Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-05T11:53:44.177Z Has data issue: false hasContentIssue false

The Implications of Skewed Risk Perception for a Dutch Coastal Land Market: Insights from an Agent-Based Computational Economics Model

Published online by Cambridge University Press:  15 September 2016

Tatiana Filatova
Affiliation:
Department of Management and Governance, Centre for Studies in Technology and Sustainable Development, at the University of Twente in Enschede, the Netherlands, Deltares, a Research Institute and Consultancy in Water Management, Utrecht, the Netherlands
Dawn C. Parker
Affiliation:
School of Planning at the University of Waterloo in Waterloo, Ontario
Anne van der Veen
Affiliation:
International Institute for Geo-Information Science and Earth Observation, and in the Department of Water Engineering and Management, at the University of Twente in Enschede, the Netherlands
Get access

Abstract

Dutch coastal land markets are characterized by high amenity values but are threatened by potential coastal hazards, leading to high potential damage costs from flooding. Yet, Dutch residents generally perceive low or no flood risk. Using an agent-based land market model and Dutch survey data on risk perceptions and location preferences, this paper explores the patterns of land development and land rents produced by buyers with low, highly skewed risk perceptions. We find that, compared to representative agent and uniform risk perception models, the skewed risk perception distribution produces substantially more, high-valued development in risky coastal zones, potentially creating economically significant risks triggered by the current Dutch flood protection policy.

Type
Contributed Papers
Copyright
Copyright © 2011 Northeastern Agricultural and Resource Economics Association 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Alberini, A. Rosato, P. Longo, A. and Zanatta, V. 2005. “Information and Willingness to Pay in a Contingent Valuation Study: The Value of S. Erasmo in the Lagoon of Venice.” Journal of Environmental Policy and Management 48(2): 155176.Google Scholar
Alonso, W. 1964. Location and Land Use. Cambridge, MA: Harvard University Press.CrossRefGoogle Scholar
Anas, A. 1990. “Taste Heterogeneity and Urban Spatial Structure: The Logit Model and Monocentric Theory Reconciled.” Journal of Urban Economics 28(3): 318335.Google Scholar
Anas, A. Arnott, R. and Small, K.A. 1998. “Urban Spatial Structure.” Journal of Economic Literature 36(3): 14261464.Google Scholar
Arthur, W.B. 2006. “Out-of-Equilibrium Economics and Agent-Based Modeling.” In Tesfatsion, L. and Judd, K.L. eds., Handbook of Computational Economics Volume 2: Agent-Based Computational Economics. Amsterdam: Elsevier.Google Scholar
Arthur, W.B. Durlauf, S.N. and Lane, D. 1997. The Economy as an Evolving Complex System II (Proceedings Vol. XXVII, Santa Fe Institute of Studies in the Science of Complexity). Reading, MA: Addison-Wesley.Google Scholar
Axtell, R. 2005. “The Complexity of Exchange.” The Economic Journal 115(June): F193F210.CrossRefGoogle Scholar
Barnhizer, D.A. 2003. “Givings Recapture: Funding Public Acquisition of Private Property Interests on the Coasts.” Harvard Environmental Law Review 27(2): 295375.Google Scholar
Barreteau, O. Bousquet, F. and 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.” Journal of Artificial Societies and Social Simulation 4(2): 12.Google Scholar
Bell, K.P. and Irwin, E.G. 2002. “Spatially Explicit Micro-Level Modelling of Land Use Change at the Rural-Urban Interface.” Agricultural Economics 27(3): 217232.Google Scholar
Berger, T. and Schreinemachers, P. 2006. “Creating Agents and Landscapes for Multiagent Systems from Random Samples.” Ecology and Society 11(2): 19. Available at http://222.ecologyandsociety.org/vol11/iss2/art19/ (accessed August 24, 2011).Google Scholar
Bin, O. Kruse, J.B. and Landry, C.E. 2008. “Flood Hazards, Insurance Rates, and Amenities: Evidence from the Coastal Housing Market.” Journal of Risk and Insurance 75(1): 6382.CrossRefGoogle Scholar
Bočkarjova, M. van der Veen, A. and Geurts, P.A.T.M. 2008. “How to Motivate People to Assume Responsibility and Act Upon Their Own Protection from Flood Risk in the Netherlands if They Think They Are Perfectly Safe?” Paper presented at the Society for Risk Analysis meetings in Valencia, Spain (September 22-25).Google Scholar
Bockstael, N.E. 1996. “Modeling Economics and Ecology: The Importance of a Spatial Perspective.” American Journal of Agricultural Economics 78(5): 11681180.Google Scholar
Bonabeau, E. 2002. “Agent-Based Modeling: Methods and Techniques for Simulating Human Systems.” Proceedings of the National Academy of Sciences 99(6): 72807287.Google Scholar
Brown, D.G. and Robinson, D.T. 2006. “Effects of Heterogeneity in Residential Preferences on an Agent-Based Model of Urban Sprawl.” Ecology and Society 11(1): 46. Available at http://www.ecologyandsociety.org/vol11/iss1/art46/ (accessed August 24, 2011).Google Scholar
Deltacommissie. 2008. “Working Together with Water: A Living Land Builds for Its Future.” Heerhugowaard, the Netherlands: Hollandia Printing. Available at http://www.deltacommissie.com/doc/deltareport_full.pdf (accessed August 24, 2011).Google Scholar
Duffy, J. 2006. “Agent-Based Models and Human Subject Experiments.” In Tesfatsion, L. and Judd, K.L. eds., Handbook of Computational Economics Volume 2: Agent-Based Computational Economics. Amsterdam: Elsevier.Google Scholar
Epple, D. and Platt, G.J. 1998. “Equilibrium and Local Redistribution in an Urban Economy When Households Differ in Both Preferences and Incomes.” Journal of Urban Economics 43(1): 2351.Google Scholar
Epstein, J.M. and Axtell, R. 1996. Growing Artificial Societies Social Science from the Bottom Up. Washington, D.C.: Brookings Institution Press and MIT Press.Google Scholar
Ettema, D. 2011. “A Multi-Agent Model of Urban Processes: Modelling Relocation Process and Price Setting in Housing Markets.” Computers, Environment and Urban Systems 35(1): 111.Google Scholar
Fernandez, L.E. Brown, D.G. Marans, R.W. and Nassauer, J.I. 2005. “Characterizing Location Preferences in an Exurban Population: Implications for Agent-Based Modeling.” Environment and Planning B: Planning and Design 32(6): 799820.Google Scholar
Filatova, T. 2009. “Land Markets from the Bottom Up: Micro-Macro Links in Economics and Implications for Coastal Risk Management.” Ph.D. thesis, Department of Water Engineering and Management, University of Twente, Enschede, the Netherlands.Google Scholar
Filatova, T. Mulder, J.P.M. and van der Veen, A. 2011. “Coastal Risk Management: How to Motivate Individual Economic Decisions to Lower Flood Risk?Ocean and Coastal Management 54(2): 164172. Available at http://dx.doi.org/10.1016/j.ocecoaman.2010.10.028 (accessed August 24, 2011).CrossRefGoogle Scholar
Filatova, T. Parker, D. and van der Veen, A. 2009. “Agent-Based Urban Land Markets: Agent's Pricing Behavior, Land Prices and Urban Land Use Change.” Journal of Artificial Societies and Social Simulation 12(1): 3.Google Scholar
Filatova, T. van der Veen, A. and Parker, D. 2009. “Land Market Interactions Between Heterogeneous Agents in a Heterogeneous Landscape: Tracing the Macro-Scale Effects of Individual Trade-Offs Between Environmental Amenities and Disamenities.” Canadian Journal of Agricultural Economics 57(4): 431459.Google Scholar
Frame, D.E. 1998. “Housing, Natural Hazards, and Insurance.” Journal of Urban Economics 44(1): 93109.Google Scholar
Haase, D. Kabisch, S. Haase, A. Filatova, T. van der Veen, A. Tötzer, T. Loibl, W. Scatasta, S. Schetke, S. Zuin, A. and von Walter, F. 2008. “Actors and Factors: Bridging Social Science Findings and Urban Land Use Change Modeling.” Paper presented at the fourth biennial meeting of the International Congress on Environmental Modelling and Software, Barcelona (July 7-10, 2008).Google Scholar
Hommes, C.H. 2006. “Heterogeneous Agent Models in Economics and Finance.” In Tesfatsion, L. and Judd, K.L. eds., Handbook of Computational Economics Volume 2: Agent-Based Computational Economics. Amsterdam: Elsevier.Google Scholar
Irwin, E.G. 2010. “New Directions for Urban Economic Models of Land Use Change: Incorporating Spatial Dynamics and Heterogeneity.” Journal of Regional Science 50(1): 6591.CrossRefGoogle Scholar
Kirman, A.P. 1992. “Whom or What Does the Representative Individual Represent?Journal of Economic Perspectives 6(2): 117136.Google Scholar
Kirman, A.P. and Vriend, N.J. 2001. “Evolving Market Structure: An ACE Model of Price Dispersion and Loyalty.” Journal of Economic Dynamics and Control 25: 459502.Google Scholar
Krywkow, J. Filatova, T. and van der Veen, A. 2008. “Flood Risk Perceptions in the Dutch Province of Zeeland: Does the Public Still Support Current Policies?” Paper presented at the European Conference on Flood Risk Management, Oxford, UK (September 30 through October 2, 2008).CrossRefGoogle Scholar
Kunreuther, H. and Pauly, M. 2006. “Rules Rather Than Discretion: Lessons from Hurricane Katrina.” Journal of Risk and Uncertainty 33(1/2): 101116.Google Scholar
LeBaron, B. 2006. “Agent-Based Computational Finance.” In Tesfatsion, L. and Judd, K.L. eds., Handbook of Computational Economics Volume 2: Agent-Based Computational Economics. Amsterdam: Elsevier.Google Scholar
MacDonald, D.N. Murdoch, J.C. and White, H.L. 1987. “Uncertain Hazards, Insurance, and Consumer Choice: Evidence from Housing Markets.” Land Economics 63(4): 361371.Google Scholar
Magliocca, N. Safirova, E. McConnell, V. and Walls, M. 2011. “An Economic Agent-Based Model of Coupled Housing and Land Markets (CHALMS).Computers, Environment and Urban Systems 35(3): 183191.Google Scholar
Marks, R. 2006. “Market Design Using Agent-Based Models.” In Tesfatsion, L. and Judd, K.L. eds., Handbook of Computational Economics Volume 2: Agent-Based Computational Economics. Amsterdam: Elsevier.Google Scholar
Nolan, J. Parker, D. van Kooten, G.C. and Berger, T. 2009. “An Overview of Computational Modeling in Agricultural and Resource Economics.” Canadian Journal of Agricultural Economics 57(4): 417429.Google Scholar
Parker, D.C. Brown, D.G. Filatova, T. Riolo, R. Robinson, D.T. and Sun, S. Forthcoming. “Do Land Markets Matter? A Modeling Ontology and Experimental Design to Test the Effects of Land Markets for an Agent-Based Model of Ex-Urban Residential Land-Use Change.” In Batty, M. Heppenstall, A. and Crooks, A. eds., Spatial Agent-Based Models: Principles, Concepts and Applications. Dordrecht: Springer.Google Scholar
Parker, D.C. and Filatova, T. 2008. “A Conceptual Design for a Bilateral Agent-Based Land Market with Heterogeneous Economic Agents.” Computers, Environment and Urban Systems 32(6): 454463.Google Scholar
Poelmann Commissie. 2005. “Advice of the Committee on Protection and Development of the Outside-Dikes Areas in Coastal Towns (in Dutch). Haarlem, the Netherlands: Province of Noord-Holland.Google Scholar
Poelmann Commission [see Poelmann Commissie].Google Scholar
Raaijmakers, R. Krywkow, J. and van der Veen, A. 2008. “Flood Risk Perceptions and Spatial Multi-Criteria Analysis: An Exploratory Research for Hazard Mitigation.” Natural Hazards 46: 307322.Google Scholar
Rijkswaterstaat. 2002. Towards an Integrated Coastal Zone Policy—Policy Agenda for the Coast. Ministry of Transport and Water Management, Direct Dutch Publications BV: the Hague.Google Scholar
Rijkswaterstaat. 2005. “Risk Management in Coastal Towns: Control of Chances and Consequences of Coastal Breach and Flooding in the Outside-Dikes Areas During Severae Storms (in Dutch). Ministry of Transport and Water Management, the Hague.Google Scholar
Robinson, D.T. Brown, D. Parker, D.C. Schreinemachers, P. Janssen, M.A. Huigen, M. Wittmer, H. Gotts, N. Promburom, P. Irwin, E. Berger, T. Gatzweiler, F. and Barnaud, C. 2007. “Comparison of Empirical Methods for Building Agent-Based Models in Land Use Science.” Journal of Land Use Science 2(1): 3155.Google Scholar
Schreinemachers, P. Potchanasin, C. Berger, T. and Roygrong, S. 2010. “Agent-Based Modeling for Ex Ante Assessment of Tree Crop Innovations: Litchis in Northern Thailand.” Agricultural Economics 41(6): 519536.Google Scholar
Shabman, L. and Stephenson, K. 1996. “Searching for the Correct Benefit Estimate: Empirical Evidence for an Alternative Perspective.” Land Economics 72(4): 433449.CrossRefGoogle Scholar
Simon, H. 1997. Models of Bounded Rationality Volume 3: Emperically Grounded Economic Reason. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Slovic, P. Finucane, M.L. Peters, E. and MacGregor, D.G. 2004. “Risk as Analysis and Risk as Feelings: Some Thoughts about Affect, Reason, Risk, and Rationality.” Risk Analysis 24(2): 311322.Google Scholar
Tatano, H. Yamaguchi, K. and Okada, N. 2004. “Risk Perception, Location Choice and Land-Use Patterns Under Disaster Risk: Long-Term Consequences of Information Provision in a Spatial Economy.” In Okuyama, Y. and Change, S. eds., Modelling Spatial and Economic Impacts of Disasters. Dordrecht: Springer.Google Scholar
Terpstra, T. and Gutteling, J.M. 2008. “Households’ Perceived Responsibilities in Flood Risk Management in the Netherlands.” Water Resources Development 24(4): 551561.Google Scholar
Tesfatsion, L. 2006. “Agent-Based Computational Economics: A Constructive Approach To Economic Theory.” In Tesfatsion, L. and Judd, K.L. eds., Handbook of Computational Economics Volume 2: Agent-Based Computational Economics. Amsterdam: Elsevier.Google Scholar
Tesfatsion, L. and Judd, K.L. (eds.). 2006. Handbook of Computational Economics Volume II: Agent-Based Computational Economics. Amsterdam: Elsevier.Google Scholar
Wiener, J.D. 1996. “Research Opportunities in Search of Federal Flood Policy.” Policy Sciences 29(4): 321344.Google Scholar
Wu, J. 2001. “Environmental Amenities and the Spatial Pattern of Urban Sprawl.” American Journal of Agricultural Economics 83(3): 691697.Google Scholar