H-GAC’s Forecast as “Production” Operations: General Organization,

Logistics, and Schedules

Dmitry MessenHouston-Galveston Area Council

Overview• Forecasts: 1972, 1986, 1992, 2003, 2006• Breaking up with the RTP cycle• Beginning in April 2013: Quarterly Releases– Annual data 2010-2040– Population, Employment: TAZ, CT, County, Region– Land use (type, sqft, HU): parcel

• Current, Announced Changes, Model Predictions

• No “adoption” process, no drafts/preliminaries• (de facto Public) continuous external/internal

Review focused on detection of factual “errors” (QA/QC)

2-Phase Forecasting System• Demographic Microsimulation– Population regional “totals”– Net change in households (demand for housing)– Labor force Workforce Jobs (demand for non-

res sqft)• Land Use Microsimulation– Aggregate demand List of Projects– Development Proposals Selection on ROI

Buildings• 3rd Phase (not yet implemented)– Household and job location choice

LU Data Development• Continuous, never ending process• Independent from model development and

model execution• Labor-intensive and code-intensive• Requires smart and flexible design of workflows

and data architecture• No way around it

Dynamic Land Use• Corrections to Current LU (existing buildings)– Why live with errors? Why not keep corrections?

• Announced Projects– Regionally-significant (e.g., new Exxon’s campus)– Locally-significant

• Sources– Imagery, Google’s StreetView– Plats (ordered from counties 4 times a year)– Business media– Appraisal (once a year)

Parcels and Buildings• Create and maintain our own polygons (“Master

Polygons”) and buildings• From land ownership parcels to polygons that are– Positionally accurate– Comprehensive (100% coverage)– Integrated (land, water, roads)– Meaningful (single polygon for a park, downtown

block, mall, etc)• Detect annual changes in parcels and apply them

to “Master Polygons”

Parcels and Buildings• Keep dynamic info (valuations, rents) separate

from static info (type, sqft, floors)• Tie-backs to appraisal records• Land Use data (GIS: feature classes and tables)• Create from Land Use data (+ other data)– Inputs for model simulation– Inputs for model estimation (we do not re-

estimate the model every quarter)

Non-LU Inputs• ACS Summary Tables, ACS PUMS, BEA, BLS• Once a year, but schedules are different• There’s always some update that we can include

in a quarterly release• Efficient process, takes minimal time– download, run SAS code

• Supports Currentness– Latest Planning Assumptions (Fed regs)

Decoupling of Production and R&D

• Production– Product (Forecast) is always available– Updated/Upgraded quarterly– Releases are labeled (2014Q1, 2014Q2,…)

• R&D– Model changes– New components

Distribution and Review• Distribution– Table query tool; download xls– Web-based mapping app (RLUIS); download GIS

data– Map service

• Review– Parcel-specific feedback directly from RLUIS (also

TAZ and CT)– General comments

Staffing (8)• Manager• Senior Modeler (LU)• Senior GIS Analyst (Data Development)– 2 GIS Analysts and 1 GIS Technician

• Senior Analyst (Tools, Technology, Infrastructure)– 1 GIS Analyst/Programmer

Lessons• Credibility of our work hinges on the accuracy of

the current land use and development “pipeline” data

• Enormous potential for other applications– Community planning, public health, environmental

• Benefits of transparency and openness• Challenges (difficult=interesting)– Design of workflows and procedures– Technology (SDE, network, web)– Distractions; escalating expectations

?