Employment Location 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

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Job Capacity Calculated, not stored Separate density ratios –Vary by location (zone) –Static 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

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

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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 Sufficient to model jobs w/ building tie, or necessary to model firms?

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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?