In recent years, big data has become a buzz word for them who are relying on digital technologies as it allows storage and analysis of massive data. Here, big data includes both structured and unstructured data sets which require processing capabilities of modern tools. One of the most popular tools to handle big data is Advanced Excel and its relevant add-ons (Rose, Spinks, & Canhoto, 2015).
In recent years, industries such as finance, healthcare and Information and communication technology (ICT) industries have witnessed dramatic changes as a result of big data phenomenon. In fact, a recent report by Consumer News and Business Channel (CNBC) (2015) highlighted the role of big data in the real estate sector and explained its evolution. This then has provided analysts with opportunities to restructure raw data and offer related solutions (Catella, 2015). Hence, the role of R software in identifying basic patterns in unstructured big data cannot be underestimated. This is because it helps in standardization of unfamiliar characteristics as well as estimation of important parameters (CoreLogic, 2013) . Read more »
Predictive modelling is a data driven, induction based modelling that is continuously used by big sized companies to gain useful insights into trends and risks budding in the future. The modelling on the basis of data extraction, cleansing and analysis helps in predicting the value of a target variable (Fortuny, Martens, & Provost, 2013). Most of the analytical softwares developed are used to efficiently understand how things move for an organisation as per trends indicated by a relevant factor. One of the software that helps in prediction is R, summarization and estimation of the target variable with respect to different factors (Varian, 2014). The software holds a wide scope to develop predictive models.
The real estate market has been undergoing changes due to recent policies of the government and other initiatives. The move of demonetisation which involved currency ban was initiated by the current Government. With the introduction of demonetisation, the real estate sector was shaken up due to high involvement of cash transactions. According to Singh (2016), 35-40% of the money which was exchanged in black for selling and buying of pre-owned houses in Delhi NCR region has been curbed due to demonetisation. This will lead to unsold inventory of residential and commercial premises, increasing the drag on other sectors such as financial, steel, etc.