Laserfiche WebLink
• The rent submodel uses estimated bids, along with other local characteristics, to estimate rents <br />for different real estate types at specific locations. <br />• The supply submodel projects forward real estate development by comparing rents with supply <br />costs, and locating new development based on estimated profit margins (rent minus supply <br />costs) and land supply availability. <br />In summary, households and worksites choose real estate types, situated in specific locations, so as to <br />maximize value. Developers respond by supplying real estate responsive to the demand. <br />The demand model mathematically represents the preference structures of different household market <br />segments and industry sectors using variables, and parameters for variables, identified and estimated <br />through discrete choice analysis of existing behavior (which is known through survey data). Variables <br />include neighborhood characteristics and accessibility to destinations. These quantified preferences <br />allow the model to estimate probabilities of all potential real estate choices for each defined household <br />type and worksite type. The choice is comprised of realestate types and locations. The locations <br />correspond to the post-2000 Transportation Analysis Zone (TAZ) system used in the Council's travel <br />demand model. <br />Many of the variables that determine the choice probabilities can change over time: Summarized land <br />use and remaining available land supply, industry mix, and socioeconomic mix of zones are projected <br />and updated within the model. Accessibility measures are projected and updated through iterative <br />looping with a linked travel demand model. <br />Concurrently, the rent model uses estimated bids, as well as other zonal characteristics, to calculate <br />and update rents within the model. If real estate in a certain location is highly desirable to one or more <br />market segments, rents can change, altering estimated distributions (or probabilities) of household and <br />worksite location choices, and prompting choice substitution. Ultimately, the model seeks an equilibrium <br />solution where all forecasted future households and employment are sorted into real estate choices, <br />proportionate to updated choice probabilities. <br />The discussion above concerns different market sectors valuing locations, and sorting themselves to <br />accomplish best -value results. Importantly, Cube Land allows supply response to growing and <br />changing market demand. Regional totals of target -year households and employment can differ from <br />start -year totals. To accommodate growth in households and employment — which has been forecasted <br />using the region -level forecast models — the Cube Land supply submodel projects the addition of new <br />housing and employment -bearing built space. In the Twin Cities implementation of Cube Land, the <br />major determinants of such development are land supply and estimated rents for each zonal location. <br />As rents are dynamically estimated within the model, the geographic distribution of new development is <br />likewise dynamic — with new growth precipitated by lower development costs and/or higher rents for <br />valued location characteristics. <br />Data and Variables Used in the Council's Cube Land Modeling <br />The Twin Cities implementation of Cube Land segments worksites and employment into 8 industry <br />sectors; these groups have varying preferences and use varying amounts of 5 types of employment - <br />bearing real estate. Households are segmented by socioeconomic characteristics into 5 major <br />household types (and 80 subtypes), which then select housing from 8 housing types. This <br />segmentation enables moderate representation of how real estate and location preferences vary <br />among different household and industry types. <br />Page 5 <br />