Why Use This Data Source In Your Models?
The homeownership rate provides an indication of the percentage of the population that owns as opposed to rents. This gives an indication of the stability of real estate markets as well as economic stability.
Homeownership Rate, Total US
U.S. Census Bureau
Housing Vacancies and Homeownership
Percent, Seasonally Adjusted
The data shows auto correlation and a non-normal distribution. The data should be differenced. While the Arcsin transformation, provides the best normality, the Boxcox variable will also perform well.
Data is unable to be distributed by time or geography. The roll up method used is Weighted Average.
Data shows auto correlation indicating a need for differencing
The ACF indicates 2 order differencing is appropriate.
Further differencing is reccommended
The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.35 p-value = 0.01 indicates that the data is not stationary.
The Shapiro-Wilk test returned W = 0.96 with a p-value =0.21 indicating the data follows a normal distribution.
A skewness score of 0.13 indicates the data are fairly symmetrical.
Hartigan's dip test score of 0.07 with a p-value of 0.28 inidcates the data is unimodal
Auto Correlation Function
Auto Correlation Function After Differencing
Partial Auto Correlation Function
Seasonal and Trend Decompostion
U.S. Census Bureau, Homeownership Rate for the United States [RSAHORUSQ156S], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/RSAHORUSQ156S, December 13, 2019.
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