Table of Contents
RMSE (Root Mean Square Error) 2
MAPE (Mean Absolute Percentage Error) 3
AIC (Akaike’s Information Criterion) 3
Data Analysis
Following is the qualitative data analysis of the data:
Time Series Analysis
Time series analysis is a useful technique to analyze the data over time. This technique determines the ‘change over time.’ So, to explain the prices of a residential and nonresidential property in the US, this analysis has shown effective outcomes. Following is the time series analysis as performed in Tableau Software:
The analysis showed that the time series as based on the sum of prices of the residential and nonresidential property in the US is fluctuating. The analysis showed that through the base year the property prices could not be stabilized. The base year was 2015. The index price was estimated to be in between 249 till 69. So, there is a considerable gap between trend analysis base prices. This difference is due to the deficiency between the costsof the property.
Similarly, the high level of fluctuations is associated with the prices in the market. The level of prices in the US is not stable. So, the general public is conscious of investing in residential and nonresidential property. The changes were calculated to see the future price index. The next prices are calculated on the base year of 2015. The base year will indicate the future of prices in the residential and nonresidential property. However, the survey showed that if the seasonal effects are kept constant, and the changes are estimated from 2015 till 2034, there will be no noticeable changes in the trending price and the quality of trend analysis season. This showed that the analysis does not predict any changes in the upcoming 20 years, since the variety of prices as settled by the residential authorities in the US.
Sum of Price Analysis
As the analysis showed that there is a considerable gap between the sum of prices index. But the index could not be stabilized since there is a gap between the survey prices. As well as the survey has shown a poor quality of the settlement of the cost.
Through the exponential smoothing trend analysis, the sum of prices computed has shown that the prices are fluctuating and variant in the US. At 0.50 level of alpha, where the researcher has kept Beta constant, and the co-efficient of gamma is held stable, the survey showed the following outcomes:
RMSE(Root Mean Square Error)
The RMSE value is 42.119, and this showed that the data slop in between the prices and the base year is positive. However, the slope has a limited movement, and it moves along the progression of graphs in the data. RMSE shows that data variation and the standard deviation of the residuals based on a prediction of errors in the data
MAE (Mean Absolute Error)
27.493 value of MAE refers to the level of difference between two continuous variables. The continuous variables are the base year and the predicted future prices of the residual and nonresidential property in the US. The survey showed that there is direct relationship in between the variables. However, the variables slope is non- aggressive and mostly remain the same
MASE
the mean absolute scaled error as shown in the analysis is 1.10. Thus, the measure of the accuracy of forecasts in data showed a difference in price forecast for the next 20 years. This showed that the prices would fluctuate and create a differentiation in the upcoming years for residential and nonresidential property in the US.
MAPE (Mean Absolute Percentage Error)
The mean absolute percentage error is 32.6%; this showed the level of the forecast as predicted by the future year’s index. In each period index level of MAPE, the mean value is associated with the percentage error in the property prices. This showed that the fluctuation in property prices is estimated until 32.6% in the next 20 years.
AIC (Akaike’s Information Criterion)
Akaike’s information criterion is the regression model based on all associated factors of the model. Based on theatrical framework variables, Akaike’s information criterion has shown the statically model for the undertaken research. Akaike’s information criterion showed that the study is valid and reliable. There will be a constant estimated raise in the variables prices, as shown by the data.
The survey showed that there would be a positive and direct raise in the prices of residential and nonresidential property prices in the US. However, the forecasted values for the next 20 years showed that the increase would be either constant, or it will be based on the price of the base year. So, it is not a remarkable raise. Still, the slight movement of the graph is associated with increasing prices and improving variables consistency of residential and nonresidential property in the US.
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