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A Novel Hybrid House Price Prediction Model

Author

Listed:
  • Süreyya Özöğür Akyüz

    (Bahçeşehir University)

  • Birsen Eygi Erdogan

    (Marmara University)

  • Özlem Yıldız

    (Bahçeşehir University)

  • Pınar Karadayı Ataş

    (Arel University)

Abstract

The real estate sector is evolving and changing rapidly with the increase in housing demand, and new luxury housing projects appear every day. The reliability of housing market investments is largely dependent on accurate pricing.The aim of this study is to introduce a dynamic pricing procedure that estimates house prices using the most important characteristics of a house. For this purpose, a hybrid algorithm using linear regression, clustering analysis, nearest neighbor classification and Support Vector Regression (SVR) method is proposed. Our hybrid algorithm involves using the output of one method as the input of another method for home price prediction to deal with the heteroscedastic nature of the housing data. In other words, the aim of this study is to present a hybrid algorithm that will create different housing clusters from the available data set, classify the houses to which the cluster is unknown, and make price predictions by creating separate prediction models for each class. Housing data collected through manual web scraping of Kadıköy district in Istanbul were used for training and validation of the proposed algorithm. In addition to these data, we validated our algorithm on the KAGGLE house dataset, which covers a wide range of features. The results of the hybrid algorithm were compared using multiple linear regression, Lasso, ridge regression, Support Vector Regression (SVR), AdaBoost, decision tree, random forest and XGBoost regression. Experimental results show that the proposed hybrid model is superior in terms of both Residual Mean Square Error (RMSE), Mean Absolute Value Percent Error (MAPE) and adjusted Rsquare measures for both Kadıköy and KAGGLE housing dataset.

Suggested Citation

  • Süreyya Özöğür Akyüz & Birsen Eygi Erdogan & Özlem Yıldız & Pınar Karadayı Ataş, 2023. "A Novel Hybrid House Price Prediction Model," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 1215-1232, October.
    • Handle: RePEc:kap:compec:v:62:y:2023:i:3:d:10.1007_s10614-022-10298-8
    DOI: 10.1007/s10614-022-10298-8
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    References listed on IDEAS

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    1. Mitsuru Sasaki & Kayoko Yamamoto, 2018. "Hedonic Price Function for Residential Area Focusing on the Reasons for Residential Preferences in Japanese Metropolitan Areas," JRFM, MDPI, vol. 11(3), pages 1-18, July.
    2. Steven C. Bourassa & Martin Hoesli & Elias Oikarinen, 2019. "Measuring House Price Bubbles," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 47(2), pages 534-563, June.
    3. Michele Aquaro & Natalia Bailey & M. Hashem Pesaran, 2021. "Estimation and inference for spatial models with heterogeneous coefficients: An application to US house prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(1), pages 18-44, January.
    4. Stevenson, Simon, 2004. "New empirical evidence on heteroscedasticity in hedonic housing models," Journal of Housing Economics, Elsevier, vol. 13(2), pages 136-153, June.
    5. Herath, Shanaka & Maier, Gunther, 2010. "The hedonic price method in real estate and housing market research. A review of the literature," SRE-Discussion Papers 2010/03, WU Vienna University of Economics and Business.
    6. Karl E. Case & Robert J. Shiller, 1990. "Forecasting Prices and Excess Returns in the Housing Market," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 18(3), pages 253-273, September.
    7. George Milunovich, 2020. "Forecasting Australia's real house price index: A comparison of time series and machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1098-1118, November.
    8. Gang-Zhi Fan & Seow Eng Ong & Hian Chye Koh, 2006. "Determinants of House Price: A Decision Tree Approach," Urban Studies, Urban Studies Journal Limited, vol. 43(12), pages 2301-2315, November.
    9. 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.
    10. Elli Pagourtzi & Vassilis Assimakopoulos & Thomas Hatzichristos & Nick French, 2003. "Real estate appraisal: a review of valuation methods," Journal of Property Investment & Finance, Emerald Group Publishing Limited, vol. 21(4), pages 383-401, August.
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    1. Jinqi Su & Changhong Dong & Ke Su & Lin He, 2023. "Research on the Construction of Digital Economy Index System Based on K-means-SA Algorithm," SAGE Open, , vol. 13(4), pages 21582440231, December.

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