A Hedonic House Price Model
Table of Contents
Transaction of the house property is based on the demand of the people. The determination of house price in a particular place is dependent upon the demand seen from the customer side which may be varying concerning time. So, a robust model for the house price is essential which will satisfy the desire of the customer for purchasing the house with great satisfaction (Camarrone & Hulle, 2019). In this paper, the house price model will be done concerning the required parameters. The model and evaluation are performed using the SAS analytics and the outcomes will be produced along with the model.
House prediction is one of the commercial and economic issues in the world. Many researchers and developers have proposed their view for the betterment of prediction of the house price (Fairchild & Wu, 2015). Different strategies have been used for such prediction and some of the important researches will be discussed here.
Madhuri, et al. (2019) have surveyed different regression strategies to find the effective price prediction for the house. In their survey, they have compared different prediction models like Multiple linear, Ada Boost, LASSO, Gradient boosting, Elastic Net and Ridge Regression. On the other hand, Varma et al. (2018) have applied the neural network using machine learning to predict the price. In this context, they have compared the regression model with the neural network and found the later is efficient and thus they have applied the neural network.
John & Carmelo (1994) had present their method to create a model by emphasizing the economic variables which have a great impact on house price. They have shown the impact of the variables on the house price which will create significant economic growth. With this chain of development, Miller et al. (2011) have shown the model for house price detection and prediction with the impact on economic growth. To design their model, they have surveyed about 379 metropolitan areas in the USA from the year 1980 to 2008 and collect the important parameters which have a great impact on economics.
Wang et al., (2020) have evaluated the problems related to the pricing model and proposed their strategy to predict the house price. They have conducted the risk evaluation for the price prediction of houses with the execution of the trend of price and the market volatility. With the evaluation of those risk, they have proposed their CAPM model to predict the price efficiently. Fairchild & Wu (2015) have worked on the similar issue and predict the price by evaluation of the volatility of the market. As the price of land is always changing concerning the year and time, so there will be no fixed rate of the price for a house (John & Carmelo, 1994). So, there is a trend in the price of the houses. The underlying factor of the risk is the subject of evaluation for the perfect prediction of housing price and to create a model (G. Meen, 2016).
The prediction of price will be executed depending upon the data colleceted for this context. There are multiple columns or features are present there for the determination of house price. Now, to determine or predict the price, different methods are available and one among those is the regression. Regression takes the price as the dependent variable and the features to be the independent variables (Chatterjee, et al., 2019). The analysis will be performed using SAS tool.
Choice of the independent variables is the important part of the prediction and thus the primary operation is executed for the choice of those. In this context, the correlation is applied for the choice of the variables. The correlation is the method through which the degree of relationship can be determined with the dependent variable (Wang, et al., 2020). In this price dataset, SalePrice is the dependent variable or it may be referred to as the target feature. The correlation is done to selecte the preferred features for regression analysis.
The hypothesis in this context are as follows:
H0: There is no effect of the features of the SalePrice.
H1: The features have significant effect on SalePrice.
The hypothesis test will be conducted along with the selected features and using the Regression Analysis (G. Meen, 2016). It will signify the highly significant features in this context for the prediction of SalePrice.
Finally, with the selected features, the regression have been done where the features are taken by selection from correlation. While execution of Regression model, the model fit result and the predicted average will be shown for consistency of the model (Madhuri, et al., 2019). The analysis will be shown in the next section.
It has been found for the that some of the features are highly and moderately correlated with the SalePrice. So, by rule of correlation, the influence of the highly correlated feature will be greater on the prediction for SalePrice (Varma, et al., 2018). From the analysis, the features with strong correlations are GrLivArea, OverallQual, TotalBsmtSF, YearBuilt, GarageArea, GarageCars, FullBath, HalfBath, TotRmsAbvGrd, GarageYrBlt, X1stFlrSF, X2ndFlrSF and X3SsnPorch. The analyssi is shown below:
Fig-1: Correlation analysis
So, those can be taken as the good predictor for this purpose. The regression will be doen using the selected features and the outcomes are shown in the next section.
The regression is done by taking the selected features as predictors and SalePrice as target. The model is fitted with the data which produces the names of the highly significant features as follows:
Fig-2: Model Fitting by selected features
This analysis is showing the result of the hypothesis that the Null Hypothesis will be rejected for the value of p is very less than 0.05 (Miller, et al., 2011). it can be seen that the variables like TotalBstmSF, OverallQual, RoofMati, BstmQual, HoiseStyle, LotArea etc are the highly significant predictor in this case. The predictor OverallQual is the rating value which has 10 labels starting from 1 by least rating to 10 by excellent rating. Roofmati also consist of multiple labels ans signifies the name of the roof materials. People also prefers the basement quality by the height and thus the predictor BstmQual is with multi-labels depending upon the height.
The regression outcome is shown below:
Fig-3: Regression outcome
From the regression model, the R-Squared value is obtained as 0.8045 which means above 80% of the values if variables have been correctly explained. This reflects the fact that the model is fitted well and the prediction can be doen effectively (Wang, et al., 2020).
The analysis and prediction of price is executed in this paper by considering the fetaures of the dataset. The regression model is conduted using the SAS tool and the outcomes have been shown in the respective sections. The R-Squared value if the regression model is showing that 80% of the data is explained in the model which means the model fitting is good. So, it can be said that the predition will become perfect with a referebce of the Fig-3(b) which shows that the original average and trhe predicted average value of SalePrice are alike similar which conclude the fact of the good prediction.
Camarrone, F. & Hulle, M. M. V., 2019. Fast Multiway Partial Least Squares Regression. IEEE Transactions on Biomedical Engineering, pp. 1-7.
Chatterjee, S., Kumar, S., Saha, J. & Sen, S., 2019. Hybrid Regression Model for Soil Moisture Quantity Prediction. International Conference on Opto-Electronics and Applied Optics (Optronix).
Fairchild, J. & Wu, J. M. a. S., 2015. Understanding housing market. Journal of Money, Credit and Banking, 47(7), p. 1309–1337.
G. Meen, A. M. a. Y. W., 2016. Endogenous uk Housing Cycles and the Risk Premium: Understanding the Next Housing Crisis. Economics & Management Discussion Paper, pp. 223-235.
John, C. & Carmelo, M. G., 1994. The Influence of Economic Variables on Local House Price Dynamics. Journal of Urban Economics, pp. 161-184.
Madhuri, C. R., Anuradha, G. & Pujitha, M. V., 2019. House Price Prediction Using Regression Techniques: A Comparative Study. International Conference on Smart Structures and Systems (ICSSS), pp. 22-27.
Miller, Peng, N. &., Sklarz, L. &. & Michael., 2011. House Prices and Economic Growth. The Journal of Real Estate Finance and Economics, 42(2).
Varma, A., Sarma, A., Doshi, S. & Nair, R., 2018. House Price Prediction Using Machine Learning and Neural Networks. Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 1936-1939.
Wang, Y., Liu, J. & Sriboonchitta, Y. T. a. S., 2020. Housing Risk and Its Influence on House Price: An Expected Utility Approach. Mathematical Problems in Engineering, pp. 1-16.
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