Introduction
This reports attempts to perform an analysis of the real estate properties based upon the regression analysis of the key marketing variables that determine the total residential housing marketing time. The purpose of this analysis report is to determine the relationship between the residential housing marketing time and the quality of the schools, which are associated with the marketed properties. In other words, the detailed analysis has been performed in order to determine that whether the properties are linked to higher quality schools or lower quality schools. Lastly, it has also been determined that whether the marketing time is more dependent upon those types of properties that are likely to accommodate more school age children. In response to address and analyze each of these questions, the following hypothesis have been constructed:
H1: Residential housing marketing time is significantly related to quality of the schools.
H2: There is a significant dependency of high quality schools on residential properties.
H3: There is a significant dependency of low quality schools on residential properties.
H4: Residential property marketing time is significantly dependent upon properties that accommodate more school age children.
All the tests have been performed on the extracted data to analyze the entire above alternative hypothesis.
Description of Data Set
The data has been selected for the United States and within the United States, the real estate and the school quality data has been extracted from the Miami Association of the Realtors. There are around six data sets that have been extracted from this source and the data pertains to a range of the properties and the schools. There are many variables, which are studied in the context of the real estate properties for which the data has been extracted. The key real estate variables for which the data has been extracted for all types of the properties in Miami are the number of the beds, full bathrooms, half bathrooms, garages and area.
The quality of the schools has been assumed on the basis of the average low, high and medium schooling grades of the children that are nearest to each of the respective properties. Based upon the high, low and medium grades, an average grade has also been calculated. The data for the list price and the selling prices of the properties has also been extracted in order to perform a detailed analysis. The year of the properties that are manufactures has also been extracted however; these would be considered as string data as it would not be used in the analysis.
The final number of the observations of the six data sets is 21809, 17949, 27558, 28643, 29796 and 24395. The initial number of the observations was higher in each of the data set and the number of the initial observations in each of the data set was much higher which approximately 33,000 were. However, the data was reduced due to missing values and the outliers that were present in the data. First of all, the validation of the data was performed through the data validation tool in the excel spreadsheet and all the missing values were filled with zero. By using the ‘If’ function in the excel spreadsheet, all the outliers have also been removed from the data sets.Regression Analysis – Residential Real Estate Case Solution
Furthermore, if the histogram is constructed for any of the data ranges, then it is going to form a bell shaped curve which would ensure that the data is homogeneous. The mean of the data would be 1 and the standard deviation of the data set is 0. This is one of the requirements or one of the assumptions of linear regression statistical test which confirms that the data is homogeneous and the linear regression analysis could be performed upon the data. All the six data sets contain the same types of the real estate properties, however, the difference lies in the time period during which the data has been collected for each of the properties. The descriptive statistics for the data set 1 has been calculated which could be seen in the excel spreadsheet. The descriptive statistics data for the range of the key variables is shown in the table below:.................................
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