Explaining Changes in Transaction Volumes in Hungarian Settlements During the Crisis

The 2008 economic crisis can be analysed as a demand-side shock to the Hungarian residential real estate market. Post-crisis adjustment in prices and transaction volumes is different in smaller settlements than in larger ones. According to income data, the crisis hit smaller settlements harder. In a static framework, this would imply that transaction volumes decreased more (relatively) in smaller settlements than in larger ones. A first glance at empirical data in Hungary, however, shows the opposite: there appears to be a smaller relative decrease in transaction volumes in smaller settlements. We measure settlement size by the number of properties in the settlement in 2009, and the change in transaction volume as the number of transactions before and after the crisis, divided by settlement size (that is, the change in relative transaction volume). The unexpected negative relationship between size and the change in volume is robust to the inclusion of controls (eg. for NUTS2 region, or the distance from the nearest larger town). A more dynamic approach is warranted to address this puzzle. A possible explanation is the following. The fact that transaction volumes fall after a crisis means that sellers will receive fewer offers over a given space of time. In smaller settlements, offers are generally less frequent than in larger settlements. Therefore, in a post-crisis smaller settlement, if an offer does come along, sellers will be likely to accept it even if it is low – since they anticipate that this offer will be the only one they receive for some time. Sellers in larger settlements, however, may still find it worth their while to hold out and wait for a better offer. This means that while post-crisis adjustment in smaller settlements happens less in volume than in price, the converse is true of larger settlements. The above argument fits into an optimal stopping framework, in which sellers receive differing offers at various intervals (each offer only being available for a limited amount of time), and must decide which offer to accept. We therefore address our puzzle using an optimal stopping model.