Land Price Model

UrbanSim uses land prices as the indicator of the match between demand and supply of land at different locations and with different development types, and of the relative market valuations for attributes of housing, nonresidential space, and location. This role is important to the rationing of land and buildings to consumers based on preferences and ability to pay, as a reflection of the operation of actual real estate markets. Since prices enter the location choice utility functions for jobs and households, an adjustment in prices will alter location preferences. All else being equal, this will in turn cause higher price alternatives to become more likely to be chosen by occupants who have lower price elasticity of demand. Similarly, any adjustment in land prices alters the preferences of developers to build new construction by type of space, and the density of the construction.

We make the following assumptions:

1. Households, businesses, and developers are all price-takers, and market adjustments are made by the market in response to aggregate demand and supply relationships. Each responds, therefore, to previous period price information.
2. Location preferences and demand-supply imbalances are capitalized into land values. Building value reflects building replacement costs only, and can include variations in development costs due to terrain, environmental constraints or development policy.
3. There is a long-term structural vacancy rate for each type of property, and the relationship of current vacancy rates to this long-term vacancy rate influences price adjustments.

Land prices are modeled using a hedonic regression of land value on attributes of the land and its environment, including land use mix, density of development, proximity of highways and other infrastructure, land use plan or zoning constraints, and neighborhood effects. The hedonic regression may be estimated from sales transactions if there are sufficient transactions on all property types, and if there is sufficient information on the lot and its location. An alternative is to use tax assessor records on land values, which are part of the database typically assembled to implement the model. Although assessor records may contain biases in their assessment, they do provide virtually complete coverage of the land (with notable exceptions and gaps for exempt or publicly owned property).

The hedonic regression equation encapsulates interactions between market demand and supply, revealing an envelope of implicit valuations for location and structural characteristics. These relative prices have been documented to be relatively consistent over time, with the acknowledgement that the relative values at specific locations change as their underlying characteristics change. Because the hedonic regression includes variables that are to be maintained as part of the simulation system, these can be used to update relative prices over time.

In addition to these relative prices captured by the hedonic regression, the overall price level within the market for each type of real estate moves over time in response to shifts between supply and demand. These fluctuations can be tied to the relationship between the actual market vacancy rate and the long-term structural vacancy rate. As the current vacancy rate falls below the structural rate, price levels rise, and when the current vacancy rate exceeds the structural level, they fall.

These two effects on prices are combined in the land price model. The estimated hedonic regression equation is used to establish relative prices, and the intercept of the equation is adjusted based on the relative position of the current and structural vacancy rate, as follows:

$$P_{ilt} =\alpha + \delta V^c_{it} + \beta X_{ilt}$$ (20)

where: 


 		 $p_{ilt}$ is the price of land per acre of development type i, at location l in time
    t 


 		 $V^c_{it}$ is the current vacancy rate at time t, weighting local and regional
    vacancy 


		 $X_{ilt}$ is a vector of locational and site attributes 


 		 $\alpha$ and $\beta$ are estimated parameters 


 		 $\delta$ is set by the user based on sensitivity testing

Prices are updated annually, after all construction and market activity is completed. These end of year prices are then used as the values of reference for market activities in the subsequent year.

The independent variables influencing land prices can be organized into site characteristics, regional accessibility, urban-design scale effects, and market conditions, as shown below:

• Site characteristics
  Development type
  Land use plan
  Environmental constraints

• Regional accessibility
  Access to population and employment

• Urban design-scale
  Land use mix and density
  Proximity to highway and arterials

• Market Conditions
  Vacancy rates