# Using Machine Learning Models and Actual Transaction Data for Predicting Real Estate Prices
## 2 The Literature Review of Real Estate Price Predictions  
Some studies of real estate prices predictions are presented as follows. [PERSON] et al. [6] employed the concept of big data to predict housing sale data in Iowa, using three models to forecast house sale prices: linear regression, random forest, and gradient boosting. The numerical results indicated that the gradient boosting model outperforms the other forecasting models in terms of forecasting accuracy. [PERSON] et al. [7] presented a logistic smooth transition autoregressive fractionally integrated process to predict housing price volatility in the U.S.A. and analyzed complicated statistical models based on assumptions of the variance process. The numerical results revealed that the Markov-switching multifractal and fractionally integrated generalized autoregressive conditional heteroscedastic models provide satisfied forecasting accuracy. [PERSON] [8] developed a news article-based forecast model to predict Jeonse prices in South Korea. The Internet search intensity of keywords from news was treated as the independent variable. The numerical results showed that the designed models obtain more accurate results than time series techniques. [PERSON] et al. [9] used genetic algorithms to forecast real estate rental prices when geographic locations and four real estate attributes are considered. The multivariate regression technique was performed to forecast the same data. Numerical results indicated that the genetic algorithms are superior to the multivariate regression in term of prediction accuracy. [PERSON] and [PERSON] [10] designed a house price prediction system by machine learning approaches to help house sellers or real estate agents with house price evaluations. In their investigation, data were collected from the Multiple Listing Service of the Metropolitan Regional Information Systems. The numerical results indicated that repeated incremental pruning to produce error reduction (RIPPER) algorithm obtains more accurate forecasting results than the other forecasting models. [PERSON] and [PERSON] [11] employed dynamic model averaging and dynamic model selection to forecast house price growth rates in the 50 states of the U.S.A. The presented forecasting system captures house price growth rates by varying the model and the coefficients' change over time and across locations. Thus, the forecasting results provided by the proposed system are substantial. [PERSON] et al. [12] designed a hybrid model as an early warning system, including ensemble empirical mode decomposition and support vector regression, for predicting sudden house price drops. The proposed forecasting approach generates superior results over other forecasting models in terms of forecasting accuracy. [PERSON] et al. [13] developed a housing price analysis system using information from public websites to analyze the total ratio of the average value and standard deviation of housing prices. They reported that the designed housing price analysis system is a helpful way for obtaining insights into housing prices. [PERSON] et al. [14] employed fuzzy adaptive networks to forecast pre-owned housing prices in Taipei by taking both objective variables and subjective variables into considerations. The empirical results indicated that the fuzzy adaptive networks outperform back-propagation neural networks and the adaptive network fuzzy inference system in terms of forecasting accuracy. [PERSON] and [PERSON] [15] used random forest to appraise residential estate of Saint Petersburg, Russia and reported that the random forest approach outperforms the other forecasting methods in prediction accuracy. In addition, the authors claimed that the random forest approach is capable of dealing with missing values and categorical variables. [PERSON] and [PERSON] [16] presented an ensemble learning system integrating ordinary least squares linear regression and support vector regression to appraise real estate.