Planning and Uncertainty: How Dutch Municipalities try to Reduce Uncertainty on the Market for Industrial Properties
Planning and Uncertainty: How Dutch Municipalities try to Reduce Uncertainty on the Market for Industrial Properties1
Recently, it has been suggested that public policy should play a stronger role in reducing and containing uncertainty in land and property markets by making this an explicit policy objective. (Adams et al. 2003; Adams, Dunse and White 2005). Adams et al. (2005) consequently argue that the issue of uncertainty should be included when analysing the impact of public policy on land and property markets. It is conjectured that different forms and combinations of public policy have different impacts on uncertainty reduction, although this has not yet been rigorously explored empirically (for an exception see Allmendinger 2006). Adams and Tiesdell (2010) thus observe that an answer is still awaited to the ‘important’ question posed by Healey, namely:
Do plans stabilize land and property markets, creating greater certainty, thereby reducing transaction costs and by, limiting the potential for ‘over-building’ in a property market, producing greater efficiency in the relation between supply and demand in the land markets? (1992: 13)
The same could apply for the likely impact of other than regulatory tools on uncertainty in land and property markets.
A problem for this type of study, which is brought forward by Adams et al. (2003), is that the containment of uncertainty has never been considered as an explicit objective of public policy for land and property development, since the main focus of policy has been on the management of supply and demand. Adams et al. therefore conclude: ‘It is thus perhaps unfair to judge a system by objectives that it has never formally articulated for itself’ (2003: 40).
This is true perhaps in the case of passive forms of public planning, by means of a framework of regulations. When, however a public agency itself is actively involved in buying, servicing and selling land, one might assume that the containment of uncertainty is a crucial objective, since it is the public agency itself that bears the development risks. The way public agencies themselves form expectations of an uncertain future under these circumstances might thus shed light on the more general question of the impact of public policy on uncertainty. An example of this type of case is provided by the market for Dutch industrial land, where most development is initiated by public authorities themselves. Therefore, this paper will investigate how municipalities try to reduce the uncertainty about the future demand for industrial land. We do this by analysing how they actually make forecasts of demand and how these estimates influence their development decisions by means of structured and semi-structured interviews with municipalities (making use of the study by the Netherlands Environmental Assessment Agency (2009) mentioned in footnote 1).
In the next section the interrelationships between planning and uncertainty are examined in general. In section 2 the way expectations are formed about uncertainties in the markets for land and property is discussed by reviewing the literature and previous research. This is followed by an account of the public development process for industrial land in the Netherlands, including the way in which municipalities form expectations of future demand according to the relevant literature. Section 4 presents the results from the survey of practice and Section 5 concludes.
Planning and Uncertainty
In the question quoted above, Healey (1992) restricted the potential impact on uncertainty to that caused by development plans. It is argued here that the discussion should not be restricted to the development control function of planning. Development plans, legal agreements and financial incentives all come under the title ‘planning controls’, but all have different effects (Allmendinger 2006: 6). In this respect, Tiesdell and Allmendinger advocate ‘deconstruct[ing] the notion of planning as a homogenous and regulatory activity’ (2005: 58). They argue that the heterogeneous nature of planning should be emphasized, for instance by showing that land-use regulation is only one part of planning activity. For this purpose, they have developed a typology of planning tools (for slightly different typologies see Adams, Disberry, Hutchison and Munjoma 2002; Geuting 2007; Segeren, Verwest, Needham and Buitelaar 2007). This identifies four types of planning tools, characterized by how they intend to affect the decision environment of market actors:
• Market shaping tools such as development plans, which provide the overarching context within which market agents make decisions.
• Market regulation tools which seek to regulate and control market actions by defining the parameters (for example choices available) of the decision context.
• Capacity building tools: although difficult to define precisely, these tools aim to ‘build’ the capacity of actors to identify and develop more desirable strategies.
Tiesdell and Allmendinger (2005) regard direct state interventions, including the provision of serviced building land and infrastructure, as a special type of market stimulation, apart from fiscal measures such as subsidies and tax breaks. By making serviced land available – creating a framework of property rights – public agencies try to encourage others, such as building developers, to take actions which meet the aims of the planning agency.
Few studies have empirically investigated the impact of these different planning tools on land and property markets. Jackson and Watkins (2007), although they did not use precisely the same typology, find that a proactive policy environment, with a strong emphasis on town-centre management and improvements to the shopping environment, will exert a significant influence on retail property values. They argue that there is little evidence of the perceived negative effects of the regulatory aspects of planning activity (also see Jackson and Watkins 2005). Jackson and Watkins focus on the impact of different planning tools on retail property values, but Allmendinger (2006) investigates the impact of different tools on a broader set of indicators, a set which captures a market actor’s decision environment. He did this by means of a survey and focus groups amongst key actors in the development industry. These indicators include the impact of planning tools on supply and demand, on information, and also on the extent to which uncertainty is reduced. The findings indicate that market shaping and several stimulating tools (for example grants and incentives) are able to reduce uncertainty while regulation and taxation (stimulation) increase uncertainty. Finally, capacity building has no significant impact.
Allmendinger (2006) does not explicitly address which sources of uncertainty are affected by these various planning tools. The relation between public policy and uncertainty in land and property markets is typically discussed in terms of the consequences of development control. Developers’ desires to build can be severely constrained due to time-consuming and uncertain processes involved when a planning authority decides whether a proposed development meets prescribed rules. Certainly in the UK context, no developer buying land speculatively without planning permission knows whether or not planning permission will be granted (Mayer and Somerville 2000; Ball, Allmendinger and Hughes 2009; Ball 2011). Mayo and Sheppard (2001) call this type of planning ‘stochastic development control’, as there is some randomness in the planning authority permitting a project. Leishman et al. (2000) refer to this as planning uncertainty. In addition, they distinguish a second type of uncertainty faced by developers. Key development variables such as construction costs, property values and interest rates may turn out differently from those which were expected at the outset of development. One could add to these variables the ‘general’ uncertainty whether there is (still) demand for land and property at the time the development project is completed. This type of uncertainty concerning the balance between supply and demand is labelled as market uncertainty (Leishman et al. 2000). When public agencies want to limit the potential for over-building in order to prevent commercial developers collectively ‘shooting themselves in the foot’ (Healey 1992) it is this latter source of uncertainty that can be reduced. The way to do this is not so much by stimulating the demand, but by reducing or phasing the supply of land (in the case of market regulation tools, by limiting development plans). This paper focuses on the latter type of uncertainty in land and property markets, and on how public agencies themselves try to reduce it when they are actively involved in land development.
Expectations in Land and Property Development
Since it takes several years to complete a development project, agents involved in land and property development have a strong interest in forming accurate expectations of future conditions: their success depends on making good forecasts of the key development variables. The literature about land and property development addresses how commercial market agents form expectations. In this respect, two ways of forming expectations are distinguished (see for example Ball, Lizieri and MacGregor 1998; Wheaton 1999; Barras 2005; Fuerst and McAllister 2010). The first is derived from the behavioural assumption in macroeconomics that expectations are formed rationally (see Muth 1961; Lucas 1972). This hypothesis implies that expectations – informed predictions of the future – are formed by taking account of all available information, including an understanding of the underlying ‘economic model’. Consequently, Ball et al. (1998) assert that agents in land and property development use their knowledge of the property market, of the wider economy, and of the best available theories about how those two function and interrelate.
When applied to property development, this assumption means, according to Wheaton (1999: 215), that agents perfectly understand the equations that describe market behaviour and thus can make correct forecasts of rents – conditional on a particular future demand variable. When this (exogenous) demand variable in the model is known with certainty, then the rational expectation is equivalent to perfect foresight. If an unanticipated shock occurs to the demand variable, its effect on other variables can be forecast, even though the shock itself came as a shock to the market agents. Market agents have perfect foresight with respect to the effects, but not the timing, of the shock (Wheaton 1999: 215). Ball et al. (1998) add to this that the longer a project lasts, the greater the error of the forecasts. But as the error is assumed to be random, the consequence is as likely to be an over- as an under-estimation of demand. Thus, agents do not make systematic mistakes in forecasting future demand. It has to be emphasized that the rational expectations assumption is an aggregate hypothesis that does not necessarily explain expectation forming by an individual market agent; such expectations may be subject to a greater error than the theory suggests. However, although agents’ expectations may be individually wrong, they are correct on average.
The second set of behavioural assumptions might be termed irrational or myopic expectations. The assumption is that market agents make their decisions on the basis of the current values of the important variables which affect their decisions. According to Wheaton (1999) many kinds of such ‘irrationality’ can be conceived. For instance, current levels of the relevant variables can be extrapolated, but also the previous growth rates of those variables (expectations are formed by trend extrapolation). Ball et al. (1998: 153) distinguish between naïve and adaptive expectations. Economic agents hold naïve expectations when they expect tomorrow to be the same as the present and adaptive expectations when they gradually adjust their views to new experience based on past changes.
Wheaton (1999) argues that although irrationality of economic agents is generally dismissed, some form of irrationality may still be occurring in land and property development. More recent studies are inconclusive about this. For office development, Tsolacos and Mcgough (1999) have investigated whether predictions are characterized by rational expectations. Their model suggests that expectations relating to office building development are indeed rational. On the other hand, a study by Antwi and Henneberry (1995) which makes use of behaviouralist modelling, indicates that commercial developers’ future expectations are conditioned by past experience, for example expectations are formed by trend extrapolation. In this respect, they report that developers make use of trend extrapolation especially during periods of extreme market change (also see Henneberry and Rowley 2002). Using a similar methodology, Leishman et al. (2000) find that amongst house builders, uncertainty results in conservative forecasts of price levels, and that the predictions of house builders are based on current values. Nevertheless, the estimates that house builders make are not far removed from expectations if they had had perfect foresight. Finally, research across 20 European office markets by Fuerst and McAllister (2010) reveals the existence of divergent expectations. In some markets, developers seem to form myopic expectations, whereas in other markets developers did not respond to very strong demand signals. That latter might indicate rational expectations because the demand increases were only temporary and soon to be reversed.
Public Intervention in the Dutch Industrial Land Market
Public Land Development
In the Netherlands, other than in most other countries, development takes place in two related but distinct stages, each within its own market: land development and property development. Land is often exchanged twice before construction takes place. First the initial owner (supplier) sells (agricultural) land to a land developer (demander). In the ‘secondary’ market of serviced building plots (Halleux 2008: 260; Van der Krabben and Buitelaar 2011), the land developers become the suppliers, while the demanders of land are commercial property developers (building for sale or lease), final users (building for own use) and in the case of the land market for housing: housing associations. Building plots for industrial uses are normally located on ‘formal’ industrial estates, and our study is restricted to such formal estates. These are sites which the local land-use plan (bestemmingsplan) designates for activities in the sectors commerce, manufacturing and commercial services. Sites that are designated exclusively for offices are usually not covered. Since land for industrial uses may also be acquired on locations outside industrial estates, this study does not cover the total supply in the industrial land market. However, between 60 and 70 per cent of all employment in the sectors manufacturing and transport is located on (formal) industrial estates (Weterings, Knoben and Amsterdam 2008). Furthermore, we assume that industrial land transactions – the acquisition of building plots on which construction still has to take place – occur only on designated industrial estates, because industrial real estate transactions in other urban areas concern mainly second-hand industrial properties. That is, undeveloped land is hardly developed outside formal industrial estates.
Because Dutch municipalities can act as a legal person they can themselves develop land. Since World War II, municipalities have pursued an ‘active land policy’ with respect to industrial land. And they continue to do so, in contrast to housing development, where an active municipal land policy has come under pressure in the last decade or so (Buitelaar 2010). An active land strategy implies that municipalities acquire (agricultural) land, service it, provide the necessary infrastructure and re-parcel it into building plots, before it is sold off to interested parties (Needham 1997). Municipalities bear the corresponding risks and reap any profits. This strategy has been institutionalized through municipal land development departments. On industrial estates, approximately 73 per cent of all available building plots are owned and supplied as serviced land by municipalities, while private developers and semi-public regional development companies own respectively 13 per cent and 14 per cent (Segeren, Needham and Groen 2005; Van der Krabben and Buitelaar 2011). In this respect, Dutch municipalities are demanders of raw building land and suppliers of serviced building plots. Building construction can start immediately on those serviced building plots.