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Demographic Study I Student Membership Forecast 19 <br /> has a similar meaning: For all Kindergarten to birth ratios over 1.0 we know that more new people with children have <br /> moved in between having their kids and age 5 than those that have moved out. Put more simply, places with a <br /> Kindergarten to birth ratio over 1.0 are attracting new residents with young children who are then enrolled in the <br /> school system. A ratio under 1.0 is just the opposite: people are having children and then leaving before enrolling their <br /> children in public school (or are choosing other schooling options). <br /> When we have historical GPRs calculated we are able to forecast enrollment. We begin by creating a forecast of <br /> GPRs. Many simply either use the most recent year's GPR, or some form of weighted average of the last several <br /> years, and then hold each of these GPRs (by school and by grade) constant for the remainder of the forecast period. <br /> This works well when there isn't a trend to the GPRs—that is, when they are not increasing or decreasing. When they <br /> are, we are much more likely to use time series forecasting methods to allow these values to vary throughout the <br /> forecast period. What we choose is entirely dependent on which model best fits the local context. <br /> Once we have final GPRs we are able to apply these data to current births and enrollment creating a baseline <br /> enrollment forecast. However, while GPR models are great when we have historical trends that are very likely to <br /> continue into the future, they can break down when things change. In enrollment forecasting, one of the mostly likely <br /> ways that they will break down is through residential development that changes historical patterns. <br /> Forecasting Development <br /> Whenever we are building a forecast, we always collect data on developments to ensure that there will not be new <br /> pressures on areas that are not already experiencing it. These data are usually obtained from a mixture of county and <br /> municipal planning or GIS departments. Once we receive these data, we compare the developments to what we are <br /> seeing in the baseline forecast and GPR data. Are we seeing new growth that isn't similar to development or migration <br /> trends in the past? How different are they? <br /> Once we decide that we need to incorporate development information into our forecast, we go through a multi-step <br /> process to ensure that we are incorporating it in a way that does not double-count students. It is important to note the <br /> goal of our forecasting process here. We are not trying to forecast the total number of students who might live in a <br /> particular new development, we are trying to forecast the number of net new students within a particular attendance <br /> zone that the development might bring3. This requires us to go through multiple steps in incorporating developments <br /> into our model: <br /> 1. We begin by looking at GRPs and birth-to-kindergarten ratios for the attendance zone. Are we already seeing <br /> ratios over 1? If so, some in-migration is already occurring, so we need to adjust to make sure we don't double- <br /> count students. <br /> 3 This is different from the goals of reports such as those from SAPFOTAC where we are looking at the potential total impact of <br /> each development instead of the net impact as we are. (e.g., <br /> https://www.orangecountync.gov/DocumentCenterNiew/31319/20hat 25-SAPFOTAC-Annual-Report---Certified-060325). <br /> CAR 0 L I N A �I T CAROLINA � Demographic 16 <br /> DEMOGR PHY lJJNC <br /> POPULATION CENTER Analytics Advisors <br />