Predicting house prices is a challenging task that many researchers have attempted to
address. As accurate house prices allow better informing parties in the real estate market …

This article investigates spatial linkages in returns, idiosyncratic risks and volatilities across
19 US regional housing markets. Using Case & Shiller housing price indices from 1995 …

Spatial modeling of economic phenomena requires the adoption of complex econometric
tools, which allow us to deal with important methodological issues, such as spatial …

This study investigates whether flipping activities impose an externality on the transaction
prices of the neighboring nonflipped properties. Using a data set of residential property …

Real estate transactions data usually consist of a collection, over time, of spatial layers of
individual transactions. Despite the presence of a temporal dimension, it is not uncommon …

The x-minute city/neighborhood concept has been introduced in numerous cities globally
and is considered a new management paradigm to support low-carbon and sustainable non …

Spatial linkages in returns have not yet received much attention in an asset pricing context,
however, they can capture important information about idiosyncratic externalities associated …

We introduce five nonparametric kriging‐type predictors for spatial data where only the
variable of interest, without covariates, is recorded. The proposed methods seek to fully …

In housing price regression, a large bundle of non-separable structural and location
characteristics, potentially affecting prices nonlinearly, constitute the relevant set of …

Purpose–Studies into ripple effects have previously focused on the interconnections
between house price movements across cities over space and time. These interconnections …