Estimating the HARA land-use model for housing planning based on hedonic price analysis

HARA is a land-use model that uses a search algorithm to find the optimal spatial allocation of new housing demands in an urban plan area. In the model, the plan area is represented as a grid of cells. A core element of the algorithm is a function that is used to evaluate the value of a cell for each possible land-use given its location. An optimum is found by stepwise improving an initial allocation based on the value function. In this paper we show that the value function can be specified as the net value of a (housing) development given the land costs, the construction costs and the market value of the development at a location. Specified in that way the solution generated represents an optimum as well as a market equilibrium (maximum net value for developers). A critical prerequisite for this is, however, that the value-function is specified such that it accurately represent buyers’ willingness-to-pay for dwelling and location characteristics in the housing market. In the paper, we show how the value function can be estimated using hedonic price analysis. The analysis is carried out based on a large housing transaction data set from The Netherlands. The trade-off between living in a green environment and having high-level urban facilities in the proximity of the residential location is of special interest for housing planning. This trade-off has become more significant even given the growing environmental concerns for creating climate-adapted as well as compact cities. We present the results of an application of the estimated model where we investigate the nature of this trade-off and the impact this has on the green-versus-compact character of urban development. We anticipate that the trade-off may change with the increasing importance of good climate performance (robustness for extreme weather conditions). Using the model as an experimentation tool, we consider what the impacts of changes in the trade-off will be for the spatial planning of housing.