READY SIGNAL CONTROL DATA

Months of Supply

Why Use This Data Source In Your Models?

Months of supply: The length of time it would take for the existing supply of homes for sale to be bought up at the market’s current pace of sales, assuming no new homes came on the market. This is calculated by dividing inventory by home sales. It’s typically used to measure the balance between supply and demand; higher supply indicates a buyer’s market, while lower supply indicates a seller’s market. Data scientists rely on it to classify markets as buyer’s or seller’s markets. Economically, it captures macro trends in housing supply and demand, reflecting broader economic stability or instability.

Seasonal Impact

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Automated Data Profiling

Ready Signal automatically profiles each data set and offers up suggested industry standard data science treatments to utilize with these data in your models.

Suggested Treatment:

The data shows auto correlation, seasonality and a non-normal distribution. The data should be differenced and seasonally adjusted. While the Log transformation, provides the best normality, the Arcsin variable will also perform well.

Grain Transformation:

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.

Following first order differencing, no further differencing is required based on the differenced ACF at lag one of -0.63

Trend Analysis:

The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.42 p-value = 0.01 indicates that the data is not stationary.

Distribution Analysis:

The Shapiro-Wilk test returned W = 0.97 with a p-value =0.00 indicating the data does not follow a normal distribution.

A skewness score of 0.70 indicates the data are moderately skewed.

Hartigan's dip test score of 0.01 with a p-value of 0.99 inidcates the data is unimodal

Statistics (Pearson P/ df, lower => more normal)

No transform

2.31

Box-cox

1.22

Log_b(x-a)

1.12

sqrt(x+a)

1.19

exp(x)

42.72

arcsinh(x)

1.12

Yeo-Johnson

1.22

OrderNorm

1.27

Auto Correlation Function

Auto Correlation Function After Differencing

Partial Auto Correlation Function

Seasonal and Trend Decompostion

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