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The present study used a new perspective that integrates the commonly observed market characteristics to analyse why the ripple effect is formed. This study proposed a model to infer that in a regional housing market with a relatively low trading probability, low trading volume, high price volatility, and low price efficiency coexist and cause the market to lag behind other regions in information response, forming a ripple effect. The empirical tests used data cover four regions of the United States (Northeast, Midwest, South, and West) between January 1999 and May 2020, and showed that the housing market in the South had the greatest liquidity and the lowest housing price volatility, however, housing prices were more informative in the South region. The empirical results also revealed the characteristics of the ripple effect in the US housing market. Changes in house prices occur earlier in the South, and information is transmitted from the South to the Northeast.

Features of the ripple effect in the US regional housing markets: a viewpoint of nonsynchronous trading