Data-driven fuzzy rule extraction for housing price prediction in Malang, East Java

Febrita, Ruth Ema; Alfiyatin, Adyan Nur; Taufiq, Hilman; Mahmudy, Wayan Firdaus

doi:10.1109/icacsis.2017.8355058

Cited by 14 publications

(4 citation statements)

References 15 publications

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“…The contribution of location attribute to house price drops as predicted from first class residential districts to fifth class residential districts. [5] pointed out that the four objects which most affected the house price are hospitals, schools, campuses, and leisure parks, which can be included in the locational attributes.…”

Section: Fig1 Types Of Attributes Used In Previous Studymentioning

confidence: 99%

“…This model will provide a lot of information and knowledge to home buyers, property investors and house builders, such as the valuation of house prices in the present market, which will help them determine house prices. Meanwhile, this model can help potential buyers decide the characteristics of a house they want according to their budget [5]. Previous studies focused on analyzing the attributes that affect house price and predicting house price based on the model of machine learning separately.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

House Price Prediction using a Machine Learning Model: A Survey of Literature

Zulkifley¹,

Rahman²,

Ubaidullah³

et al. 2020

IJMECS

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Section: Fig1 Types Of Attributes Used In Previous Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

House Price Prediction using a Machine Learning Model: A Survey of Literature

Zulkifley¹,

Rahman²,

Ubaidullah³

et al. 2020

IJMECS

View full text Add to dashboard Cite

“…The ML model that provides the best prediction results will be beneficial for researchers, home buyers, property investors, and house builders in terms of gaining a lot of knowledge and information of the house price values in the present sector. Additionally, this model can facilitate potential buyers to determine the characteristics of a house they prefer that adhere to their budget [4]. Prediction of house prices in Kuala Lumpur would be a significant research as Kuala Lumpur is the capital city of Malaysia that offers a range of facilities www.ijacsa.thesai.org including efficient public transportation, shopping malls, and many more compared to states in a rural area such as Perlis has yet to provide.…”

Section: Introductionmentioning

confidence: 99%

Advanced Machine Learning Algorithms for House Price Prediction: Case Study in Kuala Lumpur

Abdul-Rahman¹,

Zulkifley²,

Ibrahim³

et al. 2021

IJACSA

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House price is affected significantly by several factors and determining a reasonable house price involves a calculative process. This paper proposes advanced machine learning (ML) approaches for house price prediction. Two recent advanced ML algorithms, namely LightGBM and XGBoost were compared with two traditional approaches: multiple regression analysis and ridge regression. This study utilizes a secondary dataset called 'Property Listing in Kuala Lumpur', gathered from Kaggle and Google Map, containing 21984 observations with 11 variables, including a target variable. The performance of the ML models was evaluated using mean absolute error (MAE), root mean square error (RMSE), and adjusted r-squared value. The findings revealed that the house price prediction model based on XGBoost showed the highest performance by generating the lowest MAE and RMSE, and the closest adjusted r-squared value to one, consistently outperformed other ML models. A new dataset which consists of 1300 samples was deployed at the model deployment stage. It was found that the percentage of the variance between the actual and predicted price was relatively small, which indicated that this model is reliable and acceptable. This study can greatly assist in predicting future house prices and the establishment of real estate policies.

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“…The two-stage clustering method using K-Means and Fuzzy Inference System also have been done to cluster house data [12]. The data are clustered into four predefined clusters based on house price: cheap, medium, expensive and very expensive.…”

Section: Introductionmentioning

confidence: 99%

High Dimensional Data Clustering using Self-Organized Map

2019

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As the population grows and e economic development, houses could be one of basic needs of every family. Therefore, housing investment has promising value in the future. This research implements the Self-Organized Map (SOM) algorithm to cluster house data for providing several house groups based on the various features. K-means is used as the baseline of the proposed approach. SOM has higher silhouette coefficient (0.4367) compared to its comparison (0.236). Thus, this method outperforms k-means in terms of visualizing high-dimensional data cluster. It is also better in the cluster formation and regulating the data distribution.

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Data-driven fuzzy rule extraction for housing price prediction in Malang, East Java

Cited by 14 publications

References 15 publications

House Price Prediction using a Machine Learning Model: A Survey of Literature

House Price Prediction using a Machine Learning Model: A Survey of Literature

Advanced Machine Learning Algorithms for House Price Prediction: Case Study in Kuala Lumpur

High Dimensional Data Clustering using Self-Organized Map

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