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Development and Application of a Land Use Model for Santiago de Chile

Universidad de Chile. www.mussa.cl. www.citilabs.com. Development and Application of a Land Use Model for Santiago de Chile. Francisco Martínez Universidad de Chile. Introduction. ASSESS URBAN POLICIES. Evaluation of Zone Regulation Plans Max or min lot sizes Building density

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Development and Application of a Land Use Model for Santiago de Chile

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  1. Universidad de Chile www.mussa.cl www.citilabs.com Development and Application of a Land Use Model for Santiago de Chile Francisco Martínez Universidad de Chile

  2. Introduction ASSESS URBAN POLICIES • Evaluation of Zone Regulation Plans • Max or min lot sizes • Building density • Land use banned (residential, indust., commercial) • Max height of buildings • Incentives: subsidies or taxes • Sensitive to transport policies • Optimal regulation plans

  3. Introduction APPLICATIONS • Equilibrium predictions • Create scenarios for transport studies • Evaluation of mega projects (Transatiago BRT, Cerillos Airport, Central Ring) • Optimal Location (subsidies) • Land use under externalities • Schools: minimum transport cost • Emissions: minimum emission and tradable CO2 permits

  4. Model structure

  5. The Equilibrium Model Model inputs • Growth: N° households and firms (Hh) • Transport (acchi, atti) • Regulations on supply and land use • Incentives or taxes for allocation of residential and commercial activities

  6. The Equilibrium Model The model problem Predict location, rents and supply with: • Land Market: auction • Agents (households and firms h ):rational, diverse tastes, competing for land, externalities. • Space (zones i ):heterogeneous attributes, limited space and regulated. • Real State Industry (v) variety of options, maximize profit

  7. The Equilibrium Model Results and notation • Land use (Svi, qhvi) • Allocation (Hhvi) • Rents (rvi) • Consumers and producers surpluses

  8. The Equilibrium Model Current land use Transport Willingness to pay Households and firms Incentives Subsidies Taxes Auction Location Rents (1) externalities (3)b Supply Land lots Real estate Regulations (2) economies of scale Population HH & firms Equilibrium: all find a location

  9. Mathematic Formulation Demand and Supply models

  10. Mathematic Formulation The Bid function Supply specific bid Consumer’s income Subsidy or Tax: To consumer type h for locationg at dewlling type v in zone i Attributes Dwelling Accesibility, Attractivenes. Zonal (externalities) Consumer’s utility level

  11. Mathematic Formulation Externalities Endogenous Attributes Example: Location Externalities Attribute defined by allocation of consumers and supply in zone i Average income of residents Bids depend on endogenous variables: land use and built environment

  12. Mathematic Formulation Auction fixed-point Adjusts externalities (1) Allocation by auctions Auction probability Hh: Number of agents in cluster h Location bid: Deterministic term Constraints Income budget. Theoretical obs.: !max bidder implies max utility¡

  13. Mathematic Formulation Cut-offfactors Composite cut-off

  14. Mathematic Formulation Real estate rents Real estate rents: depends on amenities/externalities and utility level Expected max bid for real estate v located at zone i

  15. Mathematic Formulation Real estate supply Supply: Total Nr of real estate units ProductionCost with scale/scope economies Rents Subsidies or taxes Regulations (2) Supply MNL fixed-point

  16. Mathematic Formulation Equilibrium Condition: every agents is allocated Nr agents type h to be allocated Allocation probability: Probability that consumidor type h is best bidder on real estate type v in zone i Supply: Nr of real estate type v available in zona i Equilibrium logsum fixed-point Adjusts utility levels (3)

  17. Mathematic Formulation Resume of equilibrium equations System of fixed point (1) Allocation w/ externalities... Equilibrium ............................... (2) Supply w/ econ. scale................... (3)

  18. Calibration Parameters Calibration

  19. Calibration supply Santiago supply model

  20. Calibration Supply Data collection Sources of data: • OD trips household survey 2001 • Real estate rents • Household income • Tax records • Supply by real estate type and zone • Real estate attributes

  21. Calibration supply Data collection Residential land use (m2)

  22. Calibration supply Data collection Total housing floor space (m2)

  23. Calibration supply Data collection Total floor space of buildings (m2)

  24. Calibration supply Data collection Average residents income

  25. Calibration supply Rents per month Data Analysis Supply vs. Real estate (houses) Number of real estate units

  26. Calibration supply Data Analysis Number of real estate (house) units vs. built houses floor space Number of real estate units Built floor space

  27. Calibration supply Data Analysis Number of real estate (house) units vs. average residents’ income Number of real estate units Average income

  28. Calibration supply Santiago supply model Additional explaining variables Classic profit: rent minus direct costs (building and land)

  29. Calibration supply Supply model calibration: by type Houses Estimated parameter Standard error Rents Floor space Land price x floor space Residents Income Available zone land Departments buildings Estimated parameter Standard error Rents Floor space Land price x floor space Residents Income Available zone land

  30. Calibration demand Santiago demand model

  31. Calibration demand Typology HOUSEHOLDS CLUSTERS Socioconomic segments: 5 income levels 3 levels of car ownership 5 Levels of household size MUSSA Santiago: 65 household types; 16 million inhabitants

  32. Calibration demand Typology FIRMS Segments by: Commercial type Business size Industry Retail Service Education Other MUSSA Santiago: 5 types of firms

  33. Calibration demand REAL ESTATE SUPPLY Typology Types by: 700 Zones 12 Real estate building type MUSSA Santiago: 8.400 location options

  34. Calibration demand Accessibility attributes • Use balancing factors Anpi:from trip distribution model, by agent n, time period p and residential zone i: • Interpolate missing values: spatially for each agent type • Aggregate on periods • Normalize between 0-1

  35. Calibration demand Calibration Methodology: Bids Bid functions: linear-in-parameters multi-variate functional form Parameters per income level n Examples of variables regarding their sub-index: Household xh: Household Income Zone xi: Residents average income, zone sevices Household-zone xhi: accessibility Real estate-zone xvi: Built floor space of real estate type v in zone i

  36. Calibration demand Calibration Methodology: Bids Maximum likelihood estimators of the parameters set b With d obtained from the observed data:

  37. Calibration demand Calibration Methodology: Rents Linear least squared regression rvi0 is the observed value of rents E(B)vi is the expected maximum bid obtained as the logsum of bids

  38. Calibration demand Residential Data • Data sources 2001: • OD survey: residents location, socioeconomics, rents and trips • Tax records: land use • Transport model ESTRAUS: trip balancing factors • Variables collected • Household characteristics (size, income, car ownership, age of household’s main adult) • Real estate attributes (type, land lot size, floor space, height) • Zone attributes (land use, average residents income, land use densities, accessibility)

  39. Calibration demand Data Analysis Land use pattern Average land use density by residents income level (m2 of land use/zone area)

  40. Calibration demand Data Analysis Floor space pattern Average floor space by income level and household size (m2)

  41. Calibration demand Data Analysis Zone average of residents income Average zone income compared with the household income in the same zone (Ch$ 2001)

  42. Calibration demand Data Analysis Accessibility Average accessibility by income level and car ownership

  43. Calibration demand NON-Residential Data • Data sources 2001: • Tax records: land use • Transport model ESTRAUS: trip balancing factors • Variables collected • Firms características (business type) • Real estate (type, land lot size, floor space, height) • Zone attributes (land use, zone average income, density, attractiveness)

  44. Calibration demand NON-Residential Data Attributes by business type

  45. Calibration demand Parameter estimates Residential BIDS Model

  46. Calibration demand Parameter estimates NON Residential BIDS Models

  47. Calibration demand Parameter estimates Residential RENTS Model

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