This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Abdul-Rahman, S., Zulkifley, N. H., Ibrahim, I., & Mutalib, S. (2021). Advanced machine learning algorithms for house price prediction: Case study in Kuala Lumpur. International Journal of Advanced Computer Science and Applications, 12, pp. 736–745. doi: 10.14569/IJACSA.2021.0121291.Abdul-RahmanS.ZulkifleyN. H.IbrahimI.MutalibS.2021Advanced machine learning algorithms for house price prediction: Case study in Kuala LumpurInternational Journal of Advanced Computer Science and Applications1273674510.14569/IJACSA.2021.0121291Open DOISearch in Google Scholar
Awad, M., & Khanna, R. (2015). Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers, Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers. Apress, Berkeley, CA. doi: 10.1007/978-1-4302-5990-9.AwadM.KhannaR.2015Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers, Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System DesignersApressBerkeley, CA10.1007/978-1-4302-5990-9Open DOISearch in Google Scholar
Ayesha, S., Hanif, M. K., & Talib, R. (2020). Overview and comparative study of dimensionality reduction techniques for high dimensional data. Information Fusion, 59, pp. 44–58. doi: 10.1016/j.inffus.2020.01.005.AyeshaS.HanifM. K.TalibR.2020Overview and comparative study of dimensionality reduction techniques for high dimensional dataInformation Fusion59445810.1016/j.inffus.2020.01.005Open DOISearch in Google Scholar
Cao, Y., Ashuri, B., & Baek, M. (2018). Prediction of unit price bids of resurfacing highway projects through ensemble machine learning. Journal of Computing in Civil Engineering, 32, p. 04018043. doi: 10.1061/(asce)cp.1943-5487.0000788.CaoY.AshuriB.BaekM.2018Prediction of unit price bids of resurfacing highway projects through ensemble machine learningJournal of Computing in Civil Engineering320401804310.1061/(asce)cp.1943-5487.0000788Open DOISearch in Google Scholar
Chen, M., Liu, Y., Arribas-Bel, D., & Singleton, A. (2022). Assessing the value of user-generated images of urban surroundings for house price estimation. Landscape and Urban Planning, 226, p. 104486. doi: 10.1016/j.landurbplan.2022.104486.ChenM.LiuY.Arribas-BelD.SingletonA.2022Assessing the value of user-generated images of urban surroundings for house price estimationLandscape and Urban Planning22610448610.1016/j.landurbplan.2022.104486Open DOISearch in Google Scholar
Chen, J. H., Ong, C. F., Zheng, L., & Hsu, S. C. (2017). Forecasting spatial dynamics of the housing market using support vector machine. International Journal of Strategic Property Management, 21, pp. 273–283. doi: 10.3846/1648715X.2016.1259190.ChenJ. H.OngC. F.ZhengL.HsuS. C.2017Forecasting spatial dynamics of the housing market using support vector machineInternational Journal of Strategic Property Management2127328310.3846/1648715X.2016.1259190Open DOISearch in Google Scholar
Claesen, M., & De Moor, B. (2015). Hyperparameter Search in Machine Learning.ClaesenM.De MoorB.2015Hyperparameter Search in Machine LearningSearch in Google Scholar
Gondia, A., Siam, A., El-Dakhakhni, W., & Nassar, A. H. (2020). Machine learning algorithms for construction projects delay risk prediction. Journal of Construction Engineering and Management, 146, p. 04019085. doi: 10.1061/(asce)co.1943-7862.0001736.GondiaA.SiamA.El-DakhakhniW.NassarA. H.2020Machine learning algorithms for construction projects delay risk predictionJournal of Construction Engineering and Management1460401908510.1061/(asce)co.1943-7862.0001736Open DOISearch in Google Scholar
Ho, W. K. O., Tang, B.-S., & Wong, S. W. (2021). Predicting property prices with machine learning algorithms. Journal of Property Research, 38, pp. 48–70. doi: 10.1080/09599916.2020.1832558.HoW. K. O.TangB.-S.WongS. W.2021Predicting property prices with machine learning algorithmsJournal of Property Research38487010.1080/09599916.2020.1832558Open DOISearch in Google Scholar
Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24, pp. 417–441. doi: 10.1037/h0071325.HotellingH.1933Analysis of a complex of statistical variables into principal componentsJournal of Educational Psychology2441744110.1037/h0071325Open DOISearch in Google Scholar
Hu, L., He, S., Han, Z., Xiao, H., Su, S., Weng, M., & Cai, Z. (2019). Monitoring housing rental prices based on social media: An integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policies. Land Use Policy, 82, pp. 657–673. doi: 10.1016/j.landusepol.2018.12.030.HuL.HeS.HanZ.XiaoH.SuS.WengM.CaiZ.2019Monitoring housing rental prices based on social media: An integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policiesLand Use Policy8265767310.1016/j.landusepol.2018.12.030Open DOISearch in Google Scholar
Jiang, Z., & Shen, G. (2019). Prediction of house price based on the back propagation neural network in the Keras deep learning framework. In: 2019 6th International Conference on Systems and Informatics (ICSAI), pp. 1408–1412. doi: 10.1109/ICSAI48974.2019.9010071.JiangZ.ShenG.2019Prediction of house price based on the back propagation neural network in the Keras deep learning frameworkIn:2019 6th International Conference on Systems and Informatics (ICSAI)1408141210.1109/ICSAI48974.2019.9010071Open DOISearch in Google Scholar
Khalafallah, A. (2008). Neural network based model for predicting housing market performance. Tsinghua Science and Technology, 13, pp. 325–328. doi: 10.1016/S1007-0214(08)70169-X.KhalafallahA.2008Neural network based model for predicting housing market performanceTsinghua Science and Technology1332532810.1016/S1007-0214(08)70169-XOpen DOISearch in Google Scholar
Kim, S., & Kim, H. (2016). A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting, 32, pp. 669–679. doi: 10.1016/j.ijforecast.2015.12.003.KimS.KimH.2016A new metric of absolute percentage error for intermittent demand forecastsInternational Journal of Forecasting3266967910.1016/j.ijforecast.2015.12.003Open DOISearch in Google Scholar
Kim, H., Kwon, Y., & Choi, Y. (2020). Assessing the impact of public rental housing on the housing prices in proximity: Based on the regional and local level of price prediction models using long short-term memory (LSTM). Sustainability, 12, p. 7520. doi: 10.3390/su12187520.KimH.KwonY.ChoiY.2020Assessing the impact of public rental housing on the housing prices in proximity: Based on the regional and local level of price prediction models using long short-term memory (LSTM)Sustainability12752010.3390/su12187520Open DOISearch in Google Scholar
Li, W., & Shi, H. (2011). Applying unascertained theory, principal component analysis and ACO-based artificial neural networks for real estate price determination. Journal of Software, 6. doi: 10.4304/jsw.6.9.1672-1679.LiW.ShiH.2011Applying unascertained theory, principal component analysis and ACO-based artificial neural networks for real estate price determinationJournal of Software610.4304/jsw.6.9.1672-1679Open DOISearch in Google Scholar
Luo, H., Zhao, S., & Yao, R. (2021). Determinants of housing prices in Dalian City, China: Empirical study based on hedonic price model. Journal of Urban Planning and Development, 147, p. 05021017. doi: 10.1061/(asce)up.1943-5444.0000698.LuoH.ZhaoS.YaoR.2021Determinants of housing prices in Dalian City, China: Empirical study based on hedonic price modelJournal of Urban Planning and Development1470502101710.1061/(asce)up.1943-5444.0000698Open DOISearch in Google Scholar
Pal, R. (2017). Validation methodologies. Predictive Modeling of Drug Sensitivity, pp. 83–107. doi: 10.1016/b978-0-12-805274-7.00004-x.PalR.2017Validation methodologiesPredictive Modeling of Drug Sensitivity8310710.1016/b978-0-12-805274-7.00004-xOpen DOISearch in Google Scholar
Park, B., & Kwon Bae, J. (2015). Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data. Expert Systems with Applications, 42, pp. 2928–2934. doi: 10.1016/j.eswa.2014.11.040.ParkB.Kwon BaeJ.2015Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing dataExpert Systems with Applications422928293410.1016/j.eswa.2014.11.040Open DOISearch in Google Scholar
Patel, D. A., & Jha, K. N. (2015). Neural network model for the prediction of safe work behavior in construction projects. Journal of Construction Engineering and Management, 141, p. 04014066. doi: 10.1061/(asce)co.1943-7862.0000922.PatelD. A.JhaK. N.2015Neural network model for the prediction of safe work behavior in construction projectsJournal of Construction Engineering and Management1410401406610.1061/(asce)co.1943-7862.0000922Open DOISearch in Google Scholar
Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2, pp. 559–572. doi: 10.1080/14786440109462720.PearsonK.1901LIII. On lines and planes of closest fit to systems of points in spaceThe London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science255957210.1080/14786440109462720Open DOISearch in Google Scholar
Peng, T.-C., & Wang, C.-C. (2022). The application of machine learning approaches on real-time apartment prices in the Tokyo metropolitan area. Social Science Japan Journal, 25, pp. 3–28. doi: 10.1093/ssjj/jyab029.PengT.-C.WangC.-C.2022The application of machine learning approaches on real-time apartment prices in the Tokyo metropolitan areaSocial Science Japan Journal2532810.1093/ssjj/jyab029Open DOISearch in Google Scholar
Phan, T. D. (2019). Housing price prediction using machine learning algorithms: The case of Melbourne City, Australia. In: Proceedings – 2018 International Conference on Machine Learning and Data Engineering (iCMLDE), pp. 8–13. doi: 10.1109/iCMLDE.2018.00017.PhanT. D.2019Housing price prediction using machine learning algorithms: The case of Melbourne City, AustraliaIn:Proceedings – 2018 International Conference on Machine Learning and Data Engineering (iCMLDE)81310.1109/iCMLDE.2018.00017Open DOISearch in Google Scholar
Piao, Y., Chen, A., & Shang, Z. (2019). Housing price prediction based on CNN. In: 2019 9th International Conference on Information Science and Technology (ICIST). IEEE, pp. 491–495. doi: 10.1109/ICIST.2019.8836731.PiaoY.ChenA.ShangZ.2019Housing price prediction based on CNNIn:2019 9th International Conference on Information Science and Technology (ICIST)IEEE49149510.1109/ICIST.2019.8836731Open DOISearch in Google Scholar
Poterba, J. M. (1984). Tax subsidies to owner-occupied housing: An asset-market approach. The Quarterly Journal of Economics, 99, p. 729. doi: 10.2307/1883123.PoterbaJ. M.1984Tax subsidies to owner-occupied housing: An asset-market approachThe Quarterly Journal of Economics9972910.2307/1883123Open DOISearch in Google Scholar
Qiao, X., & Guo, H. (2014). Research on the effect of the exchange rate of RMB on housing prices based on the VAR model. In: ICCREM 2014. American Society of Civil Engineers, Reston, VA, pp. 1251–1259. doi: 10.1061/9780784413777.148.QiaoX.GuoH.2014Research on the effect of the exchange rate of RMB on housing prices based on the VAR modelIn:ICCREM 2014American Society of Civil Engineers, Reston, VA1251125910.1061/9780784413777.148Open DOISearch in Google Scholar
Rafiei, M. H., & Adeli, H. (2016). A novel machine learning model for estimation of sale prices of real estate units. Journal of Construction Engineering and Management, 142, p. 04015066. doi: 10.1061/(asce)co.1943-7862.0001047.RafieiM. H.AdeliH.2016A novel machine learning model for estimation of sale prices of real estate unitsJournal of Construction Engineering and Management1420401506610.1061/(asce)co.1943-7862.0001047Open DOISearch in Google Scholar
Reddy, G. T., Reddy, M. P. K., Lakshmanna, K., Kaluri, R., Rajput, D. S., Srivastava, G., & Baker, T. (2020). Analysis of dimensionality reduction techniques on big data. IEEE Access, 8, pp. 54776–54788. doi: 10.1109/ACCESS.2020.2980942.ReddyG. T.ReddyM. P. K.LakshmannaK.KaluriR.RajputD. S.SrivastavaG.BakerT.2020Analysis of dimensionality reduction techniques on big dataIEEE Access8547765478810.1109/ACCESS.2020.2980942Open DOISearch in Google Scholar
Sahibinden.com. (2021). Sahibinden Available at [www.sahibinden.com/kategori/emlak]. sahibinden.com. URL www.sahibinden.com/kategori/emlak [accessed 29 April, 2021].Sahibinden.com2021SahibindenAvailable at [www.sahibinden.com/kategori/emlak]. sahibinden.com. URL www.sahibinden.com/kategori/emlak [accessed 29 April, 2021].Search in Google Scholar
Sanjar, K., Bekhzod, O., Kim, J., Paul, A., & Kim, J. (2020). Missing data imputation for geolocation-based price prediction using KNN-MCF method. ISPRS International Journal of Geo-Information,9, p. 227. doi: 10.3390/ijgi9040227.SanjarK.BekhzodO.KimJ.PaulA.KimJ.2020Missing data imputation for geolocation-based price prediction using KNN-MCF methodISPRS International Journal of Geo-Information922710.3390/ijgi9040227Open DOISearch in Google Scholar
Seya, H., & Shiroi, D. (2021). A Comparison of Residential Apartment Rent Price Predictions Using a Large Data Set: Kriging Versus Deep Neural Network. Geographical Analysis 0, pp. 1–22. doi: 10.1111/gean.12283.SeyaH.ShiroiD.2021A Comparison of Residential Apartment Rent Price Predictions Using a Large Data Set: Kriging Versus Deep Neural NetworkGeographical Analysis012210.1111/gean.12283Open DOISearch in Google Scholar
Shi, H. (2009). Determination of real estate price based on principal component analysis and artificial neural networks. In: 2009 2nd International Conference on Intelligent Computing Technology and Automation (ICICTA). IEEE, pp. 314–317. doi: 10.1109/ICICTA.2009.83.ShiH.2009Determination of real estate price based on principal component analysis and artificial neural networksIn:2009 2nd International Conference on Intelligent Computing Technology and Automation (ICICTA)IEEE31431710.1109/ICICTA.2009.83Open DOISearch in Google Scholar
Shiha, A., Dorra, E. M., & Nassar, K. (2020). Neural networks model for prediction of construction material prices in Egypt using macroeconomic indicators. Journal of Construction Engineering and Management, 146, p. 04020010. doi: 10.1061/(asce)co.1943-7862.0001785.ShihaA.DorraE. M.NassarK.2020Neural networks model for prediction of construction material prices in Egypt using macroeconomic indicatorsJournal of Construction Engineering and Management1460402001010.1061/(asce)co.1943-7862.0001785Open DOISearch in Google Scholar
Son, H., Kim, C., & Kim, C. (2012). Hybrid principal component analysis and support vector machine model for predicting the cost performance of commercial building projects using pre-project planning variables. Automation in Construction, 27, pp. 60–66. doi: 10.1016/j.autcon.2012.05.013.SonH.KimC.KimC.2012Hybrid principal component analysis and support vector machine model for predicting the cost performance of commercial building projects using pre-project planning variablesAutomation in Construction27606610.1016/j.autcon.2012.05.013Open DOISearch in Google Scholar
Stukhart, G. (1982). Inflation and the construction industry. Journal of the Construction Division, 108, pp. 546–562. doi: 10.1061/JCCEAZ.0001063.StukhartG.1982Inflation and the construction industryJournal of the Construction Division10854656210.1061/JCCEAZ.0001063Open DOISearch in Google Scholar
Wang, X., & Zhang, J. (2013). Principal component analysis of influencing factors of the development of China's real estate market. In: ICCREM 2013. American Society of Civil Engineers, Reston, VA, pp. 1027–1035. doi: 10.1061/9780784413135.098.WangX.ZhangJ.2013Principal component analysis of influencing factors of the development of China's real estate marketIn:ICCREM 2013American Society of Civil Engineers, Reston, VA1027103510.1061/9780784413135.098Open DOISearch in Google Scholar
Wang, F., Zou, Y., Zhang, H., & Shi, H. (2019). House price prediction approach based on deep learning and ARIMA model. In: Proceedings of 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT), pp. 303–307. doi: 10.1109/ICCSNT47585.2019.8962443.WangF.ZouY.ZhangH.ShiH.2019House price prediction approach based on deep learning and ARIMA modelIn:Proceedings of 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT)30330710.1109/ICCSNT47585.2019.8962443Open DOISearch in Google Scholar
Wen, H., Gui, Z., Tian, C., Song, Y., & Zhou, G. (2022). Expressway proximity effects on property prices in Hangzhou, China: Multidimensional housing submarket approach. Journal of Urban Planning and Development, 148, p. 04021070. doi: 10.1061/(asce)up.1943–5444.0000757.WenH.GuiZ.TianC.SongY.ZhouG.2022Expressway proximity effects on property prices in Hangzhou, China: Multidimensional housing submarket approachJournal of Urban Planning and Development1480402107010.1061/(asce)up.1943–5444.0000757Open DOISearch in Google Scholar
Xiao, L., & Yan, T. (2019). Prediction of house price based on RBF neural network algorithms of principal component analysis. In: ICIIBMS 2019 – 4th International Conference on Intelligent Informatics and Biomedical Sciences. Institute of Electrical and Electronics Engineers Inc., pp. 315–319. doi: 10.1109/ICIIBMS46890.2019.8991474.XiaoL.YanT.2019Prediction of house price based on RBF neural network algorithms of principal component analysisIn:ICIIBMS 2019 – 4th International Conference on Intelligent Informatics and Biomedical SciencesInstitute of Electrical and Electronics Engineers Inc.31531910.1109/ICIIBMS46890.2019.8991474Open DOISearch in Google Scholar
Yue, W., Ni, C., Tian, C., Wen, H., & Fang, L. (2020). Impacts of an urban environmental event on housing prices: Evidence from the Hangzhou Pesticide plant incident. Journal of Urban Planning and Development, 146, p. 04020015. doi: 10.1061/(ASCE)UP.1943-5444.0000564.YueW.NiC.TianC.WenH.FangL.2020Impacts of an urban environmental event on housing prices: Evidence from the Hangzhou Pesticide plant incidentJournal of Urban Planning and Development1460402001510.1061/(ASCE)UP.1943-5444.0000564Open DOISearch in Google Scholar
Zhai, D., Shang, Y., Wen, H., & Ye, J. (2018). Housing price, housing rent, and rent-price ratio: Evidence from 30 Cities in China. Journal of Urban Planning and Development, 144, p. 04017026. doi: 10.1061/(ASCE)UP.1943-5444.0000426.ZhaiD.ShangY.WenH.YeJ.2018Housing price, housing rent, and rent-price ratio: Evidence from 30 Cities in ChinaJournal of Urban Planning and Development1440401702610.1061/(ASCE)UP.1943-5444.0000426Open DOISearch in Google Scholar
Zhan, D., Kwan, M.-P., Zhang, W., Xie, C., & Zhang, J. (2021). Impact of the quality of urban settlements on housing prices in China. Journal of Urban Planning and Development, 147, p. 05021044. doi: 10.1061/(ASCE)UP.1943-5444.0000764.ZhanD.KwanM.-P.ZhangW.XieC.ZhangJ.2021Impact of the quality of urban settlements on housing prices in ChinaJournal of Urban Planning and Development1470502104410.1061/(ASCE)UP.1943-5444.0000764Open DOISearch in Google Scholar
Zhang, Q. (2021). Housing price prediction based on multiple linear regression. Scientific Programming, 2021, pp. 1–9. doi: 10.1155/2021/7678931.ZhangQ.2021Housing price prediction based on multiple linear regressionScientific Programming20211910.1155/2021/7678931Open DOISearch in Google Scholar
Zhang, L., Li, T., Ma, C., & Wen, H. (2020). Measuring the spatial and temporal diffusion of urban house prices in East China. Journal of Urban Planning and Development, 146, p. 04020017. doi: 10.1061/(asce)up.1943-5444.0000572.ZhangL.LiT.MaC.WenH.2020Measuring the spatial and temporal diffusion of urban house prices in East ChinaJournal of Urban Planning and Development1460402001710.1061/(asce)up.1943-5444.0000572Open DOISearch in Google Scholar
Zhang, C., Xiong, M., & Wei, X. (2022). Influence of accessibility to urban service amenities on housing prices: Evidence from Beijing. Journal of Urban Planning and Development, 148, p. 05021063. doi: 10.1061/(asce)up.1943-5444.0000795.ZhangC.XiongM.WeiX.2022Influence of accessibility to urban service amenities on housing prices: Evidence from BeijingJournal of Urban Planning and Development1480502106310.1061/(asce)up.1943-5444.0000795Open DOISearch in Google Scholar
Zheng, S., & Yan, L. (2017). Influence of policy adjustment on housing prices: An empirical analysis based on Chinese data since 2008. In: ICCREM 2016. American Society of Civil Engineers, Reston, VA, pp. 1093–1106. doi: 10.1061/9780784480274.136.ZhengS.YanL.2017Influence of policy adjustment on housing prices: An empirical analysis based on Chinese data since 2008In:ICCREM 2016American Society of Civil Engineers, Reston, VA1093110610.1061/9780784480274.136Open DOISearch in Google Scholar