ENVISION TOMORROW has a variety of analysis tools that allow users to uncover interesting patterns in a variety of data from property assessor parcel data to census geometries.
The Redevelopment Candidate wizard allows users to identify which parcels in a study area are more or less likely to be "ripe" for redevelopment. The wizard includes two methods for analyzing and mapping redevelopment readiness. The first uses effective year built of structures and assumptions about typical building life cycle time periods to estimate what year a building's structure value will fall below the land value. The second method maps the total value per acre of each parcel based on a quantile clustering. The number of quantile breaks is user-defined. The tool uses four data fields commonly included in assessor parcel dataset: effective year built, improvement / building value, land value and total value per square foot.
The Workforce Housing Model Model is used to identify areas with an imbalance between housing and jobs, and between household income and worker wage. It also shows the impact of this spatial jobs-housing imbalance on trip generation. A precursor to job-worker balance is wage-income balance. Wage-income balance indicates that the yearly salary of residents is similar to the wages paid by jobs in the area.
The Balanced Housing model is used to analyze a community’s existing housing supply, including the matches and mismatches by age, household income and tenure (rental or owner-occupied). It is also used to conduct a capacity analysis of development potential and a forecast of future age and income cohorts. Using this information, the app is used to create a series of policy and strategic recommendations for a balanced, sustainable future housing supply along with targeted goals that can be used to determine a community’s future progress in implementing the plan.
The site-level travel model allows users to estimate transportation outcomes of development scenarios. When users "paint" development types using Envision Tomorrow, they are changing the mix and types of housing and jobs within a district. These changes impact travel behavior. The model is sensitive to changes in a variety of variables, commonly referred to as the "D" variables. These variables include Density, Design, Destinations, Demographics and Diversity of land uses.
The purpose of the Fiscal Impact Tool (FIT) is to consider the short and long-term changes in revenue and spending associated with of municipal services, environmental protection, economic development, transportation, and infrastructure. A very real challenge is that many projects and services are paid for on the local level, and in any given scenario, some communities may see more success than others.