A Machine Learning Framework for House Price Estimation

Conference paper


Ghorashi, S., Awonaike, A. and Hammad, R. 2021. A Machine Learning Framework for House Price Estimation. 21st International Conference on Intelligent Systems Design and Applications (ISDA 2021). Online 13 - 15 Dec 2021
AuthorsGhorashi, S., Awonaike, A. and Hammad, R.
TypeConference paper
Abstract

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 a 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.

KeywordsHouse price estimation; Machine learning; Neighborhood data
Year2021
Conference21st International Conference on Intelligent Systems Design and Applications (ISDA 2021)
Accepted author manuscript
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File Access Level
Anyone
Publication process dates
Accepted03 Nov 2021
Deposited29 Nov 2021
Web address (URL)http://www.mirlabs.org/isda21/
Copyright holder© 2021 The Authors
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