# Comparing Machine Learning Techniques for House Price Prediction

Konstantinos Panagiotis Fouriotis, Athanasios Tsadiras

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Konstantinos Panagiotis Fouriotis, Athanasios Tsadiras. Comparing Machine Learning Techniques for House Price Prediction. 19th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2023, León, Spain. pp.292-303, 10.1007/978-3-031-34107-6_23. hal-05498067

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# Comparing Machine Learning Techniques for House Price Prediction

Konstantinos Panagiotis Fourkiotis(✉) and Athanasios Tsadiras

Aristotle University of Thessaloniki, Thessaloniki, Greece
kfourkio@csd.auth.gr, tsadiras@econ.auth.gr

**Abstract.** One sector that already is feeling the impact of Artificial Intelligence (AI) is the real estate industry. AI is being used in the real estate sector to improve various aspects of the industry such as property search, pricing, marketing, and risk management. There are various techniques used for predicting house prices, including linear regression, decision tree and random forest. These algorithms can take into account factors such as location, square footage, number of bedrooms and bathrooms, and other relevant characteristics of the property. Predicting house prices with AI has many advantages over traditional methods, such as the ability a) to handle large amounts of data and b) to identify patterns and trends that might be overlooked by humans. It can also help us understand why housing prices are changing, and what factors are driving these changes. This information can be invaluable for real estate agents, investors, and homeowners who need to make informed decisions regarding the market. This paper provides a comparison of the performance of various Machine Learning algorithms in their attempt to predict the price of houses. The regression methods that are compared include Support Vector Machine, Kernel Ridge, Gradient Boosting, Lasso, Random Forest, XGB, LGBM, Average and Voting Regressor. The comparison shows that the best algorithm was Voting Regressor for R Squared metric and for RMSLE metric was the Average model. In conclusion, AI has the potential to bring new levels of accuracy and insight into the real estate industry.

**Keywords:** Machine Learning · Real Estate · House Prices · Regression

## 1 Introduction

Machine Learning (ML) is a subfield of Artificial Intelligence (AI), that utilizes algo-
rithms and statistical models to enable computers to learn and improve by using data
without human intervention. It's a crucial aspect of AI and is used to educate AI systems to
carry out various functions such as image identification, natural language understanding
and decision-making [1].

In this paper an analysis of the use of ML in house price prediction is presented. House
price predictions can be beneficial for various parties involved in the real estate market.
For buyers, predictions can assist in making informed decisions during purchasing a
property, determining if a property is over or underpriced and negotiating better deals.
Sellers can use predictions to price their properties correctly and avoid pricing errors. Investors can use predictions to identify properties that are likely to increase in value and make better investment decisions. Real estate agents can use predictions to provide accurate property valuations and to find potential buyers, while banks and lenders can use predictions to evaluate the risk of loan defaults and make more informed loaning decisions. Additionally, government can use predictions to create policies, taxations and regulations. Overall, predictions of house prices can be a valuable tool for anyone involved in the real estate market as they can assist in making informed decisions and identifying growth opportunities [2].

When it comes to predicting house prices, there is no one-size-fits-all algorithm. It is crucial to recognize that there is no single algorithm that can be applied to all cases. The selection of an appropriate algorithm depends on several factors, including the data quality, the complexity of the problem, and the choice of evaluation metrics.

The best algorithm to use depends on factors such as the size and complexity of the dataset, the level of accuracy needed, and the resources available for computation. Candidate algorithms are for example Support Vector Machine, Kernel Ridge, Gradient Boosting, Lasso, Random Forest, XGB, LGBM, Averaged models and Voting Regressor. These techniques are commonly used in machine learning for regression problems. By applying these methods to housing price data, a better understanding of trends and patterns in the housing market can be identified.

The current state of housing prices reflects the overall economic climate and is of concern to both buyers and sellers. Factors that affect housing prices include the number of bedrooms and bathrooms, as well as location and accessibility to amenities such as highways, schools, malls, and job opportunities [3].

This paper is organized into seven sections. After the introduction in Sect. 1, Sect. 2 provides a literature review of relevant studies and practices in this field. Section 3 outlines the dataset and the problem definition of this paper. Section 4 presents the preprocess and the Data Analysis of the Dataset. In Sect. 5 is being depicted the implementation of the ML models and in Sect. 6 has been illustrated the results and the model evaluation. Finally, Sect. 7 summarizes the key conclusions of the study and gives some suggestions for future research which indicate regional differences in housing prices and shifts in consumer preferences towards less densely populated areas.

## 2 Literature Review

This is a review of current literature on the prediction of housing prices that analyzes the existing research on the topic, including the methods and techniques used to predict house prices, evaluating their effectiveness, and identifying their strengths and limitations. There are many relevant studies, with the most important be the following:

Bahia [4] independently forecasts home prices in the real estate market using data mining techniques. To arrive at the most accurate prediction, the authors estimate the median home value in Boston suburbs based on 13 neighborhood attributes and a sample size of 506. Using SVM, LSSVM which is a variant of the Support Vector Machine (SVM) algorithm that solves for both the decision boundary and the regression function simultaneously[5], and PLS which is a statistical method used for analyzing relationships
between multiple latent variables[6], algorithms, Mu et al. [7] found that SVM and LSSVM were superior in dealing with nonlinearity, predicting Boston housing values with the goal of aiding real estate developers in making better decisions. In their study, Banerjee et al. [8] found that the RF technique was the most accurate for predicting house prices, although it had the most overfitting, while the SVM technique proved to be the most consistent and reliable. Park et al. [9], using a sample of 5359 row houses in Virginia, researched the accuracy of ML models in determining house prices and found that the RIPPER algorithm significantly improved price prediction compared to the RF and Naive Bayesian algorithms. In the Netherlands, Voutas Chatzidis [3] used different regression-based machine algorithms to predict house prices and found that CatBoost obtained the best results with an accuracy rate of 90% among LGBM, XGBM, CatBoost, and RF algorithms. XGBM was found to perform the best among various machine learning techniques by Kok et al. [10] in their analysis of real estate appraisals based on 84,305 observations from three states in the US from 2011 to 2016. In the Czech Republic, Hromada [11] presents software for real estate evaluation and analysis of advertisements by collecting and analyzing over 650,000 price quotations every 6 months and storing all advertisements in a database for credibility analysis. Hong [12] compared the performance of three machine learning algorithms (XGBM, LGBM, CatBoost) with the traditional hedonic price model (HPM) in predicting transaction prices of apartments in Seoul. The results showed that machine learning algorithms have better predictive power than OLS, a statistical method for estimating the parameters of a linear regression model to minimize the sum of the squared differences between the observed and predicted values [13], with CatBoost being the most effective in predicting prices even with the presence of outliers. The ensemble model, consisting of the three algorithms, was also found to have higher accuracy compared to the individual algorithms.

In comparison with the Boston house prices dataset, in which has been represented a challenging task of predicting the median value of owner-occupied homes in the city. A plethora of machine learning algorithms have been employed, including Random Forest, Support Vector Machine, XGBoost, LightGBM, and CatBoost, and their performances were measured using the mean squared error. The findings indicate that the choice of the right machine learning algorithm is crucial for accurate prediction of house prices. Further research is needed to determine the best performing algorithm for different data sets and evaluation criteria, and to improve the accuracy of house price prediction models.

In this study, we have thoroughly evaluated a variety of widely utilized techniques to provide a comprehensive understanding of their efficacy and strengths. The methods we have analyzed have been carefully selected based on their prevalence and proven track record in the field, ensuring that our findings are reliable and relevant to the current state of the industry.

# 3 Dataset and Problem Definition

The dataset we used in this study is the Ames Iowa House Price dataset provided on Kaggle [14]. The problem statement of the dataset was to predict the sale price of houses in Ames, Iowa based on the containing 79 explanatory variables describing almost every
aspect of residential homes in Ames, Iowa. The data set contains 1458 house-cases and included information such as the number of bedrooms and bathrooms, square footage, and various features of the property like the type of roof, heating, and cooling system, etc.

It's important to evaluate the performance of different applied algorithms by using suitable evaluation metric such as RMSLE and R-Squared. RMSLE measures the error between the predicted and actual values, while R squared measures how well the model fits the data. The RMSLE metric was chosen because it computes the proportional error between the predicted and true values, while also being resilient to outliers. R-Squared is an intuitive and widely used standardized metric that works well for simple regression models and can be used with other metrics for a more complete picture of model performance [15]. The evaluation of models in house price prediction is based on two metrics: RMSLE and R-squared. The determination of a suitable threshold value for distinguishing between good and bad models depends on various factors. An RMSLE score of 0 to 0.5 is considered excellent, while an R-squared score between 0.7 and 0.9 is generally viewed as good. Equation (1) represents the RMSLE formula, while Eq. (2) represents the R-Squared formula. RMSLE is an extension of the Mean Squared Error (MSE) that is used primarily when predictions have large variances, as is the case in our dataset. The house price values range from 34,900 to 755,000 and so we don't want to penalize deviations in the forecast as much as we do with the MSE.

$$RMSLE = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (\log(pi + 1) - \log(a_i + 1))^2} \quad (1)$$

$$R^2 = 1 - \frac{\text{sum squared regression(SSR)}}{\text{total sum of squares(SST)}} = 1 - \frac{\sum (y_i - \hat{y}_i)^2}{\sum (y_i - \bar{y})^2} \quad (2)$$

# 4 Preprocessing the Dataset and Data Analysis

In this study, 9 different machine learning algorithms were used to develop house price prediction models for the data set described in Sect. 3. In order to achieve this, the standard machine learning workflow presented in Fig. 1 has been followed, covering all crucial steps such as pre-processing, data analysis, algorithm implementation, and model evaluation.

<figure><img src="image_3.png" /><figcaption>Fig. 1. Machine Learning Workflow.</figcaption></figure>

The dataset is split into a train set and a test set with the training set to have 1166 observations, and the test set to contain 292 observations. The data processing begins
by identifying and removing outliers in the dataset. Outliers since they are values that fall outside of the typical range of values, they can skew the results of the analysis. By removing outliers, the analysis is more likely to be accurate and representative of the data.

An analysis of the Sale Price variable is conducted, as it is the target variable that we want to predict. The target variable is skewed to the right, which is a problem for linear models that require normally distributed data. To fix this, we need to transform the variable to make it more normal. The SalePrice distribution is depicted in Fig. 2. With population mean = 180932.92 and standard deviation = 79467.79. A distribution plot for the variable "SalePrice" is also depicted in Fig. 3. With the log-transformation of the target variable is performed to make the distribution more normal. In the study, we log-transformed the sale price target variable and the skewed numerical features to normalize their distributions. The output of this transformation is a more symmetrical distribution, which lessens the impact of outliers and boosts the performance of the prediction model.

<figure><img src="image_4.png" /><figcaption>Fig. 2. SalePrice Distribution.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Fig. 3. SalePrice Distribution log1p.</figcaption></figure>

The next step in our workflow is Feature Engineering, where new features are created, or existing features are transformed to improve the performance of the model. This process starts by concatenating the training and testing data into the same dataframe, making it easier to work with the data. Missing values in the data are identified, and variables with high missing ratios such as PoolQC, Misc Feature and Alley are examined in more detail in Table 1. The approach to impute missing values in a dataset using Python code is to fill in missing values with appropriate values based on the nature of the feature. In this case, it is filling in missing values with "None", "0", or the most frequent value, depending on the feature. In the case of "PoolQC" and "Misc Feature", it makes sense to replace missing values with "None" to reflect the absence of the feature. For other Features, for example "LotFrontage", is a common approach to dealing with missing data, as it allows the data to be used in statistical analyses and machine learning models. Imputing missing values with the most frequent value may be appropriate for categorical features such as "MasVnrType", which describes the type of masonry veneer on the house. The goal is to prepare data for predictive modeling.

Data Correlation is also examined, with a Correlation map in Fig. 4. to see how features are correlated with SalePrice. Missing values are then input by proceeding sequentially through features with missing values. Additional feature engineering is performed
<table><caption>**Table 1.** Percent missing data by feature</caption><thead><tr><th colspan="2">Percent missing data by feature</th></tr></thead><tbody><tr><td>PoolQC</td><td>99.691464</td></tr><tr><td>MiscFeature</td><td>96.400411</td></tr><tr><td>Alley</td><td>93.212204</td></tr><tr><td>Fence</td><td>80.425094</td></tr><tr><td>FireplaceQu</td><td>48.680151</td></tr><tr><td>LotFrontage</td><td>16.660953</td></tr><tr><td>GarageFinish</td><td>5.450806</td></tr><tr><td>GarageQual</td><td>5.450806</td></tr></tbody></table>

by transforming some numerical variables that are categorical, such as MSSubClass, OverallCond, YrSold and label encoding categorical variables that may contain information in their ordering set. A new feature, TotalAreaSquareFeet, has also been added. To better determine house prices based on important area features, a new feature TotalAreaSquareFeet has been added, which sums up the area of basement, first and second floor of each house. The final number of variables in the dataset is 79, including the TotalAreaSquareFeet feature. The dummy categorical features are then obtained because most machine learning algorithms cannot work with categorical data directly. In the “Data Processing” section of the notebook, utilizes the Pandas corr() method to compute the pairwise correlation among all the numerical features. Such an approach is crucial for detecting possible relationships and patterns between the variables.

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Correlation map with SalePrice.</figcaption></figure>
In order to get the best results of the RMSLE and R-squared metrics we will try to find the best parameters with the best measurements. Essentially, we will find the optimal parameters. We will not achieve this manually, using the test and repeat method, but it will be done automatically using the GridSearch method.

In the following section, a comprehensive overview of the algorithms employed in this paper will be represented, highlighting their key contributions and significance to the research findings.

# 5 Application of Algorithms

The next step in the workflow is the application of the algorithms. The algorithms that were examined in our study were classified in categories and they are presented below. To get the most of each of these, various values for their hypermeters were examined and they are presented in Table 2. The values of the hyperparameters that achieve the best performance in our experiments are shown in bold. A 5-fold cross validation strategy was followed with the train/test split rule to follow the 80/20 strategy in the train set.

## 5.1 Linear Models

Elastic Net regression is a hybrid algorithm between LASSO and Ridge regression. ElasticNet regression is used to regularize and select the important predictor variables to obtain a simple model with the most significant predictor variables despite high multicollinearity between the predictors [16].

Lasso is a regularization technique that is used to prevent overfitting in linear regression models. It adds a penalty term to the linear regression objective function, which encourages the model to use only a subset of the input features [17].

## 5.2 Non-linear Models

Support Vector Machines are commonly used methods which is a supervised learning algorithm that can be used for classification and regression problems. Support Vector Machines (SVM) works by finding the hyperplane that best separates data points into different classes, using a mathematical optimization process that maximizes the margin between the hyperplane and the closest data points [18].

Kernel Ridge Regression is an algorithm that merges Ridge Regression with the use of kernels. The application of kernels makes it possible for KRR to handle non-linearly separable data by transforming the input into a higher-dimensional feature space where linear regression can be carried out [19].

## 5.3 Ensemble Models

Random Forest is an ensemble algorithm that creates multiple decision trees and combines their predictions to make a final prediction. It is a powerful algorithm for handling large and complex datasets, and it is often used for regression and classification problems [20]. A machine learning algorithm that utilizes multiple decision trees to make
predictions by aggregating the results from individual trees. It works by randomly selecting subsets of features and data points, which helps to prevent overfitting and increase accuracy [21].

Gradient Boosting is another ensemble algorithm that creates multiple decision trees and combines their predictions to make a final prediction. The algorithm is known for its ability to handle large and complex datasets, and it is often used for regression and classification problems [22].

XGBoost (eXtreme Gradient Boosting) is a scalable tree boosting system that is designed for speed and performance and is particularly effective for large datasets with complex features. It is an ensemble learning technique that combines the predictions of several decision trees to improve the accuracy and robustness of the model [23].

LightGBM (Light Gradient Boosting Machine) is a highly efficient gradient boosting decision tree model that is designed for large-scale datasets and high-dimensional features. It uses a novel tree-growing algorithm and several other techniques to achieve faster training speed, lower memory usage, and higher accuracy compared to other gradient boosting models [24].

XGB (XGBoost) and LGBM (Light GBM) are two popular gradient boosting libraries that are widely used in machine learning and industry projects [25, 26].

An Average Model is a technique where multiple models are trained on the same dataset and their predictions are averaged to obtain a final prediction. This technique is often used in ensemble methods like the Bagging Regressor [27]. In our case, we used an average of all 6 models created by the application of the algorithms presented above and in Table 2.

Voting Regressor is an ensemble meta-estimator that combines multiple machine learning regression models to improve the overall performance and accuracy of the predictions [28]. Each of the created base models gets a vote on what the final prediction should be, and the voting regressor considers all the votes to make its final prediction. In our case a Voting Regressor was examined, using the methods Support Vector Machine, XGB and LGBM.

As shown in the table below, we have passed the values of the most influential hyperparameters for each of the algorithms used in our research.

<table><caption>**Table 2.** Optimized Hyperparameter Values for Regression Algorithms</caption><thead><tr><th>Algorithm</th><th>Hyper Parameter</th><th>Values</th></tr></thead><tbody><tr><td>ElasticNet</td><td>alpha<br>L1_ratio<br>random_state</td><td>[0.0004,0.004, 0.04]<br>[0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]<br>[1,2,3,4,5,6,7,8,9]</td></tr><tr><td>Lasso</td><td>Alpha<br>random_state</td><td>[0.000391,0.001,0.01,0.1,1]<br>[1,5,10]</td></tr><tr><td>Support Vector Machine</td><td>C<br>gamma<br>epsilon</td><td>[100,50,25]<br>[0.0003,0.0004,0.0005,0.0006]<br>[0.0001,0.0002,0.0003]</td></tr></tbody></table>

(continued)
<table><caption>**Table 2.** (continued)</caption><thead><tr><th>Algorithm</th><th>Hyper Parameter</th><th>Values</th></tr></thead><tbody><tr><td rowspan="4">Kernel Ridge Regression</td><td>Alpha</td><td>[0.5,0.6,0.7,0.9,]</td></tr><tr><td>coef0</td><td>[5,6,7]</td></tr><tr><td>degree</td><td>[3,4,5,6]</td></tr><tr><td>kernel</td><td>polynomial</td></tr><tr><td rowspan="4">Random Forest</td><td>max_depth</td><td>[16,17,18]</td></tr><tr><td>max_features</td><td>None</td></tr><tr><td>minSamplesLeaf</td><td>[5,10,15]</td></tr><tr><td>minSamplesSplit nEstimators</td><td>[5,10,15]</td></tr><tr><td rowspan="4">Gradient Boosting</td><td>learning_rate</td><td>[0.01,1,10]</td></tr><tr><td>loss</td><td>Huber</td></tr><tr><td>max_depth</td><td>[2,20]</td></tr><tr><td>subsample</td><td>[0.8,0.9,1.0]</td></tr><tr><td rowspan="4">XGB</td><td>gamma</td><td>[0.005, 0.0045,0.35,0.5]</td></tr><tr><td>learning_rate</td><td>[0.01,0.1,0.001]</td></tr><tr><td>max_depth</td><td>[3,0.3,0.003]</td></tr><tr><td>min_child_weight nEstimators</td><td>[1.7817,0.178,0.0178]</td></tr><tr><td rowspan="6">LGBM</td><td>bagging_fraction</td><td>[0.7,0.8]</td></tr><tr><td>learning_rate</td><td>[0.7,0.8]</td></tr><tr><td>min_data_in_leaf</td><td>[0.05,0.06]</td></tr><tr><td>nEstimators</td><td>[6,7]</td></tr><tr><td>num_leaves</td><td>[5,6]</td></tr><tr><td>objective</td><td>regression</td></tr><tr><td>Voting</td><td>Weights: [SVR, XGB, LGBM]</td><td>[0.2,0.6,0.2], [0.6,0.2,0.2], [0.40,0.40,0.20], [0.20,0.40,0.40]</td></tr></tbody></table>

# 6 Results and Models Evaluation

Using the optimized values of hyperparameters of Table 2, the models created were tested on the test data. Table 3 presents the results of the metrics for each model, where the best value to be in bold. The number in parentheses after the name of the algorithm presents its rank according to the RMSLE metric. This metric is the most important for our case, as discussed in Sect. 3. Furthermore, it was proposed by the definition of the problem in Kaggle.

Our analysis of various techniques shows that the method which obtains the highest score using the Root Mean Squared Logarithmic Error metric, is the Average method, with a score of 0.1071 and for the R Square metric is the Voting method, with a score of 0.9193. The Average method combines the next six algorithms such as Support Vector Machine, Random Forest, ElasticNet, Gradient Boosting, Kernel Ridge Regression and
<table><caption>**Table 3.** Comparison of Regression Algorithm Performance on Test Set</caption><thead><tr><th rowspan="2">Methods</th><th colspan="2">Test Set Metrics</th></tr><tr><th>RMSLE</th><th>R<sup>2</sup></th></tr></thead><tbody><tr><td colspan="3"><strong>Linear Methods</strong></td></tr><tr><td>ElasticNet (5)</td><td>0.1146</td><td>0.9082</td></tr><tr><td>Lasso (4)</td><td>0.1145</td><td>0.9084</td></tr><tr><td colspan="3"><strong>Non-Linear Methods</strong></td></tr><tr><td>Support Vector Machine (7)</td><td>0.1200</td><td>0.8993</td></tr><tr><td>Kernel Ridge Regression (8)</td><td>0.1200</td><td>0.4369</td></tr><tr><td colspan="3"><strong>Ensemble Methods</strong></td></tr><tr><td>Random Forest (9)</td><td>0.1325</td><td>0.8768</td></tr><tr><td>Gradient Boosting (2)</td><td>0.1087</td><td>0.9175</td></tr><tr><td>XGB (6)</td><td>0.1155</td><td>0.9067</td></tr><tr><td>LGBM (10)</td><td>0.1348</td><td>0.9157</td></tr><tr><td>Average (1)</td><td>0.1071</td><td>0.9010</td></tr><tr><td>Voting (3)</td><td>0.1093</td><td>0.9193</td></tr></tbody></table>

Lasso methods. The Voting method combines the SVR, XGB and LGBM algorithms with respectively weights of 0.4, 0.4 and 0.2 per cent.

Since both Average method and Voting method achieve high performances (1st and 3rd positions in the rank), it can be concluded that the specific problem of the house price prediction is quite complicated and ensemble methods can be useful because they can combine and aggregate the advantages of various algorithms.

# 7 Conclusion and Future Work

In this paper several linear, non-linear and ensemble algorithms were applied in the problem of predicting house prices and their performance was measured. The dataset used contains a number of house attributes that were preprocessed to improve the prediction accuracy. The root mean square logarithmic error (RMSLE) and R-Squared metrics were used in this study. In the field of house price prediction, it is crucial to recognize that there is no single algorithm that can be applied to all cases. The selection of an appropriate algorithm depends on several factors, including the data quality, the complexity of the problem, and the choice of evaluation metrics. After extended experiments (grid search) on different hypermeters of the applied algorithms, the best examined hypermeters were used while evaluating their performance in the test set. It was found that the Average model method achieves the best RMSLE score, followed by Gradient Boosting and the Voting method. Also, it was found that the Voting method achieves the best R Squared score, followed by XGB and LGBM method. Ensemble methods were found to be valuable in our case because they can accumulate the advantages of various individual algorithms.
It would be interesting for the conclusions of this study to be examined in other data sets that regards house price prediction. Furthermore, other methods can be examined to identify if they are beneficial. Such methods could be neural networks and deep learning, that were not examined mainly because of the not big enough size of the data set. Moreover, various preprocessing techniques can be examined in order to see their contribution towards the precision of the house price predictions.

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