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Data mining is now commonly applied in the real estate market. Data mining's ability to extract relevant knowledge from raw data makes it very useful to predict house prices, key housing attributes, and many more. Research has stated that the fluctuations in house prices are often a concern for house owners and the real estate market. A survey of literature is carried out to analyze the relevant attributes and the most efficient models to forecast the house prices. The findings of this analysis verified the use of the Artificial Neural Network, Support Vector Regression and XGBoost as the most efficient models compared to others. Moreover, our findings also suggest that locational attributes and structural attributes are prominent factors in predicting house prices. This study will be of tremendous benefit, especially to housing developers and researchers, to ascertain the most significant attributes to determine house prices and to acknowledge the best machine learning model to be used to conduct a study in this field. Machine learning is an important component of the growing field of data science. Through the use of statistical methods, different type of algorithms is trained to make classifications or predictions, and to uncover key insights in this project. These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics. Machine learning algorithms build a model based on this project data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of datasets, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.
A total of 14 articles were reviewed and evaluated to capture all attributes that influences the price of house. [3] in his article stated that square footage of unit of a house is the most importance variable in predicting price of a house, followed by number of bathrooms and number of bedrooms. Apart from that, the study suggests that the worth of the house increases by 2.6% if the floor space of the house is raised by 100 square feet. They also conclude that when the building's operating year is 1 year lower, the value increase by 0.3 percent. In addition, the price of a house would increase by 10.4 or 13.7 percent, with one more bedroom or one more bathroom. From previous study reviewed, 19 attributes are stated to have been widely used by other researchers to evaluate house price. The attributes from 12 articles, which is shown in Fig. 1, are both grouped and tabled in a bar graph. It is therefore obvious what attributes researchers have used extensively to determine house prices. The number in the above bar graph represents the cumulative papers that used the attributes as predictor. The diagram above shows that a shopping mall, hospital access and a house's lot size are the main attributes used to establish house prices. The research in recent years has dominated debates concerning locational attributes, including access to shopping centers and hospitals, as well as structural attributes, including number of bedrooms and the lot size of the house. In fact, [16] described the location as an important house price predictor. 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. In comparison, 8 of 14 studies have used the structural attributes to determine house prices, including number of bedrooms, number of bathrooms and the lot size of the house. [38] clarified that the three major attributes that influence home selling rates are the total square feet, overall efficiency and the total number of bathroom units. This finding is in line with [7] who discovered that the number of bedrooms and bathrooms and the home price have significant relations. In a similar vein, [17] stressed that an added floor, bedroom and washroom add 13 per cent, 16 per cent and 2 per cent to the home price, respectively. In addition to the locational and structural attributes, many researchers use the attributes of neighborhood to determine house prices. This can be seen in [12], where he claimed that neighborhood influences affected the house price, because citizens are likely to select a better neighborhood today. The neighborhood attributes also include low crime rates, pleasant scenery and quiet atmosphere. These factors will determine whether the price of house is high or low. Although only a few researchers chose economic attribute, including individual income and the expenses of constructing a house, as the factor in determining a house price, we agree that economic attributes do have a major impact on house prices. [9] stated in his study that a house price can be determined based on an individual income because the government plays a role in setting a house price dependent on individual financial conditions. [12] supports this study in saying that the relationship between house price and income is important to describe the affordability of a house. This is one of the factors leading to each person's affordability to own or rent a house. An evaluation of the main attributes that impact the house price is crucial and related to the first research question of this study. After evaluating the main attributes impacting house price determination, the data mining methodology (which is in the context of this study is a predicting model) can be used to estimate house price. In order to forecast house price, predictive modeling such as support vector regression or artificial neural network were used. Predictive modeling uses data mining to forecast what it observed during the study phase. Fig. 2 shows types of predictive model that is used by researchers in previous study. However, four popular prediction models, or more known as classifiers, used by researchers to construct this predictive model which are multiple linear regression, supporting vector regression, artificial neural network and classifier gradient booster. Disadvantages ? An existing methodology doesn’t implement Multiple Linear Regression and Support Vector Regression methods. ? The system not implemented artificial neural network known as ANN.
House price prediction can be done by using a multiple prediction models (Machine Learning Model) such as support vector regression, artificial neural network, and more. There are many benefits that home buyers, property investors, and house builders can reap from the house-price model. 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. Previous studies focused on analyzing the attributes that affect house price and predicting house price based on the model of machine learning separately. However, this article combines such a both predicting house price and attributes together. A. Locational Location is considered to be the most significant feature of house price determination [6, 9–11]. [12] in his study also observed the significant of location attributes in deciding house price. The location of the property was classified in a fixed locational attribute. All of these studies point to the close association between locational attributes such as distance from the closest shopping center, or position offering views of hills or shore, and house price variations. B. Structural Another significant feature influencing the house price is structural structure or some research has listed it as physical attributes [10, 13]. Structural characteristic is a feature that people may identify, whether number of bedrooms and bathrooms, or floor space, or garage and patio. These structural attributes, often offered by house builders or developers to attract potential buyers, therefore meet the potential buyers' wishes. In [14] in his earlier study, structural attributes would be the key consideration for house hunters in determining what to purchase as such attributes represent their market value. In their earlier study, [15] stated that all these attributes have a positive relationship to rising house prices [16]. C. Neighbourhood Neighborhood qualities can be included in deciding house price. According to [13], efficiency of public education, community social status and proximity to shopping malls typically improve the worth of a property. There is a substantial rise in house prices from the fifth-class suburban community to affluent neighborhood as predicted [16]. Nonetheless, [13] study found that these qualities tend to be cultural based, as they are not similarly relevant in all cultures. Advantages ? Support vector regression is a predictive model based on SVM, a neural network that usually has three layers, a powerful form of supervised learning. The model is based on a subset of training data. The advantages of support vector regression are that it is capable of processing non-linear results, provides only one possible optimal solution, and able to overcome a small sample learning issues. ? Regression analysis is a model used to determine the relationship between variables. In order to evaluate the correlation of the variables, the correlation coefficient or regression equation can be used. Multiple regression models can determine which characteristics are the most important to explain the dependent variable. Multiple regression analysis also allows certain price predictions by capturing independent and dependent variable data. ? The artificial neural network model has always been selected when a non-linear attribute is involved. The analysis of home price estimation should also use this model as a spatial consideration for the price of housing is also non-linear.