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Community resilience and house prices: A machine learning approach

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  • Zheng, Yi

Abstract

Using community resilience data at the county level in the United States obtained from the Census Bureau, we find that improvements in community resilience are associated with an increase in real estate values. Our machine learning approach indicates that community resilience plays a significant role in shaping real estate value. Furthermore, we demonstrate that the Extra Trees Regressor (ETR) method performs the best based on the root mean squared error (RMSE) standard and is effective in predicting real estate prices in a different tested sample. Finally, we conduct a grid search, exploring various parameters to further reduce RMSE and optimize our ETR method.

Suggested Citation

  • Zheng, Yi, 2023. "Community resilience and house prices: A machine learning approach," Finance Research Letters, Elsevier, vol. 58(PB).
    • Handle: RePEc:eee:finlet:v:58:y:2023:i:pb:s1544612323007729
    DOI: 10.1016/j.frl.2023.104400
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    References listed on IDEAS

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    More about this item

    Keywords

    Community resilience; Real estate property; Machine learning;
    All these keywords.

    JEL classification:

    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • G51 - Financial Economics - - Household Finance - - - Household Savings, Borrowing, Debt, and Wealth
    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General

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