A Machine Learning Framework for House Price Estimation
Awonaike, A., Ghorashi, S. and Hammad, R. 2022. A Machine Learning Framework for House Price Estimation. 21st International Conference on Intelligent Systems Design and Applications (ISDA 2021). Online 13 - 15 Dec 2021 Springer. https://doi.org/10.1007/978-3-030-96308-8_90
|Awonaike, A., Ghorashi, S. and Hammad, R.
House prices estimation has been the focus of both commercial and academic researches with various approaches being explored. Depending on the location, size, age, time and other factors, the value of houses may vary. This paper presents a modularized, process oriented, data enabled and machine learning based framework, designed to help the decision makers within the housing ecosystem to have more realistic estimation of the house prices. The development of the framework leverages the Design Science Research Methodology (DSRM) and the HM Land Registry Price Paid Data is ingested into the framework as the base transactions data. 1.1 million London based transaction records between January 2011 and December 2020 have been exploited for model design and evaluation. The proposed framework also leverages a range of neighborhood data including the location of rail stations, supermarkets and bus stops to explore the possible impact on house prices. Five machine learning algorithms have been exploited and three evaluation metrics have been presented and with a focus on RMSE. Results show that an increase in the variety of parameters enables improved accuracy which ultimately will enable decision making. The potential for future work based on this paper can explore the impact of the introduction of other groups of data on the accuracy of machine learning models designed for the estimation of house prices.
|House price estimation; Machine learning; Neighborhood data
|21st International Conference on Intelligent Systems Design and Applications (ISDA 2021)
|Accepted author manuscript
File Access Level
|27 Mar 2022
|Publication process dates
|03 Nov 2021
|29 Nov 2021
|Intelligent Systems Design and Applications: 21st International Conference on Intelligent Systems Design and Applications (ISDA 2021) Held During December 13–15, 2021
|Hong, T. P.
|Nogueira Rios, T.
|Digital Object Identifier (DOI)
|Web address (URL)
|© 2022 The Authors
Accepted author manuscript
|Ghorashi - ISDA2021 Conference.pdf
|File access level: Anyone
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