The Impact of Luxury Housing on Neighborhood Housing Prices: An Application of the Spatial Difference-in-Differences Method

Abstract

This study investigated the spatial spillover effects of luxury housing during and after construction, in regards to increases in housing prices in neighboring areas as well as the spatial dependence of neighboring housing. This study focused on already completed luxury housing in Taipei, Taiwan. First, the nearest-neighbor matching approach of propensity score matching was used to overcome the problem of data heterogeneity. The difference-in-differences (DD) method and spatial econometrics were used for analysis. The empirical results indicated that the spatial error model had the best goodness of fit. This indicated that housing prices increased by 13.0% during construction of luxury housing nearby. This indicated that housing prices increased by 5.8% after the construction of luxury housing nearby. The empirical results showed that the ongoing and completed construction of luxury housing had spillover effects on housing prices. The effect of ongoing construction of luxury housing was particularly large in scope, indicating its role as a predictor of psychological reaction in the market.

Does the Visa-Free Policy Promote Inbound Tourism? Evidence From China

“…Therefore, it is necessary to consider the spatial spillover effects when assessing policy effects. This is known as the spatial difference-in-differences (SDiD) method (Gu, 2021b; Liang et al, 2020).…”

“…First, none of these studies have considered the spatial spillover effects of foreign tourist flows, which tend to produce biased estimates (Butts, 2023). Second, none of these studies have conducted a placebo test based on a ''counterfactual scenario'' and therefore cannot rule out a pseudo-causality for the stimulating effect of visa liberalization on inbound tourism, as visa liberalization policies may simply be a proxy variable of the systematic differences between cities in the experimental and control groups (Gu, 2021b;Liang et al, 2020). The SDiD method used in this study effectively overcomes these shortcomings and achieves a theoretical and methodological breakthrough in inbound tourism research and visa policy evaluation.…”

Spatial Effect of Market Sentiment on Housing Price: Evidence From Social Media Data in China

“…Spatial econometric models, which incorporate spatial factors into the model by constructing a spatial weight matrix, have been widely used in studies exploring housing price volatility (Vergos & Zhi, 2018). For example, Liang et al (2020) used spatial econometric models to study the factors influencing housing prices and their spatial effect, which included direct and indirect effects. Therefore, this paper investigates the direct and indirect effects of market sentiment and each control variable on housing price based on the decomposition effect of the spatial econometric model, and provides insight into the impact of market sentiment on housing price from the perspective of the spatial effect, making up for the shortcomings in previous literature.…”

Big Tech on the Block: Examining the Impact of Tech Campuses on Local Housing Markets in the San Francisco Bay Area

“…Urban DID studies typically analyze the impact of a new housing investment or new infrastructure (e.g., a light rail line) using concentric rings to compare impacts within an inner ring with those in an outer ring. The radius of the ring varies widely, from as small as 500 feet for housing studies (e.g., Ellen et al, 2001), to the standard .25 mile radius for rail transit stations (e.g., Cao & Lou, 2018), to 500 to 1,000 meters for a wide variety of investments and interventions (Lee et al, 2020;Liang et al, 2020). In the Amazon HQ2 study, which is the study most similar to this one, the authors deployed circles of radii 1 mile from the Long Island City location and 5 miles from the Crystal City location, explaining that a more extensive radius was appropriate in the Virginia case because of commuters' reliance on cars.…”

“…Several studies traced the impact of rail transit investments on housing prices, using up to 4 years of data, if available (e.g., Cao & Lou, 2018;Diao et al, 2017) but just 1 year if not (e.g., Lee et al, 2020). Studies of the impact of housing patterns (whether new investment or foreclosures) tended to use shorter periods, typically 1 to 2 years (e.g., Lee et al, 2017;Liang et al, 2020;Z. Lin et al, 2009).…”