This paper is mainly concerned with the analysis of regional house price cycles. Based on a newly available data set consisting of the 40 largest U.S. Metropolitan Statistical Areas (MSAs), we introduce a wavelet transform based metric to study the housing cycle synchronization across MSAs. We...

This paper investigates the US housing market from just before the Great Recession onward (2006-2019) and assesses the viability of stock-flow matching in generating the observed outcomes. The paper documents that the probability a house sells declines sharply after listing for two weeks....

This study provides a test for measurement of spatial competition in residential real estate markets. Several alternative spatial competition measures are tested. We employ a Bertrand oligopoly model with differentiated products and adopt a Spatial Autoregressive model using a two stage least...

The recent house price experiences within an individual's social network affect her perceptions of the attractiveness of property investments, and through this channel have large effects on her housing market activity. Our data combine anonymized social network information from Facebook with...

I use daily data from fifty major cities to investigate the early effects of the COVID-19 pandemic on housing market in the United States. I find that starting from the second half of March, 2020, new home listings and pending home sales started to decrease. By mid-April, certain markets...

This paper demonstrates the way in which stock-flow matching with endogenous seller entry generates hot and cold spells in house sales. Potential sellers know the number of bidders remaining from the last house sale. If two or more bidders remain, the seller obtains the gains to trade through...

We focus on the housing market and examine why nonlocal home buyers (NLBs) pay 15 percent more for houses than local home buyers (LBs). We estimate a housing demand model that returns heterogeneous willingness to pay parameters for housing attributes. Our results show that NLBs are willing to...

We develop a programming algorithm that predicts a balanced-panel mix-adjusted house price index for arbitrary spatial units from repeated cross-sections of geocoded micro data. The algorithm combines parametric and non-parametric estimation techniques to provide a tight local fit where the...