Land-use regulations play a key role on both sides of the real estate market by regulating the supply of housing (e.g., through restrictions on unit density or building height) and by controlling the location and density of places of work, which are the primary drivers of the demand for housing. Developing geospatial models for this interaction between land use and home price on a spatially disaggregated level enables decisionmakers to evaluate the impact of their land-use decisions from the housing affordability perspective. However, existing standalone residential real estate pricing models are insensitive to changes in land use. In addition, the data preparation, calibration, and training of integrated land-use and transportation models is nontrivial too, and still impractical for most municipalities and planning agencies. This paper presents a simple-to-implement framework, SimP-R, for simulating changes in housing prices on a spatially disaggregated level in response to land-use change. It is composed of a residential real estate pricing model and an algorithm for computing a novel measure of supply-to-demand ratio. This paper then demonstrates the implementation of SimP-R in the city of San Francisco, with the entire Bay Area serving as the influence geography. Our findings showed our proposed measure of the supply-to-demand ratio is a strong predictor of and inversely related to housing prices. Simulation experimentation results highlighted SimP-R’s ability to capture the effect of local land-use changes on housing prices across the metropolitan area.
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