Home is where the ad is: online interest proxies housing demand

Online activity leaves digital traces of human behavior. In this paper we investigate if online interest can be used as a proxy of housing demand, a key yet so far mostly unobserved feature of housing markets. We analyze data from an Italian website of housing sales advertisements (ads). For each ad, we know the timings at which website users clicked on the ad or used the corresponding contact form. We show that low online interest—a small number of clicks/contacts on the ad relative to other ads in the same neighborhood—predicts longer time on market and higher chance of downward price revisions, and that aggregate online interest is a leading indicator of housing market liquidity and prices. As online interest affects time on market, liquidity and prices in the same way as actual demand, we deduce that it is a good proxy. We then turn to a standard econometric problem: what difference in demand is caused by a difference in price? We use machine learning to identify pairs of duplicate ads, i.e. ads that refer to the same housing unit. Under some caveats, differences in demand between the two ads can only be caused by differences in price. We find that a 1% higher price causes a 0.66% lower number of clicks

Comparison of the performance of different instruments in the stray neutron field around the CERN Proton Synchrotron

This paper discusses an intercomparison campaign carried out in several locations around the CERN Proton Synchrotron. The locations were selected in order to perform the measurements in different stray field conditions. Various neutron detectors were employed: ionisation chambers, conventional and extended range rem counters, both commercial and prototype ones, including a novel instrument called LUPIN, specifically conceived to work in pulsed fields. The attention was focused on the potential differences in the instrument readings due to dead-time losses that are expected to affect most commercial units. The results show that the ionisation chambers and LUPIN agree well with the expected H*(10) values, as derived from FLUKA simulations, showing no relevant underestimations even in strongly pulsed fields. On the contrary, the dead-time losses of the other rem counters induced an underestimation in pulsed fields that was more important for instruments characterised by a higher dead tim

Synchronization of endogenous business cycles

Comovement of economic activity across sectors and countries is a defining feature of business cycles. However, standard models that attribute comovement to propagation of exogenous shocks struggle to generate a level of comovement that is as high as in the data. In this paper, we consider models that produce business cycles endogenously, through some form of non-linear dynamics---limit cycles or chaos. These models generate stronger comovement, because they combine shock propagation with synchronization of endogenous dynamics. In particular, we study a demand-driven model in which business cycles emerge from strategic complementarities across sectors in different countries, synchronizing their oscillations through input-output linkages. We first use a combination of analytical methods and extensive numerical simulations to establish a number of theoretical results. We show that the importance that sectors or countries have in setting the common frequency of oscillations depends on their eigenvector centrality in the input-output network, and we develop an eigendecomposition that explores the interplay between non-linear dynamics, shock propagation and network structure. We then calibrate our model to data on 27 sectors and 17 countries, showing that synchronization indeed produces stronger comovement, giving more flexibility to match the data