Big data analytics predicting real estate prices

The enormous data generated on daily basis amounts to big data technologies. This large amounts of data have knowledge and hidden patterns. Real estate turning out to be another biggest application in big data. The emphasis of this paper is to map the process involved in taking large amounts of data to predict the price of a house in real estate. The real estate sounds to be a long-term investment. In this paper, the housing Sale Data from Ames, Iowa is considered for the timeframe 2006–2010 with a view to construct relevant models to estimate the final sale price of a house. Due to high number of explanatory variables several models such as linear regression, random forest and gradient boosting models have been used as tools for feature selection to determine the statistically significant characteristics that influence the final sale price of a house. It has been observed that out of all the models, the gradient boosting model returned the efficient results.

[1]  Alois Knoll,et al.  Gradient boosting machines, a tutorial , 2013, Front. Neurorobot..

[2]  Archana Singh,et al.  Mining of Social Media data of University students , 2016, Education and Information Technologies.

[3]  L. Yin,et al.  Creating sustainable urban built environments: An application of hedonic house price models in Wuhan, China , 2015 .

[4]  S. Sonka,et al.  Big Data and the Ag Sector: More than Lots of Numbers , 2014 .

[5]  J. Teye,et al.  Financing housing in Ghana: challenges to the development of formal mortgage system , 2015 .

[6]  Fuad Rahman,et al.  A novel big-data processing framwork for healthcare applications: Big-data-healthcare-in-a-box , 2016, 2016 IEEE International Conference on Big Data (Big Data).

[7]  David Wheeler,et al.  Multicollinearity and correlation among local regression coefficients in geographically weighted regression , 2005, J. Geogr. Syst..

[8]  Jayanthi Ranjan,et al.  A framework for mobile apps in colleges and universities: Data mining perspective , 2014, Education and Information Technologies.

[9]  Leilani Battle,et al.  Interactive visualization of big data leveraging databases for scalable computation , 2013 .

[10]  Omer Tene Jules Polonetsky,et al.  Privacy in the Age of Big Data: A Time for Big Decisions , 2012 .

[11]  Debashis Ghosh,et al.  Classification and Selection of Biomarkers in Genomic Data Using LASSO , 2005, Journal of biomedicine & biotechnology.

[12]  Myounggu Kang,et al.  An empirical analysis on the housing prices in the Pearl River Delta Economic Region of China , 2014 .

[13]  Manolis Maragoudakis,et al.  Sports & Nutrition Data Science using Gradient Boosting Machines , 2018, SETN.