the opportunity cost of land conversion. Additionally, if the

time of the conversion of land is assumed to be uncertain, this uncertainty effects may cause land to sell at a higher price compared to its opportunity cost ("irreversibility premium"). Geniaux, Ay and Napoléone, (2011) extend Capozza and Helsley's framework to the case of uncertainty in land use zoning, and show that when land zoning policies are poorly enforced or are expected to change, expectations for future development cause prices of undeveloped land to increase Regulation of land uses is a primary tool for municipalities to control the prices of its developed and undeveloped land, and to respond to shifting market demand (B. Hamilton, 1978; Hilber & Robert-Nicoud, 2013; Katz & Rosen, 1987; Kok, Monkkonen, & Quigley, 2014). Municipalities can control land prices by regulating the supply of available land for development, or by imposing other restrictions such as minimum lot size and development tax (Glaeser & Ward, 2009; Gyourko, Saiz, & Summers, 2008). Such regulations may benefit local owners of land, as it increases the value of their property, but they have a negative effect on municipalities' growth, as it increases the costs of development. As shown by Anas and Moses (1979); Baum-Snow (2007); Duranton and Turner (2012); Garcia-Lopez et al. (2014), highways have a significant effect on urban structure, its population, employment and zoning. Effects of highway network developments are also reflected as shocks in the demand for housing and employment in a municipality, and the municipality can respond to such shocks by converting land uses and permitting or restricting development (Garcia-López, Solé-Ollé, Viladecans-Marsal, & Marsal, 2014; B. Hamilton, 1978; Kok et al., 2014). Hamilton (1978) argues that municipal land regulation can generate monopolistic rent profits, as it exploits the relatively inelastic demand for labor (and consequently for housing) in an urban area. However, if the urban area is divided between several jurisdictions, competition between these municipalities will result in a relatively more elastic demand, and to smaller (or perhaps none) monopolistic profits. Kok et al. (2014) investigate the relationship between land use regulation and land values in the San-Francisco Bay area. Their findings show that the restrictiveness of a local municipality's land use regulation, measured by several land use regulation "restrictiveness" indexes, has a positive effect on the price of land. This finding is similar to Glaeser, Gyourko and Saks (2006), who find that zoning and heavy regulation keeps housing supply relatively inelastic, which in turn restricts population growth and keeps housing prices and wages at high levels. In contrast and in line with the predictions of Hamilton (1978), Glaeser and Ward (2009) find that when land regulation are applied in urban areas with large number of small local jurisdictions and similar level of amenities, the effect of land regulation on house prices is small and hardly exists. Additionally, land use regulation is by definition restrictive to new development, and is often used to reduce urban sprawl. Burchfield and Overman (2006) investigate the causes of urban sprawl and find that strict regulation, which is imposed by municipalities, deters developers and has a negative effect on residential development. Their results show that strict land regulations

encourage developers to develop areas outside the municipal borders, where less regulation exists and development costs are lower. Turner, Haughwout, and van der Klaauw (2011) extend the discussion of the effects of land use regulations on land values, and attempt to measure the welfare effects of such regulation. They find that reduction in land use regulation would be significantly beneficial for municipalities, particularly for land owners in the edges of towns, where regulation is most restrictive for new development. Hilber and Robert-Nicoud (2013) examine land use regulations as outcomes of political motives of local land owners. They assert that local owners of developed land benefit from stricter regulations on lands, and therefore they influence planning boards to increase these regulations. Consequently, relatively attractive urban regions with higher provision of amenities tend to be more developed, and therefore they adopt tighter land use regulations. Previous literature suggests that while land regulations generally increases land values (and is often used as a policy measure for these purposes), evidence shows that its effect on urban development is mostly negative. However, it is also evident that spatial context and competition between municipalities and jurisdictions can determine by a large extent the outcomes of land use regulation on urban development. Spatial competition between urban jurisdiction is particularly relevant in the Netherlands, where spatial planning is characterized by high population densities, and historically urbanized and poly-centered urban patterns (Hajer & Zonneveld, 2000; Koster & Rouwendal, 2013; van Oort, 2004). Although spatial planning is considered to be one of the structurally efficient systems in Europe, and national zoning plans specify permitted land uses at the neighborhood level (Koster & Rouwendal, 2013), in practice the planning system is relatively non-restrictive. While strategic spatial planning is dictated by national and province level, the only legally binding plan in the Dutch system is in the municipal level (the "Bestemmingplan"), which is also subjected to certain judicial flexibility, and is rarely overruled by high administrative authorities (de Vor & de Groot, 2011; Hajer & Zonneveld, 2000; Louw, van der Krabben, & Priemus, 2003; van Oort, 2004). Dutch municipalities are therefore an important authority in determining how spatial policy is implemented. In a densely populated spatial setting, the Dutch municipalities (408 municipalities in 2013) often compose different parts of the same urban agglomeration, while municipalities which are located farther from cities can have suburban or rural characteristics. Municipalities are also often actively involved in local land markets, in order to guide spatial planning and to ensure a sufficient supply of housing, but also to capture the value of developed land (Buitelaar, 2010; Louw et al., 2003). Since the early 1990’s municipalities have purchased over 60,000 hectares of land, mostly around large cities (van Marwijk & Pellenbarg, 2013). Despite extensive construction and conversion of agricultural land to industrial and residential uses, development projects needed to follow the central government’s planning directives, and to avoid conversion of protected land. Restricting land use regulations have been dominating the

Dutch planning system since the end of WWII. The purpose of these regulations was to direct and accommodate the growing need for urban expansion while conserving the country’s natural reserves and agricultural activity. In this manner, the Dutch “Green Heart” (groene hart), a large agricultural area of approximately 2,400 square kilometer in the middle of the Randstad (the economic center of the Netherlands) was designated in order to ensure agricultural production in proximity to large population centers (Koomen, Dekkers, & van Dijk, 2008). However, land use regulations were updated every several years to accommodate changes in population needs. For example, the ‘green heart’ shrunk to 1,800 square kilometers by 1993. Nevertheless, the Dutch planning system views regarding nature preservations were stricter, and while urban expansion needs often overruled other restrictions (and were even extended to land reclaimed from the sea), the borders of areas defined as nature reserves hardly changed since the 1960’s. Since the late 1980’s, new residential development in the Netherlands is largely directed by the Fourth memorandum for Spatial Planning Extra (abbreviated as `VINEX’). Issued in 1991, the VINEX plan for new residential neighborhoods was conceived by the Dutch government with cooperation with the provinces and municipalities, in order to accommodate the housing needs of the growing Dutch population, and it directed the construction of approximately 828,000 affordable housing units, mostly in areas outside the existing urban areas (Boeijenga, Mensink, & Grootens, 2008; Koomen et al., 2008; Lörzing, Klemm, van Leeuwen, & Soekimin, 2006; Rietveld & Wagtendonk, 2004). The VINEX plan promoted the construction of new neighborhoods in various areas in the Netherlands (see map in appendix A), as well as industrial areas, since the plan requires that employment centers would be constructed in short commuting distance from the new neighborhoods (Boeijenga et al., 2008; Kruythoff & Teule, 1997). While the development plans were to be implemented by the national government, the provinces and particularly the municipalities (who in practice determine the allocation of industrial or residential land), the success of a development projects depended much on private parties which were to occupy these new projects. This was emphasized by the fact that most VINEX neighborhood properties are marketed to the private sector (Kruythoff & Teule, 1997; Louw et al., 2003). Therefore, it became essential for municipalities to attract residents and developers of industrial and business areas, both parties expressing their strong priority towards good road transportation accessibility (Kruythoff & Teule, 1997). In this research, we show that municipalities become actively involved in land markets in order to achieve this goal and to encourage urban growth.

3. Methodology

3.1 A model for an urbanized area with decentralized housing and employment

We consider an urbanized area which is subdivided into a number of locations. We use a suffix 𝑖 to refer to residential locations (𝑖 = 1 … 𝐼) and 𝑗 for work locations (𝑗 = 1 … 𝐽). The locations are connected by a road network and 𝑑𝑖𝑖 is the distance between residential location 𝑖 and work location 𝑗.

Households in the urban area have a work location and a residential location. We consider only one-worker households. The utility of a combination of a work and residential location 𝑢𝑖𝑖 is the sum of four terms: (i) the utility of the residential location 𝑣𝑣𝑖 which depends on the housing price 𝑝𝑖, (ii) the utility of the work location 𝑣𝑣𝑖 which depends on the wage 𝑤𝑖, (iii) the travel cost 𝑡𝑖𝑖 which depends on the distance, that is: 𝑡𝑖𝑖 = 𝑡�𝑑𝑖𝑖�, and (iv) a random term 𝜀𝑖𝑖 which represents the idiosyncratic utility a household attaches to the combination of residential and work location:

𝑢𝑖𝑖 = 𝑣𝑣𝑖(𝑝𝑖) + 𝑣𝑣𝑖�𝑤𝑖� + 𝑡�𝑑𝑖𝑖� + 𝜀𝑖𝑖. (1)

We assume here that all 𝜀𝑖𝑖s are i.i.d. extreme value type I distributed, which implies that choice probabilities are given by the multinomial logit model.1

We assume a population with a given size 𝐵. Households can live in the urbanized area that we consider, or choose an outside option, which will be referred to with the suffix 0.

The choice probabilities are 𝜋𝑖𝑖:

𝜋𝑖𝑖 = 𝑒𝑣𝑣𝑖�𝑝𝑖�+𝑣𝑣𝑗�𝑤𝑗�+𝑡�𝑑𝑖𝑗�

∑ ∑ 𝑒𝑣𝑣𝑖�𝑝𝑖�+𝑣𝑣𝑗�𝑤𝑗�+𝑡�𝑑𝑖𝑗�𝑗𝑖 +𝑒𝑣0, 𝑖 = 1 … 𝐼, 𝑗 = 1 … 𝐽. (2)

In this equation 𝑣0 denotes the deterministic part of the utility of the outside option.

We assume that the number of houses 𝑆𝑖 per residential location and the number of jobs 𝐸𝑖 per employment location is fixed. The model can be extended with housing supply and labor demand equations, but for now we use these assumptions. Moreover, we assume that the total number of houses equals the total number of jobs in the urban area:

∑ 𝑆𝑖𝑖 = ∑ 𝐸𝑖𝑖 . (3)

We think of the residential and unemployment locations as zones that are originally in agricultural use. Agricultural land has everywhere the same value. Land can be withdrawn from agriculture and used for residential and industrial purposes. Note that the distributions of jobs and workers over the zones are arbitrary. A monocentric version of the model would have all jobs in one location. An alternative pattern would be that the sets of residential and job locations are equal (every zone has employment and housing). The present model can deal with both

1 Generalization to other GEV models is possible, but will not be considered here.

patterns as well as intermediate ones in which some zones have ‘mixed’ land use – that is some land is in residential use and another part in industrial use - while others are exclusively in residential or employment use. Agricultural land use may also be present in zones with housing or employment, either because demand for alternative uses does not outbid all agricultural use, or because it is protected (e.g. to preserve ‘open space’).

We assume:

𝑣𝑣𝑖(𝑝𝑖) = 𝐴𝐴𝑖 − 𝛼𝑝𝑖 (4)

𝑣𝑣𝑖�𝑤𝑖� = 𝐴𝐸𝑖 + 𝛽𝑤𝑖 (5)

Equation (4) states that the utility of a residential location is determined by the local amenities and the housing price. Similarly, equation (5) states that the utility of a work location is determined by amenities (for instance restaurants and shops) and the wage. We assume that 𝛼 and 𝛽 are positive.

3.2 Solving the model

In the short run the housing stock and labor demand per zone are given. Equilibrium requires that the number of households in a residential location is equal to the number of houses, and that the number of workers in a work location equals the number of jobs present there. That is:

∑ 𝜋𝑖𝑖𝑖 𝐵 = 𝑆𝑖, 𝑖 = 1 … 𝐼 (6)

∑ 𝜋𝑖𝑖𝑖 𝐵 = 𝐸𝑖 , 𝑗 = 1 … 𝐽 (7)

Prices and wages adjust so that (6) and (7) are satisfied.

Substitution of (2) into (4) gives after some rewriting:

𝑣𝑣𝑖(𝑝𝑖) = ln 𝑆𝑖 − ln∑ 𝑣𝑣𝑒𝑗�𝑤𝑗�+𝑡�𝑑𝑖𝑗�𝑖 + ln𝑁 − ln𝐵 (8)

where 𝑁 is the denominator on the right-hand side of (2). From (7) we derive similarly:

𝑣𝑣𝑖�𝑤𝑖� = ln𝐸𝑖 − ln∑ 𝑣𝑣𝑣𝑖(𝑝𝑖)+𝑡�𝑑𝑖𝑗�𝑖 + ln𝑁 − ln𝐵 (9)

Substituting (4) and (5) in (8) and (9), respectively, and some rewriting gives:

𝑝𝑖 = 1𝛼�𝐴𝐴𝑖 + ln∑ 𝑣𝑣𝑒𝑗�𝑤𝑗�+𝑡�𝑑𝑖𝑗�𝑖 − ln 𝑆𝑖 + (ln𝐵 − ln𝑁)� (10)

𝑤𝑖 = 1𝛽�ln𝐸𝑖 − 𝐴𝐸𝑖 − ln∑ 𝑣𝑣𝑣𝑖(𝑝𝑖)+𝑡�𝑑𝑖𝑗�𝑖 − (ln𝐵 − ln𝑁)� (11)

The first equation states that the price of residential land reflects the amenities present at the location, the accessibility of jobs from the location and the supply of housing at the location. Note that the last two terms, in braces, are not location-specific and have no impact on the price differences between the locations.

The wage equation has an opposite interpretation. More jobs imply that a higher wage is needed to get them all occupied. Better amenities and accessibility make it easier to fill the jobs and therefore lower the equilibrium wage. Also in this equation, the last two terms are common to all wages.

We can solve for 𝑁. To do this, first note that:

∑ ∑ 𝜋𝑖𝑖𝑖𝑖 = 1 − 𝑒𝑣0

𝑁. (12)

It must also be true that:

∑ ∑ 𝜋𝑖𝑖𝑖𝑖 𝐵 = ∑ 𝑆𝑖𝑖 . (13)

Simple algebra then gives:

𝑁 = 𝐵𝐵−𝑆𝑆

𝑣𝑣0. (14)

We can substitute this result in (10) and (11) to get some further simplification:

𝑝𝑖 = 1𝛼�𝐴𝐴𝑖 + ln∑ 𝑣𝑣𝑒𝑗�𝑤𝑗�+𝑡�𝑑𝑖𝑗�𝑖 − ln 𝑆𝑖 + (ln(𝐵 − 𝑆𝑆) − 𝑣0)� (15)

𝑤𝑖 = 1𝛽�ln𝐸𝑖 − 𝐴𝐸𝑖 − ln∑ 𝑣𝑣𝑣𝑖(𝑝𝑖)+𝑡�𝑑𝑖𝑗�𝑖 − (ln(𝐵 − 𝑆𝑆) + 𝑣0)� (16)

3.3 Implications for land market equilibrium

The set of equations (15) and (16) has the housing prices and wages as unknowns. They have to be solved. We interpret the housing price as the rent on residential land. That is, we assume that households can buy land in zone 𝑖 at a given price 𝑝𝑖 and buy a house on it.

The determine the rent on industrial land we need a theory of the firm. A simple version states that each job needs a fixed amount of space. The profits that result from the activity of this job in zone 𝑗 are equal to the difference between the revenues 𝑣𝑖 that may be a function of the distance 𝛿𝑖 to the highway network. The wage and the rent have to be subtracted from these revenues to get profits. If the industry works under zero profit conditions the rent 𝜌𝑖 is equal to the difference between revenues and wage:

𝜌𝑖 = 𝑣�𝛿𝑖� − 𝑤𝑖. (17)

Using this equation, we can eliminate the wages from equations (15) and (16) and reformulate them in terms of residential and industrial land prices. Using (17) we rewrite (16) as:

𝜌𝑖 = 1𝛽�(𝛽𝑣�𝛿𝑖� + 𝐴𝐸𝑖) + ln∑ 𝑣𝑣𝑣𝑖(𝑝𝑖)+𝑡�𝑑𝑖𝑗�𝑖 − ln𝐸𝑖 + (ln(𝐵 − 𝑆𝑆) − 𝑣0)� (18)

On a competitive land market the rents for all uses of land must be equal. Suppose residential zone 𝑖 and industrial zone 𝑗 both refer to the same geographical zone k. Then we must have:

𝑝𝑘 = 𝜌𝑘 = 𝑣𝑟𝑘 (19)

where 𝑣𝑟𝑘 denotes the agricultural rent.

To investigate the implications for land prices, we have to consider the future development of land use. We assume that agricultural rent does not change. As long as some land is in agricultural use in zone k, (19) implies that the rents on residential and industrial land will be equal to 𝑣𝑟𝑘. Only if demand for residential and industrial land is so strong that no farming is left can the rents 𝑝𝑘 and 𝜌𝑘 be higher than 𝑣𝑟𝑘. But as long as both land uses are present, the rents for residential and industrial land must be equal. It may of course also happen that only one type of land use is left. The important point to note is that in this setting all land in a zone always has the same rent.

In a framework like that developed by Capozza and Helsley (1989, 1990) this would imply that the price of all pieces of land in zone 𝑘 will be always equal. There may be differences between prices of land in various zones, but within a zone all pieces of land must have the same price, which is equal to the appropriately capitalized expected flow of future rents. This market equilibrium provides an important benchmark for an analysis of the impact of land use restrictions.

3.4 The impact of land use restrictions

With a competitive land market, land rents for all relevant uses of the land must be equal. That is, if a zone has some agricultural land use, the rents of land used for residential or industrial purposes in that zone (if any) must be equal to the price of agricultural land. If there is no agricultural land use, but industrial as well residential use exist the prices of land in both uses must be equal. If these properties are not present, arbitrage is possible.2

2 In practice conversion costs may be non-negligible.

The ‘no arbitrage’ situation will in general not result if there are restrictions on land use. This is the case in the Netherlands where ‘local land use plans’3 impose restrictions on land use almost everywhere. These restrictions are not necessarily binding. However, it is possible that they are and the restrictions give authorities the possibility to split the market for land into segments in which prices can be different.

There are several possible reasons why authorities could be willing to do this. One is that they want to protect open space surrounding their built-up areas. This is indeed often mentioned as an important issue in the Netherlands (see Vermeulen and Rouwendal, 2014). Other possibilities would be that a (national) planner maximizes expected utility for the inhabitants of the urbanized

area (expected utility is: 𝑊 = ln�∑ ∑ 𝑣𝑣𝑣𝑖(𝑝𝑖)+𝑣𝑒𝑗�𝑤𝑗�+𝑡�𝑑𝑖𝑗�𝑖𝑖 �, or that he maximizes the sum of the rents. It could also be the case that local authorities have such purposes for their jurisdictions.

Maximization of land rents

We now consider the behavior of a local planner who maximizes the sum of the land rents paid in an urban area, while taking the behavior of all other local authorities as given. We refer to the local jurisdiction of this planner as k. That is the relevant residential location is 𝑖 = 𝑘 and the relevantindustrial location is 𝑗 = 𝑘. Social surplus Ω is:

𝛺 = (𝑝𝑘 − 𝑃𝐴)𝑆𝑘 + (𝜌𝑘 − 𝑃𝐴)𝑘𝐸𝑘 (20)

….

4. Estimation

Our estimation strategy is based on equations (15) and (18). We are interested in the prices of residential and industrial land.

Our estimation equations are:

𝑝𝑖𝑡 = 𝛾0𝑡 + 𝛾1𝐴𝑖𝑡𝑒 + 𝛾2𝑆𝑖𝑡 + ∑ 𝛾𝑙𝑋𝑙𝑖𝑡𝑣𝑙>2 (21)

𝜌𝑖𝑡 = 𝛿0𝑡 + 𝛿1𝐴𝑖𝑡𝑣 + 𝛿2𝐸𝑖𝑡 + ∑ 𝛿𝑚𝑋𝑚𝑖𝑡𝑒𝑚>2 (22)

Both prices are specified as linear (in the parameters) equations. The explanatory variables are an accessibility measure, a supply indicator and area characteristics that reflect local amenities. The intercept is time-varying because B and N may change over time. 3 In Dutch: bestemmingsplannen.

Endogeneity is a concern for accessibility as well as supply.

5. Data

In the research and analysis we make use of several data sources regarding land values and uses. Industrial and residential land values were made available by Kadaster, the Dutch land registry. The Kadaster data we use here includes mean annual transaction price for industrial land and residential land ,both in newly developed and existing industrial or residential areas, in 120 municipalities in the west of the Netherlands. The data includes 50,799 residential land transactions and 1,545 industrial land transactions between the years 2003 and 2013. In all transactions included, the buyer side is a private household or firm. Transactions in which the buyer is a public body or commercial developer were not included. Due to limitations imposed by transaction deed registries, for many transactions it was impossible to distinguish between land value and the contract price (aanneemsom) for additional developments and the building to be constructed on the land. Identification of transactions in which the contract price was based on deed research and removal of groups of extreme positive price outliers, which often indicate that the transaction price includes additional irrelevant elements. Below we only use transactions that do not include the contract price. A second source of data regarding the values of industrial land was available from the Integral business areas information system (Integraal Bedrijventerrein Informatie Systeem, IBIS) which is operated and maintained by the Dutch government. The IBIS data includes information about the locations and approximation of annual land values of business and industrial areas in the Netherlands, between 1988-2014. Examination of the land transactions data in several municipalities, in which massive residential and industrial development took place in recent years, raises some interesting preliminary findings. Table 1 and figure 1 describe the average annual land prices for industrial and residential lands, which were sold by municipalities or commercial parties to private parties. The values of lands designated for industrial use are significantly lower. Between the years 2003-2009, transaction prices of industrial lands were on average 35-45% lower than industrial land transactions. In the years 2010-2013, this difference grew to over 55%, reaching a 65% difference in values in 2013 (541 EUR per square meter of residential land, compared with 192 EUR per square meter for industrial land).

Table 1 - Summary of annual average industrial land prices

Residential land Industrial land

Year Average price, EUR per m2

N transactions

Average price, EUR per m2

N transactions

2003 389.9 5,030 210 22 2004 443.5 6,534 290.4 95 2005 509.5 7,146 291.5 297 2006 544.7 7,281 323.3 257 2007 559.8 6,017 356.6 287 2008 622.6 4,702 397.3 247 2009 562.7 2,667 365.2 116 2010 611.1 3,811 257.6 83 2011 586.5 3,537 271.6 60 2012 556.9 2,165 250.3 44 2013 541.3 1,909 192 37

Total 538.9 50,799 291.4 1,545 Figure 1 - Mean annual land transaction prices (2003-2013)

Both residential and industrial land values reflect the price trend of houses in the Netherlands during these years- both values climbed until they peaked in 2008, after which they experienced a decrease. This decrease in prices appears to be stronger in industrial lands relative to residential lands, a drop which may reflect a decrease in demand for industrial and business areas due to economic slowdown flowing the global economic crisis which began in 2008. Out of the examined 120 municipalities, 39 had sufficient number of annual transactions in order to conduct price development comparisons between industrial and residential land values (at least five years of data over industrial transactions, between 2003 and 2013). Not surprisingly, the municipalities with the most available information are located in the province of Flevoland, the Netherlands’ “newest” province which was founded on areas reclaimed from the Zuiderzee (now IJselmeer) during the second half of the 20th century. Between 2003 and 2013, 6,100 research-valid residential land transactions and 300 research-valid industrial land transactions took In the province of Flevoland (Municipalities of Almere, Zeewolde, Lelystad, Dronten, Zeewolde, Urk and Noordostpolder). Table 2 and Figure 2 describe the annual trends in transaction prices in the municipalities of this province. Since the Flevoland province statistics are based on a smaller number of transactions, they present a larger annual variance between years. However, the data clearly shows that for most years, the transaction prices of industrial land are significantly lower than the transaction prices of industrial lands. In 2003, the average vale of industrial was 85 EUR per square meter, 73% percent lower than the price of residential prices in Flevoland during that year (324 EUR per square meter). Table 2 - Summary of annual average industrial land prices (Flevoland)

Residential land Industrial land

Year Average price, EUR per m2

N transactions

Average price, EUR per m2

N transactions

2003 196.1 737 95.4 10 2004 238.5 860 191.5 16 2005 253.2 761 186.2 63 2006 261.3 680 266.5 71 2007 253.1 640 113 64 2008 291.6 537 91.4 43 2009 315.6 562 113.3 17 2010 312.4 630 90 13 2011 321.7 559 182.2 11 2012 301.9 325 124.9 6 2013 323.9 239 85.5 11 Total 279 6,530 140 325

Figure 2 - Mean annual land transaction prices (Flevoland, 2003-2013)

To measure accessibility, we use ‘potentials’ of employment and houses, which are accessible within a commuting time of 60 minutes from each municipality’s population centre. See table X for provincial accessibility data in 2013. Accessibility was calculated for each municipality, based on Nationaal Wegen Bestand, the Dutch road network data from 2013 (Rijkswaterstaat, The Dutch ministry of infrastructure and environment, 2015). Travel time in different roads was assigned using highway maximum travel speed information (Rijkswaterstaat, 2015). Where information was unavailable, a traffic speed of 50 km/h was assumed for non-urban provincial roads (Gutiérrez, Condeço-Melhorado, & Martín, 2010) and 30 km/h for urban roads (Rietveld & Bruinsma, 1998). Supply measures are simply the numbers of houses and jobs in particular municipalities, for each year between 2003-2013 (CBS, 2015). To control for other accessibility measures, we include the number of train stations in each municipality, for each year between 2003-2013 (Koopmans, Rietveld, & Huijg, 2012, Rijkswaterstaat, 2015).

Accessibility score per provice (2013)

Province Average accessibile population within hour drive (thousands)

Average accessibile jobs within hour drive (thousands)

D. 2,285.00 900.1

F. 1,515.80 582.2 Fle. 5,151.90 2,255.90 Gld. 5,996.40 2,629.50 Gr. 1,483.70 554.6 L. 2,230.90 943.1 NB. 5,326.20 2,379.10 NH. 5,721.90 2,618.10 O. 2,817.50 1,120.90 U. 9,553.10 4,271.90 Z. 1,473.70 584 ZH. 7,669.40 3,516.80 Total 5,049.70 2,238.50

To observe the effects of land use restrictions we include the share of municipal area which was included in the Green heart in 1993, a dummy variable indicating whether the municipality has a VINEX development, and the share of nature coverage in 1996. While we expect land restrictions to positively affect land values, the share of nature coverage is also likely to affect residential housing value through its value as an attractive amenity. Since it is difficult to make the distinction between the two positive effects, we expect the estimated coefficient will reflect both of them and will consider it while interpreting the coefficient’s value.

Other amenities which are included are the number of official monuments in each municipality, which reflect the level of cultural heritage, and the number of universities in each municipalities. The number of universities variable is assumed to affect both the attractiveness of a certain municipality, and the productivity of nearby firms through spillover effects.

6. Estimation results

We begin by testing the hypothesis laid out in section 3 according which land rents are equal for all uses at the edge of the urban area. We do so by specifying equation (19) as follows:

𝑃𝑘 = 𝛼𝑘 + 𝛽𝑘𝜌𝑘 + 𝜖𝑘 (23)

If the hypothesis is correct and land rents are equal for both residential and industrial uses, we should observe that 𝛼𝑘 = 0 and 𝛽𝑘 = 1. Table 3 describes the estimation results of equation, for each sampled year.

2003 2004 2005 2006 2007 2008

VARIABLES ln_ind_price ln_ind_price ln_ind_price ln_ind_price ln_ind_price ln_ind_price

ln_res_price 1.156*** 1.505*** 1.124*** 1.232*** 1.060*** 1.201***

(0.0327) (0.0190) (0.0104) (0.0120) (0.0215) (0.0210)

Constant -1.546*** -3.510*** -1.583*** -2.039*** -1.009*** -2.033***

(0.191) (0.108) (0.0601) (0.0708) (0.129) (0.126)

Observations 840 4,889 8,324 8,001 5,609 2,842 R-squared 0.534 0.558 0.489 0.523 0.301 0.475

2009 2010 2011 2012 2013

VARIABLES ln_ind_price ln_ind_price ln_ind_price ln_ind_price ln_ind_price

ln_res_price 1.267*** 1.091*** 0.458*** 0.438*** 0.669***

(0.0469) (0.0287) (0.0721) (0.0977) (0.132)

Constant -2.336*** -1.430*** 2.105*** 2.807*** 0.756

(0.289) (0.177) (0.418) (0.598) (0.809)

Observations 1,138 1,453 997 545 405

R-squared 0.514 0.430 0.046 0.055 0.178 Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1 Note: Number of transactions frequency weights are used

The results in table 3 show that for most years we observe strongly significant coefficients, indicating at we can reject the hypothesis that values of newly developed land, and hence also its rents, are equal for both residential and industrial uses. In the years before 2011 it appears that 𝛼𝑘 is negative and that 𝛽𝑘 is larger than 1, indicating that industrial land uses are inherently lower than residential values. This trend is reversed after 2011, although the coefficients are possibly biased due to a lower number of transactions during these years. Table 4 provides the preliminary estimation results of equations (21) and (22), estimated in simple OLS.

Our results show that variables which indicate land use restrictions have a positive effect on the prices of both residential and industrial lands. However, it appears that residential land values are more sensitive to land use restrictions. These results confirm the findings of Kok et al (2014) and Glaeser et al (2006).

VINEX agreement raises residential and industrial land values by 23-25%.

Additional percentage of nature coverage raises residential land by about 0.6% and industrial land by 0.77%. This is surprising because in addition to a its restrictive effect on development, nature can also be considered as a desirable amenity which is expected to increase residential land values.

Additional share of municipal area which was included within the Green heart in 1993 increases residential land values by about 20% (30% if accessibility to industrial jobs is included), while it has an insignificant effect on industrial land values. This is also possibly because the green heart may affect residential lands through its attractiveness as an amenity.

Accessibility is found to be statistically significant but small for both residential and industrial land values. Accessibility to additional 10,000 jobs within commuting distance of one hour is expected to increase residential land values by about 0.2%. Accessibility to additional 10,000 people within commuting distance of one hour is expected to increase industrial land values by about 0.16%.

(1) (2) (3) VARIABLES ln_res_price ln_res_price ln_ind_price Accessibility to jobs 0.000251***

(1.01e-05) Accessibility to industry jobs

0.00167***

(7.23e-05) Accessibility to population

0.000163***

(7.74e-06)

Number of jobs, employment level

0.00130

(0.000859)

Housing supply -0.000553 -0.000365

(0.000779) (0.000798)

Number of official monuments 0.000215*** 0.000212*** 0.000174*

(6.71e-05) (6.87e-05) (0.000106)

Number of universities -0.0299 -0.0377 -0.0826**

(0.0248) (0.0254) (0.0412)

Number of rail stations 0.0105 0.0117 -0.00472

(0.0103) (0.0106) (0.0151)

Share of nature coverage, 1996 0.00666*** 0.00508*** 0.00787***

(0.00103) (0.00107) (0.00164)

Share of municipal area included in the GH (1993) 0.204*** 0.308*** 0.152

(0.0621) (0.0623) (0.0984)

VINEX municipality dummy 0.232*** 0.252*** 0.254***

(0.0316) (0.0323) (0.0499)

Year dummies Yes Yes Yes

Constant 5.002*** 4.805*** 3.856***

(0.0678) (0.0913) (0.108)

Observations 1,126 1,126 1,126 Adjusted R-squared 0.529 0.506 0.437 Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Table 5 provides the results of the same model, estimated as a seemingly unrelated regression. Coefficients remain relatively unchanged compared with the simple OLS regression, and they maintain high statistical significance levels.

(1) (2) VARIABLES ln_res_price ln_ind_price Accessibility to jobs 0.000250***

(1.00e-05) Accessibility to population

0.000164***

(7.66e-06)

Number of jobs, employment level

0.000851

(0.000838)

Housing supply -0.000600

(0.000761)

Number of official monuments 0.000216*** 0.000180*

(6.65e-05) (0.000105)

Number of universities -0.0296 -0.0725*

(0.0246) (0.0407)

Number of rail stations 0.0109 -0.00144

(0.0102) (0.0149)

Share of nature coverage, 1996 0.00669*** 0.00786***

(0.00102) (0.00162)

Share of municipal area include in the GH (1993) 0.207*** 0.148

(0.0616) (0.0975)

VINEX municipality dummy 0.233*** 0.261***

(0.0313) (0.0495)

Year dummies Yes Yes

Constant 5.004*** 4.080***

(0.0672) (0.138)

Observations 1,126 1,126 Adjusted R-squared 0.529 0.437 Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Since municipalities in the Netherlands are located in close proximity to each other, and they often share the same larger urban area, it is very likely that land restrictions in one municipality can affect its neighboring municipalities’ land values. Table 6 includes a seemingly unrelated regression including spatial-lag variables, constructed using inverse travel time matrix, for the land restriction variables (VINEX dummy, Share of municipality in the Green Heart and nature coverage) as well as for the number of historic monuments and number of universities. Due to strong correlation with the spatial –lag variables, the accessibility variables were excluded in this specification.

Much like the findings of Burchfield and Overman ( 2006), the model’s result show that while restriction on land development have a positive effect on land values, their effect on land values in nearby municipalities are much stronger.

While VINEX development projects increase the value of residential and industrial land by approximately 22-23%, coefficients of 0.667 and 1.236 imply that VINEX development in neighboring municipalities can increase residential and industrial land values by 94.8% and 244% respectively. These statistically significant findings suggest that municipalities can exploit the population growth in neighboring municipalities, which results from VINEX development, in order to attract firms and increase their revenues from industrial land rent. Similarly,

One percent increase in the share of a municipality’s nature coverage increases its residential and industrial land values by about 0.6%, while a similar increase in neighbouring municipalities increases the value of industrial land by 0.65% and of residential land by 0.56%. Since the coverage of nature in neighboring municipalities has no restrictive power on land development in

the own-municipality, this coefficient’s value can also be interpreted as the effect on land prices of nature as an amenity.

(1) (2) VARIABLES ln_res_price ln_ind_price Number of jobs, employment level

0.000626

(0.000805)

Housing supply -0.00122*

(0.000725)

Number of official monuments 0.000260*** 0.000278***

(6.26e-05) (9.81e-05)

Number of universities -0.0514** -0.0981**

(0.0253) (0.0419)

Number of rail stations 0.0112 -0.00490

(0.00970) (0.0142)

Share of nature coverage, 1996 0.00612*** 0.00693***

(0.00109) (0.00172)

Share of municipal area include in the GH (1993) -0.0940 -0.156

(0.0714) (0.113)

VINEX municipality dummy 0.232*** 0.221***

(0.0302) (0.0477)

PX_RCE -0.000477* -0.000504

(0.000246) (0.000388)

PX_uni -0.681*** -0.877**

(0.260) (0.410)

PX_nature 0.00567*** 0.00650***

(0.00109) (0.00174)

PX_vinex 0.667*** 1.236***

(0.103) (0.163)

PX_gh93 0.947*** 0.928***

(0.107) (0.170)

Year dummies Yes Yes

Constant 4.762*** 3.374***

(0.0868) (0.136)

Observations 1,126 1,126 Adjusted R-squared 0.588 0.512 Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Extensions

Possible further extensions include:

1 Congestion. A recent literature in transportation has established the existence of macroscopic relationship between the number of travelers and travel speed (see Geroliminis & Daganzo (2008). We may perhaps assume the presence of such a relationship to take into account congestion and use Fosgerau (2014) to introduce congestion into the model. Improving urban infrastructure through providing more or better roads or public transport gives the planner an alternative instrument to maximize rents.

2 Agglomeration benefits. It is generally assumed that cities exist because density has advantages. These agglomeration benefits can take the form of pecuniary externalities as in Krugman’s ((1991) model or production externalities as in Lucas (1988) and Lucas and Rossi-Hansberg (2002). If such agglomeration effects exist, attracting more jobs to the urban area provides additional benefits as production becomes more efficient. Co-agglomeration is an additional candidate for further investigation (Helsley & Strange, 2014).

7. References

Alonso, W. (1964). Location and land use. Toward a general theory of land rent. Cambridge, Mass.: Harvard Univ. Pr.

Boeijenga, J., Mensink, J., & Grootens, J. (2008). Vinex Atlas. Rotterdam 010. doi:vinex atlas

Brueckner, J. (2000). Urban sprawl: diagnosis and remedies. International Regional Science …, 23(2), 160–171.

Buitelaar, E. (2010). Window on the Netherlands: Cracks in the Myth: Challenges To Land Policy in the Netherlands. Tijdschrift Voor Economische En Sociale Geografie, 101(3), 349–356.

Burchfield, M., & Overman, H. (2006). Causes of sprawl: A portrait from space. The Quarterly Journal of Economics, 121(2), 587–633.

Capozza, D. R., & Helsley, R. W. (1989). The fundamentals of land prices and urban growth. Journal of Urban Economics, 306, 295–306.

Capozza, D. R., & Helsley, R. W. (1990). The stochastic city. Journal of Urban Economics, 28(2), 187–203.

De Vor, F., & de Groot, H. L. F. (2011). The Impact of Industrial Sites on Residential Property Values: A Hedonic Pricing Analysis from the Netherlands. Regional Studies, 45(February 2015), 609–623.

Fosgerau, M. (2014). Congestion in the bathtub.

Garcia-López, M.-À., Solé-Ollé, A., Viladecans-Marsal, E., & Marsal, E. (2014). Do Land Use Policies Follow Road Construction.

Geniaux, G., Ay, J.-S., & Napoléone, C. (2011). a Spatial Hedonic Approach on Land Use Change Anticipations*. Journal of Regional Science, 51(5), 967–986. doi:10.1111/j.1467-9787.2011.00721.x

Geroliminis, N., & Daganzo, C. F. (2008). Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings. Transportation Research Part B: Methodological, 42, 759–770. doi:10.1016/j.trb.2008.02.002

Glaeser, E. L., Gyourko, J., & Saks, R. (2006). Urban growth and housing supply. Journal of Economic Geography, 6(1), 71–89.

Glaeser, E. L., & Ward, B. a. (2009). The causes and consequences of land use regulation: Evidence from Greater Boston. Journal of Urban Economics, 65(3), 265–278.

Gutiérrez, J., Condeço-Melhorado, A., & Martín, J. C. (2010). Using accessibility indicators and GIS to assess spatial spillovers of transport infrastructure investment. Journal of Transport Geography, 18(1), 141–152.

Gyourko, J., Saiz, A., & Summers, A. (2008). A New Measure of the Local Regulatory Environment for Housing Markets: The Wharton Residential Land Use Regulatory Index. Urban Studies, 45(3), 693–729.

Hajer, M., & Zonneveld, W. (2000). Spatial Planning in the Network Society-Rethinking the Principles of Planning in the Netherlands. European Planning Studies, 8(3), 337–355. doi:10.1080/713666411

Hamilton, B. (1978). Zoning and the exercise of monopoly power. Journal of Urban Economics, 116–130.

Hamilton, B. W., & Röell, A. (1982). Wasteful Commuting Again. Journal of Political Economy, 90(5), 1035–1053.

Helsley, R., & Strange, W. (2014). Coagglomeration, Clusters, and the Scale and Composition of Cities. Journal of Political Economy, 122(5), 1064–1093.

Hilber, C. A. L., & Robert-Nicoud, F. (2013). On the origins of land use regulations: Theory and evidence from US metro areas. Journal of Urban Economics, 75, 29–43.

Katz, L., & Rosen, K. T. (1987). The Interjurisdictional Effects of Growth Controls on Housing Prices. The Journal of Law and Economics, 30(1), 149.

Kok, N., Monkkonen, P., & Quigley, J. M. (2014). Land use regulations and the value of land and housing: An intra-metropolitan analysis. Journal of Urban Economics, 81, 136–148.

Koomen, E., Dekkers, J., & van Dijk, T. (2008). Open-space preservation in the Netherlands: Planning, practice and prospects. Land Use Policy, 25(3), 361–377.

Koopmans, C., Rietveld, P., & Huijg, A. (2012). An accessibility approach to railways and municipal population growth, 1840–1930. Journal of Transport Geography, 25, 98–104.

Koster, H. R. A., & Rouwendal, J. (2013). Agglomeration, commuting costs, and the internal structure of cities. Regional Science and Urban Economics, 43(2), 352–366. doi:10.1016/j.regsciurbeco.2012.09.001

Krugman, P. (1991). Increasing Returns and Economic Geography. Journal of Political Economy, 99(3), 483–499.

Kruythoff, H., & Teule, R. (1997). Vinex policy moves into the implementation phase. Netherlands Journal of Housing and the Built Environment, 12(1), 113–133.

Lörzing, H., Klemm, W., van Leeuwen, M., & Soekimin, S. (2006). VINEX! Een morfologische verkenning. Ruimtelijke Planbureau, The Hague.

Louw, E., van der Krabben, E., & Priemus, H. (2003). Spatial development policy: Changing roles for local and regional authorities in the Netherlands. Land Use Policy, 20, 357–366.

Lucas, R. E. (1988). On the mechanics of economic development. Journal of Monetary Economics, 22(February), 3–42. doi:10.1016/0304-3932(88)90168-7

Lucas, R., & Rossi-Hansberg, E. (2002). On the internal structure of cities. Econometrica, 70(4), 1445–1476.

Mills, E. S. (1967). An Aggregative Model of Resource Allocation in a Metropolitan Area. The American Economic Review, 57, 197–210. doi:10.2307/1821621

Muth, R. F. (1969). Cities and housing; the spatial pattern of urban residential land use. American Journal of Agricultural Economics (Vol. 91).

Rietveld, P., & Bruinsma, F. R. (1998). Is transport infrastructure effective? : transport infrastructure and accessibility : impacts on the space economy. Advance in spatial science.

Rietveld, P., & Wagtendonk, A. J. (2004). The location of new residential areas and the preservation of open space: experiences in the Netherlands. Environment and Planning A, 36(11), 2047–2063.

Small, K. A., & Song, S. (1992). Comment “ Wasteful ” Commuting : A Resolution. Journal of Political Economy, 100(4), 888–898.

Turner, M. A., Haughwout, A., & van der Klaauw, W. (2011). Land use regulation and welfare. Federal Reserve Board of New York.

Van Marwijk, R., & Pellenbarg, M. (2013). Municipalities, land policy and changing contexts: land acquisition in the Netherlands.

Van Oort, F. (2004). Urban growth and innovation: Spatially bounded externalities in the Netherlands. Gower Publishing, Ltd.

Wheaton, W. C. (1974). A comparative static analysis of urban spatial structure. Journal of Economic Theory, 9, 223–237.

A. VINEX neighborhood and new industrial locations in the Netherlands