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The Drivers Of Housing Prices And The Impact Of Schools: Evidence From Georgia

Author

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  • MITRA L DEVKOTA

    (UNIVERSITY OF NORTH GEORGIA, DAHLONEGA, GA 30597, USA)

  • ERIC B HOWINGTON

    (VALDOSTA STATE UNIVERSITY, VALDOSTA, GA 31698, USA)

Abstract

This paper attempts to describe the drivers of housing prices in Cumming, Georgia, a rapidly growing suburban area in the southeast of the US. Data from123 single family homes were collected and analyzed using multiple regression methodology. The findings from correlation matrix indicate that the price of the house is positively associated with the number of bedrooms, number of bathrooms, square footage of the house, the lot size, and the number of parking spaces available in the house, and negatively associated with the age of the house. The results from regression analysis suggest that number of bathrooms, square footage, parking spaces, and the dummy variables for Denmark High School, Forsyth Central High School, and North Forsyth High School are statistically significant predictors of the price of the house for Cumming, Georgia. Finally, about 80% of the variation in the prices of the houses is accounted for by our regression model. These findings may have important implications for decision-making by residents, real-estate agents, house buyers and sellers, financial institutions, policymakers, and scholars alike.

Suggested Citation

  • Mitra L Devkota & Eric B Howington, 2023. "The Drivers Of Housing Prices And The Impact Of Schools: Evidence From Georgia," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 6, pages 6-12, December.
  • Handle: RePEc:cbu:jrnlec:y:2023:v:6:p:6-12
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    References listed on IDEAS

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    1. Adela Nistor & Diana Reianu, 2018. "Determinants of housing prices: evidence from Ontario cities, 2001-2011," International Journal of Housing Markets and Analysis, Emerald Group Publishing Limited, vol. 11(3), pages 541-556, May.
    2. Bin, Okmyung, 2004. "A prediction comparison of housing sales prices by parametric versus semi-parametric regressions," Journal of Housing Economics, Elsevier, vol. 13(1), pages 68-84, March.
    3. Steven C. Bourassa & Eva Cantoni & Martin Hoesli, 2010. "Predicting House Prices with Spatial Dependence: A Comparison of Alternative Methods," Journal of Real Estate Research, American Real Estate Society, vol. 32(2), pages 139-160.
    4. Tripathi, Sabyasachi, 2019. "Macroeconomic Determinants of Housing Prices: A Cross Country Level Analysis," MPRA Paper 98089, University Library of Munich, Germany.
    5. Joachim Zietz & Emily Zietz & G. Sirmans, 2008. "Determinants of House Prices: A Quantile Regression Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 37(4), pages 317-333, November.
    6. Steven Bourassa & Eva Cantoni & Martin Hoesli, 2010. "Predicting House Prices with Spatial Dependence: A Comparison of Alternative Methods," Journal of Real Estate Research, Taylor & Francis Journals, vol. 32(2), pages 139-160, January.
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