Hybrid Genetic-Based Support Vector Regression with Feng Shui Theory for Appraising Real Estate Price

2009 ◽  
Author(s):  
Chih-Hung Wu ◽  
Chi-Hua Li ◽  
I-Ching Fang ◽  
Chin-Chia Hsu ◽  
Wei-Ting Lin ◽  
...  
Keyword(s):  
Real Estate ◽  
Support Vector Regression ◽  
Support Vector ◽  
Feng Shui ◽  
Real Estate Price

Application of Support Vector Machine in Determination of Real Estate Price

2012 ◽  
Vol 461 ◽  
pp. 818-821
Author(s):  
Shi Hu Zhang
Keyword(s):  
Support Vector Machine ◽  
Real Estate ◽  
Learning Algorithm ◽  
Predictive Ability ◽  
Training Data ◽  
Small Samples ◽  
Support Vector ◽  
Data Set ◽  
Real Estate Price

The problem of real estate prices are the current focus of the community's concern. Support Vector Machine is a new machine learning algorithm, as its excellent performance of the study, and in small samples to identify many ways, and so has its unique advantages, is now used in many areas. Determination of real estate price is a complicated problem due to its non-linearity and the small quantity of training data. In this study, support vector machine (SVM) is proposed to forecast the price of real estate price in China. The experimental results indicate that the SVM method can achieve greater accuracy than grey model, artificial neural network under the circumstance of small training data. It was also found that the predictive ability of the SVM outperformed those of some traditional pattern recognition methods for the data set used here.

WaveletLp-Norm Support Vector Regression with Feature Selection

2015 ◽  
Vol 19 (3) ◽  
pp. 407-416 ◽  
Author(s):  
Ya-Fen Ye ◽  
◽  
Yuan-Hai Shao ◽  
Chun-Na Li ◽  
Keyword(s):  
Feature Selection ◽  
Real Estate ◽  
Theoretical Analysis ◽  
Support Vector Regression ◽  
Housing Prices ◽  
Support Vector ◽  
Powerful Factor ◽  
Regression Problems ◽  
Real Estate Prices ◽  
Performance Results

This paper proposes waveletLp-norm support vector regression (Lp-WSVR) to solve feature selection and regression problems effectively. Unlike conventional support vector regression (SVR), linearLp-WSVR ensures that useful features are selected based on theoretical analysis. By using the wavelet kernel,Lp-WSVR approaches any curve in quadratic continuous integral space that leads to improving regression performance. Results of experiments show the superiority ofLp-WSVR in both feature selection and regression performances. ApplyingLp-WSVR to Chinese real estate prices shows that the most significant and powerful factor contributing to Chinese housing prices is monetary growth.

scholarly journals Using Machine Learning Models and Actual Transaction Data for Predicting Real Estate Prices

2020 ◽  
Vol 10 (17) ◽  
pp. 5832 ◽  
Author(s):  
Ping-Feng Pai ◽  
Wen-Chang Wang
Keyword(s):  
Machine Learning ◽  
Real Estate ◽  
Least Squares ◽  
Support Vector Regression ◽  
Support Vector ◽  
Learning Models ◽  
Transaction Data ◽  
Price Prediction ◽  
Real Estate Prices ◽  
Machine Learning Models

Real estate price prediction is crucial for the establishment of real estate policies and can help real estate owners and agents make informative decisions. The aim of this study is to employ actual transaction data and machine learning models to predict prices of real estate. The actual transaction data contain attributes and transaction prices of real estate that respectively serve as independent variables and dependent variables for machine learning models. The study employed four machine learning models-namely, least squares support vector regression (LSSVR), classification and regression tree (CART), general regression neural networks (GRNN), and backpropagation neural networks (BPNN), to forecast real estate prices. In addition, genetic algorithms were used to select parameters of machine learning models. Numerical results indicated that the least squares support vector regression outperforms the other three machine learning models in terms of forecasting accuracy. Furthermore, forecasting results generated by the least squares support vector regression are superior to previous related studies of real estate price prediction in terms of the average absolute percentage error. Thus, the machine learning-based model is a substantial and feasible way to forecast real estate prices, and the least squares support vector regression can provide relatively competitive and satisfactory results.

Local and Regional Hour-Ahead Forecasts of Solar Irradiance with Training Data Selection and Support Vector Regression

2016 ◽  
Vol 136 (12) ◽  
pp. 898-907 ◽  
Author(s):  
Joao Gari da Silva Fonseca Junior ◽  
Hideaki Ohtake ◽  
Takashi Oozeki ◽  
Kazuhiko Ogimoto
Keyword(s):  
Support Vector Regression ◽  
Solar Irradiance ◽  
Training Data ◽  
Data Selection ◽  
Support Vector ◽  
Training Data Selection

Data Analytics to Help Improve Hydraulic Fracture Geometry Prediction Using Support Vector Regression: Case Study

2020 ◽  
Author(s):  
Avinash Wesley ◽  
Bharat Mantha ◽  
Ajay Rajeev ◽  
Aimee Taylor ◽  
Mohit Dholi ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Hydraulic Fracture ◽  
Data Analytics ◽  
Support Vector ◽  
Fracture Geometry
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