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The opening of a new metro station, as a mode of the transportation corridor, potentially could have different effects on housing prices. We have investigated its effect on the value of residential properties around those stations, using data from large expansions of the metro network in Tehran, Iran. In the period of our study (April 2010 to December 2018), forty-five metro stations were inaugurated in Tehran. We use a difference-in-difference regression method to identify the causal effect of interest, where adjacent properties are used as the treatment group and similar but distant properties as the control group. The results indicate that, on average, the adjacent properties are affected by a 3.7 percent increase in price relative to distant properties. We also extend our study by categorizing new metro stations according to the extent of ex-ante access to other modes of public transportation such as bus rapid transit (BRT). We find 2 to 11 percent positive effect of new metro stations in regions with lower public transport, while in regions with ex-ante extensive public transportation system, we find less than 2 percent positive effect.

PurposeOnline ride-hailing platforms match drivers with passengers by receiving ride requests from passengers and forwarding them to the nearest driver. In this context, the low acceptance rate of offers by drivers leads to friction in the process of driver and passenger matching. What policies by the platform may increase the acceptance rate and by how much? What factors influence drivers' decisions to accept or reject offers and how much? Are drivers more likely to turn down a ride offer because they know that by rejecting it, they can quickly receive another offer, or do they reject offers due to the availability of outside options? This paper aims to answer such questions using a novel dataset from Tapsi, a ride-hailing platform located in Iran.Design/methodology/approachThe authors specify a structural discrete dynamic programming model to evaluate how drivers decide whether to accept or reject a ride offer. Using this model, the authors quantitatively measure the effect of different policies that increase the acceptance rate. In this model, drivers compare the value of each ride offer with the value of outside options and the value of waiting for better offers before making a decision. The authors use the simulated method of moments (SMM) method to match the dynamic model with the data from Tapsi and estimate the model's parameters.FindingsThe authors find that the low driver acceptance rate is mainly due to the availability of a variety of outside options. Therefore, even hiding information from or imposing fines on drivers who reject ride offers cannot motivate drivers to accept more offers and does not affect drivers' welfare by a large amount. The results show that by hiding the information, the average acceptance rate increases by about 1.81 percentage point; while, it is 4.5 percentage points if there were no outside options. Moreover, results show that the imposition of a 10-min delay penalty increases acceptance rate by only 0.07 percentage points.Originality/valueTo answer the questions of the paper, the authors use a novel and new dataset from a ride-hailing company, Tapsi, located in a Middle East country, Iran and specify a structural discrete dynamic programming model to evaluate how drivers decide whether to accept or reject a ride offer. Using this model, the authors quantitatively measure the effect of different policies that could potentially increase the acceptance rate.