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Mesoscopic Land Use Forecast

Modeling for Scenario Planning,

Policy Analysis, and Pricing

EvaluationColby M. Brown AICP PTP

Simon Choi, Ph.D. AICP

Timothy G. Reardon

Land use models can

be classified in one of

four categories based

upon spatial resolution

and segmentation

1. Traditional (e.g.

gravity-based)

2. Scenario planning

& visioning tools

3. Micro-simulation

4. Input-output

New mesoscopic land

use models bridge

these categories

Cube Land

Issues of Scale In Land Use Modeling

Segmentation

Space

MACRO

MA

CR

O

micro

1

23

4

Answers to policy questions:

▪ Housing affordability (relationship of

household income to housing price)

▪ Jobs-housing balance

▪ Environmental justice

▪ Gentrification

▪ Local economic development

▪ Taxes and subsidies

Economic performance measures

▪ Rent

▪ Tenant income

▪ Effective subsidy

Economics of Land Use

Although not always the most accurate depiction of reality, equilibrium

models are still extremely useful policy analysis tools

Example: what level of cost/taxation results in a desired level of

housing supply in a particular zone or subarea of a region?

The equilibrium framework allows us to select a performance goal…

and then solve for the policies that achieve this target, all else equal

Equilibrium Models

Kyoto Protocol and Sustainable

Cities Donoso et. al. TRR (2006)

Greater Los Angeles region with over

18.4 million population in study area

22.1 million in 2035

3.7 M added between 2013 and 2035

Strategic model

▪ 531 land use zones

▪ Aggregate accessibility (travel model)

Test case

▪ Take two pre-established “visions” for

2035 (trend and TOD) and solve for

the real estate costs that achieve

theses scenarios – what do they cost?

▪ Applications to housing affordability

SCAG Cube Land Forecasting Model

SCAG Shadow Pricing Test Results

Land use forecasting

model purpose-built

for traffic and revenue

study in Louisville

Experts on the local

area couldn’t create

the entire forecast by

hand – but knew that

some things simply

wouldn’t happen

Solution: shadow

pricing approach used

to apply adjustments

and constrain forecast

Louisville - Ohio River Bridges Project

Image source: http://www.kyinbridges.com/maps.aspx

Design & specification:

▪ “Five-step” integrated land use and

travel demand forecasting model with

same-year feedback

▪ Residential

▪ 13 household lifecycle groups

▪ 5 housing unit types

▪ Non-residential

▪ 11 industry supersectors

▪ 7 land use types

Dynamic calibration – to match base year

and regional housing demand projections

Boston Region MPO Cube Land Model

Conclusions and Lessons Learned

Potentially threatening information from outside the model will always

creep into the planning process—no forecaster can secure a

monopoly on predictions and expectations for future development.

Old methods of dealing with this:

▪ Fight (lawsuits, claim greater credibility, build more sophisticated models etc.)

▪ Flight (give up on prediction, use “indicator models” and “visioning tools” instead)

New ways opened up by the mesoscopic economic LU-T models:

▪ Run the model “in reverse” to find out how much the outsider scenario “costs”…

shifts the debate from whose scenario is correct to the assumptions, conditions and

policies that will make one become reality versus another

▪ Explicitly input local expertise and knowledge to the model as “constraints” for a

forecast… keeping what the experts do know and letting the model fill in the rest

▪ Use dynamic calibration techniques to “chain” the baseline to an a priori scenario…

while still allowing room for robust policy scenario testing and sensitivity to changes

in transportation accessibility due to project phasing, etc.

▪ In short: use the models to engage in dialogue… based upon a common language