The Effect of Flood Risk on Residential Land Prices

Abstract

Floods are one of the most frequent natural disasters today. Hence, it is highly important to explore the effect of flood risk on residential land prices to promote the rational allocation of land resources and incorporate climate change risk control into territorial spatial planning. This paper takes the primary urban area of Hangzhou as an example, based upon data from 424 residential land plots. With spatial autocorrelation analysis and the Spatial Durbin Model (SDM) approach, the spatial effect of flood risk on residential transaction land price was investigated. The results show that, ceteris paribus, plots with high risk of flooding suffer a price discount of 8.62%. The unique mechanism of the way flood risk affects land prices was discussed further from the perspectives of land ownership and land price systems in China. Furthermore, when the land price in surrounding areas increases one percent, the land price in the area will increase 14.32%. The spatial spillover effects of land price were analyzed with the flood information disclosure system and the stakeholders’ considerations in land price comparison. The effect of flooding on residential land prices in Hangzhou is the result of government regulations and market allocations, which are fundamentally different from those of the free market allocations in many western countries. Interestingly, the risk of flooding is capitalized into the price, whether it is determined by government or market pricing. Integrating flood risk into land price determination can help promote the optimal allocation of land resources and minimize depreciation attributable to flood disasters.

Extreme Weather and Real Estate: A Case Study of the Jamaican Housing Market

“…According to Sawada et al (2018), non-significance of estimates possibly relates to illiquidity or other frictions in the real estate market. On the other hand, some studies indicate significant price reductions after flooding, especially for properties with higher flood risks (Zhai et al, 2003;Ismail et al, 2016;Dudzińska et al, 2020;Wei and Zhao, 2022). The results for Jamaica in this study must be considered in light of limitations in the available data and literature.…”

Evaluation of Urban Land Suitability under Multiple Sea Level Rise Scenarios

“…Metropolitan centers and tourist regions are vulnerable to frequent flooding [56]. As a southeastern coastal city in China, Hangzhou is susceptible to climate-related disasters, especially typhoons and heavy rainfall, which often result in flooding and inundation [9].…”

Spatial‐temporal evolution characteristics of residential land prices based on the 2010–2019 land transfer data in China

“…Since residential land is an important land‐use type related to basic housing needs, researchers have extensively studied RLP in terms of influencing factors (Chai et al, 2021; Lee, 2015, Mostafa, 2018; Yang, Hu et al, 2017), spatial‐temporal variation (Davis et al, 2017; Hu et al, 2012, 2013; Huang et al, 2018; Yang et al, 2020), and change effect (Ding & Zhao, 2014; Mou et al, 2017; Wen & Goodman, 2013). These studies generally adopted methods including multiple linear regression (MLR) (Mulley & Tsai, 2016; Yen et al, 2018; Zheng et al, 2021), hedonic models (Bourassa & Hoesli, 2022; Glaesener & Caruso, 2015; Kanasugi & Ushijima, 2018), exploratory spatial data analysis (ESDA) (An et al, 2021; Florence et al, 2011; Wei & Zhao, 2022), spatial econometric models (Glumac et al, 2019; Nichols et al, 2013; Qu et al, 2020), geographically weighted regression (GWR) (Hu et al, 2016; Mulley, 2014; Nakamura, 2019; Yuan et al, 2022), and machine learning algorithms (MLAs) (Ma et al, 2020). In summary, spatial‐temporal evolution studies of RLP mainly focus on specific cities, regions like urban circles and provinces, or multiple major cities.…”

“…These studies generally adopted methods including multiple linear regression (MLR) (Mulley & Tsai, 2016;Yen et al, 2018;Zheng et al, 2021), hedonic models (Bourassa & Hoesli, 2022;Glaesener & Caruso, 2015;Kanasugi & Ushijima, 2018), exploratory spatial data analysis (ESDA) (An et al, 2021;Florence et al, 2011;Wei & Zhao, 2022), spatial econometric models (Glumac et al, 2019;Nichols et al, 2013;Qu et al, 2020), geographically weighted regression (GWR) (Hu et al, 2016;Mulley, 2014;Nakamura, 2019;Yuan et al, 2022), and machine learning algorithms (MLAs) (Ma et al, 2020). In summary, spatial-temporal evolution studies of RLP mainly focus on specific cities, regions like urban circles and provinces, or multiple major cities.…”