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Sharma, H.; Harsora, H.; Ogunleye, B. An Optimal House Price Prediction Algorithm: XGBoost. Encyclopedia. Available online: https://encyclopedia.pub/entry/53852 (accessed on 18 November 2024).
Sharma H, Harsora H, Ogunleye B. An Optimal House Price Prediction Algorithm: XGBoost. Encyclopedia. Available at: https://encyclopedia.pub/entry/53852. Accessed November 18, 2024.
Sharma, Hemlata, Hitesh Harsora, Bayode Ogunleye. "An Optimal House Price Prediction Algorithm: XGBoost" Encyclopedia, https://encyclopedia.pub/entry/53852 (accessed November 18, 2024).
Sharma, H., Harsora, H., & Ogunleye, B. (2024, January 15). An Optimal House Price Prediction Algorithm: XGBoost. In Encyclopedia. https://encyclopedia.pub/entry/53852
Sharma, Hemlata, et al. "An Optimal House Price Prediction Algorithm: XGBoost." Encyclopedia. Web. 15 January, 2024.
An Optimal House Price Prediction Algorithm: XGBoost
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This entry is adapted from the peer-reviewed paper 10.3390/analytics3010003

An accurate prediction of house prices is a fundamental requirement for various sectors, including real estate and mortgage lending. It is widely recognized that a property’s value is not solely determined by its physical attributes but is significantly influenced by its surrounding neighborhood. Meeting the diverse housing needs of individuals while balancing budget constraints is a primary concern for real estate developers. 

house price prediction XGBoost feature engineering feature importance house price prediction hyperparameter tuning machine learning regression modeling

1. Introduction

Housing is one of the basic human needs. House price prediction is of utmost importance for real estate and mortgage lending organizations due to the significant contribution of the real estate sector to the global economy. This process is beneficial not only for businesses but also for buyers, as it helps mitigate risks and bridges the gap between supply and demand [1]. To estimate house prices, regression methods are commonly employed, utilizing numerous variables to create models [2]. An efficient and accessible housing price prediction model has numerous benefits for various stakeholders. Real estate businesses can utilize the model to assess risks and make informed investment decisions. Mortgage lending organizations can leverage it to evaluate loan applications and determine appropriate interest rates. Buyers can use the model to estimate the affordability of properties and make informed purchasing decisions. Most importantly, the recent instability of house prices has made the need for prediction models more important than before.
Previous studies [3][4][5] have applied various machine learning (ML) algorithms for house price prediction, with the focus on developing a model; not much attention has been paid to house price predictors. Researchers' literature review findings suggest that various traditional ML algorithms have been studied; however, there is a need to identify the optimal methodology for house price prediction. For example, Madhuri et al. [6] compared multiple linear regression, lasso regression, ridge regression, elastic net regression, and gradient boosting regression algorithms for house price prediction. However, their study did not propose an optimal solution. This is due to the fact that they applied regression algorithms using the default settings only, with no attempt to achieve optimality. The dearth of research regarding this underscores the need for a more comprehensive study on the diverse elements that contribute to the effectiveness of house price predictive models. By delving deeper into the identification and analysis of these influential factors, researchers can unveil valuable insights that will aid in achieving an optimal house price prediction model. This is beneficial to the real estate sector for understanding the significant factors that influence house costs.

2. Optimal House Price Prediction Algorithm

Predicting house prices provides insights into economic trends, guides investment decisions, and supports the development of effective policies for sustainable housing markets. The study by [7] emphasized the reliance of real estate investors and portfolio managers on house price predictions for making informed investment decisions. Recent market trends have demonstrated a clear connection between the accuracy of these predictions and the improved optimization of investment portfolios. Anticipating fluctuations in house prices empowers investors to proactively adapt their portfolios, seize emerging opportunities, and strategically navigate risks, leading to more robust and resilient investment outcomes. Furthermore, the authors in [8] discussed how individuals can gain a better understanding of real estate for their own personal investment and financing decisions. Similarly, [9] added that financial institutions and policymakers recognize house price trends as an economic indicator, as it is important to note that fluctuations in house prices can affect consumer spending, borrowing, and the overall economy. The study by [10] proposed an intuitive theoretical model of house prices, where the demand for housing was driven by how much individuals could borrow from financial institutions. A borrower’s level of debt depends on the level of disposable income he or she has and the current interest rate. The study showed that actual house prices and the amount individuals can borrow are related in the long run with plausible and statistically significant adjustments. The authors in [11] argued that landscape influences the real estate market, adding that macro- (foreign exchange) and micro-variables (such as transportation access, financial stability, and stocks) can change the land price, therefore these can be used to predict future land prices.
ML has revolutionized the process of uncovering patterns and making reliable predictions. This is due to the fact that ML involves the process of acquiring knowledge from past experiences in relation to specific tasks and performance criteria [12]. ML algorithms are of two main categories, namely the supervised and the unsupervised ML approach [13]. The supervised ML approach makes use of a subset of labeled data (where target variable is known) for training and testing on the remaining data to make predictions on unseen datasets [14]. Whilst the unsupervised ML approach does not require a labeled dataset, the approach facilitates the analysis (by uncovering hidden patterns) and makes prediction from unlabeled datasets [15]. In the context of house price prediction, previous studies have conceptualized the problem as a classification task [16] or a regression task [17]. The supervised ML algorithms are capable of modeling both tasks. An example of the classification approach was performed in the work of [16]. They aimed to predict whether the closing house price was greater than or less than the listing house price. They transformed the target variable as “high” when the closing price was greater than or equal to the listing price and as “low” when the closing price was lower than the listing price. Thus, their classification result showed that RIPPER (repeated incremental pruning to produce error reduction) outperformed C4.5, naïve Bayes, and AdaBoost in the Fairfax County, Virginia house dataset, which consisted of 5359 townhouse records.
Most studies have approached the house price prediction problem as a regression task to be able to provide estimates that are predictive in determining the direction of future trends. For example, in China, [18] used 9875 records of Jinan city estate market data for house price prediction. The paper showed that CatBoost was superior to multiple linear regression and random forest, with an R-squared of 91.3% and an RMSE of 772.408. In the Norwegian housing market, [19] introduced squared percentage error (SPE) loss function to improve XGBoost for a house price prediction model. Thus, they showed that their SPE loss function XGBoost algorithm—named SPE-XGBoost—achieved the lowest RMSE of 0.154. The authors in [17] used a Boston (USA) house dataset that consisted of 506 entries and 14 features to implement a random forest regressor and achieved an R-squared of 90%, an MSE (mean square error) of 6.7026, and an RMSE (root mean square error) of 2.5889. Similarly, Ref. [20] showed that lasso regression outperformed linear regression, polynomial regression, and ridge regression using the Boston house dataset, with an R-squared of 88.79% and an RMSE of 2.833. The authors in [6] used the King County housing dataset to compare multiple linear regression, ridge regression, lasso regression, elastic net regression, AdaBoost regression, and gradient boosting, and showed that gradient boosting achieved the superior result. However, it is worth stating that most of these studies applied a basic (default) regression model without considering optimizing the model and did not perform a comprehensive analysis of the feature importance. For illustration, a summary of the literature findings in Table 1 below is provided.
Table 1. Summary of the literature evidencing dataset used and their findings.
Author Dataset Findings RMSE
Zou [18] Jinan city estate market, China CatBoost is superior to multiple linear regression and random forest, with an R-squared of 91.3%. 772.408
Hjort et al. [19] Norwegian housing market SPE-XGBoost achieved the lowest RMSE compared with linear regression, nearest neighbour regression, random forest, and SE-XGBoost. 0.154
Adetunji et al. [17] Boston (USA) house dataset Random forest regressor achieved an R-squared of 90% and an MSE (mean square error) of 6.7026. 2.5889
Sanyal et al. [20] Boston (USA) house dataset Lasso regression outperformed linear regression, polynomial regression, and ridge regression with an R-squared of 88.79%. 2.833
Madhuri et al. [6] King County housing (USA) Gradient boosting showed a superior result with an adjusted R-squared of 91.77% over multiple linear regression, ridge regression, lasso regression, elastic net regression, and AdaBoost regression. 10,971,390,390
Aijohani [1] King County housing (USA) Ridge regression outperformed lasso regression and multiple linear regression with an adjusted R-squared of 67.3%. 224,121
Viana and Barbosa [21]
  • King County (KC), USA;
  • Fayette Count (FC), USA;
  • São Paulo (SP), Brazil;
  • Porto Alegre (POA), Brazil.
 Spatial interpolation attention network and linear regression showed robust performance over other models such as random forest, Lightgbm, XGboost, and auto-sklearn. 115,763 (KC)
22,783 (FC)
154,964 (SP)
94,201 (POA)
In summary, researchers reviewed the recent literature, specifically in the context of techniques utilized to provide up-to-date information on the house price prediction models. The findings showed that only a few studies considered optimality and the significance of features. To evidence this, researchers summarized the techniques (including the optimization approach) that have been used in previous studies, as shown in Table 2 below.
Table 2. Summary of the recent literature evidencing techniques/optimization.
Author(s) Method Hyperparameter
Tuning
Azimlu et al. [22] ANN, GP, Lasso, Ridge, Linear, Polynomial, SVR Not performed
Wang [23] OLS Linear Regression, Random Forest Not performed
Fan et al. [24] Ridge Linear Regression, Lasso Linear Regression, Random Forest, Support Vector Regressor (Linear Kernel and Gaussian Kernel), XGBoost GridSearchCV
Viana and Barbosa [21] Linear Regression, Random Forest, LightGBM, XGBoost, Auto-klearn, Regression Layer Keras(Hyperas)
Aijohani [1] Multiple Regression, Lasso Regression, Ridge Regression Not performed
Sharma et al. [25] Linear Regression, Gradient Boosting Regressor, Histogram Gradient Boosting Regressor, and Random Forest Not performed
Madhuri et al. [6] Multiple Regression, Lasso Regression, Ridge Regression, Elastic Net Regression, and Gradient Boosting Regression Not performed

References

  1. Aljohani, O. Developing a stable house price estimator using regression analysis. In Proceedings of the 5th International Conference on Future Networks & Distributed Systems, Dubai, United Arab Emirates, 15–16 December 2021; pp. 113–118.
  2. Manasa, J.; Gupta, R.; Narahari, N.S. Machine learning based predicting house prices using regression techniques. In Proceedings of the 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Bangalore, India, 5–7 March 2020; pp. 624–630.
  3. Dejniak, D. The Application of Spatial Analysis Methods in the Real Estate Market in South-Eastern Poland. Acta Univ. Lodz. Folia Oeconomica 2018, 1, 25–37.
  4. Rahman, S.N.A.; Maimun, N.H.A.; Razali, M.N.M.; Ismail, S. The artificial neural network model (ANN) for Malaysian housing market analysis. Plan. Malays. 2019, 17, 1–9.
  5. Yalpir, S.; Unel, F.B. Use of Spatial Analysis Methods in Land Appraisal; Konya Example. In Proceedings of the 5th International Symposium on Innovative Technologies in Engineering and Science (ISITES2017), Baku, Azerbaijan, 29–30 September 2017.
  6. Madhuri, C.R.; Anuradha, G.; Pujitha, M.V. House price prediction using regression techniques: A comparative study. In Proceedings of the 2019 International Conference on Smart Structures and Systems (ICSSS), Chennai, India, 14–15 March 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 1–5.
  7. Baum, A. Real Estate Investment: A Strategic Approach; Routledge: Oxfordshire, UK, 2015.
  8. Brueggeman, W.B.; Fisher, J.D. Real Estate Finance and Investments; McGraw-Hill: New York, NY, USA, 2018.
  9. Hill Muellbauer, J.; Murphy, A. Housing markets and the economy: The assessment. Oxf. Rev. Econ. Policy 2008, 24, 1–33.
  10. McQuinn, K.; O’Reilly, G. Assessing the role of income and interest rates in determining house prices. Econ. Model. 2008, 25, 377–390.
  11. Lee, S.H.; Kim, J.H.; Huh, J.H. Land Price Forecasting Research by Macro and Micro Factors and Real Estate Market Utilization Plan Research by Landscape Factors: Big Data Analysis Approach. Symmetry 2021, 13, 616.
  12. Zhou, L.; Pan, S.; Wang, J.; Vasilakos, A.V. Machine learning on big data: Opportunities and challenges. Neurocomputing 2017, 237, 350–361.
  13. Ogunleye, B.O. Statistical Learning Approaches to Sentiment Analysis in the Nigerian Banking Context. Ph.D. Thesis, Sheffield Hallam University, Sheffield, UK, 2021.
  14. Shobayo, O.; Zachariah, O.; Odusami, M.O.; Ogunleye, B. Prediction of stroke disease with demographic and behavioral data using random forest algorithm. Analytics 2023, 2, 604–617.
  15. Usama, M.; Qadir, J.; Raza, A.; Arif, H.; Yau, K.L.A.; Elkhatib, Y.; Al-Fuqaha, A. Unsupervised machine learning for networking: Techniques, applications and research challenges. IEEE Access 2019, 7, 65579–65615.
  16. Park, B.; Bae, J.K. Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data. Expert Syst. Appl. 2015, 42, 2928–2934.
  17. Adetunji, A.B.; Akande, O.N.; Ajala, F.A.; Oyewo, O.; Akande, Y.F.; Oluwadara, G. House price prediction using random forest machine learning technique. Procedia Comput. Sci. 2022, 199, 806–813.
  18. Zou, C. The House Price Prediction Using Machine Learning Algorithm: The Case of Jinan, China. Highlights Sci. Eng. Technol. 2023, 39, 327–333.
  19. Hjort, A.; Pensar, J.; Scheel, I.; Sommervoll, D.E. House price prediction with gradient boosted trees under different loss functions. J. Prop. Res. 2022, 39, 338–364.
  20. Sanyal, S.; Biswas, S.K.; Das, D.; Chakraborty, M.; Purkayastha, B. Boston house price prediction using regression models. In Proceedings of the 2022 2nd International Conference on Intelligent Technologies (CONIT), Hubli, India, 24–26 June 2022; IEEE: Piscataway, NJ, USA, 2022; pp. 1–6.
  21. Viana, D.; Barbosa, L. Attention-based spatial interpolation for house price prediction. In Proceedings of the 29th International Conference on Advances in Geographic Information Systems, Beijing, China, 2–5 November 2021; pp. 540–549.
  22. Azimlu, F.; Rahnamayan, S.; Makrehchi, M. House price prediction using clustering and genetic programming along with conducting a comparative study. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, Lille, France, 10–14 July 2021; pp. 1809–1816.
  23. Wang, Y. House-price Prediction Based on OLS Linear Regression and Random Forest. In Proceedings of the 2021 2nd Asia Service Sciences and Software Engineering Conference, Macau, China, 24–26 February 2021; pp. 89–93.
  24. Fan, C.; Cui, Z.; Zhong, X. House prices prediction with machine learning algorithms. In Proceedings of the 2018 10th International Conference on Machine Learning and Computing, Macau, China, 26–28 February 2018; pp. 6–10.
  25. Sharma, S.; Arora, D.; Shankar, G.; Sharma, P.; Motwani, V. House Price Prediction using Machine Learning Algorithm. In Proceedings of the 2023 7th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 23–25 February 2023; IEEE: Piscataway, NJ, USA, 2023; pp. 982–986.
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Subjects: Computer Science, Artificial Intelligence
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Hemlata Sharma
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Hitesh Harsora
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Bayode Ogunleye
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Update Date: 18 Jan 2024
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