Dimantha I De Silva (corresponding), HBA Specto Incorporated

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1 Paper Author (s) Dimantha I De Silva (corresponding), HBA Specto Incorporated (dds@hbaspecto.com) Daniel Flyte, San Diego Association of Governments (SANDAG) (Daniel.Flyte@sandag.org) Matthew Keating, San Diego Association of Governments (SANDAG) (Matthew.Keating@sandag.org) John Douglas Hunt, University of Calgary, Canada (jdhunt@ucalgary.ca) John E. Abraham, HBA Specto Incorporated (jea@hbaspecto.com) Paper Title & Number Investigating Land Use Regulation and Transportation Policy with San Diego PECAS Model [ITM # 63] Abstract PECAS Model of the San Diego Region has been developed for the San Diego Association of Governments (SANDAG). This model includes representation of the spatial economic interactions among 103 categories of industrial and institutional activities involving 83 types of commodity, labor and building space and using 236 land use zones. It is integrated with a four-step transportation demand model considering WW flows of trips. The full integrated model setup provides a simulation of the evolution of the economy of the San Diego Region to the year 2050 in one-year steps. The period from 2005 to 2012 has been used for model calibration and the period beyond 2014 is now considered for policy analysis and forecasting. Developer actions in the modification of building space are simulated on each parcel in each year of the simulation in response to expected space prices and taking account of available construction capacities. The San Diego PECAS Model is being used in the official Regional Transportation Plan (RTP) process by SANDAG which means it is part of the formal planning and evaluation activities of the Region. This has made it necessary to include constraints on numbers of residential units by type in locations in the model based on growth patterns negotiated with local governments. As part of the ongoing model development process, four alternative policy scenarios have been considered: Constrained Dwellings; Transport Cost Increase; Development Fee Reduction and High Transit. The results of these scenarios together with the reference case provide indications of the realism and usefulness of the San Diego PECAS Model and its outputs in the context of planning in the San Diego Region. In particular, the model shows the large negative influence of the negotiated dwelling constraints on the well-being of San Diego, when compared with the scenarios where development is only restricted by published zoning regulations. Statement of Financial Interest Some of the authors of the paper are employed by (and/or indirect shareholders of) HBA Specto Incorporated, and HBA Specto may be able to expand its consulting business if other agencies pursue land use models similar to the one described in this brief. Note, however, that the PECAS software framework for land use modelling was provided to SANDAG under a generous and permissive open-

2 source license (the Apache License, Version 2.0) that allows redistribution. Thus, although HBA Specto is currently the primary distributor of PECAS, the financial interest is limited to the ability of the brief s authors (as individuals and as corporations) to pursue further work (as employees or consultants) in similar projects. Statement of Innovation The San Diego PECAS model is a completed new type of land use model, being applied in a Regional Transportation Planning context. The application of the San Diego model for forecasting in a region with multiple levels of planning agencies, including local governments, has shown the practicality of using such a land use model, and some of the difficulties and opportunities associated with using such a land use model. The use of a modern spatial economic and parcel simulation model in RTP and conformity forecasting is, in itself, novel and innovative. However there are two important additional findings that are particularly important and innovative: 1) The model exposed a substantial disconnect between negotiated capacity values for the maximum development of each parcel and the legally binding maximum density values in the published zoning regulations. When the model was constructed to only respect the density maximums in published regulations, the model produced growth in certain areas contrary to the negotiated vision for San Diego. This led to concerns regarding the future growth of patterns in the region, criticism of the model for not respecting the vision, a re-evaluation of the historical and ongoing process for negotiating capacity on each parcel, and a further evaluation of published zoning regulations. 2) When the capacity values were added to the modelling framework, the consumer surplus calculations were much lower. The model suggests that restricting development through negotiation will have a large negative impact on the region, far larger than the impact of transportation policy.

3 Investigating Land Use Regulation and Transportation Policy with San Diego PECAS Model By Dimantha I De Silva Transportation Engineer, HBA Specto Incorporated Suite th Ave SW Calgary AB Canada T2P3P4 dds@hbaspecto.com, +1 (403) Daniel Flyte Regional Models Analyst, San Diego Association of Governments (SANDAG) 401 B St, Suite 800, San Diego, CA, USA, Daniel.Flyte@sandag.org, +1 (619) Matthew Keating Regional Models Analyst, San Diego Association of Governments (SANDAG) 401 B St, Suite 800, San Diego, CA, USA, Matthew.Keating@sandag.org, +1 (619) J D Hunt Professor, Department of Civil Engineering University of Calgary, 2500 University Dr NW Calgary AB Canada T2N1N4 jdhunt@ucalgary.ca, +1 (403) John E Abraham HBA Specto Incorporated Suite th Ave SW Calgary AB Canada T2P3P4 jea@hbaspecto.com, +1 (403)

4 INTRODUCTION AND OBJECTIVES In an effort to modernize its land use and transportation modeling system and adopt a more economically-grounded sub-regional forecast, SANDAG has developed the Production, Exchange, Consumption Allocation System (PECAS). This model includes representation of the spatial economic interactions among 103 categories of industrial and institutional activities involving 83 types of commodity, labor and building space and using 236 land use zones. It has been integrated with the existing transportation demand modeling system to simulate and analyze policy, and develop forecast policy alternatives. PECAS is a spatial economic Computable General Equilibrium (CGE) modeling system, and predicts the prices and spatial flows of labor and commodities throughout the economy, from production and consumption end-points. Commodity transport costs and accessibilities are treated explicitly throughout, influencing the buying and selling locations for each commodity. The rents associated with the demand for space influence developers and land-owners in a parcel-by-parcel micro-simulation of land cover change. Other factors influencing developers include construction costs, existing space (type, intensity and age), zoning regulations, and site-specific variables from GIS layers, including short-distance location amenities. SANDAG began development of PECAS in 2007, in a migration from its predecessor modeling system, the Urban Development Model (UDM). UDM is a derivation of the DRAM/EMPAL modeling system, and has served SANDAG for over 20 years as its sub-regional allocation model. UDM allocates jobs and housing based on local jurisdictions plans and policies for employment and housing capacity (Smith, Tayman, and Swanson, 2001). It uses what are referred to as access weights, a measure of the jobs and housing within a short proximity of a parcel, to influence which parcels are developed first. As it respects local plans for jobs and housing, UDM has served as a valuable planning tool for small area forecasts. However, the San Diego region is largely developed very close to the capacity its local plans allow for. As such, the bulk of San Diego s future housing growth must come from redevelopment and infill, with a much greater share from multi-family housing. Determining the location and phasing of this type of development requires much broader local policy input and economic analysis than UDM offers, which is more suitable for analyzing the phasing of

5 greenfield development. Furthermore, SANDAG saw substantial benefit to a developing a smallarea forecasting model built around a spatial input-output model framework. As a policy analysis tool, this provides greater insight into why location choices and technology choices of households and industries are made. Upon completion of the major PECAS development activities in late 2012, SANDAG wished to test a series of potential land use and transportation policy scenarios for sensitivity analysis of the modeling system. METHODOLOGY PECAS consists of two modules, the Activity Allocation (AA) module and the Space Development (SD) Module. The AA module allocates the economic agents into zones and solves the economic system with businesses and households choosing to live in buildings that are wellplaced with regard to their interactions (exchanges) with other businesses and households. Transportation costs and dis-utilities are represented, which would encourage everyone to locate close to each other, but since everybody cannot all live and produce in the same building, competition for location leads to rents (in what s called a bid rent allocation) and households and businesses trade off the travel costs for their interactions against favorable locations and advantageous rents. The SD module is responsible for evolving the building stock (the supply of space ) over time by representing the choices made by developers regarding construction (including renovation and demolition). Developers would like to construct buildings for tenants where the rents are high and the construction costs are low. The SD module is a micro-simulation of individual parcels, allowing it to consider site-specific variables, including zoning regulations, physical geography affecting construction costs (slope, soil, servicing), and site attractiveness for different uses. The SD module was constructed with a detailed assessment of zoning regulations. SANDAG staff reviewed published regulations regarding development, to determine the types and intensities of space that would be allowed and prohibited on each parcel. In the ongoing regional 3

6 planning process, SANDAG also works together with the individual municipalities, asking them to discuss and then indicate the maximum number of dwellings that would be allowed on each parcel at full build out. These were aggregated in to the land use zones (LUZ) and called the Capacities. In many cases, the initial process to review the published zoning regulations indicated higher allowed densities (units per acre) than the regional planning discussions. The PECAS model was modified so that it could also respect the Capacity values from the interagency discussion/negotiation, for consistency with the historical and ongoing regional planning process. PECAS is connected to a travel demand model, so that the travel conditions between locations can be forecast. For example, congestion can arise if the AA module forecasts many interactions between places with limited transportation service and infrastructure. The AA and SD module are run each year, with AA determining rents for SD and SD determining quantities of space for next year s AA module. The travel demand model is run less often (every fifth year in the SANDAG PECAS model), determining transportation demands from the locations in the most recent run of the AA module and calculating equilibrium transportation conditions which feedback into the next years of AA. PECAS has been operationalized in open-source software, and has been further described in various papers and reports. (Hunt and Abraham, 2005). Five scenarios are defined to analyze the sensitivities of the model: s21: The base case scenario which includes the household Capacities by each LUZ s22: Base scenario without the household LUZ Capacities. s23: No household LUZ Capacities and private vehicle operating cost increase by 3 times in the travel model. s24: No household LUZ Capacities and development fees removed in PECAS model. s25: No household LUZ Capacities and transit frequency increased by 3 times in travel model 4

7 MAJOR RESULTS Figure 1 shows the daily vehicle miles traveled (left side) and transit use (right side) under each of the five scenarios. The time steps represent the year in which the travel model is run and show the results from interaction between the PECAS model and the travel model. All alternative scenarios compared to the base s21 scenario have decreases in VMT. The biggest impact is from increasing the private vehicle operating cost (s23); increasing the transit frequencies (s25) had a lesser impact. Lifting the capacity restraint on dwellings has a similar impact on VMT as increasing the transit frequencies, as people have more freedom to change their location, and choose to live in a more efficient pattern. (S21) (S22) (S23) (S24) (S25) Figure 1: Daily Vehicle miles traveled and Transit use in Peak Period There is a similar trend in change in transit use from 2015 to 2035 for each scenario. The highest increase in transit use is by penalizing the use of private car as compared to increasing the service of transit. The land use policy change of lifting the household capacities have impacted both the daily vehicle miles traveled as well as the transit use. This shows the interaction between PECAS and the travel model working as expected where an impact on PECAS model has influenced change in travel in the travel model. When households are allowed to locate without capacity restrictions both daily vehicle miles traveled and transit use decrease as compared to the base case. 5

8 Figure 2 shows the change in location for two household types between s22 and s21. Each green dot represents an increase of 15 households (i.e. more households in the scenario without Capacity) while a red dot denotes a decrease in 15 households (i.e. more households in the scenario with the Capacities). The pattern is different for higher income (hh150plus-3plus, left side of Figure 2) and lower income (hh25_3plus, right side of Figure 2) household categories. Higher income households are being allowed to live in the most desirable coastal communities by the literal interpretation of the intensity and type restrictions in the legal zoning regulations, but forced to live in less desirable inland areas by the Capacities. Lower income households have a more complex pattern, for instance, they are allowed to congregate in the city center location (near the ocean and many jobs) when the Capacities are removed but cannot afford the most attractive coastal areas north west of the city center. Figure 2 Change in household location between s22 and s21, for high income and low income households with 3+ household members. 6

9 The model is based on spatial economics, with a consistent random utility treatment of locations of interactions, technology/lifestyle and location, for industry and household categories (Abraham and Hunt, 2007). As a result, consumer surplus/ producer surplus benefit measures can be calculated for policies by calculating the differences between two scenarios. Figure 3 shows the change in consumer surplus for high-income households with 3+ household members, comparing each scenario to the scenario S21, the base scenario with the Capacities. The figure shows that increasing vehicle operating costs by a factor of three decreases the attractiveness of the San Diego region to households in this category by a factor of about $1600 per year (the distance between the red line and the other lines in 2035), while the removal of the Capacities has a much larger impact, over $10,000 benefit per year per household by Figure 3: Change in consumer surplus for high income and low income households with 3+ household members. IMPLICATIONS FOR TRAVEL MODELING The PECAS spatial economic model for San Diego has been developed for spatial planning and forecasting by the San Diego Association of Governments. The model was developed over a few years in increments, with improvements made each year to add to the model s detail, accuracy, 7

10 fidelity and usefulness. The model is forecasting spatial patterns of growth, calculating economic impacts of policies, and allocating households and jobs to locations over time in response to transportation conditions, transportation policy, and land regulation policy. The comparison of different scenarios, with different policies, shows the impact of policies. In particular, these scenarios showed the very large impact that the consensus/negotiated dwelling unit capacity by zone have on the attractiveness of San Diego and the travel behavior of the residents. The model is showing that San Diego would be more attractive, and more efficient (operate with less total VMT travel) if development was allowed to occur according to a strict interpretation of the intensity and type restrictions in the written zoning regulations. Transport policy (in particular transit funding, and vehicle operating charges) have the expected effect on the future forecast, and have a large effect on travel behavior, but have a much smaller overall effect on the well-being of the region than the zoning regulation policy. The model is a useful tool for bringing land use interactions and impacts into the travel forecasting and planning process. For example, comparing s22 through s25 with each other allows specific policies of future development impact fees to be compared with transportation policy regarding transit frequency and automobile operating costs. This model with its direct representation of developer behavior in the regional situation also identified potential greater opportunities for pursuing economic well-being and regional attractiveness. In the San Diego context, the model was useful in identifying and forcing the discussion of a sensitive issue where the land use restrictions reported and negotiated in the long-term planning process are more restrictive than the building type and coverage (floor area ratio) restrictions in published legal definitions in the zoning regulations, and the region is forecasted to be better off economically if developers only had to respect those elements of the published zoning regulations. The model has allowed SANDAG to develop a regional transportation plan in which land use patterns are consistently forecasted alongside transportation demand patterns. The model has also led to more consideration and investigation of zoning regulations and the other mechanisms under policy control that discourage or encourage development in certain areas of the region. 8

11 REFERENCES Hunt, J. D., and J. E. Abraham. Design and implementation of PECAS: A generalized system for the allocation of economic production, exchange and consumption quantities, in Integrated Land- Use and Transportation Models: Behavioural Foundations, M. Lee. Gosselin and S. Doherty, Eds. Elsevier, Amsterdam, 2005, pp Abraham, J. E., and J. D. Hunt. Random Utility Location, Production and Exchange Choice: Additive Logit Model and Spatial Choice Microsimulations. In Transportation Research Record: Journal of the Transportation Research Board, Vol. 2003, Transportation Research Board of the National Academies, Washington, D.C., 2007, pp Hunt, J. D., Abraham, J. E., De Silva D., Zhong M., Bridges J. & Mysko J. (2007). Microsimulating Space Development in Baltimore. Paper for the 12th Internation Conference of the Hong Kong Society for Transportatino Studies. Hong Kong. Smith, S. K., J. Tayman and D. A. Swanson (2001), State and Local Population Projections: Methodology and Analysis, Kluwer Academic/Plenum Publishers, New York. 9