Changes in product characteristics on the extensive margin are an important and hitherto neglected dimension of quality change. Standard techniques for quality-adjusting price indices cannot handle such changes satisfactorily, which leads to an economically and statistically significant bias in...

We estimate the transmission of the pandemic shock in 2020 to prices in the residential and commercial real estate market by causal machine learning, using new granular data at the municipal level for Germany. We exploit differences in the incidence of Covid infections or short-time work at the...

This paper develops and solves a spatiotemporal equilibrium model in which regional wages and house prices are jointly determined with location-to-location migration flows. The agent’s optimal location choice and the resultant migration process are shown to be Markovian, with the transition...

On the 22nd of February 2011, much of the residential housing stock in the city of Christchurch, New Zealand, was damaged by an unusually destructive earthquake. Almost all of the houses were insured. We ask whether insurance was able to mitigate the damage adequately, or whether the damage from...

In geographically segmented credit markets, local real estate booms can deteriorate the funding conditions for small manufacturing firms and undermine their competitiveness. Using exogenous variation in the administrative land supply across 172 Chinese cities, we show that higher predicted real...

We study the effects of limited attention on property prices and energy efficiency (EE) investments in the housing market. Using a novel dataset, we analyse over 5 million residential property sale transactions in England and Wales, each containing information about sale price, property and...

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....

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...

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...