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Modelling industrial land market dynamics: The growth of economic activities (i.e. economic output and employment) in cities and regions results in physical changes in industrial land in the medium to long-term following an increase in demand for new industrial property developments. A considerable correlation between industrial and residential property and the economy is confirmed by the study of Wheaton [42]. A similar study by Ball and Wood [66] found evidence between aggregate economy and construction activity in the real property sector (see also Barras [52]). Therefore, the demand for industrial space can be represented as a function of economic activity in urban areas and regions. The increase in demand for new industrial space increases rental prices of the industrial estates in the short-run. Ball et al. [49] argued that changes in the nature of production and output generally results in more or less intensive use of space for a given level of output. Therefore, in the short-run firms do not change their demand for physical space but they adjust the existing space according to their new production needs. In the economic literature, the effect of transport infrastructure on industrial land development is incorporated as an additional input in the production function since accessibility to certain activities raises productivity or reduces cost of production [67, 68]. Therefore, accessibility can also be considered as an additional factor determining the demand for industrial land through influencing the profitability of industrial production. Given this literature, we can suggest that changes in economic activities, accessibility and rental prices of real property influence demand for industrial space in the market where the demand function can be stated as: Di,tL=f(Ei,t,Ri,t,Ai,t)(1) where Ei,t represents economic parameters (i.e. GDP, industrial GVA and industrial employment), Ai,t is the accessibility indicator which will be elaborated in future sections, and Ri,t is the industrial rental price of the i th region at time t. If there is excess demand for the existing industrial estates, it will rise the prices and rental prices of the industrial properties. The increase in rents and price for industrial space provides an incentive for developers to initiate new investments in the market. Therefore, developers will generate new construction investments in the long run. Property developers generally make investment decisions based on the prices in the recent past period [69]. Following Wheaton [42], we can state that supply depends on historical prices, Pi,t-s, and asset prices at the time of the delivery, Pi,t, as industrial properties are occupied by multiple tenants. The accessibility to industrial activities (i.e. jobs) is an important determinant of industrial land price, which consequently influences supply of land considering that land developers take into account the issues of accessibility of land uses and their price for their investment decisions in the market. A zone which has higher accessibility to industrial jobs imply that the subject zone has higher industrial land prices compared to others having lower accessibility values. Therefore, an accessibility measure, Ai,t can be included in the supply function as an independent variable. In order to obtain an understanding of the impact of industrial land conversions to other uses (e.g. social facilities, residential uses) on the development of industrial land use, we include a variable, Ci,t, in the supply equation indicating the areas of industrial land existing at time (t-s) and converted to other uses at time t. Considering that land supply is also dependent on the developed space in the previous periods (t-s), land-use restrictions such as land zoning policies imposed by local or national authorities, Zi,t, and natural limitations such as protected areas, water bodies or sludge areas, Ni,t, land supply function can be provided as: Si,tL=f(Pi,t,Pi,t−s,Ri,t,Ri,t−s,Ai,t,Ci,t,Zi,t,Ni,t,Si,t−s)(2) It can be suggested that industrial property supply is also determined by other factors such as construction costs, lending rates (i.e. cost of finance) and vacancy rates in the industrial property market. The focus of our study is to examine the key economic variables as well as supply-side parameters including zoning, natural restrictions and industrial land converted to other uses. The commonly used economic parameters are: GDP, industrial GVA, employment and property price (or rental price). Other factors determining the supply of land are out of the focus of the study. In the equilibrium, demand is equal to supply and there is no incentive in the property market to change the activities of the economic agents. Solving the equilibrium of demand and supply equations of Eqs (1) and (2), a reduced form of new equation of developed industrial land area, LANDi,t representing the conditions of demand and supply in the industrial property market, can be formed as follows: LANDi,t=f(Pi,t,Pi,t−s,Ri,t,Ri,t−s,Ei,t,Ei,t−s,Ai,t,Ci,t,Zi,t,Ni,t,LANDi,t−s)(3) As prices and rental prices are linked through capitalisation rates, there is a direct relationship between prices and rents in the market. Assuming that capitalisation rate remains unchanged, property price can be written as a function of rental price. However, in this study rental prices of Dutch industrial properties are not included in the analysis as the time-series of rental price data is unavailable at the regional level. As a future focus, we recommend rental prices to be considered in the analysis based on the availability of the subject data.
Based on construction demand forecasting literature, the process determining the variable of industrial land depends on the set of some related past variables in the same way as industrial land depend on the set of its past values. In our sample, data for all the related variables is specified at the regional level. In this, we can proceed to generate a dynamic panel data model considering the spatial variations in our pooled time-series data. Eq (3) can be adapted to a dynamic panel data model with the lagged effects of the dependent variable i.e. LANDi,t-s incorporated in the error term (see Maddala and Lahiri [70] for details) yit=γi+∑k=1Kαi(k)xi,t−k+εit,i=1,…,N;t=1,…,T(4) εit=ρεi,t−1+ωit,|ρ| where yit is the industrial land where the industrial construction is taking place, k represents the time lag, γi is the panel-level effect; xi,t-k is a 1×k1 vector of exogenous variables for each individual region i and at time t; αi(k) is k1×1 vector of parameters to be estimated; ωi are I.I.D. error terms over the whole sample with a variance σ2ε. The γi and ωi are assumed to be independent for each i over all t. As the explanatory variables may be correlated with the unobserved panel-level effects, the estimation of Eq (4) by using standard OLS methods makes the estimators inconsistent. Following Arellano and Bond [71], Arellano and Bover [72], and Blundell and Bond [73], the above-mentioned issues have been covered through the application of Generalised Methods of Moments (GMM) approach. Consequently, the estimators are constructed by first differencing Eq (4) to remove panel-level effects and using instruments to form moment conditions. The first-differenced version of (4) can be written as: Δyit=∑k=1Kαi(k)Δxi,t−k+Δεit,i=1,…,N;t=3,…,T(5) Δεit=ρΔεi,t−1+Δωit,|ρ|<1 in which the panel-level effects are eliminated by the first differencing operation. The GMM approach utilises instruments for the estimation of Eq (5). The details on instrumental variables are provided in the Appendix.
The data used in the analysis consists of 40 regions at the NUTS3 (nomenclature of terrestrial units for statistics) level in the Netherlands, covering each year between 2000 and 2008. The NUTS3 coincide with the so-called COROP regions in the Netherlands, which has been originally designed to reduce cross-border commuting and provides a rough approximation of functional labour market regions in the Country [74]. Therefore, the NUTS3 level is selected as the spatial unit in the analysis considering that the NUTS3 level is the closest to the spatial structure of the Dutch labour and industrial land markets [18, 75]. The industrial land-use data was obtained from land-use maps for Netherlands for the years 2000, 2003, 2006, and 2008 provided by the PBL Netherlands Environmental Assessment Agency (http://www.pbl.nl/en/) and were utilised to compute the areas of industrial land, natural protection and naturally restricted areas for land development, and large-scale urban development zones which incorporates industrial land uses [76] for each corresponding year. The areas of industrial land, protected and naturally restricted areas, and industrial land in development zones for those years where data is unavailable were calculated through applying a linear interpolation between known data points for all regions. This was done to provide continuity in the time-series data regarding the areas of industrial land. The existing data points for the years 2000, 2003, 2006 and 2008 were individually checked for each NUTS 3 region, and a linear relationship was observed between the existing data points to a large extent. The areas of industrial land converted to other land uses were computed through application of a GIS-based analysis. In this approach, two spatial datasets (vector datasets) for the two subsequent years were compared regarding the industrial land uses existing in period t-s and converted to other uses in the subsequent period t. It is noted that industrial uses were mostly converted to social and recreational activities as well as residential uses. From this analysis, the areas of industrial land converted to other uses were obtained for the years 2000, 2003, 2006 and 2008. The unknown data points between these years were developed through linear interpolation. The accessibility indicators were obtained from the work of Jacobs-Crisioni et al. [77] where potential accessibility measures were computed throughout Europe for each municipality within the modelled country and for the NUTS2 regions outside of the modelled countries (see also Jacobs-Crisioni and Koomens [78]). The potential accessibility measure is computed based on the formula: Ai,t=∑i≠jmPj,tMij,tγ where A is the accessibility at origin municipality i; P is the population count in destination municipalities j in decade t; M represents the distance-decayed travel times in minutes; γ is assumed to be -1 as the real value of distance-decay is unknown for the study area. To compute the accessibility measures, domestic and foreign destinations (see Jacobs-Crisioni and Koomen [78] for the domestic and foreign accessibility component formulas) were taken into account; and the intra-zonal destinations were considered based on the Frost and Spence [109] approach in such a way that internal distance dj is assumed to be dj=0.5AREAj/π. The formula implies that intra-zonal distances are half of the radius of a hypothetically constructed circular zone. Travel times were obtained from the TRANS-TOOLS road network using the shortest path algorithm assuming free-flow travel times (for the details, we refer to Jacobs-Crisioni and Koomen [78]; Jacobs-Crisioni et al. [77]). The accessibility measures computed for the Netherlands are originally provided at the municipality level and were aggregated to the NUTS3 level to be included in the current study. As the accessibility measures were computed for each of the decade between 1961 and 2011 [78], the values for the years from 2000 to 2008 were developed through applying a linear interpolation between 1991, 2001 and 2011. This study utilises gross domestic product (GDP), sectorial gross value added (GVA aggregated for each of the industrial sectors considered in this study), sectorial employment (EMP-in industry) and industrial property price (PRICE) data for each NUTS3 in the Netherlands from the online database provided by Eurostat [79] and the Netherlands Bureau of Statistics [80]. Some basic statistics on economic parameters is summarised in Table 1 for each NUTS3 region.
Table data removed from full text. Table identifier and caption: 10.1371/journal.pone.0183285.t001 Descriptive statistics for the economic parameters. From the table, it can be seen that there are considerable differences across regions concerning annual average values of GDP, industrial property price, industrial GVA, and employment. The highest values are observed in the regions of Greater-Amsterdam, Rijnmond, Utrecht, Rest of Groningen, and South-East Brabant, which are housing majority of the economic activities in the Netherlands. An overview of the study area and its regional sub-divisions can be seen in Fig 1.
Figure data removed from full text. Figure identifier and caption: 10.1371/journal.pone.0183285.g001 The study area. The Dutch industrial land market and land-use planning system: The lags between industrial demand and supply are sound in regions where there is scarcity of land for industrial development, strong regulations in real property markets, and strict institutional structures influencing the property transactions in the land markets. Netherlands is such an example of a region where there is scarcity of land ready-to-develop considering particularly the physical limitations in the Country (most of the land needs to be processed prior to development for the reasons of high water levels and low capacity of the soil to support the built structures in the country) strict land development restrictions imposed by various governmental layers, and a strict administrative system involving in various land-use planning activities. Land-use plans are highly restrictive in the Netherlands due to the fact that it is impossible to develop new urban areas without making changes in the land-use plans. Though land-use restrictions are important in the Country, it is also possible to have changes in the land-use plans. However, this requires a time consuming effort with the administrative procedures that may lead to uncertain outcomes. With respect to the institutional structure, there are three levels of government to lay down a strategic plan. These consist of: ‘(a) the national spatial planning decisions, (b) the provincial regional plans, (c) the municipal structure plans’ [81]. While municipal structure plans are dictated by higher governmental levels, these structure plans prepared by the Dutch municipalities (complying with provincial and national plans) are the only legally binding and highly influential plans in determining the conditions in the land market (see Hajer and Zonneveld [81]; Needham and Louw [82] for a review of the institutional structure). The local land-use plans are also subject to a certain level of judicial flexibility and rarely overturned by higher planning authorities [30, 83]. The growth of industrial development is facilitated by large-scale plans to develop new industrial land uses, and the municipalities are the main responsible bodies for the supply of industrial land. In Needham and Louw’s ([82]: 84) explanation, “in the Netherlands, the public provision of industrial land is seen as a means of implementing spatial, economic and employment policy” (see also Badcock [84]; Louw et al. [30]). Considering this highly restrictive land-use planning system, an increase in land values would be expected as the supply of land diminishes as a result of land-use restrictions. The evidence shows that its final effect on urban development is mostly negative [85]. However, it is also evident that ‘the spatial context and the competition between municipalities and jurisdictions can determine the outcomes of land-use regulation on urban development’ [85]. Spatial competition for urban jurisdictions is sound in the Netherlands, where spatial planning is characterised by ‘compact development’ policies resulting high development densities and ‘clustered de-concentration’ aiming at concentrating suburbanisation at specific locations to increase urban densities in those particular locations [74, 86]. While keeping high densities through successful management of urban development, the restrictive planning system has also led to successful open space preservation in the Country in the last five decades [87]. Nature conservation policies comprising, NATURA 2000-a European network of protected areas in the EU, and National Ecological Network (NEN)-a network of natural reserves areas put restriction on urban development to avoid conversion of protected land [88]. Koomen and Dekkers [89] suggest that there are more restrictive Buffer zone and Green Heart policies in the Netherlands, which cover important national landscapes as some of them were approved as world heritage sites by UNESCO. Regarding the preservation of Buffer zone and Green Heart, Koomen et al. [86] showed that between 1995 and 2003, the propensity of transformation from open space to urban use was significantly lower in these protected zones compared to other parts of the Randstad Area. Concerning the success in the application of open space preservation policies, a variable, namely natural limitations (see Eq 2 in the methodology section), is included in the supply equation to account for the impact of natural restrictions on urban development at the regional level. This variable represents the areas of natural protection zones such as NATURA2000 and NEN, and other natural restrictions for urban development such as water bodies and sludge areas in each NUTS3 region. Besides open space preservation, large-scale urban development zones [76] introduced by the national spatial planning authorities in the Netherlands are highly successful in facilitating urban growth as these neighbourhoods also incorporate industrial and business areas aiming at reducing the commuting distance between new neighbourhoods and employment centres [85, 90]. Though these large-scale plans were introduced by national government, they are mostly implemented at the regional and particularly municipal level with the involvement of private parties to invest in these new urban development schemes. Therefore, municipalities compete with each other to attract residents and developers of industrial and business areas to their territories [90]. There is not much difference among the regional authorities and the municipalities in the application of spatial plans. It is mentioned in Broitman and Koomen [87] that ‘the previous national planning report has provided more freedom to regional and local authorities to meet their objectives’ (they referred to: VROM et al. [91]). The industrial land in each of these large-scale development zones can be considered as a planning restriction that should be implemented by the corresponding local authorities. Therefore, we use industrial land area in each urban development zone for each corresponding NUTS3 region in the study area (see Eq 2) to represent the spatial planning policies of the government authorities implemented at the regional and municipal levels (see Levkovich and Rouwendal [85]; Broitman and Koomen [87]) for the inclusion of spatial planning policies in the models of the land markets in the Netherlands). In the Netherlands, there is a large gap between development of a land-use plan and provision of the industrial land for development [30]. Given this strict institutional and regulatory structure, municipalities have often developed their own methods of forecasting the demand for industrial land for the provision of serviced land without delay. Considering the competition among the municipalities in attracting higher numbers of firms than their neighbours, the supply of industrial land is higher than the sale/lease of land on industrial estates in the Netherlands [83]. The over-supply of industrial land can also be explained by the existence of time lags between the industrial land use and economic parameters, and an asymmetric relationship between land development and economic change. This can be observed from our data on the percentage changes of industrial land, GDP, GVA, property price and employment in the industrial sectors (Fig 2). From Fig 2, it is clear that developments in the industrial land respond slowly to the fluctuations in the economic parameters. For instance, there are considerable changes in GDP, GVA, industrial property price and the number of industrial employment between 2000 and 2008, which are associated with modest changes in the industrial land developments.
Figure data removed from full text. Figure identifier and caption: 10.1371/journal.pone.0183285.g002 Percentage changes of industrial land, GVA (industry), GDP, industrial employment and industrial land price for Netherlands, 2000–2008.Note: Data on industrial land exists only for 2000, 2003, 2006, and 2008. The data for the remaining periods was interpolated using a linear relationship with the existing data.
Considering the discussed characteristics of the industrial land market, long-term forecasts of industrial land use have become an important issue in the Netherlands. Therefore, various methods are used in Netherlands as well as other countries for the estimation of future industrial land use (for detailed review, see Beckers and Schuur [18]). Knoben and Traa [92] (cited in: Beckers and Schuur [18]) stated that employment-based approach is the most commonly used methodology in Netherlands among others. A similar approach based on employment forecasts is also applied in European and non-European countries such as Ireland, UK, and Australia [93, 94]. This methodology is based on future projection of employment by sector and multiplying these numbers with a land-use parameter to get the average size of land requirements per worker [18, 94]. There are also other variables utilised in forecasting of industrial land such as economic output and income [10] and real property values [16]. The influence of a set of common economic fundamentals on the value of real property can be found in Hendershott et al. [95] and Wilson and Zurbruegg [96] (see Beckers and Schuur [18] for a detailed review of industrial land forecasting methods and the use of employment-based method in the Netherlands). Considering the importance of key economic parameters in forecasting industrial land in Netherlands and internationally, this study focuses on the relationship between industrial land development and these indicators to uncover the influence of lags of the subject indicators on the development of industrial land use.
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