Homeownership Rate, Total US (NSA)
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 (NSA)
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Automated Data Profiling
Suggested Treatment:
Grain Transformation:
Source:
U.S. Census Bureau
Release:
Housing Vacancies and Homeownership
Units:
Percent, Not Seasonally Adjusted
Frequency:
Quarterly
Available Through:
06/30/2023
Suggested Treatment:
The data shows auto correlation and a non-normal distribution. The data should be differenced. While the Yeo Johnson transformation, provides the best normality, the Order Norm variable will also perform well.
Grain Transformation:
Data is unable to be distributed by time or geography. The roll up method used is Weighted Average.
Auto Correlation Analysis:
Data shows auto correlation indicating a need for differencing
The ACF indicates 1 order differencing is appropriate.
Further differencing is reccommended
Trend Analysis:
The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.23 p-value = 0.01 indicates that the data is not stationary.
Distribution Analysis:
The Shapiro-Wilk test returned W = 0.94 with a p-value =0.02 indicating the data does not follow a normal distribution.
A skewness score of 0.74 indicates the data are moderately skewed.
Hartigan's dip test score of 0.05 with a p-value of 0.48 inidcates the data is unimodal
Statistics (Pearson P/ df, lower => more normal)
Auto Correlation Function
Auto Correlation Function After Differencing
Partial Auto Correlation Function
Seasonal Impact
Seasonal and Trend Decompostion
Citation:
U.S. Census Bureau, Homeownership Rate for the United States [RHORUSQ156N], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/RHORUSQ156N, December 13, 2019.
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