Analyzing Post-Crisis House Selling Price Data in Estate Assignment

Assignment Task

Robbins Estate Agents is a local housing agency looking at house prices after the housing crisis that occurred between 2007-2010. The data set runs from 2011 and Robbins estate agents would like you to determine which variables most significantly impact price.

Robbins Estate Agents does not have the capacity to run this investigation internally therefore they have enlisted Buxton Analytics to carry out an in-depth analysis of the post crisis housing data.

The data set contains 1106 records and includes the following variables:

• Year,
• Land Value,
• Building Value,
• Acres,
• Above Space,
• Basement,
• Deck,
• Baths,
• Toilets,
• Fireplaces,
• Beds, Rooms,
• AC,
• Age,
• Car,
• Poor Condition,
• Good Condition,
• Price£

This information can be found in the spreadsheet.

In your role as data analyst for Buxton Analytics you will be expected to conducted preliminary analysis to complete the data set and then complete the data analysis for 6 questions.

The aim of this assignment is to determine which variables impact house selling price. The data will be analysed in assignment 1 and will be further interpreted in assignment 2.

Preliminary Analysis

Firstly, check for and remove any observations that have missing values. Briefly explain the steps that you performed within Excel and state the number of observations that remain post-removal.

Secondly, check for and remove any duplicate observations, if any. Briefly explain the steps that you performed within Excel and state the number of observations that remain post-removal/post-analysis.

Main Analysis

1. Using appropriate measures of descriptive analytics and data visualisations, analyse each numerical variable within the data set.

2. Using appropriate measures of descriptive statistics and data visualisations, analyse each categorical variable within the data set 

3. Using appropriate measures of descriptive analytics and data visualisations, analyse the variables to determine if there are any relationships between the numerical and categorical variables.

4. Undertake a correlation analysis to determine which variables are correlated with the Price variable. 

• Dummy variables may need to be created for the categorical variables. 

5. Conduct multiple regression analysis using Price as the dependent variable to determine which variables influence it

• This regression should include ONLY the numerical variables within the data set provided.
• Make sure to highlight any variables where multicollinearity may exist. 
• Rerun the regression addressing any multicollinearity as required. 
• Highlight which variables should be removed to allow the regression output to be parsimonious. Justify any removals.
• Determine whether different variables are significant depending on the year the house is sold. 

6. Conduct multiple regression analysis using Price as the dependent variable to determine which variables influence it? 

• ALL variables should be used here both numerical and categorical.
• Make sure to highlight any variables where multicollinearity may exist. 
• Rerun the regression addressing the multicollinearity as required. 
• Highlight which variables should be removed to allow the regression output to be parsimonious. Justify any removals.
• Determine whether different variables are significant depending on the year the house is sold.

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