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“Old Approach to Needs Analysis” PowerPoint Presentation
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“Old Approach to Needs Analysis”

“Old Approach to Needs Analysis”

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“Old Approach to Needs Analysis”

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  1. “Old Approach to Needs Analysis” • The standard practice in Oregon has been to extrapolate forward the past 5 or more years in housing production as the basis for determining a region’s future housing requirements. • “Demand” was assumed to be equivalent to “need”. • “New Methodology” • A guiding principal in the development of the housing needs model was that the methodology for calculating housing needs was to be driven by the demographics of the study area – by people not production.

  2. Housing Need is Defined by Housing Choice Options • Tenure - Rent versus Own • Price - What is an affordable price range • Housing type - What style of housing is desired

  3. Initial Research • Identify those demographic variables that would be highly correlated with housing needs • Two variables – age of head of household (Age - A) and household income (Income – I) - demonstrated significantly stronger correlation with housing tenure than other variables including household size • Household income is the key variable in determining the affordability component of housing needs • Age and Income data is available for each potential study area

  4. Initial Steps • Select the age and income ranges that would be used to break the study area’s population into household cohorts • Seven Age ranges - under 25, 25-34, 35-44, 45-54, 55-64, 65-74, 75 and older - were chosen • Seven Income ranges - under $10,000; 10,000-19,999; 20,000-29,999; 30,000-39,999; 40,000-49,999; 50,000-74,999; 75,000 and over - were chosen • These age and income ranges define 49 Age/Income cohorts and were determined to be the most useful ranges for calculating housing needs

  5. Initial Steps (cont.) • Find a source of demographic data for the 49 Age/Income cohorts • Census 2000 was selected as the source for this demographic data • Households in these 49 Age/Income (AI) cohorts make significantly different housing tenure choices as reflected by the Census data • Use the choices derived from the Census for each cohort as the basis for tenure parameters in the model

  6. Next Steps • Compared model results with Census 2000 data • Determined that households in certain types of cities made different housing tenure choices • Created three categories of census places with model tenure parameters for each category • Version U - Urban, college, and resort • Version M - Medium Rural (6,750 -22,500) • Version S - Small Rural (under 6,750) • Added “wealth” factor to model • Added mortgage rate factor for affordability impact

  7. Model Assumptions • Housing need is defined by cohort tenure choices and is equivalent to the actual cohort tenure data found within a large regional area • While the local supply of rental versus ownership housing may not be in equilibrium with tenure need in some markets, on a larger regional basis it is in equilibrium • Housing that is at “price ranges and rent levels commensurate with the financial capabilities of Oregon households” means that no more than 30% of a household’s income should be spent on housing costs, i.e., is affordable • The seven Income ranges in conjunction with the 30% limit on housing costs establishes the price ranges and rent levels used in the model to calculate the housing units needed at each price point

  8. Model Design Goals • Model structure should employ individual modules for each analytical component by using Excel templates. • Data needed to drive the model must be available. • Data gathering requirements for each locality should be minimized. • Parameters in the model should be easy to update and modify. • Model should be a user-friendly tool for city staff or interested parties. • Model should allow users to easily test out different growth scenarios. • Model should automatically produce tables and graphs that can be used as printed material for public dissemination of model results. • Model should reflect local conditions and characteristics. • Model should work for any size city and location. • Model should accommodate interaction with other planning goals. • Model should be flexible and have a variety of uses beyond satisfying Goal 10.

  9. Model Housing Types Five housing types have been identified for use in the model to categorize a community’s existing inventory of dwelling units. Each of these housing types can be owner occupied or renter occupied. • Single Family Units – either site built or manufactured single family dwellings on their own lot • Manufactured Dwelling Park Unit – a single family dwelling unit located in a rental park • Duplex Unit – a two-family dwelling unit located on its own lot • Tri-plex or Quad-plex Unit – a three or four-family dwelling unit • 5+ Multi-family Unit – dwelling units in buildings with 5 or more units per building

  10. Why cities should use the Model • The model represents a methodology that guides cities through a process that appropriately determines their housing needs in a way that compels them to address the types of housing needed to support their population • The output of the housing needs module then drives the land use module which calculates the land needed within their UGB for housing

  11. User Inputs for Running Model • Housing inventory by Housing Type and Price Point • Out Factor adjustment for reflecting local lifestyle choices - optional • Future population projection and household Income/Age distribution • Planned distribution of new housing units by housing type, tenure, and price point • Planned housing density by land use zone • Housing inventory by land use zone • Planned distribution of new housing units by zone • Buildable land inventory by land use zone

  12. Model templates color scheme

  13. Housing/Land Needs Templates • The following slides demonstrate the templates and graphs used in the model to calculate and display housing and land needs. • These slides are taken from several model runs and will not be internally consistent. • The model contains additional templates and graphs that are similar to those that follow.