Abstract
This paper presents an agent-based urban land market model. We first replace the centralized price determination mechanism of the monocentric urban market model with a series of bilateral trades distributed in space and time. We then run the model for agents with heterogeneous preferences for location. Model output is analyzed using a series of macro-scale economic and landscape pattern measures, including land rent gradients estimated using simple regression. We demonstrate that heterogeneity in preference for proximity alone is sufficient to generate urban expansion and that information on agent heterogeneity is needed to fully explain land rent variation over space. Our agent-based land market model serves as a computational laboratory that may improve our understanding of the processes generating patterns observed in real-world data.
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Notes
- 1.
CBD is assumed to be exogenously given. For future work it might be interesting to explore model dynamics with endogenous formation of CBD and suburban centers.
- 2.
In this particular paper we replicate the monocentric urban model that assumes that sellers are agricultural land owners and that their ask price is the same for every cell. However, the code of our program integrates the possibility to model the formation of ask prices for households and agricultural sellers.
- 3.
Proximity is defined as P = D max + 1 − D, where Dis distance of a cell to the CBD.
- 4.
The justification and properties of this demand function are discussed in detail in [3].
- 5.
For extended description of the event sequencing see [3].
- 6.
An equation that quantitatively characterizes the transaction price at a given distance from the city center, estimated using linear regression analysis. The land gradient is a typical characteristic of urban spatial structure analyzed both theoretically and empirically [2].
- 7.
The results of the linear regression model showed the best fit. The R 2values for linear, log-log, semi-log and inverse semi-log functional forms were 0.9923, 0.8166, 0.9738 and 0.8647 respectively.
- 8.
We also ran the model with the normal distribution of preferences. The results were qualitatively similar.
- 9.
A formal statistical test of the difference in significance between these estimated coefficients between the multiple model runs for both experiments is conceptually possible, but is beyond the scope of this paper.
References
Alonso W (1964) Location and land use. Harvard University Press, Cambridge, MA
Strazsheim M (1987) The theory of urban residential location. In: Mills ES (ed) Handbook of regional and urban economics. Elsevier Science Publishers B.V., Amsterdam, pp 717–757
Filatova T, Parker D, van der Veen A (2009) Agent-based urban land markets: agent’s pricing behavior, land prices and urban land use change. J Artif Soc Soc Simulat 12(1):3. Available online: http://jasss.soc.surrey.ac.uk/12/1/3.html
Kirman AP (1992) Whom or what does the representative individual represent? J Econ Perspect 6(2):117–136
Axtell R (2000) Why agents? On the varied motivations for agent computing in the social sciences. In: Working paper no 17. Center on Social and Economic Dynamics, The Brookings Institution, Washington, DC
Manski CF (2000) Economic analysis of social interactions. J Econ Perspect 14(3):115–136
Arthur WB (2006) Out-of-equilibrium economics and agent-based modeling. In: Judd KL, Tesfatsion L (eds) Handbook of computational economics, vol 2. Agent-based computational economics. Elsevier B.V., Amsterdam, pp 1551–1564
Tesfatsion L (2006) Agent-based computational economics: a constructive approach to economic theory. In: Judd KL, Tesfatsion L (eds) Handbook of computational economics, vol 2. Agent-based computational economics. Elsevier B.V., Amsterdam, pp 831–880
Parker DC, Berger T, Manson SM (eds) (2002) Agent-based models of land-use and land-cover change: report and review of an international workshop, October 4–7, 2001. LUCC report series, vol 6, LUCC Focus 1 office: Bloomington, 140
Berger T (2001) Agent-based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes, and policy analysis. Agr Econ 25(2–3):245–260
Happe K (2004) Agricultural policies and farm structures – agent-based modelling and application to EU-policy reform. IAMO Studies on the agricultural and food sector in Central and Eastern Europe, vol 30
Polhill JG, Parker DC, Gotts NM (2005) Introducing land markets to an agent based model of land use change: a design. In: Representing social reality: pre-proceedings of the third conference of the European Social Simulation Association. Verlag Dietmar Fölbach, Koblenz, Germany
Filatova T, Parker DC, van der Veen A (2007) Agent-based land markets: heterogeneous agents, land prices and urban land use change. In: Proceedings of the 4th conference of the European Social Simulation Association (ESSA’07), Toulouse, France
Brown DG, Robinson DT (2006) Effects of heterogeneity in residential preferences on an agent-based model of urban sprawl. Ecol Soc 11(1):46
Grevers W (2007) Land markets and public policy. University of Twente, Enschede, Netherlands
Parker DC, Filatova T (2008) A conceptual design for a bilateral agent-based land market with heterogeneous economic agents. Comput Environ Urban Syst 32:454–463
Hawksworth J, Swinney P, Gilbert N (2008) Agent-based modelling: a new approach to understanding the housing market. PricewaterhouseCoopers LLP, London
Robinson DT, Brown DG (2009) Evaluating the effects of land-use development policies on ex-urban forest cover: an integrated agent-based GIS approach. Int J Geogr Inform Sci 23(9):1211–1232
Anas A (1990) Taste heterogeneity and urban spatial structure – the logit model and monocentric theory reconciled. J Urban Econ 28(3):318–335
Barreteau O, Bousquet F, Attonaty J-M (2001) Role playing game for opening the black box of multi-agent systems: method and lessons of its application to Senegal River Valley irrigated systems. J Artif Soc Soc Simulat 4(2):12
Filatova T (2009) Land markets from the bottom up: micro-macro links in economics and implications for coastal risk management. PhD thesis, University of Twente, Enschede, Netherlands, p 196
Filatova T, van der Veen A, Voinov A (2008) An agent-based model for exploring land market mechanisms for coastal zone management. In: Sànchez-Marrè JBM, Comas J, Rizzoli A, Guariso G (eds) Proceedings of the iEMSs fourth biennial meeting: international congress on environmental modelling and software (iEMSs 2008), Barcelona, pp 792–800
Irwin E, Bockstael N (2007) The evolution of urban sprawl: evidence of spatial heterogeneity and increasing land fragmentation. Proc Natl Acad Sci USA 104(52):20672–20677
Irwin EG, Bockstael NE (2004) Land use externalities, open space preservation, and urban sprawl. Reg Sci Urban Econ 34:705–725
Caruso G, Peeters D, Cavailhes J, Rounsevell M (2007) Spatial configurations in a Periurban city. A cellular automata-based microeconomic model. Reg Sci Urban Econ 37(5):542–567
Parker DC, Meretsky V (2004) Measuring pattern outcomes in an agent-based model of edge-effect externalities using spatial metrics. Agr Ecosyst Environ 101(2–3):233–250
Irwin EG, Bockstael NE (2002) Interacting agents, spatial externalities and the evolution of residential land use patterns. J Econ Geogr 2:31–54
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Funding from NWO-ALW (LOICZ-NL) project 014.27.012 and the US National Science Foundation grants 0414060 and 0813799 is gratefully acknowledged.
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Filatova, T., Parker, D.C., van der Veen, A. (2010). Introducing Preference Heterogeneity into a Monocentric Urban Model: An Agent-Based Land Market Model. In: Takadama, K., Cioffi-Revilla, C., Deffuant, G. (eds) Simulating Interacting Agents and Social Phenomena. Agent-Based Social Systems, vol 7. Springer, Tokyo. https://doi.org/10.1007/978-4-431-99781-8_8
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