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How Well do Individuals Predict the Selling Prices of their Homes?

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  • Hugo Benítez-Silva
  • Selcuk Eren
  • Frank Heiland
  • Sergi Jiménez-Martín

Abstract

Self-reported home values are widely used as a measure of housing wealth by researchers employing a variety of data sets and studying a number of different individual and household level decisions. The accuracy of this measure is an open empirical question, and requires some type of market assessment of the values reported. In this research, we study the predictive power of self-reported housing wealth when estimating sales prices utilizing the Health and Retirement Study. We find that homeowners, on average, overestimate the value of their properties by between 5% and 10%. We also find a strong correlation between accuracy and the economic conditions (measured by the prevalent interest rate, the growth of household income, and the growth of median housing prices) at the time of the purchase of the property. While most individuals overestimate the value of their properties, those who bought during more difficult economic times tend to be more accurate, and in some cases even underestimate the value of their house. This cyclicality of the overestimation of house prices can provide some clues regarding the reasons for the difficulties currently faced by many homeowners.

Suggested Citation

  • Hugo Benítez-Silva & Selcuk Eren & Frank Heiland & Sergi Jiménez-Martín, 2008. "How Well do Individuals Predict the Selling Prices of their Homes?," Working Papers 2008-10, FEDEA.
  • Handle: RePEc:fda:fdaddt:2008-10
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    JEL classification:

    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models

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