Employment Location Choice 3 Current Issues. Overview Requires space (i.e. real estate market)...
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Transcript of Employment Location Choice 3 Current Issues. Overview Requires space (i.e. real estate market)...
Overview
• Requires space (i.e. real estate market)
• Models specified for sector preferences
• Some exceptions (non-RE market)
Overview
• Requires space (i.e. real estate market) Job capacity issue
• Models specified for sector preferences
• Some exceptions (non-RE market)
Overview
• Requires space (i.e. real estate market) Job capacity issue
• Models specified for sector preferences Additional variables: concentration
• Some exceptions (non-RE market)
Overview
• Requires space (i.e. real estate market) Job capacity issue
• Models specified for sector preferences Additional variables: concentration
• Some exceptions (non-RE market) Construction, Public Sector, Military
Job Capacity
• Calculated, not stored
• Separate density ratios– Vary by location (zone)– Static
buildingsbuilding_id sqft_per_jobbuilding_quality_id zone_idbuilding_type_id building_type_idimprovement_value sqft_per_jobland_areanon_residential_sqftparcel_idresidential_unitssqft_per_unitstoriestax_exempttemplate_idyear_builtzone_id
non_residential_sqft
sqft_per_job
Job Capacity in Brief
• Base data issues
– Assessor db: vacancy, sqft measurement errors
– Job data & job assignment to buildings uneven
• Difficult to:
– Determine valid ratio (new construction)
– Reconcile job & sqft data (existing buildings)
Job Capacity Problems – Existing Buildings
• Zonal ratio ≠ individual building ratios– Buildings with initially smaller employee space
ratios will lose employees until they reach the zonal ratio; the reverse also true
• Unique buildings – “too big to fail”
– Actual or product of data preparation
Short-term fix
• New construction: Adjust zonal ratios to look more reasonable
• Existing buildings:
Short-term fix
• New construction: Adjust zonal ratios to look more reasonable– Arbitrariness
• Existing buildings:
Short-term fix
• New construction: Adjust zonal ratios to look more reasonable– Arbitrariness
• Existing buildings: Reverse-engineer job
capacity computation by imputing sqft
Short-term fix
• New construction: Adjust zonal ratios to look more reasonable– Arbitrariness
• Existing buildings: Reverse-engineer job
capacity computation by imputing sqft– Complicates value calculations and indicators
downstream
Seattle Tower
Dexter Horton Building
Initial Adjustednon_residential_sqft 388,934 2,816,500 year_built 1922 1922improvement_value 0 0stories 15 166FAR 14 152 jobs assigned 5633 5633sqft_per_job 69.05 500.00
Initial Adjustednon_residential_sqft 216,571 643,794 year_built 1929 1929improvement_value 0 4,073,285 stories 27 80FAR 16 48 jobs assigned 1759 1759sqft_per_job 123.12 366.00
Potential long-term fix:Store job capacity as building attribute
• No need to continually re-compute
• Assigned for existing buildings– Retain base year capacity – Scale if assuming some unused capacity (e.g. 10%)
• Generated at construction for new buildings– non_residential_sqft not in question– Still requires an employee density calculation . . .
Potential long-term fix:Store job capacity as building attribute
Employee density as:
– Template attribute?• Variation must then be captured by template choice
– Function of unit_price?• Continuous; regionally estimated (large sample even
when segmented by building_type)• Some dynamic adjustment within the simulation• Spatial query of median unit_price to avoid outliers
ELCM Specification:Come estimate with us
• Estimation dataset– From cumulative jobs to net growth jobs (ideal: new
and relocating jobs)
• Variables– Initial set from CUSPA– Changes and additions– Future work – what variables are we missing?
• Work in progress– Gauge from estimations; validation difficult
Variables
• Building: building type, sqft, lot sqft, building age, pre-1940, FAR
• Neighborhood: zonal/proximal job density, population, avg income
• Accessibility: travel time to work; distance to arterial, freeway, and cbd
• Other?
Example: Sector Concentration
• Theoretical basis: two phenomena– Building level (firm proxy?)– Vicinity (agglomeration economies)
• Sector diffusion observed– Building-level and vicinity-only variables not yet
specified– In short-term, using a zonal sector concentration
variable as imprecise substitute• Highest average t-value among variables hints at
relevance
Building-level sector concentration
Fraction of Jobs by Building-level Sector Concentration (base year)*
0%
5%
10%
15%
20%
25%
30%
Sector concentration as % of jobs per building
Jo
bs R
ep
resen
ted
by S
ecto
r
Fraction of Jobs by Building-level Sector Concentration (2006_run_182)*
0%
2%
4%
6%
8%
10%
12%
14%
16%
Sector concentration as % of jobs per building
Job
s re
pre
sen
ted
Sufficient to model jobs w/ building tie,
or necessary to model firms?
Exceptional Sectors
• Construction – 87% Mobile– Allocate according to developer activity?
• Schools
• Government
• Military
Exceptional Sectors
• Construction
• Schools – Is scalar reasonable?– Allocate according to child population?
• Government
• Military
Exceptional Sectors
• Construction
• Schools
• Government – Is scalar reasonable?– Catch-all category difficult to model
• Military
Exceptional Sectors
• Construction
• Schools
• Government
• Military – Not currently modeled– MPD & planned employment events?