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Housing demand in Shanghai: A discrete choice approach

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  • Han, Xuehui

Abstract

In this study we apply a random utility modeling framework to analyze housing demand in the city of Shanghai. A Multinomial Logit Model taking account of latent choice sets is employed to investigate the impact of household characteristics, such as income, size and age composition, on the choice of dwelling, specified by location, size and unit price. In addition to the price and income effects on housing demand, the model identifies a quality indicator for dwelling attributes, which can be interpreted as the mean attractiveness in a money metric measure. The data used in this study are cross-sectional survey data. The estimated model is used to calculate demand elasticities and demand probabilities, for selected groups of households and types of dwellings. Among the results can be noted that the price-income ratios, the age composition and size of household are all important determinants of the demand. The impact of income distribution on housing demand is also studied.

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Bibliographic Info

Article provided by Elsevier in its journal China Economic Review.

Volume (Year): 21 (2010)
Issue (Month): 2 (June)
Pages: 355-376

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Handle: RePEc:eee:chieco:v:21:y:2010:i:2:p:355-376

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Web page: http://www.elsevier.com/locate/chieco

Related research

Keywords: Housing demand Random utility Latent choice set;

References

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  1. Dagsvik, John K. & Strøm, Steinar, 2004. "Sectoral labor supply, choice restrictions and functional form," Memorandum 13/2004, Oslo University, Department of Economics.
  2. Kelvin J. Lancaster, 1966. "A New Approach to Consumer Theory," Journal of Political Economy, University of Chicago Press, vol. 74, pages 132.
  3. McFadden, Daniel L., 1984. "Econometric analysis of qualitative response models," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 24, pages 1395-1457 Elsevier.
  4. Dagsvik, John K, 1994. "Discrete and Continuous Choice, Max-Stable Processes, and Independence from Irrelevant Attributes," Econometrica, Econometric Society, vol. 62(5), pages 1179-1205, September.
  5. Aaberge, Rolf & Colombino, Ugo & Strom, Steinar, 1999. "Labour Supply in Italy: An Empirical Analysis of Joint Household Decisions, with Taxes and Quantity Constraints," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(4), pages 403-22, July-Aug..
  6. Borsch-supan, Axel & Pitkin, John, 1988. "On discrete choice models of housing demand," Journal of Urban Economics, Elsevier, vol. 24(2), pages 153-172, September.
  7. Aaberge, Rolf & Dagsvik, John K & Strom, Steinar, 1995. " Labor Supply Responses and Welfare Effects of Tax Reforms," Scandinavian Journal of Economics, Wiley Blackwell, vol. 97(4), pages 635-59, December.
  8. Harold W. Elder & Leonard V. Zumpano, 1991. "Tenure Choice, Housing Demand, and Residential Location," Journal of Real Estate Research, American Real Estate Society, vol. 6(3), pages 341-356.
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Citations

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Cited by:
  1. repec:wyi:journl:002167 is not listed on IDEAS
  2. Ren, Yu & Xiong, Cong & Yuan, Yufei, 2012. "House price bubbles in China," China Economic Review, Elsevier, vol. 23(4), pages 786-800.
  3. Yuan Cheng & Xuehui Han, 2013. "Does large volatility help?—stochastic population forecasting technology in explaining real estate price process," Journal of Population Economics, Springer, vol. 26(1), pages 323-356, January.
  4. Akbar, Delwar & Rolfe, John & Kabir, S.M. Zobaidul, 2013. "Predicting impacts of major projects on housing prices in resource based towns with a case study application to Gladstone, Australia," Resources Policy, Elsevier, vol. 38(4), pages 481-489.

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