EmploymentLocation Choice

3 Current Issues

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 –

New Buildings

office sqft_per_job for downtown Seattle, smoothed averages

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:

• Existing buildings:

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

Microsimulation:

Wrong at building level = wrong at

macro level?

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

• Schools

• Government

• Military

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?

Questions?

Nascent improvements +

• Relocation choice model in the works– Non-random job destruction model too?

• Constrained sampling & bid process – Any difference if employee or employer is the

chooser?