Estimation of Bid Functions for Location Choice and Price Modeling with a Latent Variable Approach
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A new approach for the estimation of bid-rent functions for residential location choice is proposed. The method is based on the bid-auction approach and considers that the expected maximum bid of the auction is a latent variable that can be related to observed price indicators through a measurement equation. The method has the advantage of allowing for the estimation of the parameters of the bid function that explain the heterogeneous preferences of households for location while simultaneously adjusting the expected maximum bid to reproduce realistic values. The model is applied and validated for a case study on the city of Brussels. Results show that the proposed model outperforms other methods for bid-rent estimation, both in terms of real estate prices and spatial distribution of agents, especially when detailed data describing the real estate goods and their prices is not available.
KeywordsLocation choice Bid function Auction Real estate Rent
Research in this article has been funded by the European Commission’s Seventh Framework Programme and the Complex Engineering Systems Institute (ICM: P-05-004-F, CONICYT: FBO16). The authors would like to thank the SustainCity team ( www.sustaincity.org) for their contribution with data collection and processing.
- Anas A (1982) Residential location markets and urban transportation: economic theory, econometrics, and policy analysis with discrete choice models. Academic Press, LondonGoogle Scholar
- Ben-Akiva ME, Lerman SR (1985) Discrete choice analysis: theory and application to travel demand. MIT Press, CambridgeGoogle Scholar
- Bierlaire M (2003) Biogeme: a free package for the estimation of discrete choice models. In: Proceedings of the Swiss transport research conference. Ascona, SwitzerlandGoogle Scholar
- Bierlaire M, Fetiarison M (2009) Estimation of discrete choice models: extending biogeme. In: Proceedings of the 9th swiss transport research conference. Ascona, SwitzerlandGoogle Scholar
- DiPasquale D, Wheaton W (1996) Urban economics and real-estate markets. Prentice Hall, Englewood Cliffs, New JerseyGoogle Scholar
- Fujita M, Krugman P, Venables A (1999) The spatial economy: cities, regions and international trade. MIT Press, CambridgeGoogle Scholar
- Glaeser EL (2008) Cities, agglomeration, and spatial equilibrium. Oxford University Press, OxfordGoogle Scholar
- Horner MW (2004) Spatial dimensions of urban commuting: a review of major issues and their implications for future geographic research. Prof Geogr 56:170–173Google Scholar
- McFadden D (1978) Modeling the choice of residential location. In: Karlqvist A (ed)Spatial interaction theory and residential location.North-Holland, Amsterdam, pp 75–96Google Scholar
- Miyamoto A, Kitazume K (1989) A land-use model based on random utility/rent-bidding analysis (rurban). Transport policy management and technology - towards 2001 selected proceedings of the fifth world conference on transport research, YokohamaGoogle Scholar
- Wegener M (2008) The irpud model: overview, Technical report, Spiekermann & Wegener,Urban Reg Res. http://irpud.raumplanung.unidortmund.de/irpud/pro/mod/mod.htm