Serial correlation and seasonality in the real estate market

In this article, a two-step, two-sample method and a Bayesian method are proposed to estimate the serial correlation and the seasonally of the price behavior of the residential housing market. The Bayesian method is found to be superior to the alternative two-step methods. The empirical results based on the Bayesian approach support the rejection of the random-walk hypothesis in the real estate market. Seasonality is not significant; however, there is still a clear indication that the returns associated with seasonal dummies are strongest in the second quarter, with the first quarter following closely.     

A Bayesian Approach to the Construction and Comparison of Alternative House Price Indices

Several repeat-sales models have been advanced over the years for estimating real estate price indices. This article proposes a general model which incorporates earlier works as special cases and compares the alternative repeat-sales models using posterior odds ratios as criteria. While the existing literature estimates the real estate indices from the sampling point of view, in this article indices are constructed and then compared using a Bayesian approach. In general, the two-error term models outperform the one-error models. The model with a nontemporal component proposed by Goetzmann and Spiegel is found to be superior in three out of four cities. There is a significant discrepancy among the returns and indices obtained from different models.