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Analysis of Consumers’ Perceptions of Buying Conditions for Houses

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  • Pami Dua

Abstract

This paper examines the determinants of consumers' buying attitudes for houses. Data on buying attitudes are from responses to the Surveys of Consumer Attitudes conducted by the Survey Research Center, University of Michigan. The determinants considered include current and expected interest rates, current and expected real disposable income and house prices. The empirical estimates show that a long-run relationship exists between buying attitudes for houses and each of the above variables. Each of these determinants also Granger cause buying perceptions. Generalized impulse responses show that shocks to each of the above variables have a predictable and permanent impact on buying attitudes. Furthermore, generalized variance decompositions suggest that both current and expected interest rates explain a large proportion of the variation in consumers’ perceptions towards buying houses. Since consumers' attitudes towards buying houses are likely to be translated into actual purchases, this study shows that in order of importance, interest rates - both current and future - have the maximum impact on decisions to purchase houses followed by expectations of real disposable income.
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  • Pami Dua, 2008. "Analysis of Consumers’ Perceptions of Buying Conditions for Houses," The Journal of Real Estate Finance and Economics, Springer, vol. 37(4), pages 335-350, November.
    • Handle: RePEc:kap:jrefec:v:37:y:2008:i:4:p:335-350
    DOI: 10.1007/s11146-007-9084-0
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    Cited by:

    1. David C. Ling & Joseph T.L. Ooi & Thao T.T. Le, 2015. "Explaining House Price Dynamics: Isolating the Role of Nonfundamentals," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(S1), pages 87-125, March.
    2. Yuan, Cheng & He, Xilong & Kim, Yoonsu, 2017. "Home ownership, housing price and social security expenditure," China Economic Review, Elsevier, vol. 46(C), pages 290-305.
    3. MeiChi Huang, 2022. "Time‐varying impacts of expectations on housing markets across hot and cold phases," International Finance, Wiley Blackwell, vol. 25(2), pages 249-265, August.
    4. Rangan Gupta & Chi Keung Marco Lau & Vasilios Plakandaras & Wing-Keung Wong, 2019. "The role of housing sentiment in forecasting U.S. home sales growth: evidence from a Bayesian compressed vector autoregressive model," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 32(1), pages 2554-2567, January.
    5. Hamid Baghestani & Ajalavat Viriyavipart, 2019. "Do factors influencing consumer home-buying attitudes explain output growth?," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 46(5), pages 1104-1115, August.
    6. Huang, MeiChi, 2014. "Bubble-like housing boom–bust cycles: Evidence from the predictive power of households’ expectations," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(1), pages 2-16.
    7. Huang, MeiChi, 2018. "Time-varying diversification strategies: The roles of state-level housing assets in optimal portfolios," International Review of Economics & Finance, Elsevier, vol. 55(C), pages 145-172.
    8. MeiChi Huang, 2019. "Risk diversification gains from metropolitan housing assets," Review of Financial Economics, John Wiley & Sons, vol. 37(4), pages 453-481, October.
    9. Steffen Heinig & Anupam Nanda & Sotiris Tsolacos, 2016. "Which Sentiment Indicators Matter? An Analysis of the European Commercial Real Estate Market," ICMA Centre Discussion Papers in Finance icma-dp2016-04, Henley Business School, University of Reading.
    10. Chung-Chu Liu & Jason C. H. Chen & Che-Cheong Poon, 2019. "Perception Types Of Home Buyers By Q Methodology: A Comparative Study Of Hong Kong, Taiwan, And The Usa," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 64(01), pages 235-257, March.

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    More about this item

    Keywords

    Consumers’ buying attitudes; Survey data; Cointegration; Generalized impulse responses and variance decompositions;
    All these keywords.

    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • R20 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - General

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