- Internal Code :
- Subject Code : NIT3171
- University : Victoria University Assignment Help Era is not sponsored or endorsed by this college or university.
- Subject Name : business analytics
ICT Business Analysis and Data Visualization
Contents
Introduction.
Business Analysis problem..
Methodology.
Result & discussion.
Conclusion.
Introduction to Analytics in Commercial Real Estate
The task involves the application of business analysis/data mining techniques to solve the business problem by taking the example of Melbourne housing –a real estate business. The task aims to prepare a business analytics report to solve the business problem identified in Melbourne's real estate business by making use of the Data mining technique. This report gives the general overview of available business analysis tools that can be applied to the Melbourne housing dataset and discusses linear regression on the dataset in particular.
Business Analysis Problem
Business Analysis is a broad field which makes use of data analytics and statistical methods to address day to day real business problem. It makes use of all the quantitative and statistical models to draw insights from the data using predictive modelling techniques. Based on the insights generated from the data appropriate business strategies will be developed. The Business analyst makes use of the insights generated from the data and discusses the insights with the management of the Business to take proper decisions. Some of the benefits of implementing business analytics in the business or organization include
- Precise and Accurate information transformation
- Business Efficiency
- Planning business by knowing future challenges
- It uses bot data and analytics to make an informed decision
- Cost reduction
Business analytics implementation also have some difficulties like
- Lack of skilled employee
- Data maintenance and security issues
The real estate business often faces some challenges in terms of investment, operations and finances. Data relating to location, pricing, appraisals, industry trends, competition, vacancy rates are now accessible. The big challenge is to get the data and applying appropriate tools to analyse data and draw proper insights(Peneth,2020) . The present study tries to analyse the following problem
- Which type of house expensive in the city
- The magnitude of price increase
- Role of a particular location or distance in determining house price
Methodology of Analytics in Commercial Real Estate
There are various data mining models are available to address the business problem. To generate proper insights from the data first we need to understand the data and how it can be applied to find a solution for the business problem.
Data Mining Implementation Process
Data Mining Models
For the present problem, the Linear regression model is used to draw the insights from the data
Linear Regression on the Melbourne Dataset
Linear regression is a statistical technique used widely in machine learning.Linear regression measures the cause and effect relationship between the two variables.X and Y . X is calle d the dependent variable and Y is called the independent variable. When linear regression involves only one independent variable it is called as simple linear regression.If the regression involves more than one independent variable it is called as multiple linear regression. The simple linear regression will be given by the equation
Y=b0+b1x1
where Y is dependent variable
X1 is independent variable
b0 is intercept and
b1 is slope
Slope gives the unit change in Y for the given unit change in X.intercept gives the value of Y when all the variables included in the model is set to 0.For the present case study multiple regression model is used to predict the price.This technique will help us to analyze which variables are most affecting the price of the houses, which is one of the important things in any real estate analysis scenario. This should also help us ignore a few variables that are not affecting the price much and focus on only the relevant ones.
Result & Discussion on Analytics in Commercial Real Estate
From the regression result, we found out that, the coefficient of the room is positive which means the price increases with the increase number of the room. Similarly, the coefficient of price is slightly negative with the year built, which means there hasn’t been much increase in the price amount from the past.The visualization diagram of the price with the predicted price clearly shows that there won’t be big increase on the price near the future as the price would remain constant between the range most probably up to $1430977.
Conclusion on Analytics in Commercial Real Estate
As a business analyst, we have worked on the Melbourne data set which has got 21 attributes and 2000 instances. First of all, we found out what types of data are there in the data set. i.e nominal and numeric data. Then we normalized the big table into a smaller table using the normal form which has got similar properties. Then we finally did the data analysis on these data and used linear regression for our data mining techniques. Hence, from the data mining techniques and the analysis, we found the important relationship between the price with the other variables. That means the price is increased with the increase of the no. of the room with less distance difference with the CBD. More houses are built in early 2000 which is ranged less than 2865500. So we can find that people would be naturally interested in the new houses though in some of them the distance and rooms may affect a little.
References for Analytics in Commercial Real Estate
Ml|linear Regression.(2018).Retrived from Https://www.Geeksforgeeks.Org/ml-Linear- Regression/
Paneth,M.(2020).Data analytics done right : Successful adoption of analytics in commercial real estate. Retrieved from https://commercialobserver.com/2020/03/data- analytics- done-right-successful-adoption-of-analytics-in-commercial- real-estate/
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