Abstract
The broad and consistent real estate characteristics are frequently listed individually from the enquiring price and the overall description. Thus, these characteristics or the features are individually listed in a prepared organized way, such that they can be effortlessly compared across the entire range of prospective houses. Though, every house has its own distinctive features, such as a particular view, balcony 1 or 2, parking area, kids park, or type of sink, the sellers can provide a précis of all the important description of the house. Thus, the given real estate features can be measured by the probable buyers, but it seems to be nearly impossible to make available an automated evaluation on all features or variables due to the huge variety. This is as well true in the erstwhile direction: house sellers have to formulate an estimation of the worth based on its characteristics or features in similarity to the existing market price of related houses. Using the machine learning or the hypothesis function, an automated system is to be creating to predict the house price.
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
Cebula RJ (2009) The hedonic pricing model applied to the housing market of the city of savannah and its savannah historic landmark district. Rev Regional Studies 39(1):9–22
Fan G-Z, Ong SE, Koh HC (2006) Determinants of house price: a decision tree approach. Urban Stud 43(12):2301–2315
Gu J, Zhu M, Jiang L (2011) Housing price based on genetic algorithm and support vector machine. Exp Syst Appl 38:3383–3386
Selim H (2009) Determinants of house prices in Turkey: Hedonic regression versusartificial neural network. Exp Syst Appl 36:2843–2852
Lim WT et al (2016) Housing price prediction using neural networks. Natural computation. In: 2016 12th international conference on IEEE fuzzy systems and knowledge discovery (ICNC-FSKD)
Shinde N, Gawande K (2018) Valuation of house prices using predictive techniques. Int J Adv Electron Comput Sci 5(6): 34–40. ISSN: 2393-2835
Bourassa SC, Cantoni E, Hoesli M (2010) Predicting house prices with spatial dependence: a comparison of alternative methods. J Real Estate Res 32(2):139–160
Lancaster KJ (1966) A new approach to consumer theory. J Polit Econ 74(2):132–157
Rosen S (1974) Hedonic prices and implicit markets: product differentiation in pure competition. J Polit Econ 82(1):34–55
Stacy Sirmans G, Macpherson DA, Zietz EN (2005) The composition of hedonic pricing models. J Real Estate Lit 13(1):3–43
Smola AJ, Scholkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–205
Shiller RJ (2007) Understanding recent trends in house prices and home ownership. National Bureau of Economic Research, Working Paper 13553, Oct 2007
Li, Chu K-H (2017) Prediction of real estate price variation based on economic parameters. In: 2017 international conference on applied system innovation (ICASI). IEEE
Wu JY (2017) Housing price prediction using support vector regression. In: 2017 Master’s projects, 540
Park B, Bae JK (2015) Using machine learning algorithms for housing price prediction: the case of Fairfax County, Virginia housing data. Exp Syst Appl 42(6):2928–2934
Pow N, Janulewicz E, Liu L (2014) Applied machine learning project 4 prediction of real estate property prices in Montréal
Bhuriya D et al (2017) Stock market predication using a linear regression. In: 2017 international conference of electronics, communication and aerospace technology (ICECA), vol 2. IEEE
Cherny L (1995) The MUD registers: conversational modes of action in a text-based virtual reality, Linguistics Department. Palo Alto, CA: Stanford University
Wang C, Wu H (2018) A new machine learning approach to house estimation. NTMSCI 6(4):165–171
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shukla, S.S.P., Pandey, S.K., Bharadwaj, U., Yadav, A.K. (2021). Assessment of Real House Price Using Machine Learning. In: Kumar Shukla, S., Raman, S.N., Bhattacharjee, B., Bhattacharjee, J. (eds) Advances in Geotechnics and Structural Engineering. Lecture Notes in Civil Engineering, vol 143. Springer, Singapore. https://doi.org/10.1007/978-981-33-6969-6_60
Download citation
DOI: https://doi.org/10.1007/978-981-33-6969-6_60
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-6968-9
Online ISBN: 978-981-33-6969-6
eBook Packages: EngineeringEngineering (R0)