Abstract
Big Data has transformed the workings of real estate firms by improving the efficiency, cutting costs and by enhancing decision making. It helps them to become more agile for improved customer satisfaction and experiences. In the past, real estate businesses had to follow traditional methods by following past trends and professional expertise to make major decisions. Big Data has become much easier to access accurate real data, make conclusions and to even predict future prices of properties. This research uses machine learning algorithms for the appraisal of property prices in New York City. The methods are applied to the data sample of about 80,000 properties, which have sufficient information about each property and its demographic aspects. By further analysis and modelling, it is observed that model with Feature Engineering has performed much better that the model in which Random Forest was implemented. The conclusions drawn from the empirical study would be beneficial for real estate agents and people who are looking forward to invest in New York properties and understand the variation of property prices of New York in the post covid era in comparison to the pre covid era.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Baldominos, A., Blanco, I., Moreno, A.J., Iturrarte, R., Bernárdez, Ó., Afonso, C.: Identifying real estate opportunities using machine learning. Appl. Sci. 8(11), 1–24 (2018)
Basak, D., Srimanta, P., Patranabis, D.C.: Support vector regression. Neural Inf. Proces. Lett. Rev. 11(10), 203–224 (2007)
Boser, B E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: D. Haussler (ed.), 5th Annual ACM Workshop on COLT pp. 144–152. ACM Press (1992)
Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)
Breiman, L.: Arcing the edge, Technical Report. Department of Statistics, University of California, Berkeley (1997)
Breiman, L.: Predicting Games and Arcing Algorithms. Department of Statistics, University of California, Berkeley (1998)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and Regression Trees. Chapman & Hall/CRC Press, New York (1984)
Cai, Y.-D., Zhou, G.-P., Chou, K.-C.: Support vector machines for predicting membrane protein types by using functional domain composition. Biophys. J. 84(5), 3257–3263 (2003)
Corporate Finance Institute Random Forest. Corporate Finance Institute Education Inc. (2020)
Cortes, C., Vapnik, V.: Supporrt vector networks. Mach. Learn. 20(3), 273–297 (1995)
Cohen, J.P., Friedt, F.L., Lautier, J.P.: The Impact of the Coronavirus Pandemic on New York City Real Estate: First Evidence (2022)
Mohd, T., Masrom, S., Johari, N.: Machine learning housing price prediction in Petaling Jaya, Selangor, Malaysia. Int. J. Recent Technol. Eng. 8(2S11), 542–546 (2019)
Muralidharan, S., Phiri, K., Sinha, S.K., Kim, B.: Analysis and prediction of real estate prices: a case of the Boston housing market. Issues Inf. Syst. 19(2), 109–118 (2018)
Park, B.H., Bae, J.K.: Using machine learning algorithms for housing price prediction: the case of Fairfax County, Virginia housing data. Exp. Syst. Appl. 42(6) (2015)
Rafiei, M.H., Adeli, H.: A novel machine learning model for estimation of sale prices of real estate units. J. Constr. Eng. Manag. 142(2), 04015066 (2016)
Rychetsky, M.: Algorithms and Architectures for Machine Learning Based on Regularized Neural Networks and Support Vector Approaches. Shaker Verlag Gmbh (2001)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995). https://doi.org/10.1007/978-1-4757-3264-1
Varian, H.R.: Big data: New tricks for econometrics. J. Econ. Perspect. 28(2), 3–28 (2014)
Wang, C.C., Wu, H.: A new machine learning approach to house price estimation. New Trends Math. Sci. 6(4), 165–171 (2018)
Ho, W.K.O., Tang, B-S., Wong. S.W.: Predicting property prices with machine learning algorithms. J. Prop. Res. (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Jacob, C.B., Asha, K. (2023). Real-Estate Housing Market Analytics and Prediction Using Big Data for Post Pandemic Era. In: Abraham, A., Hanne, T., Gandhi, N., Manghirmalani Mishra, P., Bajaj, A., Siarry, P. (eds) Proceedings of the 14th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2022). SoCPaR 2022. Lecture Notes in Networks and Systems, vol 648. Springer, Cham. https://doi.org/10.1007/978-3-031-27524-1_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-27524-1_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-27523-4
Online ISBN: 978-3-031-27524-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)