Long-term prospects of land value uplift in planned new urban centres: Measurement, modelling and predictions
Land value uplift and its ‘capture’ enable local communities to invest in public infrastructure, facilities and services. This topic is trending among fast-growing cities in most countries. Areas that already show high demand for development receive particular attention due to good prospects of land value capture (LVC). However, cities anticipating fast growth frequently desire to plan new urban centres in currently low-demand areas where the potential for radical urban transformation is high, but the prima facie prospects of LVC are poor. Urban planners often recognise that good land value gains could eventually arise in low-demand areas, but there are few existing methods to help cities estimate when and under what conditions LVC would become a real prospect in the planned new urban centres. This research aims to develop a new modelling approach that starts to fill this gap. It develops an extended spatial equilibrium model capable of systematically measuring, modelling and predicting the financial and economic prospects of land value gains alongside changes in social costs and benefits. To see how alternative policy measures affect land values, particularly in and surrounding the planned new urban centres, the modelling method combines spatial equilibrium simulation of business and household activities with spatio-temporal scenarios of land use and transport supply. The scenario designs can consider all the main types of planning and transport infrastructure decision levers relevant to specific stages of development and their impacts on land value changes. The model is tested in Greater Shanghai, which is chosen as the case study area. Shanghai’s expanding mega-city region has a long tradition of planning new urban centres since the 1980s, with both successes and failures, thus providing the necessary data for model building. The mega-city region represents a middle level of data availability, which provides a typical setting to test the multi-source data method for model calibration and validation. The case study model has been used to test a range of future scenarios within the broad development targets of the Shanghai 2035 Plan. Retrospective measurements (2000–2015) show that despite long-standing efforts to promote new urban centres, Shanghai has so far remained a monocentric city regarding land prices. A spatial equilibrium model built on the empirical data of this period indicates that insufficient job opportunities in the planned subsidiary centres are a central challenge in turning Shanghai into a polycentric metropolis. The modelled scenarios for 2015–2035 show, for instance, that annual land prices in the subsidiary centres would rise by 0.5% if they were pure residential developments, and by 14.2% if they were focused on employment growth. The model results also show the critical importance of coordination between jobs, housing and transport development in generating land value uplift. This is because LVC initiatives that are principally aimed at financial gains may conflict with residents’ well-being. A series of scenarios exploring locations for the new subsidiary urban centres relative to the historic core indicate that there could be difficult trade-offs in designing LVC. Compared with the currently planned sub-centres at 40 km from the historic core, a closer ring of subsidiary centres at 30 km could generate significant additional economic gains locally (i.e. public profits from LVC – equivalent to 0.5% of Shanghai’s gross domestic product (GDP) per year), but this would be at the cost of consumer utility losses (equivalent to –0.3% of GDP per year). This research provides city leaders, urban planners, urban designers, developers, businesses and local communities a new approach to measure, model and predict the effects of urban interventions. The approach allows stakeholders to explore together how to develop and fund sustainable urban development outside the traditional LVC geographies, particularly in the long term.