No abstract

Housing taxation is an important policy instrument that shapes households' choices about homeownership and renting as well as the evolution of the housing market. We study the effects of housing taxation in a model with search and matching frictions in the property market and a competitive rental market. We show a new transmission channel for a housing tax reform that works through a 'shifting' effect from landlords to tenants. We calibrate the model in order to estimate the long-run effects of a recent housing market taxation reform and the extent of property tax capitalization on house prices. We show that property taxation on owner-occupied dwellings has a negative effect on property and rental prices, whereas taxes on second homes have opposite qualitative effects. The simultaneous increase in both these instruments may mitigate the dynamics of prices and rents as well as the change in the ratio between the share of owners and renters, leading to a partial capitalization taxation on prices.

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.

We provide an analytical framework for assessing financial stability risks arising from the real estate sector in Italy. This framework consists of two blocks: three complementary early warning models (EWMs) and a broad set of indicators related to the real estate market, to credit and to households. We focus separately on households and on firms engaged in construction, management and investment services in the real estate sector. Since in Italy there have been no real estate-related systemic banking crises, as vulnerability indicator we consider a continuous indicator represented by the ratio between the annual flow of bad debts related to the real estate sector and banks' capital and reserves. We contribute to the recent literature on EWMs by implementing a Bayesian Model Averaging (BMA) based on linear regression models with a continuous dependent variable of vulnerability and an ordered logit model with a discrete dependent variable of vulnerability classes. Both models exhibit good predictive abilities. Based on the BMA projections for the period from the third quarter of 2015 to the second quarter of 2016, banking vulnerability related to the real estate sector is expected to gradually decline.

This paper studies the impact of foreclosures on house prices in Italy using a large dataset of listings. We estimate that the foreclosure discount is considerable, and this would suggest a high degree of market segmentation and limited spillovers from foreclosures to the market for non-foreclosed homes. However, we find that the entry of foreclosures into the market increases the propensity of home sellers to adjust their list price. Moreover, foreclosure listings have a significant and negative impact on the prices of non-foreclosed homes nearby. Our evidence is quantitatively consistent with the recent literature on the impact of foreclosures on the US housing market.JEL Classification: R31.