Cover Image

Using machine learning to forecast residential property prices in overcoming the property overhang issue

Overhang property issue has sustained over the past ten years in Malaysia. Major overhang property issue was contributed from the unsold residential property. Though the government announced to build a data system and provide the housing data to prevent a mismatch of supply-demand in the property ma...

Full description

Saved in:
Bibliographic Details
Main Authors: Lim, W. Y., Abu Bakar, N. A., Hassan, N. H., Mohd. Zainuddin, N. M., Mohd. Yusoff, R. C., Ab. Rahim, N. Z.
Format: Conference or Workshop Item
Language:English
Published: 2021
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/id/eprint/96030/1/LimWanYee2021_UsingMachineLearningtoForecast.pdf
http://eprints.utm.my/id/eprint/96030/
http://dx.doi.org/10.1109/IICAIET51634.2021.9573830
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utm.96030
record_format eprints
spelling my.utm.960302022-07-03T03:51:55Z http://eprints.utm.my/id/eprint/96030/ Using machine learning to forecast residential property prices in overcoming the property overhang issue Lim, W. Y. Abu Bakar, N. A. Hassan, N. H. Mohd. Zainuddin, N. M. Mohd. Yusoff, R. C. Ab. Rahim, N. Z. T Technology (General) Overhang property issue has sustained over the past ten years in Malaysia. Major overhang property issue was contributed from the unsold residential property. Though the government announced to build a data system and provide the housing data to prevent a mismatch of supply-demand in the property market, there are still not many relevant studies or research on predicting residential property prices. Hence, it is essential to understand the factors that influence the price of residential properties. The study aims to predict the price of a residential property by using a machine learning algorithm. Three algorithms were selected, namely Decision Tree, Linear Regression, and Random Forest, tested against the training and testing datasets obtained from the Malaysian Valuation and Property Services Department. Results show that the Random Forest model produced high accuracy with lower r_squared (R2), RMSE, and MAE values. Significantly, the study has contributed a new insight into essential property features that primarily influence the property price, which will be useful for property developers and buyers who wish to invest in the property market. 2021 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/96030/1/LimWanYee2021_UsingMachineLearningtoForecast.pdf Lim, W. Y. and Abu Bakar, N. A. and Hassan, N. H. and Mohd. Zainuddin, N. M. and Mohd. Yusoff, R. C. and Ab. Rahim, N. Z. (2021) Using machine learning to forecast residential property prices in overcoming the property overhang issue. In: 3rd IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2021, 13 September 2021 - 15 September 2021, Kota Kinabalu, Sabah. http://dx.doi.org/10.1109/IICAIET51634.2021.9573830
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Lim, W. Y.
Abu Bakar, N. A.
Hassan, N. H.
Mohd. Zainuddin, N. M.
Mohd. Yusoff, R. C.
Ab. Rahim, N. Z.
Using machine learning to forecast residential property prices in overcoming the property overhang issue
description Overhang property issue has sustained over the past ten years in Malaysia. Major overhang property issue was contributed from the unsold residential property. Though the government announced to build a data system and provide the housing data to prevent a mismatch of supply-demand in the property market, there are still not many relevant studies or research on predicting residential property prices. Hence, it is essential to understand the factors that influence the price of residential properties. The study aims to predict the price of a residential property by using a machine learning algorithm. Three algorithms were selected, namely Decision Tree, Linear Regression, and Random Forest, tested against the training and testing datasets obtained from the Malaysian Valuation and Property Services Department. Results show that the Random Forest model produced high accuracy with lower r_squared (R2), RMSE, and MAE values. Significantly, the study has contributed a new insight into essential property features that primarily influence the property price, which will be useful for property developers and buyers who wish to invest in the property market.
format Conference or Workshop Item
author Lim, W. Y.
Abu Bakar, N. A.
Hassan, N. H.
Mohd. Zainuddin, N. M.
Mohd. Yusoff, R. C.
Ab. Rahim, N. Z.
author_facet Lim, W. Y.
Abu Bakar, N. A.
Hassan, N. H.
Mohd. Zainuddin, N. M.
Mohd. Yusoff, R. C.
Ab. Rahim, N. Z.
author_sort Lim, W. Y.
title Using machine learning to forecast residential property prices in overcoming the property overhang issue
title_short Using machine learning to forecast residential property prices in overcoming the property overhang issue
title_full Using machine learning to forecast residential property prices in overcoming the property overhang issue
title_fullStr Using machine learning to forecast residential property prices in overcoming the property overhang issue
title_full_unstemmed Using machine learning to forecast residential property prices in overcoming the property overhang issue
title_sort using machine learning to forecast residential property prices in overcoming the property overhang issue
publishDate 2021
url http://eprints.utm.my/id/eprint/96030/1/LimWanYee2021_UsingMachineLearningtoForecast.pdf
http://eprints.utm.my/id/eprint/96030/
http://dx.doi.org/10.1109/IICAIET51634.2021.9573830
_version_ 1738510314385702912
score 13.211869

Similar Items