How do pandemics affect urban housing markets? This paper studies historical outbreaks of the plague in 17th-century Amsterdam and cholera in 19th-century Paris to answer this question. Based on micro-level transaction data, we show outbreaks resulted in large declines in house prices, and...

This article presents a hierarchical Trend Model for selling prices of houses, addressing three main problems: the spatial and temporal dependence of selling prices and the dependency of price index changes on housing quality. In this model the general price trend, cluster-level price trends,...

We propose a novel and intuitive risk-neutral valuation model for real estate derivatives. We first model the underlying efficient market price of real estate and then construct the observed index value with an adaptation of the price update rule by Blundell and Ward (1987). The resulting index...

This paper examines what determines the correlation between prices and turnover in European housing markets. Using a panel vector autoregressive model, we find that there is a particularly strong feedback mechanism between prices and turnover. Momentum effects are another important reason why...

This paper deals with unobserved heterogeneity in hedonic price models, arising from missing property and locational characteristics. In specific, commercial real estate is very heterogeneous, and data on detailed property characteristics are often lacking. We show that adding mutually...

In this article we employ detailed internet search data to examine price and liquidity dynamics of the Dutch housing market. The article shows that the number of clicks on online listed properties proxies demand and the amount of listed properties proxies supply. The created market tightness...

This paper concerns the estimation of granular property price indices in commercial real estate and residential markets. We specify and apply a repeat sales model with multiple stochastic log price trends having a hierarchical additive structure: One common log price trend and cluster specific...

This paper proposes a methodology for using machine learning regression models to create price indices. In our study we developed six commercial real estate price indeces for the city of New York from year 2000 to 2019. The regression models used in this study are eXtreme Gradient Boosting Tree...

The general purpose of a dynamic factor model (DFM) is to summarize a large number of time series into a few common factors. Here we explore a number of DFM specifications applied to 80 granular, non-overlapping indexes of commercial property prices in the US, quarterly from 2001 to 2017. We...